A non-destructive testing method, device and electronic equipment for non-metallic materials

Abstract

The present application provides a non-destructive testing method, device and electronic equipment for non-metallic materials, and relates to the technical field of data processing. In the method, microwave data of non-metallic materials are obtained, and the microwave data includes material microwave data and environmental electromagnetic wave data; the material microwave data and environmental electromagnetic wave data are analyzed to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters; the material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters are denoised to obtain microwave signals; the microwave signals are analyzed using a preset analysis model to obtain analysis results, and the analysis results are used to indicate whether the non-metallic material is abnormal. The implementation of the technical solution provided by the present application is convenient for improving the accuracy of non-destructive testing of non-metallic materials.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a non-destructive testing method, device and electronic equipment for non-metallic materials. Background Art

[0002] Non-metallic materials are used in many equipment, such as aerospace, nuclear industry, petrochemical industry, etc. Non-destructive testing can detect damage and defects inside the material to ensure the safety and stability of the equipment during operation.

[0003] In the detection of abnormal defects in non-metallic materials, traditional detection methods usually identify defects based on single data. However, this method often does not fully consider the interference factors of complex electromagnetic environments, resulting in inaccurate detection results. In complex electromagnetic environments, non-metallic materials may be affected by a variety of factors, such as the size and fluctuation of electromagnetic waves. These factors may interfere with data analysis, thereby affecting the accuracy of non-destructive testing.

[0004] Therefore, there is an urgent need for a non-destructive testing method, device and electronic equipment for non-metallic materials. Summary of the invention

[0005] The present application provides a method, device and electronic equipment for non-destructive testing of non-metallic materials, which are convenient for improving the accuracy of non-destructive testing of non-metallic materials.

[0006] In a first aspect of the present application, a non-destructive testing method for non-metallic materials is provided, the method comprising: acquiring microwave data of the non-metallic material, the microwave data comprising material microwave data and environmental electromagnetic wave data; analyzing the material microwave data and the environmental electromagnetic wave data to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters; denoising the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal; analyzing the microwave signal using a preset analysis model to obtain an analysis result, the analysis result being used to indicate whether the non-metallic material has an abnormality.

[0007] By adopting the above technical solution, more accurate material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters can be obtained by analyzing the material microwave data and the environmental electromagnetic wave data. These parameters can more accurately reflect the performance and state of the material, thereby improving the accuracy of defect detection. Traditional methods for detecting abnormal defects in non-metallic materials often only consider the influence of the material itself, without considering the interference of environmental factors. By simultaneously acquiring material microwave data and environmental electromagnetic wave data, the above method can better consider the influence of environmental factors on the detection results, thereby improving the reliability of detection. By denoising the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters, the electromagnetic wave noise and interference in the data can be effectively removed, and the quality of the microwave signal can be improved. This helps to improve the accuracy and stability of defect detection. By using a preset analysis model to analyze the microwave signal, the analysis process can be simplified and the analysis efficiency can be improved. At the same time, the preset analysis model is usually optimized and verified, and can provide more accurate analysis results.

[0008] Optionally, the acquiring of microwave data of non-metallic materials specifically includes: responding to a detection request input by a user; according to the detection request, controlling a detection device to transmit microwaves to the non-metallic material, the detection device being used to detect the non-metallic material; receiving a detection data packet sent by the detection device, the detection data packet storing microwave data associated with the non-metallic material; performing microwave extraction on the detection data packet to obtain the microwave data, the microwave extraction including material microwave extraction and environmental electromagnetic wave extraction.

[0009] By adopting the above technical solution, the method can quickly respond to the detection request input by the user, thereby improving the efficiency and accuracy of obtaining microwave data. By controlling the detection equipment to emit microwaves to the non-metallic material, the microwave data inside the material can be accurately obtained, avoiding the errors and interference that may exist in the traditional method. By performing microwave extraction on the detection data packet, microwave data associated with the non-metallic material can be obtained. This method can efficiently extract data and improve the speed and accuracy of data processing. This method extracts material microwaves and environmental electromagnetic waves separately, which can more accurately analyze the microwave characteristics of the material itself and avoid the interference of environmental factors on the analysis results.

[0010] Optionally, the analyzing the material microwave data and the ambient electromagnetic wave data to obtain material microwave characteristic parameters and ambient electromagnetic wave characteristic parameters specifically includes: using a frequency domain analysis algorithm and a time domain analysis algorithm to analyze the material microwave data to obtain first analysis data, wherein the first analysis data is the frequency domain data and the time domain data corresponding to the material microwave data; using the frequency domain analysis algorithm and the time domain analysis algorithm to analyze the ambient electromagnetic wave data to obtain second analysis data, wherein the second analysis data is the frequency domain data and the time domain data corresponding to the ambient electromagnetic wave data; performing data screening on the first analysis data to obtain the material microwave characteristic parameters; performing data screening on the second analysis data to obtain the ambient electromagnetic wave characteristic parameters.

[0011] By adopting the above technical solution, the method adopts two methods, frequency domain analysis and time domain analysis, to perform a more comprehensive analysis of microwave data. Frequency domain analysis can reveal the characteristics of microwaves at different frequencies, while time domain analysis can reveal the changes of microwaves in the time domain. By screening the first analysis data and the second analysis data, the parameters of the material microwave characteristics and the environmental electromagnetic wave characteristics can be accurately extracted. These parameters can more accurately reflect the acoustic characteristics of the material and the environment, and provide more accurate data support for subsequent analysis and judgment. By adopting frequency domain analysis and time domain analysis algorithms, the data analysis process can be completed faster and the efficiency of data analysis can be improved. At the same time, this analysis method can also reduce the interference of human factors and improve the accuracy of data analysis. By analyzing the material microwave data and the environmental electromagnetic wave data separately, the acoustic characteristics of the material and the environment can be more clearly separated. This helps to more accurately determine whether the material is abnormal, and also better understand the impact of environmental factors on the acoustic characteristics of the material.

[0012] Optionally, the data screening of the first analysis data to obtain the material microwave characteristic parameters specifically includes: obtaining a preset material microwave characteristic parameter range; using the material microwave characteristic parameter range to data screen the first analysis data to obtain the material microwave characteristic parameters; the data screening of the second analysis data to obtain the ambient electromagnetic wave characteristic parameters specifically includes: obtaining a preset ambient electromagnetic wave characteristic parameter range; using the ambient electromagnetic wave characteristic parameter range to data screen the second analysis data to obtain the ambient electromagnetic wave characteristic parameters.

[0013] By adopting the above technical solution, by obtaining the preset material microwave characteristic parameter range and the environmental electromagnetic wave characteristic parameter range, the first analysis data and the second analysis data are screened, which can ensure that the screened data is consistent with the preset parameter range, thereby improving the accuracy of the data. By screening the data with the preset parameter range, the standardization and normalization of the analysis process can be ensured. In this way, the interference of human factors on the analysis results can be avoided, and the objectivity and reliability of the analysis can be improved. By using the preset parameter range for data screening, the process of data analysis can be simplified. There is no need to perform complex data processing and algorithm calculations, and the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters can be directly extracted from the screened data. By screening the data with the preset parameter range, the time and energy of manual screening and judgment can be reduced, and work efficiency can be improved. At the same time, this method can also reduce the possibility of errors and omissions and improve the accuracy of data analysis.

[0014] Optionally, the denoising of the material microwave characteristic parameters and the ambient electromagnetic wave characteristic parameters to obtain the microwave signal specifically includes: using a blind source separation algorithm to denoise the material microwave characteristic parameters and the ambient electromagnetic wave characteristic parameters to obtain the material microwave signal and the ambient electromagnetic wave signal; and performing signal reconstruction on the material microwave signal and the ambient electromagnetic wave signal to obtain the microwave signal.

[0015] By adopting the above technical solution, by using the blind source separation algorithm to denoise the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters, the electromagnetic wave noise and interference in the data can be effectively removed, and the quality of the microwave signal can be improved. This helps to improve the accuracy and reliability of subsequent analysis. By denoising the material microwave characteristics and the environmental electromagnetic wave characteristics separately, the microwave signals of the material and the environment can be more clearly separated. This helps to more accurately determine whether the material is abnormal, and also better understand the impact of environmental factors on the acoustic properties of the material. By reconstructing the signal of the material microwave signal and the environmental electromagnetic wave signal, a more complete microwave signal can be obtained. This method can make up for the information that may be lost in the denoising process and improve the integrity of the signal. The blind source separation algorithm can adapt to noise interference in various complex electromagnetic environments. This helps to improve the accuracy and reliability of non-destructive testing and analysis of non-metallic materials in complex electromagnetic environments.

[0016] Optionally, the microwave signal is analyzed using a preset analysis model to obtain an analysis result, which specifically includes: obtaining the amplitude and frequency of the microwave signal; judging whether the amplitude is within a preset amplitude range, the preset amplitude range is determined based on historical analysis data, and one preset amplitude range corresponds to a non-metallic material; judging whether the frequency is within a preset frequency range, the preset frequency range is determined based on the historical analysis data, and one preset frequency range corresponds to a non-metallic material; if it is determined that the amplitude is within the preset amplitude range, and the frequency is within the preset frequency range, then generating a first analysis result, the first analysis result is used to indicate that the non-metallic material is normal.

[0017] By adopting the above technical solution, the analysis process of microwave signals can be standardized by presetting the analysis model. This method can avoid the interference of human factors on the analysis results and improve the objectivity and reliability of the analysis. By presetting the analysis model, the microwave signal can be quickly analyzed to obtain the corresponding analysis results. This method can reduce the time and energy of manual analysis and judgment and improve the efficiency of data analysis. The preset analysis model can determine different amplitude and frequency ranges according to historical analysis data, corresponding to different non-metallic materials. This method can adapt to the microwave signal analysis of various materials and improve the versatility and adaptability of the analysis. By judging whether the amplitude and frequency are within the preset range, it is possible to more accurately judge whether the non-metallic material is normal. This method can reduce the possibility of misjudgment and missed judgment and improve the accuracy of analysis.

[0018] Optionally, the method also includes: if it is determined that the amplitude is not within the preset amplitude range and the frequency is not within the preset frequency range, or, if it is determined that the amplitude is within the preset amplitude range and the frequency is not within the preset frequency range, or, if it is determined that the amplitude is not within the preset amplitude range and the frequency is within the preset frequency range, then generating a second analysis result, the second analysis result being used to indicate that there is an abnormality in the non-metallic material.

[0019] By adopting the above technical solution, by considering different situations such as the amplitude is not within the preset range, the frequency is not within the preset range, and the amplitude is within the preset range but the frequency is not within the preset range, the abnormal situations that may exist in non-metallic materials can be more comprehensively covered. This helps to improve the accuracy and completeness of abnormality detection. By generating a second analysis result based on different abnormal situations, it is possible to more finely judge whether there is an abnormality in the non-metallic material. This method can provide more specific and detailed abnormal information, which is helpful for subsequent defect repair and processing. For different non-metallic materials, the amplitude and frequency of their microwave signals may vary greatly. By presetting different amplitude and frequency ranges, it is possible to better adapt to the characteristics of different materials and improve the accuracy and reliability of abnormality detection. When the microwave signal of a non-metallic material is abnormal, the second analysis result is generated in a timely manner, which is conducive to subsequent reminders to users to take timely measures to repair and process, so as to avoid causing greater losses.

[0020] In a second aspect of the present application, a non-destructive testing device for non-metallic materials is provided, the non-destructive testing device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire microwave data of the non-metallic material, the microwave data comprising material microwave data and environmental electromagnetic wave data; the processing module is used to analyze the material microwave data and the environmental electromagnetic wave data to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters; the processing module is also used to denoise the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal; the processing module is also used to analyze the microwave signal using a preset analysis model to obtain an analysis result, and the analysis result is used to indicate whether the non-metallic material has an abnormality.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. By analyzing the material microwave data and the environmental electromagnetic wave data, more accurate material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters can be obtained. These parameters can more accurately reflect the performance and state of the material, thereby improving the accuracy of defect detection. Traditional methods for detecting abnormal defects in non-metallic materials often only consider the influence of the material itself, without considering the interference of environmental factors. By simultaneously acquiring material microwave data and environmental electromagnetic wave data, the above method can better consider the impact of environmental factors on the detection results, thereby improving the reliability of detection. By denoising the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters, the electromagnetic wave noise and interference in the data can be effectively removed, and the quality of the microwave signal can be improved. This helps to improve the accuracy and stability of defect detection. By using a preset analysis model to analyze microwave signals, the analysis process can be simplified and the analysis efficiency can be improved. At the same time, the preset analysis model is usually optimized and verified, and can provide more accurate analysis results;

[0025] 2. By screening the first analysis data and the second analysis data, the parameters of the material's microwave characteristics and the environmental electromagnetic wave characteristics can be accurately extracted. These parameters can more accurately reflect the acoustic characteristics of the material and the environment, and provide more accurate data support for subsequent analysis and judgment. By adopting frequency domain analysis and time domain analysis algorithms, the data analysis process can be completed faster and the efficiency of data analysis can be improved. At the same time, this analysis method can also reduce the interference of human factors and improve the accuracy of data analysis. By analyzing the material microwave data and the environmental electromagnetic wave data separately, the acoustic characteristics of the material and the environment can be more clearly separated. This helps to more accurately determine whether there are abnormalities in the material, and also better understand the impact of environmental factors on the acoustic characteristics of the material;

[0026] 3. By denoising the microwave characteristics of the material and the characteristics of the environmental electromagnetic wave, the microwave signals of the material and the environment can be separated more clearly. This helps to more accurately determine whether there are abnormalities in the material, and also better understand the impact of environmental factors on the acoustic properties of the material. By reconstructing the signal of the material microwave signal and the environmental electromagnetic wave signal, a more complete microwave signal can be obtained. This method can make up for the information that may be lost in the denoising process and improve the integrity of the signal. The blind source separation algorithm can adapt to noise interference in various complex electromagnetic environments. This helps to improve the accuracy and reliability of non-destructive testing and analysis of non-metallic materials in complex electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic flow chart of a non-destructive testing method for non-metallic materials provided in an embodiment of the present application.

[0028] Figure 2A schematic diagram of a module of a non-destructive testing device for non-metallic materials provided in an embodiment of the present application.

[0029] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0030] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 31, processor; 32, communication bus; 33, user interface; 34, network interface; 35, memory. DETAILED DESCRIPTION

[0031] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0032] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" 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 "for example" or "for example" is intended to present related concepts in a specific way.

[0033] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0034] Non-metallic materials, such as polymers, ceramics, and glass, play an important role in aerospace, nuclear industry, petrochemical industry, and other fields. These materials may have internal damage or defects during manufacturing and use due to various reasons. In order to ensure the safety and stability of these devices during operation, non-destructive testing technology is widely used to detect damage and defects inside the materials.

[0035] Traditional methods for detecting abnormal defects in non-metallic materials usually identify defects based on a single piece of data. However, this method is not ideal in complex electromagnetic environments. Because in complex electromagnetic environments, non-metallic materials may be interfered by a variety of factors, such as the size and fluctuation of electromagnetic waves. These interference factors may cause data anomalies, thereby affecting the accuracy of defect identification.

[0036] In order to solve the above technical problems, the present application provides a non-destructive testing method for non-metallic materials. Figure 1 , Figure 1 A schematic flow chart of a non-destructive testing method for non-metallic materials provided in an embodiment of the present application. The non-destructive testing method is applied to a server and includes steps S110 to S140, which are as follows:

[0037] S110, acquiring microwave data of non-metallic materials, where the microwave data includes material microwave data and environmental electromagnetic wave data.

[0038] Specifically, a server refers to a computer system that provides services to the outside world through the network. In this scenario, a server is a dedicated computer or server cluster that is used to process and store information related to microwave data of non-metallic materials. The server obtains microwave data from non-metallic materials in some way, such as through user upload or real-time acquisition. Non-metallic materials refer to materials that do not have metallic properties, such as plastics, glass, ceramics, etc. These materials have different characteristics in microwave propagation. Microwave data refers to data obtained through microwave detection technology. These data may include characteristics such as wave amplitude, frequency, waveform, etc., which are used to describe the acoustic properties inside the material. Among them, material microwave data refers to microwave data obtained from the inside of non-metallic materials. These data reflect the acoustic properties of the material itself, such as the density, hardness, structure, etc. of the material. Environmental electromagnetic wave data refers to microwave data obtained in the environment surrounding non-metallic materials. These data may be affected by environmental factors, such as the size and fluctuation of electromagnetic waves.

[0039] In a possible implementation, microwave data of non-metallic materials is obtained, specifically including: responding to a detection request input by a user; controlling a detection device to transmit microwaves to the non-metallic material according to the detection request, wherein the detection device is used to detect the non-metallic material; receiving a detection data packet sent by the detection device, wherein the detection data packet stores microwave data associated with the non-metallic material; performing microwave extraction on the detection data packet to obtain microwave data, wherein the microwave extraction includes material microwave extraction and environmental electromagnetic wave extraction.

[0040] Specifically, when a user needs to obtain microwave data of a non-metallic material, the server will respond to the user's request and start to execute subsequent steps. The server will control the detection device to emit microwaves to the non-metallic material according to the user's detection requirements. Microwaves are high-frequency electromagnetic waves that can penetrate the surface of a material and detect its internal structure and characteristics. When the microwave detection is completed, the detection device will package the acquired microwave data into a data packet and send it to the server through the network. After the server receives the detection data packet, it will process and analyze the data to extract microwave data related to the non-metallic material. This process includes operations such as filtering, noise reduction, and feature extraction of microwave data. In the process of extracting microwave data, the server will distinguish between material microwaves and environmental electromagnetic waves. Material microwaves are microwaves related to the characteristics of the non-metallic material itself, while environmental electromagnetic waves are related to the surrounding high-intensity electromagnetic environment factors. By extracting these two types of microwave data separately, the characteristics and state of the non-metallic material can be more accurately analyzed. In an embodiment of the present application, the server and the detection device communicate via a wireless network.

[0041] For example, when a user enters a test request to test the internal structure of a plastic product, the server will control the test equipment to emit microwaves to the plastic product. During the test, the test equipment will collect microwave data related to the internal structure of the plastic product, and package the data into data packets and send them to the server. After receiving the data packets, the server will process and analyze the data and extract the microwave data related to the plastic product. In this process, the server will distinguish between material microwaves and environmental electromagnetic waves. For example, the electromagnetic signals inside the plastic product belong to material microwaves, while the electromagnetic waves in the surrounding air belong to environmental electromagnetic waves. By extracting these two types of data separately, the internal structure and state of the plastic product can be analyzed more accurately.

[0042] S120, analyzing the material microwave data and the environmental electromagnetic wave data to obtain the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters.

[0043] Specifically, material microwave data and environmental electromagnetic wave data are microwave data obtained by the server from non-metallic materials and the surrounding environment. They contain information about the acoustic properties of materials and the environment. In this scenario, the server will conduct an in-depth analysis of the material microwave data and environmental electromagnetic wave data to obtain electromagnetic wave characteristic parameters related to the material and the environment. The material microwave characteristic parameters describe the acoustic properties of non-metallic materials, such as the density, hardness, and internal structure of the material. By analyzing the material microwave data, these parameters can be extracted to provide a basis for subsequent defect detection, material classification, etc. The environmental electromagnetic wave characteristic parameters describe the electromagnetic wave characteristics of the surrounding environment, such as the size and fluctuation of electromagnetic waves. By analyzing the environmental electromagnetic wave data, these parameters can be extracted to provide a basis for subsequent environmental impact analysis, electromagnetic wave noise control, etc.

[0044] In a possible implementation, the material microwave data and the environmental electromagnetic wave data are analyzed to obtain the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters, specifically including: using a frequency domain analysis algorithm and a time domain analysis algorithm to analyze the material microwave data to obtain first analysis data, the first analysis data being the frequency domain data and the time domain data corresponding to the material microwave data; using a frequency domain analysis algorithm and a time domain analysis algorithm to analyze the environmental electromagnetic wave data to obtain second analysis data, the second analysis data being the frequency domain data and the time domain data corresponding to the environmental electromagnetic wave data; performing data screening on the first analysis data to obtain the material microwave characteristic parameters; performing data screening on the second analysis data to obtain the environmental electromagnetic wave characteristic parameters.

[0045] Specifically, frequency domain analysis converts the signal from the time domain to the frequency domain for analysis, which can reveal the frequency components and frequency characteristics of the signal; time domain analysis directly analyzes the signal in the time domain, which can reveal the timing characteristics and changing patterns of the signal. The first analysis data is the data obtained by performing frequency domain and time domain analysis on the material microwave data, including frequency domain data and time domain data. The second analysis data is the data obtained by performing frequency domain and time domain analysis on the environmental electromagnetic wave data, which also includes the corresponding frequency domain data and time domain data. Data screening is used to extract parameters related to the acoustic characteristics of the material or environment from the analysis data, including filtering, noise reduction, feature extraction and other operations to obtain more accurate and meaningful parameters.

[0046] For example, suppose a nondestructive test is performed on a plastic product. The server will use frequency domain analysis algorithms and time domain analysis algorithms to process the material microwave data. Frequency domain analysis can obtain the frequency components and frequency characteristics of plastic products, while time domain analysis can obtain the timing characteristics and change patterns of plastic products. In this way, the server can obtain the first analysis data, namely the frequency domain data and time domain data of the material microwave data. The second analysis data can be obtained in the same way. For another example, based on the material microwave characteristic parameters, the server can determine whether the plastic product has internal defects or abnormal structures; based on the environmental electromagnetic wave characteristic parameters, the server can understand the electromagnetic wave interference in the surrounding environment, providing a basis for subsequent process adjustments or equipment maintenance.

[0047] In a possible implementation, the first analysis data is screened to obtain material microwave characteristic parameters, specifically including: obtaining a preset material microwave characteristic parameter range; using the material microwave characteristic parameter range to screen the first analysis data to obtain material microwave characteristic parameters; the second analysis data is screened to obtain environmental electromagnetic wave characteristic parameters, specifically including: obtaining a preset environmental electromagnetic wave characteristic parameter range; using the environmental electromagnetic wave characteristic parameter range to screen the second analysis data to obtain environmental electromagnetic wave characteristic parameters.

[0048] Specifically, the preset material microwave characteristic parameter range is a set of parameter ranges predetermined in advance, which is used to screen and analyze material microwave data, and is based on prior knowledge, experience or experimental results to determine the effective parameter range related to the acoustic characteristics of the material. Similarly, the preset environmental electromagnetic wave characteristic parameter range is also a set of parameter ranges predetermined in advance, which is used to screen and analyze environmental electromagnetic wave data. Data screening is used to screen out parameters that meet the preset parameter range from the analysis data, including operations such as comparison, filtering, and threshold setting to extract parameters related to the acoustic characteristics of the material or environment. Therefore, by screening data with a preset parameter range, the standardization and normalization of the analysis process can be ensured. This can avoid the interference of human factors on the analysis results and improve the objectivity and reliability of the analysis. By using a preset parameter range for data screening, the process of data analysis can be simplified. Without the need for complex data processing and algorithm calculations, the material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters can be directly extracted from the screened data. By screening data with a preset parameter range, the time and energy of manual screening and judgment can be reduced, and work efficiency can be improved. At the same time, this method can also reduce the possibility of errors and omissions and improve the accuracy of data analysis.

[0049] S130, denoising the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal.

[0050] Specifically, denoising is used to eliminate or reduce the electromagnetic wave noise components in the signal to extract a purer and more meaningful signal. In microwave analysis, denoising can be used to improve the clarity and accuracy of microwave signals. The material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters are obtained through frequency domain and time domain analysis, describing the acoustic properties of the material and the environment. However, these parameters may contain electromagnetic wave noise components and need to be denoised to obtain a more accurate microwave signal. Microwave signals are microwave data that have been denoised and can more accurately describe the acoustic properties of materials or environments.

[0051] In a possible implementation, the material microwave characteristic parameters and the ambient electromagnetic wave characteristic parameters are denoised to obtain a microwave signal, specifically including: using a blind source separation algorithm to denoise the material microwave characteristic parameters and the ambient electromagnetic wave characteristic parameters to obtain a material microwave signal and an ambient electromagnetic wave signal; and reconstructing the material microwave signal and the ambient electromagnetic wave signal to obtain a microwave signal.

[0052] Specifically, the blind source separation algorithm is used to separate the original independent signal from the mixed signal. In microwave analysis, the blind source separation algorithm can be used to eliminate the influence of electromagnetic wave interference and other irrelevant signals to extract pure microwave signals. The material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters describe the acoustic properties of the material and the environment, but may contain electromagnetic wave noise components and other interference signals. The material microwave signal and the environmental electromagnetic wave signal are pure microwave signals obtained after denoising the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters through the blind source separation algorithm. Signal reconstruction is used to reconstruct the original microwave signal based on the denoised signal, including operations such as interpolation and filtering to restore the integrity and accuracy of the signal.

[0053] S140. Analyze the microwave signal using a preset analysis model to obtain an analysis result, where the analysis result is used to indicate whether there is an abnormality in the non-metallic material.

[0054] Specifically, the preset analysis model is a predetermined analysis model used to analyze and judge microwave signals. This model is built and trained based on a neural network to determine microwave features associated with material anomalies. Microwave signals are microwave data that have been denoised and reconstructed, and contain acoustic information related to the material or environment. The analysis result is the conclusion or result obtained after analyzing the microwave signal using the preset analysis model. This result can indicate whether there are abnormalities in the material, such as defects, cracks, inhomogeneities, etc., so that non-destructive testing of non-metallic materials can be completed based on the analysis results.

[0055] Therefore, by analyzing the material microwave data and the environmental electromagnetic wave data, more accurate material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters can be obtained. These parameters can more accurately reflect the performance and state of the material, thereby improving the accuracy of defect detection. Traditional methods for detecting abnormal defects in non-metallic materials often only consider the influence of the material itself, without considering the interference of environmental factors. By simultaneously acquiring material microwave data and environmental electromagnetic wave data, the above method can better consider the influence of environmental factors on the detection results, thereby improving the reliability of detection. By denoising the material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters, the electromagnetic wave noise and interference in the data can be effectively removed, and the quality of the microwave signal can be improved. This helps to improve the accuracy and stability of defect detection. By using a preset analysis model to analyze the microwave signal, the analysis process can be simplified and the analysis efficiency can be improved. At the same time, the preset analysis model is usually optimized and verified, and can provide more accurate analysis results.

[0056] In a possible implementation, a preset analysis model is used to analyze the microwave signal to obtain an analysis result, which specifically includes: obtaining the amplitude and frequency of the microwave signal; judging whether the amplitude is within a preset amplitude range, the preset amplitude range is determined based on historical analysis data, and one preset amplitude range corresponds to a non-metallic material; judging whether the frequency is within a preset frequency range, the preset frequency range is determined based on historical analysis data, and one preset frequency range corresponds to a non-metallic material; if it is determined that the amplitude is within the preset amplitude range, and the frequency is within the preset frequency range, then a first analysis result is generated, and the first analysis result is used to indicate that the non-metallic material is normal.

[0057] Specifically, after the server performs preliminary processing on the microwave signal, the information related to the material characteristics can be extracted by measuring and calculating the amplitude and frequency of the microwave signal. Based on the historical analysis data, a preset amplitude range related to a specific non-metallic material can be determined. By comparing the amplitude of the microwave signal with the preset amplitude range, it can be determined whether the material is normal. Among them, the server pre-stores preset ranges corresponding to a variety of non-metallic materials. Similarly, the server can determine a preset frequency range related to a specific non-metallic material based on historical analysis data. By comparing the frequency of the microwave signal with the preset frequency range, it can be further determined whether the material is normal. If the amplitude and frequency of the microwave signal are both within the corresponding preset ranges, a first analysis result indicating that the material is normal can be generated.

[0058] In one possible implementation, if it is determined that the amplitude is not within the preset amplitude range and the frequency is not within the preset frequency range, or, if it is determined that the amplitude is within the preset amplitude range and the frequency is not within the preset frequency range, or, if it is determined that the amplitude is not within the preset amplitude range and the frequency is within the preset frequency range, a second analysis result is generated, and the second analysis result is used to indicate that there is an abnormality in the non-metallic material.

[0059] Specifically, if it is determined that the amplitude is not within the preset amplitude range, it means that the amplitude of the microwave signal exceeds the expected range, which may mean that there is an abnormality or defect inside the material. If it is determined that the frequency is not within the preset frequency range, it means that the frequency of the microwave signal is inconsistent with expectations, which may mean that the internal structure of the material has changed or there is inhomogeneity. Alternatively, although the amplitude is within the normal range, the abnormality of the frequency may mean that there is some abnormality inside the material. Alternatively, if it is determined that the amplitude is not within the preset amplitude range, but the frequency is within the preset frequency range, the abnormality of the amplitude may indicate that there is an abnormality inside the material, and the normality of the frequency can provide additional reference information. In the above three cases, the server will generate a second analysis result to indicate that there is an abnormality in the non-metallic material. This result can remind the user to further inspect the material to determine the nature and extent of the abnormality.

[0060] Therefore, by considering different situations such as the amplitude is not within the preset range, the frequency is not within the preset range, and the amplitude is within the preset range but the frequency is not within the preset range, the abnormal situations that may exist in non-metallic materials can be more comprehensively covered. This helps to improve the accuracy and completeness of abnormality detection. By generating a second analysis result based on different abnormal situations, it is possible to more finely judge whether there is an abnormality in the non-metallic material. This method can provide more specific and detailed abnormal information, which is helpful for subsequent defect repair and processing. For different non-metallic materials, the amplitude and frequency of their microwave signals may vary greatly. By presetting different amplitude and frequency ranges, it is possible to better adapt to the characteristics of different materials and improve the accuracy and reliability of abnormality detection. When the microwave signal of a non-metallic material is abnormal, the second analysis result is generated in a timely manner, which is conducive to reminding the user to take timely measures to repair and process it in time to avoid causing greater losses.

[0061] The present application also provides a non-destructive testing device for non-metallic materials, referring to Figure 2 , Figure 2 A module schematic diagram of a non-destructive testing device for non-metallic materials provided in an embodiment of the present application. The non-destructive testing device is a server, and the server includes an acquisition module 21 and a processing module 22, wherein the acquisition module 21 acquires microwave data of the non-metallic material, and the microwave data includes material microwave data and environmental electromagnetic wave data; the processing module 22 analyzes the material microwave data and the environmental electromagnetic wave data to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters; the processing module 22 denoises the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal; the processing module 22 uses a preset analysis model to analyze the microwave signal to obtain an analysis result, and the analysis result is used to indicate whether the non-metallic material is abnormal.

[0062] In a possible implementation, the acquisition module 21 acquires microwave data of non-metallic materials, specifically including: the acquisition module 21 responds to a detection request input by a user; the processing module 22 controls the detection device to emit microwaves to the non-metallic material according to the detection request, and the detection device is used to detect the non-metallic material; the acquisition module 21 receives a detection data packet sent by the detection device, and the detection data packet stores microwave data associated with the non-metallic material; the processing module 22 performs microwave extraction on the detection data packet to obtain microwave data, and the microwave extraction includes material microwave extraction and environmental electromagnetic wave extraction.

[0063] In a possible implementation, the processing module 22 analyzes the material microwave data and the environmental electromagnetic wave data to obtain the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters, specifically including: the processing module 22 uses the frequency domain analysis algorithm and the time domain analysis algorithm to analyze the material microwave data to obtain the first analysis data, and the first analysis data is the frequency domain data and the time domain data corresponding to the material microwave data; the processing module 22 uses the frequency domain analysis algorithm and the time domain analysis algorithm to analyze the environmental electromagnetic wave data to obtain the second analysis data, and the second analysis data is the frequency domain data and the time domain data corresponding to the environmental electromagnetic wave data; the processing module 22 performs data screening on the first analysis data to obtain the material microwave characteristic parameters; the processing module 22 performs data screening on the second analysis data to obtain the environmental electromagnetic wave characteristic parameters.

[0064] In a possible implementation, the processing module 22 performs data screening on the first analysis data to obtain material microwave characteristic parameters, specifically including: the acquisition module 21 obtains a preset material microwave characteristic parameter range; the processing module 22 uses the material microwave characteristic parameter range to perform data screening on the first analysis data to obtain material microwave characteristic parameters; the processing module 22 performs data screening on the second analysis data to obtain environmental electromagnetic wave characteristic parameters, specifically including: the acquisition module 21 obtains a preset environmental electromagnetic wave characteristic parameter range; the processing module 22 uses the environmental electromagnetic wave characteristic parameter range to perform data screening on the second analysis data to obtain environmental electromagnetic wave characteristic parameters.

[0065] In a possible implementation, the processing module 22 denoises the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal, which specifically includes: the processing module 22 uses a blind source separation algorithm to denoise the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a material microwave signal and an environmental electromagnetic wave signal; the processing module 22 reconstructs the material microwave signal and the environmental electromagnetic wave signal to obtain a microwave signal.

[0066] In a possible implementation, the processing module 22 uses a preset analysis model to analyze the microwave signal to obtain an analysis result, which specifically includes: the acquisition module 21 acquires the amplitude and frequency of the microwave signal; the processing module 22 determines whether the amplitude is within a preset amplitude range, the preset amplitude range is determined based on historical analysis data, and one preset amplitude range corresponds to a non-metallic material; the processing module 22 determines whether the frequency is within a preset frequency range, the preset frequency range is determined based on historical analysis data, and one preset frequency range corresponds to a non-metallic material; if the processing module 22 determines that the amplitude is within the preset amplitude range and the frequency is within the preset frequency range, a first analysis result is generated, and the first analysis result is used to indicate that the non-metallic material is normal.

[0067] In one possible implementation, if the processing module 22 determines that the amplitude is not within the preset amplitude range and the frequency is not within the preset frequency range, or, if the processing module 22 determines that the amplitude is within the preset amplitude range and the frequency is not within the preset frequency range, or, if the processing module 22 determines that the amplitude is not within the preset amplitude range and the frequency is within the preset frequency range, a second analysis result is generated, and the second analysis result is used to indicate that there is an abnormality in the non-metallic material.

[0068] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0069] The present application also provides an electronic device, referring to Figure 3 , Figure 3 The electronic device may include: at least one processor 31 , at least one network interface 34 , a user interface 33 , a memory 35 , and at least one communication bus 32 .

[0070] The communication bus 32 is used to realize the connection and communication between these components.

[0071] The user interface 33 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 33 may also include a standard wired interface and a wireless interface.

[0072] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0073] Among them, the processor 31 may include one or more processing cores. The processor 31 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 35, and calling data stored in the memory 35. Optionally, the processor 31 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 31 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 31, but may be implemented separately through a chip.

[0074] The memory 35 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 35 includes a non-transitory computer-readable storage medium. The memory 35 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 35 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 35 may optionally be at least one storage device located away from the aforementioned processor 31. As Figure 3 As shown, the memory 35 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a non-destructive testing method for non-metallic materials.

[0075] exist Figure 3In the electronic device shown, the user interface 33 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 31 can be used to call an application program stored in the memory 35 for a non-destructive testing method for non-metallic materials. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0076] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0077] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0078] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, 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 service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0080] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] 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.

[0082] 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 computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0083] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A non-destructive testing method for non-metallic materials, characterized in that: The method comprises: Acquiring microwave data of non-metallic materials, wherein the microwave data includes material microwave data and environmental electromagnetic wave data; Analyzing the material microwave data and the environmental electromagnetic wave data to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters; De-noising the microwave characteristic parameters of the material and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal; Analyzing the microwave signal using a preset analysis model to obtain an analysis result, wherein the analysis result is used to indicate whether the non-metallic material has an abnormality; The analyzing the material microwave data and the environmental electromagnetic wave data to obtain the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters specifically includes: Analyzing the material microwave data using a frequency domain analysis algorithm and a time domain analysis algorithm to obtain first analysis data, where the first analysis data is frequency domain data and time domain data corresponding to the material microwave data; The frequency domain analysis algorithm and the time domain analysis algorithm are used to analyze the environmental electromagnetic wave data to obtain second analysis data, where the second analysis data is the frequency domain data and the time domain data corresponding to the environmental electromagnetic wave data; Screening the first analysis data to obtain microwave characteristic parameters of the material; Performing data screening on the second analysis data to obtain the environmental electromagnetic wave characteristic parameters; The adopting of a preset analysis model to analyze the microwave signal to obtain an analysis result specifically includes: Acquiring the amplitude and frequency of the microwave signal; Determine whether the amplitude is within a preset amplitude range, wherein the preset amplitude range is determined based on historical analysis data, and one preset amplitude range corresponds to one non-metallic material; Determine whether the frequency is within a preset frequency range, wherein the preset frequency range is determined according to the historical analysis data, and one preset frequency range corresponds to one non-metallic material; If it is determined that the amplitude is within the preset amplitude range and the frequency is within the preset frequency range, a first analysis result is generated, and the first analysis result is used to indicate that the non-metallic material is normal.

2. The non-destructive testing method for non-metallic materials according to claim 1, characterized in that: The obtaining of microwave data of non-metallic materials specifically includes: responding to a detection request input by a user; According to the detection request, controlling the detection device to transmit microwaves to the non-metallic material, the detection device being used to detect the non-metallic material; receiving a detection data packet sent by the detection device, wherein the detection data packet stores microwave data associated with the non-metallic material; The detection data packet is subjected to microwave extraction to obtain the microwave data, wherein the microwave extraction includes material microwave extraction and environmental electromagnetic wave extraction.

3. The non-destructive testing method for non-metallic materials according to claim 1, characterized in that: The screening of the first analysis data to obtain the microwave characteristic parameters of the material specifically includes: Obtaining a preset range of material microwave characteristic parameters; Using the microwave characteristic parameter range of the material to screen the first analysis data, to obtain the microwave characteristic parameter of the material; The screening of the second analysis data to obtain the environmental electromagnetic wave characteristic parameters specifically includes: Obtaining the preset environmental electromagnetic wave characteristic parameter range; The second analysis data is screened using the environmental electromagnetic wave characteristic parameter range to obtain the environmental electromagnetic wave characteristic parameter.

4. The non-destructive testing method for non-metallic materials according to claim 1, characterized in that: The denoising of the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal specifically includes: Using a blind source separation algorithm to denoise the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a material microwave signal and an environmental electromagnetic wave signal; Signal reconstruction is performed on the material microwave signal and the environmental electromagnetic wave signal to obtain the microwave signal.

5. The non-destructive testing method for non-metallic materials according to claim 1, characterized in that: The method further comprises: If it is determined that the amplitude is not within the preset amplitude range, and the frequency is not within the preset frequency range, or, If it is determined that the amplitude is within the preset amplitude range and the frequency is not within the preset frequency range, or, If it is determined that the amplitude is not within the preset amplitude range and the frequency is within the preset frequency range, a second analysis result is generated, and the second analysis result is used to indicate that an abnormality exists in the non-metallic material.

6. A non-destructive testing device for non-metallic materials, characterized in that: The nondestructive testing device comprises an acquisition module (21) and a processing module (22), wherein: The acquisition module (21) is used to acquire microwave data of non-metallic materials, wherein the microwave data includes material microwave data and environmental electromagnetic wave data; The processing module (22) is used to analyze the material microwave data and the environmental electromagnetic wave data to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters; The processing module (22) is also used to remove noise from the material microwave characteristic parameters and the environmental electromagnetic wave characteristic parameters to obtain a microwave signal; The processing module (22) is further used to analyze the microwave signal using a preset analysis model to obtain an analysis result, wherein the analysis result is used to indicate whether the non-metallic material has an abnormality; The processing module (22) is also used for analyzing the material microwave data and the environmental electromagnetic wave data to obtain material microwave characteristic parameters and environmental electromagnetic wave characteristic parameters, specifically comprising: using a frequency domain analysis algorithm and a time domain analysis algorithm to analyze the material microwave data to obtain first analysis data, the first analysis data being the frequency domain data and the time domain data corresponding to the material microwave data; using the frequency domain analysis algorithm and the time domain analysis algorithm to analyze the environmental electromagnetic wave data to obtain second analysis data, the second analysis data being the frequency domain data and the time domain data corresponding to the environmental electromagnetic wave data; performing data screening on the first analysis data to obtain the material microwave characteristic parameters; performing data screening on the second analysis data to obtain the environmental electromagnetic wave characteristic parameters; The processing module (22) is also used to analyze the microwave signal using a preset analysis model to obtain an analysis result, which specifically includes: obtaining the amplitude and frequency of the microwave signal; judging whether the amplitude is within a preset amplitude range, the preset amplitude range is determined according to historical analysis data, and one preset amplitude range corresponds to one non-metallic material; judging whether the frequency is within a preset frequency range, the preset frequency range is determined according to the historical analysis data, and one preset frequency range corresponds to one non-metallic material; if it is determined that the amplitude is within the preset amplitude range, and the frequency is within the preset frequency range, then generating a first analysis result, the first analysis result being used to indicate that the non-metallic material is normal.

7. An electronic device, characterized in that: The electronic device comprises a processor (31), a memory (35), a user interface (33) and a network interface (34), wherein the memory (35) is used to store instructions, the user interface (33) and the network interface (34) are both used to communicate with other devices, and the processor (31) is used to execute the instructions stored in the memory (35) so that the electronic device executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 5 is performed.

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