A method and system for detecting floor quality defects

Abstract

The application relates to the technical field of floor quality defect detection, and particularly provides a floor quality defect detection method and system, which comprises the following steps: obtaining initial temperature distribution information of a to-be-detected floor after a high-temperature pressing process is completed, and obtaining preset thermal physical properties corresponding to environment parameters and the to-be-detected floor; obtaining a transmission time of the to-be-detected floor from an initial temperature distribution information acquisition point to an ultrasonic detection point; obtaining predicted temperature distribution information of the to-be-detected floor at the ultrasonic detection point according to the initial temperature distribution information, the environment parameters, the preset thermal physical properties and the transmission time; correcting an ultrasonic signal obtained at the ultrasonic detection point according to the predicted temperature distribution information to obtain a first corrected ultrasonic signal; and performing floor quality defect detection according to the first corrected ultrasonic signal; the floor quality defect detection method can realize high-precision and high-reliability detection of floor quality defects after high-temperature pressing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of floor quality defect detection, in particular to a floor quality defect detection method and system. BACKGROUND

[0002] In the field of composite floor production, ultrasonic non-destructive testing technology is widely used because it can effectively identify internal defects such as cavities or delamination. With the improvement of industrial automation level, integrating ultrasonic detection systems into production lines to improve quality control efficiency and product reliability has become a development trend to ensure stable performance of floor products in long-term use.

[0003] However, in actual production environment, when the floor being detected just comes out from the high-temperature pressing process, it carries residual heat and the temperature distribution is uneven, causing the ultrasonic wave propagation speed to change. This temperature effect interferes with the accurate interpretation of the acoustic signal, causing the detection result to deviate, and may cause false positives or missed internal defects, thereby affecting the reliability of the quality control system. Specifically, the unevenness of temperature makes the sound wave propagation time unable to be stably predicted, causing the system to be unable to accurately distinguish between internal defects and signal abnormalities caused by temperature, ultimately reducing the detection accuracy.

[0004] There is currently no effective technical solution to the above problems. It should be noted that the above information disclosed in this part is only used to understand the background of the present application concept, and therefore can contain information that does not constitute prior art. SUMMARY

[0005] The purpose of the present application is to provide a floor quality defect detection method and system that can detect high-temperature pressed floor quality defects with high precision and high reliability.

[0006] In a first aspect, the present application provides a floor quality defect detection method, comprising the following steps:

[0007] S1, obtaining initial temperature distribution information of a floor to be tested after completing a high-temperature pressing process, and obtaining environmental parameters and preset thermal physical properties corresponding to the floor to be tested;

[0008] S2, obtaining the transmission time of the floor to be tested from the initial temperature distribution information acquisition point to the ultrasonic detection point;

[0009] S3, obtaining the predicted temperature distribution information of the floor to be tested at the ultrasonic detection point according to the initial temperature distribution information, the environmental parameters, the preset thermal physical properties and the transmission time;

[0010] S4, correcting the ultrasonic signal obtained at the ultrasonic detection point according to the predicted temperature distribution information to obtain a first corrected ultrasonic signal;

[0011] S5, detecting the floor quality defect according to the first corrected ultrasonic signal.

[0012] In a second aspect, the present application also provides a floor quality defect detection system, comprising:

[0013] An information acquisition module is configured to acquire initial temperature distribution information of a to-be-tested floor after a high-temperature pressing process, and to acquire environmental parameters and preset thermal physical properties corresponding to the to-be-tested floor.

[0014] A transmission time acquisition module is configured to acquire a transmission time of the to-be-tested floor from a point of acquisition of the initial temperature distribution information to an ultrasonic detection point.

[0015] A temperature distribution prediction module is configured to acquire predicted temperature distribution information of the to-be-tested floor at the ultrasonic detection point according to the initial temperature distribution information, the environmental parameters, the preset thermal physical properties, and the transmission time.

[0016] A signal correction module is configured to correct an ultrasonic signal acquired at the ultrasonic detection point according to the predicted temperature distribution information, to obtain a first corrected ultrasonic signal.

[0017] A defect detection module is configured to detect the floor quality defect according to the first corrected ultrasonic signal.

[0018] As can be seen from the above, the floor quality defect detection method and system provided by the present application introduce an accurate temperature prediction and signal correction link before ultrasonic detection, so as to take the key influencing factor of temperature into account, to effectively solve the problem of ultrasonic signal distortion caused by temperature non-uniformity in the floor detection after the high-temperature pressing process in the prior art. That is, the floor quality defect detection method of the present application can effectively distinguish the signal change caused by the real internal defect from the signal change caused by the temperature effect, so that the interpretation of the ultrasonic signal is no longer disturbed by the temperature non-uniformity of the floor, thereby realizing high-precision and high-reliability detection of the floor quality defect after the high-temperature pressing process, and effectively improving the accuracy and reliability of the floor quality defect detection. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of a floor quality defect detection method provided by an embodiment of the present application.

[0020] Figure 2 A schematic diagram of a floor quality defect detection method provided by an embodiment of the present application.

[0021] Figure 3 A structural schematic diagram of a floor quality defect detection system provided by an embodiment of the present application.

[0022] Label: 1, information acquisition module; 2, transmission time acquisition module; 3, temperature distribution prediction module; 4, signal correction module; 5, defect detection module. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0024] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0025] In a first aspect, as shown in Figure 1 and Figure 2 The present application provides a floor quality defect detection method, which comprises the following steps:

[0026] S1, acquiring initial temperature distribution information of a to-be-tested floor after completing a high-temperature pressing process, and acquiring environmental parameters and preset thermal physical properties corresponding to the to-be-tested floor;

[0027] S2, acquiring transmission time of the to-be-tested floor from an initial temperature distribution information acquisition point to an ultrasonic detection point;

[0028] S3, acquiring predicted temperature distribution information of the to-be-tested floor at the ultrasonic detection point according to the initial temperature distribution information, the environmental parameters, the preset thermal physical properties, and the transmission time;

[0029] S4, correcting an ultrasonic signal acquired at the ultrasonic detection point according to the predicted temperature distribution information, to obtain a first corrected ultrasonic signal;

[0030] S5, performing floor quality defect detection according to the first corrected ultrasonic signal.

[0031] The to-be-tested floor refers to a floor product that needs to be tested for quality defects after a high-temperature pressing process. The floor usually still has a high residual temperature during testing, and the internal temperature distribution thereof can be uneven. The initial temperature distribution information refers to the overall or partial temperature distribution data of the to-be-tested floor measured at a specific area (preferably the product outlet end of the high-temperature pressing process) after the to-be-tested floor completes the high-temperature pressing process. The information can include the surface temperature of the floor and the internal temperature gradient, etc. The information is used to represent the thermal state of the floor at the beginning of the transportation process. The environmental parameters refer to the environmental conditions of the detection site, such as the ambient temperature and the ambient humidity, etc. These parameters will affect the heat dissipation speed of the floor and the propagation characteristics of the ultrasonic waves. The preset thermal physical properties refer to the inherent thermal and physical characteristics of the to-be-tested floor material, such as the thermal conductivity, the specific heat capacity, the density, the relationship curve between the sound speed and the temperature, etc. These properties are the basis for predicting the temperature change of the floor and the propagation behavior of the ultrasonic waves. The transmission time refers to the time period during which the to-be-tested floor is transported from the place where the initial temperature distribution information is obtained to the place where the ultrasonic testing is performed. During this period, the floor continues to dissipate heat, and the temperature changes. The ultrasonic testing point refers to the specific position where the ultrasonic probe contacts the to-be-tested floor to emit and receive ultrasonic signals. The predicted temperature distribution information refers to the temperature distribution state of the to-be-tested floor at the ultrasonic testing point, which is calculated by a heat conduction model or an empirical model based on the initial temperature distribution information, the environmental parameters, the preset thermal physical properties, and the transmission time. This information is a key basis for correcting the ultrasonic signals. The ultrasonic signal refers to the sound wave signal received by the ultrasonic probe after transmitting through the to-be-tested floor. The waveform, amplitude, propagation time, etc. of the ultrasonic signal carry the internal structure information of the floor. The first corrected ultrasonic signal refers to the ultrasonic signal corrected by the predicted temperature distribution information. Since the first corrected ultrasonic signal eliminates the influence of temperature on the ultrasonic signal, the first corrected ultrasonic signal can more accurately reflect the real defect situation inside the floor. It should be understood that the floor quality defect detection method provided by the present application is usually implemented on an automatic production line. The to-be-tested floor is moved by a conveyor belt or other conveying device after high-temperature pressing, and ultrasonic testing is performed at a specific testing station. The entire process needs to be equipped with corresponding sensors, data acquisition equipment, computing units, and ultrasonic testing equipment.

[0032] The floor quality defect detection method of the present application realizes accurate correction of the high-temperature floor ultrasonic signal through a series of coordinated steps, thereby improving the accuracy of defect detection.

[0033] Firstly, the initial temperature distribution information of the to-be-tested floor after completing the high-temperature pressing process is obtained in step S1, and the environmental parameters and the preset thermal physical properties corresponding to the to-be-tested floor are obtained. Specifically, the initial temperature distribution information can be obtained in various ways. For example, an infrared thermal imager can be used to scan the surface of the floor just out of the pressing process to obtain a two-dimensional temperature distribution image, or a plurality of contact temperature sensors can be arranged on the production line to measure the initial temperature of the floor at different positions and depths, and then the overall initial temperature distribution is obtained by interpolation or modeling. The environmental parameters can be obtained by deploying environmental temperature sensors and environmental humidity sensors between the ultrasonic detection point and the initial temperature distribution information acquisition point and the ultrasonic detection point to monitor the temperature and humidity of the environment in real time. The preset thermal physical properties are usually determined in advance by experimental measurement or consulting material database according to the material composition and structure of the floor. These property data can be stored in the database of the system for subsequent calculation and calling.

[0034] Secondly, the transmission time of the to-be-tested floor from the initial temperature distribution information acquisition point to the ultrasonic detection point is obtained in step S2. The transmission time can be obtained in various ways: 1. A photoelectric sensor or a travel switch is arranged on the production line to trigger the timer when the floor passes through the initial temperature distribution information acquisition point, and to stop the timer when the floor reaches the ultrasonic detection point, so as to accurately measure the transmission time of the floor between the two points; 2. If the conveying speed of the production line is constant, the physical distance between the initial temperature distribution information acquisition point and the ultrasonic detection point can be measured, and then the transmission time can be calculated by dividing the conveying speed.

[0035] Then, the predicted temperature distribution information of the to-be-tested floor at the ultrasonic detection point is obtained according to the initial temperature distribution information, the environmental parameters, the preset thermal physical properties and the transmission time in step S3. The predicted temperature distribution information can be obtained by establishing a heat conduction model. For example, numerical simulation methods such as finite element analysis (FEA) or finite difference method (FDM) can be used, the initial temperature distribution information is taken as the initial condition of the model, the environmental parameters are taken as the boundary conditions, the preset thermal physical properties are taken as the material parameters, and the transmission time is taken as the simulation time, so as to calculate the temperature distribution state of the floor at the ultrasonic detection point. The predicted temperature distribution information can also be predicted based on empirical formula or lookup table. For example, a temperature decay curve or model of the floor under different initial temperatures, environmental conditions and transmission times is established through a large amount of experimental data. In actual detection, the real-time acquired parameters are directly looked up or substituted into the empirical formula to calculate the predicted temperature distribution information.

[0036] Subsequently, step S4 corrects the ultrasonic signal obtained by the ultrasonic detection point according to the predicted temperature distribution information to obtain a first corrected ultrasonic signal. Specifically, step S4 can correct the original ultrasonic signal according to the predicted temperature distribution information by using a database or model of the propagation speed and attenuation coefficient of ultrasonic waves at different temperatures established in advance. For example, if it is predicted that the temperature in a certain area is high, the time of flight (TOF) of the signal can be compensated according to the change of the ultrasonic propagation speed at this temperature, so as to eliminate the influence of the change of the sound speed caused by the temperature on the depth measurement, and at the same time, the gain or attenuation compensation of the amplitude of the signal can also be performed according to the influence of the temperature on the attenuation of the ultrasonic wave, so as to ensure that the true amplitude of the signal can be accurately reflected. For example, a signal processing algorithm based on temperature compensation can be used to adjust the waveform, spectrum or energy of the ultrasonic signal, so as to eliminate the interference of the temperature on the signal characteristics and obtain a first corrected ultrasonic signal closer to the true situation.

[0037] Finally, step S5 detects the floor quality defect according to the first corrected ultrasonic signal. After obtaining the first corrected ultrasonic signal, accurate defect detection can be performed. For example, the corrected ultrasonic signal can be compared with the ultrasonic signal characteristics of a pre-set defect-free floor. If the corrected signal has significant abnormalities in waveform, amplitude, propagation time or spectrum, etc., it can be judged that there is a defect, such as a cavity, delamination, crack, etc., in the area. Specifically, threshold judgment, pattern recognition, machine learning, etc. algorithms can be used to analyze the first corrected ultrasonic signal, for example, by analyzing the arrival time, amplitude attenuation, waveform distortion, etc. characteristics of the echo signal, combining the pre-set defect discrimination standard, automatically identifying whether there is a quality defect in the floor, and outputting the detection result. It should be understood that the floor quality defect detection based on the ultrasonic signal is a mature existing technology, and its working principle and working process will not be discussed in detail here. The overall working principle of the floor quality defect detection method of the present application is to compensate the ultrasonic signal by temperature, thereby effectively solving the interference of the non-uniformity of the temperature on the detection accuracy in the detection of the high-temperature floor. The traditional existing detection method often ignores the actual temperature state of the floor during detection, resulting in distortion of the ultrasonic signal due to temperature change, and further causing misjudgment or missed detection.

[0038] The scheme of the present application firstly obtains the initial temperature distribution information of the to-be-tested floor after high-temperature pressing, environmental parameters and preset thermal physical properties through step S1. These information is the basis for establishing the floor cooling model and provides comprehensive input for subsequent temperature prediction. Subsequently, in step S2, the transmission time of the floor from the initial temperature information acquisition point to the ultrasonic detection point is accurately obtained. The transmission time is the duration experienced by the floor during the cooling process, which is crucial for accurately predicting the temperature at the detection point. Based on the above-obtained initial temperature distribution information, environmental parameters, preset thermal physical properties and transmission time, step S3 is performed to obtain the predicted temperature distribution information of the to-be-tested floor at the ultrasonic detection point. This step simulates the heat dissipation and temperature change of the floor during the transmission process through a heat conduction model or an empirical model, thereby obtaining the accurate temperature state of the floor at the actual detection time. This prediction process is lacking in traditional prior art, which enables the system to real-time master the actual thermal state of the floor at the detection time, thereby providing an accurate basis for subsequent signal correction. For example, in traditional methods, the propagation speed and attenuation coefficient of the ultrasonic signal are usually considered as constants, but in high-temperature floor detection, this assumption will lead to significant errors. The present application can dynamically adjust these parameters by predicting the temperature, making the signal analysis more accurate. Then, step S4 corrects the ultrasonic signal obtained at the ultrasonic detection point according to the predicted temperature distribution information to obtain the first corrected ultrasonic signal. This correction process is the core innovation of the present application, which utilizes the law of the change of ultrasonic propagation characteristics at different temperatures to compensate for the distortion in the original ultrasonic signal caused by temperature, so that the corrected signal can more truly reflect the structural information of the floor and eliminate temperature interference. For example, if the temperature of a certain area is higher, causing the sound speed to increase, traditional methods may misjudge that the thickness of this area is thinner or there is some kind of defect. However, the present application can eliminate this illusion through temperature correction and ensure that the signal anomaly is truly derived from physical defects. Finally, in step S5, the floor quality defect detection is performed according to the first corrected ultrasonic signal. Since the signal has eliminated the interference of temperature factors, the signal-to-noise ratio of this temperature-corrected signal is higher and the defect characteristics are more obvious. That is, the abnormal characteristics reflected by the first corrected ultrasonic signal of the present application are more likely to be derived from real internal defects, so the present application can more accurately identify the cavities, delamination and other defects in the floor, thereby significantly reducing the false positive rate and the missed detection rate.In summary, the floor quality defect detection method provided by the application introduces the key influencing factor of temperature into consideration by introducing the steps of accurate temperature prediction and signal correction before ultrasonic detection, so as to effectively solve the problem of ultrasonic signal distortion caused by temperature non-uniformity in the floor detection after the high-temperature pressing process of the traditional prior art, that is, the floor quality defect detection method can effectively distinguish the signal changes caused by real internal defects and the signal changes caused by temperature effects, so that the interpretation of the ultrasonic signal is no longer disturbed by the temperature non-uniformity of the floor, thereby realizing high-precision and high-reliability detection of the floor quality defect after high-temperature pressing, and further effectively improving the accuracy and reliability of the floor quality defect detection.

[0039] In some preferred embodiments, the environmental parameters include environmental temperature and environmental humidity. The environmental temperature refers to the air temperature of the environment in which the floor to be detected is located during transportation, which directly affects the rate at which the floor to be detected loses heat by convection and radiation. The environmental humidity refers to the water vapor content in the air of the environment in which the floor to be detected is located, which may indirectly affect the heat transfer process by affecting the thermal physical properties of the air (such as thermal conductivity, specific heat capacity, etc.), especially when considering the evaporation or condensation of water. In actual application, the environmental temperature and the environmental humidity can be monitored and obtained in real time by temperature sensors and humidity sensors arranged on the transportation path of the floor to be detected. This embodiment makes the prediction of the temperature change of the floor during transportation more accurate by taking the environmental temperature and the environmental humidity as part of the environmental parameters, so that the predicted temperature distribution information can more truly reflect the actual thermal state of the floor and provide a more reliable data basis for subsequent defect detection, thereby effectively improving the reliability and precision of the floor quality defect detection.

[0040] In some preferred embodiments, step S3 comprises:

[0041] S31, determining a preliminary cooling coefficient according to the environmental parameters and the preset thermal physical properties;

[0042] S32, obtaining preliminary temperature distribution information according to the initial temperature distribution information, the environmental parameters, the transportation time and the preliminary cooling coefficient;

[0043] S33, correcting the ultrasonic signals obtained at the ultrasonic detection points according to the preliminary temperature distribution information to obtain second corrected ultrasonic signals;

[0044] S34, obtaining the statistical central value of the depth information corresponding to different structure intact areas of the floor to be detected according to the second corrected ultrasonic signals;

[0045] S35, adjusting the preliminary cooling coefficient according to the deviation of all statistical central values from the preset nominal thickness to obtain a target cooling coefficient.

[0046] S36, obtaining predicted temperature distribution information of the to-be-tested floor at the ultrasonic detection point according to the initial temperature distribution information, the environmental parameter, the transmission time and the target cooling coefficient. The preliminary cooling coefficient in step S31 can be understood as a preliminary estimation of the cooling rate of the floor based on environmental conditions and material inherent properties, which can be obtained by theoretical calculation, empirical formula or historical data analysis to provide an initial and adjustable benchmark for subsequent temperature prediction. The preliminary temperature distribution information in step S32 refers to the temperature distribution prediction at the ultrasonic detection point calculated by a heat conduction model or an empirical model based on the preliminary cooling coefficient, combined with the initial temperature distribution information, the environmental parameter and the transmission time. The second corrected ultrasonic signal in step S33 refers to the signal obtained after the original ultrasonic signal is preliminarily temperature-compensated and corrected using the preliminary temperature distribution information. The statistical central value of the depth information corresponding to the different structure intact areas of the to-be-tested floor obtained in step S34 refers to identifying the areas with complete internal structure and no defects of the floor in the preliminarily corrected ultrasonic signal (the second corrected ultrasonic signal), and calculating the statistical average or median of the depth information of the ultrasonic echo corresponding to these areas. These statistical central values reflect the actual thickness of the floor in these areas. Step S35 can inversely deduce the accuracy of the preliminary cooling coefficient by comparing the deviation between the statistical central value of the actually measured depth information of the structure intact area and the preset nominal thickness. Specifically, if there is a deviation between the statistical central value and the nominal thickness, it means that the preliminary cooling coefficient may not be accurate enough and needs to be adjusted. Step S35 can make the predicted temperature distribution information closer to the actual situation by adjusting the preliminary cooling coefficient according to the deviation between all statistical central values and the preset nominal thickness, so that the ultrasonic signal corrected based on the temperature information can more accurately reflect the real thickness of the floor. Step S36 uses the target cooling coefficient to obtain more accurate predicted temperature distribution information of the to-be-tested floor at the ultrasonic detection point, combined with the initial temperature distribution information, the environmental parameter and the transmission time. The predicted temperature distribution information will be used for subsequent final ultrasonic signal correction. Specifically, the calculation formula of the predicted temperature distribution information can be: ; wherein T 预 (x,y) represents the predicted temperature of the position with coordinates (x,y) on the to-be-tested floor, T 环境 represents the environmental temperature, T 初始 (x,y) represents the initial temperature of the position with coordinates (x,y) in the initial temperature distribution information, e represents the natural logarithm base, k represents the target cooling coefficient, and t represents the transmission time. It should be understood that the predicted temperature distribution information of this embodiment is composed of all T 预 (x,y).

[0047] The scheme of the present application effectively solves the temperature prediction deviation problem caused by the uncertainty of preset thermal physical properties or environmental parameters by introducing an adaptive cooling coefficient adjustment mechanism. Specifically, first, temperature prediction is performed based on the preliminary cooling coefficient and the ultrasonic signal is preliminarily corrected. Subsequently, the actual depth information of the intact area of the floor structure is obtained by using the preliminarily corrected ultrasonic signal. Since these areas are intact, their depth should be consistent with the preset nominal thickness. If there is a deviation between the actual depth and the nominal thickness, it indicates that there is an error in the preliminary temperature prediction, which in turn affects the correction accuracy of the ultrasonic signal. Therefore, by taking these deviations as feedback, the preliminary cooling coefficient is iteratively adjusted until the deviation between the statistical central value and the nominal thickness is minimized, thereby obtaining a target cooling coefficient that is more consistent with the actual cooling process. Finally, by using this optimized target cooling coefficient, more accurate predicted temperature distribution information can be obtained and the deviation problem that may exist when using a fixed preset cooling coefficient can be overcome, so that the temperature prediction result is closer to the real thermal state of the floor, thereby providing a more reliable temperature compensation basis for the subsequent ultrasonic signal correction, and further improving the accuracy and reliability of floor quality defect detection and reducing the risk of misjudgment and missed judgment.

[0048] In some preferred embodiments, the following is illustrated by a specific example. Assume that the initial temperature distribution information of the floor to be measured after the high-temperature pressing process has been obtained by an infrared thermal imager. The environmental parameters (such as ambient temperature and ambient humidity) have been monitored in real time by sensors. The preset thermal physical properties (such as the specific heat capacity and the thermal conductivity coefficient of the floor material) have been obtained from a material database. In step S31, the system preliminarily calculates a cooling coefficient, for example 0.05 ℃ / s / m², according to these environmental parameters and the preset thermal physical properties. In step S32, the preliminary cooling coefficient is used to predict the preliminary temperature distribution information of the floor at the ultrasonic detection point, in combination with the initial temperature distribution information, the environmental parameters and the transmission time. In step S33, the original ultrasonic signal obtained by the ultrasonic detector is preliminarily corrected based on the preliminary temperature distribution information to obtain a second corrected ultrasonic signal. Since the ultrasonic signal in the region of a floor with a perfect structure usually exhibits clear and stable characteristics, i.e. for the region of a floor with a perfect structure, the ultrasonic detection point receives clear surface echoes and bottom echoes, the arrival time of the bottom echoes (i.e. the time for ultrasonic waves to propagate back and forth in the material) is directly related to the actual thickness of the floor and the propagation speed of ultrasonic waves at this temperature, the energy attenuation of the ultrasonic signal should be within the expected range, and there should be no abnormal internal reflections or signal interruptions, so step S34 can identify different regions of the floor to be measured with a perfect structure by analyzing the second corrected ultrasonic signal, and calculate the statistical central value of the depth information of these regions, for example, the signal is first analyzed in the time domain or the frequency domain to identify the main surface echoes and bottom echoes, and then the actual thickness of the point is calculated according to the time of flight of the bottom echoes, in combination with the propagation speed of ultrasonic waves in the material at this temperature, and then the calculated actual thickness is compared with the preset nominal thickness, if the deviation between the two is within an acceptable range, it is preliminarily judged that the region is a region with a perfect structure, and it is checked whether there are abnormal internal echoes in the signal, if there are additional echoes that do not belong to normal interlayer reflections or the energy of the bottom echoes is significantly attenuated or even disappears, it indicates that the region may have defects such as cavities or delamination, and the region is not a region with a perfect structure. In step S35, the deviation of all statistical central values from the preset nominal thickness is calculated, for example, the nominal thickness is 10 mm, and the statistical central value is 9.8 mm, so there is a deviation of 0.2 mm, and then according to this 0.2 mm deviation, it is judged that the preliminary cooling coefficient may be too small, resulting in a higher predicted temperature, and thus the ultrasonic correction is insufficient. The system will fine-tune the preliminary cooling coefficient according to a preset adjustment algorithm, for example, increase it to 0.052 ℃ / s / m² as a new target cooling coefficient, and this adjustment process can be iterated until the deviation of the statistical central value from the nominal thickness is within an acceptable range. In step S36, the final predicted temperature distribution information of the floor to be measured at the ultrasonic detection point is recalculated and obtained using this adjusted target cooling coefficient.This final predicted temperature distribution information will be used in the final correction of the ultrasonic signal in step S4 to ensure the accuracy of defect detection.

[0049] In some preferred embodiments, step S35 comprises:

[0050] S351, analyzing whether the deviation is caused by the inaccuracy of the preliminary cooling coefficient according to the comparison result of the distribution of the deviation of all statistical center values from the preset nominal thickness and the initial temperature distribution information;

[0051] S352, adjusting the preliminary cooling coefficient according to the deviation between all statistical center values and the preset nominal thickness to obtain a target cooling coefficient when it is analyzed that the deviation is caused by the inaccuracy of the preliminary cooling coefficient;

[0052] S353, generating an alarm information when it is analyzed that the deviation is not caused by the inaccuracy of the preliminary cooling coefficient.

[0053] The working principle of step S351 is that if the cooling coefficient deviates from the actual value, it will cause the system to undercompensate or overcompensate the sound speed in certain temperature regions (for example, if the cooling coefficient is too large, the system will predict that the product cools faster, the actual temperature is higher than the predicted temperature, and the calculated depth will be systematically larger), and this deviation will be reflected in the deviation of the statistical central value from the preset nominal thickness, and the distribution pattern of this deviation will be consistent or inversely related to the distribution pattern of the initial temperature distribution information, that is, when the preliminary cooling coefficient deviates from the actual thermal physical properties of the material, the distribution of the deviation of the statistical central value from the preset nominal thickness in the intact structure area presents a specific pattern related to the actual temperature distribution, therefore step S351 can identify whether there is a correlation pattern between the two by comparing the distribution of the deviation of the statistical central value from the preset nominal thickness in the intact structure area with the initial temperature distribution information, thereby inferring whether the deviation is caused by the inaccurate preliminary cooling coefficient, for example, the area with higher initial temperature, the deviation of the statistical central value from the preset nominal thickness in the intact structure area is also systematically larger or smaller, then it can be judged that the main cause of this overall deviation is that the cooling coefficient and other temperature calculation parameters are inaccurate. On the contrary, if there is no obvious correlation between the distribution of the deviation of the statistical central value from the preset nominal thickness in the intact structure area and the initial temperature distribution, it indicates that the overall deviation may be caused by other factors, such as calibration drift of the ultrasonic sensor itself or slight fluctuations in the nominal thickness of the raw material. This comparison mechanism can distinguish deviations from different sources, thereby ensuring that adjustments are only made when the cooling coefficient is indeed the main cause, avoiding false attribution and inappropriate adjustments. Specifically, when it is analyzed that the deviation is caused by the inaccurate preliminary cooling coefficient, step S352 adjusts the preliminary cooling coefficient according to the deviation between all statistical central values and the preset nominal thickness to obtain a more accurate target cooling coefficient, and this adjustment process can use iterative optimization algorithms such as least squares method or gradient descent method to make the predicted temperature distribution information more consistent with the depth information reflected by the actual ultrasonic signal; when it is analyzed that the deviation is not caused by the inaccurate preliminary cooling coefficient, step S353 generates an alarm information, which can include detailed data such as the type, location, and degree of the deviation, the purpose of which is to prompt the operator or subsequent processing system in a timely manner. The floor to be measured may have structural defects or other abnormalities caused by non-temperature cooling model errors, which need to be further checked manually or processed specifically, thereby avoiding misjudging actual defects as model errors and making invalid corrections.

[0054] The scheme of the present application effectively solves the misjudgment problem that may be caused by blindly adjusting the cooling coefficient in the traditional method by introducing an intelligent analysis mechanism for the deviation reason. Specifically, by comparing the deviation distribution with the initial temperature distribution information, it can accurately identify whether the deviation is caused by inaccurate prediction of the cooling coefficient or other abnormal situations. When it is confirmed that it is a cooling coefficient problem, targeted adjustment is performed to ensure that the subsequent predicted temperature distribution information is more accurate. When it is found that the deviation is not caused by the cooling coefficient, an alarm is immediately sent out to clearly indicate the measurement abnormality and avoid misjudging the real defect as a model error, thereby significantly improving the accuracy and reliability of the floor quality defect detection. Through the above technical scheme, the present application can effectively avoid the cooling coefficient misadjustment caused by unclear deviation reason, thereby ensuring the accuracy of the predicted temperature distribution information. This not only improves the accuracy of the ultrasonic signal based on temperature correction, but more importantly, it can timely identify and distinguish structural deviations caused by non-cooling coefficient reasons, such as actual internal defects, thereby avoiding the covering or misjudgment of real defects. As a result, the reliability and accuracy of the overall floor quality defect detection method are significantly improved, reducing the risk of missed detection and false positives, and providing a more solid technical guarantee for floor production quality control.

[0055] In some preferred embodiments, the following is described by a specific example. Assuming that during the detection of a certain batch of floor, a series of statistical center values are obtained in step S34, and compared with the preset nominal thickness, it is found that the statistical center values of the local area deviate significantly from the nominal thickness. At this time, the system will execute step S351 to analyze the distribution of these deviations. If it is found that these local deviations are highly related to a certain specific high temperature area in the initial temperature distribution information, for example, the cooling speed of a certain area is abnormally slow after high-temperature pressing, causing a large difference between the actual temperature at the ultrasonic detection point and the temperature predicted by the preliminary cooling coefficient, thereby affecting the depth calculation of the ultrasonic signal, then the system will judge that the deviation is caused by inaccurate preliminary cooling coefficient. Subsequently, in step S352, the system will iteratively adjust the preliminary cooling coefficient according to these deviations until the predicted temperature distribution information can more accurately reflect the actual temperature of the area, so that the corrected ultrasonic signal can more accurately reflect the real thickness of the floor. Conversely, if in step S351, the system finds that the distribution of these local deviations has little to do with the initial temperature distribution information, the system will judge that the deviation is not caused by inaccurate preliminary cooling coefficient, but more likely caused by structural defects such as cavities and delamination in the floor. At this time, the system will immediately generate an alarm information indicating that a measurement abnormality has occurred and suggest parameter correction for the ultrasonic detection point, thereby avoiding incorrect cooling coefficient adjustment caused by deviation due to parameter abnormality of the ultrasonic detection point, ensuring the accuracy and timeliness of the defect detection.

[0056] In some preferred embodiments, step S352 comprises:

[0057] A1, determining an initial adjustment step size according to the deviation of all statistical center values from the preset nominal thickness;

[0058] A2, adjusting the preliminary cooling coefficient according to the initial adjustment step size;

[0059] A3, analyzing whether the deviation of all statistical center values from the preset nominal thickness after adjustment of the preliminary cooling coefficient is 0, if yes, taking the adjusted preliminary cooling coefficient as the target cooling coefficient and executing step S36, if not, executing step A4;

[0060] A4, adjusting the initial adjustment step size according to the change trend of the deviation of all statistical center values from the preset nominal thickness before and after adjustment of the preliminary cooling coefficient, and then returning to step A2.

[0061] In step A1, the initial adjustment step size can be determined according to the absolute value of the deviation of all statistical center values from the preset nominal thickness or the statistical distribution (e.g. standard deviation) thereof. For example, when the deviation is large, a larger initial adjustment step size can be set to speed up the convergence; when the deviation is small, a smaller step size can be set to improve the adjustment accuracy. The step size aims to provide a starting correction amount for the iterative adjustment of the preliminary cooling coefficient.

[0062] In step A2, the preliminary cooling coefficient is adjusted according to the determined initial adjustment step size, and the adjustment direction should be such that the deviation between the statistical center values and the preset nominal thickness is reduced. For example, if the statistical center values are generally smaller than the nominal thickness, it may mean that the preliminary cooling coefficient is too high, resulting in a lower predicted temperature and thus smaller depth information obtained after correction of the ultrasonic signal, in which case the preliminary cooling coefficient should be reduced; otherwise, it should be increased.

[0063] In step A3, the effect of the adjustment of the preliminary cooling coefficient is evaluated, i.e. whether the deviation of all statistical center values from the preset nominal thickness after adjustment is 0. If the deviation has been eliminated (the deviation of all statistical center values from the preset nominal thickness after adjustment is 0), it is considered that the adjusted preliminary cooling coefficient is accurate, and it is taken as the target cooling coefficient and the subsequent step S36 is continued. If the deviation still exists (the deviation of all statistical center values from the preset nominal thickness after adjustment is not 0), further iterative adjustment is needed.

[0064] In step A4, the initial adjustment step is dynamically adjusted according to the change trend of the deviation between all statistical center values and the preset nominal thickness before and after the preliminary cooling coefficient adjustment. For example, if the deviation decreases after adjustment, but the decrease is not enough, the step can be kept or slightly decreased; if the deviation increases after adjustment, the step may need to be adjusted in the opposite direction or decreased to avoid overshooting; if the adjustment effect is not obvious, the step may need to be increased. This dynamic adjustment mechanism makes the adjustment process of the cooling coefficient more intelligent and efficient, and can converge to the accurate target cooling coefficient faster.

[0065] The scheme of the present application solves the problem that the preliminary cooling coefficient adjustment may not be accurate by introducing an iterative adjustment mechanism. First, the present scheme determines an initial adjustment step according to the deviation between all statistical center values and the preset nominal thickness to make a tentative adjustment to the preliminary cooling coefficient. Then, whether the adjusted deviation is zero is evaluated to judge the adjustment effect, and if not, the step is dynamically adjusted according to the change trend of the deviation, and a new round of adjustment is returned. This iterative process of feedback control makes the finally determined target cooling coefficient gradually approach the true value, thereby ensuring the accuracy of the predicted temperature distribution information and laying a foundation for the accurate correction of the subsequent ultrasonic signal. Through the above technical scheme, the present application can realize adaptive and iterative optimization of the preliminary cooling coefficient, significantly improve the accuracy of the preliminary cooling coefficient, and compared with simple deviation analysis, the scheme can more effectively identify and correct the deviation caused by inaccurate preliminary cooling coefficient, avoid misjudgment or missed detection due to inaccurate cooling coefficient, and therefore the embodiment can effectively improve the accuracy of the predicted temperature distribution information, and then make the correction of the ultrasonic signal more accurate, and finally improve the reliability and accuracy of the floor quality defect detection and reduce the frequency of manual intervention and calibration.

[0066] In some preferred embodiments, the following is illustrated by a specific example. Assume that in a certain floor quality defect detection, preliminary cooling coefficients and preliminary temperature distribution information are obtained through steps S31 and S32, and statistical center values of the depth information corresponding to different structural intact areas of the floor under test are obtained according to steps S33 and S34. When these statistical center values deviate significantly from the preset nominal thickness, the system will start the iterative adjustment process of step S352. Specifically, the system first calculates an initial adjustment step according to the deviation of all statistical center values from the preset nominal thickness, for example, if the average deviation is -0.5 mm, the system may set an initial step of 0.01 (unit: adjustment amount of cooling coefficient). Then, the system subtracts the step from the preliminary cooling coefficient to obtain a new preliminary cooling coefficient. Next, the system will use this new preliminary cooling coefficient to recalculate the predicted temperature distribution information and obtain the statistical center values again. If the deviation of the statistical center values from the nominal thickness still exists at this time, the system will analyze the trend of the deviation, for example, if the deviation changes from -0.5 mm to -0.2 mm, indicating that the adjustment direction is correct and effective, the system may slightly reduce the adjustment step, for example, to 0.008, to more finely approach the target value; if the deviation changes to -0.8 mm, indicating that the adjustment direction is incorrect, the system will adjust the step in the opposite direction, for example, to -0.01, and re-adjust. This process will continue to iterate until the deviation of the statistical center values from the preset nominal thickness is 0. Once this condition is met, the current preliminary cooling coefficient is determined as the target cooling coefficient and used for subsequent prediction of temperature distribution information (step S36). Through this iterative and feedback mechanism, even if the initial preliminary cooling coefficient has a large deviation, the system can adaptively find an accurate cooling coefficient, thereby ensuring the accuracy of subsequent defect detection.

[0067] In some preferred embodiments, step S4 comprises:

[0068] S41, correcting the ultrasonic signals obtained by the ultrasonic detection points according to the predicted temperature distribution information to obtain preliminary ultrasonic signals;

[0069] S42, obtaining surface echo features according to the preliminary ultrasonic signals;

[0070] S43, obtaining the surface state of the floor under test according to the surface echo features;

[0071] S44, correcting the preliminary ultrasonic signals according to the surface state of the floor under test to obtain first corrected ultrasonic signals.

[0072] The purpose of step S41 is to preliminarily eliminate the influence of temperature changes on the propagation speed and attenuation of ultrasonic waves, so that the signal is preliminarily standardized in the temperature dimension. Step S42 can obtain the surface echo characteristics from the preliminary ultrasonic signal by analyzing the part of the ultrasonic signal that reflects the floor surface reflection characteristics (for example, parameters such as the amplitude, phase, waveform shape, or arrival time of the surface echo, which can reflect the energy loss and propagation path change of the ultrasonic wave when interacting with the floor surface). Step S43 obtains the surface state of the floor to be measured according to the surface echo characteristics, which means that by analyzing the surface echo characteristic parameters, the physical characteristics of the floor surface are inferred, such as surface roughness, whether there is a coating, attachments (such as dust, oil stains), or whether the surface layer structure is uniform. Step S44 corrects the preliminary ultrasonic signal according to the surface state of the floor to be measured to obtain the first corrected ultrasonic signal, which means that this embodiment further considers the influence of the surface state of the floor to be measured on the ultrasonic signal on the basis of the preliminary temperature correction, and makes a secondary correction to the ultrasonic signal based on the surface state of the floor to be measured. For example, if the surface roughness is high, the signal amplitude may need to be compensated; if there is a specific coating, the frequency response or attenuation model of the signal may need to be adjusted.

[0073] The scheme of the present application effectively solves the possible limitations of temperature correction alone by introducing a secondary correction mechanism for the ultrasonic signal. First, the preliminary temperature correction of the ultrasonic signal in step S41 ensures the accuracy of the signal in the temperature dimension. Second, through steps S42 and S43, the system can identify and quantify the surface echo characteristics of the floor to be measured, and then obtain its surface state. Since the propagation and reflection characteristics of the ultrasonic signal are closely related to the surface state of the material, the present application can compensate for signal distortion caused by factors such as surface roughness, attachments, or uneven surface layer structure by further correcting the preliminary ultrasonic signal based on the obtained surface state. This step-by-step and comprehensive correction method ensures that the final first corrected ultrasonic signal can more accurately reflect the true structural information of the floor interior, thereby providing a more reliable data basis for subsequent defect detection. Through the above technical scheme, the present application can significantly improve the accuracy and reliability of floor quality defect detection. Compared with correction methods that only consider temperature effects, the present application effectively eliminates the interference of surface factors (such as roughness, attachments, etc.) on the ultrasonic signal by introducing an evaluation and correction of the floor surface state, making the final ultrasonic signal used for defect detection more pure and true. This dual correction mechanism ensures that even in less than ideal surface conditions, defects within the floor can be accurately identified, thereby reducing the false positive rate and the false negative rate, and improving the performance and practical value of the overall detection system.

[0074] In some preferred embodiments, the following is illustrated by a specific example. Assume that the original ultrasonic signal acquired by the ultrasonic detection point of the to-be-tested floor is affected by both temperature and surface roughness. First, in step S41, the original ultrasonic signal is preliminarily corrected according to the predicted temperature distribution information, for example, by adjusting the propagation speed and attenuation coefficient of the signal, to obtain a preliminary ultrasonic signal. Next, in step S42, the amplitude, waveform width and other characteristic parameters of the surface echo are extracted from the preliminary ultrasonic signal, for example, if the surface echo amplitude is low and the waveform is widened, it may indicate that the surface roughness is high. In step S43, according to these surface echo characteristics, it is determined that the surface of the to-be-tested floor is in a "rough surface" state. Finally, in step S44, according to the "rough surface" state, a corresponding correction strategy is determined, for example, additional amplitude gain compensation is performed on the preliminary ultrasonic signal or a specific filtering algorithm is applied to eliminate the signal attenuation and scattering effects caused by the rough surface, and finally a first corrected ultrasonic signal for defect detection is obtained. In this way, even if the floor surface has roughness, accurate ultrasonic signals can be obtained, thereby improving the accuracy of defect detection.

[0075] In some preferred embodiments, step S42 comprises:

[0076] S421, filtering the preliminary ultrasonic signal to reduce environmental noise;

[0077] S422, identifying a surface echo effective area according to the energy distribution or waveform morphology of the preliminary ultrasonic signal;

[0078] S423, extracting preliminary surface echo characteristic parameters from the preliminary ultrasonic signal according to the surface echo effective area;

[0079] S424, correcting the preliminary surface echo characteristic parameters according to the preset surface layer structure scattering characteristics to obtain surface echo characteristics.

[0080] Step S421 eliminates or significantly reduces various environmental noises (such as electrical noise, mechanical vibration noise, etc.) introduced in the process of collecting the ultrasonic signal by filtering the preliminary ultrasonic signal. Step S421 can effectively improve the signal-to-noise ratio of the signal by using appropriate filtering algorithms (such as low-pass filtering, band-pass filtering, or adaptive filtering, etc.), so as to lay a foundation for subsequent signal analysis. Step S422 identifies the effective area of the surface echo according to the energy distribution or waveform form of the preliminary ultrasonic signal, which means that the part of the signal corresponding to the floor surface echo is determined by analyzing the energy concentration area of the ultrasonic signal in the time domain or frequency domain, or its specific waveform characteristics (such as peak value, rising edge, falling edge, etc.). For example, the surface echo usually appears as the wave packet with the strongest energy or the first to arrive in the signal, and its waveform form may also have specific regularity. Through these characteristics, the starting and ending points of the surface echo can be accurately located, so as to demarcate its effective area. Step S423 is equivalent to extracting the preliminary surface echo characteristic parameters from the preliminary ultrasonic signal according to the effective area of the surface echo. The parameters can include extracting the amplitude, arrival time, frequency component, phase information, waveform width, etc. of the echo. These parameters are key indicators for characterizing the state of the floor surface. For example, the echo amplitude may be related to the surface roughness or the degree of acoustic impedance matching, and the arrival time reflects the length of the sound wave propagation path. Since the floor surface layer may have microstructure inhomogeneity or roughness, which causes scattering of ultrasonic waves on the surface, thereby affecting the accuracy of the echo signal, step S424 can compensate or correct the extracted preliminary characteristic parameters by using the pre-established surface layer structure scattering model or empirical data, combined with the surface layer structure scattering characteristics, to eliminate the errors caused by the scattering effect, so as to obtain more real and accurate surface echo characteristics.

[0081] The scheme of the present application ensures the accurate acquisition of surface echo features through multi-stage fine processing of the preliminary ultrasonic signal. First, the environmental noise is effectively suppressed through filtering processing, improving the signal quality. Second, the effective area of the surface echo can be accurately identified by analyzing the energy distribution or waveform morphology of the signal, so as to avoid the interference of irrelevant signals. Third, the key preliminary surface echo feature parameters are extracted from the identified effective area, providing basic data for subsequent correction. Finally, considering the scattering effect that may exist in the floor surface layer, the preliminary feature parameters are corrected according to the preset surface layer structure scattering characteristics, further eliminating the error caused by the surface structure, so as to obtain more real and reliable surface echo features. This series of steps work together to ensure the accuracy and reliability of the surface echo features, providing a solid foundation for subsequent floor surface state judgment and accurate correction of the ultrasonic signal. Through the above technical scheme, the influence of environmental noise and surface layer structure scattering on the ultrasonic signal can be effectively overcome, making the acquisition process of the surface echo feature more accurate and robust. Therefore, the embodiment can more accurately judge the surface state of the floor to be measured, and then more accurately correct the preliminary ultrasonic signal, so that the first corrected ultrasonic signal obtained has higher reliability, significantly improving the accuracy and reliability of the floor quality defect detection.

[0082] In some preferred embodiments, step S44 comprises:

[0083] S441, determining a correction strategy according to the surface state of the floor to be measured;

[0084] S442, correcting the preliminary ultrasonic signal according to the correction strategy to obtain a first corrected ultrasonic signal.

[0085] The surface state of the to-be-tested floor refers to various physical or chemical characteristics that the floor surface may have, such as surface roughness, surface flatness, surface attachments (such as dust, oil stains), and the like. These surface states will directly affect the propagation characteristics of the ultrasonic wave when it enters or leaves the floor, resulting in attenuation, scattering or distortion of the ultrasonic signal. The correction strategy can be understood as a series of correction parameters or correction algorithms that are preset or calculated in real time for different surface states. For example, when the surface roughness is high, a specific filtering algorithm or gain adjustment can be used to compensate for signal loss; when there are specific attachments, corresponding signal denoising or feature enhancement methods can be used. The preliminary ultrasonic signal is the signal corrected by the predicted temperature distribution information, but the influence of the surface state on the signal has not yet been completely eliminated. The first corrected ultrasonic signal is the more accurate ultrasonic signal used for defect detection. According to the correction strategy, the preliminary ultrasonic signal is corrected, which means that the specific correction parameters or algorithms determined according to the surface state of the to-be-tested floor are applied to the preliminary ultrasonic signal to eliminate or weaken the interference introduced by the surface state, so that the signal more truly reflects the internal structural information of the floor.

[0086] The scheme of the present application can more systematically and accurately process the ultrasonic signal distortion problem caused by the surface state of the floor by refining the correction process of the ultrasonic signal into two steps of first determining the correction strategy and then correcting. First, through the S441 step, the system can select or generate the most suitable correction method according to the actual detected surface state of the to-be-tested floor, avoiding the problems of insufficient correction or excessive correction that may be caused by using a single or general correction method. Second, in the S442 step, the preliminary ultrasonic signal is corrected based on the determined correction strategy, ensuring the accuracy and effectiveness of the correction process, so that the first corrected ultrasonic signal can restore the propagation characteristics of the ultrasonic wave in an ideal state to the greatest extent, thereby providing a high-quality data basis for subsequent defect detection. Through the above technical scheme, the present application can significantly improve the pertinence and accuracy of ultrasonic signal correction. Specifically, the present application can effectively deal with signal interference caused by the diversity of the floor surface, such as different degrees of surface roughness, stains or coatings, by explicitly determining the correction strategy according to the surface state of the to-be-tested floor. Therefore, the embodiment can make the first corrected ultrasonic signal more accurately reflect the real structure of the floor, reducing the risk of misjudgment or omission caused by surface factors, thereby improving the reliability and accuracy of floor quality defect detection.

[0087] In some preferred embodiments, step S2 comprises:

[0088] S21, obtaining the operating parameters of the transmission path of the to-be-tested floor from the initial thermal state parameter acquisition point to the ultrasonic detection point;

[0089] S22, determining the transmission time of the to-be-tested floor from the initial thermal state parameter acquisition point to the ultrasonic detection point according to the operation parameter.

[0090] The operation parameter of step S21 can be understood as various dynamic or static parameters describing the movement process of the to-be-tested floor between the initial thermal state parameter acquisition point and the ultrasonic detection point on the production line. Specifically, the operation parameter can include but is not limited to the length of the transmission path, the running speed of the transmission equipment (for example, the speed of the conveyor belt), the number and residence time of intermediate stopping points, and any process flow parameters that can affect the transmission time of the floor. The acquisition of these parameters can be realized by real-time monitoring by sensors, preset configuration data reading, or manual input, etc. For example, the speed of the conveyor belt can be acquired in real time by a speed sensor or an encoder installed on the production line, and the residence time of the floor at a specific position can be detected by a photoelectric sensor or a limit switch. The determination of the transmission time according to the operation parameter of step S22 refers to accurately obtaining the total time required for the to-be-tested floor to move from the initial thermal state parameter acquisition point to the ultrasonic detection point based on the operation parameter through calculation or logical judgment. For example, if the transmission path is a fixed-length conveyor belt, and the conveyor belt runs at a constant speed, the transmission time can be calculated by dividing the path length by the running speed. If there are multiple transmission sections or intermediate stops, the transmission time can be composed of the sum of the transmission time of each section and the residence time of each stop. Since the accuracy of the transmission time directly affects the actual temperature prediction of the floor at the ultrasonic detection point, this embodiment can improve the accuracy and reliability of the transmission time by determining the transmission time of the to-be-tested floor from the initial thermal state parameter acquisition point to the ultrasonic detection point according to the operation parameter, to ensure the accuracy of the subsequent temperature prediction.

[0091] The scheme of the present application makes the calculation of the transmission time more accurate and reliable by refining the acquisition of the transmission time into acquiring the running parameters of the transmission path and determining the transmission time based on these parameters. In the actual production environment, the floor may undergo a complex transmission process between the completion of the high-temperature pressing process and the ultrasonic detection point, including conveyors of different speeds, intermediate cooling areas, short pauses, etc. If the transmission time is estimated only by experience or rough estimation, it may result in a large error. By explicitly acquiring the running parameters and calculating based on these parameters, the dynamic changes in the transmission process can be effectively captured, thereby obtaining a transmission time closer to the actual situation. As can be seen, the scheme can significantly improve the accuracy of the transmission time acquisition. The accurate transmission time is the basis for subsequent prediction of the temperature distribution information of the floor to be tested at the ultrasonic detection point. The more accurate the transmission time, the closer the predicted temperature distribution information to the actual situation. Therefore, the present scheme can provide more reliable input for subsequent correction of the ultrasonic signal based on the temperature distribution information, thereby improving the accuracy and reliability of the floor quality defect detection and effectively avoiding detection errors caused by inaccurate transmission time estimation, thereby improving the performance and efficiency of the overall detection system.

[0092] In a second aspect, as shown in Figure 3 The present application also provides a floor quality defect detection system, which comprises:

[0093] An information acquisition module 1 is configured to acquire initial temperature distribution information of a floor to be tested after completing a high-temperature pressing process, and to acquire environmental parameters and preset thermal physical properties corresponding to the floor to be tested;

[0094] A transmission time acquisition module 2 is configured to acquire the transmission time of the floor to be tested from the point of acquiring the initial temperature distribution information to the ultrasonic detection point;

[0095] A temperature distribution prediction module 3 is configured to acquire predicted temperature distribution information of the floor to be tested at the ultrasonic detection point according to the initial temperature distribution information, the environmental parameters, the preset thermal physical properties, and the transmission time;

[0096] A signal correction module 4 is configured to correct the ultrasonic signal acquired at the ultrasonic detection point according to the predicted temperature distribution information to obtain a first corrected ultrasonic signal;

[0097] A defect detection module 5 is configured to perform floor quality defect detection according to the first corrected ultrasonic signal.

[0098] The floor quality defect detection system provided in the application comprises an information acquisition module 1, a transmission time acquisition module 2, a temperature distribution prediction module 3, a signal correction module 4 and a defect detection module 5. The floor quality defect detection system provided in the embodiment is used to execute the steps in the floor quality defect detection method provided in the first aspect, and the principle of the floor quality defect detection system provided in the embodiment is the same as that of the floor quality defect detection method provided in the first aspect, which will not be discussed in detail here.

[0099] As can be seen from the above, the floor quality defect detection method and system provided in the application can effectively solve the problem of ultrasonic signal distortion caused by temperature non-uniformity in the floor detection after the high-temperature pressing process in the prior art by introducing the precise temperature prediction and signal correction links before ultrasonic detection, that is, the floor quality defect detection method provided in the application can effectively distinguish the signal changes caused by real internal defects from the signal changes caused by temperature effects, so that the interpretation of the ultrasonic signal is no longer disturbed by the temperature non-uniformity of the floor, thereby realizing high-precision and high-reliability detection of the floor quality defect after high-temperature pressing, and effectively improving the accuracy and reliability of the floor quality defect detection.

[0100] In the embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the above-mentioned units is only a logical function division, and another division mode can be used in actual implementation. For example, a plurality of units or components can be combined or integrated into another robot, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed entities can be indirect coupling or communication connection through some communication interfaces, devices or units, which can be electrical, mechanical or other forms.

[0101] In addition, the functional modules in each of the embodiments of the application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0102] In this document, relationship terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that these entities or operations have any such actual relationship or order.

[0103] The above merely provides an example of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting defects in flooring quality, characterized in that, The method for detecting floor quality defects includes the following steps: S1. Obtain the initial temperature distribution information of the floor under test after the high-temperature pressing process, and obtain the environmental parameters and the preset thermophysical properties corresponding to the floor under test. S2. Obtain the transmission time of the floor under test from the initial temperature distribution information acquisition point to the ultrasonic detection point; S3. Obtain the predicted temperature distribution information of the floor under test at the ultrasonic detection point based on the initial temperature distribution information, the environmental parameters, the preset thermophysical properties, and the transmission time. S4. Correct the ultrasonic signal obtained from the ultrasonic detection point according to the predicted temperature distribution information to obtain a first corrected ultrasonic signal. S5. Detect floor quality defects based on the first corrected ultrasonic signal; Step S3 includes: S31. Determine the preliminary cooling coefficient based on the environmental parameters and the preset thermophysical properties; S32. Obtain preliminary temperature distribution information based on the initial temperature distribution information, the environmental parameters, the transmission time, and the preliminary cooling coefficient; S33. Correct the ultrasonic signal obtained from the ultrasonic detection point based on the preliminary temperature distribution information to obtain a second corrected ultrasonic signal. S34. Obtain the statistical center value of the depth information corresponding to different structurally intact areas of the floor under test based on the second corrected ultrasonic signal. S35. Adjust the preliminary cooling coefficient according to the deviation of all the statistical center values ​​from the preset nominal thickness to obtain the target cooling coefficient; S36. Obtain the predicted temperature distribution information of the floor under test at the ultrasonic detection point based on the initial temperature distribution information, the environmental parameters, the transmission time, and the target cooling coefficient; Step S35 includes: S351. Based on the distribution of all the statistical center values ​​and the comparison results of the deviations from the preset nominal thickness and the initial temperature distribution information, analyze whether the deviation is caused by the inaccuracy of the preliminary cooling coefficient; S352. When the analysis shows that the deviation is caused by the inaccuracy of the preliminary cooling coefficient, the preliminary cooling coefficient is adjusted according to the deviation between all the statistical center values ​​and the preset nominal thickness to obtain the target cooling coefficient. S353. When the analysis shows that the deviation is not caused by the inaccuracy of the preliminary cooling coefficient, an alarm message is generated; The formula for calculating the predicted temperature distribution information is: ； Among them, T 预 (x,y) represents the predicted temperature at coordinates (x,y) on the floor to be measured, T 环境 Indicates ambient temperature, T 初始 (x,y) represents the initial temperature at coordinate (x,y) in the initial temperature distribution information, e represents the base of the natural logarithm, k represents the target cooling coefficient, and t represents the transmission time.

2. The method for detecting floor quality defects according to claim 1, characterized in that, The environmental parameters include ambient temperature and ambient humidity.

3. The method for detecting floor quality defects according to claim 1, characterized in that, Step S352 includes: A1. Determine the initial adjustment step size based on the deviation of all the statistical center values ​​from the preset nominal thickness; A2. Adjust the initial cooling coefficient according to the initial adjustment step size; A3. Analyze whether the deviation between all the statistical center values ​​and the preset nominal thickness after the initial cooling coefficient adjustment is 0. If yes, take the adjusted initial cooling coefficient as the target cooling coefficient and execute step S36. If no, execute step A4. A4. Adjust the initial adjustment step size according to the trend of the deviation between all the statistical center values ​​and the preset nominal thickness before and after the initial cooling coefficient adjustment, and then return to step A2.

4. The method for detecting floor quality defects according to claim 1, characterized in that, Step S4 includes: S41. Correct the ultrasonic signal obtained from the ultrasonic detection point according to the predicted temperature distribution information to obtain a preliminary ultrasonic signal. S42. Obtain surface echo characteristics based on the preliminary ultrasonic signal; S43. Obtain the surface state of the floor to be tested based on the surface echo characteristics; S44. The preliminary ultrasonic signal is corrected according to the surface condition of the floor to be tested to obtain a first corrected ultrasonic signal.

5. The method for detecting floor quality defects according to claim 4, characterized in that, Step S42 includes: S421. Filter the preliminary ultrasonic signal to reduce environmental noise; S422. Identify the effective area of ​​surface echo based on the energy distribution or waveform shape of the preliminary ultrasonic signal; S423. Extract preliminary surface echo characteristic parameters from the preliminary ultrasonic signal based on the effective area of ​​the surface echo; S424. The preliminary surface echo characteristic parameters are corrected according to the preset surface layer structure scattering characteristics to obtain the surface echo characteristics.

6. The method for detecting floor quality defects according to claim 4, characterized in that, Step S44 includes: S441. Determine a correction strategy based on the surface condition of the floor to be tested; S442. The preliminary ultrasonic signal is corrected according to the correction strategy to obtain a first corrected ultrasonic signal.

7. The method for detecting floor quality defects according to claim 1, characterized in that, Step S2 includes: S21. Obtain the operating parameters of the transmission path of the floor under test from the initial thermal state parameter acquisition point to the ultrasonic detection point; S22. Determine the transmission time of the floor under test from the initial thermal state parameter acquisition point to the ultrasonic detection point based on the operating parameters.

8. A floor quality defect detection system, characterized in that, The floor quality defect detection system is used in the steps of the floor quality defect detection method as described in any one of claims 1-7. The floor quality defect detection system includes: The information acquisition module is used to acquire the initial temperature distribution information of the floor under test after the high-temperature pressing process, and to acquire environmental parameters and the preset thermophysical properties corresponding to the floor under test. The transmission time acquisition module is used to acquire the transmission time of the floor under test from the initial temperature distribution information acquisition point to the ultrasonic detection point; The temperature distribution prediction module is used to obtain the predicted temperature distribution information of the floor under test at the ultrasonic detection point based on the initial temperature distribution information, the environmental parameters, the preset thermophysical properties, and the transmission time. A signal correction module is used to correct the ultrasonic signal obtained by the ultrasonic detection point according to the predicted temperature distribution information to obtain a first corrected ultrasonic signal. The defect detection module is used to detect floor quality defects based on the first corrected ultrasonic signal.

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