Classification of rock aggregate fracture signals and evaluation method and equipment for damage characteristics

Abstract

The present invention belongs to the field of rock dynamics technology, and specifically discloses a method and device for classifying rock aggregate fracture signals and evaluating damage characteristics. The method includes: based on the fracture signals collected during the quasi-dynamic test, by analyzing their waveform characteristics in the time domain and space, a method for classifying fracture signals with different aggregation degrees is first proposed, and then a method for analyzing and evaluating rock damage characteristics is given in combination with the statistical results of fracture signals of different categories. The device is used to implement and integrate the above method. The present invention can comprehensively evaluate the damage characteristics of rocks under quasi-dynamic loading through statistical analysis of fracture signals of different categories, providing a scientific basis for the safety assessment of rock engineering.

Description

Technical Field

[0001] The present invention belongs to the field of rock dynamics technology, and relates to a method and device for classifying rock aggregate fracture signals and evaluating damage characteristics, and more specifically, to a method for classifying rock instantaneous aggregate fracture signals and evaluating damage characteristics during millisecond loading under quasi-dynamic conditions. Background Art

[0002] Under the action of earthquakes, explosions, and missile penetration, dynamic loads will be generated at the power source in the rock mass and propagate in the medium in the form of attenuated stress waves. Among them, in the vicinity of the power source, the dynamic load is mainly a strong impact load, so the destruction and instability of the rock mass are basically within the expected range. In contrast, the rock mass at a certain distance from the power source will be affected by the attenuated stress wave and conform to the characteristics of quasi-dynamic loads. Although the quasi-dynamic load has a smaller amplitude and energy, its dynamic action will cause more hidden damage and more random instability in the rock mass, thus posing a great threat to the safe construction and stable operation of the corresponding rock mass engineering, such as deep mineral resource power mining, water conservancy and hydropower chamber group excavation, and underground important national defense engineering operation and maintenance.

[0003] During the whole process of rock mass destruction, the action of external load will cause the release of energy and the generation of micro-vibration. Combined with the corresponding monitoring technology, vibration electrical signals associated with rock failure characteristics, such as acoustic emission signals, can be obtained. Under the action of quasi-static loads, the test duration is relatively long and generally within the range of tens of minutes, so the severity of rock failure is not high and the corresponding vibration signal has simple characteristics. In contrast, the duration of quasi-dynamic tests is generally tens of milliseconds, which is 3 to 4 orders of magnitude different from quasi-static tests, which will cause the rock to produce vibration signals with different aggregation degrees during the instantaneous destruction process. Since the difference in the degree of aggregation of vibration signals corresponds to the difference in rock failure characteristics, the classification and evaluation of rock aggregation failure signals under quasi-dynamic loading will be conducive to mastering the quasi-dynamic failure behavior of rocks during millisecond loading duration, thereby facilitating the construction safety and healthy operation of rock mass engineering. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a method for classifying the types of rock aggregate fracture signals under quasi-dynamic loading and evaluating the damage characteristics. The method is based on the fracture signals collected during the quasi-dynamic test. By analyzing the waveform characteristics in the time domain and space, a method for classifying fracture signals with different aggregation degrees is first proposed. Then, a method for analyzing and evaluating rock damage characteristics is given in combination with the statistical results of fracture signals of different categories. Through the statistical analysis of fracture signals of different categories, the damage characteristics of rocks under quasi-dynamic loading can be comprehensively evaluated, providing a scientific basis for the safety assessment of rock engineering.

[0005] To achieve the above object, according to one aspect of the present invention, a method for classifying rock aggregate fracture signals and evaluating damage characteristics is proposed, comprising the following steps:

[0006] Step 1, performing a quasi-dynamic loading test on a rock sample, and monitoring a fracture signal generated by fracture of the rock sample during the loading process;

[0007] Step 2: Select a single signal from the rupture signal and draw a time domain curve of the signal amplitude; determine the duration of the single signal amplitude greater than the threshold and the corresponding start and end time according to the threshold characteristics required for signal triggering; within the above duration range, determine the amplitude extreme value of the signal above the threshold, which is recorded as the first amplitude extreme value;

[0008] Step 3, averaging the first amplitude extreme values ​​in step 2, determining the average value corresponding to all the first amplitude extreme values, comparing and analyzing the magnitude relationship between all the first amplitude extreme values ​​and the average value, extracting the extreme value whose first amplitude extreme value is greater than the average value, and recording it as the second amplitude extreme value;

[0009] Step 4: Based on the second amplitude extreme value determined in step 3, all extreme value data points are sorted in time order to form a set, and the amplitude extreme value in the set is judged and determined by comparing and analyzing the size relationship between each data point in the set and the adjacent data points on both sides thereof, recorded as the third amplitude extreme value, and the above extreme values ​​are sorted in time order;

[0010] Step 5, based on the set of the third amplitude extreme values, sequentially calculating the time intervals between adjacent third amplitude extreme values, and determining the time average value corresponding to the time intervals between all adjacent third amplitude extreme values;

[0011] Step 6: According to the time interval between the adjacent third amplitude extreme values ​​and the time average value, the relationship between the time interval and the time average value is compared and analyzed to determine the aggregation of the rupture signal;

[0012] Step 7: Repeat steps 2 to 6 to complete the classification of different types of fracture signals, and then statistically analyze the number and proportion of fracture signals of different categories, and evaluate the failure characteristics of rocks under quasi-dynamic loading based on this.

[0013] As a further preferred embodiment, in step 1, the strain rate corresponding to the quasi-dynamic load needs to be within 10 -2 ~10 0 s -1 within the scope;

[0014] Preferably, in step 2, a single signal is selected from the recorded rupture signals for analysis, and a waveform diagram of the single signal in the time domain is drawn; the single signal includes: an acoustic emission signal;

[0015] The calculation model of the first amplitude extreme value includes:

[0016] V i ∈{V1},(V i -V i-1 )>0 and (V i -V i+1 )>0

[0017] Where V i is the first amplitude extreme value, {V1} represents the set of the first amplitude extreme values, and i represents the i-th amplitude data point in a single signal; where V i The time corresponding to the data point should be within the duration range.

[0018] As further preferred, in step 3, the calculation model of the amplitude average value and the second amplitude extreme value corresponding to the first amplitude extreme value includes:

[0019]

[0020] V j ∈{V2},(V j -V j-1 )>0 and (V j -V j+1 )>0

[0021] Where V mean is the average value corresponding to the first amplitude extreme value, V i is the amplitude extreme value of the i-th signal in the set {V1} composed of the first amplitude extreme value in step 2, n is the number of data points in the set {V1}, V j is the second amplitude extreme value, {V2} represents the set composed of the second amplitude extreme values, and j represents the jth amplitude data point in the set {V1} composed of the first amplitude extreme values ​​in step 2.

[0022] As further preferred, in step 4, the calculation model of the third amplitude extreme value includes:

[0023] V k ∈{V3},(V k -V k-1 )>0 and (V k -V k+1 )>0

[0024] Where V k is the third amplitude extreme value, {V3} represents the set composed of the third amplitude extreme value, and k represents the kth amplitude data point in the set {V2} composed of the second amplitude extreme value in step three.

[0025] As further preferred, in step 5, the calculation model of the time average value includes:

[0026]

[0027] In the formula, t mean is the average time interval between adjacent amplitude extreme values, t i is the time corresponding to the i-th second amplitude extreme value in the set determined in step three, and m is the number of time intervals of the second amplitude extreme values ​​determined in step three.

[0028] As a further preferred embodiment, step six specifically includes the following steps:

[0029] The aggregation of the rupture signal is judged based on the time interval between adjacent second amplitude extreme values ​​and the time average value. If the time interval between adjacent second amplitude extreme values ​​is greater than the time average value, it is considered that the adjacent second amplitude extreme values ​​correspond to two separate signals. The aggregation of the rupture signal is judged by determining the number of separate signals in the rupture signal.

[0030] As a further preferred embodiment, step six specifically includes the following steps:

[0031] The failure characteristics of rocks under quasi-dynamic loading are evaluated by combining the number and proportion of different types of fracture signals. When the number and proportion of signals with lower aggregation in the fracture signals are high, the degree of rock damage under this loading condition is small and the severity is low. When the number and proportion of signals with higher aggregation in the fracture signals are high, the degree of rock damage in this case is large and the severity is high.

[0032] According to another aspect of the present invention, a system for classifying rock aggregate fracture signals and evaluating damage characteristics is provided, which is used to implement the method of any one of the above embodiments or a combination of multiple embodiments, including:

[0033] The first main control module is used to perform a quasi-dynamic loading test on the rock sample and monitor the fracture signal generated by the fracture of the rock sample during the loading process;

[0034] The second main control module is used to select a single signal from the rupture signal and draw a time domain curve of the signal amplitude; determine the duration of the single signal amplitude being greater than the threshold and the corresponding start and end time according to the threshold characteristics required for signal triggering, and determine the amplitude extreme value of the signal above the threshold within the above duration range, which is recorded as the first amplitude extreme value;

[0035] The third main control module is used to average the first amplitude extreme values, determine the average value corresponding to all the first amplitude extreme values, compare and analyze the magnitude relationship between all the first amplitude extreme values ​​and the average value, extract the extreme value of the first amplitude extreme value that is greater than the average value, and record it as the second amplitude extreme value;

[0036] The fourth main control module is used to sort all extreme value data points in time order and form a set according to the determined second amplitude extreme value, judge and determine the amplitude extreme value in the set by comparing and analyzing the size relationship between each data point in the set and the adjacent data points on both sides thereof, record it as the third amplitude extreme value, and sort the above extreme values ​​in time order;

[0037] A fifth main control module, used for calculating the time intervals between adjacent third amplitude extreme values ​​in sequence according to the set of third amplitude extreme values, and determining the time average value corresponding to the time intervals between all adjacent third amplitude extreme values;

[0038] The sixth main control module is used to compare and analyze the relationship between the time interval and the time average value according to the time interval between adjacent third amplitude extreme values ​​and the time average value, so as to judge the aggregation of the rupture signal;

[0039] The seventh main control module is used to classify different types of fracture signals, and then statistically analyze the number and proportion of different types of fracture signals, and evaluate the failure characteristics of rocks under quasi-dynamic loading based on this.

[0040] According to another aspect of the present invention, there is also provided an electronic device, comprising:

[0041] At least one processor, at least one memory and a communication interface; wherein,

[0042] The processor, memory and communication interface communicate with each other;

[0043] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for classifying rock aggregate fracture signals and evaluating damage characteristics according to any one of the above embodiments or a combination of multiple embodiments.

[0044] According to another aspect of the present invention, a non-transitory computer-readable storage medium is also provided, characterized in that the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute a method for classifying rock aggregate fracture signals and evaluating damage characteristics according to any of the above-mentioned embodiments or a combination of multiple embodiments.

[0045] In general, the above technical solution conceived by the present invention has the following technical advantages compared with the prior art:

[0046] 1. The present invention provides a method for classifying the aggregated fracture signals of rocks under quasi-dynamic loading and evaluating the damage characteristics. The method is based on the fracture signals collected during the quasi-dynamic test. By analyzing the waveform characteristics of the fracture signals in the time domain and space, a method for classifying fracture signals of different aggregation degrees is first proposed. Then, a method for analyzing and evaluating the damage characteristics of rocks is given in combination with the statistical results of fracture signals of different categories. Through the statistical analysis of fracture signals of different categories, the damage characteristics of rocks under quasi-dynamic loading can be comprehensively evaluated, providing a scientific basis for the safety assessment of rock engineering.

[0047] 2. The present invention can accurately monitor the signals generated by rock samples during the fracture process through quasi-dynamic loading tests, and improve the accuracy of monitoring the rock fracture process by extracting the characteristics of a single signal. By setting a threshold, fracture signals with significant characteristics can be screened out, noise and non-critical signals can be excluded, and the critical stages of rock fracture can be more effectively identified.

[0048] 3. Based on the time interval and time average, the present invention can effectively judge the aggregation of fracture signals and classify them, which is helpful to understand the fracture characteristics of different types of rocks. Through statistical analysis of fracture signals of different categories, the failure characteristics of rocks under quasi-dynamic loading can be comprehensively evaluated, providing a scientific basis for the safety assessment of rock engineering.

[0049] 4. The repeated execution of the present invention ensures the reliability and universality of the results, and is suitable for the evaluation of the failure characteristics of different rock types. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Flow chart of a method for classifying rock aggregate fracture signals and evaluating damage characteristics under quasi-dynamic loading according to a preferred embodiment of the present invention

[0051] Figure 2 A waveform diagram of an acoustic emission signal recorded in a quasi-dynamic test according to an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of extreme amplitude data points having an amplitude higher than an average value in a rupture signal according to an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of extreme value data points further determined in a set of extreme amplitude values ​​having amplitudes higher than an average value involved in an embodiment of the present invention;

[0054] Figure 5 A schematic diagram of a rupture signal consisting of five separate signals according to an embodiment of the present invention;

[0055] Figure 6A schematic diagram of the number of rupture signals composed of different numbers of individual signals in a quasi-dynamic test according to an embodiment of the present invention;

[0056] Figure 7 It is a schematic diagram of the proportion of rupture signals composed of different numbers of individual signals in the quasi-dynamic test involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a method for classifying rock aggregate fracture signals and evaluating damage characteristics under quasi-dynamic loading. Taking a sandstone sample under quasi-dynamic loading as an example, the specific implementation principle of the present invention is described in detail.

[0059] Step 1: The sandstone material is made into a specimen of a certain size, such as 50mm wide and 100mm high, and the specimen is subjected to a quasi-dynamic uniaxial compression test at an axial loading speed of 100mm / s using the RMT401 test system. The acoustic emission sensor and data acquisition system are used to record the fracture signals generated in the sandstone specimen during the millisecond loading process.

[0060] Step 2: Select a single signal from the above recorded rupture signals for analysis and draw a waveform diagram of the signal in the time domain. Figure 2 As shown, the voltage threshold corresponding to the signal trigger is about 0.03V. According to the voltage threshold, the duration corresponding to the signal amplitude being higher than the 0.03V voltage threshold, as well as the start and end times, are determined to be 283.8μs, 0μs, and 283.8μs, respectively. Within the above duration range, the amplitude data points in the signal are analyzed as follows to determine the set of the first amplitude extreme values:

[0061] V i ∈{V1},(V i -V i-1 )>0 and (V i -V i+1 )>0 (1)

[0062] Where V i is the first amplitude extreme value, {V1} represents the set of the first amplitude extreme values, and i represents the i-th amplitude data point in a single signal; where V iThe time corresponding to the data point should be within the duration range.

[0063] Step 3: Using the set of first amplitude extreme values ​​{V1} determined in step 2, calculate the amplitude average value and the second amplitude extreme value corresponding to the first amplitude extreme value using the following formula:

[0064]

[0065] V j ∈{V2},(V j -V j-1 )(V j -V j+1 )>0 (3)

[0066] Where V mean is the average value corresponding to the first amplitude extreme value, V i is the amplitude extreme value of the i-th signal in the set {V1} composed of the first amplitude extreme value in step 2, n is the number of data points in the set {V1}, V j is the second amplitude extreme value, {V2} represents the set composed of the second amplitude extreme values, and j represents the jth amplitude data point in the set {V1} composed of the first amplitude extreme values ​​in step 2.

[0067] Based on the above formula, the average value of the amplitude extreme value in the signal is calculated to be 0.25 V. Then, the amplitude extreme values ​​in the rupture signal whose amplitude is higher than the average value are determined according to formula (3) and are sorted in chronological order. Figure 3 As shown, the red solid circle represents the amplitude extreme value determined by the above method.

[0068] Step 4: Calculate the third set of amplitude extreme values ​​in the set of amplitude extreme values ​​determined in step 3 using the following formula:

[0069] V k ∈{V3},(V k -V k-1 )>0 and (V k -V k+1 )>0 (4)

[0070] Where V k is the third amplitude extreme value, {V3} represents the set composed of the third amplitude extreme value, and k represents the kth amplitude data point in the set {V2} composed of the second amplitude extreme value in step three.

[0071] like Figure 4 As shown, the purple circle represents the third amplitude extreme value further determined in this step.

[0072] Step 5: Based on the set of the third amplitude extreme values ​​determined in step 4, the time intervals between adjacent amplitude extreme values ​​are calculated in sequence, and the average value corresponding to the time interval is calculated to be 22.39 μs using the following formula:

[0073]

[0074] In the formula, t mean is the average time interval between adjacent amplitude extreme values, t i is the time corresponding to the i-th second amplitude extreme value in the set determined in step three, and m is the number of time intervals of the second amplitude extreme values ​​determined in step three.

[0075] Step 6: Use the average value of the above time interval to judge the clustering of adjacent amplitude extreme values, so as to classify the fracture signal. If the time interval between adjacent amplitude extreme values ​​is greater than the average value of the time interval determined in step 5, it is considered that the two amplitude extreme values ​​correspond to two independent signals. In one embodiment of the present invention, a fracture signal intelligent classification method based on a convolutional neural network is provided. The method classifies fracture signals through a deep learning algorithm to achieve accurate identification and classification of signals. In a quasi-dynamic uniaxial compression test of a sandstone sample, it is assumed that the collected fracture signal is:

[0076] S(t)=0.5sin(2π100t)+0.2sin(2π200t)+N(t) (6)

[0077] Among them, the main frequency is 100Hz, the secondary frequency is 200Hz, the duration is 1s, and the noise N(t) is random noise. The signal after denoising is:

[0078] S denoise (t)=0.5sin(2π100t)+0.2sin(2π200t) (7)

[0079] After normalization:

[0080]

[0081] Signal energy calculation:

[0082]

[0083] S norm (t)=sin(2π100t)+0.4sin(2π200t) (10)

[0084]

[0085] Due to the orthogonality of sinusoidal signals of different frequencies, the cross-term integral is zero, so:

[0086]

[0087] Manually label the rupture signals, establish training sets and test sets, label different types of rupture signals, and use multi-layer neural networks to build a convolutional neural network model, including input layer, convolution layer, pooling layer, fully connected layer and output layer. Use the labeled training set to train the convolutional neural network model, optimize the model parameters through the back propagation algorithm, and improve the classification accuracy. Figure 5 As shown in the figure, the time interval between the extreme value represented by the black circle and the other extreme value points on its left and right sides is greater than the average time interval determined in step 5, so it is considered to correspond to a separate signal, that is, the gray shaded part in the figure. Figure 5 It can be seen that the rupture signal is mainly composed of 5 separate signals, so its category can be divided into a rupture signal composed of 5 signals.

[0088] Step 7: Repeat steps 2 to 6 above to classify the rupture signals recorded during the test. According to the analysis results, the number of individual signals contained in the rupture signals recorded in the test is 1, 2, 3, 4, 5, 6, 7, and the number of rupture signals is 8, 7, 7, 7, 6, 3, 3 (e.g. Figure 6 As shown), the proportions are 19.5%, 17.1%, 17.1%, 17.1%, 14.6%, 7.3% and 7.3% respectively (as shown Figure 7 shown).

[0089] Step 8: According to the number and proportion of signals of different categories, the destruction of rock under quasi-dynamic loading conditions is mainly dominated by aggregated fracture signals. During the test, among the recorded fracture signals, the number of fracture signals composed of one single signal is 8, accounting for 19.5%. In contrast, the number of fracture signals formed by the aggregation of multiple single signals is significantly larger, and occupies the vast majority of the proportion, reaching 80.5%. Specifically, the number of fracture signals formed by the aggregation of 2 single signals is 7, accounting for 17.1%; the number of fracture signals formed by the aggregation of 3 single signals is also 7, accounting for 17.1%; the number of fracture signals formed by the aggregation of 4 single signals is 7, accounting for 17.1%. These data show that under quasi-dynamic loading conditions, the destruction of rock is mainly dominated by fracture signals formed by the aggregation of multiple single signals, among which the fracture signals formed by the aggregation of 2, 3, and 4 single signals are the main ones. When rock is subjected to external loading, its internal structure will gradually accumulate stress. When the stress is concentrated to a certain extent, microcracks will first appear in some weak parts. As these microcracks continue to develop and aggregate, they will form local fracture events, which will then be recorded by acoustic emission sensors. These fracture events aggregate within a certain time and space range to form fracture signals that are aggregated from multiple individual signals. Due to the complexity of the internal structure of the rock and the unevenness of stress distribution, these aggregated fracture signals are often more representative and typical than single fracture signals. In this experiment, the fracture signals were intelligently classified using a convolutional neural network (CNN), which can more accurately identify and analyze these aggregated fracture signals. The CNN model can automatically extract multidimensional features from the signal and perform deep learning through a multi-layer neural network structure, thereby improving the accuracy and reliability of signal classification.

[0090] According to another aspect of the present invention, a system for inverting blasting damage range based on crack density and acoustic wave threshold is provided. The system is used to implement the method of any embodiment or a combination of multiple embodiments described above, including:

[0091] The first main control module is used to perform a quasi-dynamic loading test on the rock sample and monitor the fracture signal generated by the fracture of the rock sample during the loading process;

[0092] The second main control module is used to select a single signal from the rupture signal and draw a time domain curve of the signal amplitude; determine the duration of the single signal amplitude being greater than the threshold and the corresponding start and end time according to the threshold characteristics required for signal triggering, and determine the amplitude extreme value of the signal above the threshold within the above duration range, which is recorded as the first amplitude extreme value;

[0093] The third main control module is used to average the first amplitude extreme values, determine the average value corresponding to all the first amplitude extreme values, compare and analyze the magnitude relationship between all the first amplitude extreme values ​​and the average value, extract the extreme value of the first amplitude extreme value that is greater than the average value, and record it as the second amplitude extreme value;

[0094] The fourth main control module is used to sort all extreme value data points in time order and form a set according to the determined second amplitude extreme value, judge and determine the amplitude extreme value in the set by comparing and analyzing the size relationship between each data point in the set and the adjacent data points on both sides thereof, record it as the third amplitude extreme value, and sort the above extreme values ​​in time order;

[0095] A fifth main control module, used for calculating the time intervals between adjacent third amplitude extreme values ​​in sequence according to the set of third amplitude extreme values, and determining the time average value corresponding to the time intervals between all adjacent third amplitude extreme values;

[0096] The sixth main control module is used to compare and analyze the relationship between the time interval and the time average value according to the time interval between adjacent third amplitude extreme values ​​and the time average value, so as to judge the aggregation of the rupture signal;

[0097] The seventh main control module is used to classify different types of fracture signals, and then statistically analyze the number and proportion of different types of fracture signals, and evaluate the failure characteristics of rocks under quasi-dynamic loading based on this.

[0098] In addition, the method of the embodiment of the present invention is implemented by relying on electronic devices, so it is necessary to introduce the relevant electronic devices. Based on this purpose, the embodiment of the present invention provides an electronic device, such as Figure 3 As shown, the electronic device includes: at least one processor, a communication interface, at least one memory, and a communication bus, wherein at least one processor, the communication interface, and at least one memory communicate with each other through the communication bus. At least one processor can call the logic instructions in at least one memory to execute all or part of the steps of the method provided by the aforementioned various method embodiments.

[0099] Furthermore, the logic instructions in the at least one memory mentioned above can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each method embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0100] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0101] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0102] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for classifying rock aggregate fracture signals and evaluating damage characteristics, characterized in that: The following steps are involved: Step 1, performing a quasi-dynamic loading test on a rock sample, and monitoring a fracture signal generated by fracture of the rock sample during the loading process; Step 2: Select a single signal from the rupture signal and draw a time domain curve of the signal amplitude; determine the duration of the single signal amplitude greater than the threshold and the corresponding start and end time according to the threshold characteristics required for signal triggering; within the above duration range, determine the amplitude extreme value of the signal above the threshold, which is recorded as the first amplitude extreme value; Step 3, averaging the first amplitude extreme values ​​in step 2, determining the average value corresponding to all the first amplitude extreme values, comparing and analyzing the magnitude relationship between all the first amplitude extreme values ​​and the average value, extracting the extreme value whose first amplitude extreme value is greater than the average value, and recording it as the second amplitude extreme value; The calculation model of the amplitude average value and the second amplitude extreme value corresponding to the first amplitude extreme value includes: V j ∈{V2},(V j -V j-1 )(V j -V j+1 )>0 Where V mean is the average value corresponding to the first amplitude extreme value, V i is the amplitude extreme value of the i-th signal in the set {V1} composed of the first amplitude extreme value in step 2, n is the number of data points in the set {V1}, V j is the second amplitude extreme value, {V2} represents the set composed of the second amplitude extreme values, and j represents the jth amplitude data point in the set {V1} composed of the first amplitude extreme values ​​in step 2; Step 4: Based on the second amplitude extreme value determined in step 3, all extreme value data points are sorted in time order to form a set, and the amplitude extreme value in the set is judged and determined by comparing and analyzing the size relationship between each data point in the set and the adjacent data points on both sides thereof, recorded as the third amplitude extreme value, and the third amplitude extreme values ​​are sorted in time order; The calculation model of the third amplitude extreme value includes: V k ∈ {V3}, (V k -V k-1 ) > 0 and (V k -V k+1 ) > 0 Where V k is the third amplitude extreme value, {V3} represents the set composed of the third amplitude extreme value, and k represents the kth amplitude data point in the set {V2} composed of the second amplitude extreme value in step 3; Step 5, based on the set of the third amplitude extreme values, sequentially calculating the time intervals between adjacent third amplitude extreme values, and determining the time average value corresponding to the time intervals between all adjacent third amplitude extreme values; Step 6: According to the time interval between the third adjacent amplitude extreme values ​​and the time average value, the relationship between the time interval and the time average value is compared and analyzed to judge the aggregation of the rupture signal; according to the time interval between the third adjacent amplitude extreme values ​​and the time average value, the aggregation of the rupture signal is judged. If the time interval between the third adjacent amplitude extreme values ​​is greater than the time average value, it is considered that the third adjacent amplitude extreme values ​​correspond to two separate signals. By determining the number of separate signals in the rupture signal, the aggregation of the rupture signal is judged; Step 7: Repeat steps 2 to 6 to complete the classification of different types of fracture signals, and then statistically analyze the number and proportion of fracture signals of different categories, and evaluate the failure characteristics of rocks under quasi-dynamic loading based on this.

2. A method for classifying rock aggregate fracture signals and evaluating damage characteristics according to claim 1, characterized in that: In step 1, the strain rate corresponding to the quasi-dynamic load must be within 10 -2 ~10 0 s -1 within the range.

3. A method for classifying rock aggregate fracture signals and evaluating damage characteristics according to claim 1, characterized in that: In step 2, a single signal is selected from the recorded rupture signals for analysis, and a waveform diagram of the single signal in the time domain is drawn; The single signal includes: an acoustic emission signal; The calculation model of the first amplitude extreme value includes: V i ∈ {V1}, (V i -V i-1 ) > 0 and (V i -V i+1 ) > 0 Where V i is the first amplitude extreme value, {V1} represents the set of the first amplitude extreme values, and i represents the i-th amplitude data point in a single signal; where V i The time corresponding to the data point should be within the duration range.

4. A method for classifying rock aggregate fracture signals and evaluating damage characteristics according to claim 1, characterized in that: In step 5, the calculation model of the time average value includes: In the formula, t mean is the average time interval between adjacent amplitude extreme values, t i is the time corresponding to the i-th second amplitude extreme value in the set determined in step three, and m is the number of time intervals of the second amplitude extreme values ​​determined in step three.

5. The method for classifying rock aggregate fracture signals and evaluating damage characteristics according to claim 1 is characterized in that: Step six specifically includes the following steps: The failure characteristics of rocks under quasi-dynamic loading are evaluated by combining the number and proportion of different types of fracture signals. When the number and proportion of signals with lower aggregation in the fracture signals are high, the degree of rock damage under this loading condition is small and the severity is low. When the number and proportion of signals with higher aggregation in the fracture signals are high, the degree of rock damage in this case is large and the severity is high.

6. A rock aggregate fracture signal classification and damage characteristic evaluation system, characterized in that: include: The first main control module is used to perform a quasi-dynamic loading test on the rock sample and monitor the fracture signal generated by the fracture of the rock sample during the loading process; The second main control module is used to select a single signal from the rupture signal and draw a time domain curve of the signal amplitude; determine the duration of the single signal amplitude being greater than the threshold and the corresponding start and end time according to the threshold characteristics required for signal triggering, and determine the amplitude extreme value of the signal above the threshold within the above duration range, which is recorded as the first amplitude extreme value; The third main control module is used to average the first amplitude extreme values, determine the average value corresponding to all the first amplitude extreme values, compare and analyze the magnitude relationship between all the first amplitude extreme values ​​and the average value, extract the extreme value of the first amplitude extreme value that is greater than the average value, and record it as the second amplitude extreme value; The calculation model of the amplitude average value and the second amplitude extreme value corresponding to the first amplitude extreme value includes: V j ∈{V2},(V j -V j-1 )(V j -V j+1 )>0 Where V mean is the average value corresponding to the first amplitude extreme value, V i is the amplitude extreme value of the i-th signal in the set {V1} composed of the first amplitude extreme value in step 2, n is the number of data points in the set {V1}, V j is the second amplitude extreme value, {V2} represents the set composed of the second amplitude extreme values, and j represents the jth amplitude data point in the set {V1} composed of the first amplitude extreme values ​​in step 2; The fourth main control module is used to sort all extreme value data points in time order and form a set according to the determined second amplitude extreme value, judge and determine the amplitude extreme value in the set by comparing and analyzing the size relationship between each data point in the set and the adjacent data points on both sides thereof, record it as the third amplitude extreme value, and sort the third amplitude extreme values ​​in time order; The calculation model of the third amplitude extreme value includes: V k ∈ {V3}, (V k -V k-1 ) > 0 and (V k -V k+1 ) > 0 Where V k is the third amplitude extreme value, {V3} represents the set composed of the third amplitude extreme value, and k represents the kth amplitude data point in the set {V2} composed of the second amplitude extreme value in step 3; A fifth main control module, used for calculating the time intervals between adjacent third amplitude extreme values ​​in sequence according to the set of third amplitude extreme values, and determining the time average value corresponding to the time intervals between all adjacent third amplitude extreme values; The sixth main control module is used to compare and analyze the relationship between the time interval and the time average value according to the time interval between the adjacent third amplitude extreme values ​​and the time average value, so as to judge the aggregation of the rupture signal; the aggregation of the rupture signal is judged according to the time interval between the adjacent third amplitude extreme values ​​and the time average value. If the time interval between the adjacent third amplitude extreme values ​​is greater than the time average value, it is considered that the adjacent third amplitude extreme values ​​correspond to two separate signals, and the aggregation of the rupture signal is judged by determining the number of separate signals in the rupture signal; The seventh main control module is used to classify different types of fracture signals, and then statistically analyze the number and proportion of different types of fracture signals, and evaluate the failure characteristics of rocks under quasi-dynamic loading based on this.

7. An electronic device, characterized in that: include: At least one processor, at least one memory and a communication interface; wherein, The processor, memory and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for classifying rock aggregate fracture signals and evaluating damage characteristics as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute a method for classifying rock aggregate fracture signals and evaluating damage characteristics as described in any one of claims 1 to 5.

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