Mechanical experiment data pre-processing method based on mechanical model

Abstract

The invention relates to the technical field of mechanical data processing, in particular to a mechanical experiment data pre-processing method based on a mechanical model. The method comprises the following steps: acquiring a mechanical experiment setting condition and a mechanical experiment measurement target, and carrying out mechanical model calibration establishment to obtain a stress-strain experiment mechanical model; carrying out noise point radius circular filtering processing by recording mechanical experiment parameter data in real time, and carrying out interpolation compensation and standardization processing to obtain mechanical experiment parameter standardization data; performing mechanical experiment fitting processing on the stress-strain experiment mechanical model to obtain stress-strain mechanical experiment fitting output prediction data; performing model prediction error analysis on the mechanical experiment parameter data to obtain stress-strain mechanical experiment model prediction error data; and performing error correction optimization on the mechanical experiment parameter data to obtain mechanical experiment parameter error correction data. The method can improve the quality of mechanical data and the reliability of analysis results.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical data processing, and in particular to a mechanical experiment data pre-processing method based on a mechanical model. Background Art

[0002] In mechanical experiments, the accuracy and reliability of mechanical experimental data are crucial for research and application. In practical applications, mechanical experimental data are usually affected by many factors, including the accuracy of measuring instruments, environmental changes, and the uncertainty of experimental operations. By combining mechanical models and data processing technology, accurate preprocessing of mechanical experimental data is achieved, and a mechanical model related to the experimental data is established according to the actual situation of mechanical experiments and mechanical theory. The model can describe the mechanical behavior and response involved in the experiment and provide a theoretical basis for data processing. The constructed mechanical model is used to perform model-driven preprocessing of mechanical experimental data. Through model prediction and fitting, the noise and systematic errors in the data are identified and removed, thereby improving the accuracy and reliability of mechanical experimental data. At the same time, the mechanical experimental data are corrected by applying the mechanical model, the deviations and errors in the experimental results are adjusted, and the flexibility and adaptability of the data preprocessing process are ensured. However, the traditional mechanical experimental data preprocessing method lacks an in-depth understanding of the noise distribution of mechanical experimental data, and cannot effectively remove the noise and errors, resulting in the data preprocessing results being unable to fully reflect the true situation of mechanical behavior. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a mechanical experiment data pre-processing method based on a mechanical model to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a mechanical experiment data preprocessing method based on a mechanical model comprises the following steps:

[0005] Step S1: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets, and calibrating and establishing a mechanical model according to the mechanical experiment setting conditions and mechanical experiment measurement targets to obtain a stress-strain experimental mechanical model; recording mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronizing the mechanical experiment parameter data to a data processing unit;

[0006] Step S2: using a data processing unit to perform noise point radius circle filtering on the mechanical experiment parameter data to obtain mechanical experiment parameter noise filtering data; performing interpolation compensation and standardization processing on the mechanical experiment parameter noise filtering data to obtain mechanical experiment parameter standardized data;

[0007] Step S3: performing mechanical experiment fitting processing on the stress-strain experimental mechanical model based on the mechanical experiment parameter standardization data to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient; performing model prediction error analysis on the mechanical experiment parameter data based on the stress-strain mechanical experiment fitting output prediction data to obtain stress-strain mechanical experiment model prediction error data;

[0008] Step S4: performing error correction optimization on the mechanical experiment parameter data based on the stress-strain mechanical experiment model prediction error data to obtain mechanical experiment parameter error correction data.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets;

[0011] Step S12: performing an analytical process on the influence of mechanical properties on the mechanical experiment setting conditions to obtain the description data of the influence of mechanical properties on the mechanical experiment conditions;

[0012] Step S13: performing mechanical influence feature mapping analysis on the mechanical experiment measurement target based on the mechanical property influence description data of the mechanical experiment conditions, and obtaining mechanical property influence feature data of the mechanical experiment measurement target under different mechanical experiment conditions;

[0013] Step S14: obtaining an initial mechanical model according to the mechanical experiment measurement target, and establishing a dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experiment measurement target under different mechanical experiment conditions, so as to obtain a stress-strain experimental mechanical model;

[0014] Step S15: Record the mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronize the mechanical experiment parameter data to the data processing unit.

[0015] Furthermore, the establishment of dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experimental measurement target under different mechanical experimental conditions described in step S14 includes the following steps:

[0016] Based on the characteristic data of the influence of mechanical properties of the mechanical experimental measurement target under different mechanical experimental conditions, the initial mechanical model is subjected to mechanical applicable condition restriction analysis, and the mechanical experimental applicable constraint conditions of the mechanical model under different mechanical experimental conditions are obtained;

[0017] Based on the applicable constraints of the mechanical model under different mechanical experimental conditions, the dynamic response parameter calibration analysis of the initial mechanical model is carried out to obtain the applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions;

[0018] According to the mechanically applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions, the initial mechanical model is gradually calibrated and optimized to establish a stress-strain experimental mechanical model.

[0019] Further, step S2 includes the following steps:

[0020] Step S21: using a data processing unit to perform noise frequency domain distribution analysis on the mechanical experiment parameter data to obtain a mechanical experiment parameter noise frequency domain distribution spectrum;

[0021] Step S22: performing noise point location and annotation processing on the mechanical experiment parameter noise frequency domain distribution spectrum to obtain a mechanical experiment parameter frequency domain distribution noise point annotated spectrum;

[0022] Step S23: performing noise point radius circle filtering processing on the corresponding mechanical experiment parameter data based on the noise point annotation spectrum of the mechanical experiment parameter frequency domain distribution to obtain mechanical experiment parameter noise filtering data;

[0023] Step S24: performing data missing point identification analysis on the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter data missing points; performing missing point interpolation compensation processing on the mechanical experiment parameter noise filtering data based on the mechanical experiment parameter data missing points to obtain mechanical experiment parameter interpolation compensation data;

[0024] Step S25: Standardize the mechanical experiment parameter interpolation compensation data to obtain mechanical experiment parameter standardized data.

[0025] Further, step S23 includes the following steps:

[0026] Step S231: performing point distance quantization calculation on any two adjacent noise points in the frequency domain distribution noise point annotation spectrum of the mechanical experiment parameter, and obtaining the point distance measurement value between any two adjacent noise points in the frequency domain spectrum of the mechanical experiment parameter;

[0027] Step S232: comparing and judging the point distance measurement value between any two adjacent noise points in the mechanical experiment parameter frequency domain spectrum according to the preset noise point distance filter threshold, when the point distance measurement value between any two adjacent noise points is greater than the preset noise point distance filter threshold, no processing is performed; when the point distance measurement value between any two adjacent noise points is less than or equal to the preset noise point distance filter threshold, the point distance measurement value between the corresponding two adjacent noise points is used as the noise point radius;

[0028] Step S233: based on the noise point radius, a radius circle area is drawn at any two corresponding adjacent noise points in the noise point annotation spectrum of the mechanical experiment parameter frequency domain distribution, so as to obtain the noise point radius circle area of ​​the mechanical spectrum;

[0029] Step S234: extracting the timestamps of the mechanical experiment parameter data points in the mechanical spectrum noise point radius circle area to obtain the timestamps of the mechanical experiment parameter noise point radius circle area data points;

[0030] Step S235: Based on the timestamps of the data points in the radius circle area of ​​the mechanical experiment parameter noise points, the corresponding mechanical experiment parameter data are subjected to timestamp synchronization filtering processing to obtain mechanical experiment parameter noise filtering data.

[0031] Furthermore, the missing point interpolation compensation processing of the mechanical experiment parameter noise filtering data based on the missing points of the mechanical experiment parameter data described in step S24 includes the following steps:

[0032] Perform spatial distribution analysis on the missing points of mechanical experimental parameter data to obtain the spatial distribution positions of the missing points of mechanical experimental parameter data;

[0033] Based on the spatial distribution of the missing points of the mechanical experimental parameters, the left and right limits of the missing points of the mechanical experimental parameters are quantitatively calculated for the noise filtering data, and the left limit value of the missing points of the mechanical experimental parameters and the right limit value of the missing points of the mechanical experimental parameters are obtained;

[0034] The left limit value of the missing point of the mechanical experimental parameters and the right limit value of the missing point of the mechanical experimental parameters are calculated by arithmetic average to obtain the limit average value of the missing point of the mechanical experimental parameters;

[0035] Based on the limiting average value of the missing points of the mechanical experiment parameters, the missing point interpolation compensation processing is performed on the spatial distribution positions of the missing points of the mechanical experiment parameters corresponding to the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter interpolation compensation data.

[0036] Further, step S3 includes the following steps:

[0037] Step S31: inputting the standardized data of mechanical experiment parameters into the stress-strain experiment mechanical model to perform mechanical experiment dynamic response behavior prediction processing to obtain stress-strain experiment dynamic response behavior prediction data;

[0038] Step S32: performing a mechanical experiment behavior field fitting analysis on the stress-strain experiment mechanical model based on the stress-strain experiment dynamic response behavior prediction data to generate a stress-strain experiment dynamic response behavior fitting output field;

[0039] Step S33: performing mechanical experiment fitting parameter output prediction on the stress-strain experiment dynamic response behavior fitting output field to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient;

[0040] Step S34: actually measuring the elastic modulus and damping coefficient of the mechanical experiment parameter data to obtain the actual elastic modulus and the actual damping coefficient of the mechanical experiment;

[0041] Step S35: Based on the prediction data output from the stress-strain mechanics experiment fitting, a model prediction error analysis is performed on the actual elastic modulus and the actual damping coefficient of the mechanics experiment to obtain the stress-strain mechanics experiment model prediction error data.

[0042] Further, step S33 includes the following steps:

[0043] Perform mechanical experiment response behavior parameter analysis on the stress-strain experiment dynamic response behavior fitting output field to obtain a mechanical experiment model dynamic response behavior fitting parameter set;

[0044] The model elastic modulus is predicted and calculated for the dynamic response behavior fitting parameter set of the mechanical experimental model to obtain the predicted elastic modulus of the stress-strain mechanical experimental model;

[0045] Perform dynamic response behavior characteristic analysis on the dynamic response behavior fitting output field of the stress-strain experiment to obtain dynamic response behavior characteristic data of the stress-strain experiment;

[0046] Based on the elastic modulus predicted by the stress-strain mechanics experimental model and the characteristic data of the dynamic response behavior of the stress-strain experiment, the damping coefficient calculation formula of the stress-strain experimental model is used to predict and calculate the model damping coefficient of the dynamic response behavior fitting output field of the stress-strain experiment, and the predicted damping coefficient of the stress-strain mechanics experimental model is obtained;

[0047] The elastic modulus predicted by the stress-strain mechanics experimental model and the damping coefficient predicted by the stress-strain mechanics experimental model are processed by parameter summary to obtain the stress-strain mechanics experimental fitting output prediction data.

[0048] Furthermore, the calculation formula of the damping coefficient of the stress-strain experimental model is specifically:

[0049]

[0050] Where c is the damping coefficient predicted by the stress-strain mechanics experimental model, ω nis the inherent damping ratio of the mechanical experimental model, E is the elastic modulus predicted by the stress-strain mechanical experimental model, α is the proportional coefficient of the elastic modulus of the mechanical experimental model, T is the total duration of the prediction calculation time window, t is the prediction calculation time variable parameter, δ(t) is the stress at time t in the stress-strain mechanical experiment, μ δ is the average value of stress dynamic response behavior, σ δ is the standard deviation of the stress dynamic response behavior, ε(t) is the strain at time t in the stress-strain mechanics experiment, μ ε is the average value of the dynamic response behavior of strain, σ ε is the standard deviation of the strain dynamic response behavior, and η is the correction coefficient of the damping coefficient predicted by the stress-strain mechanics experimental model.

[0051] Further, step S4 includes the following steps:

[0052] Step S41: performing nonlinear error distribution characteristic analysis on the stress-strain mechanical experimental model prediction error data to obtain nonlinear distribution characteristic data of the mechanical experimental model error under different stress and strain conditions;

[0053] Step S42: performing error correction strategy optimization design on corresponding mechanical experiment parameter data based on nonlinear distribution characteristic data of mechanical experiment model errors under different stress and strain conditions to generate a mechanical experiment parameter error correction optimization strategy;

[0054] Step S43: performing error correction optimization on the mechanical experiment parameter data according to the mechanical experiment parameter error correction optimization strategy to obtain mechanical experiment parameter error correction data.

[0055] Beneficial effects of the present invention:

[0056] Compared with the prior art, the mechanical experiment data preprocessing method based on the mechanical model proposed in the present invention has the beneficial effect of obtaining the mechanical experiment setting conditions and the mechanical experiment measurement targets, which is the basis and premise of the mechanical experiment process. The successful execution of this step can ensure the systematicness and goal clarity of the experiment, and provide a clear basis for subsequent experimental design and data analysis by obtaining the experimental setting conditions (such as loading methods, geometric characteristics of material samples, environmental conditions, etc.) and measurement targets (such as stress, strain, failure mode, etc.). By clarifying these conditions and targets, it can be ensured that every link of the experiment is closely connected with the experimental purpose, thereby improving the relevance and effectiveness of subsequent experimental results, and ensuring the smooth progress of the entire mechanical experiment and the high quality of data preprocessing. By calibrating and establishing the mechanical model according to the mechanical experiment setting conditions and the mechanical experiment measurement targets, an accurate stress-strain experimental mechanical model is established. By obtaining the initial mechanical model and calibrating it according to the mechanical experiment setting conditions and the mechanical experiment measurement targets, it can be ensured that the model can truly reflect the mechanical behavior of the material under actual application conditions. The dynamic response calibration process will adjust the model parameters in combination with the actual experimental data to adapt to different experimental conditions. This precise modeling not only improves the accuracy and reliability of the mechanical model, but also enables it to better predict the performance of the material under different working conditions, thereby providing basic data guarantee for subsequent processing processes. At the same time, by recording the mechanical experiment parameter data in real time from the mechanical experimental equipment and synchronizing the mechanical experiment parameter data to the data processing unit, the timeliness and accuracy of the mechanical experiment parameter data can be ensured. This process can comprehensively monitor various parameters in the experimental process, such as stress, strain, loading rate, etc., to ensure the continuity and integrity of the data. Real-time data recording can not only instantly capture any abnormalities or changes in the experimental process, but also support real-time analysis and adjustment of the experimental dynamic process, and also provide key real-time information support for subsequent data analysis and model calibration. Secondly, the data processing unit is used to perform noise point radius circle filtering on the mechanical experimental parameter data. The key to this process is that it effectively reduces the impact of noise on the experimental data by filtering out the frequency components around the noise points, and can suppress the noise components within the radius circle around the noise points in the spectrum, so as to better achieve an in-depth understanding of the noise distribution of mechanical experimental data. Through this processing, the useful information in the experimental data can be made more prominent, and the noise components are effectively removed, thereby improving the quality and reliability of the data. The implementation of this step not only improves the clarity of the experimental data, but also provides a cleaner data basis for subsequent analysis and modeling, which helps to more accurately reveal the actual mechanical properties of the material.By interpolating and compensating the noise filtering data of mechanical experimental parameters, the gaps in the missing point data can be effectively filled and the integrity of the data can be restored. Such processing ensures the continuity of data analysis, improves data quality, and helps to understand the experimental results and material properties more comprehensively. Furthermore, by performing standardization processing, standardized data of mechanical experimental parameters can be obtained. The core advantage of this process is that it can convert data of different scales and ranges into a unified standard, making the data more comparable. Standardization processing can eliminate the scale differences caused by different data sources or measurement conditions, making the data more consistent and reliable during analysis and modeling. Then, by fitting the stress-strain experimental mechanics model based on the standardized data of mechanical experimental parameters, the stress-strain mechanical experiment fitting output prediction data can be obtained, including the prediction of elastic modulus and damping coefficient. The key to this step is that through prediction analysis, the expected theoretical value can be provided for the experimental results, making the experimental design and implementation more targeted. The predicted elastic modulus and damping coefficient can be used as the standard for experimental verification to help researchers identify and correct potential model deviations. Accurate prediction data not only helps to optimize experimental conditions, but also provides support for the accuracy of actual measurements, improves the accuracy of the model, and reduces uncertainty and risk in practical applications. In addition, by performing model prediction error analysis on mechanical experimental parameter data based on the prediction data of stress-strain mechanical experiment fitting output, the gap between model prediction and actual measurement can be identified and quantified, providing a basis for model improvement. Error analysis helps to reveal the limitations of the model under different experimental conditions, guide subsequent model optimization and parameter adjustment, thereby improving the overall prediction ability and application accuracy of the model. By systematically analyzing and reducing prediction errors, the reliability of the model in practical applications can be enhanced, and the accuracy and practical value of mechanical experimental data can be improved. Finally, the mechanical experiment parameter data is error corrected and optimized based on the predicted error data of the stress-strain mechanical experimental model. The key to this step is to implement the correction strategy of optimal design according to the predicted error of the mechanical experimental model obtained from the previous analysis, and actually improve and optimize the mechanical experimental parameter data, so as to obtain more accurate mechanical experimental error correction data. The error correction optimization step can effectively apply the theoretical optimization strategy to the actual data, reduce errors, and improve the accuracy of the experimental results. The corrected data can not only provide more accurate experimental results, but also help verify and calibrate the model to make it more in line with the actual situation. This optimization process has a direct and positive impact on reducing experimental errors and thus improving data reliability. At the same time, the corrected data can provide a more reliable basis for further experimental analysis and application, improve the overall quality and practical application value of mechanical experiments, and thus fully reflect the actual situation of mechanical experimental behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0058] Figure 1 It is a schematic diagram of the steps of the mechanical experiment data pre-processing method based on the mechanical model of the present invention;

[0059] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0060] Figure 3 for Figure 2 Detailed step flow chart of step S15 in FIG. DETAILED DESCRIPTION

[0061] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 The present invention provides a mechanical experiment data pre-processing method based on a mechanical model, the method comprising the following steps:

[0065] Step S1: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets, and calibrating and establishing a mechanical model according to the mechanical experiment setting conditions and mechanical experiment measurement targets to obtain a stress-strain experimental mechanical model; recording mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronizing the mechanical experiment parameter data to a data processing unit;

[0066] Step S2: using a data processing unit to perform noise point radius circle filtering on the mechanical experiment parameter data to obtain mechanical experiment parameter noise filtering data; performing interpolation compensation and standardization processing on the mechanical experiment parameter noise filtering data to obtain mechanical experiment parameter standardized data;

[0067] Step S3: performing mechanical experiment fitting processing on the stress-strain experimental mechanical model based on the mechanical experiment parameter standardization data to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient; performing model prediction error analysis on the mechanical experiment parameter data based on the stress-strain mechanical experiment fitting output prediction data to obtain stress-strain mechanical experiment model prediction error data;

[0068] Step S4: performing error correction optimization on the mechanical experiment parameter data based on the stress-strain mechanical experiment model prediction error data to obtain mechanical experiment parameter error correction data.

[0069] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the mechanical experiment data preprocessing method based on the mechanical model of the present invention. In this example, the mechanical experiment data preprocessing method based on the mechanical model includes the following steps:

[0070] Step S1: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets, and calibrating and establishing a mechanical model according to the mechanical experiment setting conditions and mechanical experiment measurement targets to obtain a stress-strain experimental mechanical model; recording mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronizing the mechanical experiment parameter data to a data processing unit;

[0071] In an embodiment of the present invention, the specific setting conditions of the mechanical experiment are determined by obtaining, including the type, geometry, loading method, boundary conditions, etc. of the material used in the experiment. For example, if the experimental object is a welded joint, the size, weld type, and welding parameters (such as current, welding speed, etc.) of the welded joint need to be confirmed. The mechanical experiment measurement target is also clarified, such as measuring the tensile strength, fatigue life, and other performance indicators of the joint. These conditions and targets can be obtained by consulting the experimental design documents, communicating with relevant engineers, and conducting preliminary research on the actual welding process, thereby obtaining the mechanical experiment setting conditions and the mechanical experiment measurement targets. At the same time, the previously obtained mechanical experiment setting conditions are analyzed in depth to determine the influence of these conditions on the mechanical properties of the material, such as the influence of temperature gradient on material strength, residual stress generated during welding, etc., and the mechanical influence characteristics of the corresponding mechanical experiment measurement targets are mapped and analyzed by combining the mechanical experimental conditions and mechanical characteristics influence obtained by the previous analytical analysis, so as to associate the mechanical characteristics influence with the experimental measurement target by using statistical analysis methods and data mining technology. For example, the specific influence of different welding parameters (such as current, welding speed) on the tensile strength of the welded joint is determined by regression analysis. This process also includes generating mechanical characteristics influence characteristics under different experimental conditions. Characteristic data, such as stress-strain distribution characteristic data of materials at different welding speeds, and a preliminary mechanical model is obtained according to the mechanical experimental measurement target obtained by previous analysis. The model can be based on an existing theoretical model or previous research, and the initial model is dynamically calibrated by using the mechanical property influence characteristic data obtained by previous analysis. During the calibration process, the model parameters are adjusted through an optimization algorithm so that it can accurately predict the actual mechanical response under different mechanical experimental conditions. This includes using the least squares method to fit the model parameters, or using more complex optimization methods (such as genetic algorithms) to improve the accuracy of the model, thereby obtaining a stress-strain experimental mechanical model. Then, the parameter data in the mechanical experimental equipment is recorded in real time and synchronized to the data processing unit. This requires the installation of sensors and data acquisition systems during the mechanical experiment, such as strain gauges, force sensors and temperature sensors. These sensors can measure and transmit mechanical parameter data (such as stress, strain, temperature, etc.) in real time. The data acquisition system must have a high sampling rate and data storage function to transmit the sensor data to the data processing unit. In the data processing unit, the recorded data is sorted and analyzed through a real-time data processing and storage system (such as a database system or real-time data processing software), and stored in a data warehouse for subsequent analysis and verification.

[0072] Step S2: using a data processing unit to perform noise point radius circle filtering on the mechanical experiment parameter data to obtain mechanical experiment parameter noise filtering data; performing interpolation compensation and standardization processing on the mechanical experiment parameter noise filtering data to obtain mechanical experiment parameter standardized data;

[0073] In an embodiment of the present invention, a frequency domain analysis tool is used by a data processing unit to perform noise frequency domain distribution analysis on mechanical experimental parameter data, so as to convert time domain data into frequency domain data through Fourier transform, and use software such as MATLAB or SciPy library in Python to calculate the frequency spectrum of the noise signal. The generated spectrum diagram shows the frequency distribution and intensity of the noise in the data, and the noise frequency domain distribution spectrum diagram is processed by using image processing tools (such as OpenCV or the image processing toolbox in MATLAB) to locate and mark noise points. The noise point is located by setting a threshold or using an image segmentation algorithm (such as the Otsu algorithm) to detect the noise peak and mark the position of the noise point. On the frequency domain spectrum diagram, different noise points are highlighted or marked to obtain a noise point annotation spectrum diagram. At the same time, based on the obtained noise point annotated spectrum, a filter is used to filter the noise points at the corresponding time sequence position in the mechanical experiment parameter data with a radius circle, so as to select an appropriate circular filter, the filter radius needs to be set based on the distribution characteristics of the noise points, and the circular filter is used to filter the noise points by using tools such as MATLAB's filter function or Python's SciPy library, so as to obtain the mechanical experiment parameter noise filtering data. Then, the mechanical experiment parameter noise filtering data obtained after the noise filtering is previously obtained by performing data missing point identification and analysis to identify the missing points in the mechanical experiment parameter noise filtering data, and the corresponding data missing points are interpolated and compensated by combining the average limit value of the mechanical experiment parameter at the distribution position of the data missing point and applying the interpolation algorithm to make up for the missing part in the mechanical experiment parameter data, and the mechanical experiment parameter data previously obtained after the interpolation compensation is standardized by using a standardization method (such as Z-score standardization or Min-Max standardization) to convert the data value to a unified standard range, so as to eliminate the scale effect of the data in the subsequent analysis, ensure the consistency and comparability of the data, and finally obtain the mechanical experiment parameter standardized data.

[0074] Step S3: performing mechanical experiment fitting processing on the stress-strain experimental mechanical model based on the mechanical experiment parameter standardization data to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient; performing model prediction error analysis on the mechanical experiment parameter data based on the stress-strain mechanical experiment fitting output prediction data to obtain stress-strain mechanical experiment model prediction error data;

[0075] In an embodiment of the present invention, the standardized data of mechanical experimental parameters obtained after previous standardization are input into the stress-strain experimental mechanical model to perform prediction processing on the dynamic response behavior of the mechanical experiment, so as to use these data to predict the dynamic response behavior of the sample, and simulate and analyze the dynamic behavior of the sample under different stress and strain conditions, and perform fitting analysis of the mechanical experimental behavior field in the corresponding stress-strain experimental mechanical model by combining the dynamic response behavior prediction data obtained by previous prediction analysis, so as to process the dynamic response behavior prediction data by using a fitting algorithm (such as the least squares method or the curve fitting method), and compare the predicted data with the experimental results, and use a fitting tool (such as Curve Fitting in MATLAB) to calculate the dynamic response behavior prediction data. Toolbox) generates the behavior output field of the mechanical model, which describes the actual response characteristics of the material under different stress and strain conditions. At the same time, the behavior statistical analysis of the mechanical experimental response is performed on the dynamic response behavior fitting output field obtained by the previous fitting analysis, so as to fit the dynamic response of the mechanical experimental model through dynamic response analysis methods, such as Fourier transform, and by using data fitting technology (such as least squares method or Bayesian regression), so as to fit and analyze the dynamic response behavior parameter set, which includes stress response, strain response, frequency response, amplitude response and phase response, etc., and also extract the corresponding stress response fitting parameters and strain response fitting parameters from them using the elastic modulus calculation formula (for example, Where δ is stress and ε is strain) is predicted and calculated to obtain the elastic modulus reflecting the rigidity characteristics of the material under dynamic loading, thereby obtaining the elastic modulus predicted by the stress-strain mechanics experimental model. The dynamic response behavior characteristics in the dynamic response behavior fitting output field obtained by the previous fitting analysis are also deeply analyzed to extract the main characteristics from the fitted dynamic response data, such as the stress response mean, stress response standard deviation, strain response mean, strain response standard deviation, frequency characteristics and other dynamic response behavior characteristics, and the model damping coefficient is predicted and calculated by combining the elastic modulus predicted by the stress-strain mechanics experimental model and the dynamic response behavior characteristic parameters to predict and calculate the corresponding predicted damping parameters of the model, thereby obtaining the predicted damping coefficient of the stress-strain mechanics experimental model, and the predicted elastic modulus and the predicted damping coefficient of the stress-strain mechanics experimental model obtained by the previous prediction calculation are sorted and summarized to obtain the stress-strain mechanics experimental fitting output prediction data. Then, the elastic modulus and damping coefficient of the previously obtained mechanical experimental parameter data are actually measured and calculated under standardized experimental conditions, so as to analyze the corresponding actual elastic modulus and actual damping coefficient through data analysis (such as calculating the elastic modulus from the slope of the stress-strain curve, and obtaining the damping coefficient through vibration testing), and the model prediction error is statistically calculated for the corresponding actual elastic modulus and actual damping coefficient by combining the stress-strain mechanical experiment fitting output prediction data previously predicted by the model, so as to quantify the error between the calculated prediction value and the actual measurement value, and also compare the predicted data with the actual data by applying error analysis methods (such as root mean square error (RMSE) calculation), analyze the specific values ​​of the elastic modulus and damping coefficient errors, and integrate and summarize them, so as to finally obtain the stress-strain mechanical experiment model prediction error data.

[0076] Step S4: performing error correction optimization on the mechanical experiment parameter data based on the stress-strain mechanical experiment model prediction error data to obtain mechanical experiment parameter error correction data.

[0077] In an embodiment of the present invention, a nonlinear error distribution characteristic analysis is performed on the stress-strain mechanics experimental model prediction error data obtained by the previous analysis in the stress-strain mechanics experimental model, and these error data are modeled by a nonlinear regression analysis method, such as polynomial regression or support vector regression, and the error distribution is fitted by using a tool such as MATLAB's Curve Fitting Toolbox, thereby identifying the nonlinear distribution characteristics of the model prediction error under different stress and strain conditions, and optimizing the error correction strategy for the corresponding mechanical experimental parameter data by combining the nonlinear distribution characteristics of the model prediction error under different stress and strain conditions obtained by the previous analysis, so as to design a corresponding error correction strategy. Specifically, by defining the optimization objective function as the error between the experimental data and the corrected model prediction value, and setting constraints to ensure the rationality of the correction strategy, a set of optimal mechanical experimental parameter error correction strategies are designed to minimize the gap between the predicted error and the actual error, and then, by combining the previously formulated generation Under the guidance of the mechanical experimental parameter error correction optimization strategy, the mechanical experimental parameters with errors in the actual mechanical experimental parameter data are corrected, so that the correction strategy can be applied to the experimental data, and the experimental parameters can be adjusted through specific correction formulas or algorithms. Taking practical applications as an example, the written Python script or MATLAB program can be used to apply the optimization strategy to the experimental parameter data, and the experiment can be repeated and the corrected data can be recorded. The experimental data before and after the correction can be compared to verify the correction effect, ensuring that the corrected data more accurately reflects the actual stress and strain state, thereby improving the prediction accuracy of the mechanical experimental model and finally obtaining the mechanical experimental parameter error correction data.

[0078] Further, step S1 includes the following steps:

[0079] Step S11: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets;

[0080] Step S12: performing an analytical process on the influence of mechanical properties on the mechanical experiment setting conditions to obtain the description data of the influence of mechanical properties on the mechanical experiment conditions;

[0081] Step S13: performing mechanical influence feature mapping analysis on the mechanical experiment measurement target based on the mechanical property influence description data of the mechanical experiment conditions, and obtaining mechanical property influence feature data of the mechanical experiment measurement target under different mechanical experiment conditions;

[0082] Step S14: obtaining an initial mechanical model according to the mechanical experiment measurement target, and establishing a dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experiment measurement target under different mechanical experiment conditions, so as to obtain a stress-strain experimental mechanical model;

[0083] Step S15: Record the mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronize the mechanical experiment parameter data to the data processing unit.

[0084] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0085] Step S11: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets;

[0086] In an embodiment of the present invention, the specific setting conditions of the mechanical experiment are determined by obtaining, including the type of material used in the experiment, geometric shape, loading method, boundary conditions, etc. For example, if the experimental object is a welded joint, it is necessary to confirm the size of the welded joint, the weld type, and welding parameters (such as current, welding speed, etc.). At the same time, by clarifying the measurement objectives of the mechanical experiment, such as measuring the tensile strength, fatigue life and other performance indicators of the joint, these conditions and objectives can be obtained by consulting the experimental design documents, communicating with relevant engineers, and conducting preliminary research on the actual welding process, and finally obtaining the mechanical experiment setting conditions and mechanical experiment measurement objectives.

[0087] Step S12: performing an analytical process on the influence of mechanical properties on the mechanical experiment setting conditions to obtain the description data of the influence of mechanical properties on the mechanical experiment conditions;

[0088] In an embodiment of the present invention, an in-depth analysis is performed on the previously acquired mechanical experiment setting conditions to determine the influence of these conditions on the mechanical properties of the material, and finite element modeling is performed through mechanical analysis software (such as ANSYS, ABAQUS), and the material behavior under different setting conditions is simulated, thereby obtaining data such as the stress distribution and strain state of the material under different conditions. For example, for welded joints, it is necessary to analyze the influence of welding heat input on the microstructure of the material and simulate the temperature field and stress field distribution during the welding process. These analysis results will generate data describing the influence of mechanical properties, such as the influence of temperature gradient on material strength, residual stress generated during welding, etc., and finally obtain data describing the influence of mechanical experimental conditions on mechanical properties.

[0089] Step S13: performing mechanical influence feature mapping analysis on the mechanical experiment measurement target based on the mechanical property influence description data of the mechanical experiment conditions, and obtaining mechanical property influence feature data of the mechanical experiment measurement target under different mechanical experiment conditions;

[0090] In an embodiment of the present invention, a mapping analysis of mechanical influence characteristics is performed on the corresponding mechanical experimental measurement target by combining the mechanical property influence description data of the mechanical experimental conditions obtained by previous analytical analysis, so as to associate the mechanical property influence description data with the experimental measurement target by using statistical analysis methods and data mining technology. For example, the specific influence of different welding parameters (such as current, welding speed) on the tensile strength of the welded joint is determined by regression analysis. This process also includes generating mechanical property influence characteristic data under different experimental conditions, such as stress-strain distribution characteristic data of materials at different welding speeds, and finally obtaining the mechanical property influence characteristic data of the mechanical experimental measurement target under different mechanical experimental conditions.

[0091] Step S14: obtaining an initial mechanical model according to the mechanical experiment measurement target, and establishing a dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experiment measurement target under different mechanical experiment conditions, so as to obtain a stress-strain experimental mechanical model;

[0092] In an embodiment of the present invention, a preliminary mechanical model is obtained by obtaining a mechanical experimental measurement target based on a previous analysis. The model can be based on an existing theoretical model or a previous study. For example, a stress-strain model for a welded joint is selected as an initial model, and the initial model is dynamically calibrated by utilizing the mechanical property influence characteristic data obtained from the previous analysis. During the calibration process, the model parameters are adjusted through an optimization algorithm so that it can accurately predict the actual mechanical response under different mechanical experimental conditions. This includes using the least squares method to fit the model parameters, or using more complex optimization methods (such as genetic algorithms) to improve the accuracy of the model, thereby optimizing and establishing a mechanical model that can accurately describe the stress-strain relationship, and finally obtaining a stress-strain experimental mechanical model.

[0093] Step S15: Record the mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronize the mechanical experiment parameter data to the data processing unit.

[0094] In an embodiment of the present invention, parameter data in the mechanical experimental equipment is recorded in real time and synchronized to a data processing unit. This requires the installation of sensors and data acquisition systems during the mechanical experiment, such as strain gauges, force sensors, and temperature sensors. These sensors can measure and transmit mechanical parameter data (such as stress, strain, temperature, etc.) in real time. The data acquisition system must have a high sampling rate and data storage function to transmit the sensor data to the data processing unit. In the data processing unit, the recorded data is sorted and analyzed through a real-time data processing and storage system (such as a database system or real-time data processing software), and stored in a data warehouse for subsequent analysis and verification.

[0095] Furthermore, the establishment of dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experimental measurement target under different mechanical experimental conditions described in step S14 includes the following steps:

[0096] Based on the characteristic data of the influence of mechanical properties of the mechanical experimental measurement target under different mechanical experimental conditions, the initial mechanical model is subjected to mechanical applicable condition restriction analysis, and the mechanical experimental applicable constraint conditions of the mechanical model under different mechanical experimental conditions are obtained;

[0097] In an embodiment of the present invention, by combining the mechanical property data of the mechanical experimental measurement target under different mechanical conditions obtained previously using high-precision sensors and data acquisition systems, these mechanical property influencing characteristic data include different loads, ambient temperature, humidity, etc., and by using numerical analysis methods (such as finite element analysis) to perform applicable condition restriction analysis on the initial mechanical model. This process includes simulating the model under various mechanical conditions to ensure that the model can accurately reflect the mechanical properties in the experiment. The analysis results are used to determine the applicable constraints of the model under specific mechanical conditions, such as the maximum allowable load, stress distribution, etc. These constraints will be used for subsequent model adjustments to ensure its accuracy and reliability in practical applications, and finally obtain the applicable constraints of the mechanical model under different mechanical experimental conditions.

[0098] Preferably, a dynamic response parameter calibration analysis is performed on the initial mechanical model based on the applicable constraint conditions of the mechanical experiment of the mechanical model under different mechanical experimental conditions to obtain the mechanical applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions;

[0099] In an embodiment of the present invention, a dynamic response parameter calibration analysis is performed on the corresponding initial mechanical model by combining the applicable constraint conditions of the mechanical experiment of the mechanical model obtained by the previous analysis under different mechanical experimental conditions, so as to experiment on the initial mechanical model by using dynamic test equipment (such as a vibration table) to obtain the dynamic response data of the model under different conditions, and the experimental data is processed by system identification technology to obtain the dynamic response characteristic parameters of the mechanical model, such as damping ratio, natural frequency, etc., to ensure that the model can accurately predict the dynamic response under actual working conditions, and finally obtain the mechanically applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions.

[0100] Preferably, the initial mechanical model is subjected to step-by-step response calibration optimization according to the mechanically applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions, so as to establish a stress-strain experimental mechanical model.

[0101] In an embodiment of the present invention, the initial mechanical model is gradually optimized by performing response calibration by combining the mechanically applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions obtained by previous analysis, so as to gradually adjust the model parameters under different experimental conditions by utilizing an optimization algorithm (such as a genetic algorithm or a particle swarm optimization). The optimization process includes multiple iterations, and each iteration adjusts the model parameters and recalculates the stress-strain relationship to obtain an optimized stress-strain experimental mechanical model. This model can accurately reflect the mechanical behavior in the actual working environment, and finally a stress-strain experimental mechanical model is established.

[0102] Further, step S2 includes the following steps:

[0103] Step S21: using a data processing unit to perform noise frequency domain distribution analysis on the mechanical experiment parameter data to obtain a mechanical experiment parameter noise frequency domain distribution spectrum;

[0104] Step S22: performing noise point location and annotation processing on the mechanical experiment parameter noise frequency domain distribution spectrum to obtain a mechanical experiment parameter frequency domain distribution noise point annotated spectrum;

[0105] Step S23: performing noise point radius circle filtering processing on the corresponding mechanical experiment parameter data based on the noise point annotation spectrum of the mechanical experiment parameter frequency domain distribution to obtain mechanical experiment parameter noise filtering data;

[0106] Step S24: performing data missing point identification analysis on the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter data missing points; performing missing point interpolation compensation processing on the mechanical experiment parameter noise filtering data based on the mechanical experiment parameter data missing points to obtain mechanical experiment parameter interpolation compensation data;

[0107] Step S25: Standardize the mechanical experiment parameter interpolation compensation data to obtain mechanical experiment parameter standardized data.

[0108] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps:

[0109] Step S21: using a data processing unit to perform noise frequency domain distribution analysis on the mechanical experiment parameter data to obtain a mechanical experiment parameter noise frequency domain distribution spectrum;

[0110] In an embodiment of the present invention, a frequency domain analysis tool is used by a data processing unit to perform noise frequency domain distribution analysis on mechanical experiment parameter data, so as to convert time domain data into frequency domain data through Fourier transform, and use software such as MATLAB or SciPy library in Python to calculate the frequency spectrum of the noise signal. The generated spectrum diagram shows the frequency distribution and intensity of the noise in the data, which helps to identify the frequency domain characteristics of the noise. The frequency domain distribution spectrum diagram will show the noise intensity at different frequencies, provide an in-depth understanding of the noise source and its influence on the mechanical parameters, and finally obtain the frequency domain distribution spectrum diagram of the mechanical experiment parameter noise.

[0111] Step S22: performing noise point location and annotation processing on the mechanical experiment parameter noise frequency domain distribution spectrum to obtain a mechanical experiment parameter frequency domain distribution noise point annotated spectrum;

[0112] In an embodiment of the present invention, the frequency domain distribution spectrum of mechanical experiment parameter noise is processed by using an image processing tool (such as the image processing toolbox in OpenCV or MATLAB) to locate and mark the noise points. The noise points are located by setting a threshold or using an image segmentation algorithm (such as the Otsu algorithm) to detect the noise peak and mark the position of the noise point. On the frequency domain spectrum, different noise points are highlighted or marked to finally obtain a marked spectrum of the frequency domain distribution noise points of the mechanical experiment parameters.

[0113] Step S23: performing noise point radius circle filtering processing on the corresponding mechanical experiment parameter data based on the noise point annotation spectrum of the mechanical experiment parameter frequency domain distribution to obtain mechanical experiment parameter noise filtering data;

[0114] In an embodiment of the present invention, based on the obtained frequency domain distribution noise point annotation spectrum of the mechanical experimental parameter, a filter is used to perform radius circular filtering processing on the noise points at the corresponding time series positions in the mechanical experimental parameter data, so as to select an appropriate circular filter, the filter radius needs to be set based on the distribution characteristics of the noise points, and the circular filter is applied to filter the noise points by utilizing tools such as MATLAB's filter function or the SciPy library in Python. This processing step will reduce the impact of noise on the experimental data, thereby generating experimental data after noise filtering processing, and finally obtaining mechanical experimental parameter noise filtered data.

[0115] Step S24: performing data missing point identification analysis on the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter data missing points; performing missing point interpolation compensation processing on the mechanical experiment parameter noise filtering data based on the mechanical experiment parameter data missing points to obtain mechanical experiment parameter interpolation compensation data;

[0116] In the embodiment of the present invention, the missing points in the noise filtering data of the mechanical experiment parameters obtained after the noise filtering are analyzed by identifying the missing points, so as to identify the missing points in the noise filtering data of the mechanical experiment parameters, thereby obtaining the missing points in the mechanical experiment parameter data. At the same time, the corresponding missing points are interpolated and compensated by combining the average limit value of the mechanical experiment parameters at the distribution position of the missing points and applying the interpolation algorithm to make up for the missing parts in the mechanical experiment parameter data, thereby obtaining a complete mechanical experiment data set, and finally obtaining the mechanical experiment parameter interpolation compensation data.

[0117] Step S25: Standardize the mechanical experiment parameter interpolation compensation data to obtain mechanical experiment parameter standardized data.

[0118] In an embodiment of the present invention, the interpolation compensation data of the mechanical experimental parameters previously obtained after interpolation compensation are standardized by using a standardization method (such as Z-score standardization or Min-Max standardization) to convert the data values ​​into a unified standard range. For example, an appropriate standardization function (such as the normalize function in MATLAB or sklearn.preprocessing.StandardScaler in Python) can be selected to standardize the processed data so as to eliminate the scale effect of the data in subsequent analysis, ensure the consistency and comparability of the data, and finally obtain standardized data of the mechanical experimental parameters.

[0119] Further, step S23 includes the following steps:

[0120] Step S231: performing point distance quantization calculation on any two adjacent noise points in the frequency domain distribution noise point annotation spectrum of the mechanical experiment parameter, and obtaining the point distance measurement value between any two adjacent noise points in the frequency domain spectrum of the mechanical experiment parameter;

[0121] In the embodiment of the present invention, by first selecting any two adjacent noise points in the frequency domain distribution noise point annotation spectrum of the mechanical experiment parameter, for example, assuming that there are noise points A and B in the frequency domain distribution noise point annotation spectrum of the mechanical experiment parameter, the positions of these two points are coordinates (x A ,y A ) and (x B ,y B ), and calculate the distance between the two points by using the Euclidean distance formula, which is: The distance value d ABIt is the point distance measurement value between noise point A and noise point B. Repeat this process, calculate the distance of all adjacent noise point pairs in the spectrum, obtain the point distance measurement value of all adjacent noise point pairs, and finally obtain the point distance measurement value between any two adjacent noise points in the frequency domain spectrum of mechanical experimental parameters.

[0122] Step S232: comparing and judging the point distance measurement value between any two adjacent noise points in the mechanical experiment parameter frequency domain spectrum according to the preset noise point distance filter threshold, when the point distance measurement value between any two adjacent noise points is greater than the preset noise point distance filter threshold, no processing is performed; when the point distance measurement value between any two adjacent noise points is less than or equal to the preset noise point distance filter threshold, the point distance measurement value between the corresponding two adjacent noise points is used as the noise point radius;

[0123] In the embodiment of the present invention, by presetting the corresponding noise point distance filtering threshold, for example, d th =5, the point distance measurement value d between any two adjacent noise points in the frequency domain spectrum of the mechanical experimental parameters obtained by previous quantitative calculation AB Compare one by one, when the calculated point distance measurement value d AB Greater than the set threshold d th When , it remains the same without any processing; and when the calculated point distance measurement value d AB Less than or equal to the set threshold d th When the point is at a distance measurement value d AB As the noise point radius between any two adjacent noise points, noise points with a smaller distance can be screened out, and their radius is used in subsequent noise point processing steps.

[0124] Step S233: based on the noise point radius, a radius circle area is drawn at any two corresponding adjacent noise points in the noise point annotation spectrum of the mechanical experiment parameter frequency domain distribution, so as to obtain the noise point radius circle area of ​​the mechanical spectrum;

[0125] In the embodiment of the present invention, based on the previously determined noise point radius, a radius circle area is drawn at any two corresponding adjacent noise points in the noise point annotation spectrum of the mechanical experimental parameter frequency domain distribution. Taking noise point A as an example, if its radius is r A =d AB , then draw a circle with a radius of r on the spectrum with the noise point A as the center A The process is repeated for each pair of adjacent noise points that meet the distance condition, thereby obtaining several radius circle areas. The overlapping parts of these circle areas will represent the influence area between the noise points, and finally the radius circle area of ​​the noise point in the mechanical spectrum is obtained.

[0126] Step S234: extracting the timestamps of the mechanical experiment parameter data points in the mechanical spectrum noise point radius circle area to obtain the timestamps of the mechanical experiment parameter noise point radius circle area data points;

[0127] In the embodiment of the present invention, all mechanical experimental parameter data points are extracted from the circular area with the radius of the noise point of the mechanical spectrum previously determined. Assuming that there are data points D1, D2, ..., D n , these data points have timestamps T1, T2, …, T n This step realizes the extraction and processing of the timestamps of these data points and records all the timestamps. The timestamp extraction can be realized through the metadata or tags of the data points to ensure that the time information of all relevant data points in the circular area is obtained, and finally the timestamps of the data points in the radius circle area of ​​the mechanical experimental parameter noise point are obtained.

[0128] Step S235: Based on the timestamps of the data points in the radius circle area of ​​the mechanical experiment parameter noise points, the corresponding mechanical experiment parameter data are subjected to timestamp synchronization filtering processing to obtain mechanical experiment parameter noise filtering data.

[0129] In an embodiment of the present invention, the corresponding mechanical experiment parameter data is filtered with timestamp synchronization by combining the timestamps of the data points in the radius circle area of ​​the mechanical experiment parameter noise points extracted previously. Assuming that the original data set contains data point D and its corresponding timestamp T, the purpose of the timestamp synchronization filtering is to filter the data points with similar timestamps in the mechanical experiment parameter data using a time window method to reduce the impact of noise. For example, the time window can be set to Δt=0.5, and the data points with a timestamp difference less than or equal to Δt seconds are filtered and removed. These data points will be removed as noise points to remove the impact caused by the noise points, more accurately reflect the actual experimental parameters, and finally obtain the mechanical experiment parameter noise filtered data.

[0130] Furthermore, the missing point interpolation compensation processing of the mechanical experiment parameter noise filtering data based on the missing points of the mechanical experiment parameter data described in step S24 includes the following steps:

[0131] Perform spatial distribution analysis on the missing points of mechanical experimental parameter data to obtain the spatial distribution positions of the missing points of mechanical experimental parameter data;

[0132] In an embodiment of the present invention, spatial statistical analysis tools (such as Kriging model or other geographic information system (GIS) tools) are used to evaluate and analyze the spatial distribution of the previously determined missing points of mechanical experimental parameter data, so as to evaluate and analyze the distribution of the corresponding missing data points. These tools can identify the distribution pattern of the missing data points in space, thereby displaying the specific spatial location of the missing points, and finally obtaining the spatial distribution location of the missing points of the mechanical experimental parameters.

[0133] Preferably, based on the spatial distribution position of the missing points of the mechanical experiment parameters, the mechanical experiment parameter noise filtering data is quantitatively calculated for the left and right limits of the missing points to obtain the left limit value of the missing points of the mechanical experiment parameters and the right limit value of the missing points of the mechanical experiment parameters;

[0134] In an embodiment of the present invention, the spatial distribution position of the missing points of the mechanical experimental parameters obtained by combining the previous distribution analysis is used to perform limit quantification calculations at the corresponding distribution positions in the mechanical experimental parameter noise filtering data. For each missing point, the left and right limit values ​​of the noise filtering data near the spatial distribution position are calculated. This can be achieved by selecting a number of data points within a certain radius around the missing point and performing data limit calculations on these data points. The data processing method used in the limit quantification calculation may include local statistical analysis or regression analysis, so as to obtain the left limit value and the right limit value of each missing point, and finally obtain the left limit value of the missing point of the mechanical experimental parameter and the right limit value of the missing point of the mechanical experimental parameter.

[0135] Preferably, the left limit value of the missing point of the mechanical experiment parameter and the right limit value of the missing point of the mechanical experiment parameter are calculated by arithmetic average to obtain the limit average value of the missing point of the mechanical experiment parameter;

[0136] In an embodiment of the present invention, the left limit value of the missing point of the mechanical experimental parameter obtained by the previous left and right limit calculations and the right limit value of the missing point of the mechanical experimental parameter are calculated by arithmetic averaging, and the left limit value and the right limit value of each missing point obtained by the previous analysis are added and divided by 2 to determine the limit average value of each missing point of the mechanical experimental parameter, thereby ensuring that the limit average value of each missing point is accurately calculated, and finally obtaining the limit average value of the missing point of the mechanical experimental parameter.

[0137] Preferably, based on the limiting average value of the missing points of the mechanical experiment parameters, the missing point interpolation compensation processing is performed on the spatial distribution positions of the corresponding missing points of the mechanical experiment parameters in the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter interpolation compensation data.

[0138] In an embodiment of the present invention, interpolation compensation processing is performed on the spatial distribution positions of the missing points of the mechanical experiment parameters corresponding to the mechanical experiment parameter noise filtering data by combining the limiting average values ​​of the missing points of the mechanical experiment parameters obtained by previous quantitative calculation, so as to insert the positions of the missing point data according to the calculation results of the limiting average values ​​to replace the original missing data points. In specific implementation, the accuracy and consistency of the compensated data are ensured by writing an interpolation algorithm or using data processing software to perform interpolation operations. This processing process can effectively fill the missing points, improve the integrity and reliability of the mechanical experiment parameter data, and finally obtain the interpolation compensation data of the mechanical experiment parameters.

[0139] Further, step S3 includes the following steps:

[0140] Step S31: inputting the standardized data of mechanical experiment parameters into the stress-strain experiment mechanical model to perform mechanical experiment dynamic response behavior prediction processing to obtain stress-strain experiment dynamic response behavior prediction data;

[0141] In an embodiment of the present invention, the standardized data of mechanical experimental parameters obtained after previous standardization are input into the stress-strain experimental mechanical model to perform prediction processing on the dynamic response behavior of the mechanical experiment. The model uses these data to predict the dynamic response behavior of the sample, and simulates and analyzes the dynamic behavior of the sample under different stress and strain conditions using finite element analysis software (such as ANSYS or ABAQUS), and finally obtains the stress-strain experimental dynamic response behavior prediction data.

[0142] Step S32: performing a mechanical experiment behavior field fitting analysis on the stress-strain experiment mechanical model based on the stress-strain experiment dynamic response behavior prediction data to generate a stress-strain experiment dynamic response behavior fitting output field;

[0143] In an embodiment of the present invention, a fitting analysis of the mechanical experiment behavior field is performed in the corresponding stress-strain experiment mechanical model by combining the stress-strain experiment dynamic response behavior prediction data obtained by previous prediction analysis, so as to process the dynamic response behavior prediction data by using a fitting algorithm (such as the least squares method or the curve fitting method), and compare the predicted data with the experimental results, and use a fitting tool (such as the Curve Fitting Toolbox in MATLAB) to generate the behavior field of the mechanical model. This output field depicts the actual response characteristics of the material under different stress and strain conditions, and finally generates a stress-strain experiment dynamic response behavior fitting output field.

[0144] Step S33: performing mechanical experiment fitting parameter output prediction on the stress-strain experiment dynamic response behavior fitting output field to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient;

[0145] In an embodiment of the present invention, a behavior statistical analysis of the mechanical experimental response is performed on the stress-strain experimental dynamic response behavior fitting output field obtained by the previous fitting analysis, so as to fit the dynamic response of the mechanical experimental model through a dynamic response analysis method, such as Fourier transform, and by using a data fitting technique (such as least squares method or Bayesian regression), so as to obtain a parameter set of the dynamic response behavior through fitting analysis, and these parameter sets include stress response, strain response, frequency response, amplitude response and phase response, etc. At the same time, by extracting the corresponding stress response fitting parameters and strain response fitting parameters from them, the elastic modulus calculation formula (for example, Wherein δ is stress and ε is strain) is predicted and calculated to obtain the elastic modulus reflecting the rigidity characteristics of the material under dynamic loading, so as to obtain the elastic modulus predicted by the stress-strain mechanics experimental model. The dynamic response behavior characteristics in the fitting output field of the stress-strain experiment dynamic response behavior obtained by fitting analysis are also deeply analyzed to extract the main characteristics from the dynamic response data after fitting, such as the dynamic response behavior characteristics such as the stress response mean, the stress response standard deviation, the strain response mean, the strain response standard deviation, and the frequency characteristics, and the model damping coefficient is predicted and calculated by combining the elastic modulus predicted by the stress-strain mechanics experimental model and the dynamic response behavior characteristic parameters to predict and calculate the corresponding predicted damping parameters of the model, so as to obtain the stress-strain mechanics experimental model predicted damping coefficient. Then, the stress-strain mechanics experimental model predicted elastic modulus and the stress-strain mechanics experimental model predicted damping coefficient obtained by the previous prediction calculation are sorted and summarized to generate the fitting output prediction data of the stress-strain mechanics experimental model, and finally the stress-strain mechanics experimental fitting output prediction data is obtained.

[0146] Step S34: actually measuring the elastic modulus and damping coefficient of the mechanical experiment parameter data to obtain the actual elastic modulus and the actual damping coefficient of the mechanical experiment;

[0147] In an embodiment of the present invention, the elastic modulus and the damping coefficient of the mechanical experiment parameter data previously obtained are actually measured and calculated under standardized experimental conditions, and experiments are performed using a universal material testing machine (such as the Instron series), samples under different stress and strain conditions are loaded, and their response data are recorded. Through data analysis (such as calculating the elastic modulus from the slope of the stress-strain curve and obtaining the damping coefficient through vibration testing), the actual elastic modulus and the actual damping coefficient of the mechanical experiment are finally obtained.

[0148] Step S35: Based on the prediction data output from the stress-strain mechanics experiment fitting, a model prediction error analysis is performed on the actual elastic modulus and the actual damping coefficient of the mechanics experiment to obtain the stress-strain mechanics experiment model prediction error data.

[0149] In an embodiment of the present invention, by combining the stress-strain mechanics experiment fitting output prediction data previously predicted by the model, the corresponding actual elastic modulus of the mechanical experiment and the actual damping coefficient of the mechanical experiment are statistically calculated to quantify the error between the calculated prediction value and the actual measurement value, and by applying an error analysis method (such as root mean square error (RMSE) calculation), the predicted data and the actual data are compared, the specific values ​​of the elastic modulus and the damping coefficient errors are analyzed, and they are integrated and summarized to finally obtain the stress-strain mechanics experiment model prediction error data.

[0150] Further, step S33 includes the following steps:

[0151] Perform mechanical experiment response behavior parameter analysis on the stress-strain experiment dynamic response behavior fitting output field to obtain a mechanical experiment model dynamic response behavior fitting parameter set;

[0152] In an embodiment of the present invention, a statistical analysis of the behavior of the mechanical experimental response is performed on the output field of the dynamic response behavior of the stress-strain experiment obtained by the previous fitting analysis, so as to extract the characteristic data of the dynamic response behavior of the stress-strain experiment through a dynamic response analysis method, such as Fourier transform, and fit the dynamic response of the mechanical experimental model by using a data fitting technique (such as the least squares method or Bayesian regression), so as to obtain a parameter set of the dynamic response behavior through fitting analysis. These parameter sets include stress response, strain response, frequency response, amplitude response and phase response, etc., which form the basic data of the mechanical experimental model, and finally obtain a fitting parameter set of the dynamic response behavior of the mechanical experimental model.

[0153] Preferably, a model elastic modulus prediction calculation is performed on the dynamic response behavior fitting parameter set of the mechanical experimental model to obtain a stress-strain mechanical experimental model predicted elastic modulus;

[0154] In the embodiment of the present invention, the corresponding stress response fitting parameters and strain response fitting parameters are extracted from the dynamic response behavior fitting parameter set of the mechanical experimental model, and by using the elastic modulus calculation formula (for example, Where δ is stress and ε is strain), the stress response fitting parameters and the strain response fitting parameters are substituted into the calculation. The prediction calculation of the elastic modulus can be performed using finite element analysis software or a custom calculation program to ensure that the parameters input during the calculation are accurate, and the elastic modulus is corrected according to the characteristics of the dynamic response model to obtain the elastic modulus that reflects the rigidity characteristics of the material under dynamic loading, and finally the elastic modulus predicted by the stress-strain mechanics experimental model is obtained.

[0155] Preferably, a dynamic response behavior characteristic analysis is performed on the dynamic response behavior fitting output field of the stress-strain experiment to obtain dynamic response behavior characteristic data of the stress-strain experiment;

[0156] In an embodiment of the present invention, an in-depth analysis is performed on the dynamic response behavior characteristics in the fitting output field of the dynamic response behavior of the stress-strain experiment obtained previously through fitting analysis, so as to extract the main characteristics from the fitted dynamic response data, such as the stress response mean, the stress response standard deviation, the strain response mean, the strain response standard deviation, the frequency characteristics, etc., and these characteristic data are obtained by adopting time domain analysis methods (such as calculating the transient response time, the peak position, etc.) and frequency domain analysis methods (such as Fourier transform, power spectral density analysis), so as to provide the dynamic response characteristics of the material under the actual mechanical experimental working conditions, and finally obtain the characteristic data of the dynamic response behavior of the stress-strain experiment.

[0157] Preferably, based on the elastic modulus predicted by the stress-strain mechanics experimental model and the characteristic data of the dynamic response behavior of the stress-strain experiment, the model damping coefficient is predicted and calculated on the fitting output field of the dynamic response behavior of the stress-strain experiment using the damping coefficient calculation formula of the stress-strain experimental model to obtain the damping coefficient predicted by the stress-strain mechanics experimental model;

[0158] In an embodiment of the present invention, a suitable stress-strain experimental model damping coefficient calculation formula is formed by combining the inherent damping ratio of the mechanical experimental model, the predicted elastic modulus of the stress-strain mechanical experimental model, the proportional coefficient of the elastic modulus of the mechanical experimental model, the total duration of the predicted calculation time window, the predicted calculation time variable parameter, stress, the average value of the stress dynamic response behavior, the standard deviation of the stress dynamic response behavior, strain, the average value of the strain dynamic response behavior, the standard deviation of the strain dynamic response behavior and related parameters to perform a model damping coefficient prediction calculation on the stress-strain experimental dynamic response behavior fitting output field, so as to predict and calculate the corresponding predicted damping parameters of the model, and finally obtain the predicted damping coefficient of the stress-strain mechanical experimental model.

[0159] Preferably, the elastic modulus predicted by the stress-strain mechanics experimental model and the damping coefficient predicted by the stress-strain mechanics experimental model are subjected to parameter aggregation processing to obtain stress-strain mechanics experimental fitting output prediction data.

[0160] In an embodiment of the present invention, the elastic modulus predicted by the stress-strain mechanics experimental model and the damping coefficient predicted by the stress-strain mechanics experimental model obtained by previous prediction and calculation are sorted and summarized, and these two parameters are integrated by using statistical analysis methods (such as regression analysis or data fitting) to generate fitting output prediction data of the stress-strain mechanics experimental model, and finally the stress-strain mechanics experimental fitting output prediction data is obtained.

[0161] Furthermore, the calculation formula of the damping coefficient of the stress-strain experimental model is specifically:

[0162]

[0163] Where c is the damping coefficient predicted by the stress-strain mechanics experimental model, ω n is the inherent damping ratio of the mechanical experimental model, E is the elastic modulus predicted by the stress-strain mechanical experimental model, α is the proportional coefficient of the elastic modulus of the mechanical experimental model, T is the total duration of the prediction calculation time window, t is the prediction calculation time variable parameter, δ(t) is the stress at time t in the stress-strain mechanical experiment, μ δ is the average value of stress dynamic response behavior, σ δ is the standard deviation of the stress dynamic response behavior, ε(t) is the strain at time t in the stress-strain mechanics experiment, μ ε is the average value of the dynamic response behavior of strain, σ ε is the standard deviation of the strain dynamic response behavior, and η is the correction coefficient of the damping coefficient predicted by the stress-strain mechanics experimental model.

[0164] The present invention obtains a stress-strain experimental model damping coefficient calculation formula by using a specific mathematical model and verifying it, which is used to predict and calculate the model damping coefficient of the stress-strain experimental dynamic response behavior fitting output field. The stress-strain experimental model damping coefficient calculation formula can accurately predict the damping coefficient of the model by combining the dynamic response characteristics of elastic modulus, stress and strain, and improve the accuracy of the experimental model. The integral processing and response behavior characteristic analysis in the formula make the capture of dynamic behavior more comprehensive, taking into account the instantaneous changes of stress and strain, and can better reflect the damping characteristics under actual experimental conditions. It also improves the comprehensive evaluation ability of the damping characteristics of the experimental model by integrating a variety of dynamic response parameters (such as stress, strain and their statistical characteristics) into one formula. By predicting the damping coefficient, it is possible to optimize the experimental design and material selection, improve the seismic resistance and stability of the structure, and reduce potential engineering risks. In addition, by introducing a correction coefficient, it is possible to adjust according to the actual situation in the mechanical experiment, so that the mechanical experimental model is more suitable for specific application scenarios. Therefore, the formula fully considers the stress-strain mechanical experimental model prediction damping coefficient c, the mechanical experimental model inherent damping ratio ω n , stress-strain mechanical experimental model predicts elastic modulus E, mechanical experimental model elastic modulus proportionality coefficient α, predicts the total duration of the calculation time window T, predicts the calculation time variable parameter t, stress δ(t) at time t in stress-strain mechanical experiment, and the average value of stress dynamic response behavior μ δ , standard deviation of stress dynamic response behavior σ δ , strain ε(t) at time t in stress-strain mechanics experiment, average value μ of strain dynamic response behavior ε , standard deviation of strain dynamic response behavior σ ε , the correction coefficient η of the damping coefficient predicted by the stress-strain mechanics experimental model, according to the mutual correlation between the damping coefficient c predicted by the stress-strain mechanics experimental model and the above parameters, constitutes a functional relationship This formula can realize the prediction and calculation process of the model damping coefficient of the output field of the dynamic response behavior of the stress-strain experiment. At the same time, the introduction of the correction coefficient η of the stress-strain mechanics experimental model to predict the damping coefficient can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the calculation formula of the damping coefficient of the stress-strain experimental model.

[0165] Further, step S4 includes the following steps:

[0166] Step S41: performing nonlinear error distribution characteristic analysis on the stress-strain mechanical experimental model prediction error data to obtain nonlinear distribution characteristic data of the mechanical experimental model error under different stress and strain conditions;

[0167] In an embodiment of the present invention, a nonlinear error distribution characteristic analysis is performed on the stress-strain mechanics experimental model prediction error data obtained by the previous analysis in the stress-strain mechanics experimental model, and these error data are modeled by a nonlinear regression analysis method, such as polynomial regression or support vector regression, and the error distribution is fitted by using tools such as MATLAB's Curve Fitting Toolbox, thereby identifying the nonlinear distribution characteristics of the model prediction error under different stress and strain conditions, and plotting the error distribution diagram through data visualization tools (such as Python's Matplotlib or Seaborn) to clearly show the error change trend under different conditions, and finally obtain the nonlinear distribution characteristic data of the mechanical experimental model error under different stress and strain conditions.

[0168] Step S42: performing error correction strategy optimization design on corresponding mechanical experiment parameter data based on nonlinear distribution characteristic data of mechanical experiment model errors under different stress and strain conditions to generate a mechanical experiment parameter error correction optimization strategy;

[0169] In an embodiment of the present invention, the error correction strategy of the corresponding mechanical experimental parameter data is optimized by combining the nonlinear distribution characteristic data of the mechanical experimental model error under different stress and strain conditions obtained by previous analysis to design a corresponding error correction strategy, and the model parameters are optimized by algorithms such as genetic algorithms or particle swarm optimization algorithms using the identified nonlinear error distribution data. This process can be implemented using MATLAB's Optimization Toolbox or Python's SciPy library. Specifically, by defining the optimization objective function as the error between the experimental data and the corrected model prediction value, and setting constraints to ensure the rationality of the correction strategy, a set of optimal mechanical experimental parameter error correction strategies are designed to minimize the gap between the predicted error and the actual error, and finally a mechanical experimental parameter error correction optimization strategy is designed and generated.

[0170] Step S43: performing error correction optimization on the mechanical experiment parameter data according to the mechanical experiment parameter error correction optimization strategy to obtain mechanical experiment parameter error correction data.

[0171] In an embodiment of the present invention, under the guidance of a previously formulated and generated mechanical experiment parameter error correction optimization strategy, error correction is performed on the mechanical experiment parameters with errors in the actual mechanical experiment parameter data, so that the correction strategy is applied to the experimental data, and the experimental parameters are adjusted by a specific correction formula or algorithm. Taking actual application as an example, a written Python script or MATLAB program can be used to apply the optimization strategy to the experimental parameter data, and the experiment can be repeated and the corrected data can be recorded, and the experimental data before and after the correction can be compared to verify the correction effect, thereby ensuring that the corrected data more accurately reflects the actual stress and strain state, thereby improving the prediction accuracy of the mechanical experiment model, and finally obtaining mechanical experiment parameter error correction data.

[0172] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0173] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A mechanical experimental data preprocessing method based on a mechanical model, characterized in that: The following steps are involved: Step S1: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets, and calibrating and establishing a mechanical model according to the mechanical experiment setting conditions and mechanical experiment measurement targets to obtain a stress-strain experimental mechanical model; recording mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronizing the mechanical experiment parameter data to a data processing unit; Step S2: using a data processing unit to perform noise point radius circle filtering on the mechanical experiment parameter data to obtain mechanical experiment parameter noise filtering data; performing interpolation compensation and standardization processing on the mechanical experiment parameter noise filtering data to obtain mechanical experiment parameter standardized data; Step S3: performing mechanical experiment fitting processing on the stress-strain experimental mechanical model based on the mechanical experiment parameter standardization data to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient; Based on the prediction data output from the stress-strain mechanics experiment fitting, the model prediction error analysis is performed on the mechanical experiment parameter data to obtain the stress-strain mechanics experiment model prediction error data; Step S4: performing error correction optimization on the mechanical experiment parameter data based on the stress-strain mechanical experiment model prediction error data to obtain mechanical experiment parameter error correction data.

2. The mechanical experiment data pre-processing method based on the mechanical model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: obtaining mechanical experiment setting conditions and mechanical experiment measurement targets; Step S12: performing an analytical process on the influence of mechanical properties on the mechanical experiment setting conditions to obtain the description data of the influence of mechanical properties on the mechanical experiment conditions; Step S13: performing mechanical influence feature mapping analysis on the mechanical experiment measurement target based on the mechanical property influence description data of the mechanical experiment conditions, and obtaining mechanical property influence feature data of the mechanical experiment measurement target under different mechanical experiment conditions; Step S14: obtaining an initial mechanical model according to the mechanical experiment measurement target, and establishing a dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experiment measurement target under different mechanical experiment conditions, so as to obtain a stress-strain experimental mechanical model; Step S15: Record the mechanical experiment parameter data in real time from the mechanical experiment equipment, and synchronize the mechanical experiment parameter data to the data processing unit.

3. The mechanical experiment data pre-processing method based on the mechanical model according to claim 2 is characterized in that: The establishment of dynamic response calibration of the initial mechanical model based on the mechanical property influence characteristic data of the mechanical experimental measurement target under different mechanical experimental conditions described in step S14 includes the following steps: Based on the characteristic data of the influence of mechanical properties of the mechanical experimental measurement target under different mechanical experimental conditions, the initial mechanical model is subjected to mechanical applicable condition restriction analysis, and the mechanical experimental applicable constraint conditions of the mechanical model under different mechanical experimental conditions are obtained; Based on the applicable constraints of the mechanical model under different mechanical experimental conditions, the dynamic response parameter calibration analysis of the initial mechanical model is carried out to obtain the applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions; According to the mechanically applicable dynamic response calibration parameters of the mechanical model under different mechanical experimental conditions, the initial mechanical model is gradually calibrated and optimized to establish a stress-strain experimental mechanical model.

4. The mechanical experiment data pre-processing method based on mechanical model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: using a data processing unit to perform noise frequency domain distribution analysis on the mechanical experiment parameter data to obtain a mechanical experiment parameter noise frequency domain distribution spectrum; Step S22: performing noise point location and annotation processing on the mechanical experiment parameter noise frequency domain distribution spectrum to obtain a mechanical experiment parameter frequency domain distribution noise point annotated spectrum; Step S23: performing noise point radius circle filtering processing on the corresponding mechanical experiment parameter data based on the noise point annotation spectrum of the mechanical experiment parameter frequency domain distribution to obtain mechanical experiment parameter noise filtering data; Step S24: performing data missing point identification analysis on the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter data missing points; performing missing point interpolation compensation processing on the mechanical experiment parameter noise filtering data based on the mechanical experiment parameter data missing points to obtain mechanical experiment parameter interpolation compensation data; Step S25: Standardize the mechanical experiment parameter interpolation compensation data to obtain mechanical experiment parameter standardized data.

5. The mechanical experiment data pre-processing method based on mechanical model according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: performing point distance quantization calculation on any two adjacent noise points in the frequency domain distribution noise point annotation spectrum of the mechanical experiment parameter, and obtaining the point distance measurement value between any two adjacent noise points in the frequency domain spectrum of the mechanical experiment parameter; Step S232: comparing and judging the point distance measurement value between any two adjacent noise points in the mechanical experiment parameter frequency domain spectrum according to the preset noise point distance filter threshold, when the point distance measurement value between any two adjacent noise points is greater than the preset noise point distance filter threshold, no processing is performed; when the point distance measurement value between any two adjacent noise points is less than or equal to the preset noise point distance filter threshold, the point distance measurement value between the corresponding two adjacent noise points is used as the noise point radius; Step S233: based on the noise point radius, a radius circle area is drawn at any two corresponding adjacent noise points in the noise point annotation spectrum of the mechanical experimental parameter frequency domain distribution, so as to obtain the noise point radius circle area of ​​the mechanical spectrum; Step S234: extracting the timestamps of the mechanical experiment parameter data points in the mechanical spectrum noise point radius circle area to obtain the timestamps of the mechanical experiment parameter noise point radius circle area data points; Step S235: Based on the timestamps of the data points in the radius circle area of ​​the mechanical experiment parameter noise points, the corresponding mechanical experiment parameter data are subjected to timestamp synchronization filtering processing to obtain mechanical experiment parameter noise filtering data.

6. The mechanical experiment data pre-processing method based on mechanical model according to claim 4 is characterized in that: The missing point interpolation compensation processing of the mechanical experiment parameter noise filtering data based on the missing points of the mechanical experiment parameter data in step S24 includes the following steps: Perform spatial distribution analysis on the missing points of mechanical experimental parameter data to obtain the spatial distribution positions of the missing points of mechanical experimental parameter data; Based on the spatial distribution of the missing points of the mechanical experimental parameters, the left and right limits of the missing points of the mechanical experimental parameters are quantitatively calculated for the noise filtering data, and the left limit value of the missing points of the mechanical experimental parameters and the right limit value of the missing points of the mechanical experimental parameters are obtained; The left limit value of the missing point of the mechanical experimental parameters and the right limit value of the missing point of the mechanical experimental parameters are calculated by arithmetic average to obtain the limit average value of the missing point of the mechanical experimental parameters; Based on the limiting average value of the missing points of the mechanical experiment parameters, the missing point interpolation compensation processing is performed on the spatial distribution positions of the missing points of the mechanical experiment parameters corresponding to the mechanical experiment parameter noise filtering data to obtain the mechanical experiment parameter interpolation compensation data.

7. The mechanical experiment data pre-processing method based on mechanical model according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: inputting the standardized data of mechanical experiment parameters into the stress-strain experiment mechanical model to perform mechanical experiment dynamic response behavior prediction processing to obtain stress-strain experiment dynamic response behavior prediction data; Step S32: performing a mechanical experiment behavior field fitting analysis on the stress-strain experiment mechanical model based on the stress-strain experiment dynamic response behavior prediction data to generate a stress-strain experiment dynamic response behavior fitting output field; Step S33: performing mechanical experiment fitting parameter output prediction on the stress-strain experiment dynamic response behavior fitting output field to obtain stress-strain mechanical experiment fitting output prediction data, wherein the stress-strain mechanical experiment fitting output prediction data includes the stress-strain mechanical experiment model predicted elastic modulus and the stress-strain mechanical experiment model predicted damping coefficient; Step S34: actually measuring the elastic modulus and damping coefficient of the mechanical experiment parameter data to obtain the actual elastic modulus and the actual damping coefficient of the mechanical experiment; Step S35: Based on the prediction data output from the stress-strain mechanics experiment fitting, a model prediction error analysis is performed on the actual elastic modulus and the actual damping coefficient of the mechanics experiment to obtain the stress-strain mechanics experiment model prediction error data.

8. The mechanical experiment data pre-processing method based on mechanical model according to claim 1 is characterized in that: Step S33 includes the following steps: Perform mechanical experiment response behavior parameter analysis on the stress-strain experiment dynamic response behavior fitting output field to obtain a mechanical experiment model dynamic response behavior fitting parameter set; The model elastic modulus is predicted and calculated for the dynamic response behavior fitting parameter set of the mechanical experimental model to obtain the predicted elastic modulus of the stress-strain mechanical experimental model; Perform dynamic response behavior characteristic analysis on the dynamic response behavior fitting output field of the stress-strain experiment to obtain dynamic response behavior characteristic data of the stress-strain experiment; Based on the elastic modulus predicted by the stress-strain mechanics experimental model and the characteristic data of the dynamic response behavior of the stress-strain experiment, the damping coefficient calculation formula of the stress-strain experimental model is used to predict and calculate the model damping coefficient of the dynamic response behavior fitting output field of the stress-strain experiment, and the predicted damping coefficient of the stress-strain mechanics experimental model is obtained; The elastic modulus predicted by the stress-strain mechanics experimental model and the damping coefficient predicted by the stress-strain mechanics experimental model are processed by parameter summary to obtain the stress-strain mechanics experimental fitting output prediction data.

9. The mechanical experiment data pre-processing method based on mechanical model according to claim 8 is characterized in that: The calculation formula of the damping coefficient of the stress-strain experimental model is specifically: Where c is the damping coefficient predicted by the stress-strain mechanics experimental model, ω n is the inherent damping ratio of the mechanical experimental model, E is the elastic modulus predicted by the stress-strain mechanical experimental model, α is the proportional coefficient of the elastic modulus of the mechanical experimental model, T is the total duration of the prediction calculation time window, t is the prediction calculation time variable parameter, δ(t) is the stress at time t in the stress-strain mechanical experiment, μ δ is the average value of stress dynamic response behavior, σ δ is the standard deviation of the stress dynamic response behavior, ε(t) is the strain at time t in the stress-strain mechanics experiment, μ ε is the average value of the dynamic response behavior of strain, σ ε is the standard deviation of the strain dynamic response behavior, and η is the correction coefficient of the damping coefficient predicted by the stress-strain mechanics experimental model.

10. The mechanical experiment data pre-processing method based on mechanical model according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing nonlinear error distribution characteristic analysis on the stress-strain mechanical experimental model prediction error data to obtain nonlinear distribution characteristic data of the mechanical experimental model error under different stress and strain conditions; Step S42: performing error correction strategy optimization design on corresponding mechanical experiment parameter data based on nonlinear distribution characteristic data of mechanical experiment model errors under different stress and strain conditions to generate a mechanical experiment parameter error correction optimization strategy; Step S43: performing error correction optimization on the mechanical experiment parameter data according to the mechanical experiment parameter error correction optimization strategy to obtain mechanical experiment parameter error correction data.

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