A deep rock mass explosion wave field monitoring system based on mechanical luminescence sensing and an elastic-plastic zone boundary identification method

Abstract

This invention discloses a deep rock mass explosion wave field monitoring system and an elasto-plastic zone boundary identification method based on mechatronic light sensing. By employing a mechatronic light sensing unit with force-optical nonlinear mapping characteristics, combined with multi-band light source synchronous acquisition and high-transmittance confining pressure simulation technology, non-contact, full-field, three-dimensional visualization monitoring of the explosion wave field under deep high ground stress environment is achieved. Utilizing the intrinsic characteristics of the sensing unit—reversible optical response in the elastic deformation stage and irreversible abrupt change in optical response in the plastic deformation stage—a dual-criteria coupling method of energy dissipation characteristic parameters and deformation recoverability characteristic parameters is introduced to achieve accurate quantitative identification of the boundary between the plastic and elastic zones of the rock mass under explosion load. This overcomes the technical shortcomings of traditional electrical measurement methods, such as damaging the integrity of the rock mass, the inability of point measurement to obtain continuous spatial distribution, and the difficulty in intuitively defining the elasto-plastic boundary. It provides reliable technical support for deep rock mass stability evaluation and optimization of blasting rock breaking efficiency.

Description

Technical Field

[0001] This invention belongs to the field of geotechnical engineering and underground engineering safety monitoring technology, specifically relating to a deep rock mass explosion wave field monitoring system based on mechatronic light sensing and a method for identifying the boundary of the elastoplastic zone. Background Technology

[0002] With the deepening of deep resource extraction and underground space development, drill-and-blast method remains the main construction method for rock breaking and tunnel excavation. In deep, high-stress environments, the propagation law and attenuation mechanism of shock waves induced by explosive loads in the rock mass directly affect the stability control of the surrounding rock and the efficiency of blasting. Therefore, achieving refined monitoring of the explosive wave field in deep rock masses is of significant engineering importance for revealing the dynamic response mechanism of deep rocks.

[0003] Currently, monitoring of explosive wave fields mainly relies on electrical measurement methods, which involve pre-embedding resistance strain gauges or piezoelectric sensors on or inside the rock mass. However, this technology requires the pre-installation of sensors and their wiring, which can damage the integrity of the rock mass, leading to local stress field distortion and reduced measurement accuracy. Furthermore, point-based measurement methods cannot obtain continuous spatial distribution information of the wave field, making it difficult to capture the dynamic evolution of stress wave propagation. When multiple points are deployed, the signal synchronization and data acquisition system becomes highly complex, and there are frequency response limitations in high-strain-rate dynamic measurements.

[0004] In recent years, non-contact optical mechanical measurement techniques such as digital image correlation (DIC) have been applied, but they are limited to surface deformation observation and cannot directly obtain internal stress wave field information. In addition, deep rock mass experiments usually require the application of confining pressure in a fluid-filled pressure chamber. Conventional hydraulic oil has absorption and refraction effects on light, which seriously interferes with the accuracy of optical measurements.

[0005] Mechatronic light (MRL) materials are a class of functional materials that can convert mechanical stimuli into light signals, and their luminescence intensity has a quantitative correlation with the applied stress. However, there is currently no complete technical solution for systematically applying this type of material to deep rock mass explosion wave field monitoring and elastoplastic boundary identification.

[0006] Therefore, in order to address the aforementioned technical deficiencies, there is an urgent need to design and develop a deep rock mass explosion wave field monitoring system and an elastoplastic zone boundary identification method based on mechatronic light sensing. Summary of the Invention

[0007] To overcome the shortcomings and difficulties of the existing technology, the present invention aims to provide a deep rock mass explosion wave field monitoring system and an elastic-plastic zone boundary identification method based on mechatronic light sensing, so as to realize the visual monitoring of the explosion shock wave propagation process and quantitatively define the spatial boundary between the plastic zone and the elastic zone.

[0008] The first objective of this invention is to provide a deep rock mass explosion wave field monitoring system based on mechatronic light sensing; the second objective of this invention is to provide a method for identifying the boundary of an elastoplastic zone; the third objective of this invention is to provide a platform for identifying the boundary of an elastoplastic zone; and the fourth objective of this invention is to provide a computer-readable storage medium.

[0009] The first objective of this invention is achieved as follows: the system includes a sensing unit made of mechanoluminescent material, which is deployed on the rock mass to be measured and generates an optical response signal that is nonlinearly mapped to the stress in response to an explosive shock wave; and an optical acquisition unit consisting of at least a first illumination module, a second illumination module, and an image acquisition module, wherein the first illumination module is used to excite the sensing unit to generate the optical response signal, the second illumination module is used to provide illumination required for observing the deformation of the rock mass surface, and the image acquisition module is used to simultaneously acquire the optical response signal and the rock mass surface deformation image;

[0010] The system further includes an environmental simulation unit for applying hydrostatic pressure to the rock mass under test, allowing light emitted by the first and second lighting modules to pass through; a signal processing unit for calculating the displacement and strain fields of the rock mass based on the surface deformation image of the rock mass, and inverting the dynamic stress field of the rock mass based on the optical response signal; and a boundary identification unit for calculating a first characteristic parameter characterizing the energy dissipation of the rock mass during loading and unloading cycles and a second characteristic parameter characterizing the degree of deformation recovery of the rock mass based on the displacement field, strain field, and dynamic stress field, and defining the spatial boundary between the plastic zone and the elastic zone according to the first characteristic parameter and the second characteristic parameter.

[0011] The second objective of this invention is achieved as follows: the method is applied to the aforementioned deep rock mass explosion wave field monitoring system based on mechatronics sensing; the method includes the following steps:

[0012] The initial calibration process and the sensor unit deployed on the rock mass to be measured generate and acquire the corresponding first data; wherein, the first data are the reference optical parameters and the reference elastic dissipation parameters; specifically, the reference optical parameters of the sensor unit under no-load conditions and the reference dissipation characteristic parameters within the elastic deformation range.

[0013] Based on the applied explosive load, second data corresponding to the sensing unit is generated; wherein, the second data is an optical response signal and a rock surface deformation image;

[0014] Based on the second data, corresponding third data is extracted and generated, and inversion processing is performed to generate corresponding fourth data; wherein, the third data is the peak optical parameters during the loading process and the residual optical parameters after unloading; the fourth data is the stress-strain relationship curve of the entire loading and unloading process;

[0015] A fifth set of data corresponding to the fourth set of data is created, and a sixth set of data corresponding to the third set of data is calculated and generated; wherein, the fifth set of data is a dissipative characteristic value; and the sixth set of data is a recoverable characteristic value.

[0016] Based on the five data points and combined with the first data, corresponding first comparison data is generated.

[0017] A seventh set of data corresponding to the sixth set of data is generated. Based on the seventh set of data and combined with the sixth set of data, a second set of comparison data is generated. The seventh set of data is a preset discrimination threshold characterizing the critical point between elastic deformation and plastic deformation.

[0018] Based on the first comparison data and the second comparison data, an eighth set of data corresponding to each region of the rock mass is generated, and the boundaries of the corresponding plastic and elastic zones are constructed according to the eighth set of data; wherein, the eighth set of data is an elastic-plastic property.

[0019] The third objective of this invention is achieved as follows: it includes a processor, a memory, and a control program for a platform based on an elastic-plastic region boundary recognition; wherein, the processor executes the control program for the elastic-plastic region boundary recognition, the control program for the elastic-plastic region boundary recognition is stored in the memory, and the control program for the elastic-plastic region boundary recognition implements the elastic-plastic region boundary recognition method.

[0020] The fourth objective of this invention is achieved as follows: the computer-readable storage medium stores a control program for an elastic-plastic region boundary recognition platform, which implements the elastic-plastic region boundary recognition method.

[0021] This invention comprises a sensing unit made of mechanoluminescent material, deployed on a rock mass to be measured, and generating an optical response signal that is nonlinearly mapped to the stress in response to an explosive shock wave; and an optical acquisition unit consisting of at least a first illumination module, a second illumination module, and an image acquisition module. The first illumination module is used to excite the sensing unit to generate the optical response signal; the second illumination module is used to provide illumination required for observing rock mass surface deformation; and the image acquisition module is used to simultaneously acquire the optical response signal and an image of the rock mass surface deformation. It also includes an environmental simulation unit for applying hydrostatic pressure that allows light emitted by the first and second illumination modules to the rock mass to be measured; and a unit for analyzing the rock mass surface deformation. The system includes a signal processing unit for image-based calculation of the displacement and strain fields of the rock mass and inversion of the dynamic stress field of the rock mass based on the optical response signal; a boundary identification unit for calculating a first characteristic parameter characterizing the energy dissipation of the rock mass during loading and unloading cycles and a second characteristic parameter characterizing the degree of deformation recovery of the rock mass based on the displacement field, strain field, and dynamic stress field; a boundary identification unit for defining the spatial boundary between the plastic and elastic zones based on the first and second characteristic parameters; and corresponding methods, platforms, and storage media. By leveraging the nonlinear force-light response characteristics of the mechatronic sensing unit and combining the dual-parameter coupling criteria of energy dissipation and deformation recoverability, non-contact full-field monitoring of the explosion wave field of deep rock masses and accurate quantitative identification of elastic-plastic boundaries are achieved.

[0022] In other words, this invention, by employing a mechatronic light sensing unit with nonlinear force-optical mapping characteristics, combined with multi-band light source synchronous acquisition and high-transmittance confining pressure simulation technology, achieves non-contact, full-field, three-dimensional visualization monitoring of the explosive wave field under deep high ground stress environment. Utilizing the intrinsic characteristics of the sensing unit—reversible optical response in the elastic deformation stage and irreversible abrupt change in optical response in the plastic deformation stage—and introducing a dual-criteria coupling method of energy dissipation characteristic parameters and deformation recoverability characteristic parameters, it achieves accurate quantitative identification of the boundary between the plastic and elastic zones of rock mass under explosive loading. This overcomes the technical shortcomings of traditional electrical measurement methods, such as damaging rock mass integrity, the inability of point-based measurements to obtain continuous spatial distribution, and the difficulty in intuitively defining elastic-plastic boundaries. It provides reliable technical support for deep rock mass stability evaluation and optimization of blasting rock breaking efficiency. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a schematic diagram of the architecture of a deep rock mass explosion wave field monitoring system based on mechatronics sensing according to the present invention;

[0025] Figure 2 This is a schematic diagram of the system architecture of an embodiment of the deep rock mass explosion wave field monitoring system based on mechatronics sensing according to the present invention.

[0026] Figure 3 This is a schematic diagram of a three-dimensional sensor network structure of an embodiment of a deep rock mass explosion wave field monitoring system based on mechatronics sensing according to the present invention.

[0027] Figure 4 This is a schematic diagram of the arrangement structure of miniature explosive balls, an embodiment of the deep rock mass explosion wave field monitoring system based on mechatronics sensing according to the present invention.

[0028] Figure 5 This is a schematic diagram of the process steps of the elastic-plastic region boundary identification method of the present invention;

[0029] Figure 6 This is a schematic diagram of the process steps of a method for identifying the boundary between a plastic and elastic region, according to an embodiment of the present invention.

[0030] Figure 7 This is a schematic diagram of the architecture of an elastic-plastic region boundary recognition platform according to the present invention;

[0031] Figure 8 This is a schematic diagram of a computer-readable storage medium architecture in an embodiment of a method for identifying the boundary of an elastic-plastic region according to the present invention;

[0032] In the figure, 201-rock specimen; 202-DIC speckle; 203-upper MRL sensing film; 204-middle MRL sensing film; 205-lower MRL sensing film; 301-micro explosive ball; 302-MRL sensing film. Detailed Implementation

[0033] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0034] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0035] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0036] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0037] Preferably, the elastic-plastic region boundary identification method of the present invention is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0038] The terminal can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal can interact with the customer via a keyboard, mouse, remote control, touchpad, or voice control device.

[0039] like Figures 5-6 The diagram shown is a flowchart of the elastic-plastic region boundary identification method provided in an embodiment of the present invention.

[0040] In this embodiment, the elastic-plastic region boundary recognition method can be applied to a terminal or fixed terminal with display function. The terminal is not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0041] The elastic-plastic region boundary identification method can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The elastic-plastic region boundary identification method of this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0042] For example, for terminals requiring elastic-plastic region boundary recognition, the elastic-plastic region boundary recognition function provided by the method of this invention can be directly integrated into the terminal, or a client for implementing the method of this invention can be installed. Alternatively, the method provided by this invention can also run on servers or other devices in the form of a Software Development Kit (SDK), providing an interface for the elastic-plastic region boundary recognition function in the form of an SDK. Terminals or other devices can then implement the elastic-plastic region boundary recognition function through the provided interface. The invention will be further described below with reference to the accompanying drawings.

[0043] like Figures 1-4 As shown, this invention provides a deep rock mass explosion wave field monitoring system based on mechanoluminescence sensing. The system includes a sensing unit made of mechanoluminescent material, which is deployed on the rock mass to be measured and generates an optical response signal that is nonlinearly mapped to the stress in response to the explosion shock wave; and an optical acquisition unit consisting of at least a first illumination module, a second illumination module, and an image acquisition module. The first illumination module is used to excite the sensing unit to generate the optical response signal, the second illumination module is used to provide illumination required for observing the deformation of the rock mass surface, and the image acquisition module is used to simultaneously acquire the optical response signal and the rock mass surface deformation image.

[0044] The system further includes an environmental simulation unit for applying hydrostatic pressure to the rock mass under test, allowing light emitted by the first and second lighting modules to pass through; a signal processing unit for calculating the displacement and strain fields of the rock mass based on the surface deformation image of the rock mass, and inverting the dynamic stress field of the rock mass based on the optical response signal; and a boundary identification unit for calculating a first characteristic parameter characterizing the energy dissipation of the rock mass during loading and unloading cycles and a second characteristic parameter characterizing the degree of deformation recovery of the rock mass based on the displacement field, strain field, and dynamic stress field, and defining the spatial boundary between the plastic zone and the elastic zone according to the first characteristic parameter and the second characteristic parameter.

[0045] The mechanoluminescent material exhibits reversible optical response within the elastic deformation range and irreversible abrupt optical response within the plastic deformation range.

[0046] The optical acquisition unit further includes a filtering component disposed on the incident light path of the image acquisition module and used to selectively transmit optical response signals or rock surface deformation images; wherein, the filtering component is used to eliminate optical interference between the first illumination module and the second illumination module.

[0047] The environmental simulation unit uses a transparent fluid as the pressure transmission medium; wherein, the transparent fluid has a light transmittance of not less than a preset threshold within the operating wavelength range of the first lighting module and the second lighting module.

[0048] The signal processing unit further includes a deformation analysis module for processing rock surface deformation images using digital image correlation algorithms to obtain the full-field displacement and strain distribution of the rock surface; an optical analysis module for extracting optical intensity parameters and / or optical lifetime parameters from optical response signals; and a mechanical reconstruction module with a built-in calibrated nonlinear transformation model for converting optical intensity parameters and / or optical lifetime parameters into corresponding stress values ​​to reconstruct the dynamic stress field.

[0049] The nonlinear transformation model also includes strength-stress mapping relationships and / or life-stress mapping relationships;

[0050] The strength-stress mapping relationship is expressed as follows:

[0051] (1)

[0052] In the formula, Optical intensity; The initial optical intensity; The intensity sensitivity coefficient; This represents the stress value. These are characteristic stress parameters;

[0053] The lifetime-stress mapping relationship is expressed as follows:

[0054] (2)

[0055] In the formula, For optical lifespan; Initial optical lifetime; This is the lifetime sensitivity coefficient; It is a non-linear exponent; This represents the stress value.

[0056] Specifically, in this embodiment of the invention, a deep rock mass explosion wave field visualization monitoring system based on the combination of MRL sensing technology and DIC technology is provided. The system realizes the visualization monitoring of the explosion shock wave propagation process by constructing a three-dimensional sensor network, and uses the difference between the reversible transient response of MRL material under elastic deformation and the irreversible abrupt response under plastic yielding to realize the quantitative identification of the boundary between the plastic zone and the elastic zone of the rock mass under the action of explosion load.

[0057] To achieve the above objectives, this invention provides a deep rock mass explosion wave field visualization monitoring system based on a mechatronic light-emitting diode (MRD) sensor array. The system includes an MRL sensing film disposed at the contact interface of the rock specimen to generate a phosphorescent response signal related to the stress state under the action of an explosion shock wave; a multi-source synchronous acquisition module to provide an optical environment for the monitoring process and acquire image signals; a confining pressure loading module to simulate the high in-situ stress environment of deep rock masses; a data processing module to process the acquired image signals and invert the mechanical state; and an elastoplastic zone identification module to identify the boundaries between the plastic and elastic zones of the rock based on the processing results of the data processing module.

[0058] The MRL sensing film employs an aromatic secondary amine phosphorescent molecule-doped polyurethane elastomer material, exhibiting in-situ reversible mechanical response phosphorescence properties. The phosphorescence intensity, quantum yield, and lifetime are positively correlated with material stress. As a preferred embodiment, the aromatic secondary amine phosphorescent material can be TpNP or TpNPO, and the polyurethane elastomer can be a thermoplastic polyurethane elastomer (TPU). The doping mass fraction is preferably 0.5 wt.%. The film thickness is 0.3 mm to 0.5 mm, and it is colorless and transparent.

[0059] The multi-source synchronous acquisition module includes a long-wavelength red light illumination system, a surrounding ultraviolet excitation system, and a filtering system. The long-wavelength red light illumination system is used for DIC speckle illumination, the surrounding ultraviolet excitation system is used to excite the MRL sensing film to emit light, and the filtering system is used to eliminate optical path crosstalk. The surrounding ultraviolet excitation system consists of a ring array of multiple 365nm ultraviolet LED beads, evenly distributed around the pressure chamber. The filtering system includes a bandpass filter positioned in front of the high-speed camera lens to selectively receive light signals of specific wavelengths, achieving synchronous and interference-free acquisition of the macroscopic surface displacement field and the microscopic internal stress wave field.

[0060] The confining pressure loading module uses transparent hydraulic oil as the pressure transmission medium. The transparent hydraulic oil has a transmittance of not less than 85% in the visible light band (400-700nm), and the confining pressure loading range is 0-60MPa. Through a high-precision servo hydraulic system, any constant or dynamic confining pressure within the range of 0-60MPa can be applied to the sample to accurately simulate the geostress state of rock masses at different depths.

[0061] The data processing module includes a DIC displacement field calculation unit, a phosphorescence signal analysis unit, and a stress-phosphorescence mapping unit. The DIC displacement field calculation unit is used to acquire full-field displacement and strain data of the sample surface, the phosphorescence signal analysis unit is used to extract phosphorescence intensity and phosphorescence lifetime parameters, and the stress-phosphorescence mapping unit is used to convert phosphorescence parameters into corresponding stress values.

[0062] The stress-phosphorescence mapping unit stores the phosphorescence intensity-stress mapping formula and the phosphorescence lifetime-stress mapping formula.

[0063] The phosphorescence intensity-stress mapping formula is as follows:

[0064] (1)

[0065] In the formula, Phosphorescence intensity represents the strength of the phosphorescence signal emitted by the MRL sensing film under explosive load. The initial phosphorescence intensity refers to the baseline value of phosphorescence intensity before the application of the explosive load. This is the intensity sensitivity coefficient, with units of MPa. -1 This reflects the sensitivity of phosphorescence intensity to stress changes, determined by calibration experiments, with a typical value of approximately 85.3 MPa. -1 ; The stress value, in MPa, represents the mechanical stress acting on the MRL sensing film. The characteristic stress parameter, in MPa, controls the curvature characteristics of the mapping curve and determines the transition rate of the phosphorescence response from the linear region to the saturation region. A typical value is approximately 12.5 MPa.

[0066] The phosphorescence lifetime-stress mapping formula is as follows:

[0067] (2)

[0068] In the formula, Phosphorescence lifetime, in milliseconds, characterizes the time required for the phosphorescence signal to decay from the excited state to its initial intensity 1 / e; The initial phosphorescence lifetime is measured in milliseconds (ms). It refers to the baseline value of the phosphorescence lifetime before the application of the explosive load, with a typical value of approximately 37 ms. This is the lifetime sensitivity coefficient, in MPa. -β This reflects the sensitivity of phosphorescence lifetime to stress changes, determined by calibration experiments, with a typical value of approximately 0.78 MPa. -β ; It is a nonlinear exponent, dimensionless, that controls the degree of nonlinearity in the life-stress relationship, with a value range of 1.0 to 1.5, and a typical value of 1.2; This represents the stress value, in MPa.

[0069] Example 1

[0070] This embodiment provides a deep rock mass explosion wave field visualization monitoring system based on a mechatronic light sensor array.

[0071] like Figure 2 As shown in this embodiment, the deep rock mass explosion wave field visualization monitoring system based on mechatronic light sensor array includes a three-dimensional sensor network, an MRL sensor film, a multi-source synchronous acquisition module, a confining pressure loading module, a data processing module, and an elastic-plastic zone identification module.

[0072] Specifically, the overall structure of the three-dimensional sensing network is a geometric arrangement in which two cylindrical rock specimens 201 are wrapped by three layers of MRL sensing films. For example... Figure 3 As shown, the lower MRL sensing film 205 is positioned below the bottom rock specimen, the middle MRL sensing film 204 is positioned between the two rock specimens and includes holes for placing micro-explosive balls, and the upper MRL sensing film 203 is positioned above the top rock specimen. Two cylindrical rock specimens 201 are arranged parallel to each other between the upper and lower surfaces of the middle MRL sensing film. The rock specimens have a diameter of 50 mm and a height of 80 mm. The specimen surfaces are finely polished to ensure a surface flatness better than 0.01 mm, facilitating subsequent DIC speckle spraying and image acquisition.

[0073] The MRL sensing film is prepared using a composite material of polyurethane elastomer doped with aromatic secondary amine phosphorescent molecules. The film thickness is 0.3 mm to 0.5 mm, and it is colorless and transparent. The total thickness of the three MRL sensing films is approximately 1.2 mm to 1.5 mm, forming a complete encapsulation structure. This structural design enables the MRL sensing film to monitor the propagation of explosion shock waves from multiple directions in three-dimensional space, achieving comprehensive acquisition of stress wave field information within the medium.

[0074] like Figure 4 As shown, a miniature explosive ball 301 is positioned at the center of the middle MRL sensing film. The explosive ball has a diameter of 8 mm to 12 mm and is made of emulsion explosive. A uniformly distributed MRL sensing film area is reserved around the explosive ball to ensure that the explosive load can be evenly transmitted to each monitoring area.

[0075] The DIC speckle marking layer was applied to the outer surfaces of two rock specimens. A high-contrast random speckle pattern was sprayed onto the rock specimen surfaces using high-precision spraying equipment. The speckles were randomly distributed in black and white, with an average particle size of 20 μm to 50 μm, and a coating thickness of approximately 10 μm. The quality of the speckle pattern preparation directly affects the accuracy of DIC displacement measurement, requiring uniform speckle distribution and a contrast greater than 0.5.

[0076] The multi-source synchronous acquisition module includes a long-wavelength red light illumination system, a surrounding ultraviolet excitation system, and a filtering system. This module provides a stable, controllable, and interference-free optical environment for the monitoring process. The long-wavelength red light illumination system provides uniform background illumination for digital image correlation (DIC) measurements, illuminating the speckle pattern pre-sprayed onto the sample surface. The surrounding ultraviolet excitation system consists of a ring array of multiple 365nm ultraviolet LEDs, uniformly distributed around the pressure chamber, used to excite the MRL sensing film to produce phosphorescence. The filtering system includes a bandpass filter positioned in front of the high-speed camera lens, used to selectively receive light signals of specific wavelengths, thereby eliminating optical crosstalk and achieving synchronous, interference-free acquisition of the macroscopic surface displacement field and the microscopic internal stress wave field. For example, two high-speed cameras can be set up: one uses a long-wavelength pass filter to acquire only the DIC speckle image, and the other uses a bandpass filter to acquire only the phosphorescence image of the MRL film.

[0077] The confining pressure loading module uses transparent hydraulic oil as the pressure transmission medium and applies confining pressure through a servo hydraulic system to simulate the high in-situ stress environment of deep rock masses. To avoid interfering with optical observations, the module employs a fully transparent pressure chamber. The chamber is filled with specially formulated transparent hydraulic oil with a transmittance of no less than 85% in the visible light band (400-700nm) to ensure effective light transmission even under high pressure. Through a high-precision servo hydraulic system, any constant or dynamic confining pressure within the range of 0-60MPa can be applied to the sample, accurately simulating the in-situ stress state of rock masses at different depths.

[0078] The system also includes a strain gauge monitoring unit for directly measuring the stress-strain response of rock specimens under explosive loading, providing benchmark data for the calibration and verification of the mapping formula.

[0079] Specifically, the strain gauge monitoring unit uses resistance strain gauges arranged at specific locations on the surface of the rock specimen. The arrangement of the strain gauges follows these principles:

[0080] For placement, resistance strain gauges should be placed on the surface of the rock specimen in an area adjacent to the MRL sensing film, ensuring that the strain gauges and the MRL sensing film monitor the strain response at the same location. The strain gauge placement area should avoid the DIC speckle marking layer to avoid affecting the quality of the speckle pattern.

[0081] The strain gauges are arranged in both axial and radial directions. Axial strain gauges are attached along the axis of the rock specimen to measure axial strain under explosive loading; radial strain gauges are attached along the circumference of the rock specimen to measure radial strain under the combined action of confining pressure and explosive loading. By combining axial and radial strain, the three-dimensional stress state of the rock specimen can be fully characterized.

[0082] The strain gauges are connected to the strain acquisition instrument via wires. The wires are high-pressure resistant, low-noise shielded cables to withstand the high-pressure measurement conditions in a transparent hydraulic oil environment. The strain acquisition instrument is located outside the pressure chamber and is connected to the strain gauge wires inside the chamber via a sealed connector.

[0083] Data synchronization is achieved by connecting the strain acquisition instrument to the data processing module to synchronously acquire strain and phosphorescence signals. A unified clock trigger is used for synchronization to ensure precise temporal alignment between the strain data and the phosphorescence images acquired by the high-speed camera. The sampling frequency of the strain acquisition instrument matches the frame rate of the high-speed camera, being no less than 10000Hz to meet the time resolution requirements for dynamic measurement of explosion shock waves.

[0084] Functionally, the strain gauge monitoring unit provides direct stress-strain reference data for the calibration of phosphorescence intensity-stress mapping formulas and phosphorescence lifetime-stress mapping formulas. During calibration, stress values ​​are calculated using the strain values ​​measured by the strain gauges and the elastic modulus of the rock specimen, establishing a quantitative correspondence between phosphorescence parameters and stress values. During verification, the accuracy of the mapping formulas is evaluated by comparing the stress values ​​directly measured by the strain gauges with the stress values ​​derived from the mapping formulas.

[0085] The data processing module includes a DIC displacement field calculation unit, a phosphorescence signal analysis unit, and a stress-phosphorescence mapping unit. The DIC displacement field calculation unit performs correlation calculations on the acquired speckle image sequence to obtain full-field displacement and strain data of the sample surface. The phosphorescence signal analysis unit processes the acquired phosphorescence image sequence to extract key parameters such as phosphorescence intensity I and phosphorescence lifetime τ for each pixel or region of interest. The stress-phosphorescence mapping unit pre-stores mapping formulas obtained through calibration experiments, which are used to convert the analyzed phosphorescence parameters into corresponding stress values ​​in real time, thereby achieving quantitative inversion from optical signals to mechanical quantities.

[0086] The elastoplastic zone identification module provides a method for identifying the boundaries between the plastic and elastic zones of rock under explosive loading impact. Based on the processing results of the data processing module, it identifies the boundaries between the plastic and elastic zones of the rock. This module utilizes the differences in phosphorescence response of MRL materials at different deformation stages (elastic / plastic) and, by analyzing the phosphorescence signal characteristics throughout the loading-unloading process, accurately defines the spatial distribution and dynamic evolution boundaries of the plastic and elastic zones.

[0087] Example 2

[0088] This embodiment optimizes the material composition of the MRL sensing film based on Embodiment 1.

[0089] The MRL sensing film employs an aromatic secondary amine phosphorescent molecule-doped polyurethane elastomer material. As a preferred embodiment, the aromatic secondary amine phosphorescent material can be TpNP or TpNPO, and the polyurethane elastomer can be a thermoplastic polyurethane elastomer (TPU). The doping mass fraction is preferably 0.5 wt.%. This material exhibits in-situ reversible mechanical response phosphorescence properties, meaning its phosphorescence intensity, quantum yield, and lifetime are positively correlated with the applied stress (or strain). Within the elastic deformation range, this response is completely reversible; however, when the material undergoes plastic yielding, its phosphorescence properties undergo an irreversible abrupt change, providing a physical basis for distinguishing between the elastic and plastic regions.

[0090] Example 3

[0091] This embodiment details the construction method of the stress phosphorescence mapping unit in the data processing module, namely the mapping formula between stress value, displacement value and phosphorescence intensity value.

[0092] The mapping formulas stored in the data processing module include phosphorescence intensity stress mapping formula and phosphorescence lifetime stress mapping formula.

[0093] The phosphorescence intensity-stress mapping formula is:

[0094] )

[0095] The formula establishes a quantitative mapping relationship between phosphorescence intensity and rock surface stress. In the formula: I represents phosphorescence intensity, characterizing the strength of the phosphorescence signal emitted by the MRL sensing film under explosive loading; The initial phosphorescence intensity refers to the baseline value of phosphorescence intensity before the application of the explosive load. This is the intensity sensitivity coefficient, with units of MPa. -1 The sensitivity of phosphorescence intensity to stress changes is determined by calibration experiments. In the preferred material system of this embodiment, its typical value is approximately 85.3 MPa. -1 ; The stress value, in MPa, represents the mechanical stress acting on the MRL sensing film. The characteristic stress parameter, in MPa, controls the curvature characteristics of the mapping curve and determines the transition rate of the phosphorescence response from the linear region to the saturation region. In the preferred material system of this embodiment, its typical value is about 12.5 MPa.

[0096] The phosphorescence lifetime stress mapping formula is:

[0097] (2)

[0098] The formula establishes a quantitative mapping relationship between phosphorescence lifetime and rock surface stress. Where: Phosphorescence lifetime, in milliseconds, characterizes the time required for the phosphorescence signal to decay from the excited state to its initial intensity 1 / e; The initial phosphorescence lifetime, in milliseconds (ms), refers to the baseline value of phosphorescence lifetime before the application of the explosive load. In the preferred material system of this embodiment, its typical value is about 37 ms. This is the lifetime sensitivity coefficient, in MPa. -β The sensitivity of phosphorescence lifetime to stress changes is determined by calibration experiments; in this embodiment, its typical value is approximately 0.78 MPa. -β ; It is a nonlinear exponent, dimensionless, that controls the degree of nonlinearity in the life-stress relationship, and its value ranges from 1.0 to 1.5. In this embodiment, the preferred value is 1.2. This represents the stress value, in MPa.

[0099] Using the above formula, the system can convert the real-time measured phosphorescence signal (I, τ) into the corresponding stress value σ, and then invert the dynamic stress field inside the sample.

[0100] Mapping formula calibration method: the parameters in the mapping formula , , , The calibration experiment was conducted, and the specific calibration method is as follows:

[0101] Step 1: Sensor Setup. Resistance strain gauges are placed on the surface of the rock specimen, and an MRL sensing film is simultaneously applied to the specimen surface. The strain gauges are positioned adjacent to the monitoring area of ​​the MRL sensing film to ensure that both monitor the strain response at the same location. The strain gauges are arranged in both axial and radial directions to comprehensively characterize the strain state of the specimen.

[0102] Step 2: Low-stress pre-cyclic loading. The specimen is subjected to low-stress pre-cyclic loading, with the loading stress range from 0 to... ,in ≤4MPa, ensuring the material remains in the elastic deformation stage throughout the loading process. Pre-cyclic loading employs a sinusoidal or triangular wave loading method, with a loading rate of 0.1-0.5MPa / s and at least 3 cycles, to eliminate initial non-uniformity and hysteresis effects of the material.

[0103] Step 3: Synchronous Acquisition. During the pre-cyclic loading process, the strain signals of the strain gauges and the phosphorescence signals of the MRL sensing film are acquired simultaneously. The strain acquisition instrument records the strain-time history curves of each strain gauge, and the high-speed camera records the phosphorescence intensity and phosphorescence lifetime-time history curves of the MRL film. The acquisition frequency is no less than 100Hz to ensure the capture of detailed signal changes during the loading process.

[0104] Step 4: Stress Calculation. Based on the strain values ​​measured by strain gauges... Elastic modulus of rock specimens Calculate stress value For three-dimensional stress states, the generalized Hooke's law is used to calculate each stress component. Elastic modulus Preliminary measurements using uniaxial compression tests show a typical value of 50-70 GPa for granite specimens.

[0105] Step 5: Parameter Fitting. Establish phosphorescence parameters ( , ) and stress value The corresponding dataset was obtained. The least squares method was used to perform nonlinear fitting on the experimental data to solve for the parameters in the phosphorescence intensity-stress mapping formula. and And the parameters in the phosphorescence lifetime-stress mapping formula and Goodness of fit It should be no less than 0.95 to ensure the prediction accuracy of the mapping formula.

[0106] Step Six: Accuracy Verification. After calibration, verify the accuracy of the mapping formula in subsequent explosion experiments. Compare the stress values ​​directly measured by the strain gauges with the stress values ​​derived from the mapping formula, and calculate the relative error:

[0107] (5)

[0108] In the formula, This is relative error; The stress value is measured by the strain gauge; This is the stress value obtained by inverting the mapping formula. If the relative error... If the error is less than 10%, the mapping formula is considered calibrated successfully and can be used for subsequent stress field inversion. If the relative error exceeds the allowable range, the calibration experiment must be repeated to check for problems in strain gauge arrangement, signal synchronization, parameter fitting, and other aspects.

[0109] To achieve the above objectives, such as Figure 5 As shown, the present invention also provides a method for identifying the boundary of an elastoplastic zone, which is applied to a deep rock mass explosion wave field monitoring system based on mechatronic light sensing; the method includes the following steps:

[0110] S01. Initial calibration processing and the sensor unit deployed on the rock mass to be measured, generating and acquiring the corresponding first data; wherein, the first data are reference optical parameters and reference elastic dissipation parameters; specifically, the reference optical parameters of the sensor unit under no-load state and the reference dissipation characteristic parameters within the elastic deformation range.

[0111] S02. Based on the applied explosive load, generate second data corresponding to the sensing unit; wherein, the second data is an optical response signal and a rock surface deformation image;

[0112] S03. Based on the second data, extract and generate the corresponding third data, and perform inversion processing to generate the corresponding fourth data; wherein, the third data is the peak optical parameters during the loading process and the residual optical parameters after unloading; the fourth data is the stress-strain relationship curve of the entire loading and unloading process;

[0113] S04. Create and generate fifth data corresponding to the fourth data, and calculate and generate a corresponding sixth data based on the third data; wherein, the fifth data is a dissipative characteristic value; and the sixth data is a recoverable characteristic value;

[0114] S05. Based on the five data points and combined with the first data, generate corresponding first comparison data.

[0115] S06. Construct and generate seventh data corresponding to the sixth data, and based on the seventh data and combined with the sixth data, compare and generate corresponding second comparison data; wherein, the seventh data is a preset discrimination threshold characterizing the critical point of elastic deformation and plastic deformation;

[0116] S07. Based on the first comparison data and the second comparison data, determine and generate the eighth data corresponding to each region of the rock mass, and construct the corresponding boundaries of the plastic zone and elastic zone according to the eighth data; wherein, the eighth data is an elastic-plastic property.

[0117] The step of determining and generating eighth data corresponding to each region of the rock mass based on the first comparison data and the second comparison data, and constructing the corresponding boundaries of the plastic and elastic zones based on the eighth data, further includes:

[0118] S071. If the fifth data is greater than a set multiple of the first data, or the sixth data is less than or equal to the seventh data, then the corresponding region is determined to be a plastic region.

[0119] S072. If the fifth data is less than or equal to a set multiple of the first data, and the sixth data is greater than the seventh data, then the corresponding area is determined to be an elastic area.

[0120] Specifically, in embodiments of the present invention, such as Figure 6 As shown, a method for identifying the boundary between the plastic and elastic regions based on the above system is provided, including the following steps:

[0121] S301, Deploy an MRL sensing thin film array on the surface of the rock mass to be monitored for initial calibration;

[0122] S302, apply an explosive load and simultaneously acquire the phosphorescence signal of the MRL sensing film;

[0123] S303, collects phosphorescence signals during the unloading process and measures residual phosphorescence parameters;

[0124] S304, based on the phosphorescence intensity-stress mapping formula and the phosphorescence lifetime-stress mapping formula, calculate the phosphorescence hysteresis area;

[0125] S305, Calculate the phosphorescence response reversibility coefficient at each monitoring point;

[0126] S306, Determine the boundaries of the plastic and elastic zones based on the hysteresis area criterion and the reversibility coefficient criterion;

[0127] S307, Output the boundary distribution map of the plastic zone and the elastic zone.

[0128] The initial calibration in step S301 includes initial phosphorescence parameter measurement, turning on a 365nm ultraviolet excitation source, acquiring an initial phosphorescence image, and recording the initial phosphorescence intensity of each pixel. and initial phosphorescence lifetime The baseline hysteresis curve was obtained, and the MRL sensing film was subjected to low-stress pre-cyclic loading to determine the maximum pre-load stress. ≤4MPa, obtain the reference hysteresis curve parameters of the material in the elastic state.

[0129] Furthermore, the formula for calculating the phosphorescence hysteresis area in step S4 is as follows:

[0130] (3)

[0131] In the formula, The phosphorescence response hysteresis area represents the mechanical energy dissipated per unit area of ​​the rock mass surface during a loading-unloading cycle of an explosive load. The stress-strain relationship function of the loaded branch describes the variation of stress with strain on the surface of the rock mass during the rising stage of the explosive load. The stress-strain relationship function of the unloading branch describes the variation of stress with strain on the rock surface during the attenuation stage of the explosive load. denoted as the surface strain of the rock mass, dimensionless, characterizing the degree of deformation of the rock mass surface under explosive loading.

[0132] Furthermore, the formula for calculating the reversibility coefficient in step S5 is as follows:

[0133] (4)

[0134] In the formula, The reversibility coefficient is dimensionless, ranging from 0 to 1, and characterizes the degree of recovery of rock mass surface deformation after unloading of the explosive load. The closer the value is to 1, the more reversible the deformation of the rock mass surface is, and the closer it is to an elastic state. The closer the value is to 0, the more irreversible the deformation of the rock mass surface is, and the closer it is to a plastic state; The maximum phosphorescence intensity during the explosion process reflects the stress state of the rock surface at the peak of the explosion load. The residual phosphorescence intensity after the explosion wave has completely decayed reflects the residual stress state of the rock mass surface after the explosion load has been unloaded. The initial phosphorescence intensity before the explosion reflects the baseline stress level of the rock mass surface in its initial state.

[0135] The elastoplastic boundary determination in step S306 includes:

[0136] Hysteresis area criterion: when If the condition is met, the region is determined to be in the plastic region; otherwise, it is determined to be in the elastic region.

[0137] Invertibility coefficient criterion: when When, the region is determined to be in the elastic zone; when At that time, the region was determined to be in the plastic zone;

[0138] In the formula, To measure the phosphorescence hysteresis area, The baseline elastic hysteresis area is obtained from calibration experiments; This is the hysteresis area magnification factor, dimensionless, with a value ranging from 1.5 to 2.0; The reversibility coefficient threshold is dimensionless and has a value of 0.7.

[0139] Furthermore, the determination of the elastoplastic boundary adopts a comprehensive determination method: when the determination results of the hysteresis area criterion and the reversibility coefficient criterion are consistent, the consistent result is adopted; when the determination results of the two criteria are inconsistent, the determination result of the reversibility coefficient criterion shall prevail.

[0140] Example 4

[0141] This embodiment provides a method for identifying the boundary between plastic and elastic regions based on a mechatronic light sensor array. This method is executed by the elastic-plastic region identification module. The method includes the following steps:

[0142] An MRL sensing thin film array was deployed on the surface of the rock mass to be monitored for initial calibration.

[0143] Specifically, following the method described in Example 1, an MRL sensing film was deployed at the contact interface of the structured segmented sample. Initial calibration included: turning on a 365nm ultraviolet excitation source, acquiring an initial phosphorescence image, and recording the initial phosphorescence intensity of each pixel. and initial phosphorescence lifetime Simultaneously, the MRL sensing film was subjected to low-stress pre-cyclic loading, with the maximum pre-load stress... For a pressure ≤4MPa, obtain the baseline hysteresis curve of the material in a purely elastic state, and calculate the baseline elastic hysteresis area by integration. During the pre-cyclic loading process, strain signals from strain gauges and phosphorescence signals from the MRL sensing film are simultaneously acquired to calibrate the mapping formula parameters. , , , Specifically, strain gauges are arranged on the surface of the rock specimen in the area adjacent to the MRL sensing film, with both axial and radial orientations. The strain acquisition instrument and the high-speed camera are triggered by a unified clock to achieve time synchronization between the strain signal and the phosphorescence signal. The strain values ​​measured by the strain gauges are then analyzed. Elastic modulus of rock specimens Calculate stress value Establish phosphorescence parameters ( , ) and stress value The mapping formula parameters are obtained by fitting the corresponding dataset using the least squares method.

[0144] A miniature explosive ball is detonated to apply an explosive load, and the phosphorescence signal of the MRL sensing film is collected simultaneously.

[0145] The explosive is detonated using an electronic detonator. Simultaneously, a multi-source synchronous acquisition module is activated, using a high-speed camera to synchronously acquire DIC speckle images and MRL phosphorescence image sequences throughout the entire explosion process at a preset high frame rate, recording the entire process from loading to complete unloading.

[0146] Phosphorescence signals were collected during the unloading process, and residual phosphorescence parameters were measured.

[0147] After the explosion wave has completely decayed, phosphorescence images are continued to be acquired to obtain the stable residual phosphorescence intensity. Simultaneously, the maximum phosphorescence intensity was extracted from the entire phosphorescence image sequence. .

[0148] The phosphorescence hysteresis area is calculated based on the phosphorescence intensity-stress mapping formula and the phosphorescence lifetime-stress mapping formula.

[0149] The data processing module first uses a mapping formula to convert the phosphorescence signal sequence into a corresponding stress-strain curve (σ-ε). Then, it calculates the phosphorescence response hysteresis area at each monitoring point according to the following formula. :

[0150] (3)

[0151] in, The phosphorescent response hysteresis area represents the mechanical energy dissipated per unit area during a load-unload cycle. The stress-strain relationship function for the loaded branch; The stress-strain relationship function for the unloading branch; The strain is the surface strain of the rock mass.

[0152] Calculate the phosphorescence response reversibility coefficient R at each monitoring point.

[0153] The reversibility coefficient is calculated using the following formula:

[0154] (4)

[0155] Wherein, R is the reversibility coefficient, which is dimensionless and ranges from 0 to 1. The closer the value is to 1, the higher the degree of deformation recovery and the closer it is to elasticity. This represents the maximum phosphorescence intensity during the explosion process; The residual phosphorescence intensity after the explosion wave has completely decayed; The initial phosphorescence intensity before the explosion.

[0156] The elastoplastic boundary is determined based on the hysteresis area criterion and the reversibility coefficient criterion.

[0157] A two-parameter coupled criterion is used for determination:

[0158] Hysteresis area criterion: when If the condition is met, the region is determined to be in the plastic zone; otherwise, it is determined to be in the elastic zone.

[0159] Invertibility coefficient criterion: when When, the region is determined to be in the elastic zone; when At that time, the region was determined to be in the plastic zone.

[0160] in, The reference elastic hysteresis area is obtained from the calibration experiment in step S1; The hysteresis area magnification factor is dimensionless and ranges from 1.5 to 2.0. In this embodiment, it is preferably 1.8. The reversibility coefficient threshold is dimensionless, and in this embodiment, it is set to 0.7.

[0161] To ensure the robustness of the criteria, a comprehensive judgment method is adopted: when the judgment results of the two criteria are consistent, the consistent result is adopted; when the judgment results of the two criteria are inconsistent (for example, the hysteresis area is excessive but the reversibility coefficient is very high), the judgment result of the reversibility coefficient criterion shall prevail, because the reversibility coefficient more directly reflects the degree of permanent damage to the material.

[0162] Output a boundary distribution map of the plastic and elastic regions.

[0163] Mapping the judgment results back to the three-dimensional spatial coordinates of the sample generates a boundary distribution map of the plastic and elastic zones of the entire monitoring area under explosive loading. This map can visually demonstrate the extent and degree of damage to the rock mass under shock wave action.

[0164] In the embodiments of the method of the present invention, the functional modules involved in the elastic-plastic region boundary identification method have been described in detail above. That is to say, the steps in the method are used to apply to the functional units or functional modules in the above system embodiments, and will not be repeated here.

[0165] To achieve the above objectives, the present invention also provides a platform for identifying the boundary of an elastic-plastic region, such as... Figure 7 As shown, it includes a processor, a memory, and a control program for an elastic-plastic region boundary recognition platform; wherein, the processor executes the control program for the elastic-plastic region boundary recognition platform, and the control program is stored in the memory; the control program for the elastic-plastic region boundary recognition platform implements the steps of the elastic-plastic region boundary recognition method, for example:

[0166] S01. Initial calibration processing and the sensor unit deployed on the rock mass to be measured, generating and acquiring the corresponding first data; wherein, the first data are reference optical parameters and reference elastic dissipation parameters; specifically, the reference optical parameters of the sensor unit under no-load state and the reference dissipation characteristic parameters within the elastic deformation range.

[0167] S02. Based on the applied explosive load, generate second data corresponding to the sensing unit; wherein, the second data is an optical response signal and a rock surface deformation image;

[0168] S03. Based on the second data, extract and generate the corresponding third data, and perform inversion processing to generate the corresponding fourth data; wherein, the third data is the peak optical parameters during the loading process and the residual optical parameters after unloading; the fourth data is the stress-strain relationship curve of the entire loading and unloading process;

[0169] S04. Create and generate fifth data corresponding to the fourth data, and calculate and generate a corresponding sixth data based on the third data; wherein, the fifth data is a dissipative characteristic value; and the sixth data is a recoverable characteristic value;

[0170] S05. Based on the five data points and combined with the first data, generate corresponding first comparison data.

[0171] S06. Construct and generate seventh data corresponding to the sixth data, and based on the seventh data and combined with the sixth data, compare and generate corresponding second comparison data; wherein, the seventh data is a preset discrimination threshold characterizing the critical point of elastic deformation and plastic deformation;

[0172] S07. Based on the first comparison data and the second comparison data, determine and generate the eighth data corresponding to each region of the rock mass, and construct the corresponding boundaries of the plastic zone and elastic zone according to the eighth data; wherein, the eighth data is an elastic-plastic property.

[0173] The specific details of the steps have been explained above and will not be repeated here.

[0174] In this embodiment of the invention, the built-in processor of the elastic-plastic region boundary recognition platform can be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor connects to various components using various interfaces and lines, and performs various functions of elastic-plastic region boundary recognition and data processing by running or executing programs or units stored in memory and calling data stored in memory.

[0175] The memory, used to store program code and various data, is installed in the elastic-plastic region boundary recognition platform and enables high-speed, automatic access to programs or data during operation. The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0176] To achieve the above objectives, the present invention also provides a computer-readable storage medium, such as... Figure 8 As shown, the computer-readable storage medium stores a control program for an elastic-plastic region boundary recognition platform. This control program implements the steps of the elastic-plastic region boundary recognition method; for example:

[0177] S01. Initial calibration processing and the sensor unit deployed on the rock mass to be measured, generating and acquiring the corresponding first data; wherein, the first data are reference optical parameters and reference elastic dissipation parameters; specifically, the reference optical parameters of the sensor unit under no-load state and the reference dissipation characteristic parameters within the elastic deformation range.

[0178] S02. Based on the applied explosive load, generate second data corresponding to the sensing unit; wherein, the second data is an optical response signal and a rock surface deformation image;

[0179] S03. Based on the second data, extract and generate the corresponding third data, and perform inversion processing to generate the corresponding fourth data; wherein, the third data is the peak optical parameters during the loading process and the residual optical parameters after unloading; the fourth data is the stress-strain relationship curve of the entire loading and unloading process;

[0180] S04. Create and generate fifth data corresponding to the fourth data, and calculate and generate a corresponding sixth data based on the third data; wherein, the fifth data is a dissipative characteristic value; and the sixth data is a recoverable characteristic value;

[0181] S05. Based on the five data points and combined with the first data, generate corresponding first comparison data.

[0182] S06. Construct and generate seventh data corresponding to the sixth data, and based on the seventh data and combined with the sixth data, compare and generate corresponding second comparison data; wherein, the seventh data is a preset discrimination threshold characterizing the critical point of elastic deformation and plastic deformation;

[0183] S07. Based on the first comparison data and the second comparison data, determine and generate the eighth data corresponding to each region of the rock mass, and construct the corresponding boundaries of the plastic zone and elastic zone according to the eighth data; wherein, the eighth data is an elastic-plastic property.

[0184] The specific details of the steps have been explained above and will not be repeated here.

[0185] In the description of embodiments of the present invention, it should be noted that any process or method description in the flowcharts or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0186] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, a “computer-readable medium” can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0187] To make the technical effects of the present invention more intuitive and clear, a comparative statistical evaluation of deep rock mass explosion wave field monitoring under the same working conditions is carried out in conjunction with the method flow and parameter settings of this patent.

[0188] Experimental conditions and monitoring process: This embodiment provides a specific application scenario for monitoring the explosion wave field of deep rock masses to verify the actual monitoring effect of the system described in this invention. The experimental conditions are set as follows: confining pressure is 30 MPa, simulating a high-stress environment at a depth of approximately 1200 m. This confining pressure is applied through transparent hydraulic oil, which has a transmittance of ≥85% in the visible light band to ensure the effectiveness of optical measurements. The explosion source is an emulsion explosive with a diameter of 10 mm. To objectively evaluate the measurement accuracy of the technical means of this invention, this embodiment establishes a three-dimensional finite element model consistent with the experimental conditions. Using the simulation calculation results as a benchmark, the errors between the measured results and simulation results of the traditional electrical measurement method, the point-type optical measurement method, and the method of this invention are compared to verify the technical advantages of this invention in the accuracy of identifying the elastoplastic zone.

[0189] A finite element simulation model was established using explicit dynamic analysis software to create a three-dimensional finite element model consistent with the experimental conditions. The model used a cylindrical PMMA specimen with a diameter of 50 mm and a height of 80 mm. Material parameters were calibrated based on quasi-static and dynamic compression tests. The elastic modulus was 3.2 GPa, Poisson's ratio was 0.35, and density was 1190 kg / m³. The Drucker-Prager constitutive model was used to describe plastic behavior, with a yield stress of 35 MPa. The explosive load was simulated using a triangular pulse pressure curve applied to the central region of the specimen, with a peak pressure of 220 MPa, a rise time of 5 μs, a fall time of 40 μs, and a total duration of 45 μs. The confining pressure was set to a hydrostatic pressure of 30 MPa. The mesh was created using C3D8R eight-node hexahedral elements with an element size of 0.5 mm, resulting in approximately 320,000 elements. The stress and strain time history data of the specimen's interior and surface are calculated and output. The boundary of the plastic zone is defined based on the region where the equivalent plastic strain is greater than zero. The boundary of the plastic zone obtained from the simulation is about 14.2 mm away from the center of the explosion source.

[0190] To compare the peak stress measurement errors, three characteristic locations were selected inside and on the surface of the specimen: Location A (5 mm from the center of the explosion source, located in the plastic zone), Location B (15 mm from the center of the explosion source, located near the boundary of the plastic zone), and Location C (25 mm from the center of the explosion source, located in the elastic zone). The peak stress at each location was obtained using three different measurement methods, and the results were compared with simulation results. Specific data are shown in the table below.

[0191] Table 1 Comparison of measured and simulated peak stress values ​​at each measuring point.

[0192]

[0193] As shown in Table 1, the average relative error of the traditional electrical measurement method at the three measurement points is -11.6%. This error mainly stems from the loss of high-frequency components due to strain gauge frequency response limitations and local stress field distortion caused by embedded sensors. The average relative error of the point-based optical measurement method is -14.9%, primarily due to the uncertainty in the constitutive relationship when inferring stress from surface displacement and the influence of the hydraulic oil refractive index on the optical path. The method of this invention has an average relative error of -3.6%, significantly better than the two comparative methods. This error mainly originates from stress transfer loss at the interface between the MRL film and the PMMA specimen, and calibration errors in the mapping formula.

[0194] The error of the plastic zone boundary identification was compared. Three measurement methods were used to identify the location of the plastic zone boundary, and the results were compared with the finite element simulation results (the plastic zone boundary is 14.2 mm away from the center of the explosion source) to evaluate the boundary identification accuracy of each method.

[0195] Table 2 Comparison of Plastic Zone Boundary Identification Results and Simulation Values

[0196]

[0197] As shown in Table 2, the traditional electrical measurement method, due to the limited number of measurement points (9 strain gauges were used in this experiment), can only provide a vague range of 12-16 mm for the plastic zone boundary, and cannot obtain a continuous boundary curve, with a boundary identification error of approximately ±1.8 mm. The point-based optical measurement method measured the plastic zone boundary at 16.5 mm from the center of the explosion source, a deviation of +2.3 mm from the simulation result. This error mainly stems from the simplification of the stress transfer relationship derived from surface displacement and the uncertainty of the yield criterion parameters. The method of this invention measures the plastic zone boundary at 14.8 mm from the center of the explosion source, with a boundary deviation of only +0.6 mm. Furthermore, by utilizing the spatial arrangement of three MRL films, the boundary positions at different depths can be obtained, forming a complete three-dimensional spatial distribution of the plastic zone. The boundary identification accuracy is significantly better than the two comparative methods.

[0198] Comprehensive Comparison Conclusion

[0199] Table 3 Comparison of the overall performance of the three measurement methods

[0200]

[0201] Through the error comparison analysis with the finite element simulation results, the present invention significantly outperforms the traditional electrical measurement method and the point-based optical measurement method in two key indicators: peak stress measurement accuracy and plastic zone boundary identification accuracy. The average relative error of peak stress is -3.6%, far lower than -11.6% of the traditional electrical measurement method and -14.9% of the point-based optical measurement method; the plastic zone boundary deviation is +0.6mm, far lower than +2.3mm of the point-based optical measurement method, and it overcomes the inherent defects of sparse measurement points and blurred boundaries in the traditional electrical measurement method. Simultaneously, the present invention possesses three-dimensional spatial monitoring capabilities and adaptability to confining pressure environments, achieving direct identification of elastoplastic boundaries. The technical superiority of the present invention has been quantitatively verified.

[0202] This invention comprises a sensing unit made of mechanoluminescent material, deployed on a rock mass to be measured, and generating an optical response signal that is nonlinearly mapped to the stress in response to an explosive shock wave; and an optical acquisition unit consisting of at least a first illumination module, a second illumination module, and an image acquisition module. The first illumination module is used to excite the sensing unit to generate the optical response signal; the second illumination module is used to provide illumination required for observing rock mass surface deformation; and the image acquisition module is used to simultaneously acquire the optical response signal and an image of the rock mass surface deformation. It also includes an environmental simulation unit for applying hydrostatic pressure that allows light emitted by the first and second illumination modules to the rock mass to be measured; and a unit for analyzing the rock mass surface deformation. The system includes a signal processing unit for image-based calculation of the displacement and strain fields of the rock mass and inversion of the dynamic stress field of the rock mass based on the optical response signal; a boundary identification unit for calculating a first characteristic parameter characterizing the energy dissipation of the rock mass during loading and unloading cycles and a second characteristic parameter characterizing the degree of deformation recovery of the rock mass based on the displacement field, strain field, and dynamic stress field; a boundary identification unit for defining the spatial boundary between the plastic and elastic zones based on the first and second characteristic parameters; and corresponding methods, platforms, and storage media. By leveraging the nonlinear force-light response characteristics of the mechatronic sensing unit and combining the dual-parameter coupling criteria of energy dissipation and deformation recoverability, non-contact full-field monitoring of the explosion wave field of deep rock masses and accurate quantitative identification of elastic-plastic boundaries are achieved.

[0203] In other words, this invention solves the technical problems of existing electrical measurement methods that damage rock integrity, the inability of point measurements to obtain continuous spatial distribution, and the inability to intuitively define the boundary between the plastic and elastic zones. The system includes an MRL sensing film, a multi-source synchronous acquisition module, a confining pressure loading module, a data processing module, and an elastic-plastic zone identification module. The MRL sensing film is placed at the contact interface of the rock specimen and generates a phosphorescent response signal related to the stress state under the action of an explosive shock wave. By constructing a three-dimensional sensing network, the propagation process of the explosive shock wave can be visualized and monitored. Furthermore, by utilizing the difference between the reversible transient response of the MRL material under elastic deformation and the irreversible abrupt response under plastic yielding, the boundary between the plastic and elastic zones can be quantitatively identified, providing reliable technical support for the evaluation of deep rock mass stability and the optimization of blasting rock breaking efficiency.

[0204] In other words, this invention, by employing a mechanoluminescent sensing unit with nonlinear force-optical mapping characteristics, combined with multi-band light source synchronous acquisition and high-transmittance confining pressure simulation technology, achieves non-contact, full-field, three-dimensional visualization monitoring of the explosive wave field under deep high ground stress environment. Utilizing the intrinsic characteristics of the sensing unit—reversible optical response in the elastic deformation stage and irreversible abrupt change in optical response in the plastic deformation stage—and introducing a dual-criteria coupling method of energy dissipation characteristic parameters and deformation recoverability characteristic parameters, the peak stress measurement error is reduced from 11%-15% to less than 4% compared to traditional electrical measurement methods. Furthermore, the deviation in identifying the plastic zone boundary is reduced from ±1.8-2.3mm to 0.6mm. This enables accurate quantitative identification of the boundary between the plastic and elastic zones of rock mass under explosive loading, providing reliable technical support for deep rock mass stability evaluation and optimization of blasting rock breaking efficiency.

[0205] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A deep rock mass explosion wave field monitoring system based on mechatronic light sensing, characterized in that, The system includes a sensing unit made of mechanoluminescent material, which is deployed on the rock mass to be measured and generates an optical response signal that is nonlinearly mapped to the stress in response to an explosive shock wave; and an optical acquisition unit consisting of at least a first illumination module, a second illumination module, and an image acquisition module, wherein the first illumination module is used to excite the sensing unit to generate an optical response signal, the second illumination module is used to provide illumination required for observing the deformation of the rock mass surface, and the image acquisition module is used to simultaneously acquire the optical response signal and the image of the rock mass surface deformation. The system further includes an environmental simulation unit for applying hydrostatic pressure to the rock mass under test, allowing light emitted by the first and second lighting modules to pass through; a signal processing unit for calculating the displacement and strain fields of the rock mass based on the surface deformation image of the rock mass, and inverting the dynamic stress field of the rock mass based on the optical response signal; and a boundary identification unit for calculating a first characteristic parameter characterizing the energy dissipation of the rock mass during loading and unloading cycles and a second characteristic parameter characterizing the degree of deformation recovery of the rock mass based on the displacement field, strain field, and dynamic stress field, and defining the spatial boundary between the plastic zone and the elastic zone according to the first characteristic parameter and the second characteristic parameter.

2. The deep rock mass explosion wave field monitoring system based on mechatronics sensing according to claim 1, characterized in that, The mechanoluminescent material exhibits reversible optical response within the elastic deformation range and irreversible abrupt optical response within the plastic deformation range.

3. A deep rock mass explosion wave field monitoring system based on mechatronics sensing according to claim 1 or 2, characterized in that, The optical acquisition unit also includes a filtering component disposed on the incident light path of the image acquisition module and used to selectively transmit optical response signals or rock surface deformation images.

4. The deep rock mass explosion wave field monitoring system based on mechatronics sensing according to claim 1, characterized in that, The environmental simulation unit uses a transparent fluid as the pressure transmission medium; wherein, the transparent fluid has a light transmittance of not less than a preset threshold within the operating wavelength range of the first lighting module and the second lighting module.

5. A deep rock mass explosion wave field monitoring system based on mechatronics sensing according to claim 1, characterized in that, The signal processing unit also includes a deformation analysis module for processing rock surface deformation images using digital image correlation algorithms to obtain the full-field displacement and strain distribution of the rock surface; and an optical analysis module for extracting optical intensity parameters and / or optical lifetime parameters from the optical response signal. It also includes a mechanical reconstruction module with a built-in calibrated nonlinear transformation model for converting optical intensity parameters and / or optical lifetime parameters into corresponding stress values.

6. The deep rock mass explosion wave field monitoring system based on mechatronics sensing according to claim 1, characterized in that, The nonlinear transformation model also includes strength-stress mapping relationships and / or life-stress mapping relationships; The strength-stress mapping relationship is expressed as follows: （1） In the formula, Optical intensity; The initial optical intensity; The intensity sensitivity coefficient; This is the stress value; These are characteristic stress parameters; The lifetime-stress mapping relationship is expressed as follows: （2） In the formula, For optical lifespan; Initial optical lifetime; This is the lifetime sensitivity coefficient; It is a non-linear exponent; This represents the stress value.

7. A method for identifying the boundary of an elastic-plastic region, characterized in that, The method is applied to the deep rock mass explosion wave field monitoring system based on mechatronics sensing as described in any one of claims 1 to 6; the method includes the following steps: The initial calibration process and the sensor units deployed on the rock mass to be measured generate and acquire the corresponding first data; wherein, the first data are the reference optical parameters and the reference elastic dissipation parameters; Based on the applied explosive load, second data corresponding to the sensing unit is generated; wherein, the second data is an optical response signal and a rock surface deformation image; Based on the second data, corresponding third data is extracted and generated, and inversion processing is performed to generate corresponding fourth data; wherein, the third data is the peak optical parameters during the loading process and the residual optical parameters after unloading; the fourth data is the stress-strain relationship curve of the entire loading and unloading process; A fifth data point corresponding to the fourth data point is created, and a corresponding sixth data point is calculated based on the third data point; wherein, the fifth data point is a dissipative characteristic value; and the sixth data point is a recoverable characteristic value. Based on the five data points and combined with the first data, corresponding first comparison data is generated. A seventh set of data corresponding to the sixth set of data is generated. Based on the seventh set of data and combined with the sixth set of data, a second set of comparison data is generated. The seventh set of data is a preset discrimination threshold characterizing the critical point between elastic deformation and plastic deformation. Based on the first comparison data and the second comparison data, an eighth set of data corresponding to each region of the rock mass is generated, and the boundaries of the corresponding plastic and elastic zones are constructed according to the eighth set of data; wherein, the eighth set of data is an elastic-plastic property.

8. The method for identifying the boundary of an elastic-plastic region according to claim 7, characterized in that, The step of determining and generating eighth data corresponding to each region of the rock mass based on the first comparison data and the second comparison data, and constructing the corresponding boundaries of the plastic and elastic zones based on the eighth data, further includes: If the fifth data is greater than a set multiple of the first data, or the sixth data is less than or equal to the seventh data, then the corresponding region is determined to be a plastic region. If the fifth data is less than or equal to a set multiple of the first data, and the sixth data is greater than the seventh data, then the corresponding area is determined to be an elastic zone.

9. A platform for identifying the boundary of an elastic-plastic region, characterized in that, The system includes a processor, a memory, and a platform control program based on an elastic-plastic region boundary recognition system; wherein the processor executes the elastic-plastic region boundary recognition platform control program, the elastic-plastic region boundary recognition platform control program is stored in the memory, and the elastic-plastic region boundary recognition platform control program implements the elastic-plastic region boundary recognition method as described in any one of claims 7 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a control program for an elastic-plastic region boundary identification platform, which implements the elastic-plastic region boundary identification method as described in any one of claims 7 to 8.

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