Mechanical property detection method and device and storage medium

Abstract

The invention discloses a mechanical property detection method and device and a storage medium, relates to the technical field of material property detection, and aims to solve the problem that a traditional welding seam detection method cannot realize nondestructive detection of the mechanical property of a welding seam. The mechanical property detection method comprises the steps that a target sound wave characteristic parameter of a first detection point in a to-be-detected object is obtained, and the target sound wave characteristic parameter can be obtained by extracting an ultrasonic detection signal of the first detection point and conducting correlation detection on the target sound wave characteristic parameter and a real mechanical property parameter obtained through a destructive mechanical test. Based on the target acoustic wave characteristic parameters and a mechanical property regression equation, target mechanical property parameters are obtained, and the mechanical property regression equation is used for reflecting the incidence relation between the acoustic wave characteristic parameters and the mechanical property, so that ultrasonic nondestructive testing of the mechanical property of the welding seam is achieved. And inputting the target mechanical property parameters into the mechanical property grade model to obtain a mechanical property detection result so as to realize grading inspection of the mechanical property of the welding seam.

Description

Technical Field

[0001] The present application relates to the technical field of material performance testing, and in particular to a mechanical performance testing method, device and storage medium. Background Art

[0002] In the industrial field, girth seams (i.e. girth welds) are widely used in various welding scenarios. If there is a deviation in the welding process, the mechanical properties of the weld will not meet the quality requirements, resulting in insufficient overall strength of the structure and easy damage. Therefore, after welding, it is usually necessary to test the mechanical properties of the weld to determine whether the mechanical properties of the weld meet the quality requirements, thereby ensuring the safety of the welded structure.

[0003] However, the traditional weld inspection method requires the destruction of the pipeline and cannot achieve non-destructive inspection of the mechanical properties of the weld. Therefore, a new weld inspection method is needed to inspect the weld. Summary of the invention

[0004] The purpose of this application is to provide a mechanical property detection method, device and storage medium, aiming to solve the problem that traditional weld detection methods cannot achieve non-destructive detection of weld mechanical properties.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for detecting mechanical properties, the method comprising: obtaining target acoustic wave characteristic parameters of a first detection point in an object to be detected, the target acoustic wave characteristic parameters being acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. Based on the target acoustic wave characteristic parameters and a mechanical property regression equation, target mechanical property parameters of the object to be detected are obtained, the mechanical property regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical properties, and the target mechanical property parameters are used to indicate the mechanical properties of the object to be detected. Based on the target mechanical property parameters, a mechanical property detection result of the object to be detected is obtained.

[0007] The mechanical property detection method provided in the embodiment of the present application is as follows: the target acoustic wave characteristic parameters of the first detection point in the object to be detected can be obtained, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. Afterwards, the target mechanical property parameters of the object to be detected can be obtained based on the target acoustic wave characteristic parameters and the mechanical property regression equation. The mechanical property regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical properties, and the target mechanical property parameters are used to indicate the mechanical properties of the object to be detected. Afterwards, the mechanical property detection results of the object to be detected can be obtained based on the target mechanical property parameters. In this way, by associating the acoustic wave with the mechanical properties, the mechanical properties of the object to be detected can be obtained without destroying the object to be detected. In this way, the mechanical properties of the object to be detected can be obtained by non-destructive testing. In addition, by detecting multiple first detection points, a comprehensive detection of the object to be detected can be achieved.

[0008] In some embodiments, obtaining the target acoustic wave characteristic parameter of the first detection point in the object to be detected includes: obtaining a plurality of preset acoustic wave characteristic parameters. The plurality of preset acoustic wave characteristic parameters are processed based on the principal component analysis (PCA) algorithm to obtain a plurality of mechanical property experimental parameters, wherein one preset acoustic wave characteristic parameter corresponds to one mechanical property experimental parameter. Based on the plurality of mechanical property experimental parameters, the target acoustic wave characteristic parameter is obtained, and the target acoustic wave characteristic parameter is the preset acoustic wave characteristic parameter with the smallest difference between the plurality of mechanical property experimental parameters and the actual mechanical property parameter of the object to be detected.

[0009] In some embodiments, the object to be detected includes: multiple detection areas, the target acoustic wave characteristic parameters include multiple regional acoustic wave characteristic parameters, one regional acoustic wave characteristic parameter corresponds to one detection area, and the regional acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the corresponding detection area. Based on the target acoustic wave characteristic parameters and the mechanical property regression equation, the target mechanical property parameters of the object to be detected are obtained, including: based on the multiple regional acoustic wave characteristic parameters and the mechanical property regression equation, multiple regional mechanical property parameters are obtained, one regional mechanical property parameter corresponds to one regional acoustic wave characteristic parameter, and the regional mechanical property parameters are used to indicate the mechanical properties of any one of the multiple detection areas. Wherein, the target mechanical property parameters include multiple regional mechanical property parameters. The mechanical property detection result of the object to be detected based on the target mechanical property parameters is obtained, including: based on a preset detection threshold and the multiple regional mechanical property parameters, determining the preset performance level of the object to be detected. Based on the target mechanical property parameters, the mechanical property detection result of the object to be detected is obtained, including: based on the preset detection threshold and the multiple regional mechanical property parameters, determining the preset performance level of the object to be detected. Wherein, the mechanical property detection result of the object to be detected includes the preset performance level of the object to be detected.

[0010] In some embodiments, based on a preset detection threshold and multiple regional mechanical performance parameters, a preset performance level of the object to be detected is determined, including: when multiple regional mechanical performance parameters are greater than or equal to the preset detection threshold, the mechanical performance level of the object to be detected is determined to be a preset first level. When some of the regional mechanical performance parameters are greater than or equal to the preset detection threshold, and some of the regional mechanical performance parameters are less than the preset detection threshold, the mechanical performance level of the object to be detected is determined to be a preset second level. When multiple regional mechanical performance parameters are less than the preset detection threshold, the mechanical performance level of the object to be detected is determined to be a preset third level.

[0011] In some embodiments, the mechanical property regression equation is determined by: obtaining the real mechanical property parameters and the acoustic wave characteristic parameters of the second detection point in the object to be detected, and processing the real mechanical property parameters and the acoustic wave characteristic parameters of the second detection point based on a linear regression algorithm to obtain the mechanical property regression equation.

[0012] In some embodiments, the real mechanical performance parameters and the acoustic wave characteristic parameters of the second detection point are processed based on a linear regression algorithm to obtain a mechanical performance regression equation, including: processing the real mechanical performance parameters and multiple acoustic wave characteristic parameters of the second detection point based on a correlation test algorithm to obtain a correlation coefficient of each acoustic wave characteristic parameter in the multiple acoustic wave characteristic parameters, and the correlation coefficient is used to indicate the degree of association between the acoustic wave characteristic parameter and the real mechanical performance parameter. Based on the correlation coefficient of each acoustic wave characteristic parameter, multiple related acoustic wave characteristic parameters are determined from the multiple acoustic wave characteristic parameters, and the related acoustic wave characteristic parameters are acoustic wave characteristic parameters whose correlation coefficients are less than a preset coefficient threshold. The real mechanical performance parameters and multiple related acoustic wave characteristic parameters are processed based on a linear regression algorithm to obtain a mechanical performance regression equation.

[0013] In some embodiments, obtaining the acoustic wave characteristic parameters of the second detection point includes: obtaining an ultrasonic detection signal of the second detection point. Extracting characteristic parameters of the ultrasonic detection signal to obtain ultrasonic characteristic parameters, where the ultrasonic characteristic parameters are characteristic parameters associated with the microstructure of the object to be detected. Extracting the ultrasonic detection signal based on a spectrum analysis algorithm to obtain frequency characteristic parameters, where the frequency characteristic parameters are characteristic parameters associated with the microstructure of the object to be detected. Based on the ultrasonic characteristic parameters and the frequency characteristic parameters, the acoustic wave characteristic parameters of the second detection point are obtained.

[0014] In a second aspect, the present application provides a mechanical property detection device, which includes an acquisition module and a processing module.

[0015] The acquisition module is used to acquire the target acoustic wave characteristic parameters of the first detection point in the object to be detected, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. The processing module is used to acquire the target acoustic wave characteristic parameters of the first detection point in the object to be detected, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. The processing module is also used to obtain the mechanical property detection result of the object to be detected based on the target mechanical property parameters.

[0016] In some embodiments, the acquisition module is used to acquire multiple preset acoustic wave characteristic parameters. The processing module is used to process the multiple preset acoustic wave characteristic parameters based on the principal component analysis (PCA) algorithm to obtain multiple mechanical property experimental parameters, where one preset acoustic wave characteristic parameter corresponds to one mechanical property experimental parameter. The processing module is also used to obtain a target acoustic wave characteristic parameter based on multiple mechanical property experimental parameters, where the target acoustic wave characteristic parameter is a preset acoustic wave characteristic parameter with the smallest difference between the multiple mechanical property experimental parameters and the actual mechanical property parameter of the object to be detected.

[0017] In some embodiments, the object to be detected includes: multiple detection areas, the target acoustic wave characteristic parameters include multiple regional acoustic wave characteristic parameters, one regional acoustic wave characteristic parameter corresponds to one detection area, and the regional acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the corresponding detection area. The processing module is used to obtain multiple regional mechanical performance parameters based on multiple regional acoustic wave characteristic parameters and mechanical performance regression equations, one regional mechanical performance parameter corresponds to one regional acoustic wave characteristic parameter, and the regional mechanical performance parameters are used to indicate the mechanical properties of any one of the multiple detection areas. Among them, the target mechanical performance parameters include multiple regional mechanical performance parameters. The processing module is used to determine the preset performance level of the object to be detected based on a preset detection threshold and multiple regional mechanical performance parameters. Among them, the mechanical performance detection result of the object to be detected includes the preset performance level of the object to be detected.

[0018] In some embodiments, the processing module is used to determine that the mechanical property level of the object to be detected is a preset first level when the mechanical property parameters of multiple regions are greater than or equal to the preset detection threshold. The processing module is also used to determine that the mechanical property level of the object to be detected is a preset second level when the mechanical property parameters of some regions among the multiple mechanical property parameters are greater than or equal to the preset detection threshold, and the mechanical property parameters of some regions are less than the preset detection threshold. The processing module is also used to determine that the mechanical property level of the object to be detected is a preset third level when the mechanical property parameters of multiple regions are less than the preset detection threshold.

[0019] In some embodiments, the acquisition module is used to acquire the real mechanical performance parameters and the acoustic wave characteristic parameters of the second detection point in the object to be detected. The processing module is also used to process the real mechanical performance parameters and the acoustic wave characteristic parameters of the second detection point based on a linear regression algorithm to obtain a mechanical performance regression equation.

[0020] In some embodiments, the processing module is used to process the real mechanical performance parameters and multiple acoustic wave characteristic parameters of the second detection point based on the correlation test algorithm to obtain the correlation coefficient of each acoustic wave characteristic parameter in the multiple acoustic wave characteristic parameters, and the correlation coefficient is used to indicate the degree of association between the acoustic wave characteristic parameter and the real mechanical performance parameter. The processing module is also used to determine multiple related acoustic wave characteristic parameters from the multiple acoustic wave characteristic parameters based on the correlation coefficient of each acoustic wave characteristic parameter, and the related acoustic wave characteristic parameters are acoustic wave characteristic parameters whose correlation coefficients are less than a preset coefficient threshold. The processing module is also used to process the real mechanical performance parameters and multiple related acoustic wave characteristic parameters based on the linear regression algorithm to obtain a mechanical performance regression equation.

[0021] In some embodiments, the acquisition module is used to acquire the ultrasonic detection signal of the second detection point. The processing module is used to extract characteristic parameters of the ultrasonic detection signal to obtain ultrasonic characteristic parameters, which are characteristic parameters associated with the microstructure of the object to be detected. The processing module is also used to extract the ultrasonic detection signal based on a spectrum analysis algorithm to obtain frequency characteristic parameters, which are characteristic parameters associated with the microstructure of the object to be detected. The processing module is also used to obtain the acoustic wave characteristic parameters of the second detection point based on the ultrasonic characteristic parameters and the frequency characteristic parameters.

[0022] In a third aspect, the present application provides a device for detecting mechanical properties, the device comprising: a processor and a memory; the processor and the memory are coupled; the memory is used to store one or more programs, the one or more programs comprising computer-executable instructions, and when the mechanical properties detection device is running, the processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect and any possible implementation of the first aspect.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a computer, the computer executes the method described in the above-mentioned first aspect and any possible implementation of the first aspect.

[0024] In a fifth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the method described in the first aspect and any possible implementation of the first aspect.

[0025] In a sixth aspect, the present application provides a computer program product comprising instructions, which, when executed by a computer, enables the computer to execute the method described in the above-mentioned first aspect and any possible implementation manner of the first aspect.

[0026] In the above scheme, the technical problems that can be solved and the technical effects achieved by the mechanical properties detection device, computer equipment, computer storage medium, chip or computer program product can refer to the technical problems and technical effects solved by the above-mentioned first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A schematic diagram of a flow chart of a method for detecting mechanical properties provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of an example of a method for detecting mechanical properties provided in an embodiment of the present application;

[0030] Figure 3 A schematic diagram of a flow chart of another method for detecting mechanical properties provided in an embodiment of the present application;

[0031] Figure 4 A schematic diagram of an example of obtaining an ultrasonic detection signal provided in an embodiment of the present application;

[0032] Figure 5 A longitudinal wave waveform diagram of a longitudinal wave detection signal provided in an embodiment of the present application;

[0033] Figure 6 A shear wave waveform diagram of a shear wave detection signal provided in an embodiment of the present application;

[0034] Figure 7 A schematic diagram of an example of a tensile test piece provided in an embodiment of the present application;

[0035] Figure 8 An example schematic diagram of an X8 steel ring weld provided in an embodiment of the present application;

[0036] Fig. 9 A schematic diagram of the structure of a mechanical property detection device provided in an embodiment of the present application;

[0037] Fig.10A schematic diagram of the structure of another mechanical property detection device provided in an embodiment of the present application;

[0038] Fig.11 A conceptual partial view of a computer program product provided for an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] In the description of this application, it should be understood that the terms "upper", "lower", "left", "right", "front", "back", "inside", "outside", etc. indicate directions or positional relationships based on the directions or relative positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on this application. Unless otherwise specified, the above-mentioned directional description can be flexibly set in the process of actual application under the condition that the relative positional relationship shown in the accompanying drawings is met.

[0041] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0042] In the present application, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, article or device including the element.

[0043] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0044] In the industrial field, girth seams (i.e. girth welds) are widely used in various welding scenarios. If there is a deviation in the welding process, the performance of the weld will not meet the quality requirements, resulting in insufficient overall strength of the structure and easy damage. Therefore, after welding, it is usually necessary to test the performance of the weld to determine whether the performance of the weld meets the quality requirements, thereby ensuring the safety of the welded structure.

[0045] The mechanical property testing of some girth welds is mainly based on destructive testing. For example, a destructive test can be performed using a tensile testing machine to explore the effect of preheating temperature on the tensile strength of X80 steel girth welds. However, destructive testing can only detect the tensile properties (i.e. mechanical properties) of the destroyed structure at a specific point, and cannot measure the tensile properties of other welds, and cannot achieve comprehensive testing of welds. Therefore, it is not suitable for testing in-service structures.

[0046] The mechanical property detection of some girth welds is mainly based on ultrasonic testing. After the ultrasonic wave passes through the polycrystalline metal material, the ultrasonic signal received by the probe often carries a large amount of information reflecting the internal microstructure of the material. Since the microstructure of the material determines the acoustic properties of the material, the microstructure of the material also determines the mechanical properties of the material. Therefore, the mechanical properties of the material can be detected by the characteristic parameters of the acoustic wave. For example, the laser ultrasonic method can be used to calibrate the precipitated phase content inside the alloy. For another example, the near-surface hardness of alloy steel can be characterized by critical longitudinal waves.

[0047] However, during the welding process, different areas of the weld joint will be subjected to different degrees of heat in different situations, and a variety of microstructures will be obtained. In addition, the alloy composition of different weld joint areas is complex. Therefore, the traditional ultrasonic detection method of the mechanical properties of the weld is not effective. The correspondence between the single acoustic wave parameter and the weld structure is not strong, and it is difficult to analyze the acoustic characteristics of the weld.

[0048] Therefore, a new weld inspection method is urgently needed to inspect welds.

[0049] In order to solve the above technical problems, the embodiment of the present application provides a method for detecting mechanical properties. In this method, the target acoustic wave characteristic parameters of the first detection point in the object to be detected can be obtained, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. Afterwards, the target mechanical performance parameters of the object to be detected can be obtained based on the target acoustic wave characteristic parameters and the mechanical performance regression equation. The mechanical performance regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical performance, and the target mechanical performance parameters are used to indicate the mechanical properties of the object to be detected. Afterwards, the mechanical performance test results of the object to be detected can be obtained based on the target mechanical performance parameters. In this way, by associating the acoustic wave with the mechanical performance, the mechanical properties of the object to be detected can be obtained without destroying the object to be detected. In this way, the mechanical properties of the object to be detected can be obtained by non-destructive testing. In addition, by detecting multiple first detection points, a comprehensive detection of the object to be detected can be achieved.

[0050] The implementation environment of the embodiments of the present application is introduced below.

[0051] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0052] like Figure 1 As shown, a method for detecting mechanical properties provided in an embodiment of the present application comprises:

[0053] S101, obtaining target acoustic wave characteristic parameters of a first detection point in an object to be detected.

[0054] The first detection point is any detection position of the object to be detected. The target acoustic wave characteristic parameter is an acoustic wave characteristic parameter related to the mechanical properties of the object to be detected.

[0055] It should be noted that the present application does not limit the number of target acoustic wave characteristic parameters. For example, the number of target acoustic wave characteristic parameters can be 1, 4, 6, 8, or 10.

[0056] In a possible implementation, multiple preset acoustic wave characteristic parameters can be obtained. Afterwards, the multiple preset acoustic wave characteristic parameters can be processed based on the principal component analysis (PCA) algorithm to obtain multiple mechanical property experimental parameters. Among them, one preset acoustic wave characteristic parameter corresponds to one mechanical property experimental parameter. Afterwards, based on the multiple mechanical property experimental parameters, a target acoustic wave characteristic parameter can be obtained, and the target acoustic wave characteristic parameter is the preset acoustic wave characteristic parameter with the smallest difference between the multiple mechanical property experimental parameters and the actual mechanical property parameter of the object to be detected.

[0057] For example, the actual mechanical property parameter of the object to be detected is 0.7. The mechanical property experimental parameter corresponding to the preset acoustic wave characteristic parameter 1 is 0.3, the mechanical property experimental parameter corresponding to the preset acoustic wave characteristic parameter 2 is 0.9, and the mechanical property experimental parameter corresponding to the preset acoustic wave characteristic parameter 3 is 0.6, then the target acoustic wave characteristic parameter is the preset acoustic wave characteristic parameter 3.

[0058] Optionally, multiple preset acoustic wave characteristic parameters can be processed based on the principal component analysis (PCA) algorithm to obtain multiple transformed preset acoustic wave characteristic parameters. Among them, one preset acoustic wave characteristic parameter corresponds to one transformed preset acoustic wave characteristic parameter, and then the multiple transformed preset acoustic wave characteristic parameters can be input into the mechanical property regression equation to obtain multiple mechanical property experimental parameters. Among them, one transformed acoustic wave characteristic parameter corresponds to one mechanical property experimental parameter. The target acoustic wave characteristic parameter is the transformed preset acoustic wave characteristic parameter with the smallest difference between the actual mechanical property parameter of the object to be detected and the multiple mechanical property experimental parameters.

[0059] Exemplarily, the actual mechanical property parameter of the object to be tested is 0.7. After inputting the transformed preset acoustic wave characteristic parameter 1 into the mechanical property regression equation, the mechanical property experimental parameter 0.3 is obtained, after inputting the transformed preset acoustic wave characteristic parameter 2 into the mechanical property regression equation, the mechanical property experimental parameter 0.9 is obtained, after inputting the transformed preset acoustic wave characteristic parameter 3 into the mechanical property regression equation, the mechanical property experimental parameter 0.6 is obtained, and the target acoustic wave characteristic parameter is the transformed preset acoustic wave characteristic parameter 3.

[0060] In this way, the target acoustic wave characteristic parameters with the strongest correlation with the actual mechanical performance parameters among multiple preset acoustic wave characteristic parameters can be obtained, so that the target mechanical performance parameters obtained by the mechanical performance regression equation can be more accurate.

[0061] In another possible implementation, the mechanical property detection device pre-stores a plurality of target acoustic wave characteristic parameters and object types of the detection object, and there is a corresponding relationship between the target acoustic wave characteristic parameters and the object type. Based on the object type of the detection object, the target acoustic wave characteristic parameters of the first detection point in the detection object can be determined from the plurality of target acoustic wave characteristic parameters.

[0062] It should be noted that the present application does not limit the object to be inspected. For example, the object type to be inspected may be a weld (such as a girth weld), a partial area of ​​a weld, a pipe, or a steel plate.

[0063] S102, obtaining target mechanical property parameters of the object to be detected based on the target acoustic wave characteristic parameters and the mechanical property regression equation.

[0064] The mechanical property regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical properties, and the target mechanical property parameters are used to indicate the mechanical properties of the object to be detected.

[0065] It should be noted that the present application does not limit the expression form of the mechanical property regression equation. For example, the mechanical property regression equation can be a multivariate linear regression equation.

[0066] In a possible implementation, the target acoustic wave characteristic parameters may be normalized, and then the target acoustic wave characteristic parameters may be input into a mechanical property regression equation to obtain the target mechanical property parameters of the object to be detected.

[0067] It should be noted that the present application does not limit the expression form of the mechanical property regression equation.

[0068] Optionally, the object to be detected includes: multiple detection areas, the target acoustic wave characteristic parameters include multiple regional acoustic wave characteristic parameters, one regional acoustic wave characteristic parameter corresponds to one detection area, and the regional acoustic wave characteristic parameter is an acoustic wave characteristic parameter related to the mechanical properties of the corresponding detection area. Among them, the target mechanical property parameters include multiple regional mechanical property parameters.

[0069] In another possible implementation, multiple regional mechanical property parameters can be obtained based on multiple regional acoustic wave characteristic parameters and mechanical property regression equations. One regional mechanical property parameter corresponds to one regional acoustic wave characteristic parameter, and the regional mechanical property parameter is used to indicate the mechanical properties of any one of the multiple detection areas.

[0070] It should be noted that the present application does not limit the number of regional acoustic wave characteristic parameters. For example, the number of regional acoustic wave characteristic parameters can be 1, 4, 5, 7, or 8.

[0071] Exemplarily, the plurality of detection areas include an upper area of ​​the first detection point and a lower area of ​​the first detection point, the upper area is used to indicate the detection area above the straight line including the first detection point and parallel to the horizontal line, and the lower area is used to indicate the detection area below the straight line including the first detection point and parallel to the horizontal line. The regional acoustic wave characteristic parameters of the upper area are input into the mechanical property regression equation to obtain regional mechanical property parameters 1, and the regional acoustic wave characteristic parameters of the lower area are input into the mechanical property regression equation to obtain regional mechanical property parameters 2.

[0072] In this way, by testing the mechanical properties of different areas, the mechanical properties of each area can be obtained more accurately, making the mechanical properties test results more accurate.

[0073] S103. Obtaining mechanical property test results of the object to be tested based on the target mechanical property parameters.

[0074] Optionally, the mechanical property test result of the object to be tested is the target mechanical property parameter.

[0075] In a possible implementation, the mechanical property test result of the object to be tested includes a preset performance level of the object to be tested. The preset performance level of the object to be tested can be determined based on a preset test threshold and multiple regional mechanical property parameters.

[0076] Optionally, a mechanical property grade model may be obtained. Afterwards, a preset detection threshold and multiple regional mechanical property parameters may be input into the mechanical property grade model to obtain a preset performance grade of the object to be detected. The mechanical property grade model is used to evaluate the mechanical properties of the object to be detected.

[0077] It should be noted that the present application does not limit the mechanical property grade model. For example, the mechanical property grade model can be a classification model, a statistical model, or a neural network model.

[0078] In a possible design, when the mechanical performance parameters of multiple regions are all greater than or equal to the preset detection threshold, the mechanical performance level of the object to be detected can be determined to be the preset first level. When the mechanical performance parameters of some regions among the multiple regional mechanical performance parameters are greater than or equal to the preset detection threshold, and the mechanical performance parameters of some regions are less than the preset detection threshold, the mechanical performance level of the object to be detected is determined to be the preset second level. When the mechanical performance parameters of multiple regions are all less than the preset detection threshold, the mechanical performance level of the object to be detected is determined to be the preset third level.

[0079] For example, Figure 2 As shown, the regional performance level of a detection area of ​​the first detection point whose regional mechanical performance parameter is greater than or equal to the preset detection threshold is Class A, and the regional performance level of a detection area of ​​the first detection point whose regional mechanical performance parameter is less than the preset detection threshold is Class B. If the regional performance level of the upper area and the regional performance level of the lower area are both Class A, the mechanical performance level of the object to be detected is the preset first level (i.e., Class I). If the regional performance level of the upper area is Class A and the regional performance level of the lower area is Class B; or, the regional performance level of the upper area is Class B and the regional performance level of the lower area is Class A, the mechanical performance level of the object to be detected is the preset second level (i.e., Class II); if the regional performance level of the upper area and the regional performance level of the lower area are both Class B, the mechanical performance level of the object to be detected is the preset third level (i.e., Class III).

[0080] It should be noted that the present application does not limit the preset detection threshold. For example, the preset detection threshold may be 380 MPa, 450 MPa, 490 MPa, 580 MPa, or 630 MPa.

[0081] In this way, the mechanical properties of each area can be evaluated by preset performance levels, and the mechanical properties of each area of ​​the object to be tested can be obtained more quickly.

[0082] Based on the above technical solution, the target acoustic wave characteristic parameters of the first detection point in the object to be detected can be obtained, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. Afterwards, the target mechanical performance parameters of the object to be detected can be obtained based on the target acoustic wave characteristic parameters and the mechanical performance regression equation. The mechanical performance regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical performance, and the target mechanical performance parameters are used to indicate the mechanical properties of the object to be detected. Afterwards, the mechanical performance test results of the object to be detected can be obtained based on the target mechanical performance parameters. In this way, by associating the acoustic wave with the mechanical performance, the mechanical properties of the object to be detected can be obtained without destroying the object to be detected. In this way, the mechanical properties of the object to be detected can be obtained through non-destructive testing. In addition, by detecting multiple first detection points, a comprehensive detection of the object to be detected can be achieved.

[0083] like Figure 3 As shown, another method for detecting mechanical properties provided in the embodiment of the present application, the mechanical property regression equation is determined by the following method:

[0084] S301, obtaining real mechanical performance parameters and acoustic wave characteristic parameters of a second detection point in the object to be detected.

[0085] In a possible implementation, an ultrasonic detection signal of the second detection point may be obtained. Then, characteristic parameters may be extracted from the ultrasonic detection signal to obtain ultrasonic characteristic parameters, which are characteristic parameters associated with the microstructure of the object to be detected. Then, based on the ultrasonic characteristic parameters, the acoustic wave characteristic parameters of the second detection point may be obtained.

[0086] Optionally, the ultrasonic detection signal includes a longitudinal wave detection signal and a shear wave detection signal.

[0087] It should be noted that the present application does not limit the method for obtaining the ultrasonic detection signal of the second detection point. For example, the method for obtaining the ultrasonic detection signal of the second detection point can be: determine the mechanical properties to be detected, and detect a detection point in a 20mm thick X80 steel ring weld based on the ultrasonic diffraction time difference method (time of flight diffraction, TOFD). If the longitudinal wave detection signal of the second detection point is obtained, the wedge angle is 22 degrees. If the transverse wave detection signal of the second detection point is obtained, the wedge angle is selected to be 30 degrees. Among them, the probe center frequency is 5MHz, and the probe spacing is 40mm.

[0088] It should be understood that in the process of obtaining the ultrasonic detection signal based on TOFD technology, the second detection point can be detected in combination with the sound velocity of the sound wave in the object to be detected and the geometric feature information of the object to be detected. The geometric feature information of the object to be detected includes the wedge angle and the probe spacing, so that the ultrasonic signal can detect the overall information of the object to be detected.

[0089] For example, Figure 4 As shown, it is a schematic diagram of an example of obtaining an ultrasonic detection signal provided by an embodiment of the present application. A schematic diagram of an example of obtaining an ultrasonic detection signal of a second detection point. The beam transmitting device transmits an ultrasonic wave at the second detection point, and then the beam receiving device can receive the ultrasonic wave at the receiving point. Among them, sound wave 1 is a direct wave, sound wave 2 and sound wave 3 are scattered waves, and sound wave 4 is a bottom surface echo. When sound wave 1 is a trough, sound wave 2 is a peak, sound wave 3 is a trough, and sound wave 4 is a peak. The distance between the second detection point and the receiving point is 2S.

[0090] Optionally, a longitudinal wave waveform of the longitudinal wave detection signal and a shear wave waveform of the shear wave detection signal can be obtained. Afterwards, characteristic parameters of the longitudinal wave detection signal can be extracted based on the longitudinal wave waveform, and characteristic parameters of the shear wave detection signal can be extracted based on the shear wave waveform to obtain ultrasonic characteristic parameters.

[0091] For example, Figure 5 As shown in FIG. 1 , a longitudinal wave waveform diagram of a longitudinal wave detection signal provided in an embodiment of the present application is used to indicate the change of the amplitude of the longitudinal wave over time. Figure 6 As shown, a shear wave waveform diagram of a shear wave detection signal provided in an embodiment of the present application is used to indicate changes in the amplitude of the shear wave over time.

[0092] In another possible implementation, an ultrasonic detection signal of the second detection point may be obtained. Then, characteristic parameters may be extracted from the ultrasonic detection signal to obtain ultrasonic characteristic parameters, which are characteristic parameters associated with the microstructure of the object to be detected. Then, the ultrasonic detection signal may be extracted based on a spectrum analysis algorithm to obtain frequency characteristic parameters, which are characteristic parameters associated with the microstructure of the object to be detected. Then, based on the ultrasonic characteristic parameters and the frequency characteristic parameters, the acoustic wave characteristic parameters of the second detection point may be obtained.

[0093] It should be noted that the present application does not limit the spectrum analysis algorithm. For example, the spectrum analysis algorithm may be Fourier transform, wavelet decomposition, or both.

[0094] Optionally, the frequency characteristic parameters include the highest frequency of the signal, nonlinear characteristic information of the object to be detected, and wavelet components of the signal.

[0095] It is understandable that by extracting the ultrasonic detection signal of the detection point, a plurality of acoustic wave characteristic parameters associated with the microstructure of the object to be detected can be obtained. Since the microstructure of the object to be detected is associated with the mechanical properties of the object to be detected, the mechanical properties of the object to be detected can be associated with the acoustic wave characteristic parameters, thereby realizing non-destructive detection of the object to be detected.

[0096] It should be noted that the present application does not limit the method of obtaining the true mechanical performance parameters. For example, the true mechanical performance parameters can be obtained through destructive tests (such as tensile tests).

[0097] For example, Figure 7 , which is a schematic diagram of an example of a tensile test piece provided in an embodiment of the present application. Among them, tensile test piece 1 is a tensile test piece obtained in the upper area of ​​the second detection point of the object to be detected 701, and tensile test piece 2 is a tensile test piece obtained in the lower area of ​​the second detection point of the object to be detected 701. Tensile test piece 702 is obtained by processing tensile test piece 1 or tensile test piece 2. Tensile test piece 702 is used to perform a tensile test to obtain the true mechanical performance parameters of the second detection point.

[0098] It should be noted that the present application does not limit the second detection point, for example, the second detection point can be any position of the object to be detected. The present application does not limit the number of second detection points, for example, the number of second detection points can be 1, 4, 8, 12, or 17.

[0099] For example, Figure 8 , which is a schematic diagram of an example of an X8 steel ring weld provided in an embodiment of the present application. Eight detection points 801 are arranged on the outer surface of the X8 steel ring weld.

[0100] S302, processing the real mechanical performance parameters and the acoustic wave characteristic parameters of the second detection point based on a linear regression algorithm to obtain a mechanical performance regression equation.

[0101] In a possible implementation, the real mechanical performance parameter and multiple acoustic wave characteristic parameters of the second detection point can be processed based on a correlation test algorithm to obtain a correlation coefficient of each acoustic wave characteristic parameter in the multiple acoustic wave characteristic parameters, and the correlation coefficient is used to indicate the degree of association between the acoustic wave characteristic parameter and the real mechanical performance parameter. Afterwards, based on the correlation coefficient of each acoustic wave characteristic parameter, multiple related acoustic wave characteristic parameters can be determined from the multiple acoustic wave characteristic parameters, and the related acoustic wave characteristic parameters are acoustic wave characteristic parameters whose correlation coefficients are less than a preset coefficient threshold. Afterwards, the real mechanical performance parameter and multiple related acoustic wave characteristic parameters can be processed based on a linear regression algorithm to obtain a mechanical performance regression equation.

[0102] Optionally, the relevant acoustic wave characteristic parameters include relevant acoustic wave characteristic parameters of the longitudinal wave detection signal and relevant acoustic wave characteristic parameters of the shear wave detection signal.

[0103] It should be noted that the present application does not impose any restrictions on the relevant acoustic wave characteristic parameters of the longitudinal wave detection signal and the relevant acoustic wave characteristic parameters of the shear wave detection signal.

[0104] Optionally, relevant acoustic wave characteristic parameters of the longitudinal wave detection signal include but are not limited to at least one of the following: the direct wave amplitude of the longitudinal wave detection signal, the peak value of the direct wave peak of the longitudinal wave detection signal, the arrival time of the direct wave peak of the longitudinal wave detection signal, the peak value of the scattered wave peak of the longitudinal wave detection signal, the arrival time of the scattered wave of the longitudinal wave detection signal, the bottom surface reflected wave amplitude of the longitudinal wave detection signal, the arrival time of the bottom surface reflected wave of the longitudinal wave detection signal, and the signal-to-noise ratio of the longitudinal wave detection signal.

[0105] It should be noted that the description of the relevant acoustic wave characteristic parameters of the shear wave detection signal can refer to the description of the relevant acoustic wave characteristic parameters of the longitudinal wave detection signal, and this application will not elaborate on this.

[0106] In the embodiment of the present application, the degree of correlation between the acoustic wave characteristic parameters and the actual mechanical performance parameters is inversely proportional to the correlation coefficient.

[0107] It should be noted that the present application does not limit the preset coefficient threshold. For example, the preset coefficient threshold may be 0.3, 0.35, 0.4, 0.45, or 0.5.

[0108] Exemplarily, the preset coefficient threshold is 0.5, the correlation coefficient of the acoustic wave characteristic parameter 1 is 0.1, the correlation coefficient of the acoustic wave characteristic parameter 2 is 0.1, and the correlation coefficient of the acoustic wave characteristic parameter 1 is 0.6. Then the acoustic wave characteristic parameter 1 is the relevant acoustic wave characteristic parameter.

[0109] Exemplarily, the mechanical property regression equation may satisfy Formula 1:

[0110]

[0111] Among them, a is the direct wave amplitude of the longitudinal wave detection signal, b is the peak value of the direct wave peak of the longitudinal wave detection signal, c is the arrival time of the direct wave peak of the longitudinal wave detection signal, d is the peak value of the scattered wave peak of the longitudinal wave detection signal, e is the arrival time of the scattered wave of the longitudinal wave detection signal, f is the bottom surface reflection wave amplitude of the longitudinal wave detection signal, g is the arrival time of the bottom surface reflection wave of the longitudinal wave detection signal. h is the direct wave amplitude of the longitudinal wave detection signal.

[0112] In this way, the correlation test algorithm can obtain the relevant acoustic wave characteristic parameters that are more correlated with the mechanical performance parameters. The linear regression algorithm can be used to associate the relevant acoustic wave characteristic parameters with the true mechanical performance parameters, so that the mechanical performance parameters obtained by the mechanical performance regression equation can be more accurate.

[0113] Based on the above technical solution, the real mechanical performance parameters and the acoustic wave characteristic parameters of the second detection point in the object to be detected can be obtained. Afterwards, the real mechanical performance parameters and the acoustic wave characteristic parameters of the second detection point can be processed based on the linear regression algorithm to obtain a mechanical performance regression equation. In this way, the real mechanical performance parameters can be associated with the acoustic wave characteristic parameters through the mechanical performance regression equation. In this way, the mechanical performance parameters can be obtained through the mechanical performance regression equation without destroying the object to be detected.

[0114] In some embodiments, the acoustic wave characteristic parameters of multiple second detection points can be obtained, and an acoustic wave characteristic database can be established based on the acoustic wave characteristic parameters of the multiple second detection points. The mechanical properties of multiple second detection points can be obtained, and a mechanical properties database can be established based on the mechanical properties of the multiple second detection points.

[0115] The embodiments of the present application will be introduced below in conjunction with specific examples. Exemplarily, the object to be detected is an X80 steel girth weld with a thickness of 20 mm. An ultrasonic detection signal of a detection point of the X80 steel girth weld can be obtained. The ultrasonic detection signal includes a longitudinal wave detection signal and a transverse wave detection signal. Then, feature parameters are extracted from the longitudinal wave detection signal and the transverse wave detection signal to obtain ultrasonic feature parameters. Then, the tensile property parameters (i.e., mechanical property parameters) of the X80 steel girth weld can be obtained. The tensile property parameters of the X80 steel girth weld are obtained through a tensile test. Then, based on a linear regression algorithm, the ultrasonic feature parameters and the tensile property parameters are processed to obtain a mechanical property regression equation for the X80 steel girth weld. Then, the ultrasonic feature parameters are input into the mechanical property regression equation to obtain the target mechanical property parameters of the X80 steel girth weld. Then, based on a preset detection threshold and the mechanical property parameters of multiple regions, the preset performance grade of the X80 steel girth weld can be determined.

[0116] The above mainly introduces the solution provided by the embodiments of the present application from the perspective of methods. It can be understood that in order to implement the above functions, the mechanical property detection device includes the corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should easily realize that in combination with the steps of the mechanical property detection methods of the examples described in the embodiments disclosed in the present application, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0117] The embodiments of the present application also provide a mechanical property detection device. The mechanical property detection device can be a server, or the CPU in the above server, or the module for detecting mechanical properties in the above server, or the client for detecting mechanical properties in the above server.

[0118] The embodiments of the present application can divide the mechanical property detection device into functional modules or functional units according to the above method examples. For example, each functional module or functional unit can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware, or in the form of a software functional module or functional unit. Among them, the division of modules or units in the embodiments of the present application is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0119] The embodiments of the present application provide a mechanical property detection device. As Fig. 9As shown, the mechanical property detection device may include: an acquisition module 901 and a processing module 902 .

[0120] The acquisition module 901 is used to acquire the target acoustic wave characteristic parameters of the first detection point in the object to be detected, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. The processing module 902 is used to acquire the target acoustic wave characteristic parameters of the first detection point in the object to be detected, and the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected. The processing module 902 is also used to obtain the mechanical property detection result of the object to be detected based on the target mechanical property parameters.

[0121] Fig.10 1 is a schematic diagram of another mechanical property detection device according to an exemplary embodiment. The mechanical property detection device may include a processor 1002, and the processor 1002 is used to execute application code to implement the mechanical property detection method in this application.

[0122] The processor 1002 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

[0123] like Fig.10 As shown, the mechanical property detection device may further include a memory 1003. The memory 1003 is used to store application program codes for executing the solution of the present application, and is controlled to be executed by the processor 1002.

[0124] The memory 1003 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 1003 may exist independently and be connected to the processor 1002 via the bus 1004. The memory 1003 may also be integrated with the processor 1002.

[0125] like Fig.10 As shown, the mechanical property detection device may further include a communication interface 1001, wherein the communication interface 1001, the processor 1002, and the memory 1003 may be coupled to each other, for example, via a bus 1004. The communication interface 1001 is used to exchange information with other devices, for example, to support information exchange between the mechanical property detection device and other devices.

[0126] It should be pointed out that Fig.10 The equipment structure shown in the figure does not constitute a limitation on the detection device of the mechanical properties, except Fig.10 In addition to the components shown, the mechanical property detection device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] In actual implementation, the functions implemented by the processing module 902 can be Fig.10 The processor 1002 shown calls the program code in the memory 1003 to implement.

[0128] The present application also provides a computer-readable storage medium, on which instructions are stored. When the instructions in the computer-readable storage medium are executed by a processor of a computer device, the computer can execute the method for detecting mechanical properties provided in the above-mentioned embodiment. For example, the computer-readable storage medium may be a memory 1003 including instructions, and the above instructions may be executed by a processor 1002 of a computer device to complete the above method. Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, a non-temporary computer-readable storage medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0129] Fig.11 A conceptual partial view of a computer program product provided by an embodiment of the present application is schematically shown, where the computer program product includes a computer program for executing a computer process on a computing device.

[0130] In one embodiment, the computer program product is provided using a signal bearing medium 1100. The signal bearing medium 1100 may include one or more program instructions that, when executed by one or more processors, may provide the above-described Figure 1 , Figure 3 Thus, for example, reference to Figure 1 , Figure 3 In the embodiment shown in , one or more features of S101 to S103 may be undertaken by one or more instructions associated with the signal bearing medium 1100. In addition, Fig.11 The program instructions in also describe example instructions.

[0131] In some examples, the signal bearing medium 1100 may include a computer readable medium 1101, such as, but not limited to, a hard drive, a compact disk (CD), a digital video disk (DVD), a digital tape, a memory, a read-only memory (ROM) or a random access memory (RAM), and the like.

[0132] In some implementations, the signal bearing medium 1100 may include a computer recordable medium 1102 such as, but not limited to, a memory, a read / write (R / W) CD, a R / W DVD, or the like.

[0133] In some implementations, signal bearing medium 1100 may include communication medium 1103 such as, but not limited to, digital and / or analog communication media (eg, fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

[0134] The signal bearing medium 1100 may be communicated by a wireless form of communication medium 1103. The one or more program instructions may be, for example, computer executable instructions or logic implemented instructions.

[0135] In some examples, the mechanical property detection device can be configured to provide various operations, functions, or actions in response to one or more program instructions in computer-readable medium 1101, computer-recordable medium 1102, and / or communication medium 1103.

[0136] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0139] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes various media that can store program codes, such as-U disk, mobile hard disk, ROM, RAM, disk or CD.

[0141] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0142] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting mechanical properties, characterized in that: The method comprises: Acquiring a target acoustic wave characteristic parameter of a first detection point in the object to be detected, wherein the target acoustic wave characteristic parameter is an acoustic wave characteristic parameter related to the mechanical properties of the object to be detected; Based on the target acoustic wave characteristic parameters and the mechanical property regression equation, the target mechanical property parameters of the object to be detected are obtained, wherein the mechanical property regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical properties, and the target mechanical property parameters are used to indicate the mechanical properties of the object to be detected; Based on the target mechanical property parameters, a mechanical property test result of the object to be tested is obtained.

2. The method according to claim 1, characterized in that The step of obtaining target acoustic wave characteristic parameters of a first detection point in the object to be detected includes: Obtaining multiple preset sound wave characteristic parameters; The plurality of preset acoustic wave characteristic parameters are processed based on a principal component analysis (PCA) algorithm to obtain a plurality of mechanical property experimental parameters, wherein one preset acoustic wave characteristic parameter corresponds to one mechanical property experimental parameter; Based on the multiple mechanical property experimental parameters, the target acoustic wave characteristic parameter is obtained, and the target acoustic wave characteristic parameter is a preset acoustic wave characteristic parameter with the smallest difference between the multiple mechanical property experimental parameters and the actual mechanical property parameter of the object to be detected.

3. The method according to claim 1 or 2, characterized in that: The object to be detected includes: a plurality of detection areas, the target acoustic wave characteristic parameters include a plurality of regional acoustic wave characteristic parameters, one regional acoustic wave characteristic parameter corresponds to one detection area, and the regional acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the corresponding detection area; The step of obtaining the target mechanical property parameters of the object to be detected based on the target acoustic wave characteristic parameters and the mechanical property regression equation includes: Based on the multiple regional acoustic wave characteristic parameters and the mechanical property regression equation, multiple regional mechanical property parameters are obtained, one regional mechanical property parameter corresponds to one regional acoustic wave characteristic parameter, and the regional mechanical property parameter is used to indicate the mechanical property of any one of the multiple detection areas; wherein the target mechanical property parameter includes the multiple regional mechanical property parameters; The step of obtaining the mechanical property test result of the object to be tested based on the target mechanical property parameter comprises: Determining a preset performance level of the object to be detected based on a preset detection threshold and the multiple regional mechanical performance parameters; Wherein, the mechanical property test result of the object to be tested includes a preset performance level of the object to be tested.

4. The method according to claim 3, characterized in that The step of determining a preset performance level of the object to be detected based on a preset detection threshold and the plurality of regional mechanical performance parameters includes: When the mechanical property parameters of the plurality of regions are all greater than or equal to the preset detection threshold, determining that the mechanical property level of the object to be detected is a preset first level; When the mechanical property parameters of some regions among the multiple regional mechanical property parameters are greater than or equal to the preset detection threshold, and the mechanical property parameters of some regions are less than the preset detection threshold, determining that the mechanical property level of the object to be detected is the preset second level; When the mechanical property parameters of the multiple regions are all smaller than the preset detection threshold, it is determined that the mechanical property level of the object to be detected is a preset third level.

5. The method according to claim 1, characterized in that The mechanical property regression equation is determined by the following method: Acquiring true mechanical performance parameters and acoustic wave characteristic parameters of a second detection point in the object to be detected; The true mechanical property parameters and the acoustic wave characteristic parameters of the second detection point are processed based on a linear regression algorithm to obtain the mechanical property regression equation.

6. The method according to claim 5, characterized in that The method of processing the true mechanical property parameters and the acoustic wave characteristic parameters of the second detection point based on a linear regression algorithm to obtain the mechanical property regression equation includes: Processing the true mechanical performance parameter and a plurality of acoustic wave characteristic parameters of the second detection point based on a correlation test algorithm to obtain a correlation coefficient of each of the plurality of acoustic wave characteristic parameters, wherein the correlation coefficient is used to indicate a degree of correlation between the acoustic wave characteristic parameter and the true mechanical performance parameter; Based on the correlation coefficient of each acoustic wave characteristic parameter, determining a plurality of correlated acoustic wave characteristic parameters from the plurality of acoustic wave characteristic parameters, the correlated acoustic wave characteristic parameters being acoustic wave characteristic parameters whose correlation coefficients are less than a preset coefficient threshold; The true mechanical property parameters and the multiple related acoustic wave characteristic parameters are processed based on the linear regression algorithm to obtain the mechanical property regression equation.

7. The method according to claim 5 or 6, characterized in that: Acquiring the acoustic wave characteristic parameters of the second detection point, including: Acquire an ultrasonic detection signal of the second detection point; Extracting characteristic parameters of the ultrasonic detection signal to obtain ultrasonic characteristic parameters, wherein the ultrasonic characteristic parameters are characteristic parameters associated with the microstructure of the object to be detected; The ultrasonic detection signal is extracted based on a spectrum analysis algorithm to obtain frequency characteristic parameters. The frequency characteristic parameter is a characteristic parameter associated with the microstructure of the object to be detected; Based on the ultrasonic characteristic parameter and the frequency characteristic parameter, the acoustic wave characteristic parameter of the second detection point is obtained.

8. A mechanical property detection device, characterized in that: The device comprises an acquisition module and a processing module: The acquisition module is used to acquire target acoustic wave characteristic parameters of a first detection point in the object to be detected, wherein the target acoustic wave characteristic parameters are acoustic wave characteristic parameters related to the mechanical properties of the object to be detected; The processing module is used to obtain the target mechanical property parameters of the object to be detected based on the target acoustic wave characteristic parameters and the mechanical property regression equation, wherein the mechanical property regression equation is used to reflect the correlation between the acoustic wave characteristic parameters and the mechanical property, and the target mechanical property parameters are used to indicate the mechanical property of the object to be detected; The processing module is further used to obtain the mechanical property test results of the object to be tested based on the target mechanical property parameters.

9. A mechanical property detection device, characterized in that: include: Processor and memory; The processor is coupled to the memory; The memory is used to store one or more programs, and one or more of the programs include computer-executable instructions. When the mechanical properties detection device is running, the processor executes the computer-executable instructions stored in the memory to enable the mechanical properties detection device to perform the method described in any one of claims 1-7.

10. A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, characterized in that: When a computer executes the instruction, the computer performs the method according to any one of claims 1 to 7.

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