Acoustic emission event processing method and system based on percolation model

Abstract

The invention relates to the technical field of acoustic emission event data processing, and provides an acoustic emission event processing method and system based on a percolation model, and the method comprises the steps: carrying out the cube coverage traversal of a to-be-processed acoustic emission event, and recognizing an occupied cube and an empty cube; clustering the acoustic emission events on the basis of a percolation model, and sorting the acoustic emission events according to spatial correlation and the proportion of a clustered group to a cube; group numbers obtained after sorting are used as importance degree indexes of all the groups so as to reflect the correlation strength of the acoustic emission events; and setting a noise reduction level according to the group number, and removing the acoustic emission events in the low-number group according to the noise reduction level to obtain acoustic emission event data after noise reduction processing. By means of the percolation model, according to the spatial correlation of the acoustic emission events, the acoustic emission events are sequenced and subjected to hierarchical noise reduction, key events and secondary events can be distinguished, the characteristics of the damage accumulation area are highlighted, and therefore the subsequent data processing efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field related to acoustic emission event data processing, and in particular, to an acoustic emission event processing method and system based on a percolation model. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Acoustic emission events refer to the phenomenon that elastic waves are released when a material is subjected to force or external forces due to the generation, expansion or friction sliding of internal cracks. This elastic wave can be captured by the acoustic emission sensor arranged on the surface of the material, and the characteristic parameters of the event, such as occurrence time, energy, amplitude, etc., can be extracted through signal processing technology. Acoustic emission technology is a non-destructive detection method and is widely used in rock mechanics, metal material failure monitoring, and composite material health assessment. In rock mechanics, acoustic emission events are an important tool for studying rock fracture behavior. By arranging multiple sensors and calculating the time difference between the elastic waves released during the fracture and reaching different sensors, the spatial location of the fracture inside the rock can be determined. These fracture locations are recorded as a series of positioning events, which provides an important basis for studying the expansion process of rock cracks.

[0004] The inventors found in their research that the existing recognition system based on acoustic emission events has the following problems:

[0005] 1) AE events are often huge in number, including key events and a large number of irrelevant or weakly related events. Existing AE events are considered to be equivalent, and it is impossible to quickly identify the key events most closely related to rock fracture or material damage, which affects the analysis efficiency.

[0006] 2) Due to the huge amount of acoustic emission event data, the acoustic emission characteristics of the destruction concentration area are easily masked by weakly correlated events, which increases the difficulty of analysis.

[0007] 3) Due to the limitations of the measurement environment and the equipment itself, acoustic emission events often contain noise signals. These noises will not only interfere with the judgment of destructive behavior, but may also mask the characteristics of important acoustic emission events. Summary of the invention

[0008] In order to solve the above problems, the present disclosure proposes an acoustic emission event processing method and system based on the percolation model. With the help of the percolation model, the acoustic emission events are sorted and graded for noise reduction according to the spatial correlation of the acoustic emission events, which can distinguish key events from minor events and highlight the characteristics of the damage concentration area, thereby improving the subsequent data processing efficiency and realizing the accurate tracking of the evolution of internal damage of the material.

[0009] In order to achieve the above objectives, the present disclosure adopts the following technical solutions:

[0010] One or more embodiments provide a method for processing acoustic emission events based on a percolation model, comprising the following steps:

[0011] For the acoustic emission events to be processed, cube coverage traversal is performed to identify occupied cubes and empty cubes;

[0012] Acoustic emission events are clustered based on the percolation model, and the acoustic emission events are sorted according to the spatial correlation based on the proportion of cubes occupied by cluster groups.

[0013] After sorting, the group numbers are obtained as the importance index of each group to reflect the correlation strength of the acoustic emission events;

[0014] The noise reduction level is set according to the group number, and the acoustic emission events in the low-numbered groups are removed according to the noise reduction level to obtain the acoustic emission event data after noise reduction processing.

[0015] One or more embodiments provide an acoustic emission event processing system based on a percolation model, including:

[0016] A preparation module is configured to perform a cube coverage traversal for an acoustic emission event to be processed, and identify occupied cubes and empty cubes;

[0017] A sorting module is configured to cluster the acoustic emission events based on the percolation model, and sort the acoustic emission events according to the spatial correlation according to the proportion of the cube occupied by the clustering group;

[0018] The numbering module is configured to obtain group numbers after sorting as an indicator of the importance of each group to reflect the strength of the correlation of the acoustic emission events;

[0019] The noise reduction module is configured to set a noise reduction level according to the group number, remove the acoustic emission events in the low-numbered group according to the noise reduction level, and obtain the acoustic emission event data after noise reduction processing.

[0020] An electronic device comprises a memory and a processor and computer instructions stored in the memory and running on the processor. When the computer instructions are run by the processor, the steps in the above-mentioned acoustic emission event processing method based on the percolation model are completed.

[0021] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the steps in the above-mentioned acoustic emission event processing method based on the percolation model are completed.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] In the present disclosure, by finely calibrating and sorting the spatial distribution characteristics of acoustic emission events, key events closely related to rupture behavior are quickly identified, significantly reducing the workload of analysis; percolation model clustering is used in combination with a sorting algorithm to effectively highlight the key acoustic emission characteristics of the destruction cluster area to prevent them from being masked by weakly correlated events. A hierarchical denoising method based on group numbering is used to remove noise events in a targeted manner, retain key event characteristics, improve data quality, and enhance the reliability of model predictions. The combination of cube cover traversal and percolation model enables the system to adapt to data sets of different spatial distributions and complexities, and can be effectively applied in a variety of materials and scenarios.

[0024] The advantages of the present disclosure and the advantages of additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and the description thereof are used to explain the present disclosure but do not constitute a limitation of the present disclosure.

[0026] Figure 1 is a flow chart of an acoustic emission event processing method according to Embodiment 1 of the present disclosure; DETAILED DESCRIPTION

[0027] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0029] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof. It should be noted that, in the absence of conflict, the various embodiments in the present disclosure and the features in the embodiments can be combined with each other. The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0030] Terminology explanation: High-frequency adjacent spacing: The adjacent spacing of acoustic emission events has the highest frequency in the range close to the average value (indicating that the number of acoustic emission events in this part is the largest). The adjacent spacing in this range is called high-frequency adjacent spacing.

[0031] Example 1

[0032] In the technical solutions disclosed in one or more embodiments, Figure 1 As shown, a method for processing acoustic emission events based on a percolation model comprises the following steps:

[0033] Step 1: for the acoustic emission event to be processed, perform cube coverage traversal to identify occupied cubes and empty cubes;

[0034] Step 2: Cluster the acoustic emission events based on the percolation model, and sort the acoustic emission events according to the spatial correlation according to the proportion of the cube occupied by the cluster group;

[0035] Step 3: After sorting, the group numbers are obtained as the importance index of each group to reflect the correlation strength of the acoustic emission events;

[0036] Step 4: Set the noise reduction level according to the group number, remove the acoustic emission events in the low-numbered group according to the noise reduction level, and obtain the acoustic emission event data after noise reduction processing;

[0037] The above process is equivalent to the preprocessing process of the acoustic emission event data. Based on the preprocessed acoustic emission events, the internal changes of the material to be tested can be further identified, such as the generation, expansion or friction of internal cracks in the rock mass can be identified.

[0038] In this embodiment, by finely calibrating and sorting the spatial distribution characteristics of acoustic emission events, key events closely related to rupture behavior are quickly identified, significantly reducing the workload of analysis; percolation model clustering is used in combination with a sorting algorithm to effectively highlight the key acoustic emission characteristics of the destruction cluster area to prevent them from being masked by weakly correlated events. A hierarchical denoising method based on group numbering is used to remove noise events in a targeted manner, retain key event characteristics, improve data quality, and enhance the reliability of model predictions. The combination of cube cover traversal and percolation model enables the system to adapt to data sets of different spatial distributions and complexities, and can be effectively applied in a variety of materials and scenarios.

[0039] In step 1, cube coverage traversal is performed, and the implementation method includes:

[0040] Step 1.1, divide the study area into cube grids of different sizes. The study area is the measurement object of the acoustic emission sensor, which can be rocks, measured materials, etc.;

[0041] Step 1.2, counting whether each cube contains an acoustic emission event: a cube containing an acoustic emission event is an occupied cube; a cube not containing an acoustic emission event is an empty cube;

[0042] Step 1.3: Count the positions and quantities of the occupied cubes to provide a basis for subsequent analysis.

[0043] In step 2, clustering is performed based on the percolation model, and the implementation method includes:

[0044] Step 2.1, performing percolation model clustering on occupied cubes, and classifying adjacent cubes into the same group; the adjacent cubes are a group of coplanar cubes;

[0045] Step 2.2, identify all groups occupying the cube and select the largest group;

[0046] In the above scheme, in the preparation stage of acoustic emission event noise reduction, the number of cubes occupied in each group is counted, and the group occupying the largest number of cubes is called the largest group, thereby identifying the largest group.

[0047] In step 2, the method of sorting the acoustic emission events according to the spatial correlation based on the proportion of the cube occupied by the cluster group includes the following steps:

[0048] Step 2-1, determine the high-frequency adjacent spacing of acoustic emission events;

[0049] Specifically, the relationship curve between the number of occupied cubes of the largest group and the size of the covered cube is calculated. The cube size corresponding to the peak of the curve is the high-frequency adjacent spacing d of the acoustic emission event. p ;

[0050] The covering cube refers to a cube whose size is set when performing covering traversal. For example, if the size of the covering cube used for traversal is 10, the number of occupied cubes covered is 5. When the size of the covering cube used for traversal is set to 5, the number of occupied cubes covered may be 12.

[0051] Step 2-2, determining the minimum and maximum values ​​of the adjacent spacings of acoustic emission events, comprises the following steps:

[0052] Step 2-21, count the proportion of the number of cubes occupied by the largest group in the total number of cubes occupied, and draw a curve of the proportion of the number of cubes occupied by the largest group versus the size of the covering cube, that is, the curve of the proportion of the number of cubes occupied by the largest group;

[0053] Step 2-22: In the curve of the proportion of the number of cubes occupied by the largest group, the corresponding covering cube size at the point where the curve starts to increase is the minimum value d of the adjacent spacing. min ;

[0054] Specifically, the curve of the proportion of the number of cubes occupied by the largest group is positive S-shaped. The corresponding covering cube size at the place where the curve of the number of cubes occupied by the largest group begins to increase is determined as the minimum value d of the adjacent spacing of acoustic emission. min ;

[0055] Step 2-23: In the curve of the proportion of the number of cubes occupied by the largest group, the corresponding covered cube size at the point where the curve value is equal to 1 is the maximum value d of the adjacent spacing of the acoustic emission. max ;

[0056] Step 2-3, setting the size of the coverage cube according to the maximum and minimum values ​​of the adjacent spacing of the acoustic emission and the adjacent spacing of the high frequency;

[0057] Specifically, set the size range of the covering cube to d min ~d max , the preferred size can be set to high frequency adjacent spacing d p ;

[0058] Step 2-4, perform coverage traversal according to the set coverage cube, perform percolation model clustering again, and generate multiple cluster groups;

[0059] Specifically, according to the determined covering cube size, the acoustic emission events are covered and the occupied cubes are identified, and the occupied cubes are clustered using the percolation model;

[0060] Step 2-5, count the proportion of the number of cubes occupied by each cluster group in the total number of cubes occupied, and sort the cluster groups according to the proportion; specifically, sort in ascending order;

[0061] The interval between acoustic emission events can be considered as a random variable. Studies have shown that the interval values ​​are most frequent in the range close to the mean, indicating that these events are the most numerous and more likely to come from the damage concentration area. Small intervals usually indicate a higher degree of damage, while high-frequency intervals correspond to more important acoustic emission events.

[0062] This embodiment can quickly identify the events most relevant to the damage characteristics through sorting, which can avoid the problem that the acoustic emission characteristics of the damage concentration area are masked by weakly related events. By sorting, key events can be distinguished from minor events, which can highlight the characteristics of the damage concentration area, thereby improving data processing efficiency and achieving accurate tracking of damage evolution.

[0063] In step 3, the group numbers obtained after sorting are used as the importance index of each group to reflect the strength of the correlation of the acoustic emission events. Specifically, the groups are numbered consecutively according to the sorting order. The group that occupies a small proportion of the number of cubes has a small number, and the group that occupies a large proportion of the number of cubes has a large number.

[0064] Specifically, after sorting in ascending order, the numbers are numbered consecutively, and the numbers may start from 1 and increase in sequence;

[0065] In step 4, set the noise reduction level according to the group number, specifically:

[0066] The group number corresponds to the noise reduction level, and the smallest number corresponds to the minimum noise reduction level, such as number 1 corresponds to noise reduction level 1 (minimum noise reduction level); the largest number corresponds to the maximum noise reduction level;

[0067] In step 4, remove the acoustic emission events in the low-numbered group according to the noise reduction level. Specifically, set the target noise reduction level and select the acoustic emission events to be eliminated:

[0068] Step 41, identifying the target group to be deleted according to the correspondence between the noise reduction level and the group number;

[0069] Step 42, deleting the acoustic emission events covered by the target group to complete the noise reduction;

[0070] In this embodiment, by setting the noise reduction level, the acoustic emission events in the low-proportion group are flexibly screened and eliminated; the high-level group is retained (important events), and the low-level group is eliminated (irrelevant events). After clustering, sorting and then noise reduction can achieve hierarchical noise reduction.

[0071] Example 2

[0072] Based on Example 1, this embodiment provides an acoustic emission event processing system based on a percolation model, including:

[0073] A preparation module is configured to perform a cube coverage traversal for an acoustic emission event to be processed, and identify occupied cubes and empty cubes;

[0074] A sorting module is configured to cluster the acoustic emission events based on the percolation model, and sort the acoustic emission events according to the spatial correlation according to the proportion of the cube occupied by the clustering group;

[0075] The numbering module is configured to obtain group numbers after sorting as an indicator of the importance of each group to reflect the strength of the correlation of the acoustic emission events;

[0076] The noise reduction module is configured to set a noise reduction level according to the group number, remove the acoustic emission events in the low-numbered group according to the noise reduction level, and obtain the acoustic emission event data after noise reduction processing.

[0077] The above system uses cube cover traversal technology to map acoustic emission events into a series of cubes through spatial discretization to identify their occupancy status. This method transforms complex spatial distribution problems into discrete cube calibration problems, which helps to improve the structured degree of data. The sorting module clusters acoustic emission events through the percolation model, explores the spatial correlation of each group and the proportion of occupied cubes, and highlights the groups with significant spatial correlation through sorting. This process combines spatial geometry and event distribution laws to make key events easier to identify. The numbering module assigns numbers to each group to intuitively indicate the importance of the group, forming a basis for event screening in descending order of number priority. Finally, the denoising module gradually eliminates the noise components in low-numbered groups by setting the denoising level, thereby achieving efficient denoising of event data.

[0078] It should be noted here that the various modules in this embodiment correspond one-to-one to the various steps in Example 1, and the specific implementation process is the same, which will not be repeated here.

[0079] Example 3

[0080] This embodiment provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are run by the processor, the steps in the acoustic emission event processing method based on the percolation model of Example 1 are completed.

[0081] Example 4

[0082] This embodiment provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps in the method for processing acoustic emission events based on the percolation model of Embodiment 1 are completed.

[0083] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0084] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. An acoustic emission event processing method based on a percolation model, characterized in that: The steps include: For the acoustic emission events to be processed, cube coverage traversal is performed to identify occupied cubes and empty cubes; Acoustic emission events are clustered based on the percolation model, and the acoustic emission events are sorted according to the spatial correlation based on the proportion of cubes occupied by cluster groups. After sorting, the group numbers are obtained as the importance index of each group to reflect the correlation strength of the acoustic emission events; The noise reduction level is set according to the group number, and the acoustic emission events in the low-numbered groups are removed according to the noise reduction level to obtain the acoustic emission event data after noise reduction processing.

2. The acoustic emission event processing method based on the percolation model according to claim 1, characterized in that: Perform a cube cover traversal, including: Divide the area to be studied into cube grids of different sizes; Count whether each cube contains an acoustic emission event: the cube containing an acoustic emission event is an occupied cube; the cube not containing an acoustic emission event is an empty cube; Count the positions and quantities of occupied cubes.

3. The method for processing acoustic emission events based on the percolation model according to claim 1, characterized in that: Clustering based on percolation model, including: Perform percolation model clustering on occupied cubes, and classify adjacent cubes into the same group; the adjacent cubes are a group of coplanar cubes; Identify all cliques that occupy the cube and select the largest clique.

4. The method for processing acoustic emission events based on the percolation model according to claim 1, characterized in that: The method for sorting acoustic emission events according to spatial correlation based on the proportion of cube occupied by cluster groups includes the following steps: Determine the high frequency adjacent spacing of acoustic emission events; Determine the minimum and maximum values ​​of the adjacent spacing between acoustic emission events; The size of the coverage cube is set according to the maximum and minimum values ​​of the adjacent spacing of the acoustic emission and the adjacent spacing of the high frequency; Perform covering traversal according to the set covering cube, perform percolation model clustering again, and generate multiple clustering groups; Count the proportion of cubes occupied by each cluster group in all cubes occupied, and sort the groups by proportion.

5. The method for processing acoustic emission events based on the percolation model according to claim 4, characterized in that: Determining the minimum and maximum values ​​of the adjacent spacing between acoustic emission events includes the following steps: Count the proportion of the number of cubes occupied by the largest group in the total number of occupied cubes, plot the curve of the proportion of the number of cubes occupied by the largest group versus the size of the covering cube, and obtain the curve of the proportion of the number of cubes occupied by the largest group; In the curve of the proportion of the number of cubes occupied by the largest group, the corresponding covered cube size at the point where the curve starts to increase is the minimum value of the adjacent spacing; In the curve of the proportion of the number of cubes occupied by the largest group, the corresponding covering cube size when the curve value is equal to 1 is the maximum value of the adjacent spacing of acoustic emission.

6. The method for processing acoustic emission events based on the percolation model according to claim 1, characterized in that: After sorting, the group numbers are obtained as the importance index of each group. Specifically, they are numbered consecutively according to the sorting order. The group that occupies a small proportion of the number of cubes has a small number, and the group that occupies a large proportion of the number of cubes has a large number.

7. The method for processing acoustic emission events based on a percolation model according to claim 1, characterized in that: Set the noise reduction level according to the group number. Specifically, the group number corresponds to the noise reduction level, the minimum number corresponds to the minimum noise reduction level, and the maximum number corresponds to the maximum noise reduction level. Alternatively, remove AE events in low-numbered groups according to the noise reduction level, specifically: According to the correspondence between the noise reduction level and the group number, the target group to be deleted is identified; Delete the AE events covered by the target group to complete the noise reduction. 8.Acoustic emission event processing system based on percolation model, characterized in that: include: A preparation module is configured to perform a cube coverage traversal for an acoustic emission event to be processed, and identify occupied cubes and empty cubes; A sorting module is configured to cluster the acoustic emission events based on the percolation model, and sort the acoustic emission events according to the spatial correlation according to the proportion of the cube occupied by the clustering group; The numbering module is configured to obtain group numbers after sorting as an indicator of the importance of each group to reflect the strength of the correlation of the acoustic emission events; The noise reduction module is configured to set a noise reduction level according to the group number, remove the acoustic emission events in the low-numbered group according to the noise reduction level, and obtain the acoustic emission event data after noise reduction processing.

9. An electronic device, characterized in that: The invention comprises a memory and a processor and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the method for processing acoustic emission events based on the percolation model according to any one of claims 1 to 7 are completed.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps in the acoustic emission event processing method based on the percolation model according to any one of claims 1 to 7.

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