Tunnel damage monitoring method and system

Abstract

The invention discloses a tunnel damage monitoring method and system, and belongs to the technical field of data processing. The method comprises the following steps: acquiring tunnel operation data and tunnel damage data, and transmitting the data to a control center; creating a score data set; creating a damaged data set; constructing a tunnel damage monitoring model, and training the damage monitoring model until the damage monitoring model reaches the standard; and continuously collecting real-time tunnel operation data, inputting the real-time tunnel operation data into the tunnel damage monitoring model to obtain a real-time damage score, and if the real-time damage score exceeds a preset damage score threshold, determining real-time tunnel damage data through the tunnel damage monitoring model. According to the scheme, potential damage or abnormal conditions can be found in time, and the safety of the tunnel is ensured. The data is automatically processed by using the model, so that the data processing efficiency is improved. The model captures a mode and a trend in a real situation by analyzing historical data and real-time data, ensures that a result is consistent with the actual situation, and improves the reliability of an analysis result.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a tunnel damage monitoring method and system. Background Art

[0002] As an important part of highway transportation, the safety and stability of tunnels are of great significance to ensuring smooth traffic and protecting people's lives and property. However, due to the complex geological environment in which tunnels are located, they may be affected by many factors during construction and operation, resulting in damage to the tunnel structure. Therefore, tunnel damage monitoring is a necessary means to ensure the safe operation of tunnels.

[0003] Tunnel damage monitoring is currently done manually. With their professional knowledge and practical experience, inspectors use tools such as crack microscopes and measuring rulers to conduct a comprehensive inspection of tunnel lining cracks, quality, water leakage, etc., and record in detail data such as crack location, length, width, and lining quality. By collating and analyzing the test results, the safety and stability of the tunnel structure are evaluated, and a maintenance and reinforcement plan is formulated accordingly.

[0004] However, manual inspection takes a lot of time and manpower, especially in long tunnels or tunnel groups, and the inspection speed is slow, which affects the normal traffic on the road. Due to the complex internal environment of the tunnel and poor lighting conditions, manual inspection is easily limited by vision, resulting in low detection accuracy and possible missed detection or misjudgment. Summary of the invention

[0005] The embodiments of the present application provide a tunnel damage monitoring method and system, which solve the technical problems in the prior art that manual detection of tunnel damage requires a lot of time and manpower, especially in the case of long tunnels or tunnel groups, the detection speed is slow, affecting the normal traffic on the road; and due to the complex internal environment of the tunnel and poor lighting conditions, manual detection is easily subject to visual limitations, resulting in low detection accuracy and the possibility of missed detection or misjudgment.

[0006] In a first aspect, an embodiment of the present application provides a tunnel damage monitoring method, the method comprising:

[0007] In the initial stage of tunnel operation, tunnel operation data and tunnel damage data at each preset collection time point are obtained, and the tunnel operation data and tunnel damage data are transmitted to the control center; wherein the tunnel operation data include tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; tunnel damage data include tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff;

[0008] receiving a damage score of the tunnel operation data sent by the control center, marking a damage score label of the tunnel operation data according to the damage score, and creating a scoring data set according to the tunnel operation data and the damage score label;

[0009] Obtaining a preset damage score threshold, and dividing the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold;

[0010] Determine abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, label the abnormal tunnel operation data with a tunnel damage label according to the abnormal tunnel damage data, and create a damage data set according to the abnormal tunnel operation data and the tunnel damage label;

[0011] Constructing a tunnel damage monitoring model, and training the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard;

[0012] Real-time tunnel operation data is continuously collected, and the real-time tunnel operation data is input into a tunnel damage monitoring model to obtain a real-time damage score. If the real-time damage score exceeds a preset damage score threshold, the real-time tunnel damage data is determined by the tunnel damage monitoring model.

[0013] Furthermore, after continuously collecting real-time tunnel operation data and inputting the real-time tunnel operation data into a tunnel damage monitoring model to obtain a real-time damage score, the method further includes:

[0014] If the real-time damage score is lower than a preset damage score threshold, inputting the tunnel operation data into a preset maintenance detection model to obtain a real-time maintenance score;

[0015] If the real-time maintenance score exceeds a preset maintenance score threshold, a maintenance type is determined by a preset maintenance detection model, and the maintenance type is transmitted to a control center.

[0016] Furthermore, after determining the real-time tunnel damage data by using the tunnel damage monitoring model, the method further includes:

[0017] Acquire tunnel geometry data, tunnel material data, tunnel boundary data, and tunnel load data, and create a three-dimensional tunnel model according to the tunnel geometry data;

[0018] Setting material properties of the three-dimensional tunnel model according to the tunnel material data, and setting boundary data of the three-dimensional tunnel model according to the tunnel boundary data;

[0019] The tunnel grid of the three-dimensional tunnel model is divided according to the tunnel geometry data and the tunnel material data, and the tunnel load data is input into the three-dimensional tunnel model.

[0020] Furthermore, after meshing the tunnel three-dimensional model according to the tunnel geometry data and the tunnel material data, and inputting the tunnel load data into the tunnel three-dimensional model, the method further includes:

[0021] Obtaining historical monitoring results, and determining historical tunnel damage data, historical damage causes, and historical damage impacts based on the historical monitoring results;

[0022] Acquire simulation requirements, set simulation parameters of the tunnel three-dimensional model according to the simulation requirements, input the historical tunnel damage data into the tunnel three-dimensional model for simulation testing, and obtain tunnel simulation results;

[0023] Determining whether the three-dimensional model of the tunnel meets a preset simulation standard according to the tunnel simulation results, historical damage causes, and historical damage impacts;

[0024] If the preset simulation standard is reached, it is determined that the tunnel 3D model has passed the simulation test.

[0025] Furthermore, after determining whether the tunnel three-dimensional model meets a preset simulation standard according to the tunnel simulation result and the historical tunnel damage data, the method further includes:

[0026] If the preset simulation standard is not met, the material property adjustment range, boundary data adjustment range and tunnel mesh adjustment range are determined according to the tunnel geometry data, tunnel material data, tunnel boundary data and tunnel load data;

[0027] Within the material property adjustment range, boundary data adjustment range and tunnel mesh adjustment range, the material properties, boundary data and tunnel mesh are continuously adjusted according to the preset material property adjustment step size, the preset boundary data adjustment step size and the preset tunnel mesh adjustment step size, and the simulation test is re-performed after each adjustment until the tunnel three-dimensional model passes the simulation test.

[0028] Furthermore, after the tunnel three-dimensional model passes the simulation test, the method further includes:

[0029] The real-time tunnel damage data is input into the tunnel three-dimensional model to obtain the real-time damage cause and the real-time damage impact, and the real-time damage cause and the real-time damage impact are sent to the control center.

[0030] Furthermore, the training process of the preset maintenance detection model includes:

[0031] Obtaining historical maintenance score records, and determining historical tunnel operation data and historical maintenance scores according to the historical maintenance records;

[0032] annotating the historical tunnel operation data with a maintenance score tag according to the historical maintenance score, and determining a maintenance score data set according to the historical tunnel operation data and the maintenance score tag;

[0033] Determine abnormal historical tunnel operation data according to the historical tunnel operation data and a preset maintenance score threshold, and obtain a historical maintenance type of the abnormal historical tunnel operation data;

[0034] marking the maintenance type label of the abnormal historical tunnel operation data according to the historical maintenance type, and creating a maintenance type data set according to the abnormal historical tunnel operation data and the maintenance type label;

[0035] A maintenance detection model is constructed, and the maintenance detection model is trained according to the maintenance score data set and the maintenance type data set until the maintenance detection model reaches a preset maintenance detection model training standard.

[0036] Furthermore, after determining the real-time tunnel damage data by using the tunnel damage monitoring model, the method further includes:

[0037] The real-time tunnel damage data, tunnel operation data and real-time damage score are hashed respectively to obtain hash values ​​corresponding to the real-time tunnel damage data, tunnel operation data and real-time damage score;

[0038] The real-time tunnel damage data, tunnel operation data and hash values ​​corresponding to the real-time damage score are stored in the blockchain according to the preset smart contract.

[0039] Furthermore, after obtaining the real-time tunnel damage data, the tunnel operation data, and the hash value corresponding to the real-time damage score, the method further includes:

[0040] Generate a zero-knowledge proof based on the real-time tunnel damage data, the tunnel operation data, the real-time damage score, and the hash values ​​corresponding to the real-time tunnel damage data, the tunnel operation data, and the real-time damage score;

[0041] Accordingly, the real-time tunnel damage data, tunnel operation data and hash values ​​corresponding to the real-time damage score are stored in the blockchain according to the preset smart contract, including:

[0042] The zero-knowledge proof and the hash value corresponding to the real-time tunnel damage data, tunnel operation data and real-time damage score are stored in the blockchain according to a preset smart contract.

[0043] According to a second aspect of the present application, a tunnel damage monitoring system is provided, the system comprising:

[0044] A data acquisition module is used to obtain tunnel operation data and tunnel damage data at each preset acquisition time point in the initial stage of tunnel operation, and transmit the tunnel operation data and tunnel damage data to the control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff;

[0045] A scoring data set creation module, configured to receive a damage score of tunnel operation data sent by a control center, label the tunnel operation data with a damage score tag according to the damage score, and create a scoring data set according to the tunnel operation data and the damage score tag;

[0046] A data division module, used for obtaining a preset damage score threshold, and dividing the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold;

[0047] A damage data set creation module, configured to determine abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, label the abnormal tunnel operation data with a tunnel damage label according to the abnormal tunnel damage data, and create a damage data set according to the abnormal tunnel operation data and the tunnel damage label;

[0048] A model building module, used to build a tunnel damage monitoring model, and train the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard;

[0049] The real-time data determination module is used to continuously collect real-time tunnel operation data, input the real-time tunnel operation data into the tunnel damage monitoring model, and obtain a real-time damage score. If the real-time damage score exceeds a preset damage score threshold, the real-time tunnel damage data is determined by the tunnel damage monitoring model.

[0050] Compared with the prior art, this application has the following beneficial effects:

[0051] The embodiment of the present application uses the above-mentioned tunnel damage monitoring method to automatically collect and analyze tunnel operation data, thereby avoiding the tediousness and time-consuming of manual detection, especially in the case of long tunnels or tunnel groups, and significantly improving the monitoring efficiency. By using a variety of high-precision sensors such as piezoelectric ceramic sensors, optical fiber sensors, accelerometers, etc., combined with a tunnel damage monitoring model, it is possible to capture small changes in the tunnel structure, improve the accuracy of monitoring, and reduce missed detection and misjudgment. By real-time collection and analysis of tunnel operation data, potential damage or abnormal conditions can be discovered in a timely manner, and an early warning mechanism can be automatically triggered to ensure the safety of the tunnel and prevent accidents. The use of models to automatically process data improves the efficiency of data processing. The model captures patterns and trends in real situations by analyzing historical data and real-time data, ensuring that the results are consistent with the actual situation, providing a scientific basis for the formulation of maintenance and reinforcement plans, and improving the accuracy and reliability of decision-making. Through hash processing and blockchain storage technology, the integrity and non-tamperability of tunnel damage data are ensured, the security of data is improved, and a reliable basis is provided for subsequent data analysis and decision-making. Through real-time monitoring and analysis, potential safety hazards can be identified in advance, accidents can be avoided, and maintenance costs caused by accidents can be reduced. At the same time, the data-based maintenance detection model can guide a reasonable maintenance plan, avoid excessive maintenance, and further reduce maintenance costs. The use of blockchain technology to store data and zero-knowledge proofs improves data transparency and traceability, and enhances public trust in tunnel safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the tunnel damage monitoring method provided in Example 1 of the present application;

[0053] Figure 2 It is a flow chart of the tunnel damage monitoring method provided in the second embodiment of the present application;

[0054] Figure 3 It is a flow chart of the tunnel damage monitoring method provided in Example 3 of the present application;

[0055] Figure 4 is a schematic diagram of the structure of a tunnel damage monitoring system provided in Embodiment 4 of the present application; DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0058] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0059] The tunnel damage monitoring method provided by the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0060] Embodiment 1

[0061] Figure 1 Schematic diagram of the process of tunnel damage monitoring method provided in Example 1 of the present application. Figure 1 As shown, the specific steps include:

[0062] S101, acquiring tunnel operation data and tunnel damage data at each preset collection time point in the initial stage of tunnel operation, and transmitting the tunnel operation data and tunnel damage data to a control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; and the tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff.

[0063] First, the usage scenario of this solution can be to establish a damage monitoring model by collecting data in the early stage of tunnel operation, and deploy the model to collect data in real time after the model passes the standard to determine the damage score and damage data scenario.

[0064] Based on the above usage scenarios, it can be understood that the executor of the present application can be a tunnel damage monitoring system, and no excessive limitations are made here.

[0065] The initial stage of tunnel operation can refer to the early period when a tunnel has just been put into use or has undergone significant construction. During this stage, monitoring data is particularly important to ensure the safety of the structure and identify potential damage.

[0066] Each preset collection time point may refer to a time point set in advance during the monitoring process, which is used to collect tunnel operation and damage data. For example, data collection may be set every hour, every day, or after a specific event (such as rainfall or earthquake).

[0067] Tunnel operation data may include data reflecting the state of the tunnel structure and environmental conditions, among which tunnel stress data may be data describing the distribution of forces within the tunnel. Tunnel strain data may be data describing the degree of deformation of tunnel materials due to external forces. Tunnel temperature data may be data recording temperature changes within the tunnel, which may affect material properties and structural safety. Tunnel vibration data may be data reflecting the vibration of the tunnel structure under operation or the influence of external factors.

[0068] Tunnel damage data may be data describing the damage to the tunnel structure, wherein tunnel displacement data may be data reflecting the displacement of the tunnel in a specific direction. Tunnel deformation data may be data describing the change in the shape of the tunnel, which may include lateral and longitudinal changes. Tunnel crack data may be data recording the presence, location and size of cracks in the tunnel structure.

[0069] The control center can be a centralized monitoring and data analysis facility, responsible for receiving, storing and analyzing data collected from the tunnel, and performing real-time monitoring and early warning.

[0070] Piezoelectric ceramic sensors are sensors that work using the piezoelectric effect and can measure stress and strain changes. They generate electrical signals when subjected to mechanical pressure and can therefore be used to monitor small deformations in tunnel structures.

[0071] Fiber optic sensors can be sensors that use fiber optic technology to measure physical quantities (such as temperature, strain, etc.). Fiber optic sensors have high sensitivity and anti-interference capabilities.

[0072] Accelerometers can be sensors used to measure vibration and movement inside a tunnel, capable of detecting changes in the tunnel's acceleration due to external forces, such as earthquakes or traffic.

[0073] The handheld terminal can be a portable device that workers use to collect and record tunnel damage data such as displacement, deformation and crack conditions in real time.

[0074] The frequency and time of data collection can be set according to the operation and environmental conditions of the tunnel. For example, data collection can be set every hour, every day, or after a specific event (such as rainfall or increased traffic load). Piezoelectric ceramic sensors are installed at key parts of the tunnel structure to monitor the stress and strain of the tunnel in real time. Fiber optic sensors are arranged in the tunnel to measure temperature changes and can be used with strain sensors to improve the accuracy of the data. Accelerometers are placed at different locations in the tunnel to monitor vibrations and evaluate external influences. The sensor system is then used to automatically collect data regularly to ensure the real-time and accuracy of the data. Workers use handheld devices in the tunnel to regularly check and record the displacement, deformation and cracks of the tunnel. This can be manually entered or obtained through image recognition. Wireless communication technology (such as Wi-Fi, LTE or dedicated wireless networks) is used to transmit the real-time collected data to the control center.

[0075] S102, receiving a damage score for tunnel operation data sent by a control center, marking a damage score label for the tunnel operation data according to the damage score, and creating a scoring data set according to the tunnel operation data and the damage score label.

[0076] The damage score can be a numerical value obtained after analyzing the tunnel operation data, indicating the health status or degree of damage of the tunnel structure under specific conditions. The score is usually calculated based on a series of parameters (such as stress, strain, temperature, etc.). The higher the score, the greater the damage risk.

[0077] The damage score label can directly use the damage score value to indicate the health status of the tunnel. For example, the damage score can be directly used as a label (such as 0.2, 0.5, 0.8, etc.), and these values ​​can be used in subsequent regression analysis or other models that require numerical input.

[0078] The scoring dataset can be a database formed by summarizing all tunnel operation data and their corresponding damage scoring labels, which is usually used to train and verify damage monitoring models. Each record includes tunnel operation data (such as stress, strain, temperature, etc.) and the corresponding damage scoring label.

[0079] Real-time tunnel operation data and calculated damage scores can be received from the control center. The received damage scores are associated with the corresponding tunnel operation data (such as stress, strain, temperature, etc.). A corresponding damage score label is assigned to each tunnel operation data. The tunnel operation data marked with the damage score label is then organized into a scoring data set.

[0080] S103: Obtain a preset damage score threshold, and divide the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold.

[0081] The preset damage score threshold may be a specific value used to distinguish between normal and abnormal tunnel operation states. For example, if the damage score is higher than the threshold, it is considered that potential damage exists.

[0082] Normal tunnel operation data may be data with a damage score lower than or equal to a preset threshold, indicating that the tunnel is in a safe operating state.

[0083] Abnormal tunnel operation data may be data with a damage score higher than a preset threshold, indicating potential damage or risk to the tunnel.

[0084] The preset damage score threshold can be obtained from the system configuration or database. Each tunnel operation data is traversed and compared with the threshold according to the damage score. The tunnel operation data that is lower than or equal to the preset damage score threshold is normal tunnel operation data, and the tunnel operation data that is higher than the preset damage score threshold is abnormal tunnel operation data.

[0085] S104, determining abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, marking a tunnel damage label of the abnormal tunnel operation data according to the abnormal tunnel damage data, and creating a damage data set according to the abnormal tunnel operation data and the tunnel damage label.

[0086] Abnormal tunnel damage data may refer to damage information corresponding to abnormal tunnel operation data, and may include detailed data on actual damage conditions of the tunnel, such as displacement, deformation, cracks, etc.

[0087] Tunnel damage labels can refer to specific values ​​that mark tunnel damage conditions. These values ​​can be used to quantify the degree, type or location of damage. For example, damage labels can be the width of cracks (in millimeters), the amount of displacement (in millimeters) or the classification of damage (such as minor, moderate, severe). These labels play a key role in analysis and modeling, and help to build effective damage monitoring models.

[0088] The damage dataset can be a collection of abnormal tunnel operation data and its corresponding damage labels, which is usually used for subsequent data analysis, model training or decision support.

[0089] By analyzing the damage scores of tunnel operation data, abnormal tunnel operation data can be identified. These abnormal data are usually records with damage scores exceeding the preset threshold. Determine which tunnel damage data (such as displacement, deformation, cracks, etc.) are associated with the identified abnormal operation data. Abnormal operation data can be matched with damage data by timestamp, sensor location or other identifiers. Then, based on the identified abnormal tunnel damage data, the corresponding tunnel damage label is annotated for each abnormal tunnel operation data. Finally, all abnormal tunnel operation data and their corresponding tunnel damage labels are aggregated to create a damage dataset. This dataset will provide a basis for subsequent analysis and modeling, and help build a more effective damage monitoring model.

[0090] S105, constructing a tunnel damage monitoring model, and training the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard.

[0091] The tunnel damage monitoring model can be a model based on machine learning or deep learning, which is used to identify and evaluate the damage of the tunnel. The model predicts the health status and potential damage of the tunnel by analyzing the input tunnel operation data (such as stress, displacement, temperature, etc.) and the corresponding damage data (such as crack width, displacement, etc.).

[0092] The preset damage monitoring model training standard can refer to the indicators used to evaluate the performance of the model during the training process. Specifically, it can include accuracy: the proportion of correct predictions made by the model. Precision and recall: measure the accuracy and missed detection rate of the model in detecting damage respectively. F1 score: the harmonic mean of precision and recall, used to comprehensively evaluate the performance of the model. Loss function: an indicator monitored during the training process, which is usually hoped to be minimized.

[0093] The scoring dataset and the damage dataset can be organized into a format suitable for model input. Preprocess the data, including standardization, denoising, and missing value processing. Select an appropriate model architecture based on the data characteristics, such as support vector machine, decision tree, neural network, etc. Use the scoring dataset and the damage dataset to train the model and adjust the model parameters to improve performance. During the training process, regularly verify the performance of the model on the validation set to ensure that the model is not overfitted. Evaluate the performance of the model on the test set and calculate indicators such as accuracy, precision, and recall. Compare the preset damage monitoring model training standards to determine whether the model meets the requirements. If the model performance does not meet the preset standards, it can be optimized by adjusting model parameters, improving data quality, or increasing the amount of training data. Once the model meets the preset standards, save it and deploy it in actual applications for real-time tunnel damage monitoring.

[0094] S106, continuously collect real-time tunnel operation data, input the real-time tunnel operation data into a tunnel damage monitoring model to obtain a real-time damage score, and if the real-time damage score exceeds a preset damage score threshold, determine the real-time tunnel damage data through the tunnel damage monitoring model.

[0095] Real-time tunnel operation data refers to the operation status data collected in real time by various sensors and monitoring equipment during the operation of the tunnel. Specifically, it can include stress data: the stress state of the tunnel structure. Strain data: the deformation of the tunnel material. Temperature data: the temperature changes inside and outside the tunnel. Vibration data: the dynamic response of the tunnel, such as vibration caused by vehicle traffic or other external factors.

[0096] The real-time damage score can refer to a numerical indicator generated by analyzing the real-time tunnel operation data through the tunnel damage monitoring model. This score is used to quantify the health status of the tunnel. It usually calculates damage-related features based on the input data to reflect the potential degree of damage. The higher the score, the greater the risk of damage.

[0097] Real-time tunnel damage data may refer to specific damage information determined by the model when the real-time damage score exceeds a preset threshold, and may include damage type: such as cracks, displacement, deformation, etc. Damage location: the specific location where the damage occurs. Damage degree: quantitative information such as crack width and displacement.

[0098] Real-time tunnel operation data can be continuously collected through sensors to ensure the accuracy and completeness of the data. The real-time collected data is cleaned and preprocessed to remove noise and outliers to ensure the quality of the input data. The processed real-time tunnel operation data is input into the trained tunnel damage monitoring model. The model performs inference based on the input real-time tunnel operation data and calculates the real-time damage score. The real-time damage score will reflect the health of the current tunnel status and is usually a numerical value. The real-time damage score is compared with the preset damage score threshold. If the real-time damage score exceeds the preset threshold, the input data is analyzed by the tunnel damage monitoring model to determine the corresponding real-time tunnel damage data.

[0099] The technical solution provided in this embodiment obtains tunnel operation data and tunnel damage data at each preset collection time point in the initial stage of tunnel operation, and transmits the tunnel operation data and tunnel damage data to a control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by a piezoelectric ceramic sensor, tunnel temperature data transmitted by an optical fiber sensor, and tunnel vibration data transmitted by an accelerometer; the tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by a handheld terminal of a staff member; receives a damage score for the tunnel operation data sent by the control center, annotates a damage score label for the tunnel operation data according to the damage score, and creates a scoring data set according to the tunnel operation data and the damage score label; obtains a preset damage score threshold, and generates a score based on the damage score and damage score label. The damage score threshold divides the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data; determines the abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, marks the tunnel damage label of the abnormal tunnel operation data according to the abnormal tunnel damage data, and creates a damage data set according to the abnormal tunnel operation data and the tunnel damage label; constructs a tunnel damage monitoring model, trains the damage monitoring model according to the score data set and the damage data set, until the damage monitoring model reaches the preset damage monitoring model training standard; continuously collects real-time tunnel operation data, inputs the real-time tunnel operation data into the tunnel damage monitoring model, obtains a real-time damage score, and if the real-time damage score exceeds the preset damage score threshold, determines the real-time tunnel damage data through the tunnel damage monitoring model. Through the above-mentioned tunnel damage monitoring method, by continuously collecting and analyzing tunnel operation data, potential damage or abnormal conditions can be discovered in time to ensure the safety of the tunnel. The model is used to automatically process data, thereby improving the efficiency of data processing. The model captures patterns and trends in real situations by analyzing historical data and real-time data, ensures that the results are consistent with the actual situation, and improves the reliability of the analysis results.

[0100] Based on the above technical solution, optionally, after inputting the real-time tunnel operation data into the tunnel damage monitoring model to obtain the real-time damage score, the method further includes:

[0101] If the real-time damage score is lower than a preset damage score threshold, inputting the tunnel operation data into a preset maintenance detection model to obtain a real-time maintenance score;

[0102] If the real-time maintenance score exceeds a preset maintenance score threshold, a maintenance type is determined by a preset maintenance detection model, and the maintenance type is transmitted to a control center.

[0103] In this solution, the preset maintenance detection model can be an algorithm or model developed based on historical data and expert knowledge to assess the maintenance needs of equipment or structures (such as tunnels). The model may include machine learning algorithms, rule engines, or physics-based models to analyze data and generate maintenance recommendations.

[0104] The real-time maintenance score may be a quantitative value generated by a preset maintenance detection model, reflecting the current maintenance demand or status of the tunnel.

[0105] The preset maintenance score threshold may be a set value, and when the real-time maintenance score exceeds this value, the system will deem that the tunnel requires maintenance.

[0106] Maintenance type refers to the specific maintenance measures or operations determined based on real-time maintenance scores and analysis results, which may include regular inspection, cleaning, maintenance, and parts replacement.

[0107] The tunnel operation data (such as stress, temperature, displacement, etc.) can be continuously obtained and input into the maintenance detection model. The input real-time operation data is analyzed using the preset maintenance detection model to generate a real-time maintenance score. The real-time maintenance score is compared with the preset maintenance score threshold. If the score is lower than the threshold, it means that maintenance is not required at the moment. If it exceeds the threshold, the maintenance type needs to be further determined. If the real-time maintenance score exceeds the threshold, the preset maintenance detection model is used to analyze the current status and determine the specific maintenance type. The determined maintenance type information is transmitted to the control center for subsequent maintenance arrangements and execution.

[0108] In this solution, through real-time monitoring and analysis, potential maintenance needs can be quickly identified, accident risks can be reduced, and safe operation of the tunnel can be ensured. Analysis based on real-time data can reduce unnecessary maintenance, avoid excessive maintenance, and thus reduce maintenance costs.

[0109] Based on the above technical solution, the optional, preset maintenance detection model training process includes:

[0110] Obtaining historical maintenance score records, and determining historical tunnel operation data and historical maintenance scores according to the historical maintenance records;

[0111] annotating the historical tunnel operation data with a maintenance score tag according to the historical maintenance score, and determining a maintenance score data set according to the historical tunnel operation data and the maintenance score tag;

[0112] Determine abnormal historical tunnel operation data according to the historical tunnel operation data and a preset maintenance score threshold, and obtain a historical maintenance type of the abnormal historical tunnel operation data;

[0113] marking the maintenance type label of the abnormal historical tunnel operation data according to the historical maintenance type, and creating a maintenance type data set according to the abnormal historical tunnel operation data and the maintenance type label;

[0114] A maintenance detection model is constructed, and the maintenance detection model is trained according to the maintenance score data set and the maintenance type data set until the maintenance detection model reaches a preset maintenance detection model training standard.

[0115] In this solution, the historical maintenance score record may be a record of score information of past tunnel maintenance, including the effect, quality and execution of the maintenance.

[0116] The historical tunnel operation data may include monitoring data of the tunnel in the historical period, such as operating parameters such as stress, displacement, temperature, etc., reflecting the status and performance of the tunnel.

[0117] The historical maintenance score can be a maintenance effect score based on historical tunnel operation data, which is usually evaluated based on the operation data after actual maintenance.

[0118] The maintenance score tag may be a score value used to mark historical tunnel operation data for subsequent data analysis and model training.

[0119] The maintenance score dataset may be a data set including historical tunnel operation data and its corresponding maintenance score labels, which is used to train and evaluate the maintenance detection model.

[0120] Abnormal historical tunnel operation data may refer to tunnel operation data showing abnormal characteristics in historical records, which are usually caused by operating conditions or other factors beyond the normal range.

[0121] The historical maintenance type may be a maintenance category indicating maintenance performed for a specific abnormal situation in historical records, such as routine maintenance, emergency repair, preventive maintenance, and the like.

[0122] The maintenance type label may be used to mark the maintenance type corresponding to the abnormal historical tunnel operation data, so as to facilitate subsequent analysis and model training.

[0123] The maintenance type dataset may be a data set including abnormal historical tunnel operation data and its corresponding maintenance type labels, which is used to train a maintenance detection model.

[0124] The preset maintenance inspection model training standard can define the performance indicators that the model needs to achieve during the training process, such as accuracy, recall rate or other key performance indicators to ensure the effectiveness of the model.

[0125] Historical maintenance records can be extracted from the maintenance management system or database, which usually include information such as the date of each maintenance, maintenance type, executor, maintenance content and score. To ensure the completeness and accuracy of the records, it may be necessary to confirm with relevant management personnel or maintenance personnel. By associating the date and time in the maintenance record, the tunnel operation data (such as stress, displacement, temperature, etc.) in the corresponding time period are extracted from the monitoring system. The maintenance score is calculated for each maintenance record based on the corresponding tunnel operation data to ensure the standardization and consistency of the score. According to the historical maintenance score, it is annotated with the corresponding historical tunnel operation data, and a maintenance score label is assigned to each historical data. The historical tunnel operation data and its corresponding maintenance score label are then combined to form a maintenance score dataset. Ensure that the dataset is cleaned and organized for subsequent model training. Using the preset maintenance score threshold, analyze the historical tunnel operation data and identify abnormal historical data with a score below the threshold. Record the timestamps and related features of these abnormal data in preparation for subsequent analysis. By finding the maintenance records corresponding to the abnormal historical tunnel operation data, the historical maintenance type (such as emergency maintenance, regular maintenance, etc.) of these data is extracted. Assign maintenance type labels to the abnormal historical tunnel operation data according to the historical maintenance type for subsequent data analysis and modeling. Then combine the abnormal historical tunnel operation data and its corresponding maintenance type labels to form a maintenance type dataset. Ensure that the dataset format is unified and perform appropriate data cleaning. Select an appropriate machine learning algorithm (such as decision tree, random forest, or neural network) to build a maintenance detection model. Use the maintenance score dataset and maintenance type dataset for model training and adjust hyperparameters to optimize model performance. Until the model reaches the preset maintenance detection model training standards (such as accuracy, recall rate, etc.), ensure its effectiveness in predicting maintenance needs.

[0126] In this solution, training a more accurate maintenance detection model can improve its ability to predict future maintenance needs, thereby achieving more effective resource allocation.

[0127] Embodiment 2

[0128] Figure 2 is a flow chart of the tunnel damage monitoring method provided in the second embodiment of the present application, such as Figure 2 As shown, the specific method includes the following steps:

[0129] S201, acquiring tunnel geometry data, tunnel material data, tunnel boundary data and tunnel load data, and creating a three-dimensional tunnel model according to the tunnel geometry data.

[0130] Tunnel geometry data may include information about the shape and dimensions of the tunnel, such as: Length: the overall length of the tunnel. Width: the cross-sectional width of the tunnel. Height: the longitudinal height of the tunnel. Shape: such as circular, rectangular or other special shapes. Slope: the inclination or slope information of the tunnel.

[0131] Tunnel material data can include the physical and mechanical properties of the various materials that make up the tunnel, such as concrete: elastic modulus, compressive strength, density, etc. Steel bars: tensile strength, yield strength, etc. Lining materials: performance data such as epoxy resin or other special materials.

[0132] Tunnel boundary data defines the constraints and boundary conditions of the tunnel, such as fixed points: fixed support points at both ends of the tunnel or at specific locations. Free ends: open ends or unfixed locations of the tunnel. Contact conditions: such as the contact relationship between the soil and the tunnel lining.

[0133] Tunnel load data can refer to various external forces and influences applied to the tunnel, such as traffic load: the weight of vehicles and their distribution within the tunnel. Earth pressure: the lateral pressure of the surrounding soil on the tunnel structure. Wind pressure: the force of external air currents on the tunnel surface. Seismic force: the force applied to the tunnel in the event of an earthquake.

[0134] A tunnel 3D model can be a computer-generated 3D representation used to simulate and analyze the physical structure of a tunnel. It contains the above-mentioned geometry, materials, boundaries, and load data, and is constructed using computer-aided design (CAD) software or finite element analysis (FEA) tools.

[0135] Laser rangefinders, total stations and other tools can be used for on-site measurements to obtain the length, width, height, shape, etc. of the tunnel. Consult design drawings and construction drawings to extract relevant geometric information. Obtain geological survey data to understand the topographic changes at the tunnel location, and combine the above-obtained data into geometric data. Take samples on site and conduct laboratory tests to determine the physical and mechanical properties of materials such as concrete and steel bars. Refer to relevant material standards and specifications to obtain property data of commonly used materials. Consult the material selection records in the design stage to obtain tunnel material data. Determine the fixed and free boundary positions of the tunnel according to the design drawings, inspect the tunnel support structure on site, record the support points and contact conditions, and obtain tunnel boundary data. Obtain the number and weight data of passing vehicles through traffic monitoring. Obtain the physical properties of the surrounding soil to calculate the soil pressure. Refer to the load test data and earthquake records of similar tunnels to obtain tunnel load data.

[0136] The acquired geometric data can be input using computer-aided design (CAD) software (such as AutoCAD, Revit) or 3D modeling tools (such as SketchUp, 3ds Max). In the modeling software, the corresponding material properties, including mechanical properties, are assigned to the model. Fixed points, free ends, and contact conditions are defined based on the acquired boundary data. Traffic loads, soil pressure, etc. are applied to the model, and the load position and size are set according to the calculation requirements.

[0137] S202: setting material properties of the three-dimensional tunnel model according to the tunnel material data, and setting boundary data of the three-dimensional tunnel model according to the tunnel boundary data.

[0138] Material properties can refer to the physical and mechanical parameters that describe the characteristics of a material. Specifically, they can include elastic modulus: the ratio of stress to strain in a material within its elastic range. Poisson's ratio: describes the deformation of a material in a perpendicular direction when it is stretched or compressed in one direction. Density: the ratio of the mass of a material to its volume. Compressive and tensile strength: the maximum pressure and tension that a material can withstand when subjected to stress. Shear strength: the ability of a material to resist shear forces.

[0139] Boundary data can be information that defines the boundary conditions of the model. Specifically, it can include fixed boundaries: some parts of the model are fixed and cannot be moved. Free boundaries: some parts of the model can move freely and are affected by external forces. Contact conditions: contact behavior between different materials, such as friction, adhesion, etc.

[0140] You can select the parts (such as concrete, steel bars, etc.) that need to set material properties in the modeling software. Enter the acquired material data to ensure that the parameters such as elastic modulus, Poisson's ratio, density, etc. are accurate. If the software supports it, you can select the corresponding material in the material library and automatically fill in the relevant properties. Determine the position of each boundary in the modeling software based on the design drawings and on-site survey results. For fixed boundaries, select the corresponding face or edge in the model and set it to "fixed". For free boundaries, make sure no constraints are applied and allow them to deform freely. If there are contact conditions, set the contact surface and friction coefficient to ensure a realistic simulation of the interaction.

[0141] S203, dividing the tunnel grid of the three-dimensional tunnel model according to the tunnel geometry data and the tunnel material data, and inputting tunnel load data into the three-dimensional tunnel model.

[0142] Tunnel meshing is the process of dividing a 3D tunnel model into multiple small units (grids). These small units can be triangles, quadrilaterals, hexahedrons, etc. The purpose is to more accurately calculate stress, strain and other physical quantities inside the model during numerical simulation. The quality of meshing directly affects the accuracy and computational efficiency of the simulation.

[0143] Mesh generation tools can be found in the modeling or simulation software used. Set the size of the mesh according to the geometric complexity of the tunnel and the required simulation accuracy. Smaller meshes can improve accuracy, but increase the amount of calculation. Choose a suitable mesh type (such as triangles, quadrilaterals, hexahedrons, etc.) depending on the geometric characteristics of the model. Use the automatic meshing function of the software, or manually select specific areas for meshing. Ensure that the mesh is fine enough in critical areas of the tunnel (such as supports, joints, etc.) to accurately capture stress changes. Check whether the generated mesh has distortions, overlaps, or irregular shapes to ensure that the mesh quality is good. The mesh quality check tool in the software can be used to evaluate the quality of the mesh. After the meshing is completed, apply tunnel load data (such as static loads, dynamic loads, etc.) to the model according to the design requirements and actual conditions. Ensure that the load is applied in the correct position and direction, and set the time variation law as required.

[0144] In this embodiment, by acquiring detailed geometry, material, boundary and load data, a more accurate three-dimensional model can be constructed to enhance the authenticity and reliability of the simulation.

[0145] On the basis of the above technical solution, optionally, after meshing the tunnel three-dimensional model according to the tunnel geometry data and the tunnel material data, and inputting the tunnel load data into the tunnel three-dimensional model, the method further includes:

[0146] Obtaining historical monitoring results, and determining historical tunnel damage data, historical damage causes, and historical damage impacts based on the historical monitoring results;

[0147] Acquire simulation requirements, set simulation parameters of the tunnel three-dimensional model according to the simulation requirements, input the historical tunnel damage data into the tunnel three-dimensional model for simulation testing, and obtain tunnel simulation results;

[0148] Determining whether the three-dimensional model of the tunnel meets a preset simulation standard according to the tunnel simulation results, historical damage causes, and historical damage impacts;

[0149] If the preset simulation standard is reached, it is determined that the tunnel 3D model has passed the simulation test.

[0150] In this solution, the historical monitoring results can be the data collected from tunnel monitoring conducted in the past, including stress, deformation, displacement and other information recorded by various sensors.

[0151] Historical tunnel damage data may be tunnel damage records determined based on historical monitoring results, and may include specific values ​​such as crack width and displacement.

[0152] Historical damage causes can be known causes of tunnel damage, such as soil liquefaction, construction defects, geological changes, etc.

[0153] The impact of historical damage can be an assessment of the impact of past tunnel damage on structural safety, function, stability, etc.

[0154] Simulation requirements can be requirements for simulation analysis that need to be performed for specific purposes or problems, such as working conditions that need to be tested, simulated environmental conditions, etc.

[0155] The simulation parameters may be the parameters required when setting up the simulation model, including material properties, load conditions, boundary conditions, time step, etc.

[0156] Tunnel simulation results should include detailed data on the causes and effects of historical damage. For example, this may include indicators such as stress distribution, deformation degree, crack width, etc.

[0157] The preset simulation standard may be a standard for judging whether the simulation test result is qualified, and may include a threshold value of similarity with historical damage causes and effects, etc.

[0158] Monitoring data from the past period of time can be collected and organized. These data may come from various sensors, inspection records, and monitoring reports. Specific tunnel damage data, including crack width, displacement, deformation, and other parameters, can be extracted from historical monitoring results. These data provide a basis for analyzing the tunnel status. Analyze historical damage data to determine the causes of damage. For example, these may include construction defects, geological changes, load effects, and other factors. Evaluate the actual impact of historical damage on the tunnel, such as the impact on structural stability, increased maintenance costs, or safety hazards. Then, based on engineering needs, expected analysis goals, and design requirements, clarify the specific content and direction of the simulation to be performed. According to the simulation requirements, configure the simulation parameters of the tunnel 3D model, including material properties, load conditions, boundary conditions, etc. Input the determined historical tunnel damage data into the tunnel 3D model for simulation testing based on actual damage conditions. Run the simulation model to generate tunnel simulation results, including stress, deformation, crack development, and other analysis data. Compare the tunnel simulation results with the historical damage causes and effects to evaluate their similarity. Set a similarity threshold to determine whether the model results meet expectations. If the simulation results meet the preset similarity standard, the tunnel 3D model is confirmed to have passed the simulation test.

[0159] In this solution, the simulation model is used to verify the performance of the tunnel under different damage conditions to ensure the accuracy and reliability of the model, thereby enhancing the credibility of the engineering design. By comparing the similarity of the simulation results, the risks of the tunnel can be effectively assessed, potential safety hazards can be identified in advance, and necessary preventive measures can be taken.

[0160] On the basis of the above technical solution, optionally, after determining whether the tunnel three-dimensional model meets the preset simulation standard according to the tunnel simulation result and the historical tunnel damage data, the method further includes:

[0161] If the preset simulation standard is not met, the material property adjustment range, boundary data adjustment range and tunnel mesh adjustment range are determined according to the tunnel geometry data, tunnel material data, tunnel boundary data and tunnel load data;

[0162] Within the material property adjustment range, boundary data adjustment range and tunnel mesh adjustment range, the material properties, boundary data and tunnel mesh are continuously adjusted according to the preset material property adjustment step size, the preset boundary data adjustment step size and the preset tunnel mesh adjustment step size, and the simulation test is re-performed after each adjustment until the tunnel three-dimensional model passes the simulation test.

[0163] In this solution, the material property adjustment range may refer to the range in which the material properties (such as elastic modulus, strength, density, etc.) can be adjusted during the simulation process.

[0164] The boundary data adjustment range may be a range of boundary conditions that can be adjusted in the simulation.

[0165] The tunnel mesh adjustment range may be a range of mesh refinement levels in the tunnel model.

[0166] The preset material property adjustment step size may be an increment each time the material property is adjusted.

[0167] The preset boundary data adjustment step size may be a change amount when adjusting the boundary conditions.

[0168] The preset tunnel mesh adjustment step size may be a step size when adjusting mesh refinement.

[0169] Define adjustment ranges based on existing tunnel geometry, materials, boundaries, and load data. Determine the specific increments for each adjustment to ensure reasonable and controllable parameter changes during the adjustment process. Make adjustments within the material property adjustment range, using a set step size. Make adjustments within the boundary data adjustment range. Make adjustments within the tunnel mesh adjustment range. After each adjustment, rerun the simulation test and record the results. Check whether the simulation results meet the preset criteria. If not, continue with the next round of adjustments until the preset simulation criteria are met.

[0170] In this scheme, careful parameter adjustment can more accurately reflect the actual situation of the tunnel, ensure a higher match between the model and the real environment, and thus improve the credibility of the simulation results.

[0171] Based on the above technical solution, optionally, after the three-dimensional tunnel model passes the simulation test, the method further includes:

[0172] The real-time tunnel damage data is input into the tunnel three-dimensional model to obtain the real-time damage cause and the real-time damage impact, and the real-time damage cause and the real-time damage impact are sent to the control center.

[0173] In this solution, real-time damage causes can refer to specific factors or events that cause tunnel damage, such as load overload: vehicles or other objects that exceed the design load pass through the tunnel. Environmental factors: such as high temperature, humidity, soil changes, etc. Construction defects: damage caused by improper construction or material quality problems. Long-term use: natural aging or fatigue damage caused by long-term use of the tunnel.

[0174] Real-time damage impact can refer to the specific impact of damage on tunnel safety, stability and performance, for example, decreased structural stability: may increase the risk of tunnel collapse. Operational safety hazards: affect normal traffic and increase the risk of accidents. Increased maintenance costs: may require early repair or reinforcement, increasing maintenance costs.

[0175] Real-time damage data, including displacement, crack width, strain and other information, can be obtained from sensors and input into the 3D model of the tunnel. The real-time damage data can be analyzed using the model to identify potential damage causes. The model may be inferred by combining historical data, simulation results and real-time monitoring data. The real-time damage impact is calculated by the algorithm to assess the impact of the current damage on the tunnel structure. A report on the real-time damage causes and effects is generated based on the analysis results, including a detailed description of each damage and potential consequences. The real-time damage cause and real-time damage impact information is then sent to the control center via a data transmission system (such as a wireless network or a wired network) for relevant personnel to evaluate and make decisions.

[0176] In this solution, through real-time monitoring and analysis, potential safety hazards can be quickly identified, so that timely measures can be taken to avoid accidents. Understanding the causes and effects of damage can help develop more effective maintenance and repair plans and reduce maintenance costs.

[0177] Embodiment 3

[0178] Figure 3 is a flow chart of the tunnel damage monitoring method provided in Example 3 of the present application, such as Figure 3 As shown, the specific method includes the following steps:

[0179] S301, acquiring tunnel operation data and tunnel damage data at each preset collection time point in the initial stage of tunnel operation, and transmitting the tunnel operation data and tunnel damage data to the control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff.

[0180] S302, receiving a damage score for tunnel operation data sent by a control center, marking a damage score label for the tunnel operation data according to the damage score, and creating a scoring data set according to the tunnel operation data and the damage score label.

[0181] S303: Obtain a preset damage score threshold, and divide the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold.

[0182] S304, determining abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, marking a tunnel damage label of the abnormal tunnel operation data according to the abnormal tunnel damage data, and creating a damage data set according to the abnormal tunnel operation data and the tunnel damage label.

[0183] S305: construct a tunnel damage monitoring model, and train the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard.

[0184] S306, continuously collect real-time tunnel operation data, input the real-time tunnel operation data into a tunnel damage monitoring model to obtain a real-time damage score, and if the real-time damage score exceeds a preset damage score threshold, determine the real-time tunnel damage data through the tunnel damage monitoring model.

[0185] S307, performing hash processing on the real-time tunnel damage data, tunnel operation data and real-time damage score respectively to obtain hash values ​​corresponding to the real-time tunnel damage data, tunnel operation data and real-time damage score.

[0186] A hash value can be obtained by converting input data (such as real-time damage data, operating data, damage score) into a string of fixed length (usually a combination of numbers and letters) through a hash function, which is used to uniquely identify the original data.

[0187] You can select a suitable hash algorithm, such as SHA-256, MD5, etc. Usually, a relatively secure and widely used hash algorithm is selected. Convert the real-time tunnel damage data, tunnel operation data, and real-time damage score into a string format. If the data is a numeric value or other format, you need to convert it into a string first. Enter these strings into the selected hash algorithm, and the algorithm will generate the corresponding hash value.

[0188] S308, the real-time tunnel damage data, tunnel operation data and the hash value corresponding to the real-time damage score are stored in the blockchain according to the preset smart contract.

[0189] A pre-set smart contract can be a piece of code that is automatically executed on the blockchain, which specifies the rules for data storage and access. Smart contracts can ensure the integrity and immutability of data and automatically execute when specific conditions are met. For example, a smart contract can define the structure of stored hash values, how to verify data, and the permissions for authorized access.

[0190] Smart contracts can be written, and the content should include data structure: define the type of data to be stored (such as hash value, timestamp, source, etc.). Storage function: implement a function to receive the hash value and store it in the blockchain. Access control: set who can call the storage function to ensure that only authorized users or systems can perform operations. Deploy the written smart contract to the blockchain network. This process uploads the code of the smart contract to the blockchain, making it an unchangeable part. During the real-time data processing, the real-time tunnel damage data, tunnel operation data, and real-time damage score are hashed separately to obtain the corresponding hash value. Send the hash value to the smart contract through the storage function provided by the contract: Use the client of the blockchain network (such as Web3.js or Ethers.js) to connect to the blockchain. Call the storage function in the smart contract and pass in the hash value as a parameter. After submitting the storage request, wait for the blockchain network to confirm the transaction. After confirmation, the hash value will be permanently stored on the blockchain and recorded together with the timestamp and other relevant information to ensure the immutability and traceability of the data.

[0191] In this solution, blockchain storage can ensure data security and transparency to prevent tampering. Smart contracts can be automatically executed, improving efficiency and reducing human intervention.

[0192] On the basis of the above technical solution, optionally, after obtaining the real-time tunnel damage data, the tunnel operation data and the hash value corresponding to the real-time damage score, the method further includes:

[0193] Generate a zero-knowledge proof based on the real-time tunnel damage data, the tunnel operation data, the real-time damage score, and the hash values ​​corresponding to the real-time tunnel damage data, the tunnel operation data, and the real-time damage score;

[0194] Accordingly, the real-time tunnel damage data, tunnel operation data and hash values ​​corresponding to the real-time damage score are stored in the blockchain according to the preset smart contract, including:

[0195] The zero-knowledge proof and the hash value corresponding to the real-time tunnel damage data, tunnel operation data and real-time damage score are stored in the blockchain according to a preset smart contract.

[0196] In this scheme, a zero-knowledge proof can be a cryptographic protocol that enables one party (the prover) to prove the truth of a statement to another party (the verifier) ​​without revealing any specific information about the statement. In simple terms, the prover can prove that they know a secret (such as the truth of some data) without revealing the data itself.

[0197] You can determine what needs to be proved. For example, prove that "real-time tunnel damage data is valid" without revealing specific data. Choose a suitable zero-knowledge proof scheme, such as zk-SNARKs or zk-STARKs. These schemes have their own characteristics, and the specific selection depends on security, efficiency, and implementation complexity. Generate a proof using real-time tunnel damage data, tunnel operation data, and real-time damage scores. This process usually involves complex mathematical calculations. The generated proof will contain some encrypted information, but not the original data. The proof and the associated hash value are received by the verifier (which may be a blockchain node or other user). Use the verification algorithm of the zero-knowledge proof protocol to confirm the validity of the proof and ensure that the prover's statement is true. Then determine how to handle zero-knowledge proofs in smart contracts. Smart contracts need to be able to receive proofs and hash values. Deploy the smart contract to the blockchain so that it can accept zero-knowledge proofs and hash values. Connect to the network through the blockchain client. Call the storage function of the smart contract and pass in the zero-knowledge proof and the corresponding hash value as parameters. After submitting the request, wait for the blockchain network to confirm the transaction. Once confirmed, the zero-knowledge proof and hash value will be stored on the blockchain to ensure the security and immutability of the data.

[0198] In this solution, zero-knowledge proof allows the authenticity of data to be verified without exposing the data content, protecting privacy. By storing the proof and hash value on the blockchain, the data can be ensured to be tamper-proof and traceable.

[0199] Embodiment 4

[0200] Figure 4 is a schematic diagram of the structure of a tunnel damage monitoring system provided in Example 4 of the present application. Figure 4 As shown, the system is used to implement a tunnel damage monitoring system method provided in embodiments 1, 2, and 3, and the system specifically includes the following:

[0201] The data acquisition module 401 is used to acquire tunnel operation data and tunnel damage data at each preset acquisition time point in the initial stage of tunnel operation, and transmit the tunnel operation data and tunnel damage data to the control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; the tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff;

[0202] A scoring data set creation module 402 is used to receive a damage score of the tunnel operation data sent by the control center, label the damage score label of the tunnel operation data according to the damage score, and create a scoring data set according to the tunnel operation data and the damage score label;

[0203] The data division module 403 is used to obtain a preset damage score threshold, and divide the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold;

[0204] A damage data set creation module 404 is used to determine abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, label the abnormal tunnel operation data with a tunnel damage label according to the abnormal tunnel damage data, and create a damage data set according to the abnormal tunnel operation data and the tunnel damage label;

[0205] A model building module 405 is used to build a tunnel damage monitoring model, and train the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard;

[0206] The real-time data determination module 406 is used to continuously collect real-time tunnel operation data, input the real-time tunnel operation data into the tunnel damage monitoring model, and obtain a real-time damage score. If the real-time damage score exceeds a preset damage score threshold, the real-time tunnel damage data is determined by the tunnel damage monitoring model.

[0207] In the embodiment of the present application, the data acquisition module is used to obtain the tunnel operation data and tunnel damage data at each preset acquisition time point in the initial stage of tunnel operation, and transmit the tunnel operation data and tunnel damage data to the control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff; the scoring data set creation module is used to receive the damage score of the tunnel operation data sent by the control center, mark the damage score label of the tunnel operation data according to the damage score, and create a scoring data set according to the tunnel operation data and the damage score label; the data partitioning module is used to obtain a preset damage score threshold, and create a scoring data set according to the damage score and damage score threshold. The tunnel operation data is divided into normal tunnel operation data and abnormal tunnel operation data; a damage data set creation module is used to determine the abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, annotate the tunnel damage label of the abnormal tunnel operation data according to the abnormal tunnel damage data, and create a damage data set according to the abnormal tunnel operation data and the tunnel damage label; a model construction module is used to build a tunnel damage monitoring model, train the damage monitoring model according to the score data set and the damage data set until the damage monitoring model reaches the preset damage monitoring model training standard; a real-time data determination module is used to continuously collect real-time tunnel operation data, input the real-time tunnel operation data into the tunnel damage monitoring model, and obtain a real-time damage score. If the real-time damage score exceeds the preset damage score threshold, the real-time tunnel damage data is determined by the tunnel damage monitoring model. Through the above-mentioned tunnel damage monitoring system, by continuously collecting and analyzing tunnel operation data, potential damage or abnormal conditions can be discovered in time to ensure the safety of the tunnel. The model is used to automatically process data, which improves the efficiency of data processing. The model captures the patterns and trends in the real situation by analyzing historical data and real-time data, ensures that the results are consistent with the actual situation, and improves the reliability of the analysis results.

[0208] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A tunnel damage monitoring method, characterized in that: The method comprises: In the initial stage of tunnel operation, tunnel operation data and tunnel damage data at each preset collection time point are obtained, and the tunnel operation data and tunnel damage data are transmitted to the control center; wherein the tunnel operation data include tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; tunnel damage data include tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff; receiving a damage score of the tunnel operation data sent by the control center, marking a damage score label of the tunnel operation data according to the damage score, and creating a scoring data set according to the tunnel operation data and the damage score label; Obtaining a preset damage score threshold, and dividing the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold; Determine abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, label the abnormal tunnel operation data with a tunnel damage label according to the abnormal tunnel damage data, and create a damage data set according to the abnormal tunnel operation data and the tunnel damage label; Constructing a tunnel damage monitoring model, and training the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard; Real-time tunnel operation data is continuously collected, and the real-time tunnel operation data is input into a tunnel damage monitoring model to obtain a real-time damage score. If the real-time damage score exceeds a preset damage score threshold, the real-time tunnel damage data is determined by the tunnel damage monitoring model.

2. The tunnel damage monitoring method according to claim 1, characterized in that: After continuously collecting real-time tunnel operation data and inputting the real-time tunnel operation data into a tunnel damage monitoring model to obtain a real-time damage score, the method further includes: If the real-time damage score is lower than a preset damage score threshold, inputting the tunnel operation data into a preset maintenance detection model to obtain a real-time maintenance score; If the real-time maintenance score exceeds a preset maintenance score threshold, a maintenance type is determined by a preset maintenance detection model, and the maintenance type is transmitted to a control center.

3. The tunnel damage monitoring method according to claim 1, characterized in that: After determining the real-time tunnel damage data by the tunnel damage monitoring model, the method further includes: Acquire tunnel geometry data, tunnel material data, tunnel boundary data, and tunnel load data, and create a three-dimensional tunnel model according to the tunnel geometry data; Setting material properties of the three-dimensional tunnel model according to the tunnel material data, and setting boundary data of the three-dimensional tunnel model according to the tunnel boundary data; The tunnel grid of the three-dimensional tunnel model is divided according to the tunnel geometry data and the tunnel material data, and the tunnel load data is input into the three-dimensional tunnel model.

4. The tunnel damage monitoring method according to claim 3, characterized in that: After meshing the tunnel three-dimensional model according to the tunnel geometry data and the tunnel material data and inputting the tunnel load data into the tunnel three-dimensional model, the method further comprises: Obtaining historical monitoring results, and determining historical tunnel damage data, historical damage causes, and historical damage impacts based on the historical monitoring results; Acquire simulation requirements, set simulation parameters of the tunnel three-dimensional model according to the simulation requirements, input the historical tunnel damage data into the tunnel three-dimensional model for simulation testing, and obtain tunnel simulation results; Determining whether the three-dimensional model of the tunnel meets a preset simulation standard according to the tunnel simulation results, historical damage causes, and historical damage impacts; If the preset simulation standard is reached, it is determined that the tunnel 3D model has passed the simulation test.

5. The tunnel damage monitoring method according to claim 4, characterized in that: After determining whether the tunnel three-dimensional model meets the preset simulation standard according to the tunnel simulation result and the historical tunnel damage data, the method further includes: If the preset simulation standard is not met, the material property adjustment range, boundary data adjustment range and tunnel mesh adjustment range are determined according to the tunnel geometry data, tunnel material data, tunnel boundary data and tunnel load data; Within the material property adjustment range, boundary data adjustment range and tunnel mesh adjustment range, the material properties, boundary data and tunnel mesh are continuously adjusted according to the preset material property adjustment step size, the preset boundary data adjustment step size and the preset tunnel mesh adjustment step size, and the simulation test is re-performed after each adjustment until the tunnel three-dimensional model passes the simulation test.

6. The tunnel damage monitoring method according to claim 4 or 5, characterized in that: After the tunnel three-dimensional model passes the simulation test, the method further includes: The real-time tunnel damage data is input into the tunnel three-dimensional model to obtain the real-time damage cause and the real-time damage impact, and the real-time damage cause and the real-time damage impact are sent to the control center.

7. The tunnel damage monitoring method according to claim 1, characterized in that: The training process of the preset maintenance detection model includes: Obtaining historical maintenance score records, and determining historical tunnel operation data and historical maintenance scores according to the historical maintenance records; annotating the historical tunnel operation data with a maintenance score tag according to the historical maintenance score, and determining a maintenance score data set according to the historical tunnel operation data and the maintenance score tag; Determine abnormal historical tunnel operation data according to the historical tunnel operation data and a preset maintenance score threshold, and obtain a historical maintenance type of the abnormal historical tunnel operation data; marking the maintenance type label of the abnormal historical tunnel operation data according to the historical maintenance type, and creating a maintenance type data set according to the abnormal historical tunnel operation data and the maintenance type label; A maintenance detection model is constructed, and the maintenance detection model is trained according to the maintenance score data set and the maintenance type data set until the maintenance detection model reaches a preset maintenance detection model training standard.

8. The tunnel damage monitoring method according to claim 1, characterized in that: After determining the real-time tunnel damage data by the tunnel damage monitoring model, the method further includes: The real-time tunnel damage data, tunnel operation data and real-time damage score are hashed respectively to obtain hash values ​​corresponding to the real-time tunnel damage data, tunnel operation data and real-time damage score; The real-time tunnel damage data, tunnel operation data and hash values ​​corresponding to the real-time damage score are stored in the blockchain according to the preset smart contract.

9. The tunnel damage monitoring method according to claim 8, characterized in that: After obtaining the real-time tunnel damage data, the tunnel operation data, and the hash value corresponding to the real-time damage score, the method further includes: Generate a zero-knowledge proof based on the real-time tunnel damage data, the tunnel operation data, the real-time damage score, and the hash values ​​corresponding to the real-time tunnel damage data, the tunnel operation data, and the real-time damage score; Accordingly, the real-time tunnel damage data, tunnel operation data and hash values ​​corresponding to the real-time damage score are stored in the blockchain according to the preset smart contract, including: The zero-knowledge proof and the hash value corresponding to the real-time tunnel damage data, tunnel operation data and real-time damage score are stored in the blockchain according to a preset smart contract.

10. A tunnel damage monitoring system, characterized in that: The system comprises: A data acquisition module is used to obtain tunnel operation data and tunnel damage data at each preset acquisition time point in the initial stage of tunnel operation, and transmit the tunnel operation data and tunnel damage data to the control center; wherein the tunnel operation data includes tunnel stress data and tunnel strain data transmitted by piezoelectric ceramic sensors, tunnel temperature data transmitted by optical fiber sensors, and tunnel vibration data transmitted by accelerometers; tunnel damage data includes tunnel displacement data, tunnel deformation data, and tunnel crack data transmitted by handheld terminals of staff; A scoring data set creation module, configured to receive a damage score of tunnel operation data sent by a control center, label the tunnel operation data with a damage score tag according to the damage score, and create a scoring data set according to the tunnel operation data and the damage score tag; A data division module, used for obtaining a preset damage score threshold, and dividing the tunnel operation data into normal tunnel operation data and abnormal tunnel operation data according to the damage score and the damage score threshold; A damage data set creation module, configured to determine abnormal tunnel damage data corresponding to the abnormal tunnel operation data according to the tunnel damage data, label the abnormal tunnel operation data with a tunnel damage label according to the abnormal tunnel damage data, and create a damage data set according to the abnormal tunnel operation data and the tunnel damage label; A model building module, used to build a tunnel damage monitoring model, and train the damage monitoring model according to the scoring data set and the damage data set until the damage monitoring model reaches a preset damage monitoring model training standard; The real-time data determination module is used to continuously collect real-time tunnel operation data, input the real-time tunnel operation data into the tunnel damage monitoring model, and obtain a real-time damage score. If the real-time damage score exceeds a preset damage score threshold, the real-time tunnel damage data is determined by the tunnel damage monitoring model.

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