Decorative material quality detection method and device

Abstract

The embodiment of the invention provides a decoration material quality detection method and device, and relates to the technical field of decoration material quality detection technologies. The method comprises the steps that sound wave detection information fed back by a decorative material is obtained, the sound wave detection information is obtained by conducting sound wave detection on the decorative material through a microphone array, the sound wave detection information comprises first sound wave detection information and second sound wave detection information, the first sound wave detection information comprises a first sound wave signal, and the second sound wave detection information comprises a second sound wave signal; the second sound wave detection information comprises a second sound wave signal; preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal; and comparing the first three-dimensional sound field with the second three-dimensional sound field, and determining whether the decorative material has defects or not according to a comparison result of the first three-dimensional sound field and the second three-dimensional sound field, thereby improving the detection precision and efficiency of the internal quality of the decorative material.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of decoration material quality detection, and in particular, to a decoration material quality detection method and device. Background Art

[0002] With the rapid development of my country's economy, the building decoration industry is becoming increasingly prosperous, and the quality inspection of decorative materials has become an important part of ensuring the quality of building decoration projects.

[0003] Current methods for quality inspection of decorative materials mainly include visual inspection, manual measurement, and some automated inspection technologies. These methods are unable to inspect the internal conditions of decorative materials, which makes the quality inspection accuracy of traditional inspection methods poor. In addition, due to defects in decorative materials and the large thickness of some special decorative materials, they cannot be well monitored. Sound waves can penetrate decorative materials well, but unilateral sound waves are difficult to fully detect the uniformity of decorative materials, which may lead to deviations in the monitoring of some materials.

[0004] There is currently no better solution to the above problems. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for detecting the quality of decorative materials, so as to at least solve the problem of poor accuracy in detecting the internal quality of decorative materials in the related art.

[0006] According to one embodiment of the present invention, a method for detecting quality of a decorative material is provided, comprising:

[0007] Acquire acoustic wave detection information fed back by the decorative material, wherein the acoustic wave detection information is obtained by performing acoustic wave detection on the decorative material through a microphone array, the acoustic wave detection information includes first acoustic wave detection information and second acoustic wave detection information, the first acoustic wave detection information includes a first acoustic wave signal, and the second acoustic wave detection information includes a second acoustic wave signal;

[0008] Preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal;

[0009] The first three-dimensional sound field and the second three-dimensional sound field are compared, and whether the decorative material has defects is determined according to the comparison result of the first three-dimensional sound field and the second three-dimensional sound field.

[0010] In an exemplary embodiment, the emission source of the first sound wave and the emission source of the second sound wave are located in different directions of the decoration material.

[0011] In an exemplary embodiment, comparing the first three-dimensional sound field with the second three-dimensional sound field, and determining whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field, specifically includes:

[0012] Establish a standard coordinate system;

[0013] Establishing a comparison base point for the first three-dimensional sound field and the second three-dimensional sound field;

[0014] The first three-dimensional sound field and the second three-dimensional sound field overlap according to the comparison base point, and whether the comparison result exceeds an overlap threshold is determined according to the overlap degree of the first three-dimensional sound field and the second three-dimensional sound field; if it does not exceed the overlap threshold, it is determined that the decorative material has defects.

[0015] In an exemplary embodiment, after comparing the first three-dimensional sound field and the second three-dimensional sound field, and determining whether the decorative material has defects according to the comparison result of the first three-dimensional sound field and the second three-dimensional sound field, the method further includes:

[0016] When the overlap degree between the first three-dimensional sound field and the second three-dimensional sound field exceeds the overlap degree threshold, a sound field feedback parameter of the first three-dimensional sound field or the second three-dimensional sound field is obtained, and the sound field feedback parameter is compared with a standard parameter. If the difference between the sound field feedback parameter and the standard parameter exceeds a preset threshold, it is determined that the decorative material has a defect.

[0017] According to another embodiment of the present invention, there is provided a decoration material quality detection device, comprising:

[0018] A sound wave acquisition module, used for acquiring sound wave detection information fed back by the decorative material, wherein the sound wave detection information is obtained by performing sound wave detection on the decorative material through a microphone array, and the sound wave detection information includes first sound wave detection information and second sound wave detection information, wherein the first sound wave detection information includes a first sound wave signal, and the second sound wave detection information includes a second sound wave signal;

[0019] A sound field establishment module, used for preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal;

[0020] The first quality judgment module is used to compare the first three-dimensional sound field with the second three-dimensional sound field, and determine whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field.

[0021] In an exemplary embodiment, the first quality judgment module further includes:

[0022] A base point comparison module: establishing a comparison base point for the first three-dimensional sound field and the second three-dimensional sound field;

[0023] The overlap determination module overlaps the first three-dimensional sound field and the second three-dimensional sound field according to the comparison base point, and determines whether the comparison result exceeds the overlap threshold according to the overlap of the first three-dimensional sound field and the second three-dimensional sound field; if it does not exceed the overlap threshold, it is determined that the decorative material has defects.

[0024] In an exemplary embodiment, the apparatus further comprises:

[0025] The second quality judgment module is used to obtain sound field feedback parameters of the first three-dimensional sound field or the second three-dimensional sound field when the overlap between the first three-dimensional sound field and the second three-dimensional sound field exceeds the overlap threshold, and compare the sound field feedback parameters with standard parameters, if the difference between the sound field feedback parameters and the standard parameters exceeds a preset threshold, determine that the decorative material has defects.

[0026] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when run.

[0027] According to yet another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0028] Through the present invention, since sound waves have strong penetrating power, quality defects inside decorative materials can be deeply detected, thereby detecting quality problems that cannot be detected by visual inspection. Some special decorative materials have high requirements for the uniform distribution of materials. Therefore, bilateral acoustic wave imaging is adopted, which can not only fully realize the conformation of decorative materials, but also realize bilateral imaging comparison to simply judge whether there are quality problems of uniform distribution inside decorative materials. At the same time, it can also improve the accuracy of acoustic wave measurement. For example, if there are impurities or other materials or uneven distribution that cannot be penetrated by some sound waves inside decorative materials, preliminary detection of materials can be realized through bilateral acoustic wave sound field comparison to determine whether the materials meet the quality requirements. At the same time, by comparing the standard values ​​again afterwards, it can be identified whether the complete material meets the quality requirements. Therefore, the problems of low accuracy, uneven distribution and incomplete detection of internal quality detection of decorative materials can be solved, so as to achieve the effect of improving the accuracy and efficiency of internal quality detection of decorative materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of a decoration material quality detection method according to an embodiment of the present invention;

[0030] Figure 2 4 is a structural block diagram of a decoration material quality detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0033] In this embodiment, a method for detecting the quality of a decorative material is provided. Figure 1 : is a flow chart of the quality inspection of the decorative material according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0034] Step S100, obtaining sound wave detection information fed back by the decorative material, wherein the sound wave detection information is obtained by performing sound wave detection on the decorative material through a microphone array, the sound wave detection information includes first sound wave detection information and second sound wave detection information, the first sound wave detection information includes a first sound wave signal, and the second sound wave detection information includes a second sound wave signal;

[0035] In this embodiment, the detection of decorative materials by sound waves can, on the one hand, be carried out without damaging or changing the properties of the materials, thereby reducing damage caused during the detection process; on the other hand, it can deeply detect the internal conditions of the decorative materials and detect defects inside the materials, such as cracks, voids, delamination, etc. At the same time, it can quickly cover a large area of ​​decorative materials, thereby improving detection efficiency.

[0036] Among them, decorative materials include wood, stone, metal, plastic, paint and wallpaper, etc. Different artificial intelligence analysis models need to be selected for different decorative materials; in addition to the sound wave signal, the sound wave detection information can also include (but not limited to) the propagation time, amplitude, frequency, energy attenuation gradient, signal strength, echo / ringing, temperature change and other information of the sound wave in the material.

[0037] Specifically, an array composed of multiple microphones can be used to collect sound wave information. The microphone is a MEMS microphone. The decorative material is about 0.6-0.9m away from the sound wave collection array. Then, the sound wave is emitted to the decorative material through the sound source generator, and the sound wave detection information is collected by the sound wave collection array. In this embodiment, two sound wave emission sources are set, which are located on the left and right sides of the monitored decorative material respectively, to achieve complete coverage of sound wave monitoring.

[0038] Step S200, preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal;

[0039] In this embodiment, in order to ensure the accuracy of sound wave detection, it is necessary to filter out signals such as noise.

[0040] Among them, the preprocessing includes denoising and filtering the collected sound wave signals. The denoising and filtering can be performed by wavelet transform or filter. Here, adaptive filter is used for filtering and denoising. After that, the establishment of the three-dimensional sound field first defines the position and parameters of the sound source and receiver, which includes the coordinates of the sound source and the medium parameters of the sound wave propagation, such as sound speed, density, attenuation coefficient, etc. According to the propagation characteristics of sound waves in different decorative materials, the corresponding acoustic model is established. These models can be based on different theories, such as the acoustic perturbation equation (APE) theory, which is used to predict the noise generated by liquid flow (usually non-compressible flow). Then, numerical methods are used to simulate the propagation and reflection of sound waves in three-dimensional space, and the sound wave simulation is run to obtain the sound pressure value of the entire fluid area. Finally, the simulation results are analyzed and visualized to construct a three-dimensional sound field.

[0041] Step S300 , comparing the first three-dimensional sound field with the second three-dimensional sound field, and determining whether the decorative material has defects according to the comparison result between the first three-dimensional sound field and the second three-dimensional sound field.

[0042] Specifically, firstly, a standard coordinate system is established, and a comparison base point is determined for the first three-dimensional sound field and the second three-dimensional sound field. When the comparison base point is applied to the first three-dimensional sound field and the second three-dimensional sound field, the first three-dimensional sound field and the second three-dimensional sound field are overlapped according to the comparison base point. According to the overlap degree of the first three-dimensional sound field and the second three-dimensional sound field, it is determined whether the comparison result exceeds the overlap degree threshold. If it does not exceed the overlap degree threshold, it is determined that the decorative material has defects.

[0043] Among them, the comparison base point is determined according to the container shape of the decorative material or the shape of the decorative material itself, and the comparison base point is applied to the first three-dimensional sound field and the second three-dimensional sound field. The comparison base point can be set to one or more, depending on the situation. In this embodiment, the comparison base point is set to three, so as to overlap the first three-dimensional sound field and the second three-dimensional sound field, and compare the first three-dimensional sound field and the second three-dimensional sound field. The comparison coincidence is calculated by software, and the coincidence is compared with a preset coincidence threshold to determine whether the decorative material has defects, and the coincidence threshold depends on the decorative material.

[0044] After comparing the first three-dimensional sound field with the second three-dimensional sound field and determining whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field, the method further includes:

[0045] When the overlap degree between the first three-dimensional sound field and the second three-dimensional sound field exceeds the overlap degree threshold, sound field feedback parameters of the first three-dimensional sound field and the second three-dimensional sound field are obtained, and the sound field feedback parameters are compared with standard parameters. If the difference between the sound field feedback parameters and the standard parameters exceeds a preset threshold, it is determined that the decorative material has defects.

[0046] Specifically, if the overlap exceeds a preset overlap threshold, a secondary comparison can be performed to fit the first three-dimensional sound field and the second three-dimensional sound field, or to use the first three-dimensional sound field or the second three-dimensional sound field as a comparison sample to obtain its sound field feedback parameters and compare them with the standard parameters of the decorative material. If the difference between the sound field feedback parameters and the standard parameters exceeds a preset threshold, it is determined that the decorative material is defective.

[0047] In this embodiment, under normal circumstances, the absorption or reflection of sound waves by decorative materials of good quality is within a specific range, and thus the corresponding signal characteristics are also distributed according to a certain rule. If there are defects in its internal quality, such as holes or cracks, the absorption or reflection of the signal will increase, causing a large change in the signal characteristics. Therefore, by judging whether the signal characteristics are within a preset range, it is possible to determine whether the internal quality of the decorative material meets the requirements.

[0048] Since sound waves have strong penetrating power, they can deeply detect quality defects inside decorative materials, thereby also detecting quality problems that cannot be detected by visual inspection. Some special decorative materials have high requirements for the uniform distribution of materials. Therefore, the use of double-sided acoustic wave imaging can not only fully realize the conformation of decorative materials, but also realize double-sided imaging comparison to simply judge whether there are quality problems with uniform distribution inside decorative materials. At the same time, it can also improve the accuracy of acoustic wave measurement. For example, if there are impurities or other materials or uneven distribution inside decorative materials that cannot be penetrated by some sound waves, double-sided acoustic wave sound field comparison can be used to realize preliminary detection of materials and determine whether the materials meet the quality requirements. At the same time, the method of re-comparing the standard values ​​afterwards can identify whether the complete material meets the quality requirements. Therefore, the problems of low accuracy, uneven distribution and incomplete detection of internal quality detection of decorative materials can be solved, so as to improve the accuracy and efficiency of internal quality detection of decorative materials.

[0049] In this embodiment, the time domain features may also be identified through a trained machine learning model, and when the feature identification result does not meet the preset feature conditions, it is determined that the decorative material has quality defects.

[0050] In this embodiment, under normal circumstances, the absorption or reflection of sound waves by decorative materials of good quality is within a specific range, and thus the corresponding signal characteristics are also distributed according to a certain rule. If there are defects in its internal quality, such as holes or cracks, the absorption or reflection of the signal will increase, causing a large change in the signal characteristics. Therefore, by judging whether the signal characteristics are within a preset range, it is possible to determine whether the internal quality of the decorative material meets the requirements.

[0051] Among them, the machine learning algorithm can be a convolutional neural network (CNN) or recursive neural network (RNN) the same as the aforementioned first model, or an algorithm model such as a random forest algorithm, which can be selected and adjusted according to actual needs; when training, a labeled time-frequency data set can be used to train the model, and these data sets can include feature information such as time-frequency spectra and corresponding quality features; the feature condition can be whether the amplitude of the signal is within a threshold range, or whether the signal energy is within a threshold range, or whether the attenuation gradient of the signal meets the preset attenuation gradient, etc., which can be set according to actual needs.

[0052] Through the above steps, since sound waves have strong penetrating power, they can deeply detect quality defects inside building materials, thereby detecting quality problems that cannot be detected by visual inspection, solving the problem of low accuracy in internal quality inspection of decorative materials, and improving the accuracy and efficiency of internal quality inspection of decorative materials.

[0053] Among them, the machine learning algorithm can be a convolutional neural network (CNN) or recursive neural network (RNN) the same as the aforementioned first model, or an algorithm model such as a random forest algorithm, which can be selected and adjusted according to actual needs; when training, a labeled time-frequency data set can be used to train the model, and these data sets can include feature information such as time-frequency spectra and corresponding quality features; the feature condition can be whether the amplitude of the signal is within a threshold range, or whether the signal energy is within a threshold range, or whether the attenuation gradient of the signal meets the preset attenuation gradient, etc., which can be set according to actual needs.

[0054] Through the above steps, since sound waves have strong penetrating power, they can deeply detect quality defects inside building materials, thereby detecting quality problems that cannot be detected by visual inspection, solving the problem of low accuracy in internal quality inspection of decorative materials and improving the accuracy and efficiency of internal quality inspection of decorative materials.

[0055] The execution subject of the above steps may be a terminal, etc., but is not limited thereto.

[0056] In a selected embodiment, the performing feature recognition on the time domain feature by using a trained machine learning model, and determining that the decorative material has quality defects when the feature recognition result does not meet the preset feature condition includes:

[0057] Performing feature recognition on the time domain features through the trained machine learning model to obtain feature types and feature scores;

[0058] In this embodiment, the feature types include amplitude features, time features, energy features, periodic features, etc. Correspondingly, the feature scores include threshold scores, standard deviation scores, similarity scores, etc.

[0059] Performing type matching on the feature types and performing threshold matching on the feature scores;

[0060] In this embodiment, the feature type identified by the model is matched with a preset feature type library to determine whether it is a known qualified feature type, and the feature score output by the model is compared with a preset qualified threshold range to determine whether the feature score is within an acceptable range.

[0061] When the feature type is not matched, and / or the feature score exceeds a preset threshold range, it is determined that the decorative material has a quality defect.

[0062] In this embodiment, if the feature type is not successfully matched in the type library, or the feature score exceeds the preset threshold range, it means that the signal corresponding to the relevant feature is enhanced and absorbed or reflected, thereby judging that there are certain quality defects inside the decorative material.

[0063] For example, under normal circumstances, the feature type of a certain decorative material is A / B / C, and the corresponding feature scores are A1, B1, and C1. However, the feature type output by the actual model is A / B / D, and the corresponding scores are A2, B1, and D2. This means that there may be holes or cracks inside the material, which causes deviations in the feature scores, and that feature C is an empty set (so it will be replaced by feature D), thereby achieving quality detection of the decorative material, and so on.

[0064] In a selected embodiment, after performing feature recognition on the time domain feature by using the trained machine learning model to obtain a feature type and a feature score, the method further includes:

[0065] According to a preset first matrix construction rule, matrix construction processing is performed on the feature types and feature scores to obtain a feature recognition matrix;

[0066] Performing feature correlation calculation on the feature recognition matrix to obtain feature correlation values;

[0067] The feature correlation value is compared and calculated according to a preset Pearson correlation coefficient, and when the feature correlation value exceeds a preset correlation value range, it is determined that an abnormality exists in the feature recognition result.

[0068] In this embodiment, under normal circumstances, the feature type and the feature score correspond to each other. For example, if the feature type is amplitude, the maximum and minimum values ​​of its feature score are A and B respectively. If the score is greater than A or less than B, the correlation coefficient of this part will exceed the threshold range, thereby judging that the feature recognition result is abnormal, and so on.

[0069] Among them, the preset matrix construction rule can be the feature score and feature type number represented by the rows and columns of the matrix respectively, or other construction rules, which can be adjusted according to actual needs; the correlation calculation can be based on the Pearson correlation coefficient, or it can be based on other correlation calculation formulas; and before constructing the matrix, the feature types can be uniformly numbered to include the feature types in the calculation under special needs. At the same time, for different feature identification matrices, the relevant features need to be normalized to ensure that the relevant data can be calculated in a unified data format; when filling in matrix elements, for blank data, zero filling can be used to fill it, or the relevant data can be directly eliminated, which can be adjusted according to actual needs.

[0070] For example, the features and corresponding scores can be expressed as shown in Table 1:

[0071] Table 1

[0072] Rating 1 Rating 2 Rating 3 Feature 1 10 12 16 Feature 2 5 8 10 Feature 3 3 1 4

[0073] Thus, the vegetation feature matrix P is obtained: Then, the vegetation feature matrix P is correlated. When the correlation results and the similarity results between different feature matrices meet the threshold conditions, it means that the feature data corresponding to the matrix elements are normal. Otherwise, it is judged to be abnormal and the machine learning model needs to be adjusted.

[0074] In an optional embodiment, after preprocessing the sound wave signal to obtain the first sound wave signal, the method further includes:

[0075] Step S101, acquiring image information of the decorative material, and performing quality image detection on the image information to obtain a quality image detection result, wherein the image information is obtained by collecting an image of the decorative material;

[0076] In this embodiment, the purpose of capturing images of decorative materials and performing quality image detection on image information is to judge the visual quality of decorative materials from the perspective of image recognition, such as defects, flatness, etc., so as to facilitate intuitive replacement of decorative materials with quality defects.

[0077] Among them, the quality image detection results include the quality detection results of the decorative materials on the image, such as flatness, color, texture, etc.; among them, the quality image detection can be identified through the adjusted YOLO series neural network model, or through other artificial intelligence models.

[0078] Performing an associative mapping process on the quality image detection result and the feature recognition result;

[0079] Based on the association mapping result, the image acoustic wave timing matrix is ​​constructed according to the preset second matrix construction rule;

[0080] A quality correlation calculation is performed on the image acoustic wave time series matrix, and when the quality correlation calculation result does not meet a preset correlation condition, it is determined that an abnormality exists in the quality image detection and / or feature recognition.

[0081] In this embodiment, in order to further improve the accuracy of quality detection, it can also be combined with image detection, the image information is associated with the sound wave signal, and whether the image detection results and the sound wave detection results in each time period are related is judged according to the time sequence. If they are related, it means that both the image detection results and the sound wave detection results are reasonable, otherwise it means that one of the detection results is abnormal.

[0082] For example, if image detection shows that the pattern of the decorative material is A, but the ultrasonic detection shows that the pattern is B, or image detection shows that the area of ​​the decorative material is C, but the ultrasonic detection shows that the area is D, then at least one of the two is abnormal, and so on.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0084] In this embodiment, a decorative material quality detection device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0085] Figure 2 is a structural block diagram of a decoration material quality detection device according to an embodiment of the present invention. Figure 2 As shown, the device comprises:

[0086] A sound wave acquisition module, used for acquiring sound wave detection information fed back by the decorative material, wherein the sound wave detection information is obtained by performing sound wave detection on the decorative material through a microphone array, and the sound wave detection information includes first sound wave detection information and second sound wave detection information, wherein the first sound wave detection information includes a first sound wave signal, and the second sound wave detection information includes a second sound wave signal;

[0087] A sound field establishment module, used for preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal;

[0088] The first quality judgment module is used to compare the first three-dimensional sound field with the second three-dimensional sound field, and determine whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field.

[0089] In an optional embodiment, the first quality judgment module includes:

[0090] A base point comparison module: establishing a comparison base point for the first three-dimensional sound field and the second three-dimensional sound field;

[0091] The overlap determination module overlaps the first three-dimensional sound field and the second three-dimensional sound field according to the comparison base point, and determines whether the comparison result exceeds the overlap threshold according to the overlap of the first three-dimensional sound field and the second three-dimensional sound field; if it does not exceed the overlap threshold, it is determined that the decorative material has defects.

[0092] In an optional embodiment, the device further comprises:

[0093] The second quality judgment module is used to obtain sound field feedback parameters of the first three-dimensional sound field or the second three-dimensional sound field when the overlap between the first three-dimensional sound field and the second three-dimensional sound field exceeds the overlap threshold, and compare the sound field feedback parameters with standard parameters, if the difference between the sound field feedback parameters and the standard parameters exceeds a preset threshold, determine that the decorative material has defects.

[0094] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0095] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0096] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0097] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0098] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0099] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0100] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting the quality of decorative materials, characterized in that: include: Acquire acoustic wave detection information fed back by the decorative material, wherein the acoustic wave detection information is obtained by performing acoustic wave detection on the decorative material through a microphone array, the acoustic wave detection information includes first acoustic wave detection information and second acoustic wave detection information, the first acoustic wave detection information includes a first acoustic wave signal, and the second acoustic wave detection information includes a second acoustic wave signal; Preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal; The first three-dimensional sound field and the second three-dimensional sound field are compared, and whether the decorative material has defects is determined according to the comparison result of the first three-dimensional sound field and the second three-dimensional sound field.

2. The method according to claim 1, characterized in that The emission source of the first sound wave and the emission source of the second sound wave are located in different directions of the decorative material.

3. The method according to claim 2, characterized in that The comparing the first three-dimensional sound field with the second three-dimensional sound field, and determining whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field, specifically includes: Establish a standard coordinate system; Establishing a comparison base point for the first three-dimensional sound field and the second three-dimensional sound field; The first three-dimensional sound field and the second three-dimensional sound field overlap according to the comparison base point, and whether the comparison result exceeds an overlap threshold is determined according to the overlap degree of the first three-dimensional sound field and the second three-dimensional sound field; if it does not exceed the overlap threshold, it is determined that the decorative material has defects.

4. The method according to claim 3, characterized in that After comparing the first three-dimensional sound field with the second three-dimensional sound field and determining whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field, the method further includes: When the overlap degree between the first three-dimensional sound field and the second three-dimensional sound field exceeds the overlap degree threshold, sound field feedback parameters of the first three-dimensional sound field and the second three-dimensional sound field are obtained, and the sound field feedback parameters are compared with standard parameters. If the difference between the sound field feedback parameters and the standard parameters exceeds a preset threshold, it is determined that the decorative material has defects.

5. A decoration material quality detection device, characterized in that: include: A sound wave acquisition module, used for acquiring sound wave detection information fed back by the decorative material, wherein the sound wave detection information is obtained by performing sound wave detection on the decorative material through a microphone array, and the sound wave detection information includes first sound wave detection information and second sound wave detection information, wherein the first sound wave detection information includes a first sound wave signal, and the second sound wave detection information includes a second sound wave signal; A sound field establishment module, used for preprocessing the first sound wave signal and the second sound wave signal, establishing a first three-dimensional sound field according to the preprocessed first sound wave signal, and establishing a second three-dimensional sound field according to the second sound wave signal; The first quality judgment module is used to compare the first three-dimensional sound field with the second three-dimensional sound field, and determine whether the decorative material has defects according to the comparison result of the first three-dimensional sound field with the second three-dimensional sound field.

6. The device according to claim 5, characterized in that The first quality judgment module also includes: A base point comparison module: establishing a comparison base point for the first three-dimensional sound field and the second three-dimensional sound field; The overlap determination module overlaps the first three-dimensional sound field and the second three-dimensional sound field according to the comparison base point, and determines whether the comparison result exceeds the overlap threshold according to the overlap of the first three-dimensional sound field and the second three-dimensional sound field; if it does not exceed the overlap threshold, it is determined that the decorative material has defects.

7. The device according to claim 6, characterized in that The device also includes: The second quality judgment module is used to obtain sound field feedback parameters of the first three-dimensional sound field or the second three-dimensional sound field when the overlap between the first three-dimensional sound field and the second three-dimensional sound field exceeds the overlap threshold, and compare the sound field feedback parameters with standard parameters, if the difference between the sound field feedback parameters and the standard parameters exceeds a preset threshold, determine that the decorative material has defects.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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