A method and system for measuring rock deformation in a microwave rock breaking process

Abstract

The application discloses a kind of rock deformation measurement method and system in microwave rock breaking process, it is related to deformation measurement technical field, including the following steps: in the microwave rock breaking process, the multi-source information data of rock is collected, and temperature field distribution model, rock thermal expansion deformation prediction model and rock comprehensive deformation field are established, and rock thermal expansion deformation prediction field is obtained, thus combined rock comprehensive deformation field and rock thermal expansion deformation prediction field analysis eliminates thermal interference, identifies structural damage and non-structural damage area, and calculates thermal interference intensity index to non-structural damage area, dynamically adjusts microwave output power, irradiation time length and pulse duty cycle, realizes local accurate rock breaking.This method effectively eliminates the deformation measurement error caused by uneven distribution of thermal field, improves the structural damage identification accuracy and rock breaking efficiency, solves the problem of insufficient deformation measurement accuracy and damage identification ambiguity in the prior art, with the advantages of strong operability and high precision.

Description

Technical Field

[0001] This invention relates to the field of deformation measurement technology, specifically to a method and system for measuring rock deformation during microwave rock breaking. Background Technology

[0002] As deep resource mining extends into extremely hard rock formations, traditional mechanical cutting and drilling / blasting methods face bottlenecks such as rapid tool wear, significant vibration damage, and poor operational continuity. Microwave rock breaking technology, as a novel and highly efficient non-contact physical rock breaking method, utilizes the significant differences in the absorption rates of microwave energy by mineral components to generate non-uniform thermal stress within the rock, inducing crack initiation. Current technologies typically involve adjusting the power and irradiation time of a microwave generator to deliver high-energy electromagnetic radiation to the rock within a metal-shielded cavity, and then evaluating the rock-breaking effect and stress evolution by real-time monitoring of the deformation and displacement of the rock surface.

[0003] For example, Chinese invention patent CN104315989B discloses a method for measuring the deformation of a rock region, which specifically includes the following steps: 1) making a thin film paper; 2) attaching the plastic film tightly to the surface of the rock sample to be measured and forming an integral part with the rock sample; 3) placing a camera on the upper layer of an iron stand and fixing the rock sample to be measured on the lower layer of the iron stand, and taking pictures of the distribution of grids or points on the paper before the rock is deformed; 4) subjecting the rock sample to force to cause deformation, and taking pictures of the distribution of grids or points on the paper after the rock is deformed; 5) processing the distribution of grids or points on the paper before and after the rock deformation obtained in steps 3) and 4) with graphics processing software, and completing the measurement of the deformation of the rock region by measuring the positional changes of the grids or points.

[0004] For example, Chinese invention patent CN108036745B discloses an error compensation method for microwave interferometric deformation measurement, which specifically includes the following steps: S1, selecting n stable reference points for measurement and collecting deformation data; the stable reference points are points that do not undergo deformation displacement throughout the measurement time, and the radial distance from each stable reference point to the deformation measurement radar is not equal; and n≧2; S2, the radar system establishes an error compensation model based on the deformation data of the n stable reference points. The model divides the deformation monitoring error of the stable reference points into zero-mean error and gradually varying error, and simultaneously uses time-domain smoothing to remove the zero-mean error, and then uses the least squares method to obtain the gradually varying error; S3, using the obtained gradually varying error of the stable reference points to compensate for the gradually varying error of the actual test points, and compensating for the deformation measurement data of each actual test point.

[0005] However, in deep water-bearing mines or complex mining scenarios with jointed aquifers, the water content inside the rock to be broken often exhibits a significant non-uniform distribution. Because the dielectric loss factor of water molecules is much higher than that of the rock matrix minerals, local "hot spots" rapidly form in areas with high water content during the initial stages of microwave irradiation. Under this heterogeneous heating environment, the rock undergoes intense nonlinear thermal expansion due to the instantaneous heating of water. The displacement resulting from this expansion is highly intertwined in both space and time with the physical damage deformation (crack opening) caused by stress concentration in the rock structure. This "overlapping effect" of thermal expansion and structural damage deformation leads to the displacement data obtained by existing optical or mechanical measurement methods containing a large number of "thermal interference terms," ​​making it impossible to accurately isolate the residual deformation reflecting the true degree of rock damage, resulting in inaccurate rock-breaking energy efficiency assessments. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for measuring rock deformation during microwave rock breaking.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: In a first aspect, this invention discloses a method for measuring rock deformation during microwave rock breaking, comprising the following steps: acquiring multi-source information data of the rock during microwave rock breaking, including rock surface deformation data, spatial distribution data of rock water content, rock acoustic emission signals, and a temperature field image sequence of the rock surface, and constructing a temperature field distribution model based on the temperature field image sequence; performing a rock microwave energy absorption capacity analysis based on the temperature field distribution model and combined with the spatial distribution data of rock water content, thereby establishing a rock thermal expansion deformation prediction model to obtain the rock thermal expansion deformation prediction field during microwave heating; and based on the rock surface... Deformation data is used to construct a comprehensive rock deformation field. The comprehensive rock deformation field is then compared with the predicted rock thermal expansion deformation field to obtain a rock structural damage deformation field free from thermal interference. Initial anomaly identification is performed on the rock structural damage deformation field to identify suspected anomaly regions. These suspected anomaly regions are then optimized and adjusted to obtain anomalous deformation regions. The anomalous deformation regions are then spatiotemporally matched and verified with the rock acoustic emission signal. If the spatiotemporal matching verification is successful, the anomalous deformation region is determined to be a structural damage deformation region, and the structural damage deformation measurement results are output synchronously. Otherwise, it is determined to be a non-structural damage deformation region, and feedback adjustment is performed.

[0008] Secondly, this invention discloses a rock deformation measurement system for microwave rock breaking, comprising: a data acquisition module for acquiring multi-source information data of the rock during microwave rock breaking, including rock surface deformation data, spatial distribution data of rock water content, rock acoustic emission signals, and temperature field image sequences of the rock surface, and constructing a temperature field distribution model based on the temperature field image sequences; a thermal expansion deformation prediction module for performing microwave energy absorption capacity analysis of the rock based on the temperature field distribution model and combined with the spatial distribution data of rock water content, thereby establishing a rock thermal expansion deformation prediction model and obtaining the predicted field of rock thermal expansion deformation during microwave heating; and a rock structure damage deformation analysis module for... A comprehensive rock deformation field is constructed based on rock surface deformation data. The difference between the comprehensive rock deformation field and the predicted rock thermal expansion deformation field is processed to obtain a rock structural damage deformation field free from thermal interference. An abnormal deformation analysis module is used to perform initial anomaly identification processing on the rock structural damage deformation field to obtain suspected abnormal regions. The suspected abnormal regions are then detected, optimized, and adjusted to obtain abnormal deformation regions. A spatiotemporal matching verification module is used to perform spatiotemporal matching verification between the abnormal deformation regions and the rock acoustic emission signal. If the spatiotemporal matching verification is successful, the abnormal deformation region is determined to be a structural damage deformation region, and the structural damage deformation measurement results are output synchronously. Otherwise, it is determined to be a non-structural damage deformation region, and feedback adjustment is performed.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention analyzes the microwave energy absorption capacity of rocks based on a temperature field distribution model and combined with spatial distribution data of rock water content, establishes a rock thermal expansion deformation prediction model, and performs difference processing with the measured comprehensive rock deformation field. This process removes thermal interference caused by non-uniform heating from the original displacement data, resulting in a rock structural damage deformation field free of thermal interference. This solves the technical problems of insufficient deformation measurement accuracy and fuzzy damage identification caused by uneven thermal field distribution during the rock breaking process of non-uniform water-bearing rocks in existing technologies.

[0010] 2. This invention obtains the mean local deformation gradient of suspected abnormal regions to match and obtain secondary grid division specifications, and obtains image acquisition frequency adjustment value and deformation update frequency adjustment value based on the degree of specification difference value, thereby obtaining image acquisition execution frequency and deformation update execution frequency. This realizes multi-round deformation matching calculation for suspected abnormal regions, and solves the problems of untimely capture of local high gradient deformation regions and inaccurate anomaly identification caused by single sampling specifications in the prior art.

[0011] 3. This invention extracts the time of deformation and its spatial location in the abnormal deformation region, and compares them with the location and time information of the acoustic emission event obtained through spatial positioning. It performs spatiotemporal matching verification, thereby verifying the abnormal deformation region through the mutual verification of multi-source information with different physical properties. This solves the problem that a single optical deformation measurement method is easily affected by non-structural damage factors, leading to inaccurate determination of the damage region.

[0012] 4. This invention analyzes the thermal interference intensity index of the non-structural damage deformation region and compares it with the thermal interference intensity threshold to determine the thermal interference region and match the secondary microwave output execution value. Then, it performs secondary microwave rock breaking treatment and adjusts the pulse duty cycle, thereby realizing targeted optimization of microwave output parameters based on the real-time deformation response of the rock. Attached Figure Description

[0013] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein: Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the overall process of rock deformation measurement during microwave rock breaking according to the present invention. Figure 3 This is a flowchart of the thermal interference identification and microwave parameter feedback adjustment process of the present invention. Figure 4 This is a system architecture diagram of the present invention. Detailed Implementation

[0014] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0015] This invention relates to the field of deformation measurement technology, specifically to a method for measuring rock deformation during microwave rock breaking. This method collects multi-source information data, including rock surface deformation data, spatial distribution data of rock water content, rock acoustic emission signals, and rock surface temperature field image sequences, to construct a rock temperature field distribution model. It then analyzes the rock's absorption capacity for microwave energy using the spatial distribution data of rock water content, thereby establishing a rock thermal expansion deformation prediction model. By performing difference processing between the comprehensive rock deformation field and the predicted rock thermal expansion deformation field, thermal expansion deformation is separated, resulting in a structural damage deformation field reflecting the actual rock fracture behavior. This field is then verified through spatiotemporal matching using acoustic emission signals to accurately identify the rock fracture region. Furthermore, by identifying thermal interference regions within the non-structural damage deformation region and dynamically adjusting microwave rock breaking parameters, the microwave rock breaking process is optimized. This invention effectively distinguishes between rock structural damage deformation and thermal expansion deformation by constructing a thermal expansion deformation prediction model and performing deformation separation processing.

[0016] In microwave rock breaking technology, microwave energy is converted into heat energy through the absorption of electromagnetic waves by water and mineral particles inside the rock, thereby creating a temperature gradient and thermal stress within the rock, ultimately leading to rock fracture. With the increasing application of microwave rock breaking technology in mining, underground engineering, and tunnel construction, real-time monitoring and deformation identification of the rock fracture process have become crucial for improving rock breaking efficiency and safety. However, in actual microwave rock breaking processes, rocks heated by microwaves exhibit significant thermal expansion. The thermal expansion deformation and the structural damage deformation resulting from actual rock fracture share certain similarities in surface deformation characteristics. Existing technologies typically rely solely on surface deformation monitoring or acoustic emission monitoring to determine rock fracture, making it difficult to effectively distinguish between thermal expansion deformation and structural fracture deformation. This can easily lead to misjudgment of fracture identification, thus affecting rock breaking efficiency and microwave energy utilization efficiency. The purpose of this invention is to solve the problem of difficulty in distinguishing between thermal expansion deformation and structural damage deformation in existing technologies, thereby improving the accuracy of rock fracture identification and achieving dynamic optimization control of the microwave rock breaking process.

[0017] To achieve the above objectives, the present invention employs the following technical solution: During microwave rock breaking, rock surface deformation data, spatial distribution data of rock water content, rock acoustic emission signals, and rock surface temperature field image sequences are collected, and a temperature field distribution model of the rock surface is constructed based on the temperature field image sequences; based on the temperature field distribution model and combined with the spatial distribution data of rock water content, the rock's ability to absorb microwave energy is analyzed, thereby establishing a rock thermal expansion deformation prediction model and obtaining the rock thermal expansion deformation prediction field during microwave heating; further, by constructing a comprehensive rock deformation field and performing difference processing with the rock thermal expansion deformation prediction field, a rock structural damage deformation field after removing thermal expansion interference is obtained; subsequently, the spatial gradient of the structural damage deformation field is applied... Suspected anomaly regions were analyzed and identified, and local detection optimization was performed on these regions. By refining the mesh and increasing the deformation detection frequency, multiple rounds of deformation matching calculations were conducted on the suspected regions to obtain a more stable and accurate local deformation distribution. Based on this, the anomalous deformation regions were verified by spatiotemporal matching using rock acoustic emission signals. When the deformation region and the acoustic emission fracture event met the matching conditions in both time and space, the region was determined to be a structural damage deformation region; otherwise, it was determined to be a non-structural damage deformation region. For non-structural damage deformation regions, the thermal interference intensity was further analyzed. When a region was determined to be a thermal interference region, the microwave rock breaking process was dynamically controlled by adjusting microwave output power, microwave irradiation duration, and microwave pulse duty cycle, among other microwave rock breaking parameters.

[0018] like Figure 1 As shown, Figure 1 The flowchart of the method of the present invention includes the following steps: collecting multi-source information data of the rock during microwave rock breaking, including rock surface deformation data, spatial distribution data of rock water content, rock acoustic emission signals, and a temperature field image sequence of the rock surface; constructing a temperature field distribution model based on the temperature field image sequence; performing microwave energy absorption capacity analysis of the rock based on the temperature field distribution model and combined with the spatial distribution data of rock water content, thereby establishing a rock thermal expansion deformation prediction model to obtain the rock thermal expansion deformation prediction field during microwave heating; and constructing a rock surface deformation prediction model based on the rock surface deformation data. The rock comprehensive deformation field is analyzed, and the difference between the rock comprehensive deformation field and the rock thermal expansion deformation prediction field is processed to obtain the rock structural damage deformation field after removing thermal interference. The rock structural damage deformation field is subjected to initial anomaly identification processing to obtain suspected anomaly regions. The suspected anomaly regions are then detected, optimized, and adjusted to obtain anomalous deformation regions. The anomalous deformation regions are then spatiotemporally matched and verified with the rock acoustic emission signal. If the spatiotemporal matching verification is successful, the anomalous deformation region is determined to be a structural damage deformation region, and the structural damage deformation measurement results are output simultaneously. Otherwise, it is determined to be a non-structural damage deformation region, and feedback adjustment is performed.

[0019] In this embodiment, as Figure 2 As shown, Figure 2 The present invention provides a flowchart of the overall process for measuring rock deformation during microwave rock breaking. First, microwave rock breaking deformation monitoring is initiated, and image data and temperature field data of the rock surface are collected. The collected data are processed to construct a rock deformation field and spatially mesh the monitoring area. Based on this, a rock thermal expansion deformation prediction model is established, and a corresponding rock thermal expansion deformation prediction field is generated. Next, a structural damage deformation field is constructed based on the prediction results, and spatial gradient analysis is performed on the structural damage deformation field to identify deformation anomaly units. The identified deformation anomaly units are regionally marked to form suspected anomaly regions. Then, multiple rounds of deformation matching calculations are performed on the suspected anomaly regions to obtain multiple rounds of... Deformation displacement data is collected and the data is screened for consistency to obtain optimal deformation data. Based on the optimal deformation data, a local deformation distribution is constructed to identify abnormal deformation areas. Subsequently, rock acoustic emission signals are collected and event recognition processing is performed. The acoustic emission event information is then matched and verified with the abnormal deformation areas in a spatiotemporal manner. If the match is successful, it is determined to be a structural damage deformation area; if the match fails, it is determined to be a non-structural damage deformation area. For non-structural damage deformation areas, the thermal interference intensity index is further calculated and compared with a preset thermal interference threshold. When the thermal interference intensity index exceeds the thermal interference threshold, the microwave rock breaking parameters are adjusted; otherwise, the rock deformation measurement process ends.

[0020] Through deformation identification and mechanism verification, the deformation regions on the rock surface are divided into four different types, each with different formation mechanisms and physical significance. Suspected anomalous regions refer to areas where spatial gradient analysis reveals a significantly higher local deformation gradient than the surrounding area within the rock structural damage deformation field. These regions typically exhibit drastic deformation changes, possibly caused by factors such as the propagation of microcracks within the rock, localized stress concentration, or uneven thermal expansion. Therefore, this region only indicates the presence of potential anomalous deformation, but it has not yet been confirmed whether it represents actual structural fracture. After local detection optimization and spatiotemporal matching verification using acoustic emission signals, if the anomalous deformation region and the acoustic emission fracture event meet the matching conditions in both time and space, it is identified as a structural damage deformation region. This region represents actual crack propagation or structural damage within the rock, directly reflecting the rock fracture behavior during microwave rock breaking. If no corresponding fracture event occurs in the anomalous deformation region during acoustic emission verification, it is identified as a non-structural damage deformation region. This region is usually caused by thermal expansion effects or differences in thermal expansion of mineral particles and does not represent actual rock fracture. Within the non-structural damage deformation region, further classification is based on the intensity of thermal interference. When the deformation is primarily caused by a rapid increase in local temperature or concentrated microwave energy, the region is identified as a thermal interference region, essentially belonging to abnormal thermal expansion deformation. Conversely, when the deformation pertains to normal thermal expansion or structural fine-tuning after rock heating, it is determined to be a stable deformation region. Through this regional division, the true rock fracture behavior can be clearly distinguished from pseudo-deformation caused by temperature effects, thereby improving the accuracy of rock fracture identification.

[0021] By constructing a rock thermal expansion deformation prediction model and performing thermal expansion stripping processing on the comprehensive rock deformation field, the thermal expansion deformation during microwave heating is effectively eliminated, making the final rock structural damage deformation field more realistically reflect the rock fracture behavior, thus significantly improving the accuracy of rock fracture identification. Simultaneously, by optimizing the local detection of suspected abnormal areas through refined mesh division and increased image acquisition and deformation update frequencies, the accuracy of local deformation detection is improved, enabling more accurate identification of internal crack propagation areas. This scheme introduces rock acoustic emission signals for spatiotemporal matching verification, allowing deformation detection results to be cross-validated with rock fracture acoustic emission events, further improving the reliability of structural damage identification. Based on this, the microwave output power, irradiation duration, and microwave pulse duty cycle are dynamically adjusted according to the thermal interference intensity in non-structural damage deformation areas, resulting in a more reasonable microwave energy distribution, avoiding local overheating, and thus improving microwave energy utilization efficiency and rock fracture efficiency.

[0022] The rock composite deformation field and the rock thermal expansion deformation prediction field are interpolated to obtain a rock structural damage deformation field free from thermal interference. Specifically, after acquiring rock surface deformation data, the actual displacement distribution of the rock surface is first obtained through digital image correlation calculations. This displacement distribution is then reconstructed according to spatial coordinates to form a rock composite deformation field describing the true deformation state of the rock during microwave rock breaking. The rock composite deformation field and the rock thermal expansion deformation prediction field are spatially registered to ensure consistency in spatial grid division, coordinate reference, and time sampling nodes. After spatial and temporal alignment, the composite deformation value at the same spatial location is compared point-by-point with the corresponding thermal expansion deformation prediction value. The difference between the two is calculated to eliminate the influence of thermal expansion deformation caused by temperature rise. Since the rock composite deformation field includes both thermal expansion deformation and rock structural damage deformation, while the rock thermal expansion deformation prediction field only reflects deformation caused by temperature, point-by-point interpolation can yield the residual displacement distribution caused only by crack initiation, propagation, and structural failure. Finally, the residual displacement distribution is reorganized into a continuous spatial displacement field, which yields the rock structure damage deformation field after removing thermal interference.

[0023] Furthermore, a temperature field distribution model is constructed based on the temperature field image sequence. The specific method is as follows: the temperature images of each frame in the temperature field image sequence of the rock surface are processed for time synchronization so that each frame corresponds to a unique acquisition time, and the images are divided into spatial grid units according to a preset division specification; the temperature values ​​of each pixel in each spatial grid unit are extracted and averaged to obtain the average temperature of each grid unit; based on the spatial arrangement relationship of each spatial grid unit in the temperature field image sequence, the average temperature values ​​at the corresponding time are spatially rearranged to form the temperature field distribution matrix at the corresponding time; the temperature field distribution matrices at each time in the temperature field image sequence are continuously arranged to obtain the temperature field distribution model of the rock surface.

[0024] In this embodiment, the preset partitioning specification, or initial partitioning specification, refers to the standard parameters used to uniformly divide the spatial grid units when spatially discretizing the temperature field image of the rock surface. Its purpose is to convert the continuous temperature image into calculable discrete temperature units. For example, when the temperature image acquired by the infrared thermal imager has a resolution of 640×480 pixels and the effective observation area of ​​the rock sample surface is 320mm×240mm, the temperature image can be divided into grid units of 16×16 pixels. In this case, each spatial grid unit corresponds to an actual area of ​​approximately 8mm×8mm on the rock surface.

[0025] The average temperature values ​​at corresponding times are spatially rearranged to form a temperature field distribution matrix for that time. Specifically, after obtaining the average temperature value for each spatial grid cell, a unified two-dimensional coordinate numbering system is established for all spatial grid cells. For example, the upper left corner of the temperature image is used as the origin, the horizontal direction is defined as the row direction, and the vertical direction as the column direction. A unique row and column index number is assigned to each spatial grid cell. After the temperature image at a certain acquisition time is divided into grid cells and the average temperature value is calculated, the average temperature value is filled into the corresponding position according to the row and column index of each grid cell, thus constructing a two-dimensional matrix structure. Each element in the matrix corresponds to the average temperature value of a certain spatial region on the rock surface. For example, when the image is divided into 40×30 spatial grid cells, a 40×30 temperature field distribution matrix is ​​obtained. Then, the temperature field distribution matrices at consecutive acquisition times are arranged in chronological order to form a temperature field distribution model containing both time and spatial dimensions.

[0026] By constructing a temperature field distribution model of the rock surface based on a sequence of temperature field images, the temperature information in the original infrared thermographic images can be converted into temperature field data with a clear temporal and spatial structure. During microwave rock breaking, due to the non-uniform distribution of mineral composition and water content within the rock, the absorption process of microwave energy within the rock exhibits significant spatial differences, resulting in a significant non-uniform distribution of rock surface temperature in different regions. Relying solely on a single frame of temperature image or local temperature measurement points makes it difficult to accurately reflect the overall temperature variation over time and space. However, by constructing a temperature field distribution model, the spatial distribution of temperature and its temporal evolution can be described simultaneously, thereby enabling accurate identification of local hotspots and temperature gradient changes formed during microwave heating.

[0027] Furthermore, a rock thermal expansion deformation prediction model is established. The specific method is as follows: retrieve the spatial distribution data of rock water content and map it to the spatial grid structure where the temperature field distribution matrix is ​​located. The spatial grid structure includes each spatial grid cell. Based on the spatial distribution data of rock water content, the microwave specific absorptivity of each spatial grid cell is obtained, thereby obtaining the microwave energy absorption distribution on the rock surface. Based on the temperature rise rate of each spatial grid cell, thermal expansion response analysis is performed to obtain the predicted trend of thermal expansion displacement of each spatial grid cell. The predicted trends of thermal expansion displacement of the spatial grid cells are spatially combined to obtain the rock thermal expansion deformation prediction model, thereby generating the rock thermal expansion deformation prediction field.

[0028] In this embodiment, the spatial distribution data of rock moisture content is retrieved and mapped to the spatial grid structure of the temperature field distribution matrix. Specifically, the mapping method involves establishing a unified two-dimensional spatial coordinate system based on the geometric dimensions of the rock sample, using the spatial grid division specifications adopted by the temperature field distribution matrix as the reference grid structure. Subsequently, the spatial distribution data of rock moisture content is read, and spatial registration processing is performed on the moisture content data to ensure that its coordinate origin, scale, and direction are consistent with the temperature field grid. For cases where the resolution of the moisture content data is inconsistent with the resolution of the temperature field grid, an interpolation resampling method can be used to convert the moisture content data to the temperature field grid scale. For example, a neighborhood averaging method can be used to recalculate the moisture content value into the corresponding spatial grid cell, so that each spatial grid cell corresponds to a moisture content value, thereby mapping the spatial distribution data of rock moisture content to the spatial grid structure of the temperature field distribution matrix. It should be noted that all data before calculating the microwave specific absorptivity of the spatial grid cells has been normalized to eliminate the influence of dimensions.

[0029] The specific method for obtaining the microwave specific absorptivity of spatial grid cells is as follows: ; In the formula, This represents the microwave specific absorptivity of a spatial grid cell. Represents pi (π). Indicates microwave frequency. Represents the vacuum permittivity. Represents the relative permittivity loss factor. Indicates electric field strength. This indicates the density of the rock.

[0030] Microwave frequency can be obtained directly through the microwave generator control panel. The relative permittivity loss factor can be obtained by retrieving the preset physical property database based on the real-time collected rock moisture content data. The electric field strength can be directly measured by a broadband electromagnetic field strength analyzer (such as Narda NBM-550). The rock density can be obtained by consulting the physical property standard manual for this type of rock.

[0031] Thermal expansion response analysis is performed based on the temperature rise rate of each spatial grid cell to obtain the predicted trend of thermal expansion displacement for each spatial grid cell. Specifically, after obtaining the temperature change data for each spatial grid cell, the temperature rise rate of each spatial grid cell can be calculated by analyzing the temperature change trend over time. The temperature rise rate reflects the magnitude of temperature change per unit time and is an important parameter characterizing the degree of local heat accumulation. The specific method involves comparing the temperature data of the same spatial grid cell at adjacent acquisition times and calculating the relationship between temperature change and time interval to obtain the corresponding temperature rise rate. Subsequently, the influence of the temperature rise rate on the thermal expansion response of the rock is analyzed based on the theory of material thermal expansion. According to the thermal expansion mechanism of solid materials, when the material temperature rises, its internal lattice spacing increases, resulting in volumetric or linear expansion. Therefore, based on the thermal expansion characteristics of the rock material and the temperature change trend, the direction and degree of change of thermal expansion displacement of each spatial grid cell can be predicted, thus obtaining the trend of thermal expansion displacement of each grid cell during microwave heating, thereby forming the corresponding predicted trend data of thermal expansion displacement.

[0032] A rock thermal expansion deformation prediction model is obtained, from which a rock thermal expansion deformation prediction field is generated. Specifically, the predicted thermal expansion displacement trend corresponding to each spatial grid cell is arranged according to its spatial coordinate position in the temperature field distribution matrix, ensuring that the predicted displacement of each grid cell is consistent with its spatial position. Then, based on the spatial grid structure, the predicted displacements of each grid cell are uniformly organized to form a two-dimensional displacement matrix describing the thermal expansion displacement changes at various spatial locations on the rock surface. As time progresses, the displacement matrices at different time points are continuously arranged, thus forming a deformation prediction data structure that simultaneously includes spatial and temporal dimensions, thereby obtaining the rock thermal expansion deformation prediction model. The corresponding rock thermal expansion deformation prediction field is generated at any given time to describe the theoretical thermal expansion displacement distribution on the rock surface caused by temperature changes; this is the rock thermal expansion deformation prediction field.

[0033] In real-world microwave rock breaking environments, the comprehensive deformation measured on the rock surface typically includes both thermal expansion deformation and structural damage deformation. Without prediction and separation of thermal expansion deformation, displacement caused by thermal expansion can easily be misjudged as deformation due to rock structural fracture, thus affecting the accuracy of fracture identification. By constructing a thermal expansion deformation prediction model, the potential thermal expansion deformation on the rock surface can be predicted in advance based on the temperature and moisture content distribution, forming a corresponding thermal expansion deformation prediction field. In subsequent steps, by performing difference processing with the actually measured comprehensive rock deformation field, the thermal expansion interference term can be effectively removed, resulting in a more realistic distribution of structural damage deformation. This improves the overall accuracy of rock fracture identification and the efficiency of microwave rock breaking.

[0034] Furthermore, a comprehensive rock deformation field is constructed using the following method: An array of optical markers is uniformly arranged on the rock surface; continuous image acquisition of the rock surface is performed using an optical acquisition device to form a sequence of rock surface images; region matching analysis is performed on adjacent time frame images to identify the positional changes of each optical marker between different time frames; the spatial displacement of each optical marker is obtained based on the positional changes of each optical marker, resulting in a displacement dataset for each optical marker; based on the spatial distribution of the displacement dataset, spatial difference compensation is performed on the areas where no optical markers are arranged to obtain the comprehensive rock deformation field. Specifically, spatial difference compensation for the areas where no optical markers are arranged can be performed using, for example, inverse distance weighted interpolation.

[0035] In this embodiment, by constructing a comprehensive rock deformation field, continuous and complete deformation distribution information of the entire rock surface can be obtained, enabling accurate quantification of local and global deformation. This provides a reliable basis for subsequent removal of thermal interference and identification of structural damage deformation, thereby improving the accuracy of rock damage identification during microwave rock breaking.

[0036] Furthermore, suspected anomalous regions are identified through the following method: spatial gradient analysis is performed on the structural damage deformation field to obtain the local deformation gradient value of each spatial grid cell; a preset deformation gradient threshold is obtained and compared with the local deformation gradient value of each spatial grid cell; if the local deformation gradient of a certain spatial grid cell is above the deformation gradient threshold, the spatial grid cell is marked as a deformation anomalous cell; the deformation anomalous cell and its directly adjacent cells are marked as suspected anomalous regions.

[0037] In this embodiment, spatial gradient analysis is performed on the structural damage deformation field to obtain the local deformation gradient values ​​of each spatial grid cell. Specifically, the rock structure damage deformation field is discretized into a two-dimensional matrix containing multiple spatial grid cells. For any target grid cell in the matrix, the horizontal deformation gradient of the grid cell is obtained by extracting the displacement difference between two adjacent grid cells in the horizontal direction and dividing it by twice the horizontal grid spacing. Similarly, the vertical deformation gradient of the grid cell is obtained by extracting the displacement difference between two adjacent grid cells in the vertical direction and dividing it by twice the vertical grid spacing. The obtained horizontal and vertical deformation gradient values ​​are squared and summed, and the square root of the sum is taken to obtain the local deformation gradient amplitude of the spatial grid cell. This calculation process is repeated for each grid cell in the structural damage deformation field, ultimately forming the local deformation gradient value distribution of each grid cell.

[0038] By performing spatial gradient analysis on the deformation field of structural damage and dividing it into deformation anomaly units according to a preset deformation gradient threshold, regions with significant local deformation changes in rocks can be quickly identified, thus highlighting key areas where structural damage may exist. Marking deformation anomaly units and their directly adjacent areas as suspected anomaly regions effectively covers the adjacent influence zones caused by stress transfer or crack initiation, avoiding the omission of potential damage areas. Furthermore, it can capture the spatial expansion trend of structural damage at an early stage, providing reliable candidate regions for subsequent anomaly detection optimization and refined deformation data analysis, thereby improving the completeness and accuracy of damage identification.

[0039] Further, detection, optimization, and adjustment processes are performed to obtain abnormal deformation regions. Specifically, the following method is used: The mean deformation gradient of the suspected abnormal region is obtained and matched with the historical deformation database to obtain the corresponding secondary mesh partitioning specification. This is then used to perform local partitioning of the suspected abnormal region, obtaining each local mesh unit. A difference analysis is performed between the secondary mesh partitioning specification and the preset partitioning specification to obtain the specification difference value. Based on the specification difference value, an image acquisition frequency adjustment value and a deformation update frequency adjustment value are obtained. Based on the image acquisition frequency adjustment value, the image acquisition frequency is increased to obtain the image acquisition execution frequency. Specifically, the current... The image acquisition frequency is obtained by adding the image acquisition frequency adjustment value to the image acquisition frequency. The deformation update frequency is increased based on the deformation update frequency adjustment value to obtain the deformation update execution frequency. Specifically, the deformation update frequency adjustment value is added to the deformation update frequency to obtain the deformation update execution frequency. Based on the image acquisition execution frequency and the deformation update execution frequency, multiple rounds of deformation matching calculations are performed on the suspected abnormal areas to obtain a set of multiple rounds of deformation displacement data. The set of multiple rounds of deformation displacement data is subjected to consistency screening to obtain the preferred deformation data. Based on the preferred deformation data, the local deformation distribution of the suspected abnormal areas is constructed to obtain the abnormal deformation areas.

[0040] In this embodiment, the corresponding secondary mesh partitioning specification is obtained by comparing the current part deformation gradient mean with the historical part deformation gradient intervals stored in the historical deformation database. If the current part deformation gradient mean is within a certain historical part deformation gradient interval, the secondary mesh partitioning specification corresponding to the historical part deformation gradient mean interval is obtained as the corresponding secondary mesh partitioning specification.

[0041] The degree of difference between the secondary mesh division specification and the preset division specification is analyzed to obtain the specification difference value. The specific method is as follows: the difference between the current secondary mesh division specification and the preset division specification is divided by the preset division specification to obtain the specification difference value.

[0042] The image acquisition frequency adjustment value and deformation update frequency adjustment value are obtained based on the matching of specification difference degree values. The specific method is as follows: the specification difference degree value is matched with the specification difference adjustment comparison table stored in the historical deformation database. The specification difference adjustment comparison table contains each historical specification difference degree value and the corresponding image acquisition frequency adjustment reference value and deformation update frequency adjustment reference value. The historical specification difference degree value that is closest to the specification difference degree value is selected as the historical comparison specification difference degree value. If the historical comparison specification difference degree value is not unique, the value corresponding to the minimum value of the historical comparison specification difference degree value is selected as the specification difference degree comparison value. The image acquisition frequency adjustment reference value and deformation update frequency adjustment reference value corresponding to the specification difference degree comparison value are used as the image acquisition frequency adjustment value and deformation update frequency adjustment value.

[0043] Based on the image acquisition execution frequency and deformation update execution frequency, multiple rounds of deformation matching calculations are performed on suspected anomaly regions to obtain a multi-round deformation displacement data set. Specifically, at the optimized acquisition frequency, multiple consecutive frames of images are acquired on the suspected anomaly regions; the displacement change between each frame is calculated based on optical markers to obtain the local deformation vector for each round. Multiple rounds of calculation mean repeating this process multiple times, thereby forming a multi-round deformation displacement data set covering the time series, ensuring data sufficiency and temporal integrity.

[0044] To obtain optimal deformation data, a consistency screening process is performed on the multi-round deformation displacement data set. Specifically, for the displacement data set obtained from multiple rounds of deformation matching calculations in suspected abnormal areas, the mean displacement and standard deviation of each grid cell are first calculated. A screening rule is then applied to each grid cell: data whose deviation from the mean is within twice the standard deviation is retained, while data exceeding the deviation is discarded. This screening process is repeated for all rounds of data to ultimately obtain the optimal deformation data.

[0045] To construct the local deformation distribution of suspected abnormal regions, thereby obtaining abnormal deformation regions, the specific method is as follows: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The preferred mapping is to each local grid cell location, and bilinear spatial interpolation is used for continuous processing: for each non-grid node location Its deformation value The result is obtained by calculating the weighted average of the four nearest neighbor grid nodes: ; In the formula, Indicates the deformation value. Indicates preferred deformation data. Indicates the first Each grid point corresponds to the target location. Contribution to the impact The coordinates of the four neighboring grid cell nodes. The x and y coordinates of the four corner points of the rectangle are respectively. The horizontal spacing of the grid. The vertical spacing of the grid. This represents the total number of grid cell nodes. This allows the generation of a continuous local deformation distribution map, defining the abnormal deformation region as a set of continuous spatial units whose displacement amplitude exceeds a preset threshold.

[0046] By performing local secondary meshing, multiple rounds of deformation matching, and consistency screening on suspected anomalous areas, minute local deformations can be accurately captured and thermal expansion or noise interference can be eliminated, achieving high-precision identification of anomalous deformation areas. Adjusting the image acquisition frequency and deformation update frequency can dynamically adapt to the spatial complexity and temporal change rate of different regions, improving the spatiotemporal resolution of the measurement.

[0047] Furthermore, spatiotemporal matching verification is performed. The specific method is as follows: the collected rock acoustic emission signals are processed for event identification to obtain each acoustic emission event; the arrival time difference of the signals from each acoustic emission sensor is obtained, thereby spatially locating the acoustic emission events to obtain their positions; the deformation occurrence time and spatial location of the abnormal deformation area are extracted; the position and time information of the acoustic emission events are jointly compared with the abnormal deformation area. When the time difference between the deformation occurrence time of the abnormal deformation area and the acoustic emission event is less than a preset time threshold, and the spatial distance between the location of the abnormal deformation area and the acoustic emission event location result is less than a preset spatial threshold, the spatiotemporal matching verification is considered successful; otherwise, the spatiotemporal matching verification fails.

[0048] In this embodiment, the time difference of signal arrival of each acoustic emission sensor can be obtained by retrieving the logs of the acoustic emission acquisition system.

[0049] Event identification processing is performed to obtain each acoustic emission event. Specifically, the raw acoustic emission signals are recorded in real time. Bandpass filtering is applied to the raw signals to remove environmental noise and low-frequency vibration interference; the filtering frequency range is, for example, 20kHz to 400kHz. A trigger threshold is then set in the filtered signal. When the signal amplitude exceeds this threshold at a certain moment, it is determined to be an acoustic emission trigger signal, and the start time of this signal is recorded. Centered on this trigger signal, the complete waveform is extracted within a preset time window (e.g., 200μs), and its duration, peak amplitude, and ring count are calculated. If both the duration and ring count of this signal segment are greater than a preset minimum event threshold, then this signal segment is identified as an acoustic emission event. This processing procedure is repeated for all signals acquired by the sensors to obtain multiple acoustic emission events and their corresponding time information, thus forming an acoustic emission event set.

[0050] The time difference of arrival (TDOA) of signals from each acoustic emission sensor is obtained, thereby spatially locating the acoustic emission event and obtaining the location of the acoustic emission event. Specifically, spatial location can be obtained by using an acoustic emission detection system (such as the Physical Acoustics PCI-2 AESystem) and an acoustic emission source localization algorithm, such as the time difference of arrival localization method (TDOA localization).

[0051] During microwave heating and fracturing, rocks develop microcracks that propagate. This process manifests not only as surface deformation but also as acoustic emission events caused by the release of internal elastic waves. Therefore, spatiotemporal matching verification can confirm whether abnormal deformation areas are truly caused by internal crack activity, thus avoiding false deformation identification problems due to image noise, lighting variations, or measurement errors. By temporally matching the deformation occurrence time with the acoustic emission event time and spatially matching the deformation location with the sound source location, cross-verification of multi-source information can be achieved. Only when both the time difference and spatial distance meet threshold conditions is the deformation area determined to be consistent with a crack event, thereby improving the reliability of abnormal deformation identification. Furthermore, spatiotemporal matching verification can couple surface deformation information with internal crack activity information for more accurate identification of the true location and development process of rock fracture, reducing misjudgments.

[0052] Furthermore, feedback adjustments are performed, specifically as follows: The distribution ratio and duration of non-structural damage deformation regions are obtained. These ratios can be statistically obtained from the rock structural damage deformation field, and the thermal interference intensity index of the non-structural damage deformation regions is analyzed. A preset thermal interference intensity threshold is obtained and compared with the thermal interference intensity index of the non-structural damage deformation regions. If the thermal interference intensity index is above the threshold, the non-structural damage deformation region is marked as a thermal interference region; otherwise, it is marked as a stable deformation region. If the non-structural damage deformation region is a thermal interference region, microwave rock breaking parameter adjustments are performed; otherwise, microwave rock breaking parameter adjustments are not performed.

[0053] In this embodiment, the thermal interference intensity index of the non-structural damage deformation region is obtained by the following method: ; In the formula, Indicates the intensity of thermal interference. Indicates the distribution proportion weight. Indicates duration weight, This indicates the proportion of non-structural damage deformation regions. This indicates the duration of the non-structural damage deformation region. This represents the baseline value for duration.

[0054] The distribution ratio weight and duration weight can be determined using historical experimental data calibration methods, as follows: Within multiple historical microwave rock breaking experimental cycles, the distribution ratio and duration of the non-structural damage deformation region are recorded for each experimental cycle. Simultaneously, the rock temperature disturbance amplitude within the corresponding experimental cycle is collected as a thermal interference reference index. The correlation coefficient between the distribution ratio data sequence and the corresponding temperature disturbance data sequence within each experimental cycle is calculated, and the correlation coefficient between the duration data sequence and the corresponding temperature disturbance data sequence within each experimental cycle is also calculated. The correlation coefficients can be obtained using the Pearson correlation coefficient calculation method. The absolute values ​​of the correlation coefficients corresponding to the distribution ratio and the duration are processed and normalized to obtain the distribution ratio weight and duration weight, respectively. The distribution ratio weight is the proportion of the absolute value of the distribution ratio correlation coefficient to the sum of the absolute values ​​of the two correlation coefficients, and the duration weight is the proportion of the absolute value of the duration correlation coefficient to the sum of the absolute values ​​of the two correlation coefficients. The baseline value for duration can be obtained by querying a historical deformation database.

[0055] The stable deformation region is mainly caused by the normal thermal expansion of the rock or the adjustment of internal stress, and no structural fracture occurs. Therefore, there is no need to adjust the microwave rock breaking parameters. The thermal interference region is mainly caused by abnormal temperature rise due to local microwave energy concentration. This abnormal temperature rise will affect the accuracy of structural damage deformation identification. Therefore, it is necessary to control the microwave rock breaking process by adjusting the microwave output power, irradiation duration or microwave pulse duty cycle, so as to reduce thermal interference and improve rock breaking efficiency.

[0056] Furthermore, the microwave rock-breaking parameters are adjusted. The specific method is as follows: the location of local microwave energy concentration is determined based on the distribution range of the thermal interference area; a secondary microwave output execution value is obtained based on the thermal interference intensity index matching. The secondary microwave output execution value includes the microwave output power execution value and the microwave irradiation duration execution value; a secondary microwave rock-breaking process is performed based on the secondary microwave output execution value, and rock deformation monitoring is performed again during the secondary microwave rock-breaking process to determine whether there are still non-structural damage deformation areas. If there are still non-structural damage deformation areas, the microwave pulse duty cycle is adjusted; otherwise, the microwave pulse duty cycle adjustment is not performed, and the microwave rock-breaking process is completed.

[0057] In this embodiment, as Figure 3 As shown, Figure 3The flowchart for thermal interference identification and microwave parameter feedback adjustment of this invention is as follows: Non-structural damage deformation regions are identified, and statistical analysis is performed on these regions to obtain their distribution ratio. The duration of these non-structural damage deformation regions is calculated by time tracking. A thermal interference intensity index is calculated based on the distribution ratio and duration. The thermal interference intensity index is then compared with a preset thermal interference threshold. If the threshold is not exceeded, the microwave rock breaking parameter optimization process ends. If the threshold is exceeded, a secondary microwave output execution value is obtained based on the thermal interference intensity index, and the microwave output power and microwave irradiation duration are adjusted. A secondary microwave rock breaking process is then performed, and rock deformation monitoring is performed again during the process. It is determined whether non-structural damage deformation regions still exist. If not, the microwave rock breaking parameter optimization process ends; if they still exist, the microwave pulse duty cycle is adjusted, thereby completing the microwave rock breaking parameter optimization process.

[0058] The rock deformation monitoring was repeated, mainly involving the continuous acquisition, analysis, and determination of the rock surface deformation state. Specifically, the aforementioned deformation measurement system was used to continuously acquire images of the rock surface, and the deformation amount and deformation gradient of each spatial grid cell were recalculated to construct a new rock deformation field. Subsequently, abnormal deformation cells were identified in the new deformation field, and structural damage was determined in the abnormal deformation areas using the acoustic emission event spatiotemporal matching verification method, thereby distinguishing between structurally damaged deformation areas and non-structurally damaged deformation areas. Based on this, the distribution ratio and duration of non-structurally damaged deformation areas were recalculated, and a new thermal interference intensity index was calculated to determine whether non-structurally damaged deformation areas caused by thermal expansion still existed during the secondary microwave rock breaking process.

[0059] The secondary microwave output execution value is obtained based on the matching of thermal interference intensity index. The specific method is as follows: obtain the microwave rock breaking parameter mapping table stored in the historical deformation database. This mapping table stores the thermal interference intensity index interval with the corresponding microwave output power execution value and microwave irradiation duration execution value. For example, the thermal interference intensity index is divided into multiple intervals (such as low, medium and high intervals). The current thermal interference intensity index is matched with the thermal interference intensity index interval in the microwave rock breaking parameter mapping table to determine its interval. The microwave output power execution value and microwave irradiation duration execution value corresponding to the interval are read as the secondary microwave output execution value.

[0060] The specific method for adjusting the microwave pulse duty cycle is as follows: If non-structural damage deformation areas still exist after secondary microwave rock breaking treatment, these non-structural damage deformation areas are marked as secondary microwave non-structural damage deformation areas. The thermal interference intensity index of these secondary microwave non-structural damage deformation areas is obtained and recorded as the secondary microwave thermal interference intensity index. The microwave pulse duty cycle adjustment execution value is obtained based on the secondary microwave thermal interference intensity index, and then the microwave pulse duty cycle is adjusted based on this execution value. Specifically, obtaining the microwave pulse duty cycle adjustment execution value based on the secondary microwave thermal interference intensity index involves: obtaining the microwave rock breaking parameter mapping table stored in the historical deformation database. This mapping table stores the thermal interference intensity index intervals and their corresponding microwave pulse duty cycle adjustment execution values. For example, the thermal interference intensity index is divided into multiple intervals (such as low, medium, and high intervals). The current secondary microwave thermal interference intensity index is matched with the thermal interference intensity index interval in the microwave rock breaking parameter mapping table to determine its interval, and the microwave pulse duty cycle adjustment execution value corresponding to that interval is read as the microwave pulse duty cycle adjustment execution value.

[0061] By adjusting microwave rock-breaking parameters, the microwave energy output mode can be dynamically adjusted based on the thermal interference intensity index. This makes the microwave rock-breaking process more adaptable to the actual damage evolution state inside the rock, thereby improving rock-breaking efficiency and reducing measurement errors caused by thermal interference. During the initial parameter adjustment, only the microwave output power and microwave irradiation duration are adjusted. This allows for rapid adjustment of the overall energy input intensity without changing the microwave energy release mode, prioritizing the improvement of uneven microwave energy distribution or insufficient energy input. Subsequently, rock deformation monitoring is performed again to verify the adjustment effect. If non-structural damage deformation areas still exist, the microwave pulse duty cycle is further adjusted. By changing the microwave energy pulse release mode, thermal expansion interference caused by continuous heating is reduced, thus gradually optimizing the microwave energy action mode. This approach avoids increasing system control complexity due to excessive parameter adjustments at once and allows for gradual correction of the microwave action state based on real-time monitoring results, thereby improving the stability of the microwave rock-breaking process, energy utilization efficiency, and the accuracy of rock structural damage identification.

[0062] like Figure 4 As shown, Figure 4The system architecture diagram of this invention, provided in this embodiment, includes a rock deformation measurement system during microwave rock breaking, comprising: a data acquisition module for acquiring multi-source information data of the rock during microwave rock breaking, including rock surface deformation data, spatial distribution data of rock water content, rock acoustic emission signals, and temperature field image sequences of the rock surface, and constructing a temperature field distribution model based on the temperature field image sequences; a thermal expansion deformation prediction module for performing microwave energy absorption capacity analysis of the rock based on the temperature field distribution model and combined with the spatial distribution data of rock water content, thereby establishing a rock thermal expansion deformation prediction model and obtaining the predicted field of rock thermal expansion deformation during microwave heating; and a rock structure damage deformation analysis module. The system is divided into four modules: a rock structure damage deformation field and a rock structure damage deformation field. The rock structure damage deformation field is constructed based on rock surface deformation data, and the rock structure damage deformation field is compared with the rock thermal expansion deformation prediction field to obtain the rock structure damage deformation field after removing thermal interference. The abnormal deformation analysis module is used to perform initial anomaly identification processing on the rock structure damage deformation field to obtain suspected abnormal areas, and to perform detection, optimization and adjustment processing on the suspected abnormal areas to obtain abnormal deformation areas. The spatiotemporal matching verification module is used to perform spatiotemporal matching verification between the abnormal deformation areas and the rock acoustic emission signals. If the spatiotemporal matching verification is successful, the abnormal deformation area is determined to be a structural damage deformation area, and the structural damage deformation measurement results are output synchronously. Otherwise, it is determined to be a non-structural damage deformation area, and feedback adjustment is performed.

[0063] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for measuring rock deformation during microwave rock breaking, characterized in that: Includes the following steps: Multi-source information data of rocks are collected during microwave rock breaking. The multi-source information data includes rock surface deformation data, rock water content spatial distribution data, rock acoustic emission signals, and rock surface temperature field image sequence. A temperature field distribution model is constructed based on the temperature field image sequence. Based on the temperature field distribution model and combined with the spatial distribution data of rock water content, the microwave energy absorption capacity of rock is analyzed, thereby establishing a rock thermal expansion deformation prediction model and obtaining the rock thermal expansion deformation prediction field during microwave heating. A comprehensive rock deformation field is constructed based on rock surface deformation data, and the difference between the comprehensive rock deformation field and the rock thermal expansion deformation prediction field is processed to obtain a rock structure damage deformation field with thermal interference removed. Initial anomaly identification processing is performed on the rock structure damage deformation field to obtain suspected anomaly regions. Then, the suspected anomaly regions are detected, optimized, and adjusted to obtain the abnormal deformation regions. The abnormal deformation area is matched and verified with the acoustic emission signal of the rock in a time and space. If the time and space matching verification is successful, the abnormal deformation area is determined to be a structural damage deformation area, and the structural damage deformation measurement result is output synchronously. Otherwise, it is determined to be a non-structural damage deformation area, and feedback adjustment is performed.

2. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The specific method for constructing the temperature field distribution model based on the temperature field image sequence is as follows: The temperature field image sequence of the rock surface is time-synchronized so that each frame of temperature image corresponds to a unique acquisition time, and then divided into spatial grid units according to a preset division specification. Extract the temperature value of each pixel in each spatial grid cell and perform averaging to obtain the average temperature of each grid cell; Based on the spatial arrangement of each spatial grid cell in the temperature field image sequence, the temperature mean at the corresponding time is spatially rearranged to form the temperature field distribution matrix at the corresponding time. By continuously arranging the temperature field distribution matrices at each moment in the temperature field image sequence, a temperature field distribution model of the rock surface is obtained.

3. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The specific method for establishing the rock thermal expansion deformation prediction model is as follows: Retrieve spatial distribution data of rock water content and map it to the spatial grid structure where the temperature field distribution matrix is ​​located. The spatial grid structure includes each spatial grid cell. The microwave specific absorptivity of each spatial grid cell is obtained based on the spatial distribution data of rock water content, thereby obtaining the microwave energy absorption distribution on the rock surface; Thermal expansion response analysis is performed based on the temperature rise rate of each spatial grid cell to obtain the predicted trend of thermal expansion displacement of each spatial grid cell. By spatially combining the predicted trends of thermal expansion displacement of spatial grid cells, a rock thermal expansion deformation prediction model is obtained, thereby generating a rock thermal expansion deformation prediction field.

4. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The specific method for constructing the comprehensive deformation field of rocks is as follows: An array of optical marker points is uniformly arranged on the rock surface; Continuous image acquisition of the rock surface is performed using optical acquisition equipment to form a sequence of rock surface images; Region matching analysis is performed on adjacent time frame images to identify the positional changes of each optical marker point between different time frames; Based on the analysis of the positional changes of each optical marker, the spatial displacement of each optical marker is obtained, and the displacement dataset of each optical marker is obtained. Based on the spatial distribution of the displacement dataset, spatial difference compensation is performed on the areas where optical markers are not placed to obtain the comprehensive deformation field of the rock.

5. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The specific method for obtaining the suspected abnormal region is as follows: Spatial gradient analysis was performed on the structural damage deformation field to obtain the local deformation gradient values ​​of each spatial grid element; Obtain a preset deformation gradient threshold and compare it with the local deformation gradient value of each spatial grid cell. If the local deformation gradient of a spatial grid cell is above the deformation gradient threshold, then mark the spatial grid cell as a deformation anomalous cell. The deformed anomalous units and their directly adjacent units are marked as suspected anomalous regions.

6. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The process of detection, optimization, and adjustment is performed to obtain the abnormal deformation region. The specific method is as follows: The mean value of the deformation gradient of the suspected abnormal region is obtained and matched with the historical deformation database to obtain the corresponding secondary mesh division specification. Then, the suspected abnormal region is locally divided to obtain each local mesh element. The degree of difference between the secondary mesh division specification and the preset division specification is analyzed to obtain the specification difference value; The image acquisition frequency adjustment value and deformation update frequency adjustment value are obtained based on the matching of the specification difference value; The image acquisition frequency is obtained by increasing the image acquisition frequency adjustment value; The deformation update frequency is obtained by increasing the deformation update frequency adjustment value; Based on the image acquisition execution frequency and deformation update execution frequency, multiple rounds of deformation matching calculations are performed on suspected abnormal areas to obtain a set of multiple rounds of deformation displacement data. Consistency screening is performed on the multi-round deformation displacement data set to obtain the preferred deformation data. Based on the preferred deformation data, the local deformation distribution of suspected abnormal areas is constructed, thereby obtaining the abnormal deformation areas.

7. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The specific method for performing spatiotemporal matching verification is as follows: The collected acoustic emission signals from the rocks are processed for event identification to obtain each acoustic emission event; The time difference of arrival of signals from each acoustic emission sensor is obtained, thereby spatially locating the acoustic emission event and obtaining the location of the acoustic emission event. Extract the time of deformation and its spatial location in the abnormal deformation region; The location and time information of the acoustic emission event are jointly compared with the abnormal deformation area. When the time difference between the deformation of the abnormal deformation area and the time of the acoustic emission event is less than a preset time threshold, and the spatial distance between the location of the abnormal deformation area and the location result of the acoustic emission event is less than a preset spatial threshold, the spatiotemporal matching verification is determined to be successful; otherwise, the spatiotemporal matching verification is deemed to be unsuccessful.

8. The method for measuring rock deformation during microwave rock breaking according to claim 1, characterized in that: The specific method for implementing the feedback adjustment is as follows: The distribution ratio and duration of non-structural damage deformation regions were obtained, and the thermal interference intensity index of non-structural damage deformation regions was analyzed. A preset thermal interference intensity threshold is obtained and compared with the thermal interference intensity index of the non-structural damage deformation region. If the thermal interference intensity index is above the thermal interference intensity threshold, the non-structural damage deformation region is marked as a thermal interference region; otherwise, it is marked as a stable deformation region. If the non-structural damage deformation area is a thermal interference area, then microwave rock breaking parameter adjustment will be performed; otherwise, microwave rock breaking parameter adjustment will not be performed.

9. The method for measuring rock deformation during microwave rock breaking according to claim 8, characterized in that: The specific method for adjusting the microwave rock-breaking parameters is as follows: The secondary microwave output execution value is obtained based on the matching of thermal interference intensity index. The secondary microwave output execution value includes the microwave output power execution value and the microwave irradiation duration execution value. A second microwave rock breaking process is performed based on the secondary microwave output execution value. During the second microwave rock breaking process, rock deformation monitoring is performed again to determine whether there are still non-structural damage deformation areas. If there are still non-structural damage deformation areas, the microwave pulse duty cycle is adjusted; otherwise, the microwave pulse duty cycle is not adjusted, and the microwave rock breaking process is completed.

10. A system for measuring rock deformation during microwave rock breaking, characterized in that: include: The data acquisition module is used to acquire multi-source information data of rocks during microwave rock breaking. The multi-source information data includes rock surface deformation data, rock water content spatial distribution data, rock acoustic emission signals, and temperature field image sequences of rock surfaces, and constructs a temperature field distribution model based on the temperature field image sequences. The thermal expansion deformation prediction module is used to perform microwave energy absorption capacity analysis of rocks based on temperature field distribution model and combined with spatial distribution data of rock water content, thereby establishing a rock thermal expansion deformation prediction model and obtaining the rock thermal expansion deformation prediction field during microwave heating. The rock structure damage and deformation analysis module is used to construct a comprehensive rock deformation field based on rock surface deformation data, and to perform difference processing between the comprehensive rock deformation field and the rock thermal expansion deformation prediction field to obtain a rock structure damage and deformation field with thermal interference removed. The abnormal deformation analysis module is used to perform initial anomaly identification processing on the damage deformation field of rock structure, obtain suspected abnormal areas, and perform detection, optimization and adjustment processing on the suspected abnormal areas to obtain the abnormal deformation areas. The spatiotemporal matching verification module is used to perform spatiotemporal matching verification between the abnormal deformation area and the acoustic emission signal of the rock. If the spatiotemporal matching verification is successful, the abnormal deformation area is determined to be a structural damage deformation area, and the structural damage deformation measurement results are output synchronously. Otherwise, it is determined to be a non-structural damage deformation area, and feedback adjustment is performed.

Images