Rock uniaxial compression test data processing method, system, device and medium

Abstract

The application discloses a rock uniaxial compression test data processing method, system, device and medium, relates to the rock mechanics technical field, and includes the following steps: acquiring rock uniaxial compression test data collected through a rock mechanics testing machine, intercepting and processing the rock uniaxial compression test data according to a preset extreme value data intercepting method to obtain target test data; fitting the target test data to obtain a stress-strain curve after fitting, acquiring an instantaneous change rate of stress corresponding to strain in the stress-strain curve after fitting, and intercepting a target fitting curve whose instantaneous change rate meets a preset elastic stage data condition; and performing double linear regression analysis on the target fitting curve in axial strain-stress and radial strain-axial strain respectively to obtain an elastic modulus result and a Poisson's ratio result of the rock uniaxial compression test data. Through automatic processing of the flow, human error is avoided, and the efficiency, accuracy and reliability of rock uniaxial compression test data processing are improved.

Description

Technical Field

[0001] This invention relates to the field of rock mechanics, and in particular to methods, systems, equipment and media for processing data from uniaxial compression tests of rocks. Background Technology

[0002] Uniaxial compression testing of rocks is a core experimental method for evaluating the mechanical properties of rocks, widely used in geological engineering, mining engineering, and civil engineering to determine key parameters such as uniaxial compressive strength, elastic modulus, and Poisson's ratio. Traditional data processing methods mainly include the following steps: collecting axial stress, axial strain, and radial strain data of rock samples using a testing machine; manually extracting the elastic phase (usually an approximately linear segment) of the stress-strain curve; and performing a single linear regression analysis on the extracted data to calculate the elastic modulus and Poisson's ratio. However, the above methods have significant drawbacks in practical applications:

[0003] Manual interception is highly subjective: the boundary of the elastic stage depends on the operator's experience and judgment. Different people or multiple operations by the same person are prone to deviation, resulting in large dispersion of parameter calculation results (error can reach 10%-15%).

[0004] Fixed threshold has poor adaptability: Existing automated algorithms mostly use fixed strain thresholds, such as 0.05%-0.2% strain to cut off the elastic stage. However, rock types are diverse, such as brittle granite and ductile shale, whose stress-strain curves have significantly different shapes. Fixed thresholds are difficult to dynamically adapt to the complex changes in the nonlinear transition section.

[0005] High noise sensitivity: Experimental data are often subject to noise due to interference such as sensor drift and environmental vibration. Traditional single linear regression or low-order polynomial fitting methods have weak anti-interference ability, and the calculation results are significantly biased under low signal-to-noise ratio (SNR<15dB). Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a method, system, equipment, and medium for processing uniaxial compression test data of rocks. Through automated process processing, human error is avoided, thereby improving the efficiency, accuracy, and reliability of uniaxial compression test data processing. The specific solution is as follows:

[0007] In a first aspect, this application discloses a method for processing data from a uniaxial compression test of rock, including:

[0008] Obtain uniaxial compression test data of rock collected by a rock mechanics testing machine, and process the uniaxial compression test data of rock according to a preset extreme value data truncation method to obtain target test data;

[0009] The target test data is fitted to obtain a fitted stress-strain curve, and the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve is obtained. The target fitted curve that satisfies the preset elastic stage data conditions is then selected.

[0010] The target fitted curves were subjected to dual linear regression analysis of axial strain-stress and radial strain-axial strain to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock.

[0011] Optionally, acquiring uniaxial compression test data of rock collected by a rock mechanics testing machine includes:

[0012] The axial stress, axial strain, and radial strain data of rock samples are collected in real time by the sensors of the rock mechanics testing machine to obtain uniaxial compression test data of the rock.

[0013] Optionally, the step of processing the uniaxial compression test data of the rock according to a preset extreme value data truncation method to obtain the target test data includes:

[0014] Search for the minimum data value in the uniaxial compression test data of the rock, and use the minimum data value as the starting point for selection. Then, search for the maximum data value in the uniaxial compression test data of the rock in chronological order, and use the maximum data value as the ending point for selection.

[0015] The uniaxial compression test data of the rock located between the starting point and the ending point of the interception are intercepted to obtain the target test data.

[0016] Optionally, the fitting process of the target test data to obtain the fitted stress-strain curve includes:

[0017] The target experimental data are fitted with a low-order polynomial to obtain an initial fitting curve;

[0018] Based on the change in the curvature index of the initial fitted curve, candidate order polynomials are selected;

[0019] The target order polynomial of the initial fitting curve is determined from the candidate order polynomials using cross-validation.

[0020] The target experimental data are subjected to polynomial fitting using the target order polynomial to obtain the fitted stress-strain curve.

[0021] Optionally, the step of extracting the target fitting curve whose instantaneous rate of change satisfies the preset elastic stage data conditions includes:

[0022] Several local peaks of the fitted stress-strain curve are obtained by a sliding window, and each local peak is taken as the maximum peak in the corresponding elastic stage interval. The target threshold of the corresponding elastic stage interval is set based on each maximum peak.

[0023] From each elastic stage interval, target fitted data whose instantaneous rate of change of the fitted data in the fitted stress-strain curve is greater than or equal to the target threshold of the corresponding elastic stage interval are selected to obtain the target fitted curve for each elastic stage interval.

[0024] Accordingly, the bilinear regression analysis of axial strain-stress and radial strain-axial strain is performed on the target fitted curve to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock, including:

[0025] Linear regression analysis was performed on the axial strain-stress data and radial strain-axial strain data in the target fitting curves of each elastic stage interval to obtain the elastic modulus and Poisson's ratio results of each elastic stage interval of the uniaxial compression test data of the rock.

[0026] Optionally, the method for processing uniaxial compression test data of rocks further includes:

[0027] The stress fit goodness of the fitted stress-strain curve is calculated based on the true stress value of the target test data and the corresponding fitted stress value on the fitted stress-strain curve to obtain the fitting coefficient.

[0028] When the fitting coefficient is less than a preset fitting coefficient threshold, abnormal data of the fitted stress-strain curve are marked and a visualization report is generated, so as to perform the step of obtaining rock uniaxial compression test data collected by the rock mechanics testing machine based on the visualization report.

[0029] Optionally, after obtaining the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock, the method further includes:

[0030] The elastic modulus result is fed back to the testing machine control system in real time, so that the testing machine control system can control the rock mechanics testing machine to dynamically adjust the loading rate during the test process based on the elastic modulus result.

[0031] Secondly, this application discloses a data processing system for uniaxial compression tests of rocks, including a data acquisition module, a dynamic fitting module, an elastic stage extraction module, a parameter calculation module, and a verification and encapsulation module, wherein...

[0032] The data acquisition module is connected to the stress sensor and strain sensor of the rock mechanics testing machine. It is used to acquire the uniaxial compression test data of the rock collected by the rock mechanics testing machine in real time, and to process the uniaxial compression test data of the rock according to the preset extreme value data interception method to obtain the target test data.

[0033] The dynamic fitting module is used to fit the target test data to obtain the fitted stress-strain curve and to obtain the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve.

[0034] The elastic phase interception module is used to intercept the target fitting curve whose instantaneous rate of change meets the preset elastic phase data conditions.

[0035] The parameter calculation module is used to perform dual linear regression analysis on the target fitting curve for axial strain-stress and radial strain-axial strain, respectively, to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock.

[0036] The verification and encapsulation module is used to calculate the stress fit goodness of the fitted stress-strain curve based on the actual stress value of the target test data and the corresponding fitted stress value on the fitted stress-strain curve, so as to obtain the fitting coefficient; when the fitting coefficient is less than the preset fitting coefficient threshold, the abnormal data of the fitted stress-strain curve is marked and a visualization report is generated.

[0037] Thirdly, this application discloses an electronic device, including:

[0038] Memory, used to store computer programs;

[0039] A processor is used to execute the computer program to implement the steps of the aforementioned disclosed method for processing data from uniaxial compression tests of rocks.

[0040] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed method for processing data from a uniaxial compression test of rocks.

[0041] As can be seen, this application discloses a method for processing uniaxial compression test data of rock, including: acquiring uniaxial compression test data of rock collected by a rock mechanics testing machine, and truncating the uniaxial compression test data of rock according to a preset extreme value data truncation method to obtain target test data; fitting the target test data to obtain a fitted stress-strain curve, and obtaining the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve, and truncating the target fitted curve whose instantaneous rate of change meets the preset elastic stage data conditions; performing dual linear regression analysis on the target fitted curve for axial strain-stress and radial strain-axial strain respectively to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of rock. Therefore, by automatically selecting target test data through the preset extreme value data truncation method, the subjectivity of manual truncating of the elastic stage is avoided, and the discrete errors caused by manual operation are eliminated. The target fitted curve is extracted based on the instantaneous rate of change of the fitted curve. The boundary of the elastic stage is dynamically adjusted, and then the axial and radial strain data on the target fitted curve are independently fitted. It can adapt to the diverse curves of brittle rocks (significant linearity) and ductile rocks (significant nonlinearity), with a wider range of rock types and the instantaneous rate of change retains the true mechanical characteristics. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0043] Figure 1 This is a flowchart of a method for processing uniaxial compression test data of rock disclosed in this application;

[0044] Figure 2 This is a schematic diagram illustrating the results of acquiring target experimental data as disclosed in this application;

[0045] Figure 3 This is a schematic diagram of the result after fitting target experimental data as disclosed in this application;

[0046] Figure 4 This is a schematic diagram of the target fitting curve truncation result disclosed in this application;

[0047] Figure 5 This is a schematic diagram of the elastic modulus result of a target fitting curve disclosed in this application;

[0048] Figure 6 This is a schematic diagram of the Poisson's ratio result of a target fitting curve disclosed in this application;

[0049] Figure 7 This is a schematic diagram of the display result of invalid data marking disclosed in this application;

[0050] Figure 8 This is a schematic diagram of the structure of a rock uniaxial compression test data processing system disclosed in this application;

[0051] Figure 9 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0053] Uniaxial compression testing of rocks is a core experimental method for evaluating the mechanical properties of rocks, widely used in geological engineering, mining engineering, and civil engineering to determine key parameters such as uniaxial compressive strength, elastic modulus, and Poisson's ratio. Traditional data processing methods mainly include the following steps: collecting axial stress, axial strain, and radial strain data of rock samples using a testing machine; manually extracting the elastic phase (usually an approximately linear segment) of the stress-strain curve; and performing a single linear regression analysis on the extracted data to calculate the elastic modulus and Poisson's ratio. However, the above methods have significant drawbacks in practical applications:

[0054] Manual interception is highly subjective: the boundary of the elastic stage depends on the operator's experience and judgment. Different people or multiple operations by the same person are prone to deviation, resulting in large dispersion of parameter calculation results (error can reach 10%-15%).

[0055] Fixed threshold has poor adaptability: Existing automated algorithms mostly use fixed strain thresholds, such as 0.05%-0.2% strain to cut off the elastic stage. However, rock types are diverse, such as brittle granite and ductile shale, whose stress-strain curves have significantly different shapes. Fixed thresholds are difficult to dynamically adapt to the complex changes in the nonlinear transition section.

[0056] High noise sensitivity: Experimental data are often subject to noise due to interference such as sensor drift and environmental vibration. Traditional single linear regression or low-order polynomial fitting methods have weak anti-interference ability, and the calculation results are significantly biased under low signal-to-noise ratio (SNR<15dB).

[0057] Therefore, this invention provides a data processing scheme for uniaxial compression tests of rocks, which avoids human error and improves the efficiency, accuracy and reliability of data processing for uniaxial compression tests of rocks through automated processing.

[0058] Reference Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for processing uniaxial compression test data of rock, including:

[0059] Step S11: Obtain the uniaxial compression test data of rock collected by the rock mechanics testing machine, and process the uniaxial compression test data of rock according to the preset extreme value data interception method to obtain the target test data.

[0060] In this embodiment, the axial stress, axial strain, and radial strain data of the rock sample are collected in real time by sensors of a rock mechanics testing machine to obtain uniaxial compression test data. It is understood that stress sensors and strain sensors are installed on the rock mechanics testing machine. After the rock mechanics testing machine performs a uniaxial compression test on the rock, the axial stress, axial strain, and radial strain data collected by the stress sensors and strain sensors are acquired in real time as the uniaxial compression test data. The uniaxial compression test is conducted under laboratory conditions by applying gradually increasing pressure along a single axial direction (usually vertical) to a cylindrical or cubic rock sample until the rock sample fails. The above-mentioned uniaxial compression test data can be obtained through this test.

[0061] In this embodiment, the minimum data value in the uniaxial compression test data of the rock is searched, and the minimum data value is used as the starting point for truncation. The maximum data value in the uniaxial compression test data of the rock is then searched in chronological order, and the maximum data value is used as the ending point for truncation. The uniaxial compression test data of the rock located between the starting point and the ending point is then truncated to obtain the target test data. It is understood that since only elastic stage data is needed to calculate the elastic modulus and Poisson's ratio, and the elastic stage data only includes the portion before the peak strength, when truncating the uniaxial compression test data of the rock, the maximum and minimum values ​​of the entire uniaxial compression test data sequence are first found. Then, the search begins according to the data index. The search begins when the minimum value is found and ends when the maximum value is found. The truncated segment of data is the target test data. Figure 2 As shown, red dots represent uniaxial compression test data of rocks, and blue dots represent target test data. Figure 2 It is known that the target test data to be extracted are all uniaxial compression test data of rocks before the peak strength is reached. In this way, data on the elastic stage can be obtained.

[0062] Step S12: Fit the target test data to obtain the fitted stress-strain curve, and obtain the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve, and extract the target fitted curve whose instantaneous rate of change meets the preset elastic stage data conditions.

[0063] In one specific implementation, a third-order polynomial is directly used to fit the target experimental data to obtain the fitted stress-strain curve. The formula for the third-order polynomial fitting is as follows:

[0064] ;

[0065] in, This represents axial stress, with units of MPa. Represents axial strain, dimensionless. , , , This represents the polynomial coefficients determined by fitting using the least squares method.

[0066] In another specific implementation, the target experimental data is fitted with a low-order polynomial to obtain an initial fitted curve; candidate order polynomials are selected based on the curvature index change of the initial fitted curve; a target order polynomial for the initial fitted curve is determined from the candidate order polynomials using cross-validation; and the target order polynomial is used to perform polynomial fitting on the target experimental data to obtain a fitted stress-strain curve. It is understood that the target experimental data is analyzed using second derivatives to obtain the initial fitted curve. Based on the curvature change of the initial fitted curve, the target order polynomial is dynamically determined from candidate order polynomials of orders 2 to 5. If the curvature index change is greater than a preset curvature index threshold, a 4th or 5th order polynomial is used for fitting; otherwise, a 2nd or 3rd order polynomial is used. Furthermore, a cross-validation method, such as leave-one-out, is used to determine the optimal order to avoid overfitting. In this way, by determining the target order polynomial as described above, the appropriate target order polynomial can be selected for fitting the target experimental data under different scenarios, based on the specific scenario. For example, a low-order polynomial is used for brittle rocks (significant linearity) to reduce computational load; while for ductile rocks (significant nonlinearity), the fitting is automatically upgraded to a higher order to improve the curve representation capability. Polynomial fitting is then performed on the target experimental data based on the selected target order polynomial to obtain the fitted stress-strain curve.

[0067] like Figure 3 As shown, Figure 3 The image shows the fitted stress-strain curve obtained by polynomial fitting based on the target experimental data obtained from the intercepted data. In the image, the blue line represents the target experimental data, and the red line represents the fitted stress-strain curve.

[0068] In one specific implementation, the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve is calculated using the first derivative, wherein the formula for calculating the instantaneous rate of change is as follows:

[0069] ;

[0070] in, It represents the instantaneous rate of change of stress with respect to strain.

[0071] Furthermore, the target fitting curve is selected based on the instantaneous rate of change satisfying the preset elastic stage data conditions, where the preset elastic stage data conditions are:

[0072] ;

[0073] As can be seen, by filtering the data segment where the instantaneous rate of change decays to 70% of its peak, the target fitted curve can be obtained. Figure 4 As shown, the blue part represents the data that was removed, and the green part represents the target fitted curve.

[0074] In another specific implementation, several local peaks of the fitted stress-strain curve are obtained through a sliding window, and each local peak is taken as the maximum peak in the corresponding elastic stage interval. A target threshold for the corresponding elastic stage interval is set based on each maximum peak. Target fitted data with an instantaneous rate of change greater than or equal to the target threshold of the corresponding elastic stage interval are selected from each elastic stage interval to obtain the target fitted curve for each elastic stage interval. It is understood that, for complex rocks (such as layered rock masses) that may have multiple elastic stages, the derivative threshold method is improved. Specifically, multiple local peaks of the derivative curve are identified through a sliding window, and the fitted stress-strain curve is divided into multiple elastic intervals based on each local peak. Linear regression is performed on each elastic interval to calculate the equivalent elastic modulus range. This more accurately reflects the mechanical properties of rock anisotropy or layered structures; the elastic modulus interval values ​​are output, providing a basis for calculating engineering safety factors.

[0075] Step S13: Perform dual linear regression analysis on the target fitted curve for axial strain-stress and radial strain-axial strain respectively to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock.

[0076] In this embodiment, linear regression analysis is performed on the axial strain-stress data and radial strain-axial strain data in the target fitting curves of each elastic stage interval to obtain the elastic modulus and Poisson's ratio results for each elastic stage interval of the uniaxial compression test data of the rock. It can be understood that the formula for calculating the elastic modulus is:

[0077] ;

[0078] in: This represents the elastic modulus result, in GPa. This represents the axial stress increment, in MPa, and is calculated as the stress difference between the first and last ends of a linearly fitted segment. This represents the axial strain increment, dimensionless, and is taken as the strain difference between the first and last segments of a linearly fitted section. For example... Figure 5 As shown, the blue part represents the curve data of the target fitted curve, and the red part represents the fitting result of the curve data. The elastic modulus result is 39.06 GPa.

[0079] The formula for calculating Poisson's ratio is as follows:

[0080] ;

[0081] in, This indicates the Poisson's ratio result. This represents the radial strain increment of the linear fit, and is dimensionless. For example... Figure 6 As shown, the blue part represents the curve data of the target fitted curve, and the red part represents the fitting result of the curve data, with a Poisson ratio of 0.1.

[0082] In this embodiment, the stress fit goodness of the fitted stress-strain curve is calculated based on the true stress value of the target test data and the corresponding fitted stress value on the fitted stress-strain curve to obtain the fitting coefficient. When the fitting coefficient is less than a preset fitting coefficient threshold, abnormal data of the fitted stress-strain curve is marked and a visualization report is generated. Based on the visualization report, the step of obtaining uniaxial compression test data of rock collected by a rock mechanics testing machine is performed. It can be understood that the formula for calculating the fitting coefficient is as follows:

[0083] ;

[0084] in, This represents the fitting coefficient, and i represents the index number of the data point. The value range is [0,1]. This represents the actual stress value, in MPa. This represents the fitted stress value on the fitted curve, in MPa. This represents the actual average stress measured in MPa.

[0085] If the fitting coefficient is less than 0.8 (the preset fitting coefficient threshold), it indicates an anomaly in the current fitting process, meaning the current data is invalid. In this case, a semi-transparent red stamp is added to the visualization report to cover the abnormal curve range. Figure 7 The image shows an invalid result in the Poisson's ratio calculation. Then, jump back to the step of obtaining uniaxial compression test data of rock collected by a rock mechanics testing machine, until... Stop if the value is ≥0.8 or the number of iterations exceeds 3.

[0086] In this embodiment, after obtaining the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock, the method further includes: feeding the elastic modulus results back to the testing machine control system in real time, so that the testing machine control system can dynamically adjust the loading rate of the rock mechanics testing machine according to the elastic modulus results. It can be understood that feeding the calculation results (elastic modulus and Poisson's ratio results) back to the testing machine control system in real time allows the control system to dynamically adjust the loading rate according to the initial value of the elastic modulus. For example, for high-modulus rocks, the loading rate is reduced. For instance, if the elastic modulus calculation result is fed back to the testing machine in real time and the E value exceeds a preset threshold, the loading rate is automatically adjusted to 50%-80% of the original rate. If abnormal data is detected, the test is automatically paused and a prompt is made to check the sample or sensor. This avoids unnatural damage to the sample due to improper loading rate; the measured failure rate is reduced by 40%, and the test success rate is improved.

[0087] As can be seen, this application discloses a method for processing uniaxial compression test data of rock, including: acquiring uniaxial compression test data of rock collected by a rock mechanics testing machine, and truncating the uniaxial compression test data of rock according to a preset extreme value data truncation method to obtain target test data; fitting the target test data to obtain a fitted stress-strain curve, and obtaining the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve, and truncating the target fitted curve whose instantaneous rate of change meets the preset elastic stage data conditions; performing dual linear regression analysis on the target fitted curve for axial strain-stress and radial strain-axial strain respectively to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of rock. Therefore, by automatically selecting target test data through the preset extreme value data truncation method, the subjectivity of manual truncating of the elastic stage is avoided, and the discrete errors caused by manual operation are eliminated. The target fitted curve is extracted based on the instantaneous rate of change of the fitted curve. The boundary of the elastic stage is dynamically adjusted, and then the axial and radial strain data on the target fitted curve are independently fitted. It can adapt to the diverse curves of brittle rocks (significant linearity) and ductile rocks (significant nonlinearity), with a wider range of rock types and the instantaneous rate of change retains the true mechanical characteristics.

[0088] Reference Figure 8 As shown, the present invention also discloses a data processing system for uniaxial compression tests of rocks, including a data acquisition module 11, a dynamic fitting module 12, an elastic stage extraction module 13, a parameter calculation module 14, and a verification and encapsulation module 15, wherein,

[0089] The data acquisition module 11 is connected to the stress sensor and strain sensor of the rock mechanics testing machine. It is used to acquire the uniaxial compression test data of the rock collected by the rock mechanics testing machine in real time, and to process the uniaxial compression test data of the rock according to the preset extreme value data interception method to obtain the target test data.

[0090] The dynamic fitting module 12 is used to perform fitting processing on the target test data to obtain a fitted stress-strain curve and to obtain the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve.

[0091] The elastic phase interception module 13 is used to intercept the target fitting curve whose instantaneous rate of change meets the preset elastic phase data conditions.

[0092] The parameter calculation module 14 is used to perform dual linear regression analysis on the target fitting curve for axial strain-stress and radial strain-axial strain, respectively, to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock.

[0093] The verification and encapsulation module 15 is used to calculate the stress fit goodness of the fitted stress-strain curve based on the actual stress value of the target test data and the corresponding fitted stress value on the fitted stress-strain curve, so as to obtain the fitting coefficient; when the fitting coefficient is less than the preset fitting coefficient threshold, the abnormal data of the fitted stress-strain curve is marked and a visualization report is generated.

[0094] As can be seen, this application discloses a method for acquiring uniaxial compression test data of rock collected by a rock mechanics testing machine, and processing the uniaxial compression test data of rock according to a preset extreme value data truncation method to obtain target test data; fitting the target test data to obtain a fitted stress-strain curve, and obtaining the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve, and truncating the target fitted curve whose instantaneous rate of change meets the preset elastic stage data conditions; performing dual linear regression analysis on the target fitted curve for axial strain-stress and radial strain-axial strain respectively to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of rock. Therefore, by automatically selecting target test data through the preset extreme value data truncation method, the subjectivity of manual truncation of the elastic stage is avoided, and the discrete errors caused by manual operation are eliminated. The target fitted curve is extracted based on the instantaneous rate of change of the fitted curve. The boundary of the elastic stage is dynamically adjusted, and then the axial and radial strain data on the target fitted curve are independently fitted. It can adapt to the diverse curves of brittle rocks (significant linearity) and ductile rocks (significant nonlinearity), with a wider range of rock types and the instantaneous rate of change retains the true mechanical characteristics.

[0095] Furthermore, embodiments of this application also disclose an electronic device, Figure 9 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0096] Figure 9 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the rock uniaxial compression test data processing method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0097] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0098] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0099] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0100] The operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device 20 to enable the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22. It can be Windows Server, Netware, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the rock uniaxial compression test data processing method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the electronic device from external devices, as well as data collected by its own input / output interface 25.

[0101] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for processing uniaxial compression test data of rocks. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0103] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly in hardware, software modules executed by a processor, or a combination of both. The software module may be located in random access memory (RAM), memory, read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, CD-ROMs (Compact Disc-Read Only Memory), or any other form of storage medium known in the art.

[0104] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0105] The solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for processing data from a uniaxial compression test of rock, characterized in that, include: Obtain uniaxial compression test data of rock collected by a rock mechanics testing machine, and process the uniaxial compression test data of rock according to a preset extreme value data truncation method to obtain target test data; The target test data is fitted to obtain a fitted stress-strain curve, and the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve is obtained. The target fitted curve that satisfies the preset elastic stage data conditions is then selected. The target fitted curves were subjected to dual linear regression analysis of axial strain-stress and radial strain-axial strain to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock. The process of fitting the target test data to obtain the fitted stress-strain curve includes: The target experimental data are fitted with a low-order polynomial to obtain an initial fitting curve; Based on the change in the curvature index of the initial fitted curve, candidate order polynomials are selected; The target order polynomial of the initial fitting curve is determined from the candidate order polynomials using cross-validation. The target experimental data are subjected to polynomial fitting using the target order polynomial to obtain the fitted stress-strain curve; The target fitting curve for which the instantaneous rate of change satisfies the preset elastic stage data conditions includes: Several local peaks of the fitted stress-strain curve are obtained by a sliding window, and each local peak is taken as the maximum peak in the corresponding elastic stage interval. The target threshold of the corresponding elastic stage interval is set based on each maximum peak. From each elastic stage interval, target fitted data whose instantaneous rate of change of the fitted data in the fitted stress-strain curve is greater than or equal to the target threshold of the corresponding elastic stage interval are selected to obtain the target fitted curve for each elastic stage interval. Accordingly, the bilinear regression analysis of axial strain-stress and radial strain-axial strain is performed on the target fitted curve to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock, including: Linear regression analysis was performed on the axial strain-stress data and radial strain-axial strain data in the target fitting curves of each elastic stage interval to obtain the elastic modulus and Poisson's ratio results of each elastic stage interval of the uniaxial compression test data of the rock.

2. The method for processing uniaxial compression test data of rock according to claim 1, characterized in that, The acquisition of uniaxial compression test data of rock collected by a rock mechanics testing machine includes: The axial stress, axial strain, and radial strain data of rock samples are collected in real time by the sensors of the rock mechanics testing machine to obtain uniaxial compression test data of the rock.

3. The method for processing uniaxial compression test data of rock according to claim 1, characterized in that, The step of processing the uniaxial compression test data of the rock according to the preset extreme value data truncation method to obtain the target test data includes: Search for the minimum data value in the uniaxial compression test data of the rock, and use the minimum data value as the starting point for selection. Then, search for the maximum data value in the uniaxial compression test data of the rock in chronological order, and use the maximum data value as the ending point for selection. The uniaxial compression test data of the rock located between the starting point and the ending point of the interception are intercepted to obtain the target test data.

4. The method for processing uniaxial compression test data of rock according to any one of claims 1 to 3, characterized in that, Also includes: The stress fit goodness of the fitted stress-strain curve is calculated based on the true stress value of the target test data and the corresponding fitted stress value on the fitted stress-strain curve to obtain the fitting coefficient. When the fitting coefficient is less than a preset fitting coefficient threshold, abnormal data of the fitted stress-strain curve are marked and a visualization report is generated, so as to perform the step of obtaining rock uniaxial compression test data collected by the rock mechanics testing machine based on the visualization report.

5. The method for processing uniaxial compression test data of rock according to claim 1, characterized in that, After obtaining the elastic modulus and Poisson's ratio results from the uniaxial compression test data of the rock, the method further includes: The elastic modulus result is fed back to the testing machine control system in real time, so that the testing machine control system can control the rock mechanics testing machine to dynamically adjust the loading rate during the test process based on the elastic modulus result.

6. A data processing system for uniaxial compression tests of rocks, characterized in that, It includes a data acquisition module, a dynamic fitting module, an elastic phase interception module, a parameter calculation module, and a verification and encapsulation module. The data acquisition module is connected to the stress sensor and strain sensor of the rock mechanics testing machine. It is used to acquire the uniaxial compression test data of the rock collected by the rock mechanics testing machine in real time, and to process the uniaxial compression test data of the rock according to the preset extreme value data interception method to obtain the target test data. The dynamic fitting module is used to fit the target test data to obtain the fitted stress-strain curve and to obtain the instantaneous rate of change of stress with respect to strain in the fitted stress-strain curve. The elastic phase interception module is used to intercept the target fitting curve whose instantaneous rate of change meets the preset elastic phase data conditions. The parameter calculation module is used to perform dual linear regression analysis on the target fitting curve for axial strain-stress and radial strain-axial strain, respectively, to obtain the elastic modulus and Poisson's ratio results of the uniaxial compression test data of the rock. The verification and encapsulation module is used to calculate the stress fit goodness of the fitted stress-strain curve based on the actual stress value of the target test data and the corresponding fitted stress value on the fitted stress-strain curve, so as to obtain the fitting coefficient; when the fitting coefficient is less than the preset fitting coefficient threshold, the abnormal data of the fitted stress-strain curve is marked and a visualization report is generated. The dynamic fitting module is specifically used to perform low-order polynomial fitting on the target experimental data to obtain an initial fitting curve; select candidate order polynomials based on the curvature index change of the initial fitting curve; determine the target order polynomial of the initial fitting curve from the candidate order polynomials using cross-validation; and perform polynomial fitting on the target experimental data using the target order polynomial to obtain the fitted stress-strain curve. The elastic stage interception module is specifically used to obtain several local peaks of the fitted stress-strain curve through a sliding window, and take each local peak as the maximum peak in the corresponding elastic stage interval, and set the target threshold of the corresponding elastic stage interval based on each maximum peak; and select target fitted data from each elastic stage interval whose instantaneous rate of change of the fitted data in the fitted stress-strain curve is greater than or equal to the target threshold of the corresponding elastic stage interval, so as to obtain the target fitted curve of each elastic stage interval. The parameter calculation module is specifically used to perform linear regression analysis on the axial strain-stress data and radial strain-axial strain data in the target fitting curves of each elastic stage interval, so as to obtain the elastic modulus and Poisson's ratio results of each elastic stage interval of the rock uniaxial compression test data.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the rock uniaxial compression test data processing method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the steps of the rock uniaxial compression test data processing method as described in any one of claims 1 to 5.

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