Method and system for detecting lining disbonding

Abstract

The application provides a lining void detection method and system, which comprises the following steps: after the pouring of a lining area to be monitored is completed, real-time temperature distribution data collected by a self-heating distributed temperature measurement optical fiber at each sampling point of each sensing path in the lining area to be monitored is used to compensate original strain frequency shift data collected by a distributed strain measurement optical fiber at each sampling point, and a candidate void area is determined in the lining area to be monitored according to the compensated real strain evolution characteristics; after it is determined that the lining area to be monitored reaches a stable state, the thermal response characteristics of the candidate void area are collected based on the self-heating distributed temperature measurement optical fiber; and the candidate void area is subjected to void detection according to the thermal response characteristics, so as to obtain a void detection result of the candidate void area. The application realizes accurate identification, accurate positioning and reliable discrimination of the lining void by combining continuous spatial sensing of the distributed optical fiber, passive strain monitoring and active thermal excitation.

Description

Technical Field

[0001] This invention relates to the field of underground monitoring technology, and in particular to a method and system for detecting lining voids. Background Technology

[0002] In the construction of underground engineering projects such as tunnels, hydropower stations, and compressed air energy storage tunnels, the tight fit between the lining structure and the surrounding rock and soil is crucial to ensuring structural stability and sealing. Due to the complexity of concrete pouring construction and the influence of gravity and other factors, lining voids are easily formed. These voids prevent the lining from being tightly bonded to the surrounding rock, thereby altering its stress state and making localized areas weak points in the structure. Under external disturbances or groundwater pressure, these areas are prone to a series of defects such as cracking, leakage, and detachment. Therefore, the ability to efficiently and reliably identify and assess the lining voids during construction and operation is an important requirement for ensuring the safe operation of underground engineering projects.

[0003] To meet the above requirements, current technologies typically employ single-point monitoring for lining void detection. However, in complex and variable underground spaces, this monitoring method is prone to issues such as information coverage gaps due to the physical limitations of specific deployment points, feature annihilation of physical signals reflecting deformation characteristics under the thermal evolution of materials, and passivation of the perception of minute differences at the interface during the performance stabilization period. As a result, the sensitivity and reliability of lining void detection cannot be effectively guaranteed. Summary of the Invention

[0004] This invention provides a method and system for detecting voids in linings, which solves the problem that single-point monitoring in the prior art makes it difficult to effectively guarantee the sensitivity and reliability of void detection, and significantly improves the sensitivity and reliability of void detection.

[0005] This invention provides a method for detecting voids in lining, comprising: After the lining area to be monitored is poured, the raw strain frequency shift data collected by the distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and the real-time temperature distribution data collected by the self-heating distributed temperature measurement fiber at each sampling point are obtained; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension. Temperature compensation is performed on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and candidate void areas are determined in the lining area to be monitored based on the true strain evolution characteristics. After determining that the lining area to be monitored has reached a stable state, the thermal response characteristics of the candidate voided area are collected based on the self-heating distributed temperature measurement optical fiber. Based on the thermal response characteristics, the candidate voided regions are subjected to voiding detection to obtain the voiding detection results of the candidate voided regions.

[0006] According to the present invention, a method for detecting lining voids includes performing temperature compensation on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, comprising: The frequency shift deviation is calculated based on the real-time temperature distribution data and the preset temperature coefficient. Subtract the original strain frequency shift data from the frequency shift deviation to obtain the true Brillouin frequency shift; The actual strain value corresponding to each sampling point is calculated based on the actual Brillouin frequency shift and the preset strain coefficient. The true strain evolution characteristics are obtained based on the true strain values ​​at different sampling times.

[0007] According to the present invention, a method for detecting lining voids, wherein the thermal response characteristics of the candidate void region are acquired based on the self-heating distributed temperature measurement optical fiber, comprising: The initial background temperature data and real-time heating temperature data of each sampling point in the candidate de-energized region are collected based on the self-heating distributed temperature measurement optical fiber; the initial background temperature data and the real-time heating temperature data are collected before and after the heating operation by the self-heating distributed temperature measurement optical fiber, respectively. The difference between the real-time heating temperature data and the initial background temperature data is calculated to obtain the temperature rise change value corresponding to each sampling point in the candidate de-vacuolation region; The thermal response characteristics are obtained based on the temperature rise changes at different sampling times.

[0008] According to the present invention, a method for detecting voids in linings includes, in which the candidate void regions are examined based on the thermal response characteristics to obtain void detection results for the candidate void regions, comprising: In the thermal response characteristics, the temperature rise slope data of each sampling point in the candidate de-vacuolation region during the heating cycle are obtained; Based on the temperature rise slope data, a first target region is identified in the candidate vacancy region; the first target region is the region corresponding to multiple consecutive sampling points where the temperature rise slope data exceeds the reference slope range; If the first target region is identified, the void detection result is determined to include the detection result that the candidate void region is a real void region; If the first target region is not identified, the void detection result is determined to include the detection result that the candidate void region is a non-void region.

[0009] According to a method for detecting lining voids provided by the present invention, the method further includes: If the first target region is identified, the thermal response characteristics of the first target region are compared with the calibrated thermal response characteristics of multiple preset delamination types for similarity calculation. Based on similarity, the target empty type is determined from multiple preset empty types; The target de-voiding type is determined as the de-voiding type of the candidate de-voiding region; The plurality of preset de-vacuum types include water-type de-vacuum and air-type de-vacuum; the target de-vacuum type is the preset de-vacuum type corresponding to the calibrated thermal response feature that has the highest similarity to the thermal response feature of the first target region.

[0010] According to the present invention, a method for detecting lining voids is provided, wherein the plurality of sensing paths include a horizontal path attached to the arch wall, an inner horizontal path, and an undulation sensing path; The arch-top wall-attached horizontal path is a horizontal path laid out along the central axis of the arch top of the lining area to be monitored and in contact with the surface of the surrounding rock; the inner horizontal path is a horizontal path laid out along the inner support member on the central axis of the arch top; the undulation sensing path is an undulation path that extends back and forth and is alternately fixed between the surface of the surrounding rock and the inner support member.

[0011] According to the present invention, a method for detecting lining voids, wherein determining candidate void regions in the lining region to be monitored based on the actual strain evolution characteristics includes: Extract the actual strain change of each sampling point in the actual strain evolution feature; Based on the actual strain change, a second target region is identified in the lining area to be monitored; the second target region is the region corresponding to multiple consecutive sampling points where the actual strain change exceeds a preset strain range. If the second target region is identified, then the second target region is determined as the candidate empty region.

[0012] The present invention also provides a lining void detection system, comprising: The sensing unit is used to acquire, after the lining area to be monitored is poured, the raw strain frequency shift data collected by the distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and the real-time temperature distribution data collected by the self-heating distributed temperature measurement fiber at each sampling point; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension. The first detection unit is used to perform temperature compensation on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and to determine the candidate void region in the lining area to be monitored based on the true strain evolution characteristics. The second detection unit is used to collect the thermal response characteristics of the candidate voided region based on the self-heating distributed temperature measurement optical fiber after determining that the lining area to be monitored has reached a stable state; and to perform voiding inspection on the candidate voided region based on the thermal response characteristics to obtain the voiding detection result of the candidate voided region.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lining void detection method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lining void detection method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the lining void detection method as described above.

[0016] The lining void detection method and system provided by this invention constructs a continuous collaborative sensing network by deploying distributed strain measurement optical fibers under tension and self-heating distributed temperature measurement optical fibers between the surrounding rock surface and the inner support of the lining area to be monitored. After the lining is poured, the original strain frequency shift data is accurately compensated using synchronously acquired real-time temperature distribution data. By extracting the true strain evolution characteristics, the system achieves preliminary locking of candidate void areas under complex multi-physics coupling environment. After the structure enters a stable state, the active heating mechanism of the optical fibers is further triggered. By analyzing the thermal response characteristics of the candidate void areas under thermal excitation, the system achieves qualitative verification and attribute review of strain anomaly areas. This not only effectively eliminates the interference of environmental fluctuations such as hydration heat on the monitoring signal and solves the shortcomings of existing technologies in terms of spatial sensing continuity, detection sensitivity, and positioning accuracy, but also achieves accurate identification, precise positioning, and reliable judgment of lining void hazards throughout their entire life cycle, providing strong technical support for the safety assessment of underground engineering. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of the lining void detection method provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of the self-heating distributed temperature measurement optical fiber provided by the present invention.

[0020] Figure 3 This is a front view of the fiber optic cable layout along each sensing path provided by the present invention.

[0021] Figure 4 This is a side view of the fiber optic cable layout along each sensing path provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the calibration thermal response characteristics corresponding to the air evaporation type provided by the present invention.

[0023] Figure 6 This is a schematic diagram of the calibration thermal response characteristics corresponding to the water body devastation type provided by the present invention.

[0024] Figure 7 This is a schematic diagram of the actual strain evolution characteristics corresponding to the air evaporation type provided by the present invention.

[0025] Figure 8 This is a schematic diagram of the actual strain evolution characteristics corresponding to the water body de-voiding type provided by the present invention.

[0026] Figure 9 This is a schematic diagram of the lining void detection system provided by the present invention.

[0027] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0029] Lining voids refer to the phenomenon in underground structures such as tunnels, pipelines, and hydropower projects where voids form between the supporting layer (lining) and the surrounding soil or structure due to separation. This problem is prevalent in various underground engineering projects and seriously threatens the stability and safety of the structure. During the construction of compressed air energy storage (CAES) tunnels, especially in the area above the steel plate of the gas tank, lining voids are easily formed due to the difficulty of concrete lining pouring and the influence of gravity and other factors during solidification. These voids prevent the lining from tightly adhering to the surrounding rock, thus altering its stress state and making the area a weak point in the structure. Under external disturbances or groundwater pressure, this can easily induce a series of problems such as lining cracking, leakage, and detachment, thereby weakening the airtightness and pressure resistance of the gas storage system, and in severe cases, even affecting the operational safety of the entire energy storage system. Therefore, there is an urgent need to develop an efficient and reliable technical means to monitor and evaluate the pouring quality and void status of the tunnel roof lining.

[0030] Currently, traditional methods for detecting voids in tunnel arch linings mainly employ ground-penetrating radar (GPR), acoustic tomography (ATT), core drilling, and manual hammering. However, these methods all have limitations in practical applications. Specifically, GPR and ATT are easily affected by the complex internal structure of the concrete and the distribution of reinforcing steel, leading to severe signal attenuation and affecting the accuracy and reliability of the results. While core drilling can directly reflect the void situation after lining construction, it is a destructive operation that not only damages the integrity of the lining structure but also makes efficient, real-time monitoring difficult, failing to meet the requirements of modern complex engineering for rapid, accurate, and quantitative detection. Manual hammering relies on workers erecting scaffolding and judging concrete density by tapping; its results are highly dependent on the operator's experience, highly subjective, and difficult to guarantee repeatability and accuracy.

[0031] To overcome the aforementioned shortcomings, some technologies propose using single-point prediction methods with sensors for lining void detection. For example, single-point prediction methods such as resistance strain gauge monitoring and vibrating wire strain gauge monitoring are used for lining void detection. While these technologies offer high sensitivity, they still have varying degrees of drawbacks. Specifically, resistance strain gauges measure strain based on the change in resistance caused by material deformation. However, they suffer from poor long-term stability, are prone to zero-point drift, leading to distorted monitoring data. Furthermore, each sensing point can only acquire single-point information, making it difficult to form a continuous spatial strain distribution. In complex and variable underground spaces, the detection range is easily limited by the physical constraints of specific placement points, resulting in missing information coverage; physical signals reflecting deformation characteristics undergo feature annihilation under the thermal evolution of the material; and the sensitivity to subtle interface differences is dulled during the performance stabilization period. Therefore, the sensitivity and reliability of lining void detection cannot be effectively guaranteed. Vibrating wire strain gauges reflect strain by measuring the frequency change of the vibrating wire. Although they have high sensitivity, the wire is under constant tension and is susceptible to long-term effects such as material creep, causing their performance to degrade significantly over time. Similar to resistance strain gauges, they also struggle to establish a continuous spatial strain distribution. In complex and variable underground spaces, they are prone to issues such as missing information coverage due to physical limitations of specific placement points, loss of physical signals reflecting deformation characteristics due to material thermal evolution, and passivation of the perception of minute interface differences during the performance stabilization period. As a result, the sensitivity and reliability of lining void detection cannot be effectively guaranteed.

[0032] In contrast, distributed fiber optic sensing technology, as a sensing method with continuous spatial measurement capabilities, has the advantage of simultaneously acquiring information on the distribution and temporal changes of physical field parameters along the entire fiber length. Therefore, distributed fiber optic sensing technology demonstrates significant advantages in monitoring lining voids. Its greatest feature is its ability to achieve continuous monitoring along the entire line, breaking through the limitations of traditional point-measurement modes. It can act as both a sensor and a signal transmission medium, simultaneously acquiring spatial distribution and temporal evolution information of parameters such as temperature and strain throughout the entire fiber optic cable laying range. This structure-oriented, continuous imaging sensing capability greatly improves the sensitivity and accuracy of identifying and locating local anomalies in the lining (such as voids, cracks, and subsidence). Furthermore, optical fibers themselves possess advantages such as resistance to electromagnetic interference, corrosion resistance, small size, and ease of installation, making them particularly suitable for long-term monitoring needs in high-humidity, high-pressure, and complex structural environments such as tunnels.

[0033] In response, this application provides a method for detecting lining voids. This method constructs a continuous collaborative sensing network by deploying distributed strain measurement optical fibers and self-heating distributed temperature measurement optical fibers under tension between the surrounding rock surface and the inner support of the lining area to be monitored. Through the combination of passive strain monitoring and active thermal excitation, it achieves accurate identification, precise location and reliable judgment of lining void hazards throughout their entire life cycle.

[0034] Figure 1 This is a schematic flowchart of the lining void detection method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step 110: After the lining area to be monitored is poured, acquire the raw strain frequency shift data collected by the distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and the real-time temperature distribution data collected by the self-heating distributed temperature measurement fiber at each sampling point; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension.

[0035] Figure 2 This is a schematic diagram of the structure of the self-heating distributed temperature measurement optical fiber provided by the present invention.

[0036] The lining area to be monitored here refers to the area in underground engineering that has been excavated and reinforced with concrete, such as a compressed air energy storage (CAES) tunnel.

[0037] To achieve high-precision collaborative sensing, this embodiment specifically designs the structure and deployment of the two types of distributed fiber optic sensors (i.e., self-heating distributed temperature measurement fiber and distributed strain measurement fiber) involved in this application. For the self-heating distributed temperature measurement fiber, such as... Figure 2 As shown, this is a special composite optical cable integrating temperature sensing and electric heating functions. This distributed temperature measurement fiber can be a Raman scattering-based distributed temperature sensing (DTS) fiber, hereinafter referred to as DTS fiber. It not only includes the fiber core for transmitting optical signals and performing distributed temperature sensing, but also integrates conductive structures such as a metal armor layer or built-in metal heating wires (e.g., heating copper wire). This conductive structure is connected to an external power controller. When current is applied, it can utilize the thermal effect of the current to generate controllable heat along the entire length of the fiber, thereby achieving active heating of the surrounding medium. For distributed strain measurement fiber, it is a sensing fiber based on Brillouin Optical Time Domain Analysis (BOTDA) technology, hereinafter referred to as BOTDA fiber, used to capture strain information distributed along the fiber path.

[0038] In terms of deployment, to ensure the precise spatial correspondence between the DTS fiber and the BOTDA fiber, so that the temperature value sensed by the DTS fiber at each point can be used to correct the measurement value of the BOTDA fiber, in this embodiment, the DTS fiber and BOTDA fiber deployed on each sensing path can be implemented using a co-fiber integrated design, or by co-cable deployment in close parallel arrangement. That is, the DTS fiber and the BOTDA fiber are encapsulated in the same duplex multimode optical cable, or the two independent fibers are bundled or laid close together in parallel during construction. This ensures that the temperature value measured at each sampling point can accurately represent the temperature environment of the strain fiber at that location, thereby minimizing the compensation error caused by spatial distance.

[0039] The sensing path here refers to the pre-set monitoring trajectory of the optical fiber inside the lining structure, which is precisely arranged within the interlayer space formed by the surrounding rock surface and the inner support components. The inner support components can be structures such as inner steel mesh or steel arch frames.

[0040] For example, in one possible implementation, the plurality of sensing paths include a vault-wall horizontal path, an inner horizontal path, and an undulation sensing path; The arch-top wall-attached horizontal path is a horizontal path laid out along the central axis of the arch top of the lining area to be monitored and in contact with the surface of the surrounding rock; the inner horizontal path is a horizontal path laid out along the inner support member on the central axis of the arch top; the undulation sensing path is an undulation path that extends back and forth and is alternately fixed between the surface of the surrounding rock and the inner support member.

[0041] Figure 3 This is a front view of the fiber optic cable layout along each sensing path provided by the present invention. Figure 4 This is a side view of the fiber optic cable layout along each sensing path provided by the present invention.

[0042] like Figure 3 and Figure 4 As shown, in order to achieve comprehensive and multi-dimensional three-dimensional monitoring of the potential for voids in the lining, multiple sensing paths are designed as a three-dimensional combined network including the horizontal path attached to the arch wall, the horizontal path on the inner side, and the undulation sensing path. Among them, the optical fiber laid on the horizontal path attached to the arch wall is called the optical fiber 1 attached to the arch wall, the optical fiber laid on the horizontal path on the inner side is called the optical fiber 2 laid on the inner side reinforcement, and the optical fiber laid on the undulation sensing path is called the S-shaped undulation optical fiber.

[0043] Specifically, the horizontal path along the arch of the tunnel (i.e., the tunnel) is designed to closely conform to the surface of the surrounding rock. In actual construction, the optical fibers along this path are secured with high density using nail guns and custom-made clamps, ensuring direct contact between the fibers and the interface between the surrounding rock and the lining. Its technical function is to directly sense the initial peeling or gapping state between the top of the lining and the surrounding rock, serving as the first line of defense against voids.

[0044] The inner horizontal path is configured as a horizontal path laid out along the inner support (such as the inner steel mesh) on the central axis of the arch. The optical fiber is tied or fixed to the steel mesh on the side closest to the tunnel clearance, ensuring that the horizontal optical fiber remains on the central axis of the arch as much as possible during construction. Its technical function is to monitor the strain distribution on the inner side of the lining structure. Combined with the outer wall-mounted optical fiber, it can analyze the overall deformation gradient in the lining thickness direction and determine whether there is penetrating structural damage.

[0045] The undulation sensing path is suspended and configured as an alternating, fixed undulation path extending back and forth between the surrounding rock surface and the inner support (i.e., along the central axis). Figure 4 The image shows an "S"-shaped suspended optical fiber. During actual installation, the optical fiber travels up and down in an "S" shape between the fixing point on the arch rock wall and the fixing point on the inner steel reinforcement, spanning the entire thickness of the lining.

[0046] This special "S"-shaped undulating sensing path has significant technical advantages: First, compared with straight-line deployment, the undulating deployment integrates longer optical fibers on the tunnel axis per unit length, significantly improving the spatial resolution and sampling density of the monitoring; Second, the optical fibers on this path are in a suspended tensile state. When a void occurs during concrete pouring, the concrete sinking due to gravity will directly pull the optical fibers spanning it, causing significant tensile deformation. This amplifies the minute volume shrinkage or displacement into an observable strain signal, thus ensuring the sensitivity of strain measurement.

[0047] By combining the above three paths, this embodiment constructs a three-dimensional sensing network from the interface (arch top wall) to the interior (undulating path) and then to the inner edge (inner horizontal), effectively overcoming the problems of limited monitoring range and easy blind spots of traditional single-point sensors, and greatly improving the ability to capture and locate voids in the lining, especially the hidden voids at the arch top.

[0048] The tensile stress state refers to applying a certain pretension during the installation and fixing of the optical fiber to keep it taut. This setting is to ensure that the optical fiber can sensitively generate a strain response when the concrete experiences voids or slight displacement, avoiding signal loss due to fiber slack.

[0049] Optionally, in the specific monitoring process, in order to capture subtle strain and temperature changes during concrete pouring, the spatiotemporal resolution of data acquisition can be set according to actual needs. For example, a spatial sampling rate of 10 cm and a temporal sampling rate of 1 minute can be used to perform continuous measurements on the lining area to be monitored.

[0050] During the monitoring process, two different fiber optic data acquisition channels were simultaneously activated: a distributed strain measurement fiber and a self-heating distributed temperature measurement fiber. The distributed strain measurement fiber was used to record in real-time Brillouin frequency shift changes at each sampling point caused by force or temperature, forming raw strain frequency shift data. The self-heating distributed temperature measurement fiber, in the unheated passive measurement mode, was used to record in real-time background temperature changes at each sampling point, forming real-time temperature distribution data.

[0051] It should be noted that, due to the heterogeneity of the concrete medium, the initial state measurement of the above data should be carried out immediately after the pouring is completed. This high-density spatiotemporal sampling ensures that any minute physical field anomalies can be captured by the system in real time during the dynamic process of concrete solidification, laying an accurate data foundation for subsequent signal decoupling and void identification.

[0052] Step 120: Perform temperature compensation on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and determine the candidate void region in the lining area to be monitored based on the true strain evolution characteristics.

[0053] Considering that Brillouin scattering is an acousto-optic interaction phenomenon, the Brillouin frequency shift data acquired by the distributed strain measurement fiber shows a linear relationship with both the axial strain data and temperature change data in the fiber. The specific calculation formula is as follows: ; in, This is the Brillouin frequency shift variation data (i.e., the original strain frequency shift data). This is axial strain data; For strain coefficient, This is a temperature coefficient, which is typically precisely calibrated in a laboratory. This is data on temperature changes.

[0054] After concrete pouring, the cement hydration reaction releases a large amount of heat, causing drastic fluctuations in ambient temperature. The directly measured raw strain frequency shift data is actually heavily contaminated with temperature interference components, failing to distinguish the contributions of temperature data from strain data. In other words, the data measured by BOTDA fiber optic cables includes strain deviations caused by concrete temperature changes and cannot directly reflect the true mechanical state of the lining structure. Therefore, during the initial detection of lining voids, it is necessary to first decouple strain and temperature using a temperature compensation algorithm to obtain the true strain evolution characteristics.

[0055] Specifically, it utilizes synchronously acquired real-time temperature distribution data to obtain... And combined with the pre-calibrated temperature coefficient The frequency shift deviation caused by temperature is calculated, and this frequency shift deviation is subtracted from the original strain frequency shift data to obtain the true strain evolution characteristics caused only by structural deformation (i.e., ).

[0056] To visually represent the monitoring results, the corrected true strain evolution characteristics can be plotted as a waterfall plot. This plot characterizes the dynamic evolution of the true strain value over time (vertical axis) and space (horizontal axis) across the entire distributed optical fiber. By analyzing the evolution characteristics of this plot, if an abnormal strain abrupt change is observed in a certain region, that region is determined to be a suspected delamination region and thus identified as a candidate delamination region.

[0057] Step 130: After determining that the lining area to be monitored has reached a stable state, the thermal response characteristics of the candidate voided area are collected based on the self-heating distributed temperature measurement optical fiber.

[0058] Step 140: Based on the thermal response characteristics, perform a void removal test on the candidate void removal region to obtain the void removal detection result of the candidate void removal region.

[0059] The stable state here refers to the state where the concrete has completely solidified, the internal hydration heat reaction has essentially ended, and the temperature has returned to the background temperature of the natural environment. At this point, since the structural morphology has been largely solidified, it is difficult to further distinguish the specific properties of the void medium (e.g., whether it is an air cavity or a water sac) by relying solely on passive strain monitoring. Therefore, this step utilizes the active detection function of the self-heating distributed temperature measurement fiber optic cable to further detect candidate void regions.

[0060] In practice, an external power controller applies a set heating current and power to the conductive structure of the self-heating distributed temperature measurement fiber, initiating a timed heating cycle. During the heating process, the self-heating distributed temperature measurement fiber records the temperature change over time at each sampling point within the candidate de-energized region in real time. This allows for the acquisition of spatiotemporal evolution data of the real-time heating temperature under active thermal excitation, thereby obtaining thermal response characteristics. These characteristics can also be recorded and displayed using a waterfall plot.

[0061] Concrete, air, and water have drastically different thermal conductivity and specific heat capacity. Dense concrete has relatively good thermal conductivity and a large heat capacity, resulting in a slower temperature rise. However, if the void area is filled with air (i.e., air-type void), the heat is difficult to dissipate because air is a poor conductor of heat, leading to a rapid increase in temperature around the optical fiber. If the void is filled with still water (i.e., water-type void), its temperature rise characteristics also differ from those of concrete.

[0062] Therefore, after obtaining the waterfall plot of the thermal response characteristics of the candidate de-void region, the evolution characteristics of the plot can be analyzed to further determine whether the candidate de-void region is a real de-void region. Alternatively, if the candidate de-void region is determined to be a real de-void region, the evolution characteristics of the actual thermal response characteristics of the candidate de-void region can be compared with the evolution characteristics of the calibrated thermal response characteristics of different de-void types to determine the de-void type of the candidate de-void region, such as whether it is air-type de-void or water-type de-void. Finally, the de-void detection result containing detection information such as de-void location, range, and type is output.

[0063] The method provided in this embodiment constructs a continuous collaborative sensing network by deploying distributed strain measurement optical fibers under tension and self-heating distributed temperature measurement optical fibers between the surrounding rock surface and the inner support of the lining area to be monitored. After the lining is poured, the original strain frequency shift data is accurately compensated using synchronously acquired real-time temperature distribution data. By extracting the true strain evolution characteristics, the method achieves preliminary locking of candidate void areas under complex multi-physics coupling environment. After the structure enters a stable state, the active heating mechanism of the optical fibers is further triggered. By analyzing the thermal response characteristics of candidate void areas under thermal excitation, the method achieves qualitative verification and attribute review of strain anomaly areas. This not only effectively eliminates the interference of environmental fluctuations such as hydration heat on monitoring signals and solves the shortcomings of existing technologies in terms of spatial sensing continuity, detection sensitivity, and positioning accuracy, but also achieves accurate identification, precise positioning, and reliable judgment of lining void hazards throughout their entire life cycle, providing strong technical support for the safety assessment of underground engineering.

[0064] Based on the above embodiments, step 120, which involves performing temperature compensation on the original strain frequency shift data according to the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, includes the following specific process: Step 121: Calculate the frequency shift deviation based on the real-time temperature distribution data and the preset temperature coefficient.

[0065] First, based on the sensing principle of distributed strain measurement fiber optics, it is necessary to utilize synchronously acquired temperature data to quantify the contribution of temperature to frequency shift. Specifically, this involves using self-heating distributed temperature measurement fiber optics to acquire data at various sampling points. The real-time temperature distribution data, i.e., the temperature change data, is obtained relative to the initial temperature change value, and is denoted as . Combined with the temperature coefficient precisely calibrated in the laboratory for BOTDA optical fiber The sampling points on the distributed strain measurement fiber are calculated using the following formula. Frequency shift deviation due to temperature changes : .

[0066] Step 122: Subtract the original strain frequency shift data from the frequency shift deviation to obtain the true Brillouin frequency shift.

[0067] Optionally, after obtaining the frequency shift deviation caused purely by temperature, it is transferred from the BOTDA fiber at each sampling point. The measured raw strain frequency shift data, which includes the temperature and strain coupling effect (denoted as ) Subtracting from the value effectively eliminates the frequency shift bias caused by the heat of hydration and temperature rise. This yields the true Brillouin frequency shift (denoted as ) that reflects only the strain effect. The specific calculation formula is as follows: .

[0068] Step 123: Calculate the actual strain value corresponding to each sampling point based on the actual Brillouin frequency shift and the preset strain coefficient.

[0069] Subsequently, using the fiber strain coefficient pre-calibrated in the laboratory (denoted as...) ), and the sampling points obtained from the above calculations The true Brillouin frequency shift is converted into a physically meaningful true strain value (denoted as ). The specific formula is as follows: .

[0070] Step 124: Obtain the true strain evolution characteristics based on the true strain values ​​at different sampling times.

[0071] Finally, the actual strain values ​​of each sampling point in the continuous time series are summarized to construct the actual strain evolution characteristics reflecting the changes in strain over time and space. To visually represent the monitoring results, the corrected actual strain evolution characteristics can be plotted as a waterfall plot, which characterizes the dynamic evolution of the actual strain values ​​over time (vertical axis) and space (horizontal axis) across the entire distributed optical fiber. By analyzing the evolution characteristics of this plot, if an abnormal strain abrupt change is observed in a certain region, that region is determined to be a suspected delamination region and thus identified as a candidate delamination region.

[0072] This embodiment uses precise analytical calculation steps to completely separate temperature and strain from the coupled raw data, ensuring that the obtained strain characteristics can truly reflect the mechanical state of the lining structure, and providing reliable data support for accurately locating candidate void areas.

[0073] Based on the above embodiments, the specific process of acquiring the thermal response characteristics of the candidate devitrification region based on the self-heating distributed temperature measurement optical fiber in step 130 includes: Step 131: Based on the self-heating distributed temperature measurement fiber, collect the initial background temperature data and real-time heating temperature data of each sampling point in the candidate de-energized region; the initial background temperature data and the real-time heating temperature data are collected before and after the heating operation of the self-heating distributed temperature measurement fiber, respectively.

[0074] Optionally, after confirming that the structure has entered a stable state (i.e., the concrete has completely solidified and its temperature has returned to normal), the basic temperature measurement channel of the DTS fiber is first activated, the spatial resolution and temperature measurement accuracy are set, and the temperature distribution under natural conditions is recorded as initial background temperature data. Subsequently, the power controller outputs a constant power current to the conductive structure of the DTS fiber to start the heating cycle (e.g., continuous heating for several minutes to tens of minutes). During this period, the DTS fiber continuously collects the temperature of each sampling point at a high frequency (e.g., every 10 to 30 seconds), and these data are the real-time heating temperature data.

[0075] Step 132: Calculate the difference between the real-time heating temperature data and the initial background temperature data to obtain the temperature rise change value corresponding to each sampling point in the candidate de-vacuolation region.

[0076] To eliminate the influence of ambient baseline temperature (such as seasonal ground temperature differences), only the relative temperature rise caused by heating is considered.

[0077] Step 133: Obtain the thermal response characteristics based on the temperature rise changes at different sampling times.

[0078] Optionally, after obtaining the temperature rise change values ​​of different sampling points at different sampling times, the temperature rise change values ​​of each sampling point over the heating time can be aggregated to form the thermal response characteristics. These data can also be plotted as a waterfall diagram of temperature response. This characteristic intuitively reflects the heat absorption and conduction capacity of the medium in different regions, and is the core basis for distinguishing between dense concrete and void defects.

[0079] This embodiment effectively filters out the interference of ambient background temperature through a standardized active heating and differential calculation process, highlighting the temperature rise signal caused by the difference in the thermal properties of the medium, thus laying the foundation for achieving highly sensitive qualitative judgment of vacancy removal.

[0080] Based on the above embodiments, step 140, which involves performing a void removal test on the candidate void removal region according to the thermal response characteristics to obtain the void removal detection result of the candidate void removal region, includes the following specific steps: Step 141: In the thermal response characteristics, obtain the temperature rise slope data of each sampling point in the candidate vacancy region during the heating cycle.

[0081] Optionally, the rate of temperature increase over time at each sampling point during the heating phase, i.e., the temperature rise slope data, can be extracted from the acquired thermal response characteristic data. This slope is directly related to the thermal conductivity of the medium.

[0082] Step 142: Based on the temperature rise slope data, identify a first target region in the candidate vacancy region; the first target region is the region corresponding to multiple consecutive sampling points where the temperature rise slope data exceeds the reference slope range.

[0083] Step 143: If the first target region is identified, then the void detection result is determined to include the detection result of the candidate void region being a real void region.

[0084] Step 144: If the first target region is not identified, then determine that the void detection result includes the detection result of the candidate void region being a non-void region.

[0085] Optionally, a reference slope range based on the thermal response characteristics of dense concrete can be set. If a continuous sampling point (i.e., the first target area) is found within the candidate void region, and its temperature rise slope exceeds the reference range, it indicates that the temperature rise characteristics of that area are abnormal.

[0086] If a first target region matching the characteristics of abnormal temperature rise is identified, it can be determined that the previous strain anomaly was indeed caused by de-voiding, thus confirming that the candidate region is the real de-voiding region.

[0087] Conversely, if the temperature rise slope of the candidate area is no different from that of normal concrete, it indicates that although there has been strain anomaly in the area, such as micro-settlement of non-void structures, the medium is still dense concrete, and it can still be determined as a non-void area.

[0088] This embodiment introduces the quantitative indicator of temperature rise slope to establish a clear logic for vacancy detection, effectively eliminating false alarms in vacancy detection caused by simple strain monitoring, and greatly improving the accuracy of the detection results.

[0089] Furthermore, after confirming that the candidate vacancy region is a true vacancy region, the vacancy type of the candidate vacancy region can be further detected. Specific detection methods include: Step 145: If the first target region is identified, the thermal response features of the first target region are compared with the calibration thermal response features of multiple preset emptying types.

[0090] Step 146: Determine the target emptying type from multiple preset emptying types based on similarity.

[0091] Step 147: Determine the target de-voiding type as the de-voiding type of the candidate de-voiding region; wherein, the plurality of preset de-voiding types include water-type de-voiding and air-type de-voiding; the target de-voiding type is the preset de-voiding type corresponding to the calibration thermal response feature with the highest similarity to the thermal response feature of the first target region.

[0092] Optionally, after confirming that the candidate vacancy region is a true vacancy, the measured thermal response characteristics of the first target region are compared with the calibrated thermal response characteristics pre-stored in the database. These thermal response characteristics are based on typical temperature rise curves of different media (such as air and water) under standard heating conditions.

[0093] Furthermore, by calculating the similarity between the measured thermal response characteristics of the first target region and each calibrated thermal response characteristic, such as by using algorithms like Euclidean distance and correlation coefficient, the type with the highest matching degree is identified as the de-voiding type of the candidate de-voiding region.

[0094] Figure 5 This is a schematic diagram of the calibration thermal response characteristics corresponding to the air evaporation type provided by the present invention; Figure 6 This is a schematic diagram of the calibration thermal response characteristics corresponding to the water body de-voiding type provided by the present invention. The diagram shows the evolution and distribution of temperature over time (vertical axis) and space (horizontal axis). Two significant anomalous regions are clearly visible in the diagram, which represent two typical de-voiding medium response modes, respectively.

[0095] like Figure 5 As shown in the waterfall plot of the calibrated thermal response characteristics of air-type de-airing, the abnormal areas show extremely dark colors (representing high temperature values) and extremely rapid temperature rise over time, which is consistent with the characteristics of air having low thermal conductivity and difficulty in heat dissipation, leading to local heat accumulation.

[0096] like Figure 6 As shown in the waterfall plot corresponding to the calibration thermal response characteristics of the water body type void, although the temperature of the abnormal area is also higher than that of the background concrete, its color is lighter (representing a relatively lower temperature value) and the temperature rise curve is relatively flat. This is consistent with the characteristics of water body having a large specific heat capacity and strong heat absorption capacity, resulting in a lag in temperature rise.

[0097] Therefore, based on the similarity calculation results, it is possible to accurately determine whether the candidate void area is a water-type void or an air-type void, providing a key decision-making basis for formulating targeted grouting or drainage repair plans. That is, if it is determined to be an air-type void, it means that there is a dry cavity behind the lining, which may need to be filled with grout; if it is determined to be a water-type void, it means that there is a water-accumulated cavity behind the lining, suggesting that there may be a groundwater seepage channel, which requires priority to be given to sealing and drainage treatment.

[0098] The method provided in this embodiment achieves intelligent qualitative analysis of the properties of cavitary media through quantitative pattern recognition. This not only solves the problem that traditional methods cannot distinguish between cavities and water accumulation, but also provides crucial decision-making basis for subsequent development of targeted engineering repair plans (such as selecting grouting materials or processes), significantly enhancing the engineering guidance value of the test results.

[0099] Based on the above embodiments, step 120, which involves determining candidate void regions in the lining region to be monitored according to the actual strain evolution characteristics, includes the following specific steps: Step 125: Extract the actual strain change of each sampling point in the actual strain evolution feature.

[0100] Step 126: Based on the actual strain change, identify a second target region in the lining area to be monitored; the second target region is the region corresponding to multiple consecutive sampling points where the actual strain change exceeds the preset strain range.

[0101] Optionally, after temperature compensation is completed, pure true strain evolution characteristics can be obtained. For quantitative analysis, it is necessary to extract the true strain change at each sampling point at each time point from the true strain evolution characteristics.

[0102] Figure 7 This is a schematic diagram of the actual strain evolution characteristics corresponding to the air de-vacation type provided by the present invention; Figure 8 This is a schematic diagram of the actual strain evolution characteristics corresponding to the water body de-voiding type provided by the present invention.

[0103] like Figure 7 and Figure 8 As shown in the figure, the evolution of strain over time (vertical axis) and space (horizontal axis) is illustrated. The shades of color (or the magnitudes of values) in the figure represent the strain intensity.

[0104] In normal dense concrete regions, due to the volume shrinkage characteristics of concrete during the hardening process, the strain typically manifests as small shrinkage strain or remains relatively stable, within a normal preset strain range, such as... Figure 7 and Figure 8 The background area is lighter in color and has a uniform texture.

[0105] However, if a void occurs at the top of the lining, the concrete, now unsupported by the surrounding rock, will experience a slight subsidence under its own weight. This causes significant tensile stress on the embedded optical fibers (especially those suspended in an "S" shape), resulting in abrupt changes in strain at continuous sampling points compared to the normal dense concrete area. For example, if... Figure 7 and Figure 8 As shown by the obvious dark striped area, the actual strain change in this area shows a stretching trend that continues to increase over time, and this abnormal feature covers multiple consecutive sampling points in space. Therefore, this part of the area showing significant strain abrupt change characteristics can be identified as the second target area.

[0106] Step 127: If the second target region is identified, then the second target region is determined as the candidate empty region.

[0107] Optionally, once a second target region matching the above characteristics is identified in the strain evolution diagram, it indicates that there is highly suspicious structural separation or settlement behavior at that location, and it can be marked as a candidate vacancy region.

[0108] The method provided in this embodiment achieves automated initial screening of potential hazards in the entire lining by determining the quantitative threshold of strain change. This preliminary judgment based on mechanical mechanism can quickly identify key objects of concern from massive monitoring data, clarify the spatial range for active thermal detection, and thus significantly improve the efficiency, targeting, accuracy and reliability of the overall detection process.

[0109] The lining void detection system provided by the present invention is described below. The lining void detection system described below can be referred to in correspondence with the lining void detection method described above.

[0110] Figure 9 This is a schematic diagram of the lining void detection system provided by the present invention; as shown. Figure 9 As shown, the system includes: The sensing unit 910 is used to acquire, after the lining area to be monitored is poured, the original strain frequency shift data collected by the distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and the real-time temperature distribution data collected by the self-heating distributed temperature measurement fiber at each sampling point; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension. The first detection unit 920 is used to perform temperature compensation on the original strain frequency shift data according to the real-time temperature distribution data, obtain the real strain evolution characteristics corresponding to each sampling point, and determine the candidate void region in the lining area to be monitored according to the real strain evolution characteristics. The second detection unit 930 is used to collect the thermal response characteristics of the candidate voided region based on the self-heating distributed temperature measurement optical fiber after determining that the lining area to be monitored has reached a stable state; and to perform voiding inspection on the candidate voided region based on the thermal response characteristics to obtain the voiding detection result of the candidate voided region.

[0111] The system provided in this embodiment constructs a continuous collaborative sensing network by deploying distributed strain measurement optical fibers under tension and self-heating distributed temperature measurement optical fibers between the surrounding rock surface and the inner support components of the lining area to be monitored. After the lining is poured, the original strain frequency shift data is accurately compensated using synchronously acquired real-time temperature distribution data. By extracting the true strain evolution characteristics, the system initially locks down candidate void areas under complex multi-physics coupling environments. After the structure enters a stable state, the active heating mechanism of the optical fibers is further triggered. By analyzing the thermal response characteristics of candidate void areas under thermal excitation, the system achieves qualitative verification and attribute review of strain anomaly areas. This not only effectively eliminates the interference of environmental fluctuations such as hydration heat on monitoring signals and solves the shortcomings of existing technologies in terms of spatial sensing continuity, detection sensitivity, and positioning accuracy, but also achieves accurate identification, precise positioning, and reliable judgment of lining void hazards throughout their entire life cycle, providing strong technical support for the safety assessment of underground engineering.

[0112] The system provided by this invention is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0113] Figure 10 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 10As shown, the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030, and a communications bus 1040, wherein the processor 1010, the communications interface 1020, and the memory 1030 communicate with each other through the communications bus 1040. The processor 1010 can call logic instructions in the memory 1030 to execute a lining void detection method. This method includes: after the lining area to be monitored is poured, acquiring raw strain frequency shift data collected by a distributed strain measurement fiber at each sampling point along each sensing path within the lining area to be monitored, and real-time temperature distribution data collected by a self-heating distributed temperature measurement fiber at each sampling point; the sensing path is the path between the surrounding rock surface and the inner support of the lining area to be monitored, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed along the sensing path are under tension; temperature compensation is performed on the raw strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and candidate void areas are determined within the lining area to be monitored based on the true strain evolution characteristics; after determining that the lining area to be monitored has reached a stable state, thermal response characteristics of the candidate void areas are collected based on the self-heating distributed temperature measurement fiber; void detection is performed on the candidate void areas based on the thermal response characteristics to obtain the void detection result of the candidate void areas.

[0114] Furthermore, the logical instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the lining void detection method provided by the above methods. The method includes: after the lining area to be monitored is poured, acquiring raw strain frequency shift data collected by a distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and real-time temperature distribution data collected by a self-heating distributed temperature measurement fiber at each sampling point; the sensing path is between the surrounding rock surface of the lining area to be monitored and the inner support. The path is such that the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension; the original strain frequency shift data is temperature compensated according to the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and candidate void regions are determined in the lining area to be monitored according to the true strain evolution characteristics; after determining that the lining area to be monitored has reached a stable state, the thermal response characteristics of the candidate void regions are collected based on the self-heating distributed temperature measurement fiber; the void detection results of the candidate void regions are obtained by performing void detection on the candidate void regions according to the thermal response characteristics.

[0116] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the lining void detection method provided by the above methods. This method includes: after the lining area to be monitored is poured, acquiring raw strain frequency shift data collected by distributed strain measurement optical fibers at sampling points along each sensing path within the lining area to be monitored, and real-time temperature distribution data collected by self-heating distributed temperature measurement optical fibers at each sampling point; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support member, and the distributed strain measurement optical fibers are arranged along the sensing path. The distributed strain measurement fiber and the self-heating distributed temperature measurement fiber are under tension. Temperature compensation is performed on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point. Based on these true strain evolution characteristics, candidate void regions are determined in the lining region to be monitored. After determining that the lining region to be monitored has reached a stable state, the thermal response characteristics of the candidate void regions are collected based on the self-heating distributed temperature measurement fiber. Based on the thermal response characteristics, void detection is performed on the candidate void regions to obtain the void detection results.

[0117] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting voids in lining, characterized in that, include: After the lining area to be monitored is poured, the raw strain frequency shift data collected by the distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and the real-time temperature distribution data collected by the self-heating distributed temperature measurement fiber at each sampling point are obtained; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension. Temperature compensation is performed on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and candidate void areas are determined in the lining area to be monitored based on the true strain evolution characteristics. After determining that the lining area to be monitored has reached a stable state, the thermal response characteristics of the candidate voided area are collected based on the self-heating distributed temperature measurement optical fiber. Based on the thermal response characteristics, the candidate voided regions are subjected to voiding detection to obtain the voiding detection results of the candidate voided regions.

2. The method for detecting lining voids according to claim 1, characterized in that, The step of performing temperature compensation on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point includes: The frequency shift deviation is calculated based on the real-time temperature distribution data and the preset temperature coefficient. Subtract the original strain frequency shift data from the frequency shift deviation to obtain the true Brillouin frequency shift; The actual strain value corresponding to each sampling point is calculated based on the actual Brillouin frequency shift and the preset strain coefficient. The true strain evolution characteristics are obtained based on the true strain values ​​at different sampling times.

3. The method for detecting lining voids according to claim 1, characterized in that, The thermal response characteristics of the candidate devitrification region acquired based on the self-heating distributed temperature measurement optical fiber include: The initial background temperature data and real-time heating temperature data of each sampling point in the candidate de-energized region are collected based on the self-heating distributed temperature measurement optical fiber; the initial background temperature data and the real-time heating temperature data are collected before and after the heating operation by the self-heating distributed temperature measurement optical fiber, respectively. The difference between the real-time heating temperature data and the initial background temperature data is calculated to obtain the temperature rise change value corresponding to each sampling point in the candidate de-vacuolation region; The thermal response characteristics are obtained based on the temperature rise changes at different sampling times.

4. The method for detecting lining voids according to any one of claims 1-3, characterized in that, The step of performing a void removal test on the candidate void removal region based on the thermal response characteristics to obtain the void removal detection result of the candidate void removal region includes: In the thermal response characteristics, the temperature rise slope data of each sampling point in the candidate de-vacuolation region during the heating cycle are obtained; Based on the temperature rise slope data, a first target region is identified in the candidate vacancy region; the first target region is the region corresponding to multiple consecutive sampling points where the temperature rise slope data exceeds the reference slope range; If the first target region is identified, the void detection result is determined to include the detection result that the candidate void region is a real void region; If the first target region is not identified, the void detection result is determined to include the detection result that the candidate void region is a non-void region.

5. The method for detecting lining voids according to claim 4, characterized in that, The method further includes: If the first target region is identified, the thermal response characteristics of the first target region are compared with the calibrated thermal response characteristics of multiple preset delamination types for similarity calculation. Based on similarity, the target empty type is determined from multiple preset empty types; The target de-voiding type is determined as the de-voiding type of the candidate de-voiding region; The plurality of preset de-vacuum types include water-type de-vacuum and air-type de-vacuum; the target de-vacuum type is the preset de-vacuum type corresponding to the calibrated thermal response feature that has the highest similarity to the thermal response feature of the first target region.

6. The method for detecting lining voids according to any one of claims 1-3, characterized in that, The multiple sensing paths include a vaulted wall-mounted horizontal path, an inner horizontal path, and an undulation sensing path; The arch-top wall-attached horizontal path is a horizontal path laid out along the central axis of the arch top of the lining area to be monitored and in contact with the surface of the surrounding rock; the inner horizontal path is a horizontal path laid out along the inner support member on the central axis of the arch top; the undulation sensing path is an undulation path that extends back and forth and is alternately fixed between the surface of the surrounding rock and the inner support member.

7. The method for detecting lining voids according to any one of claims 1-3, characterized in that, The step of determining candidate void regions in the lining region to be monitored based on the actual strain evolution characteristics includes: Extract the actual strain change of each sampling point in the actual strain evolution feature; Based on the actual strain change, a second target region is identified in the lining area to be monitored; the second target region is the region corresponding to multiple consecutive sampling points where the actual strain change exceeds a preset strain range. If the second target region is identified, then the second target region is determined as the candidate empty region.

8. A lining void detection system, characterized in that, include: The sensing unit is used to acquire, after the lining area to be monitored is poured, the raw strain frequency shift data collected by the distributed strain measurement fiber at each sampling point of each sensing path in the lining area to be monitored, and the real-time temperature distribution data collected by the self-heating distributed temperature measurement fiber at each sampling point; the sensing path is the path between the surrounding rock surface of the lining area to be monitored and the inner support, and the distributed strain measurement fiber and the self-heating distributed temperature measurement fiber deployed on the sensing path are under tension. The first detection unit is used to perform temperature compensation on the original strain frequency shift data based on the real-time temperature distribution data to obtain the true strain evolution characteristics corresponding to each sampling point, and to determine the candidate void region in the lining area to be monitored based on the true strain evolution characteristics. The second detection unit is used to collect the thermal response characteristics of the candidate voided area based on the self-heating distributed temperature measurement optical fiber after determining that the lining area to be monitored has reached a stable state. Based on the thermal response characteristics, the candidate voided regions are subjected to voiding detection to obtain the voiding detection results of the candidate voided regions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the lining void detection method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the lining void detection method as described in any one of claims 1 to 7.

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