A multi-layer material thickness detection system and method based on a TM sensor, an electronic device and a storage medium

Abstract

The present invention relates to the technical field of material thickness detection, and discloses a multi-layer material thickness detection system, method, electronic device, and storage medium based on a TM sensor. The system comprises: an ultrasonic detection module, internally provided with a transmitting unit and a receiving unit; a feature extraction module, configured to acquire ultrasonic signals and perform feature extraction on the ultrasonic signals; a control module, electrically connected to the ultrasonic detection module and the feature extraction module, and comprising an acquisition unit, an algorithm generation unit, an analysis unit, an adjustment unit, and a storage unit; the acquisition unit, configured to acquire amplitude characteristic data, waveform characteristic data, and spectral characteristic data; and an algorithm generation unit, configured to generate a layered measurement algorithm using the amplitude characteristic data, waveform characteristic data, and spectral characteristic data as input signals, and calculate the measured thickness of each layer of material according to the layered measurement algorithm. The present invention can automatically adjust the measurement algorithm to ensure the accuracy and reliability of the measurement results.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material thickness detection, in particular to a multi-layer material thickness detection system and method based on a TM sensor, an electronic device and a storage medium. BACKGROUND

[0002] In modern industrial production, the thickness detection of multi-layer materials is an important quality control link. Multi-layer materials are widely used in the fields of aviation, automobiles, construction, etc., and the uniformity and consistency of their thickness directly affect the performance and safety of products. Traditional thickness detection methods mainly rely on manual measurement or mechanical measurement equipment, which not only has low efficiency, but also is easily affected by human factors, resulting in inaccurate measurement results.

[0003] With the development of science and technology, sensor-based thickness detection technology has gradually become the mainstream. Among them, the TM (Time-of-Flight Measurement) sensor has been widely used in the field of multi-layer material thickness detection due to its high precision, high efficiency and non-contact measurement characteristics. The TM sensor calculates the thickness of the material by measuring the propagation time of ultrasonic waves in the material, which has high measurement accuracy and repeatability.

[0004] However, the existing TM sensor-based thickness detection system still has some problems in practical application. For example, changes in environmental factors such as temperature, humidity and pressure will affect the propagation speed of ultrasonic waves, thereby affecting the accuracy of the measurement results. In addition, the density inhomogeneity of multi-layer materials also affects the thickness measurement. Therefore, how to realize high-precision multi-layer material thickness detection under the condition of environmental factors and material density changes has become a technical problem to be solved. SUMMARY

[0005] In view of this, the present application proposes a multi-layer material thickness detection system and method based on a TM sensor, an electronic device and a storage medium, aiming to solve the problem of low accuracy of measurement results for multi-layer materials in the current technology.

[0006] In one aspect, the present application proposes a multi-layer material thickness detection system based on a TM sensor, comprising:

[0007] An ultrasonic detection module is internally provided with a transmitting unit and a receiving unit, the transmitting unit is used to transmit ultrasonic waves into the multi-layer material, and the receiving unit is used to receive the echo of the ultrasonic waves transmitted by the transmitting unit on each layer of material, forming an ultrasonic signal;

[0008] The feature extraction module is configured to acquire the ultrasonic signals and perform feature extraction on the ultrasonic signals to obtain amplitude feature data, waveform feature data and frequency spectrum feature data of the ultrasonic signals corresponding to each layer of material.

[0009] The control module is electrically connected with the ultrasonic detection module and the feature extraction module, and includes an acquisition unit, an algorithm generation unit, an analysis unit, an adjustment unit and a storage unit.

[0010] The acquisition unit is configured to acquire the amplitude feature data, the waveform feature data and the frequency spectrum feature data.

[0011] The algorithm generation unit is configured to generate a layered measurement algorithm taking the amplitude feature data, the waveform feature data and the frequency spectrum feature data as input signals, and calculate a measurement thickness of each layer of material according to the layered measurement algorithm.

[0012] The analysis unit is configured to compare the measurement thickness with a standard thickness value, and determine whether to adjust the layered measurement algorithm according to a comparison result.

[0013] When it is determined to adjust the layered measurement algorithm, the analysis unit controls the acquisition unit to acquire first environment data of an external environment and second environment data inside the multi-layer material, and establishes an environment data set according to the first environment data and the second environment data, calculates an environment influence index according to the environment data set, compares the environment influence index with historical data, and adjusts the layered measurement algorithm according to a comparison result.

[0014] The adjustment unit is configured to control the acquisition unit to acquire density data corresponding to each layer of material when the environment influence index is different from the historical data, and calculate a comprehensive compensation coefficient according to the density data, the first environment data and the second environment data, and adjust the layered measurement algorithm.

[0015] The storage unit is configured to store the comprehensive compensation coefficient.

[0016] Further, when the amplitude feature data, the waveform feature data and the frequency spectrum feature data of the ultrasonic signals corresponding to each layer of material are acquired, the following steps are included.

[0017] The amplitude feature data is a maximum amplitude of the ultrasonic signals measured for each layer of material.

[0018] The waveform feature data is a rising edge time and a falling edge time of the ultrasonic signals measured for each layer of material.

[0019] The frequency spectrum feature data is a frequency distribution of the ultrasonic signals measured for each layer of material.

[0020] Further, when generating the layered measurement algorithm with the amplitude feature data, the waveform feature data and the spectrum feature data as input signals, comprising:

[0021] cleaning data of the amplitude feature data, the waveform feature data and the spectrum feature data, removing noise in the ultrasonic signal, and eliminating interference signals by wavelet transform;

[0022] normalizing the amplitude feature data, the waveform feature data and the spectrum feature data to obtain normalized amplitude feature data, normalized waveform feature data and normalized spectrum feature data;

[0023] fusing the normalized amplitude feature data, the normalized waveform feature data and the normalized spectrum feature data to obtain fusion feature data;

[0024] using machine learning algorithm to train the fusion feature data with the fusion feature data as input data, and generating the layered measurement algorithm.

[0025] Further, when comparing the measured thickness with the standard thickness value and determining whether to adjust the layered measurement algorithm according to the comparison result, comprising:

[0026] obtaining a thickness difference value by subtracting the measured thickness from the standard thickness;

[0027] comparing the thickness difference value with a thickness difference value threshold, and determining whether to adjust the layered measurement algorithm according to the comparison result;

[0028] when the thickness difference value is less than or equal to the thickness difference value threshold, the analysis unit determines not to adjust the layered measurement algorithm;

[0029] when the thickness difference value is greater than the thickness difference value threshold, the analysis unit determines to adjust the layered measurement algorithm.

[0030] Further, when determining to adjust the layered measurement algorithm, the analysis unit controls the acquisition unit to acquire first environment data of an external environment and second environment data inside the multi-layer material, and establishes an environment data set according to the first environment data and the second environment data, and calculates an environment influence index according to the environment data set, comprising:

[0031] the first environment data includes external environment temperature, external environment humidity and external environment pressure;

[0032] the second environment data includes material internal temperature, material internal humidity and material internal pressure;

[0033] The environmental influence index is obtained by the following formula:

[0034]

[0035] Wherein, C represents the environmental influence index, k represents the weight coefficient, Te represents the external environment temperature, Ti represents the material internal temperature, Tr represents the reference temperature, He represents the external environment humidity, Hi represents the material internal humidity, Hr represents the reference humidity, Pe represents the external environment pressure, Pi represents the material internal pressure, Pr represents the reference pressure, t represents the time variable, t0 and t1 represent the starting time and the ending time of the ultrasonic signal collection respectively.

[0036] Further, when the environmental influence index is compared with the historical data, and the layered measurement algorithm is adjusted according to the comparison result, comprising:

[0037] When there is a historical environmental influence index same as the environmental influence index in the historical data, the layered measurement algorithm is adjusted with the historical compensation coefficient corresponding to the historical environmental influence index;

[0038] When there is no historical environmental influence index same as the environmental influence index in the historical data, the environmental influence index is stored.

[0039] Further, when the comprehensive compensation coefficient is calculated according to the density data, the first environmental data and the second environmental data, and the layered measurement algorithm is adjusted, comprising:

[0040] The density value corresponding to the density data of each layer of material is subtracted from the density standard value corresponding to each layer of material to obtain a density difference value;

[0041] The density difference value is compared with the first density difference threshold value and the second density difference threshold value, and according to the comparison result, it is determined whether the current layer of material has a density abnormality and the abnormality type; wherein the first density difference threshold value is less than 0, and the second density difference threshold value is greater than 0;

[0042] When the density difference value is less than or equal to the first density difference threshold value, it is determined that the current layer of material has a density abnormality, and the abnormality type is material damage;

[0043] When the density difference value is greater than the first density difference threshold value and less than or equal to the second density difference threshold value, it is determined that the current layer of material does not have a density abnormality;

[0044] When the density difference value is greater than the second density difference threshold value, it is determined that the current layer of material has a density abnormality, and the abnormality type is material surface contamination.

[0045] Further, when determining whether the current layer material has a density anomaly according to the comparison result, the method comprises:

[0046] The comprehensive compensation coefficient is calculated according to the density data, the first environmental data and the second environmental data, and the comprehensive compensation coefficient is obtained by the following formula:

[0047]

[0048] wherein Zi represents the comprehensive compensation coefficient of the current layer material, a and β represent weight coefficients, D represents a normalized value of the density difference, Te represents a normalized value of the external environmental temperature, He represents a normalized value of the external environmental humidity, Pe represents a normalized value of the external environmental pressure, Ti represents a normalized value of the internal temperature of the material, Hi represents a normalized value of the internal humidity of the material, Pi represents a normalized value of the internal pressure of the material, and ρi represents a normalized value of the density of the current layer material.

[0049] The layered measurement algorithm is adjusted by using the comprehensive compensation coefficient, and the comprehensive compensation coefficient is stored in the storage unit.

[0050] Compared with the prior art, the beneficial effects of the present application are that the multi-layer material thickness detection system based on the TM sensor provided by the present application is suitable for multi-layer material thickness measurement in various complex environments. By collecting and analyzing external environmental data and material internal data in real time, the system can automatically adjust the measurement algorithm to ensure the accuracy and reliability of the measurement results. The ultrasonic detection module of the present application is used for transmitting and receiving ultrasonic signals; the feature extraction module is used for extracting key information from these signals for subsequent analysis and processing; the control module is used for coordinating the work of each module and performing algorithm generation, analysis, adjustment and storage operations; the algorithm generation unit can dynamically adjust the parameters of the layered measurement algorithm according to different material properties and detection environments to adapt to various detection scenarios; the analysis unit can predict the trend of material thickness changes according to historical data and real-time data in addition to thickness comparison and environmental impact index calculation; the adjustment unit can select appropriate compensation models and algorithms according to different material types and thickness ranges when adjusting the layered measurement algorithm to ensure the accuracy of the measurement results; and the storage unit can record the data and results of each detection in addition to storing the comprehensive compensation coefficient, which is convenient for subsequent data analysis and quality control.

[0051] In another aspect, the present application further provides a multi-layer material thickness detection method based on a TM sensor, which comprises:

[0052] S100: emit ultrasonic waves to the inside of the multi-layer material, and receive echoes of the ultrasonic waves on each layer of material to form ultrasonic signals; acquire the ultrasonic signals, and perform feature extraction on the ultrasonic signals to obtain amplitude feature data, waveform feature data and frequency spectrum feature data of the ultrasonic signals corresponding to each layer of material;

[0053] S200: collect the amplitude feature data, the waveform feature data and the frequency spectrum feature data; generate a layered measurement algorithm with the amplitude feature data, the waveform feature data and the frequency spectrum feature data as input signals, and calculate a measurement thickness of each layer of material according to the layered measurement algorithm;

[0054] S300: compare the measurement thickness with a standard thickness value, and determine whether to adjust the layered measurement algorithm according to a comparison result; when it is determined to adjust the layered measurement algorithm, collect first environment data of an external environment and second environment data of the inside of the multi-layer material, and establish an environment data set according to the first environment data and the second environment data, calculate an environment influence index according to the environment data set, compare the environment influence index with historical data, and adjust the layered measurement algorithm according to a comparison result;

[0055] S400: when the environment influence index is different from the historical data, collect density data corresponding to each layer of material, and calculate a comprehensive compensation coefficient according to the density data, the first environment data and the second environment data, and adjust the layered measurement algorithm;

[0056] S500: store the comprehensive compensation coefficient.

[0057] It can be understood that the multi-layer material thickness detection method and system based on a TM sensor have the same beneficial effects as described above, and will not be described here again. BRIEF DESCRIPTION OF DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the application. Moreover, the same reference numerals are used throughout the same figures. In the drawings:

[0059] Figure 1 a flowchart of the multi-layer material thickness detection method based on a TM sensor provided by an embodiment of the application;

[0060] Figure 2 a structural block diagram of the multi-layer material thickness detection system based on a TM sensor provided by an embodiment of the application;

[0061] Figure 3Fig. 1 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure can be embodied in various forms without being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0063] Reference Figure 1 As shown in the drawings, in some embodiments of the present application, the present embodiment provides a TM sensor-based multi-layer material thickness detection system, comprising:

[0064] An ultrasonic detection module, which is internally provided with a transmitting unit and a receiving unit, the transmitting unit is used to transmit ultrasonic waves to the inside of the multi-layer material, and the receiving unit is used to receive the echo of the ultrasonic waves transmitted by the transmitting unit on each layer of material, forming an ultrasonic signal;

[0065] A feature extraction module configured to acquire the ultrasonic signal and perform feature extraction on the ultrasonic signal, to obtain the amplitude feature data, waveform feature data and frequency spectrum feature data of the ultrasonic signal corresponding to each layer of material;

[0066] A control module electrically connected with the ultrasonic detection module and the feature extraction module, the control module comprising an acquisition unit, an algorithm generation unit, an analysis unit, an adjustment unit and a storage unit;

[0067] The acquisition unit is configured to acquire the amplitude feature data, waveform feature data and frequency spectrum feature data;

[0068] The algorithm generation unit is configured to generate a layered measurement algorithm with the amplitude feature data, waveform feature data and frequency spectrum feature data as input signals, and calculate the measurement thickness of each layer of material according to the layered measurement algorithm;

[0069] The analysis unit is configured to compare the measurement thickness with a standard thickness value, and determine whether to adjust the layered measurement algorithm according to the comparison result:

[0070] When it is determined that the layered measurement algorithm needs to be adjusted, the analysis unit controls the acquisition unit to acquire first environment data of an external environment and second environment data inside the multi-layer material, and establishes an environment data set according to the first environment data and the second environment data, calculates an environment influence index according to the environment data set, compares the environment influence index with historical data, and adjusts the layered measurement algorithm according to the comparison result;

[0071] The adjustment unit is configured to control the acquisition unit to acquire density data corresponding to each layer of material when the environment influence index is different from the historical data, and calculate a comprehensive compensation coefficient according to the density data, the first environment data and the second environment data, and adjust the layered measurement algorithm.

[0072] The storage unit is configured to store the comprehensive compensation coefficient.

[0073] In some embodiments of the present application, the TM (Time-of-Flight Measurement) sensor is a sensor based on time-of-flight measurement, which can accurately measure the propagation time of ultrasonic waves in different media, and by calculating the propagation time of ultrasonic waves in each layer of material, the thickness information of each layer of material can be obtained.

[0074] It can be understood that the ultrasonic detection module is used for transmitting and receiving ultrasonic signals, the feature extraction module is used for extracting key information from these signals for subsequent analysis and processing, the control module is used for coordinating the work of each module and performing algorithm generation, analysis, adjustment and storage operations, the algorithm generation unit can dynamically adjust the parameters of the layered measurement algorithm according to different material properties and detection environments to adapt to various detection scenarios, the analysis unit can predict the trend of material thickness change according to historical data and real-time data in addition to thickness comparison and environment influence index calculation, the adjustment unit adjusts the layered measurement algorithm to ensure the accuracy of the measurement result, and the storage unit can record the data and results of each detection in addition to storing the comprehensive compensation coefficient, which is convenient for subsequent data analysis and quality control.

[0075] In some embodiments of the present application, the historical data includes historical external environment temperature, historical external environment humidity, historical external environment pressure, historical material internal temperature, historical material internal humidity and historical material internal pressure.

[0076] It can be understood that the multi-layer material thickness detection system based on the TM sensor can provide a high-precision and high-stability detection method, and is suitable for multi-layer material thickness measurement in various complex environments. By collecting and analyzing external environment data and material internal data in real time, the system can automatically adjust the measurement algorithm to ensure the accuracy and reliability of the measurement results.

[0077] Specifically, when acquiring the amplitude feature data, the waveform feature data and the spectrum feature data of the ultrasonic signal corresponding to each layer of material, the following steps are included:

[0078] The amplitude feature data is the maximum amplitude of the ultrasonic signal measured for each layer of material.

[0079] The waveform feature data is the rising edge time and the falling edge time of the ultrasonic signal measured for each layer of material.

[0080] The spectrum feature data is the frequency distribution of the ultrasonic signal measured for each layer of material.

[0081] In some embodiments of the present application, the maximum amplitude of the ultrasonic signal of each layer of material is measured, while the rising edge time and the falling edge time reflect the propagation speed and attenuation of the ultrasonic signal in each layer of material. The frequency distribution reveals the structural characteristics of the material inside, and the spectrum feature data specifically includes the main frequency, the frequency bandwidth, the spectral centroid, the spectral entropy, the spectral roll-off point and the harmonic component. The main frequency is the strongest frequency component of the ultrasonic signal, the frequency bandwidth is the range of signal energy distribution of the ultrasonic signal, the spectral centroid is the center position of the signal frequency of the ultrasonic signal, the spectral entropy is the complexity of the signal frequency distribution of the ultrasonic signal, the spectral roll-off point is the frequency point at which the signal energy of the ultrasonic signal drops to a certain specific value, and the harmonic component is whether there is an integer multiple frequency component other than the fundamental frequency inside the multi-layer material.

[0082] Specifically, when generating a layered measurement algorithm with the amplitude feature data, the waveform feature data and the spectrum feature data as input signals, the following steps are included:

[0083] Data cleaning is performed on the amplitude feature data, the waveform feature data and the spectrum feature data to remove noise in the ultrasonic signal, and wavelet transform is used to eliminate interference signals.

[0084] The amplitude feature data, the waveform feature data and the spectrum feature data are normalized to obtain normalized amplitude feature data, normalized waveform feature data and normalized spectrum feature data.

[0085] The normalized amplitude feature data, the normalized waveform feature data and the normalized spectrum feature data are fused to obtain fused feature data.

[0086] The fusion feature data is used as input data to train the machine learning algorithm, and the hierarchical measurement algorithm is generated.

[0087] In some embodiments of the present application, the machine learning algorithm includes support vector machines, random forests, neural networks, and gradient boosting decision trees. Through the training of these algorithms, the characteristics of different material layers can be effectively identified and classified, thereby improving the measurement accuracy and reliability.

[0088] In some embodiments of the present application, when the fusion feature data is used as input data to train the machine learning algorithm, and the hierarchical measurement algorithm is generated, it includes:

[0089] First, feature selection is performed on the fusion feature data to determine the feature subset that has the greatest impact on the hierarchical measurement result. This step can be achieved through recursive feature elimination or model-based feature selection methods. Through feature selection, the complexity of the model can be reduced, the training speed can be improved, and the overfitting phenomenon can be avoided.

[0090] Next, the cross-validation technique is used to optimize the parameters of the machine learning algorithm.

[0091] Then, the optimized machine learning algorithm is applied to the fusion feature data for model training. During the training process, different optimization algorithms such as gradient descent, Newton's method, or quasi-Newton method can be used to accelerate the convergence speed of the model. After training, the generated hierarchical measurement algorithm is tested and verified.

[0092] Finally, the verified hierarchical measurement algorithm is integrated into the multi-layer material thickness detection system. The system will automatically call the corresponding hierarchical measurement algorithm based on the real-time acquisition of external environmental data and material internal data to achieve high-precision and high-stability thickness measurement.

[0093] It can be understood that using the fusion feature data as input data and training the machine learning algorithm with the fusion feature data to generate the hierarchical measurement algorithm can better adapt to complex and variable detection environments and material characteristics.

[0094] Specifically, when the measured thickness is compared with the standard thickness value, and whether to adjust the hierarchical measurement algorithm is determined according to the comparison result, it includes:

[0095] The measured thickness is subtracted from the standard thickness to obtain a thickness difference value;

[0096] The thickness difference value is compared with a thickness difference value threshold, and whether to adjust the hierarchical measurement algorithm is determined according to the comparison result;

[0097] when the thickness difference value is less than or equal to the thickness difference value threshold, the analysis unit determines not to adjust the layered measurement algorithm;

[0098] when the thickness difference value is greater than the thickness difference value threshold, the analysis unit determines to adjust the layered measurement algorithm.

[0099] In some embodiments of the present application, the standard thickness is obtained by a known calibration sample or a standard value certified by an authoritative agency.

[0100] In some embodiments of the present application, the thickness difference value threshold is pre-set according to the actual application scenario and the accuracy requirement. For example, in the case of high accuracy requirement, the thickness difference value threshold is set to 0.1 millimeter, while in the case of relatively low accuracy requirement, the thickness difference value threshold is set to 0.5 millimeter.

[0101] It can be understood that by setting a reasonable thickness difference value threshold, the accuracy and reliability of the measurement result can be ensured, and frequent adjustment of the layered measurement algorithm can be avoided, thereby improving the stability and efficiency of the system.

[0102] Specifically, when it is determined to adjust the layered measurement algorithm, the analysis unit controls the acquisition unit to acquire first environmental data of an external environment and second environmental data inside the multi-layer material, and establishes an environmental data set according to the first environmental data and the second environmental data, and calculates an environmental influence index according to the environmental data set, comprising:

[0103] The first environmental data includes external environment temperature, external environment humidity and external environment pressure;

[0104] The second environmental data includes material internal temperature, material internal humidity and material internal pressure;

[0105] The environmental influence index is obtained by the following formula:

[0106]

[0107] wherein C represents the environmental influence index, k represents a weight coefficient, Te represents the external environment temperature, Ti represents the material internal temperature, Tr represents a reference temperature, He represents the external environment humidity, Hi represents the material internal humidity, Hr represents a reference humidity, Pe represents the external environment pressure, Pi represents the material internal pressure, Pr represents a reference pressure, t represents a time variable, t0 and t1 represent the starting time and the ending time of the ultrasonic signal acquisition, respectively.

[0108] In some embodiments of the present application, the weight coefficient k is pre-set according to the actual measurement environment and material characteristics to ensure that the environmental impact index C can accurately reflect the influence of environmental changes on the propagation characteristics of ultrasonic signals. For example, in an environment with large temperature changes, the value of k needs to be set higher to ensure that the temperature factor is given sufficient attention; while in an environment with large humidity or pressure changes, the value of k needs to be adjusted accordingly.

[0109] It can be understood that the reference temperature, the reference humidity and the reference pressure are reference values for calibrating the environmental impact index C to ensure the accuracy and reliability of the calculation of the environmental impact index C. In actual application, the reference temperature, the reference humidity and the reference pressure can be set according to the specific measurement environment and material characteristics to adapt to different detection needs. For example, in an environment with small temperature fluctuations, the reference temperature can be set as the average value of the ambient temperature; while in an environment with large humidity changes, the reference humidity needs to be reasonably set according to the hygroscopicity of the material and the change of the environmental humidity. Through reasonable setting of the reference temperature, the reference humidity and the reference pressure, the measurement accuracy and stability of the multi-layer material thickness detection system can be further improved.

[0110] It can be understood that by calculating the environmental impact index C, the comprehensive influence of external environmental temperature and humidity, external environmental pressure, and material internal temperature and humidity, material internal pressure on the propagation characteristics of ultrasonic signals can be quantified. The calculation of this index helps the system to more accurately determine when the layered measurement algorithm needs to be adjusted, as well as the degree and direction of adjustment.

[0111] Specifically, when the environmental impact index is compared with the historical data, and the layered measurement algorithm is adjusted according to the comparison result, it includes:

[0112] When there is a historical environmental impact index in the historical data that is the same as the environmental impact index, the layered measurement algorithm is adjusted with the historical compensation coefficient corresponding to the historical environmental impact index;

[0113] When there is no historical environmental impact index in the historical data that is the same as the environmental impact index, the environmental impact index is stored.

[0114] In some embodiments of the present application, the historical data is stored in a database, and the database is connected to the analysis unit. The analysis unit regularly analyzes the historical data in the database to identify the trend and pattern of environmental changes. Through such analysis, the influence of future environmental changes on the measurement algorithm can be predicted and adjusted in advance, thereby further improving the prediction ability and response speed of the measurement system.

[0115] In some embodiments of the present invention, a historical compensation coefficient is calculated based on historical measurement data, environmental factors, and material properties, and is used to compensate for the impact of environmental changes on measurement results. The historical compensation coefficient can also be a comprehensive compensation coefficient corresponding to an environmental impact index, where the comprehensive compensation coefficient is the same as the first and second environmental data corresponding to the environmental impact index. Alternatively, the environmental impact index can be used as the historical compensation coefficient to adjust the hierarchical measurement algorithm.

[0116] Specifically, calculating the comprehensive compensation coefficient according to the density data, the first environmental data, and the second environmental data and adjusting the layered measurement algorithm includes:

[0117] Subtract the density value corresponding to the density data of each layer of material from the density standard value corresponding to each layer of material to obtain the density difference;

[0118] Comparing the density difference with a first density difference threshold and a second density difference threshold, and determining whether there is a density anomaly in the current layer of material and the type of anomaly based on the comparison result; wherein the first density difference threshold is less than 0 and the second density difference threshold is greater than 0;

[0119] When the density difference is less than or equal to a first density difference threshold, it is determined that a density anomaly exists in the current layer of material, and the anomaly type is material damage;

[0120] When the density difference is greater than a first density difference threshold and less than or equal to a second density difference threshold, it is determined that there is no density anomaly in the current layer of material;

[0121] When the density difference is greater than a second density difference threshold, it is determined that a density anomaly exists in the current layer of material, and the anomaly type is material surface contamination.

[0122] In some embodiments of the present invention, the density difference thresholds are pre-set based on the actual application scenario and accuracy requirements. For example, in applications where high requirements are placed on material damage detection, the first density difference threshold is set to -0.05 g / cm³, and the second density difference threshold is set to 0.1 g / cm³. In applications where high requirements are placed on material surface contamination detection, the first density difference threshold is set to -0.03 g / cm³, and the second density difference threshold is set to 0.08 g / cm³.

[0123] It can be understood that by setting a reasonable density difference threshold, the accuracy of the material density anomaly can be ensured, and the corresponding maintenance measures can be taken in time to improve the use quality and service life of the material. When it is determined that the current layer of material has a density anomaly, the analysis unit will adjust the layering measurement algorithm according to the type of anomaly to ensure the accuracy of the measurement result. The specific adjustment method is: when it is determined that the material is damaged, the amplitude characteristic data of the ultrasonic signal is corrected to compensate for the influence of the damage on the signal propagation characteristics; when it is determined that the material surface is contaminated, the ultrasonic waveform characteristic data is corrected to compensate for the influence of the contamination on the signal propagation speed and attenuation; through the above adjustment, the layering measurement algorithm can adapt to the measurement requirements under different density anomaly conditions, thereby improving the adaptability and robustness of the measurement system.

[0124] Specifically, when it is determined according to the comparison result that the current layer of material has a density anomaly, it includes:

[0125] If the abnormal type of the current layer of material is material damage, the size and position of the damaged area are analyzed, and the measurement result is corrected according to the damage degree;

[0126] If the abnormal type of the current layer of material is material surface contamination, the contaminants on the material surface are removed, and then ultrasonic detection is performed again.

[0127] In some embodiments of the present application, when the measurement result is corrected according to the damage degree, it includes: first, the boundary of the damaged area is determined through the waveform characteristic data of the ultrasonic signal, and then a correction coefficient is calculated according to the ratio of the area of the damaged area to the area of the entire measurement area. Then, the correction coefficient is applied to the amplitude characteristic data of the ultrasonic signal to correct the influence of the damage on the signal propagation characteristics. In this way, the measurement result can more accurately reflect the actual state of the material. After removing the contaminants on the material surface, the analysis unit will guide the acquisition unit to perform multiple ultrasonic signal acquisitions on the same layer of material to ensure the stability and reliability of the detection result. By multiple acquisitions and averaging, the influence of random errors can be effectively reduced, and the measurement accuracy can be improved.

[0128] It can be understood that by accurately analyzing the size and position of the damaged area, the interference of the damaged area on the thickness detection in the ultrasonic signal processing can be excluded, and the false measurement result caused by material damage can be avoided, so that the final thickness measurement data is closer to the true situation of the material; during the detection process, material damage will cause abnormal reflection and attenuation of the ultrasonic signal, thereby affecting the thickness measurement, and the correction of the damaged area can effectively reduce the measurement error caused by the damage and ensure the reliability of the thickness measurement result.

[0129] Specifically, when determining whether the current layer material has a density anomaly according to the comparison result, the method comprises the following steps:

[0130] According to the density data, the first environmental data and the second environmental data, a comprehensive compensation coefficient is calculated, and the comprehensive compensation coefficient is obtained by the following formula:

[0131]

[0132] Wherein, Zi represents the comprehensive compensation coefficient of the current layer material, α and β represent weight coefficients, D represents a normalized value of the density difference, Te represents a normalized value of the external environmental temperature, He represents a normalized value of the external environmental humidity, Pe represents a normalized value of the external environmental pressure, Ti represents a normalized value of the internal temperature of the material, Hi represents a normalized value of the internal humidity of the material, Pi represents a normalized value of the internal pressure of the material, and ρi represents a normalized value of the density of the current layer material.

[0133] The layered measurement algorithm is adjusted by using the comprehensive compensation coefficient, and the comprehensive compensation coefficient is stored in the storage unit.

[0134] In some embodiments of the present application, each layer of material corresponds to a different density compensation coefficient according to the difference in its density, thereby ensuring that the measurement system has higher precision and consistency between different material layers.

[0135] In some embodiments of the present application, after obtaining the comprehensive compensation coefficient, the layered measurement algorithm needs to be adjusted to adapt to the measurement requirements under different environmental conditions. The specific adjustment methods include: adjusting the amplitude characteristic data of the ultrasonic signal according to the comprehensive compensation coefficient to compensate for the influence of environmental parameters on the signal propagation characteristics; adjusting the ultrasonic waveform characteristic data according to the comprehensive compensation coefficient to compensate for the influence of environmental parameters on the signal propagation speed and attenuation; adjusting the collection frequency and sampling time of the ultrasonic signal according to the comprehensive compensation coefficient to adapt to the signal collection requirements under different environmental conditions. Through the above adjustment, the layered measurement algorithm can maintain high measurement precision and stability under different environmental conditions. In addition, the model can be further optimized and improved according to the requirements of the actual application scene, so as to improve the adaptability and robustness of the measurement system.

[0136] Referring to Figure 2 As shown in the drawings, in some embodiments of the present application, the present embodiment provides a multi-layer material thickness detection method based on a TM sensor, comprising the following steps:

[0137] S100: Emit ultrasonic waves to the inside of the multi-layer material and receive the echoes of the ultrasonic waves on each layer of material to form ultrasonic signals; acquire the ultrasonic signals and perform feature extraction on the ultrasonic signals to obtain amplitude feature data, waveform feature data and frequency spectrum feature data of the ultrasonic signals corresponding to each layer of material;

[0138] S200: Collect the amplitude feature data, waveform feature data and frequency spectrum feature data; generate a layered measurement algorithm with the amplitude feature data, waveform feature data and frequency spectrum feature data as input signals, and calculate the measurement thickness of each layer of material according to the layered measurement algorithm;

[0139] S300: Compare the measurement thickness with a standard thickness value, and determine whether to adjust the layered measurement algorithm according to the comparison result: when it is determined to adjust the layered measurement algorithm, collect first environmental data of the external environment and second environmental data of the inside of the multi-layer material, and establish an environmental data set according to the first environmental data and the second environmental data, calculate an environmental influence index according to the environmental data set, compare the environmental influence index with historical data, and adjust the layered measurement algorithm according to the comparison result;

[0140] S400: When the environmental influence index is different from the historical data, collect density data corresponding to each layer of material, and calculate a comprehensive compensation coefficient according to the density data, first environmental data and second environmental data, and adjust the layered measurement algorithm;

[0141] S500: Store the comprehensive compensation coefficient.

[0142] It can be understood that the multi-layer material thickness detection method based on the TM sensor has high flexibility and adaptability, and can cope with various complex detection environments and material characteristics. By using the TM sensor, accurate measurement of the thickness of the multi-layer material can be realized, ensuring the reliability of product quality and performance. The TM sensor has high sensitivity and stability, and can capture small thickness changes to provide more accurate measurement data.

[0143] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in Figure 3 The electronic device includes a processor 410, a memory 420, an input device 430 and an output device 440; the number of processors 410 in the device can be one or more, Figure 3 and an example of one processor 410 is taken; the processor 410, the memory 420, the input device 430 and the output device 440 in the device can be connected through a bus or other means, Figure 3 and an example of connection through a bus is taken.

[0144] The memory 420, as a computer readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the multi-layer material thickness detection method based on a TM sensor according to the embodiments of the present application. The processor 410 executes various function applications and data processing of the device by running the software programs, instructions and modules stored in the memory 420.

[0145] The memory 420 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; and the data storage area can store data created according to the use of the terminal and the like. In addition, the memory 420 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some examples, the memory 420 can further include a memory remotely arranged with respect to the processor 410, and the remote memory can be connected to the device through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0146] The input device 430 can be used to receive input digital or character information, and generate signal input related to the user settings and function control of the device. The output device 440 can include a display device such as a display screen.

[0147] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks.

[0149] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed methods. Figure 1

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed methods. Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed methods.

[0151] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limiting the same. Even though the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently, and any modification or replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.​

Claims

1. A multi-layer material thickness detection system based on TM sensor, characterized in that: include: An ultrasonic detection module is internally provided with a transmitting unit and a receiving unit. The transmitting unit is used to transmit ultrasonic waves into the multi-layer material, and the receiving unit is used to receive the echo of the ultrasonic waves emitted by the transmitting unit on each layer of material to form an ultrasonic signal; a feature extraction module configured to acquire the ultrasonic signal and perform feature extraction on the ultrasonic signal to obtain amplitude feature data, waveform feature data, and spectrum feature data of the ultrasonic signal corresponding to each layer of material; a control module electrically connected to the ultrasonic detection module and the feature extraction module, the control module comprising an acquisition unit, an algorithm generation unit, an analysis unit, an adjustment unit, and a storage unit; The acquisition unit is configured to acquire the amplitude characteristic data, waveform characteristic data and spectrum characteristic data; The algorithm generating unit is configured to generate a layered measurement algorithm using the amplitude characteristic data, the waveform characteristic data, and the spectrum characteristic data as input signals, and calculate the measured thickness of each layer of material according to the layered measurement algorithm; The analysis unit is configured to compare the measured thickness with a standard thickness value, and determine whether to adjust the layer measurement algorithm according to the comparison result: When it is determined that the layer measurement algorithm needs to be adjusted, the analysis unit controls the acquisition unit to acquire first environmental data of the external environment and second environmental data of the interior of the multilayer material, establishes an environmental data set based on the first environmental data and the second environmental data, calculates an environmental impact index based on the environmental data set, compares the environmental impact index with historical data, and adjusts the layer measurement algorithm based on the comparison result; The adjustment unit is configured to control the acquisition unit to acquire density data corresponding to each layer of material when the environmental impact index is different from the historical data, calculate a comprehensive compensation coefficient based on the density data, the first environmental data, and the second environmental data, and adjust the layered measurement algorithm; The storage unit is configured to store the comprehensive compensation coefficient.

2. The multi-layer material thickness detection system based on TM sensor according to claim 1, characterized in that: When obtaining the amplitude characteristic data, waveform characteristic data and spectrum characteristic data of the ultrasonic signal corresponding to each layer of material, it includes: The amplitude characteristic data is the maximum amplitude of the ultrasonic signal measured for each layer of material; The waveform characteristic data is the rising edge time and falling edge time of the ultrasonic signal measured for each layer of material; The frequency spectrum characteristic data is the frequency distribution of the ultrasonic signal measured for each layer of material.

3. The multi-layer material thickness detection system based on TM sensor according to claim 2, characterized in that: When the amplitude characteristic data, the waveform characteristic data and the spectrum characteristic data are used as input signals to generate a hierarchical measurement algorithm, the method includes: Performing data cleaning on the amplitude characteristic data, waveform characteristic data, and spectrum characteristic data to remove noise in the ultrasonic signal, and using wavelet transform to eliminate interference signals; Normalizing the amplitude characteristic data, waveform characteristic data, and spectrum characteristic data to obtain normalized amplitude characteristic data, normalized waveform characteristic data, and normalized spectrum characteristic data; fusing the normalized amplitude feature data, the normalized waveform feature data, and the normalized spectrum feature data to obtain fused feature data; The fused feature data is used as input data, and a machine learning algorithm is used to train the fused feature data to generate the hierarchical measurement algorithm.

4. The multi-layer material thickness detection system based on TM sensor according to claim 3 is characterized in that: Comparing the measured thickness with a standard thickness value and determining whether to adjust the layer measurement algorithm according to the comparison result includes: Subtracting the measured thickness from the standard thickness to obtain a thickness difference; Comparing the thickness difference with a thickness difference threshold, and determining whether to adjust the layer measurement algorithm according to the comparison result; When the thickness difference is less than or equal to the thickness difference threshold, the analyzing unit determines not to adjust the layer measurement algorithm; When the thickness difference is greater than the thickness difference threshold, the analyzing unit determines to adjust the layer measurement algorithm.

5. The multi-layer material thickness detection system based on TM sensor according to claim 4, characterized in that: When it is determined that the layer measurement algorithm needs to be adjusted, the analysis unit controls the acquisition unit to acquire first environmental data of the external environment and second environmental data inside the multilayer material, and establishes an environmental data set based on the first environmental data and the second environmental data. When calculating the environmental impact index based on the environmental data set, the process includes: The first environmental data includes external environmental temperature, external environmental humidity and external environmental pressure; The second environmental data includes the internal temperature of the material, the internal humidity of the material, and the internal pressure of the material; The environmental impact index is obtained by the following formula: Among them, C represents the environmental impact index, k represents the weight coefficient, Te represents the external environment temperature, Ti represents the internal temperature of the material, Tr represents the reference temperature, He represents the external environment humidity, Hi represents the internal humidity of the material, Hr represents the reference humidity, Pe represents the external environment pressure, Pi represents the internal pressure of the material, Pr represents the reference pressure, t represents the time variable, t0 and t1 represent the start time and end time of the ultrasonic signal acquisition, respectively.

6. The multi-layer material thickness detection system based on TM sensor according to claim 5, characterized in that: Comparing the environmental impact index with historical data and adjusting the hierarchical measurement algorithm according to the comparison results includes: When there is a historical environmental impact index identical to the environmental impact index in the historical data, adjusting the hierarchical measurement algorithm with a historical compensation coefficient corresponding to the historical environmental impact index; When there is no historical environmental impact index corresponding to the environmental impact index in the historical data, the environmental impact index is stored.

7. The multi-layer material thickness detection system based on TM sensor according to claim 6, characterized in that: Calculating a comprehensive compensation coefficient based on the density data, the first environmental data, and the second environmental data and adjusting the layered measurement algorithm includes: Subtract the density value corresponding to the density data of each layer of material from the density standard value corresponding to each layer of material to obtain the density difference; Comparing the density difference with a first density difference threshold and a second density difference threshold, and determining whether there is a density anomaly in the current layer of material and the type of anomaly based on the comparison result; wherein the first density difference threshold is less than 0 and the second density difference threshold is greater than 0; When the density difference is less than or equal to a first density difference threshold, it is determined that a density anomaly exists in the current layer of material, and the anomaly type is material damage; When the density difference is greater than a first density difference threshold and less than or equal to a second density difference threshold, it is determined that there is no density anomaly in the current layer of material; When the density difference is greater than a second density difference threshold, it is determined that a density anomaly exists in the current layer of material, and the anomaly type is material surface contamination.

8. The multi-layer material thickness detection system based on TM sensor according to claim 7, characterized in that: When the density of the current layer material is determined to be abnormal based on the comparison results, it includes: If the abnormal type of the current layer material is material damage, analyze the size and position of the damaged area and correct the measurement results according to the degree of damage; If the abnormality type of the current layer of material is surface contamination, remove the contaminants on the surface of the material and then perform ultrasonic testing again.

9. The multi-layer material thickness detection system based on TM sensor according to claim 8, characterized in that: When determining whether the current layer material has density anomaly based on the comparison results, it includes: A comprehensive compensation coefficient is calculated based on the density data, the first environment data, and the second environment data. The comprehensive compensation coefficient is obtained by the following formula: Wherein, Zi represents the comprehensive compensation coefficient of the material of the current layer, α and β represent weight coefficients, D represents the normalized value of the density difference, Te represents the normalized value of the external environment temperature, He represents the normalized value of the external environment humidity, Pe represents the normalized value of the external environment pressure, Ti represents the normalized value of the internal temperature of the material, Hi represents the normalized value of the internal humidity of the material, Pi represents the normalized value of the internal pressure of the material, and ρi represents the normalized value of the density of the material of the current layer; The layered measurement algorithm is adjusted using the comprehensive compensation coefficient, and the comprehensive compensation coefficient is stored in the storage unit.

10. A method for detecting thickness of multi-layer materials based on a TM sensor, applied to a multi-layer material thickness detection system based on a TM sensor according to any one of claims 1 to 9, characterized in that: include: Transmitting ultrasonic waves into the interior of the multilayer material and receiving echoes of the ultrasonic waves on each layer of material to form ultrasonic signals; acquiring the ultrasonic signals and performing feature extraction on the ultrasonic signals to obtain amplitude feature data, waveform feature data, and spectrum feature data of the ultrasonic signals corresponding to each layer of material; Collecting the amplitude characteristic data, waveform characteristic data, and spectrum characteristic data; generating a layered measurement algorithm using the amplitude characteristic data, waveform characteristic data, and spectrum characteristic data as input signals, and calculating the measured thickness of each layer of material according to the layered measurement algorithm; Comparing the measured thickness with a standard thickness value, and determining whether to adjust the layer measurement algorithm based on the comparison result: when it is determined that the layer measurement algorithm is to be adjusted, collecting first environmental data of an external environment and second environmental data of an interior of the multilayer material, establishing an environmental data set based on the first environmental data and the second environmental data, calculating an environmental impact index based on the environmental data set, comparing the environmental impact index with historical data, and adjusting the layer measurement algorithm based on the comparison result; When the environmental impact index is different from the historical data, density data corresponding to each layer of material is collected, and a comprehensive compensation coefficient is calculated based on the density data, the first environmental data, and the second environmental data, and the layered measurement algorithm is adjusted; The comprehensive compensation coefficient is stored.

11. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-layer material thickness detection method based on the TM sensor as claimed in claim 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for detecting thickness of multi-layer materials based on a TM sensor as claimed in claim 10 is implemented.

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