An air energy storage cavern structure based on uhpc lining and distributed optical fiber and a monitoring method thereof

Abstract

This invention discloses an air-storage cavern structure based on UHPC lining and distributed optical fiber, and its monitoring method, belonging to the field of underground cavity engineering technology for air-storage. The structure includes a concrete lining structure set on the inner wall of the natural cavern; a concrete reinforcement structure set on the inner side of the concrete lining structure, forming an energy storage chamber inside; and an optical fiber sensor embedded in the airtight protective layer. By integrating ultra-high performance concrete layered lining with distributed optical fiber sensors, a cavern gas storage system integrating structural load-bearing, multiple sealing and intelligent sensing is constructed. Based on a coupled deformation model and a leakage threshold model, a multi-dimensional early warning index and a graded early warning mechanism are established to accurately capture early micro-damage and gas leakage precursors, and automatically generate maintenance plans. This effectively solves the technical problems of the disconnect between high-performance sealing materials and intelligent monitoring systems, and the inability to identify early hidden damage in a timely manner.

Description

Technical Field

[0001] This invention relates to the field of underground cavity engineering technology for air energy storage, specifically to an air energy storage cave structure based on UHPC lining and distributed optical fiber and its monitoring method. Background Technology

[0002] Compressed air energy storage is an important long-term energy storage technology for new power systems. Its underground gas storage systems are typically constructed using natural caverns such as salt caverns or hard rock chambers. Under the long-term action of high-pressure circulating internal pressure, the surrounding rock support and inner wall lining of the gas storage caverns require excellent resistance to deformation, fatigue, and airtightness. Currently, the sealing of gas storage caverns mainly adopts rigid steel plate lining, flexible polymer membranes, or rigid-flexible composite structures. Monitoring mainly relies on point sensors, inlet and outlet pressure difference leakage rate calculation, and periodic manual inspections. At the same time, existing sensors are mostly installed by surface bonding or external mounting, which is independent of the sealing lining structure, thus compromising the integrity of the lining and causing monitoring signal distortion and weak data correlation.

[0003] Therefore, current technical solutions fail to deeply integrate high-performance sealing materials with distributed intelligent monitoring systems, resulting in structures that lack sufficient crack resistance, impermeability, and buckling resistance, and are unable to accurately detect and provide real-time warnings of internal micro-cracks, micro-deformations, and early gas channeling. Specifically, on the one hand, ordinary concrete or steel plate linings are prone to cracking and failure under high-pressure cyclic loading due to insufficient material toughness, construction joints, and stress concentration; on the other hand, point-based monitoring methods cannot locate hidden damage, often only discovering it after leakage or structural instability has occurred, resulting in a severely insufficient safety margin. Therefore, there is an urgent need for a rock cave structure and monitoring method that integrates ultra-high-performance concrete layered lining with embedded distributed fiber optic sensing to fundamentally eliminate monitoring blind spots, achieve coordinated operation of the structure and monitoring, and establish an adaptive early warning and maintenance closed loop to support the safe operation of air-storage rock caves throughout their entire lifecycle. Summary of the Invention

[0004] To address the problems mentioned in the background section, this invention provides an air-storage rock cave structure based on UHPC lining and distributed optical fiber, and a monitoring method thereof.

[0005] The above-mentioned objective of this application is achieved through the following technical solution:

[0006] An air-storage cave structure based on UHPC lining and distributed optical fiber is applied in a natural cave, including a concrete lining structure installed on the inner wall of the natural cave.

[0007] A concrete reinforcement structure is provided on the inner side of the concrete lining structure, forming an energy storage chamber inside. The concrete reinforcement structure includes a surrounding rock bonding layer, a main sealing layer, and an airtight protective layer. The surrounding rock bonding layer is provided in close contact with the inner wall of the concrete lining structure. The main sealing layer and the airtight protective layer are sequentially provided on the side of the surrounding rock bonding layer away from the concrete lining structure.

[0008] An optical fiber sensor is embedded inside the airtight protective layer. The optical fiber sensor includes a temperature sensor, a strain sensor, and a vibration sensor.

[0009] An output converter is installed at the wellhead interface of the natural rock cave to lead the signal of the fiber optic sensor out of the cave.

[0010] In a preferred embodiment, this application may be further configured as follows: the surrounding rock bonding layer is a composite structure of modified ultra-high performance concrete interface agent and thin ultra-high performance concrete, and the thickness of the surrounding rock bonding layer is 3-5 cm; the main sealing layer is an ultra-high performance concrete layer incorporating fibers, and the conventional thickness of the main sealing layer is 8-15 cm, while the thickness of the dome region is 12-18 cm; the airtight protective layer is a composite structure of nano-silicon sealing coating and ultra-thin stainless steel airtight liner, and the thickness of the ultra-thin stainless steel airtight liner is 1-2 mm.

[0011] In a preferred embodiment, the present application may be further configured such that the fiber optic sensor is a combination of DTS and DAS composite optical cable and FBG grating string, and the temperature sensor, strain sensor and vibration sensor are integrated in the same composite optical cable.

[0012] In a preferred embodiment, this application may be further configured to include a high-precision absolute pressure sensor pre-embedded at the interface between the concrete-reinforced structure and the surrounding rock and at the center of the dome, a piezometer embedded in the surrounding rock and caprock, and microseismic detectors arrayed on the ground and in the well.

[0013] In a preferred embodiment, this application can be further configured such that: the output converter is a fiber optic splice box or a fiber optic cable terminal box, which is sealed and installed at the wellhead interface, and the fiber optic sensor is connected to the fiber optic demodulator outside the well through the output converter.

[0014] The second objective of this invention is achieved through the following technical solution:

[0015] A method for monitoring air-source energy storage caverns based on UHPC lining and distributed optical fibers, for implementation in an air-source energy storage cavern structure based on UHPC lining and distributed optical fibers, includes the following steps:

[0016] S10: Real-time temperature information, real-time strain information and real-time vibration information are acquired based on distributed optical fiber sensors to generate optical fiber monitoring information;

[0017] S20: Based on a high-precision absolute pressure sensor, obtain gas storage pressure information and annular pressure information in the energy storage chamber; based on a piezometer, obtain groundwater seepage pressure information; based on a microseismic detector, obtain microseismic information; and combine the fiber optic monitoring information to generate a multi-dimensional real-time monitoring dataset.

[0018] S30: Based on the coupled deformation mechanism of ultra-high performance concrete lining and surrounding rock, a coupled deformation model and a leakage threshold model are established, and a multi-dimensional early warning index including deformation early warning threshold, leakage early warning threshold and micro-seismic early warning threshold is set.

[0019] S40: Compare each data point in the multi-dimensional real-time monitoring dataset with the corresponding multi-dimensional early warning indicators. When any data point reaches or exceeds the corresponding threshold, generate a graded early warning information.

[0020] S50: Generate a maintenance plan based on the hierarchical early warning information, and evaluate the effectiveness of the maintenance execution process in real time. Optimize the parameters of the coupled deformation model or leakage threshold model based on the evaluation results.

[0021] In a preferred embodiment, this application may be further configured such that: the real-time temperature information obtained in step S10 includes temperature distribution data of the dome, sidewall, and wellhead interface; the real-time strain information includes dome settlement data, lining microcrack development data, and surrounding rock creep data; and the real-time vibration information includes salt rock microfracture data, hard rock unloading vibration data, and microseismic signal data generated by gas escape.

[0022] In a preferred embodiment, this application can be further configured such that: the deformation warning threshold set in step S30 is when the dome settlement monitoring value reaches or exceeds 2 mm / month, or the lining strain monitoring value reaches or exceeds 1000 mm / month. ;

[0023] The leakage warning threshold is when the abnormal temperature drop monitoring value reaches or exceeds 2°C, or the daily pressure drop monitoring value reaches or exceeds 0.05MPa;

[0024] The microseismic early warning threshold is defined as a sudden increase in the frequency of microseismic events or an abnormal increase in energy.

[0025] In a preferred embodiment, this application can be further configured such that the graded early warning information generated in step S40 includes deformation early warning information, leakage early warning information and micro-vibration early warning information, and is pushed through platform pop-ups, SMS or sound and light alarms.

[0026] In a preferred embodiment, this application can be further configured as follows: after generating the maintenance plan based on the graded early warning information in step S50, the method further includes tracking and recording the maintenance execution results, and feeding back the tracking and recording data to the coupled deformation model and leakage threshold model for online optimization of model parameters.

[0027] The beneficial effects of the air-source energy storage cavern structure based on UHPC lining and distributed optical fiber and its monitoring method of the present invention are as follows:

[0028] By deeply integrating ultra-high performance concrete (UHPC) layered lining with embedded distributed fiber optic sensors, along with high-precision absolute pressure sensors, piezometers, and microseismic detectors, a cavern gas storage system integrating structural load-bearing capacity, multiple sealing, and intelligent sensing has been constructed. The high strength, high toughness, and extremely low permeability of the UHPC lining fundamentally enhance its resistance to cracking, seepage, and high-pressure cyclic internal pressure. Meanwhile, the temperature, strain, and vibration fiber optic sensors embedded in the airtight protective layer, along with complementary sensor arrays, enable real-time, multi-parameter monitoring of dome settlement, microcrack propagation, gas leakage temperature anomalies, and microseismic signals across the entire cross-section, completely eliminating blind spots in traditional point-based monitoring. Based on this, a multi-dimensional early warning index and hierarchical early warning mechanism established using a coupled deformation model and leakage threshold model can accurately capture early micro-damage and gas channeling precursors, and automatically generate maintenance plans. Simultaneously, through maintenance effect evaluation and online optimization of model parameters, the model continuously adapts to structural aging and environmental changes. This effectively solves the technical problem of the disconnect between high-performance sealing materials and intelligent monitoring systems, and the inability to identify early hidden damage in a timely manner, significantly improving the structural safety margin, sealing reliability and full life cycle adaptive operation and maintenance capabilities of gas storage caverns. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the first cross-sectional structure of an air-storage rock cave structure based on UHPC lining and distributed optical fiber in an embodiment of the present invention.

[0030] Figure 2 for Figure 1 A schematic diagram of the cross-sectional structure of section A in the middle;

[0031] Figure 3 This is a schematic diagram of the second cross-sectional structure of an air-storage rock cave structure based on UHPC lining and distributed optical fiber in an embodiment of the present invention.

[0032] Figure 4 for Figure 3 A schematic diagram of the cross-sectional structure of section B;

[0033] Figure 5 This is a schematic diagram of the third cross-sectional structure of an air-storage rock cave structure based on UHPC lining and distributed optical fiber in an embodiment of the present invention.

[0034] Figure 6 for Figure 5 A schematic diagram of the cross-sectional structure corresponding to the SS section in the middle;

[0035] Figure 7 This is a schematic diagram illustrating the implementation process of an air-source energy storage cavern monitoring method based on UHPC lining and distributed optical fiber in an embodiment of the present invention.

[0036] Among them, 1. Natural rock cave; 2. Concrete lining structure; 3. Concrete reinforcement structure; 4. Energy storage chamber; 5. Output converter; 6. Fiber optic sensor; 301. Surrounding rock bonding layer; 302. Main sealing layer; 303. Airtight protective layer; 601. Temperature sensor; 602. Strain sensor; 603. Vibration sensor. Detailed Implementation

[0037] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] like Figure 1-6 As shown, an air-storage cave structure based on UHPC lining and distributed optical fiber is applied in a natural cave 1, including a concrete lining structure 2, which is set on the inner wall of the natural cave 1.

[0039] A concrete reinforcement structure 3 is disposed inside the concrete lining structure 2, forming an energy storage chamber 4 inside. The concrete reinforcement structure 3 includes a surrounding rock bonding layer 301, a main sealing layer 302, and an airtight protective layer 303. The surrounding rock bonding layer 301 is disposed in close contact with the inner wall of the concrete lining structure 2. The main sealing layer 302 and the airtight protective layer 303 are disposed sequentially on the side of the surrounding rock bonding layer 301 away from the concrete lining structure 2.

[0040] The fiber optic sensor 6 is embedded in the airtight protective layer 303. The fiber optic sensor 6 includes a temperature sensor 601, a strain sensor 602, and a vibration sensor 603.

[0041] The converter 5 is installed at the wellhead interface of the natural rock cave 1 to lead the signal of the fiber optic sensor 6 out of the cave.

[0042] In this embodiment, a concrete lining structure 2 is provided on the inner wall of the natural cave 1 as a basic support layer; a concrete reinforcement structure 3 is provided on the inner side of the concrete lining structure 2, which forms an energy storage chamber 4. This reinforcement structure is composed of a surrounding rock bonding layer 301, a main sealing layer 302, and an airtight protective layer 303 stacked sequentially. The surrounding rock bonding layer 301 is in close contact with the inner wall of the concrete lining structure 2, the main sealing layer 302 is located in the middle, and the airtight protective layer 303 is located on the innermost side. A fiber optic sensor 6 is embedded in the airtight protective layer 303 closest to the energy storage chamber 4, and includes three types of sensors: temperature, strain, and vibration. A converter 5 is installed at the wellhead interface of the natural cave 1 to lead the signal of the fiber optic sensor 6 out of the cave. The three-layer complementary reinforcement layer achieves a firm bond with the cavern wall, a sealed main load-bearing structure, and an airtight surface protection. The fiber optic sensor 6 is directly embedded in the innermost airtight protective layer 303, which can directly sense the temperature, strain, and vibration changes near the energy storage chamber 4 without affecting the integrity of the seal. The output converter 5 ensures the sealing and reliability of the signal when it passes through the wellbore. The high-performance material structure and distributed fiber optic sensing are deeply integrated to form a composite lining system that can resist high-pressure cyclic internal pressure and sense the structural state in situ, realizing the integration of structural load-bearing, sealing protection, and intelligent monitoring.

[0043] In one embodiment, the surrounding rock bonding layer 301 is a composite structure of modified ultra-high performance concrete interface agent and thin ultra-high performance concrete, and the thickness of the surrounding rock bonding layer 301 is 3-5 cm; the main sealing layer 302 is an ultra-high performance concrete layer incorporating fibers, and the conventional thickness of the main sealing layer 302 is 8-15 cm, while the thickness in the dome area is 12-18 cm; the airtight protective layer 303 is a composite structure of nano-silicon sealing coating and ultra-thin stainless steel airtight liner, and the thickness of the ultra-thin stainless steel airtight liner is 1-2 mm.

[0044] In this embodiment, the surrounding rock bonding layer 301 is composed of a modified ultra-high performance concrete interface agent and thin ultra-high performance concrete, with a thickness of 3-5 cm. It can penetrate the micro-cracks on the surface of the surrounding rock and enhance the bonding strength with salt rock or hard rock. The main sealing layer 302 is an ultra-high performance concrete layer incorporating fibers, with a conventional thickness of 8-15 cm, and is thickened to 12-18 cm in the stress-concentrated area of ​​the dome. The fiber reinforcement can improve crack resistance and toughness. The airtight protective layer 303 is a composite structure of a nano-silicon sealing coating and an ultra-thin stainless steel airtight liner with a thickness of 1-2 mm. By setting a reasonable thickness range and material combination, the surrounding rock bonding layer 301 ensures that there is no risk of voids between the lining and the surrounding rock; the differentiated thickness of the main sealing layer 302 specifically strengthens the stress-sensitive area at the top; the nano-silicon coating of the airtight protective layer 303 fills the micropores, and the stainless steel liner provides absolute gas barrier, forming multiple airtight barriers; thereby significantly improving the mechanical compatibility and overall sealing durability of the lining in different parts, and achieving efficient use of materials while ensuring structural safety.

[0045] In one embodiment, the fiber optic sensor 6 is a combination of DTS and DAS composite optical cable and FBG grating string, and the temperature sensor 601, strain sensor 602 and vibration sensor 603 are integrated in the same composite optical cable.

[0046] In this embodiment, the fiber optic sensor 6 uses a composite optical cable of DTS (Distributed Temperature Sensing) and DAS (Distributed Acoustic Sensing), combined with an FBG (Fiber Bragg Grating) grating string. The temperature sensor 601, strain sensor 602, and vibration sensor 603 are integrated into the same composite optical cable. This integrated optical cable design can simultaneously acquire three types of parameters: temperature, strain, and vibration, avoiding the construction complexity and risk of damage to the sealing layer caused by laying multiple independent optical cables. The combination of DTS and DAS technology enables long-distance continuous distributed measurement, while the FBG grating string provides high-precision point measurement. The two complement each other to achieve surface-point collaborative monitoring, thereby obtaining the richest monitoring information with minimal pre-embedded volume, ensuring the comprehensiveness, accuracy, and structural integrity of the monitoring system.

[0047] In one embodiment, it also includes a high-precision absolute pressure sensor pre-embedded at the interface between the concrete-reinforced structure 3 and the surrounding rock and at the center of the dome, a piezometer embedded in the surrounding rock and the caprock, and microseismic detectors arrayed on the ground and in the well.

[0048] In this embodiment, high-precision absolute pressure sensors (not shown in the figure) are pre-embedded at the interface between the concrete-reinforced structure 3 and the surrounding rock, as well as at the center of the dome, to monitor the gas storage pressure and annular pressure. Piezometers (not shown in the figure) are embedded inside the surrounding rock and caprock to monitor groundwater seepage pressure. Microseismic detectors (not shown in the figure) are arrayed on the surface and in the well. These sensors, together with the embedded fiber optic sensor 6, form a complementary monitoring system. The absolute pressure sensors capture abnormal pressure fluctuations, the piezometers provide early warning of groundwater seepage and gas escape, and the fusion of microseismic detectors with DAS microseismic data from inside the tunnel improves the accuracy of microseismic location. Through the arrangement of multiple types and locations of sensors, comprehensive pressure, seepage, and microseismic monitoring of the energy storage chamber 4, lining structure, surrounding rock, and caprock is achieved, compensating for the shortcomings of single fiber optic sensing in certain physical quantities and improving the comprehensiveness and accuracy of risk identification.

[0049] In one embodiment, the output converter 5 is a fiber optic splice box or fiber optic cable terminal box, which is sealed and installed at the wellhead interface. The fiber optic sensor 6 is connected to the fiber optic demodulator outside the well through the output converter 5.

[0050] In this embodiment, the output converter 5 is a fiber optic splice box or fiber optic terminal box, sealed and installed at the wellbore interface. The fiber optic sensor 6 is connected to the fiber optic demodulator outside the wellbore through this converter. This design ensures the continuity, sealing, and maintainability of the fiber optic signal as it exits the wellbore from inside the high-pressure energy storage chamber 4; the sealed installation prevents gas leakage along the fiber optic channel; thus, it safely and reliably leads the monitoring signal from inside the wellbore to the surface monitoring system without compromising the airtightness of the wellbore interface. This is a key structural element for the effective transmission of downhole monitoring data to the external processing platform.

[0051] like Figure 7 As shown, a method for monitoring air-source energy storage caverns based on UHPC lining and distributed optical fibers is used in an air-source energy storage cavern structure based on UHPC lining and distributed optical fibers, and includes the following steps:

[0052] S10: Real-time temperature information, real-time strain information and real-time vibration information are acquired based on distributed optical fiber sensors to generate optical fiber monitoring information;

[0053] S20: Based on a high-precision absolute pressure sensor, obtain gas storage pressure information and annular pressure information in the energy storage chamber; based on a piezometer, obtain groundwater seepage pressure information; based on a microseismic detector, obtain microseismic information; and combine the fiber optic monitoring information to generate a multi-dimensional real-time monitoring dataset.

[0054] S30: Based on the coupled deformation mechanism of ultra-high performance concrete lining and surrounding rock, a coupled deformation model and a leakage threshold model are established, and a multi-dimensional early warning index including deformation early warning threshold, leakage early warning threshold and micro-seismic early warning threshold is set.

[0055] S40: Compare each data point in the multi-dimensional real-time monitoring dataset with the corresponding multi-dimensional early warning indicators. When any data point reaches or exceeds the corresponding threshold, generate a graded early warning information.

[0056] S50: Generate a maintenance plan based on the hierarchical early warning information, and evaluate the effectiveness of the maintenance execution process in real time. Optimize the parameters of the coupled deformation model or leakage threshold model based on the evaluation results.

[0057] In this embodiment, the fiber optic monitoring information is a comprehensive dataset that aggregates temperature information reflecting the temperature field distribution near the energy storage chamber, strain information reflecting structural deformation and microcrack development, and microseismic signals reflecting rock fracture or gas escape. The gas storage pressure information is the absolute pressure of the gas inside the energy storage chamber. The annular pressure information is the pressure in the void between the lining outer wall and the surrounding rock. The multi-dimensional real-time monitoring dataset is a complete real-time database formed by fusing data collected from all sensors. The coupled deformation model is a mechanical model established based on the interaction mechanism between the UHPC lining and the surrounding rock, describing the relationship between deformation and stress. The leakage threshold model is a critical condition model for judging sealing failure established based on gas seepage theory and experimental data. The multi-dimensional early warning indicators are thresholds set for deformation, leakage, and microseismic signals respectively, used to automatically trigger different levels of alarms. The graded early warning information is an early warning signal generated according to the type and degree of exceeding the standard indicators, distinguishing between deformation early warning, leakage early warning, and microseismic early warning.

[0058] Specifically, firstly, real-time temperature, strain, and vibration information are acquired using distributed fiber optic sensors to generate fiber optic monitoring information. Simultaneously, high-precision absolute pressure sensors, piezometers, and microseismic detectors are used to acquire gas pressure, annular pressure, groundwater seepage pressure, and microseismic information within the energy storage chamber, respectively, and these are fused with the fiber optic monitoring information to form a multi-dimensional real-time monitoring dataset. Then, based on the coupled deformation mechanism of the UHPC lining and surrounding rock, a coupled deformation model and a leakage threshold model are established, and early warning indicators are set, including deformation, leakage, and microseismicity. Next, each data point in the dataset is compared with its corresponding early warning indicator; when any indicator reaches or exceeds the threshold, a graded early warning message is automatically generated. Finally, a maintenance plan (such as grouting or adjusting operating pressure) is generated based on the early warning information, and the effectiveness is continuously evaluated during the maintenance process. The evaluation results are used to optimize the model parameters. By fusing multi-source heterogeneous data and performing closed-loop calibration of physical models, the entire process from data acquisition and intelligent early warning to adaptive maintenance has been automated. This enables real-time capture of minor structural anomalies and drives scientific decision-making, significantly improving the ability to identify early-stage hazards and response speed, reducing the need for manual intervention and the risk of misjudgment, and ensuring the safe operation of energy storage caverns throughout their entire life cycle.

[0059] In one embodiment, the real-time temperature information obtained in step S10 includes temperature distribution data of the dome, sidewalls and wellhead interface; the real-time strain information includes dome settlement data, lining microcrack development data and surrounding rock creep data; the real-time vibration information includes salt rock microfracture data, hard rock unloading vibration data and microseismic signal data generated by gas escape.

[0060] In this embodiment, the dome temperature distribution data is the temperature field information of the top region of the energy storage gas chamber, which can be used to monitor local cooling caused by gas leakage or the heat of salt rock karstification reaction; the sidewall temperature distribution data is the temperature change at various heights of the rock cave sidewall, reflecting the heat exchange of the surrounding rock and possible gas channeling channels; the wellhead interface temperature distribution data is the temperature in the area near the converter, focusing on monitoring temperature anomalies caused by interface sealing failure; the dome settlement data is the vertical displacement of the top structure calculated by strain sensors, which is a core indicator for measuring the safety of the dome structure; the lining microcrack development data is... Local strain concentrations or abrupt changes captured by strain sensors are used to determine crack initiation and propagation; rock creep data are strain records of slow deformation of the surrounding rock under long-term pressure, reflecting the rheological properties of salt or hard rock; salt microfracture data are vibration signals generated by tiny fractures inside the salt rock, indicating a decrease in the stability of the solution cavity; hard rock unloading vibration data are vibrations generated by the release of stress in hard rock due to excavation or pressure changes, which can be used to assess the degree of damage to the surrounding rock; micro-vibration signal data generated by gas escape are high-frequency vibrations induced by microfractures in the surrounding rock when high-pressure gas moves along the fractures, which are precursors to leakage.

[0061] Specifically, real-time temperature information includes temperature distribution data for the dome, sidewalls, and shaft interfaces, allowing for the monitoring of thermal anomalies at different locations. Real-time strain information includes data on dome settlement, lining microcrack development, and surrounding rock creep, corresponding to top structure safety, lining integrity, and long-term surrounding rock deformation, respectively. Real-time vibration information includes micro-fractures in salt rock, unloading vibrations in hard rock, and micro-seismic signals generated by gas escape, enabling the differentiation of vibration events from different sources. By incorporating these refined features into the monitoring dataset, the system can more accurately identify the location, type, and severity of potential hazards. For example, a sustained localized cooling of the dome may indicate a leak, while a sudden increase in strain in microcracks points to a risk of lining cracking. This improves the physical interpretability and diagnostic granularity of the monitoring information, providing more targeted basis for subsequent early warning and maintenance, and avoiding misjudgments or omissions caused by general alarms.

[0062] In one embodiment, the deformation warning threshold set in step S30 is when the dome settlement monitoring value reaches or exceeds 2 mm / month, or the lining strain monitoring value reaches or exceeds 1000 mm / month. ;

[0063] The leakage warning threshold is when the abnormal temperature drop monitoring value reaches or exceeds 2°C, or the daily pressure drop monitoring value reaches or exceeds 0.05MPa;

[0064] The microseismic early warning threshold is defined as a sudden increase in the frequency of microseismic events or an abnormal increase in energy.

[0065] In this embodiment, the dome settlement monitoring value is the real-time measured vertical displacement of the dome; the lining strain monitoring value is the micro-strain on or inside the lining surface measured by a fiber optic strain sensor; the abnormal temperature drop monitoring value is the decrease in temperature compared to the normal operating temperature baseline, usually caused by the expansion and heat absorption of leaked gas; the daily pressure drop monitoring value is the pressure drop in the energy storage chamber over 24 hours, which can comprehensively reflect the system's sealing performance; a sudden increase in micro-vibration frequency is when the number of micro-vibration events per unit time significantly exceeds the historical average, indicating an intensified fracturing of the surrounding rock; and an abnormal increase in energy is when the energy released by a single micro-vibration exceeds the normal fluctuation range, which may correspond to a larger-scale rupture or gas channeling.

[0066] Specifically, the deformation warning threshold is when the dome settlement reaches or exceeds 2 mm / month, or the lining strain reaches or exceeds 1000 mm / month. The leakage warning threshold is set at a temperature drop of 2°C or more, or a daily pressure drop of 0.05 MPa or more. The micro-vibration warning threshold is set at a sudden increase in micro-vibration frequency or an abnormal increase in energy. These thresholds are preset based on engineering experience, material properties, and gas seepage theory. The system automatically triggers the corresponding warning when real-time data exceeds the limit. By setting multiple parallel trigger conditions, false alarms caused by occasional fluctuations in a single indicator can be avoided, while ensuring the reliability of cross-validation across multiple physical fields. The role of this hierarchical quantitative indicator is to achieve automatic conversion from continuous monitoring to discrete alarms, enabling maintenance personnel to quickly distinguish risk types and determine severity, providing clear and operable criteria for timely and targeted maintenance measures, and effectively avoiding delays in handling due to ambiguous indicators.

[0067] In one embodiment, the graded early warning information generated in step S40 includes deformation early warning information, leakage early warning information and micro-vibration early warning information, and is pushed through platform pop-ups, SMS or sound and light alarms.

[0068] In this embodiment, the deformation warning information is an alarm generated when the deformation monitoring value reaches or exceeds the deformation warning threshold, which includes the abnormal location (such as excessive settlement at a certain point in the dome) and the current value; the leakage warning information is an alarm generated when the temperature drops abnormally or the daily pressure drops beyond the limit, indicating that there may be a gas leak point; the microseismic warning information is an alarm generated when the frequency or energy of microseismic events increases abnormally, indicating that the surrounding rock is fracturing or gas channeling activity is increasing.

[0069] Specifically, the generated tiered early warning information includes three categories: deformation warning, leakage warning, and micro-seismic warning. Each category carries the anomaly type, location, and numerical value. These warnings are pushed through multiple channels, including platform pop-ups, SMS messages, and audible and visual alarms. Pop-ups are used for highlighting on the real-time monitoring interface, SMS messages are suitable for mobile notifications, and audible and visual alarms are used for emergencies requiring immediate response. By efficiently and reliably transmitting risk signals automatically determined by the computer to relevant personnel, delays in response due to untimely or omitted information transmission are avoided, forming a closed-loop "perception-early warning-response" chain, thereby improving emergency response speed and management efficiency.

[0070] In one embodiment, after generating the maintenance plan based on the graded early warning information in step S50, the method further includes tracking and recording the maintenance execution results, and feeding back the tracking and recording data to the coupled deformation model and leakage threshold model for online optimization of model parameters.

[0071] In this embodiment, the maintenance execution result tracking and recording involves continuously monitoring and recording the changes in various data (such as strain and pressure) after implementing measures such as glue injection and pressure control.

[0072] Specifically, after generating maintenance plans based on tiered early warning information, the system tracks and records the maintenance execution results and feeds this recorded data back to the coupled deformation model and leakage threshold model for online optimization of model parameters. For example, if the strain does not decrease as expected after glue injection and joint repair, the system will automatically adjust the local stiffness parameters of that area in the model to make the next early warning more accurate. Through closed-loop optimization, the model can continuously adapt to structural aging, damage evolution, or environmental changes, avoiding the accumulation of prediction biases caused by a fixed model. This endows the monitoring system with adaptive evolution capabilities, enabling the model to maintain high fidelity over a long period, thereby extending the effective service life of the early warning system, reducing the frequency of manual calibration, and achieving truly intelligent adaptive monitoring and maintenance.

[0073] The present invention and its embodiments have been described above. This description is not restrictive. The accompanying drawings are only one embodiment of the present invention. The actual structure is not limited to this. In short, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present invention, such design should fall within the protection scope of the present invention.

Claims

1. An air-storage cave structure based on UHPC lining and distributed optical fiber, applied in a natural cave (1), characterized in that: Includes a concrete lining structure (2), which is installed on the inner wall of the natural cave (1); A concrete reinforcement structure (3) is set inside the concrete lining structure (2) and forms an energy storage chamber (4) inside. The concrete reinforcement structure (3) includes a surrounding rock bonding layer (301), a main sealing layer (302) and an airtight protective layer (303). The surrounding rock bonding layer (301) is set close to the inner wall of the concrete lining structure (2). The main sealing layer (302) and the airtight protective layer (303) are sequentially set on the side of the surrounding rock bonding layer (301) away from the concrete lining structure (2). The fiber optic sensor (6) is embedded in the airtight protective layer (303). The fiber optic sensor (6) includes a temperature sensor (601), a strain sensor (602), and a vibration sensor (603). A converter (5) is installed at the wellhead interface of the natural cave (1) to lead the signal of the fiber optic sensor (6) out of the cave.

2. The air-source energy storage cavern structure based on UHPC lining and distributed optical fiber according to claim 1, characterized in that: The surrounding rock bonding layer (301) is a composite structure of modified ultra-high performance concrete interface agent and thin ultra-high performance concrete, and the thickness of the surrounding rock bonding layer (301) is 3-5cm; the main sealing layer (302) is an ultra-high performance concrete layer with fiber incorporation, and the conventional thickness of the main sealing layer (302) is 8-15cm, while the thickness in the dome area is 12-18cm; the airtight protective layer (303) is a composite structure of nano-silicon sealing coating and ultra-thin stainless steel airtight liner, and the thickness of the ultra-thin stainless steel airtight liner is 1-2mm.

3. The air-storage rock cave structure based on UHPC lining and distributed optical fiber according to claim 1, characterized in that: The fiber optic sensor (6) is a combination of DTS and DAS composite optical cable and FBG grating string, and the temperature sensor (601), strain sensor (602) and vibration sensor (603) are integrated in the same composite optical cable.

4. The air-storage rock cave structure based on UHPC lining and distributed optical fiber according to claim 1, characterized in that: It also includes a high-precision absolute pressure sensor pre-embedded in the interface between the concrete reinforcement structure (3) and the surrounding rock and the center of the dome, a piezometer embedded in the surrounding rock and the caprock, and microseismic detectors arrayed on the ground and in the well.

5. The air-source energy storage cavern structure based on UHPC lining and distributed optical fiber according to claim 1, characterized in that: The output converter (5) is a fiber optic splice box or a fiber optic cable terminal box, which is sealed and installed at the wellhead interface. The fiber optic sensor (6) is connected to the fiber optic demodulator outside the well through the output converter (5).

6. A method for monitoring air-source energy storage caverns based on UHPC lining and distributed optical fibers, used in an air-source energy storage cavern structure based on UHPC lining and distributed optical fibers as described in any one of claims 1-5, characterized in that: Includes the following steps: S10: Real-time temperature information, real-time strain information and real-time vibration information are acquired based on distributed optical fiber sensors to generate optical fiber monitoring information; S20: Based on a high-precision absolute pressure sensor, obtain gas storage pressure information and annular pressure information in the energy storage chamber; based on a piezometer, obtain groundwater seepage pressure information; based on a microseismic detector, obtain microseismic information; and combine the fiber optic monitoring information to generate a multi-dimensional real-time monitoring dataset. S30: Based on the coupled deformation mechanism of ultra-high performance concrete lining and surrounding rock, a coupled deformation model and a leakage threshold model are established, and a multi-dimensional early warning index including deformation early warning threshold, leakage early warning threshold and micro-seismic early warning threshold is set. S40: Compare each data point in the multi-dimensional real-time monitoring dataset with the corresponding multi-dimensional early warning indicators. When any data point reaches or exceeds the corresponding threshold, generate a graded early warning information. S50: Generate a maintenance plan based on the hierarchical early warning information, and evaluate the effectiveness of the maintenance execution process in real time. Optimize the parameters of the coupled deformation model or leakage threshold model based on the evaluation results.

7. The air-storage rock cave monitoring method based on UHPC lining and distributed optical fiber according to claim 6, characterized in that: The real-time temperature information obtained in step S10 includes temperature distribution data of the dome, sidewalls and wellhead interfaces; the real-time strain information includes dome settlement data, lining microcrack development data and surrounding rock creep data; the real-time vibration information includes salt rock microfracture data, hard rock unloading vibration data and microseismic signal data generated by gas escape.

8. The air-storage rock cave monitoring method based on UHPC lining and distributed optical fiber according to claim 6, characterized in that: The deformation warning threshold set in step S30 is when the dome settlement monitoring value reaches or exceeds 2 mm / month, or the lining strain monitoring value reaches or exceeds 1000 mm / month. ; The leakage warning threshold is when the abnormal temperature drop monitoring value reaches or exceeds 2°C, or the daily pressure drop monitoring value reaches or exceeds 0.05MPa; The microseismic early warning threshold is defined as a sudden increase in the frequency of microseismic events or an abnormal increase in energy.

9. A method for monitoring air-source energy storage caverns based on UHPC lining and distributed optical fiber according to claim 6, characterized in that: The graded early warning information generated in step S40 includes deformation early warning information, leakage early warning information and micro-vibration early warning information, and is pushed through platform pop-ups, SMS or sound and light alarms.

10. A method for monitoring air-source energy storage caverns based on UHPC lining and distributed optical fiber according to claim 6, characterized in that: After generating the maintenance plan based on the graded early warning information in step S50, the method further includes tracking and recording the maintenance execution results, and feeding back the tracking and recording data to the coupled deformation model and leakage threshold model for online optimization of model parameters.

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