Monitoring and repairing method and equipment for tunnel deformation and medium

Abstract

The invention discloses a monitoring and repairing method and device for tunnel deformation and a medium, and relates to the technical field of tunnel treatment. The method comprises the steps that optical fiber sensors are arranged on the lining surface of a tunnel in a rhombic grid mode, temperature sensors are integrated for the optical fiber sensors, and digital passports are generated for the optical fiber sensors and the temperature sensors; acquiring strain field data of the lining surface through an optical fiber sensor, performing temperature compensation on the strain field data, and establishing a distribution model; stress distribution characteristics of tunnel damage and historical data of corresponding repair cases are obtained, a first repair instruction is generated, and the stress distribution characteristics of the tunnel damage comprise three-dimensional stress distribution and a failure evolution path; and performing tensor slicing processing on the distribution model, matching the distribution model with historical data, and generating a second repair instruction by adopting an optimization algorithm. According to the method, the full-section high-density monitoring of the three-dimensional stress field is realized, the integrity and traceability of the monitoring data are ensured, and the accuracy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel management, and in particular to a method, device and medium for monitoring and repairing tunnel deformation. Background Art

[0002] As urban underground space development moves towards deeper depths and larger cross-sections, deformation monitoring of tunnel structures under complex geological conditions and external loads faces severe challenges.

[0003] Traditional monitoring methods mainly rely on manual inspections and discrete sensor networks, which have technical bottlenecks such as large monitoring blind spots, significant temperature interference, and delayed identification of damage evolution paths. The fiber optic sensor networks used in existing technologies mostly adopt a rectangular layout, which has inherent defects in the characterization of shear strain fields and lacks dynamic coupling analysis with the thermal expansion characteristics of materials. Existing repair technologies are mostly based on static repair solutions based on empirical judgment, and the grouting pressure parameter settings are not well matched with on-site working conditions. Although BIM technology has been applied to tunnel engineering, it lacks deep integration with real-time monitoring data. The existing system has insufficient prediction accuracy for damage evolution, and the repair process lacks reliable digital records, resulting in the inability to trace quality disputes.

[0004] Through the above analysis, the problems and defects of the prior art are as follows: The existing tunnel deformation monitoring technologies suffer from low spatial resolution, insufficient compensation for temperature disturbances, poor dynamic adaptability of repair solutions, and insufficient data security and traceability. Summary of the invention

[0005] The embodiments of the present application provide a method, device and medium for monitoring and repairing tunnel deformation, which can solve the problems of low spatial resolution, insufficient temperature interference compensation, poor dynamic adaptability of repair solutions, and insufficient data security and traceability in tunnel deformation monitoring in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring and repairing tunnel deformation, the method comprising: arranging optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrating temperature sensors into the optical fiber sensors, and generating digital passports for the optical fiber sensors and the temperature sensors; acquiring strain field data of the lining surface through optical fiber sensors, and performing temperature compensation on the strain field data to establish a distribution model; acquiring stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generating a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; performing tensor slicing processing on the distribution model and matching it with historical data, and using an optimization algorithm to generate a second repair instruction.

[0007] In one implementation of the present application, the strain field data of the lining surface is obtained by an optical fiber sensor, and the strain field data is temperature compensated to establish a distribution model, which specifically includes: collecting real-time temperature data of the location of the optical fiber sensor through a temperature sensor; obtaining the concrete mix ratio, and calculating the thermal expansion coefficient of the location of the optical fiber sensor based on the real-time temperature data, and calculating the coupling relationship with the strain field data; separating the temperature drift component of the strain field data based on the coupling relationship to obtain a three-dimensional stress distribution model of the net mechanical strain value.

[0008] In one implementation of the present application, the stress distribution characteristics of tunnel damage and the historical data of corresponding repair cases are obtained to generate a first repair instruction, which specifically includes: matching repair cases based on the BIM component library, the repair cases including material usage, construction machinery paths and grouting pressure parameters; using a time reversal algorithm to generate a failure path sequence with a timestamp.

[0009] In one implementation of the present application, the method also includes: using a laser scanner mounted on an unmanned aerial vehicle to reverse model the repair area and compare the Mahalanobis distance of the strain field data before and after the repair; dynamically adjusting the material usage, construction machinery path and grouting pressure parameters based on the Mahalanobis distance and failure path sequence to obtain the first repair instruction, and generating a blockchain evidence log containing a holographic image of the repair process.

[0010] In one implementation of the present application, the distribution model is subjected to tensor slicing processing and matched with historical data, and an optimization algorithm is used to generate a second repair instruction, specifically including: generating annular stress slices along the tunnel axis and at preset intervals, and matching them with stress distribution characteristics; after matching to obtain the corresponding first repair instruction, an intelligent optimization algorithm is used to plan material usage, construction machinery path and grouting pressure parameters, and generate a second repair instruction including three-dimensional coordinate positioning.

[0011] In one implementation of the present application, a digital passport is generated for the optical fiber sensor and the temperature sensor, specifically including: generating a unique digital identifier for the optical fiber sensor and the temperature sensor based on the blockchain, writing the factory calibration parameters and the installation positioning coordinates; when the strain field data and the real-time temperature data are uploaded, the logical consistency of the current data with the reference curve is verified through zero-knowledge proof; if the verification fails for a preset number of consecutive times, the current optical fiber sensor is marked as abnormal, and the redundant node data reconstruction process is started.

[0012] In one implementation of the present application, a time reversal algorithm is used to generate a failure path sequence with a timestamp, specifically including: extracting the time series characteristics of stress redistribution in historical data, and constructing a prediction model of path transfer probability based on LSTM; introducing environmental corrosion factors into the prediction model to correct the crack propagation rate, and generating a multi-dimensional failure scenario tree with confidence intervals.

[0013] In one implementation of the present application, after the distribution model is subjected to tensor slicing processing and matched with historical data, and a second repair instruction is generated using an optimization algorithm, the method further includes: when executing the second repair instruction, distributed optical fiber acoustic wave sensing is synchronously used to monitor the grouting density; if the frequency domain energy of the acoustic wave signal in the grouting area is lower than a preset value, the grouting pressure parameters are automatically updated.

[0014] In a second aspect, an embodiment of the present application also provides a monitoring and repair device for tunnel deformation, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: arrange optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrate temperature sensors into the optical fiber sensors, and generate a digital passport for the optical fiber sensors and the temperature sensors; obtain strain field data of the lining surface through optical fiber sensors, and perform temperature compensation on the strain field data to establish a distribution model; obtain stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; perform tensor slicing on the distribution model and match it with historical data, and use an optimization algorithm to generate a second repair instruction.

[0015] On the third aspect, the embodiment of the present application also provides a non-volatile computer storage medium for monitoring and repairing tunnel deformation, storing computer executable instructions, and the computer executable instructions are set to: arrange optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrate temperature sensors into the optical fiber sensors, and generate digital passports for the optical fiber sensors and temperature sensors; obtain strain field data of the lining surface through optical fiber sensors, and perform temperature compensation on the strain field data to establish a distribution model; obtain the stress distribution characteristics of the tunnel damage and the corresponding historical data of the repair case, and generate a first repair instruction, wherein the stress distribution characteristics of the tunnel damage include three-dimensional stress distribution and failure evolution path; perform tensor slicing on the distribution model and match it with the historical data, and use an optimization algorithm to generate a second repair instruction.

[0016] The embodiments of the present application provide a method, device and medium for monitoring and repairing tunnel deformation. By integrating optical fiber sensors and temperature sensors arranged in a diamond grid, high-density monitoring of the full cross-section of the three-dimensional stress field is achieved. The digital passport and blockchain evidence storage technology are used to ensure the integrity and traceability of the monitoring data, and cross-verification of equipment status and data can be achieved throughout the life cycle of the tunnel. Through the combination of the BIM component library and the time reversal algorithm, a failure evolution model with time and space correlation is constructed, which improves the matching accuracy of the repair plan, and the dynamic adjustment mechanism improves the qualified rate of the grouting density. The closed-loop control of distributed optical fiber acoustic wave sensing and intelligent optimization algorithm is adopted to realize real-time feedback adjustment of the repair process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a method for monitoring and repairing tunnel deformation provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a tunnel deformation monitoring and repair device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0019] The embodiments of the present application provide a method, device and medium for monitoring and repairing tunnel deformation, which solves the problems of low spatial resolution, insufficient temperature interference compensation, poor dynamic adaptability of repair solutions, and insufficient data security and traceability in tunnel deformation monitoring in the prior art.

[0020] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0021] Figure 1 A flow chart of a method for monitoring and repairing tunnel deformation provided in an embodiment of the present application. Figure 1 As shown, a method for monitoring and repairing tunnel deformation provided in an embodiment of the present application specifically includes the following steps: Step 10: Arrange fiber optic sensors in a diamond grid on the lining surface of the tunnel, integrate temperature sensors with the fiber optic sensors, and generate digital passports for the fiber optic sensors and temperature sensors.

[0022] In this step, for example, the grid side length can be 0.5-1.2 meters, which is adjusted according to the stability of the formation. The node intersection is fixed with epoxy resin encapsulation, and the spacing between adjacent optical fibers needs to cover the curvature change of the lining surface to ensure that the spatial sampling density of the strain field is ≥20 points / ㎡; it is also possible to integrate a digital temperature sensor every 3 diamond nodes with a spacing of about 3 meters, and share the data transmission channel with the optical fiber sensor through a multiplexer.

[0023] As an optional embodiment, generating a digital passport for the optical fiber sensor and the temperature sensor may specifically include: Step 101: generating a unique digital identification for the optical fiber sensor and the temperature sensor based on the blockchain, and writing the factory calibration parameters and installation positioning coordinates; Step 102: when the strain field data and the real-time temperature data are uploaded, verifying the logical consistency of the current data with the reference curve through zero-knowledge proof; Step 103: if the verification fails for a preset number of consecutive times, marking the current optical fiber sensor as abnormal, and starting the redundant node data reconstruction process.

[0024] In this step, the factory calibration parameters can be the zero-point strain value and the temperature sensitivity coefficient; the installation positioning coordinates can be the local coordinate system based on the tunnel BIM model; as well as the sensor activation time and the first data upload time, and the data generates an unalterable hash value. Each time the data is uploaded, the zk-SNARK protocol is used to verify the consistency of the current data with the factory reference curve. For example, if the verification fails for three consecutive times, such as the data mutation exceeds the threshold of ±200με, the system automatically marks the sensor as abnormal and activates the adjacent redundant node, that is, the preset backup sensor for data interpolation and reconstruction.

[0025] Step 20: Obtain the strain field data of the lining surface through the optical fiber sensor, perform temperature compensation on the strain field data, and establish a distribution model.

[0026] As an optional embodiment, the strain field data of the lining surface is obtained by using an optical fiber sensor, and the strain field data is temperature compensated to establish a distribution model, which may specifically include: step 201: collecting real-time temperature data of the position where the optical fiber sensor is located by using a temperature sensor; step 202: obtaining the concrete mix ratio, and calculating the thermal expansion coefficient of the position where the optical fiber sensor is located based on the real-time temperature data, and calculating the coupling relationship with the strain field data; step 203: separating the temperature drift component of the strain field data based on the coupling relationship to obtain a three-dimensional stress distribution model of the net mechanical strain value.

[0027] In this step, the temperature sensor collects the surface temperature of the lining, and then the Kalman filter is used to eliminate the environmental noise and generate a temperature field thermogram; then the concrete mix parameters are input: cement type, aggregate particle size, water-cement ratio, and the real-time temperature data are combined to calculate the thermal expansion coefficient α(T)=α0+kΔT. According to the real-time temperature ΔT and the concrete mix ratio, α(T) is dynamically calculated, where α0 is the thermal expansion coefficient of concrete at the reference temperature, which is determined by the concrete mix ratio in the laboratory and is directly related to the concrete mix ratio parameters, k is the material temperature sensitivity factor, and ΔT is the difference between the real-time temperature and the reference temperature. Separation of the temperature component Δε in the optical fiber strain data T and the mechanical strain component Δε M , temperature strain ΔεT=α(T) ΔT, Δε M =Δε 总 -α(T)ΔT. In other words, the total strain Δε is always composed of the superposition of the temperature strain ΔεT and the mechanical strain ΔεM. After removing the strain caused by temperature through temperature compensation, the strain data reflecting the real deformation of the structure. Based on the compensated net mechanical strain value, it is very difficult to directly measure the three-dimensional stress distribution in tunnel monitoring, but the strain data can be obtained in real time through optical fiber sensors. Therefore, the finite element inverse analysis method is used to reconstruct the three-dimensional stress field, and the temperature-compensated strain data is converted into a three-dimensional stress tensor, and the output is in tensor form.

[0028] Step 30: Obtain stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path.

[0029] As an optional embodiment, the stress distribution characteristics of tunnel damage and the historical data of corresponding repair cases are obtained to generate a first repair instruction, which may specifically include: Step 301: matching repair cases based on the BIM component library, the repair cases including material usage, construction machinery paths and grouting pressure parameters; Step 302: using a time reversal algorithm to generate a failure path sequence with a timestamp.

[0030] In this step, for example, historical restoration cases are retrieved from the BIM (Building Information Modeling) component library to extract the following features: damage type: shear cracks, circumferential fractures, etc.; stress distribution similarity: Euclidean distance ≤ 0.15; environmental parameters: groundwater pH value, surrounding rock grade; after successful matching, the material usage in the case is automatically associated, such as the epoxy resin grouting volume Q = πr²L·filling factor, construction machinery path: six-axis robot arm motion trajectory; grouting pressure: P = 0.5-1.2MPa (the grouting pressure can be adjusted between 0.5 MPa and 1.2 MPa).

[0031] As an optional embodiment, the method may also include: Step 303: using a laser scanner mounted on an unmanned aerial vehicle to reverse model the repair area and compare the Mahalanobis distance of the strain field data before and after the repair; Step 304: dynamically adjusting the material usage, construction machinery path and grouting pressure parameters based on the Mahalanobis distance and the failure path sequence to obtain the first repair instruction, and generating a blockchain evidence log containing a holographic image of the repair process.

[0032] In this step, the Mahalanobis distance can reflect the relative position change of data points in multidimensional space and is an effective indicator for evaluating the repair effect. If the Mahalanobis distance is small, it means that the repair effect is good and the strain field difference between the repaired area and the unrepaired area is not large; if the Mahalanobis distance is large, it means that there is still a large strain difference in the repaired area, and the repair process may need to be further optimized. The repair instructions, sensor data, and image files are packaged to generate IPFS hashes and written into the blockchain evidence log to support subsequent quality traceability.

[0033] As an optional embodiment, a time reversal algorithm is used to generate a failure path sequence with a timestamp, which may specifically include: Step 3021: extracting the time series characteristics of stress redistribution in historical data, and constructing a prediction model of path transfer probability based on LSTM; Step 3022: introducing environmental corrosion factors into the prediction model to correct the crack propagation rate, and generating a multi-dimensional failure scenario tree with confidence intervals.

[0034] In this step, the historical stress data time series is input and the long short-term memory network is trained to predict the crack growth rate v: v = v 0 e kt (Introduction of corrosion factor k =0.02Cl- concentration, the corrosion factor k is used to quantify the corrosion rate of materials in the environment, Cl- represents the chloride ion concentration, and chloride ions are common corrosive ions), output the failure path sequence with timestamp, for example, the annular crack expands to 2m at t=72h, and generate a multi-dimensional scenario tree, for example, the path with probability weight ≥80% can be displayed first.

[0035] Step 40: Perform tensor slicing processing on the distribution model, match it with historical data, and use an optimization algorithm to generate a second repair instruction.

[0036] As an optional embodiment, the distribution model is subjected to tensor slicing processing and matched with historical data, and an optimization algorithm is used to generate a second repair instruction, which may specifically include: Step 401: Generate annular stress slices along the tunnel axis at a preset interval and match them with the stress distribution characteristics; Step 402: After matching and obtaining the corresponding first repair instruction, use an intelligent optimization algorithm to plan material usage, construction machinery path and grouting pressure parameters, and generate a second repair instruction including three-dimensional coordinate positioning.

[0037] In this step, for example, circumferential stress slices are generated every 0.5 meters along the axial direction of the tunnel, and the maximum principal stress and shear stress in the slices are extracted and matched with the damage patterns in the BIM case library. Then, based on the first repair instruction, intelligent optimization algorithms, genetic algorithms, particle swarm algorithms, simulated annealing algorithms, etc. are used to plan the material usage, construction machinery path and grouting pressure parameters. The optimized repair parameters are combined with the three-dimensional coordinate positioning information of the tunnel to generate a second repair instruction containing three-dimensional coordinate positioning.

[0038] As an optional embodiment, after performing tensor slicing processing on the distribution model, matching it with historical data, and using an optimization algorithm to generate a second repair instruction, the method may also include: when executing the second repair instruction, synchronously using distributed fiber optic acoustic wave sensing to monitor the grouting density; if the frequency domain energy of the acoustic wave signal in the grouting area is lower than a preset value, automatically updating the grouting pressure parameters.

[0039] In this step, distributed fiber optic acoustic wave sensors are arranged around or inside the grouting area. The sensors transmit acoustic wave signals through optical fibers and can monitor the changes in acoustic waves generated during the grouting process in real time. The time domain acoustic wave signals are converted into frequency domain signals through methods such as Fourier transform, and the energy distribution at different frequencies is analyzed. According to the frequency domain energy distribution, the density of the grouting area is evaluated, and a threshold value of the frequency domain energy of the acoustic wave signal is preset as a criterion for judging the grouting density. If the frequency domain energy of the acoustic wave signal in the grouting area is lower than the preset value, it means that the grouting material may not be fully filled or the density is insufficient. At this time, the system automatically updates the grouting pressure parameters, increases the grouting pressure or adjusts the grouting method to improve the grouting effect.

[0040] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a monitoring and repair device for tunnel deformation, the structure of which is as follows: Figure 2 shown.

[0041] Figure 2 A schematic diagram of the internal structure of a monitoring and repair device for tunnel deformation provided in an embodiment of the present application. Figure 2 As shown, the device includes: at least one processor 201; and, a memory 202 communicatively connected to the at least one processor; The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 so that the at least one processor 201 can: arrange optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrate temperature sensors into the optical fiber sensors, and generate digital passports for the optical fiber sensors and temperature sensors; obtain strain field data of the lining surface through optical fiber sensors, perform temperature compensation on the strain field data, and establish a distribution model; obtain stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; perform tensor slicing on the distribution model, match it with historical data, and use an optimization algorithm to generate a second repair instruction.

[0042] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for monitoring and repairing tunnel deformation stores computer executable instructions, wherein the computer executable instructions are configured to: arrange optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrate temperature sensors into the optical fiber sensors, and generate digital passports for the optical fiber sensors and the temperature sensors; obtain strain field data of the lining surface through the optical fiber sensors, perform temperature compensation on the strain field data, and establish a distribution model; obtain stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; perform tensor slicing on the distribution model and match it with historical data, and generate a second repair instruction using an optimization algorithm.

[0043] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0044] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0045] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0049] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0050] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0051] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0052] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0053] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for monitoring and repairing tunnel deformation, characterized in that: The method comprises: Arranging optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrating temperature sensors with the optical fiber sensors, and generating a digital passport for the optical fiber sensors and the temperature sensors; Acquiring strain field data of the lining surface through the optical fiber sensor, performing temperature compensation on the strain field data, and establishing a distribution model; Acquire stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; The distribution model is subjected to tensor slicing processing and matched with the historical data, and a second repair instruction is generated by using an optimization algorithm.

2. A method for monitoring and repairing tunnel deformation according to claim 1, characterized in that: The step of obtaining the strain field data of the lining surface by the optical fiber sensor, performing temperature compensation on the strain field data, and establishing a distribution model specifically includes: Collecting real-time temperature data of the location of the optical fiber sensor through the temperature sensor; Obtaining the concrete mix ratio, and calculating the thermal expansion coefficient of the location of the optical fiber sensor according to the real-time temperature data, and calculating the coupling relationship with the strain field data; The temperature drift component of the strain field data is separated according to the coupling relationship to obtain a three-dimensional stress distribution model of the net mechanical strain value.

3. A method for monitoring and repairing tunnel deformation according to claim 1, characterized in that: The step of obtaining stress distribution characteristics of tunnel damage and historical data of corresponding repair cases and generating a first repair instruction specifically includes: Matching the repair case based on the BIM component library, wherein the repair case includes material usage, construction machinery path and grouting pressure parameters; A time reversal algorithm is used to generate a failure path sequence with timestamps.

4. A method for monitoring and repairing tunnel deformation according to claim 3, characterized in that: The method further comprises: Using a laser scanner mounted on a drone, reverse modeling of the repaired area was performed to compare the Mahalanobis distance of the strain field data before and after the repair. The material usage, construction machinery path and grouting pressure parameters are dynamically adjusted based on the Mahalanobis distance and the failure path sequence to obtain a first repair instruction, and generate a blockchain evidence log containing a holographic image of the repair process.

5. A method for monitoring and repairing tunnel deformation according to claim 4, characterized in that: The tensor slicing process is performed on the distribution model, and the distribution model is matched with the historical data, and the second repair instruction is generated by using an optimization algorithm, specifically including: Generate circumferential stress slices along the tunnel axis at preset intervals to match the stress distribution characteristics; After the corresponding first repair instruction is matched, an intelligent optimization algorithm is used to plan the material usage, construction machinery path and grouting pressure parameters to generate a second repair instruction including three-dimensional coordinate positioning.

6. A method for monitoring and repairing tunnel deformation according to claim 1, characterized in that: The generating of a digital passport for the optical fiber sensor and the temperature sensor specifically includes: Generate a unique digital identifier for the optical fiber sensor and temperature sensor based on blockchain, and write factory calibration parameters and installation positioning coordinates; When the strain field data and real-time temperature data are uploaded, the logical consistency between the current data and the reference curve is verified by zero-knowledge proof; If the verification fails for a preset number of consecutive times, the current optical fiber sensor is marked as abnormal and the redundant node data reconstruction process is started.

7. A method for monitoring and repairing tunnel deformation according to claim 3, characterized in that: The time reversal algorithm is used to generate a failure path sequence with a timestamp, specifically including: Extracting the time series features of stress redistribution in the historical data and constructing a prediction model of path transition probability based on LSTM; The environmental corrosion factor is introduced into the prediction model to correct the crack growth rate and generate a multi-dimensional failure scenario tree with confidence intervals.

8. The method for monitoring and repairing tunnel deformation according to claim 3, characterized in that: After performing tensor slicing processing on the distribution model and matching it with the historical data, and using an optimization algorithm to generate a second repair instruction, the method further includes: When executing the second repair instruction, the grouting density is monitored synchronously using distributed optical fiber acoustic wave sensing; If the frequency domain energy of the acoustic wave signal in the grouting area is lower than a preset value, the grouting pressure parameter is automatically updated.

9. A monitoring and repair device for tunnel deformation, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Arranging optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrating temperature sensors with the optical fiber sensors, and generating a digital passport for the optical fiber sensors and the temperature sensors; Acquiring strain field data of the lining surface through the optical fiber sensor, performing temperature compensation on the strain field data, and establishing a distribution model; Acquire stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; The distribution model is subjected to tensor slicing processing and matched with the historical data, and a second repair instruction is generated by using an optimization algorithm.

10. A non-volatile computer storage medium for monitoring and repairing tunnel deformation, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Arranging optical fiber sensors in a diamond grid on the lining surface of the tunnel, integrating temperature sensors with the optical fiber sensors, and generating a digital passport for the optical fiber sensors and the temperature sensors; Acquiring strain field data of the lining surface through the optical fiber sensor, performing temperature compensation on the strain field data, and establishing a distribution model; Acquire stress distribution characteristics of tunnel damage and historical data of corresponding repair cases, and generate a first repair instruction, wherein the stress distribution characteristics of tunnel damage include three-dimensional stress distribution and failure evolution path; The distribution model is subjected to tensor slicing processing and matched with the historical data, and a second repair instruction is generated by using an optimization algorithm.

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