A tunnel health assessment method, device and medium based on digital twin

Abstract

The present application discloses a tunnel health assessment method, device, and medium based on digital twins, which relates to the field of digital twin technology. The method includes: performing spatiotemporal alignment processing on tunnel health monitoring data to obtain a structured tunnel status data set; performing tunnel equipment physical drive analysis on structured tunnel equipment status data to determine tunnel equipment performance data; performing finite element analysis on structured tunnel environment status data to determine tunnel environment status data; based on tunnel equipment performance data and tunnel environment status data, performing bidirectional coupling processing to obtain a dynamically updated digital twin; based on the dynamically updated digital twin, determining tunnel state drive simulation data through dynamic interaction of tunnel data twins; performing multi-level health index calculations on tunnel state drive simulation data to obtain a tunnel health assessment score. The present application solves the technical problem that tunnel health assessment cannot link tunnel equipment and the environment through the above method.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to a tunnel health assessment method, device, and medium based on digital twins. Background Art

[0002] As core infrastructure of transportation networks, tunnels' structural health and equipment reliability are directly linked to driving safety, operational efficiency, and overall lifecycle costs. As tunnels age, risks such as surrounding rock creep, equipment aging, and environmental corrosion intensify, potentially leading to major accidents such as lining cracking, ventilation failure, and fire. According to statistics, maintenance costs resulting from tunnel structural damage account for over 40% of total operating expenses, and sudden failures can cause serious consequences such as traffic disruptions and casualties. Therefore, tunnel health assessment technology has become crucial for ensuring infrastructure safety, and there is an urgent need for an intelligent upgrade from "passive maintenance" to "active intervention."

[0003] Current tunnel health assessment technology mainly relies on threshold alarms based on statistical analysis and data-driven machine learning assessments. Due to the isolated analysis of environmental data and equipment data, it is impossible to conduct specific analysis of the interaction between tunnel equipment and the environment, and thus cannot achieve a more comprehensive health assessment of the tunnel health status. Summary of the Invention

[0004] The embodiments of the present application provide a tunnel health assessment method, device, and medium based on digital twins, which solve the technical problem in the prior art that tunnel health assessment cannot link tunnel equipment and tunnel environment.

[0005] In a first aspect, an embodiment of the present application provides a tunnel health assessment method based on digital twins, characterized in that the method includes: acquiring tunnel health monitoring data, and performing spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status data set; wherein the structured tunnel status data set includes: structured tunnel equipment status data, structured tunnel environment status data; performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; performing finite element analysis on the structured tunnel environment status data to determine tunnel environment status data; based on the tunnel equipment performance data and the tunnel environment status data, bidirectional coupling processing is performed to obtain a dynamically updated digital twin; based on the dynamically updated digital twin, tunnel state drive simulation data is determined through dynamic interaction of the tunnel data twin; and multi-level health index calculations are performed on the tunnel state drive simulation data to obtain a tunnel health assessment score.

[0006] In one implementation of the present application, the tunnel health monitoring data is subjected to spatiotemporal alignment processing to obtain a structured tunnel status data set, specifically comprising: data cleaning of the tunnel health monitoring data to obtain pure tunnel health monitoring data; wherein the data cleaning comprises: 3 Abnormal distribution determination and wavelet noise reduction processing; timestamp alignment processing of pure tunnel health monitoring data to determine time-synchronized tunnel detection data; data space registration processing of pure tunnel health monitoring data to determine space-aligned tunnel detection data; based on time-synchronized tunnel detection data and space-aligned tunnel detection data, dual-end state feature extraction is performed to obtain a structured tunnel state data set; among which, dual-end state feature extraction includes: equipment-end abnormal feature extraction and environment-end strain gradient analysis.

[0007] In one implementation of the present application, a tunnel device physical drive analysis is performed on structured tunnel device status data to determine tunnel device performance data, specifically including: obtaining a tunnel device physical equation, and based on the structured tunnel device status data and the device physical equation, constructing a tunnel device physical initial drive through historical fault analysis; performing LSTM data-driven correction on the tunnel device physical initial drive to determine the tunnel device physical drive; and determining the tunnel device performance data based on the tunnel device physical drive through actual tunnel device performance calculation.

[0008] In one implementation of the present application, finite element analysis is performed on structured tunnel environmental status data to determine the tunnel environmental status data, specifically including: tunnel meshing the structured tunnel environmental status data to obtain tunnel mesh data; based on the tunnel mesh data, determining key risk areas through tunnel environmental status balance analysis; and performing FEM prediction updates on the key risk areas to obtain tunnel environmental status data.

[0009] In one implementation of the present application, based on tunnel equipment performance data and tunnel environment status data, bidirectional coupling processing is performed to obtain a dynamically updated digital twin, specifically including: coupling the tunnel equipment performance data with the boundary conditions of the equipment affecting the tunnel environment to determine the coupling data of the tunnel equipment to the tunnel environment; coupling the tunnel environment status data with the equipment monitoring threshold conditions to determine the coupling data of the tunnel environment to the tunnel equipment; dynamically updating the coupling data of the equipment to the tunnel environment and the coupling data of the tunnel environment to the tunnel equipment to obtain a dynamically updated digital twin.

[0010] In one implementation of the present application, based on the dynamically updated digital twin, the tunnel state driving simulation data is determined through dynamic interaction of the tunnel data twin, specifically including: obtaining the tunnel real-time monitoring data stream, and performing grid threshold simulation processing on the tunnel real-time monitoring data stream to obtain dynamically adjusted tunnel simulation configuration parameters; based on the tunnel simulation configuration parameters, the simulation configuration parameters are determined through dynamic federated learning driven processing; according to the simulation configuration parameters, the tunnel multi-objective adaptive control strategy configuration is configured to obtain the tunnel level state determination strategy; the tunnel level state determination strategy is embedded in the dynamically updated digital twin to determine the tunnel state driving simulation data.

[0011] In one implementation of the present application, a multi-level health index operation is performed on the tunnel state drive simulation data to obtain a tunnel health assessment score, specifically including: performing a tunnel equipment health status analysis on the tunnel state drive simulation data to determine a first tunnel assessment parameter; performing a tunnel environment risk assessment on the tunnel state drive simulation data to determine a second tunnel assessment parameter; performing a parameter bidirectional impact analysis on the first tunnel assessment parameter and the second tunnel assessment parameter to obtain a tunnel health assessment score.

[0012] In one implementation of the present application, after performing multi-level health index calculations on the tunnel state driven simulation data to obtain a tunnel health assessment score, the method also includes: determining the current tunnel maintenance requirements based on the tunnel health assessment score through maintenance strategy configuration; and obtaining a tunnel operation and maintenance priority sorting work order through an operation and maintenance work order priority evaluation based on the current tunnel maintenance requirements.

[0013] In a second aspect, an embodiment of the present application also provides a tunnel health assessment device based on digital twins, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein 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 so that the at least one processor can: obtain tunnel health monitoring data, and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status data set; wherein the structured tunnel status data set includes: structured tunnel equipment status data, structured tunnel environment status data; perform tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; perform finite element analysis on the structured tunnel environment status data to determine tunnel environment status data; based on the tunnel equipment performance data and the tunnel environment status data, bidirectional coupling processing is performed to obtain a dynamically updated digital twin; based on the dynamically updated digital twin, tunnel state drive simulation data is determined through dynamic interaction of the tunnel data twin; multi-level health index calculations are performed on the tunnel state drive simulation data to obtain a tunnel health assessment score.

[0014] In a third aspect, an embodiment of the present application also provides a non-volatile computer storage medium for tunnel health assessment based on digital twins, which stores computer executable instructions, characterized in that the computer executable instructions are set to: obtain tunnel health monitoring data, and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status data set; wherein the structured tunnel status data set includes: structured tunnel equipment status data, structured tunnel environment status data; perform tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; perform finite element analysis on the structured tunnel environment status data to determine tunnel environment status data; based on the tunnel equipment performance data and the tunnel environment status data, perform bidirectional coupling processing to obtain a dynamically updated digital twin; based on the dynamically updated digital twin, determine the tunnel state drive simulation data through dynamic interaction of the tunnel data twin; perform multi-level health index calculations on the tunnel state drive simulation data to obtain a tunnel health assessment score.

[0015] The embodiments of the present application provide a tunnel health assessment method, device and medium based on digital twins. Through the bidirectional dynamic coupling analysis of tunnel equipment and tunnel environment and the dynamically updated digital twins, the technical problem in the prior art that tunnel health assessment cannot link tunnel equipment and tunnel environment is solved, and tunnel health assessment based on the interaction between tunnel equipment and tunnel environment is realized, which improves the practicality and comprehensiveness of tunnel health assessment and optimizes the data discretization processing of tunnel health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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:

[0017] Figure 1 A flow chart of a tunnel health assessment method based on digital twins provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the internal structure of a tunnel health assessment device based on digital twins provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiments of the present application provide a tunnel health assessment method, device and medium based on digital twins. Through the bidirectional dynamic coupling analysis of tunnel equipment and tunnel environment and the dynamically updated digital twins, the technical problem in the prior art that tunnel health assessment cannot link tunnel equipment and tunnel environment is solved, and tunnel health assessment based on the interaction between tunnel equipment and tunnel environment is realized, which improves the practicality and comprehensiveness of tunnel health assessment and optimizes the data discretization processing of tunnel health assessment.

[0021] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flow chart of a tunnel health assessment method based on digital twins provided in an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a tunnel health assessment method based on digital twins, which specifically includes the following steps:

[0023] Step 101: Acquire tunnel health monitoring data and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status dataset.

[0024] For example, the structured tunnel status dataset includes structured tunnel equipment status data and structured tunnel environment status data. Since a comprehensive analysis of tunnel equipment and the tunnel environment is required, the acquired tunnel health monitoring data also needs to include tunnel equipment monitoring data and tunnel environment monitoring data. Temporal and spatial alignment of the tunnel health monitoring data between equipment and environment achieves a one-to-one temporal and spatial correspondence between the tunnel equipment and the tunnel environment, facilitating coupled analysis.

[0025] Specifically, the tunnel health monitoring data is subjected to spatiotemporal alignment processing to obtain a structured tunnel status data set, including: data cleaning of the tunnel health monitoring data to obtain pure tunnel health monitoring data; wherein, data cleaning includes: 3 Abnormal distribution determination and wavelet noise reduction processing; timestamp alignment processing of pure tunnel health monitoring data to determine time-synchronized tunnel detection data; data space registration processing of pure tunnel health monitoring data to determine space-aligned tunnel detection data; based on time-synchronized tunnel detection data and space-aligned tunnel detection data, dual-end state feature extraction is performed to obtain a structured tunnel state data set; among which, dual-end state feature extraction includes: equipment-end abnormal feature extraction and environment-end strain gradient analysis.

[0026] In one embodiment, first, the original tunnel health monitoring data is cleaned, specifically including using 3 Abnormal distribution is determined to eliminate outliers, and wavelet noise reduction is used to eliminate high-frequency noise to generate pure data for tunnel health monitoring.

[0027] Then, the clean data is timestamp aligned, and the sampling time intervals of different sensors are unified through an interpolation algorithm to generate time-synchronized tunnel detection data. The clean data is spatially registered and mapped to the tunnel three-dimensional model based on the sensor spatial coordinates to generate spatially aligned tunnel detection data.

[0028] Finally, dual-end state feature extraction is performed on the time-synchronized and spatially aligned data. Abnormal feature extraction on the device side is achieved through frequency domain energy distribution analysis, and strain gradient analysis on the environment side adopts spatial difference calculation, ultimately generating structured tunnel state data.

[0029] Step 102: Perform tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data.

[0030] For example, the operation of tunnel equipment is mainly realized by ventilation equipment, drainage equipment, etc. The structured tunnel equipment status data is subjected to tunnel equipment physical drive analysis based on the aging degree and operation status of the tunnel equipment, realizing equipment performance analysis at the tunnel equipment end and providing a data basis for data coupling between the tunnel equipment and the tunnel environment.

[0031] Specifically, a tunnel equipment physical drive analysis is performed on the structured tunnel equipment status data to determine the tunnel equipment performance data, specifically including: obtaining the tunnel equipment physical equation, and based on the structured tunnel equipment status data and the equipment physical equation, constructing the tunnel equipment physical initial drive through historical fault analysis; performing LSTM data-driven correction on the tunnel equipment physical initial drive to determine the tunnel equipment physical drive; and determining the tunnel equipment performance data through actual tunnel equipment performance calculation based on the tunnel equipment physical drive.

[0032] In one embodiment, a physical model based on electromechanical losses is first established for key equipment such as tunnel ventilation systems and drainage pumps. The core of this model is based on key moving components such as motors and bearings. Typical degradation patterns are extracted from historical fault data, and a mapping relationship between equipment status parameters and physical parameters is established to construct a model of the initial physical drive of the tunnel equipment.

[0033] Then, an LSTM network is used to model the residual between the physical model output value and the real-time monitoring value, and a sliding time window (24 hours) is designed to dynamically update the network weights. When a sudden abnormality of the equipment causes the residual to exceed the preset threshold, the model incremental training is triggered, and the physical equation parameters and L network weights are updated synchronously. Adversarial sample generation technology is introduced to simulate the equipment status under extreme working conditions and enhance the robustness of the model.

[0034] Finally, based on the revised physical drive model, the equipment performance degradation rate is calculated, and an equipment health assessment matrix is constructed. The performance level threshold is set in combination with the ISO standard to determine the tunnel equipment performance data.

[0035] Step 103: Perform finite element analysis on the structured tunnel environment state data to determine the tunnel environment state data.

[0036] For example, the environment of the tunnel equipment determines the overall stability of the tunnel. By performing finite element analysis on the structured tunnel environment status data, the equipment performance analysis at the tunnel equipment end is realized, providing a data basis for the data coupling between the tunnel equipment and the tunnel environment.

[0037] Specifically, finite element analysis is performed on the structured tunnel environmental status data to determine the tunnel environmental status data, including: tunnel meshing the structured tunnel environmental status data to obtain tunnel mesh data; based on the tunnel mesh data, key risk areas are determined through tunnel environmental status balance analysis; and FEM prediction updates are performed on the key risk areas to obtain tunnel environmental status data.

[0038] In one embodiment, first, an unstructured grid generation algorithm is used to dynamically adjust the grid density based on the formation radar detection data and the spatial distribution of geotechnical parameters, and automatically encrypt the grid to centimeter-level resolution in weak interlayers and fault zones.

[0039] The fiber optic sensor data and geological exploration data were then mapped to grid nodes to construct an initial multi-physics coupling model. A multi-condition coupling analysis was then performed to calculate the surrounding rock stress concentration factor and pore water pressure gradient. Monte Carlo simulations were used to assess factors such as the coefficient of variation of rock mass strength. A risk probability cloud map was generated, and a convolutional neural network was used to automatically identify the morphological characteristics of high-risk areas.

[0040] Finally, a two-way feedback channel for the finite element model of monitoring data was established to convert the distributed optical fiber strain monitoring data into boundary condition corrections in real time, thereby predicting the evolution trend of tunnel surrounding rock displacement in the next 48 hours to determine the tunnel environmental status data.

[0041] Step 104: Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing.

[0042] For example, based on tunnel equipment performance data and tunnel environment status data, through the coupling of tunnel equipment with tunnel environment and vice versa, a digital twin that satisfies bidirectional coupling and dynamic updates is realized, and tunnel health assessment based on the interaction between tunnel equipment and tunnel environment is realized.

[0043] Specifically, based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing, including: coupling the tunnel equipment performance data with the boundary conditions of the equipment affecting the tunnel environment to determine the coupling data of the tunnel equipment to the tunnel environment; coupling the tunnel environment status data with the equipment monitoring threshold conditions to determine the coupling data of the tunnel environment to the tunnel equipment; and dynamically updating the coupling data of the equipment to the tunnel environment and the coupling data of the tunnel environment to the tunnel equipment to obtain a dynamically updated digital twin.

[0044] In one embodiment, first, the ventilation equipment performance data (such as wind pressure and flow) is converted into thermal-mechanical coupling boundary conditions of the surrounding rock-support system, and the dynamic impact of equipment operation on the temperature, humidity and stress fields of the tunnel is simulated to determine the coupling data of the tunnel equipment to the tunnel environment.

[0045] Then, the surrounding rock displacement monitoring data is converted into the equipment workload coefficient, and the drainage pump start-stop threshold and ventilator speed control strategy are dynamically adjusted to determine the coupling data of the tunnel environment on the tunnel equipment.

[0046] It should be noted that the selection of coupling data can be reasonably adjusted according to the requirements of the cableway health assessment. The higher the requirements for the health assessment, the more types of coupling data there are and the greater the amount of calculation.

[0047] Finally, a cross-scale model was constructed, using finite element simulation at the macro level to simulate the overall structural response, and discrete element analysis at the micro level to analyze rock mass particle motion. A collaborative simulation engine was designed to enable interaction between stress transfer and energy dissipation data, with adaptive time step matching, between the two-scale models to produce a dynamically updated digital twin.

[0048] Step 105: Determine the tunnel state driving simulation data based on the dynamically updated digital twin through dynamic interaction with the tunnel data twin.

[0049] Exemplarily, the present application determines the tunnel state driving simulation data through dynamic interaction of tunnel data twins, realizes multi-objective adaptive interaction of tunnel states, and meets the tunnel state driving simulation requirements based on digital twins.

[0050] Specifically, based on the dynamically updated digital twin, the tunnel state driving simulation data is determined through the dynamic interaction of the tunnel data twin, specifically including: obtaining the tunnel real-time monitoring data stream, and performing grid threshold simulation processing on the tunnel real-time monitoring data stream to obtain dynamically adjusted tunnel simulation configuration parameters; based on the tunnel simulation configuration parameters, the simulation configuration parameters are determined through dynamic federated learning driven processing; according to the simulation configuration parameters, the tunnel multi-objective adaptive control strategy configuration is used to obtain the tunnel level state determination strategy; the tunnel level state determination strategy is embedded in the dynamically updated digital twin to determine the tunnel state driving simulation data.

[0051] In one embodiment, first, a sliding window sampling is performed on the real-time monitoring data stream, and dynamically adjusted tunnel simulation configuration parameters are generated in combination with grid threshold constraints. Multi-node simulation results are aggregated based on the federated learning framework, and the global simulation model is optimized through parameter weight distribution to generate revised simulation configuration parameters.

[0052] The real-time monitoring data streams include surrounding rock displacement of 0.3-2.1mm, segment joint opening of 0.5-3.8mm, fan current fluctuation of 12-18A, and ambient humidity of 70-95% RH. This data stream undergoes grid threshold processing. Based on the preset surrounding rock displacement safety threshold of 1.8mm and joint opening warning threshold of 2.5mm, high-precision simulation grid regions are dynamically divided. In areas where displacement exceeds the limit, the grid density is increased from the conventional 1m×1m to 0.2m×0.2m. This generates a tunnel simulation configuration parameter set containing locally refined parameters.

[0053] Then, based on a dynamic federated learning framework, simulation node data from the east and west tunnel construction sections was aggregated. A weighted distribution algorithm was used to optimize the global model, and a multi-objective optimization algorithm was used to configure an adaptive control strategy to balance safety, energy consumption, and equipment life indicators. In ventilation system control, the bearing vibration threshold was dynamically adjusted from 6.0 mm / s to a range of 4.5-7.2 mm / s, reducing the average daily energy consumption of the fans by 18% while shortening the alarm response time for stress overruns in high-risk segment areas to less than 30 seconds. Based on this, a tunnel-level status determination strategy was generated, consisting of a three-level response mechanism (normal monitoring, yellow warning, and red emergency response), and associated support resource scheduling plans at different levels.

[0054] Finally, the judgment strategy is embedded into the digital twin managed by blockchain, and the simulation model is updated through a dynamic interaction engine.

[0055] Step 106: Perform multi-level health index calculations on the tunnel state driving simulation data to obtain a tunnel health assessment score.

[0056] Illustratively, the present application implements tunnel health assessment based on the interaction between tunnel equipment and tunnel environment by performing multi-level health index calculations on tunnel state driven simulation data to obtain a tunnel health assessment score, thereby improving the practicality and comprehensiveness of tunnel health assessment and optimizing the data discretization processing of tunnel health assessment.

[0057] Specifically, a multi-level health index operation is performed on the tunnel state drive simulation data to obtain a tunnel health assessment score, including: performing a tunnel equipment health status analysis on the tunnel state drive simulation data to determine a first tunnel assessment parameter; performing a tunnel environment risk assessment on the tunnel state drive simulation data to determine a second tunnel assessment parameter; and performing a parameter bidirectional impact analysis on the first tunnel assessment parameter and the second tunnel assessment parameter to obtain a tunnel health assessment score.

[0058] In one embodiment, equipment operating parameters are extracted from tunnel state driving simulation data, and the equipment health index is calculated through degradation pattern matching to generate the first tunnel evaluation parameter.

[0059] Analyze the stress concentration and displacement change rate in the environmental simulation data and generate the second tunnel assessment parameters based on the risk probability model;

[0060] A coupling impact matrix was constructed, and two types of assessment parameters were integrated through a bidirectional weighting algorithm to generate a comprehensive tunnel health assessment score. A bidirectional coupling simulation generated a tunnel risk warning map, demarcating a 120-meter high-risk red section and a 350-meter yellow warning section. Maintenance work orders were generated based on the equipment health status. For emergency situations involving leakage risks and equipment aging, the system prioritized maintenance resources to ensure timely resolution of high-risk areas.

[0061] Furthermore, after performing multi-level health index calculations on the tunnel state driven simulation data to obtain a tunnel health assessment score, the method also includes: determining the current tunnel maintenance requirements based on the tunnel health assessment score through maintenance strategy configuration; and obtaining a tunnel operation and maintenance priority sorting work order through an operation and maintenance work order priority evaluation based on the current tunnel maintenance requirements.

[0062] In one embodiment, first, the equipment aging level and the environmental risk level are divided according to the tunnel health assessment score, and a maintenance strategy configuration plan is generated.

[0063] Then, a multi-objective evaluation function is constructed based on maintenance cost, risk diffusion speed and repair urgency to prioritize work orders.

[0064] Finally, based on the priority ranking, a tunnel operation and maintenance priority ranking work order including the maintenance location, recommended measures and execution time is obtained.

[0065] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a tunnel health assessment device based on digital twins, the structure of which is as follows: Figure 2 shown.

[0066] Figure 2 This is a schematic diagram of the internal structure of a tunnel health assessment device based on digital twins provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0067] at least one processor 201;

[0068] and, a memory 202 communicatively coupled to the at least one processor;

[0069] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0070] Acquire tunnel health monitoring data and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status dataset; wherein the structured tunnel status dataset includes: structured tunnel equipment status data and structured tunnel environment status data; perform tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; perform finite element analysis on the structured tunnel environment status data to determine tunnel environment status data; based on the tunnel equipment performance data and tunnel environment status data, perform bidirectional coupling processing to obtain a dynamically updated digital twin; based on the dynamically updated digital twin, determine tunnel state drive simulation data through dynamic interaction with the tunnel data twin; perform multi-level health index calculation on the tunnel state drive simulation data to obtain a tunnel health assessment score.

[0071] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for tunnel health assessment based on digital twins stores computer executable instructions, wherein the computer executable instructions are set to:

[0072] Acquire tunnel health monitoring data and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status dataset; wherein the structured tunnel status dataset includes: structured tunnel equipment status data and structured tunnel environment status data; perform tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; perform finite element analysis on the structured tunnel environment status data to determine tunnel environment status data; based on the tunnel equipment performance data and tunnel environment status data, perform bidirectional coupling processing to obtain a dynamically updated digital twin; based on the dynamically updated digital twin, determine tunnel state drive simulation data through dynamic interaction with the tunnel data twin; perform multi-level health index calculation on the tunnel state drive simulation data to obtain a tunnel health assessment score.

[0073] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0074] 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 their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0075] 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 take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] 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 block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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 1A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0079] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0080] 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.

[0081] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using 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 RAM (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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (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 transitory computer-readable media such as modulated data signals and carrier waves.

[0082] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0083] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A tunnel health assessment method based on digital twins, characterized in that: The method comprises: Acquire tunnel health monitoring data and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status data set; wherein the structured tunnel status data set includes: structured tunnel equipment status data and structured tunnel environment status data; Performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; Performing finite element analysis on the structured tunnel environment state data to determine tunnel environment state data; Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing; Determining tunnel state driving simulation data based on the dynamically updated digital twin through dynamic interaction with the tunnel data twin; performing a multi-level health index operation on the tunnel state driving simulation data to obtain a tunnel health assessment score; Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing, specifically including: Performing coupling of the tunnel equipment performance data with the tunnel environment boundary conditions to determine coupling data of the tunnel equipment to the tunnel environment; Performing equipment monitoring threshold condition coupling on the tunnel environment status data to determine coupling data of the tunnel environment to the tunnel equipment; Dynamically updating data on the coupling of the device to the tunnel environment and data on the coupling of the tunnel environment to the tunnel device to obtain a dynamically updated digital twin; Performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data, specifically including: Acquire a tunnel equipment physical equation, and construct a tunnel equipment physical initial drive based on the structured tunnel equipment state data and the equipment physical equation through historical fault analysis; Performing LSTM data-driven correction on the physical initial drive of the tunnel device to determine the physical drive of the tunnel device; The tunnel device performance data is determined by calculating the actual performance of the tunnel device according to the physical drive of the tunnel device.

2. A tunnel health assessment method based on digital twins according to claim 1, characterized in that: The tunnel health monitoring data is subjected to spatiotemporal alignment processing to obtain a structured tunnel status dataset, specifically including: Performing data cleaning on the tunnel health monitoring data to obtain clean tunnel health monitoring data; wherein the data cleaning includes: 3σ abnormal distribution determination and wavelet noise reduction processing; Performing timestamp alignment processing on the tunnel health monitoring clean data to determine time-synchronized tunnel detection data; Performing data space registration processing on the clean tunnel health monitoring data to determine spatially aligned tunnel detection data; Based on the time-synchronized tunnel detection data and the space-aligned tunnel detection data, the structured tunnel state data set is obtained through dual-end state feature extraction; wherein, the dual-end state feature extraction includes: device-end abnormal feature extraction and environment-end strain gradient analysis.

3. The tunnel health assessment method based on digital twin according to claim 1 is characterized in that: Performing finite element analysis on the structured tunnel environment state data to determine the tunnel environment state data, specifically including: Performing tunnel grid division on the structured tunnel environment state data to obtain tunnel grid data; Based on the tunnel grid data, determining key risk areas through tunnel environmental state balance analysis; Perform FEM prediction update on the key risk area to obtain the tunnel environment status data.

4. The tunnel health assessment method based on digital twin according to claim 1 is characterized in that: Based on the dynamically updated digital twin, tunnel state driving simulation data is determined through dynamic interaction with the tunnel data twin, specifically including: Acquire a tunnel real-time monitoring data stream, and perform grid threshold simulation processing on the tunnel real-time monitoring data stream to obtain dynamically adjusted tunnel simulation configuration parameters; Determining simulation configuration parameters based on the tunnel simulation configuration parameters through dynamic federated learning driven processing; According to the simulation configuration parameters, a tunnel level state determination strategy is obtained through tunnel multi-objective adaptive control strategy configuration; The tunnel level state determination strategy is embedded into the dynamically updated digital twin to determine the tunnel state driving simulation data.

5. The tunnel health assessment method based on digital twin according to claim 1 is characterized in that: Performing a multi-level health index calculation on the tunnel state driving simulation data to obtain a tunnel health assessment score, specifically including: Performing a tunnel equipment health status analysis on the tunnel state driving simulation data to determine a first tunnel evaluation parameter; Performing a tunnel environment risk assessment on the tunnel state driving simulation data to determine a second tunnel assessment parameter; A bidirectional parameter impact analysis is performed on the first tunnel evaluation parameter and the second tunnel evaluation parameter to obtain the tunnel health assessment score.

6. The tunnel health assessment method based on digital twin according to claim 1 is characterized in that: After performing multi-level health index calculations on the tunnel state driving simulation data to obtain a tunnel health assessment score, the method further includes: Based on the tunnel health assessment score, determining current tunnel maintenance needs through maintenance strategy configuration; According to the current tunnel maintenance demand, a tunnel operation and maintenance priority sorting work order is obtained through an operation and maintenance work order priority evaluation.

7. A tunnel health assessment device based on digital twins, 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: Acquire tunnel health monitoring data and perform spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status data set; wherein the structured tunnel status data set includes: structured tunnel equipment status data and structured tunnel environment status data; Performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; Performing finite element analysis on the structured tunnel environment state data to determine tunnel environment state data; Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing; Determining tunnel state driving simulation data based on the dynamically updated digital twin through dynamic interaction with the tunnel data twin; performing a multi-level health index operation on the tunnel state driving simulation data to obtain a tunnel health assessment score; Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing, specifically including: Performing coupling of the tunnel equipment performance data with the tunnel environment boundary conditions to determine coupling data of the tunnel equipment to the tunnel environment; Performing equipment monitoring threshold condition coupling on the tunnel environment status data to determine coupling data of the tunnel environment to the tunnel equipment; Dynamically updating data on the coupling of the device to the tunnel environment and data on the coupling of the tunnel environment to the tunnel device to obtain a dynamically updated digital twin; Performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data, specifically including: Acquire a tunnel equipment physical equation, and construct a tunnel equipment physical initial drive based on the structured tunnel equipment state data and the equipment physical equation through historical fault analysis; Performing LSTM data-driven correction on the physical initial drive of the tunnel device to determine the physical drive of the tunnel device; The tunnel device performance data is determined by calculating the actual performance of the tunnel device according to the physical drive of the tunnel device.

8. A non-volatile computer storage medium for tunnel health assessment based on digital twins, storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Acquiring tunnel health monitoring data and performing spatiotemporal alignment processing on the tunnel health monitoring data to obtain a structured tunnel status dataset; The structured tunnel status data set includes: structured tunnel equipment status data, structured tunnel environment status data; Performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data; Performing finite element analysis on the structured tunnel environment state data to determine tunnel environment state data; Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing; Determining tunnel state driving simulation data based on the dynamically updated digital twin through dynamic interaction with the tunnel data twin; performing a multi-level health index operation on the tunnel state driving simulation data to obtain a tunnel health assessment score; Based on the tunnel equipment performance data and the tunnel environment status data, a dynamically updated digital twin is obtained through bidirectional coupling processing, specifically including: Performing coupling of the tunnel equipment performance data with the tunnel environment boundary conditions to determine coupling data of the tunnel equipment to the tunnel environment; Performing equipment monitoring threshold condition coupling on the tunnel environment status data to determine coupling data of the tunnel environment to the tunnel equipment; Dynamically updating data on the coupling of the device to the tunnel environment and data on the coupling of the tunnel environment to the tunnel device to obtain a dynamically updated digital twin; Performing tunnel equipment physical drive analysis on the structured tunnel equipment status data to determine tunnel equipment performance data, specifically including: Acquire a tunnel equipment physical equation, and construct a tunnel equipment physical initial drive based on the structured tunnel equipment state data and the equipment physical equation through historical fault analysis; Performing LSTM data-driven correction on the physical initial drive of the tunnel device to determine the physical drive of the tunnel device; The tunnel device performance data is determined by calculating the actual performance of the tunnel device according to the physical drive of the tunnel device.

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