Tunnel deformation monitoring system and method based on wireless signals

Abstract

The invention provides a tunnel deformation monitoring system and method based on wireless signals, relates to the technical field of tunnel monitoring, and solves the problem of comprehensive monitoring limitation in tunnel safety monitoring in the existing monitoring technology. The system comprises a plurality of wireless signal monitoring units which are arranged in a tunnel according to a preset mode, each wireless signal monitoring unit is provided with a radio frequency transceiver module and an antenna system, and the wireless signal monitoring units can transmit and receive wireless electromagnetic wave signals with preset frequency; the data acquisition and processing terminal is in communication connection with the wireless signal monitoring unit, and receives key parameters of one or more combinations in the signal propagation process acquired by each unit, so as to monitor the signal according to the variable quantity of the key parameters between the continuous monitoring periods / relative to a preset initial reference value. And calculating inter-unit position change / propagation path characteristic change caused by tunnel structure deformation so as to realize tunnel deformation monitoring. According to the invention, the comprehensiveness, accuracy and early warning capability of tunnel deformation monitoring can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel monitoring, and in particular to a tunnel deformation monitoring system and method based on wireless signals. Background Art

[0002] Monitoring tunnel structure deformation is a critical step in ensuring engineering safety. Existing technologies include optical measurement, automated total station measurement, 3D laser scanning, GPS measurement, fiber optic sensing, inclinometer measurement, strain gauge measurement, synthetic aperture radar interferometry, and photogrammetry. However, these technologies all have varying degrees of limitations in their application.

[0003] Optical measurement relies on instruments like levels and theodolites. While highly accurate, it requires manual on-site operation. Its visibility is easily obstructed by construction or operating equipment within the tunnel. Furthermore, it is significantly constrained by environmental factors like lighting conditions and smoke and dust, making continuous automated monitoring difficult. While automated measurement with total stations can support automated monitoring, the equipment is expensive to purchase and maintain, and is prone to wear and tear in the harsh tunnel environment.

[0004] 3D laser scanning technology can acquire high-density point cloud data, but its data processing is labor-intensive, time-consuming, and relatively expensive. Global Positioning System (GPS) measurements work well on open surfaces, but inside tunnels, the rock and soil shield the satellite signal, causing severe signal attenuation and obstruction. This prevents the receiver from locking onto enough satellites for effective positioning, making it impractical for direct application in tunnel environments.

[0005] Fiber-optic sensing technologies such as fiber Bragg grating (FBG) or Brillouin scattering distributed measurement offer advantages in resisting electromagnetic interference. However, the sensors themselves are fragile, installation and maintenance are complex, and cost control is challenging. Inclinometers and strain gauges are mostly point sensors that can only reflect the local tilt or strain state at the point of installation, making it difficult to fully perceive the overall deformation trend of the tunnel structure.

[0006] Synthetic aperture radar interferometry is suitable for monitoring large-scale surface subsidence, but its accuracy in monitoring deformation within tunnels is limited and it is susceptible to atmospheric interference, making it difficult to increase monitoring frequency. Photogrammetry also has stringent requirements for lighting conditions and image processing algorithms, and its stability is easily disrupted in dusty and vaporous tunnel environments.

[0007] It can be seen that the existing monitoring technology has not yet achieved coordinated optimization in key performance indicators such as high-precision measurement, real-time continuous monitoring, adaptability to complex environments, low-cost deployment and maintenance, and perception of overall deformation of tunnel structures, making it difficult to meet the comprehensive needs of current tunnel safety monitoring. Summary of the Invention

[0008] The present invention aims to address the limitations of existing monitoring technologies in tunnel safety monitoring. Therefore, a wireless signal-based tunnel deformation monitoring system and method is proposed. This system constructs a wireless sensor network along key longitudinal cross-sections within the tunnel. This system measures and intelligently analyzes the multi-parameter characteristics of wireless signals to monitor tunnel deformation, effectively improving the comprehensiveness, accuracy, and early warning capabilities of tunnel deformation monitoring.

[0009] The present invention adopts the following technical solutions to achieve the purpose: A tunnel deformation monitoring system based on wireless signals, comprising: Multiple wireless signal monitoring units, each of which is arranged inside the tunnel in a preset manner, each of which is equipped with a radio frequency transceiver module and an antenna system, and each of which is used to transmit and receive radio electromagnetic wave signals of a preset frequency; a data acquisition and processing terminal, the data acquisition and processing terminal being communicatively connected to the plurality of wireless signal monitoring units, the data acquisition and processing terminal being configured to receive at least one or more combinations of key parameters of the radio electromagnetic wave signals during propagation, collected by each of the wireless signal monitoring units; The data acquisition and processing terminal is also used to calculate the relative position changes between the wireless signal monitoring units and / or the changes in the signal propagation path characteristics caused by the deformation of the tunnel structure based on the changes in the key parameters between consecutive monitoring cycles and / or relative to the preset initial baseline value, combined with the preset electromagnetic wave propagation model and deformation solution algorithm, to achieve monitoring of tunnel deformation.

[0010] Preferably, the key parameters include signal strength indication, signal phase difference and signal propagation delay.

[0011] Preferably, the radio frequency transceiver module is used to transmit and receive the radio electromagnetic wave signal at multiple different preset frequency points; the data acquisition and processing terminal is also used to calculate the change amount in the key parameters of the radio electromagnetic wave signal obtained at multiple preset frequency points through differential processing, joint solution and / or phase unwrapping.

[0012] Preferably, the deformation solution algorithm is built into the data acquisition and processing terminal, or is obtained by the data acquisition and processing terminal through external call; the deformation solution algorithm includes one or more pre-trained machine learning models, and the machine learning model takes the change in the key parameters and preset optional environmental parameters as input, and outputs the deformation amount and / or deformation mode classification result of the tunnel structure; the machine learning model is obtained after training with historical monitoring data, laboratory simulation data and / or numerical simulation data.

[0013] Preferably, the system further comprises a remote monitoring and early warning platform; the remote monitoring and early warning platform is connected to the data acquisition and processing terminal via a network communication, and the remote monitoring and early warning platform is used to receive, store and analyze relative position changes and / or signal propagation path characteristic changes from the data acquisition and processing terminal, and after integrating them into deformation monitoring data, present the tunnel deformation status and trend to the user in a graphical and / or digital manner; The remote monitoring and early warning platform is equipped with an early warning module, which is used to trigger an early warning action and notify the user through multiple preset methods when the tunnel deformation amount and / or tunnel deformation rate represented by the deformation monitoring data exceeds preset multi-level thresholds.

[0014] Overall, the monitoring system of the present invention strategically deploys multiple groups of wireless signal monitoring units along the tunnel's longitudinal axis and key cross-sections. These units can be divided into transmitting and receiving units, or they can have both transmitting and receiving capabilities. Together, these units form a spatially distributed wireless sensor network. Each unit's antenna system can be configured as directional or omnidirectional depending on the application scenario, and is equipped with a radio frequency transceiver module. The wireless signal monitoring units are used in conjunction with the data acquisition and processing terminal.

[0015] Deployed signal units utilize radio waves in specific frequency bands, such as Wi-Fi, LoRa, ZigBee, or signals in specific licensed bands, not limited to the ISM band, to communicate with each other. The monitoring system actively controls the transmitters to emit detection signals, while the receivers synchronously and precisely measure and record key signal parameters along each propagation path. These parameters are then extracted using sophisticated processing algorithms.

[0016] The monitoring system then continuously monitors changes in these signal parameters over time, or compares and analyzes them against preset or dynamically updated baseline state parameters. When the tunnel structure undergoes deformations such as convergence, settlement, or misalignment, the relative position or posture of the monitoring units changes. Consequently, the propagation path length of the wireless signal and the electromagnetic characteristics of the propagation environment will also change, leading to corresponding changes in key signal parameters. Based on a preset electromagnetic wave propagation model and deformation resolution algorithm, intelligent fusion analysis and pattern recognition of these key signal parameter changes can accurately reflect the relative displacement, posture changes, and strain distribution between monitoring units, thereby assessing the overall and local deformation status, development trends, and potential risks of the tunnel structure.

[0017] All monitoring data from the monitoring system is aggregated into its remote monitoring and early warning platform, enabling data storage, analysis, and visualization. It also features a built-in intelligent early warning mechanism. When the detected deformation or rate exceeds a preset threshold, the system automatically triggers an alarm and sends a warning message to the user. The system also supports auxiliary functions such as historical data query, deformation trend prediction, and remote configuration management.

[0018] The present invention also provides a tunnel deformation monitoring method based on wireless signals. The hardware basis of the method is the aforementioned tunnel deformation monitoring system. The method comprises the following steps: S1. In the initial stable state of the tunnel structure, deploy and calibrate multiple wireless signal monitoring units, and establish a mapping relationship model between the reference signal parameter set and key parameters and the tunnel space geometry; S2. During the monitoring process, periodically collect key parameters of radio electromagnetic wave signal transmission between different wireless signal monitoring units, including signal strength indication, signal phase difference and signal propagation delay; S3, calculating the variation of the acquired key parameters relative to the established reference signal parameter set; S4. Based on the calculated changes in key parameters, tunnel deformation data is calculated using a mapping relationship model between the key parameters and the tunnel spatial geometry; S5. Based on the calculated tunnel deformation data, trigger external warning operations and adaptive monitoring strategy adjustment operations through preset thresholds.

[0019] Preferably, in step S4, the process of calculating the tunnel deformation data is performed based on one or more combinations of changes in signal strength indication, changes in signal phase difference, and changes in signal propagation delay.

[0020] Furthermore, when calculating tunnel deformation data based on signal strength indicator changes, a theoretical model is established to correlate signal strength indicator changes with distance changes between wireless signal monitoring units. Alternatively, a preliminarily estimated distance change between different wireless signal monitoring unit nodes is achieved based on a pre-trained empirical model or lookup table. When solving tunnel deformation data based on signal phase difference changes, for the preset frequency When the propagation path length between two corresponding wireless signal monitoring units changes When the value of , is the wavelength of the radio electromagnetic wave signal; by measuring the phase difference change , the change in propagation path length can be obtained by inversion Based on multi-frequency measurement and differential phase method, combined with the change of signal strength indication and signal propagation delay, the tunnel deformation data is estimated and solved; When solving tunnel deformation data based on signal propagation delay changes, the propagation time changes of radio electromagnetic wave signals are used to calculate the tunnel deformation data. To reflect the change in the length of its propagation path Using TDOA technology, by measuring the time difference between the radio electromagnetic wave signal emitted by any wireless signal monitoring unit and the arrival of at least three other different wireless signal monitoring units, the relative position change from the signal transmission point to the corresponding signal receiving point can be determined, thereby reflecting the change in the propagation path length.

[0021] Preferably, the signal strength indication change, signal phase difference change and signal propagation delay change are combined with auxiliary information to form an input feature vector, which is input into a mapping relationship model between key parameters and tunnel space geometry for solution; the training data of the mapping relationship model comes from laboratory simulation and / or numerical simulation, and is trained using a model with the same three-dimensional structure as the tunnel to be monitored and the corresponding three types of key data: signal strength indication change, signal phase difference change and signal propagation delay change, to form a complex nonlinear mapping model from high-dimensional signal parameter feature space to actual structural deformation parameters.

[0022] Preferably, when collecting key parameters of radio electromagnetic wave signal transmission, multipath effects are suppressed by various means, including: The antenna system of the wireless signal monitoring unit is configured as a directional antenna; Use broadband signal reception technology to resolve and combine multipath components; Channel estimation techniques are used to identify and compensate for multipath parameter deviations.

[0023] In summary, due to the adoption of this technical solution, the beneficial effects of the present invention are as follows: This invention significantly improves the effectiveness of tunnel deformation monitoring. It enables real-time, continuous, and automated monitoring of the overall deformation of tunnel structural sections, overcoming the limitations of traditional point sensors that only capture local information and providing a comprehensive understanding of structural deformation trends. Furthermore, this invention offers significant economic advantages, significantly reducing the hardware cost of the monitoring unit itself. Installation and deployment are simple and quick, eliminating the need for large-scale cabling projects, effectively reducing both initial system investment and long-term maintenance costs.

[0024] The present invention also has good environmental adaptability. It adopts a wireless signal monitoring mechanism and shows strong robustness against high dust, high humidity and stray light interference in the tunnel. Through multi-frequency collaborative measurement, signal processing algorithms that are resistant to multipath interference, and electromagnetic compatibility design, the system can ensure continuous and stable operation in complex electromagnetic environments. The monitoring accuracy and intelligence level of the present invention are also improved accordingly. It can be combined with multi-source parameter fusion analysis and machine learning algorithms to improve the accuracy of deformation recognition, intelligently identify abnormal deformation patterns and realize deformation trend prediction, and provide decision support for safety warnings. In addition, the system architecture of the present invention supports high scalability, and the modular unit design concept can flexibly configure the number of monitoring units according to the actual tunnel length and monitoring accuracy requirements, and realize full-scale monitoring coverage capabilities from short-distance tunnels to large-scale tunnel networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention further illustrates its implementation and technical solutions in detail through the following drawings, which specifically include three drawings as follows: Figure 1 This is a schematic diagram of the arrangement of wireless signal monitoring units in a tunnel in the system of the present invention; Figure 2 This is an example setting diagram for wireless signal transmission at multiple preset frequency points in the present invention; Figure 3 Schematic diagram briefly describing the overall process of the tunnel deformation monitoring method of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0028] Example 1 A tunnel deformation monitoring system based on wireless signals, comprising: Multiple wireless signal monitoring units, each of which is arranged inside the tunnel in a preset manner, each of which is equipped with a radio frequency transceiver module and an antenna system, and each of which is used to transmit and receive radio electromagnetic wave signals of a preset frequency; A data acquisition and processing terminal is communicatively connected to the plurality of wireless signal monitoring units, and is used to receive at least one or more key parameters of the radio electromagnetic wave signals collected by the respective wireless signal monitoring units during the propagation process; The data acquisition and processing terminal is also used to calculate the relative position changes between wireless signal monitoring units and / or changes in signal propagation path characteristics caused by tunnel structure deformation based on the changes in key parameters between consecutive monitoring cycles and / or relative to preset initial baseline values, combined with a preset electromagnetic wave propagation model and deformation solution algorithm, to achieve monitoring of tunnel deformation.

[0029] The system deploys transmitting and receiving antennas at both ends of the tunnel and at key locations along the cross-section. Multiple antennas transmit and receive signals at different frequencies, recording signal strength attenuation and phase changes between antennas in real time. When the tunnel deforms, the relative positions of the antennas change, altering the signal transmission path and, in turn, causing changes in signal parameters. By comparing and analyzing these parameters with theoretical signal parameters derived from modeling and simulation, the system accurately reflects tunnel structure deformation, enabling highly accurate deformation monitoring.

[0030] In this embodiment, Figure 1 As shown, several wireless signal monitoring units are installed along the inner wall of the tunnel at predetermined intervals. The inner part of the tunnel can be divided into sections every 10-50 meters in the radial direction, and 3-8 units are arranged in each section, so that the key deformation areas of the section and the surrounding tunnel inner wall path can be covered. For example, in a circular tunnel, Figure 1 As shown, they can be evenly distributed along the circumference, and the transmitting units and receiving units can be set in corresponding positions; non-uniform distribution can also be adopted, that is, they can be laid out in the top, waist or bottom of the tunnel circumference.

[0031] In this embodiment, the wireless signal monitoring units exchange signals with each other through a preset wireless communication protocol, such as a customized TDMA, CSMA / CA, or a protocol based on LoRa or Zigbee, thereby forming a multi-node, multi-path wireless sensor network. The specific structure of the wireless sensor network can be star-shaped, that is, all wireless signal monitoring units communicate directly with a central controller, which is in turn connected to a data acquisition and processing terminal; it can also be a tree-shaped hierarchical structure or a mesh topology, so that multi-hop communication can be carried out between nodes; this embodiment prefers a mesh topology to enhance the robustness and coverage of wireless signal communication, and its path redundancy facilitates flexible deployment. The signal interaction between the wireless signal monitoring units includes not only the detection signal for measurement, but also the necessary control signaling and synchronization information.

[0032] In this embodiment, the data collected by each wireless signal monitoring unit is transmitted to the data collection and processing terminal via its wired or wireless data interface. If wireless backhaul is used, a hierarchical network can be constructed, with each wireless signal monitoring unit acting as an end node in the network. Data is uploaded to the data collection and processing terminal via a sink node, either integrated into some wireless signal monitoring units or as a standalone device.

[0033] In this embodiment, the core components of each wireless signal monitoring unit may specifically include: RF transceiver module: The core function is to generate, modulate, transmit, receive, demodulate, and amplify wireless signals. This module may include components such as a frequency synthesizer that generates precise carrier frequencies, a power amplifier for signal transmission, a low-noise amplifier for signal reception, a mixer, and a filter. Figure 2 As shown, it enables the wireless signal monitoring unit to transmit and receive signals at multiple preset frequency points, and can switch the operating frequency and bandwidth according to instructions; the multiple preset frequency points can be multiple channels in the 2.4GHz ISM band, or lower frequency bands such as the 433 / 868 / 915MHz bands used by LoRa, or even higher frequency bands such as millimeter waves.

[0034] Antenna System: Directional antennas such as Yagi antennas and patch antenna arrays can be used to enhance signal transmission and reception capabilities in a specific direction, or omnidirectional antennas can be used to provide wider coverage. Antenna design must consider the propagation characteristics of tunnel environments, and current, proven antenna designs can be used as needed.

[0035] The signal processing unit, which can utilize a high-performance microcontroller (MCU), digital signal processor (DSP), or field-programmable gate array (FPGA), is responsible for precisely controlling the RF module's operating mode, such as transmit power, modulation, and transmit / receive timing. It also digitizes and preprocesses the received I / Q sampled baseband signals, including digital filtering, channel estimation, RSSI calculation, phase information extraction, and delay estimation. This unit also executes the communication protocol stack with the data acquisition and processing terminal, packaging and transmitting the processed data.

[0036] Synchronization module: For measurement methods that rely on precise timestamps, including TDOA and continuous phase difference tracking, high-precision synchronization is crucial. This module is responsible for achieving time synchronization between wireless signal monitoring units and between wireless signal monitoring units and data acquisition and processing terminals. Various synchronization schemes can be adopted, such as: broadcasting global synchronization pulses via wired or wireless means, namely the IEEE 1588 PTP protocol; if some wireless signal monitoring units can intermittently receive GPS signals, GPS signals can also be used for time training; or a network time synchronization protocol based on two-way timestamp exchange, such as an optimized version of NTP, can be used. This embodiment recommends that the synchronization accuracy reach the sub-nanosecond or picosecond level to meet the requirements of high-precision ranging.

[0037] Power supply module: It can be powered by AC power, solar energy (if applicable and the solar panel cable is connected to the outside of the tunnel) or long-life battery, and should have low power consumption management function.

[0038] Data interface and communication module: used to support wired methods such as RS485 and Ethernet or wireless methods such as LoRaWAN, NB-IoT, and ZigBee to upload processed data to the data acquisition and processing terminal.

[0039] Protective housing: It is recommended to have an IP67 or higher protection level to adapt to the high humidity, dusty and dripping environment in the tunnel.

[0040] In this embodiment, the data acquisition and processing terminal is the aggregation node and computing core of the monitoring system. It can be deployed in the tunnel management center or a nearby equipment chamber. Its main components and functions are described as follows: High-performance embedded computer or industrial personal computer: Serves as the main processor, running the operating system and core monitoring and analysis software. Responsible for receiving, parsing, and verifying data from all wireless signal monitoring units, executing complex deformation resolution algorithms involving electromagnetic propagation model calculations and machine learning model inference, managing the benchmark database, storing historical data, and generating deformation reports and warnings.

[0041] Network communication interface: including Ethernet, LoRaWAN gateway interface, fiber optic interface, etc. for communicating with the wireless signal monitoring unit; as well as high-speed Ethernet, 4G / 5G module interface, etc. for connecting to the remote monitoring and early warning platform; the network communication interface must ensure reliable and efficient data transmission.

[0042] Data storage unit: uses a large-capacity hard disk or SSD to store original monitoring data, processing results, system logs, etc.

[0043] Power management system: used to ensure stable power supply support for data acquisition and processing terminals.

[0044] In this embodiment, the system also includes a remote monitoring and early warning platform; this platform is a web-based B / S architecture software system platform, which can also adopt a C / S architecture and be deployed in the cloud or at the user's local server cluster. Its main components and functions are described as follows: Data reception and management module: Receives processed deformation data, optionally receives original signal parameters, and system status information from the data acquisition and processing terminal through a secure interface, and performs structured storage, storing the data in a time series database or a relational database, while providing data backup, recovery, query, and management functions.

[0045] Data Analysis and Visualization Module: This module provides a rich set of interactive data visualization tools, such as deformation time-history curves, cross-section convergence diagrams, 3D deformation cloud maps, and deformation rate contour maps, which can be overlaid on the tunnel BIM model. This module also supports multi-dimensional data screening, comparative analysis, and trend prediction, which can be performed using time series analysis models such as ARIMA and LSTM.

[0046] Intelligent Early Warning Module: This module allows users to configure multiple deformation thresholds based on specifications or project specifics. For example, thresholds such as caution, warning, and danger can be configured for different result data, such as displacement, velocity, and acceleration. The system compares monitoring results with thresholds in real time. If a limit is exceeded, an alarm is triggered and notified via SMS, email, app push notifications, and audio and visual alarms. These push notifications include key information such as time, location, deformation, and level of limit violation. Alert rules can be dynamically adjusted and customized.

[0047] System configuration and management module: allows users to remotely configure the working parameters of the wireless signal monitoring unit, manage user information, view the system operation status, etc.

[0048] Example 2 On the basis of Example 1, this embodiment provides a tunnel deformation monitoring method based on wireless signals. The hardware basis of this method is the tunnel deformation monitoring system in Example 1. Figure 3 The key steps of this method can be summarized as follows: S1. In the initial stable state of the tunnel structure, deploy and calibrate multiple wireless signal monitoring units, and establish a mapping relationship model between the reference signal parameter set and key parameters and the tunnel space geometry; S2. During the monitoring process, periodically collect key parameters of radio electromagnetic wave signal transmission between different wireless signal monitoring units, including signal strength indication, signal phase difference and signal propagation delay; S3, calculating the variation of the acquired key parameters relative to the established reference signal parameter set; S4. Based on the calculated changes in key parameters, tunnel deformation data is calculated using a mapping relationship model between the key parameters and the tunnel spatial geometry; S5. Based on the calculated tunnel deformation data, trigger external warning operations and adaptive monitoring strategy adjustment operations through preset thresholds.

[0049] This embodiment will introduce the preferred details of each part in detail according to the above step sequence.

[0050] 1. System initialization and benchmark establishment After the monitoring system is deployed, it undergoes initial calibration. At this point, the tunnel structure is in a relatively stable or known state, such as having just completed initial support or being in the early stages of operation. The system controls all wireless signal monitoring units to conduct comprehensive signal scanning and parameter measurements. This includes transmitting and receiving signals between each wireless signal monitoring unit at all preset frequencies, recording the initial RSSI value, the precise phase difference, and the propagation delay. The phase difference must undergo phase unwrapping, and the propagation delay must be synchronized and calibrated as accurately as possible, ultimately forming a complete and high-precision set of reference signal parameters. This process continues for a certain period of time to obtain stable statistical measurement data.

[0051] At the same time, combining the tunnel's design data with an initial 3D geometric model derived from precision measurements using total stations and 3D laser scanning, along with the precise installation coordinates of each wireless signal monitoring unit (including 3D coordinates and attitude angles), the reference signal parameter set is associated with the precise spatial positions and relative relationships of the wireless signal monitoring units. This initially establishes a mapping relationship model between key parameters and the tunnel's spatial geometry. This model can be calibrated and optimized using electromagnetic simulation software, and the modeling process considers the effects of tunnel wall material, shape, humidity, and the presence of metal components on electromagnetic wave propagation.

[0052] 2. Routine Monitoring and Data Collection Once the system enters normal monitoring mode, the data acquisition and processing terminal controls the wireless signal monitoring unit to periodically transmit and receive signals according to a preset strategy. This strategy can be a fixed interval, such as once an hour, or an adaptive sampling strategy that dynamically adjusts based on historical deformation rates, or triggers encrypted observations when abnormal vibrations or sudden stress changes are detected.

[0053] The electrical signal flow of this part is as follows: the data acquisition and processing terminal sends a control instruction → the signal processing unit of the wireless signal monitoring unit receives and analyzes the instruction → controls the RF transceiver module to transmit the detection signal according to the specified parameters → the detection signal is radiated through the antenna and propagates in the tunnel space → the antenna of other wireless signal monitoring units receives the signal → the corresponding RF transceiver module processes the received signal → the signal processing unit extracts the signal parameters.

[0054] The wireless signal monitoring unit measures and records parameters such as signal strength, signal phase difference, and signal propagation delay for each path at the current moment. This raw data, or pre-processed data after filtering, averaging, and compression, along with a timestamp and the wireless signal monitoring unit's identifier, is uploaded to the data collection and processing terminal via wired or wireless communication. Data integrity and low latency must be maintained during the upload process.

[0055] 3. Calculation of key parameter changes After receiving the real-time signal parameters from each wireless signal monitoring unit, the data acquisition and processing terminal first performs data verification and time alignment, then accurately compares it with the stored reference signal parameter set or the parameter set of the previous monitoring cycle, and calculates the change in each key parameter on each propagation path; this process requires strict synchronization and data management.

[0056] Corresponding to the three key parameters of signal strength indication, signal phase difference and signal propagation delay, the calculated changes include signal strength indication change , signal phase difference changes and signal propagation delay variations .

[0057] 4. Deformation solution This part is the core step of the method execution process, which can be combined in one or more of the following ways: (1) Solution based on signal strength indicator changes. Changes in the signal strength indicator RSSI mainly reflect changes in propagation path loss. Slight changes in path length, changes in the reflection / scattering characteristics of the tunnel wall, such as changes in humidity leading to changes in dielectric constant, cracks leading to new scattering sources, etc., may all cause RSSI changes. This embodiment establishes an accurate theoretical model of RSSI changes and distance changes. The theoretical model can be a logarithmic distance path loss model, and takes into account shadow fading and multipath effects; or it can be based on an empirical model / lookup table trained with a large amount of calibration data to preliminarily estimate the distance changes between nodes. However, the signal strength indicator RSSI is susceptible to environmental interference. This embodiment uses it as an auxiliary parameter or integrates it with other key parameters.

[0058] (2) Through the principle of interference measurement, the solution is based on the change of signal phase difference. For the preset frequency The radio electromagnetic wave signal, its wavelength , is the speed of light; when the propagation path length between two corresponding wireless signal monitoring units When the value of , so by measuring the phase difference change with high precision , the sub-millimeter propagation path length change can be obtained by inversion Since the specific number of turns cannot be determined when the phase change exceeds a full cycle, this is to solve the problem. Phase ambiguity can be solved by using multi-frequency measurement, differential phase technology, or combining other parameters such as changes in signal strength indication and signal propagation delay to make a rough estimate and assist in deambiguation. Multi-frequency measurement uses the different relationships between phase change and distance change at different frequencies to achieve a joint solution, while differential phase technology compares the phase difference between adjacent moments or adjacent frequencies.

[0059] (3) Calculation based on the change of signal propagation delay, namely TOF / TDOA. The change of signal propagation time (Time of Flight, TOF) directly reflects the change of its propagation distance, namely . Measuring TOF requires that the transmitter and receiver have precisely synchronized clocks. TDOA (Time Difference of Arrival) technology can determine the relative position change of the transmitter relative to at least three different receiving units by measuring the time difference between the signal reaching these receiving units. This method has extremely high requirements for synchronization between the receiving units, but has low requirements for absolute synchronization between the transmitter and the receiving system. This embodiment achieves high-precision TOF / TDOA measurement. It is recommended to use broadband signals to obtain steep pulse edges or good correlation characteristics, as well as a high-sampling-rate ADC.

[0060] As a preferred embodiment of this invention, the application of the above three types of key parameters is based on multi-parameter intelligent fusion and machine learning. Due to objective factors such as multipath, non-line-of-sight, electromagnetic interference, and temperature and humidity changes in the tunnel environment, a single signal parameter is often difficult to accurately and robustly reflect the structural deformation. This embodiment adopts a multi-parameter intelligent fusion strategy to combine the changes in multiple key parameters from different paths and frequencies, that is, the signal strength indicator changes. , signal phase difference changes and signal propagation delay variations , and possibly auxiliary information such as temperature and humidity sensor data, as input feature vectors. Leveraging the strengths of deep neural networks (DNNs), convolutional neural networks (CNNs) for processing signal temporal or spectral features, recurrent neural networks (RNNs) / LSTMs for processing time series data, support vector machines (SVMs), random forests (RFs), and gradient boosting tree (GBDTs), a machine learning algorithm is designed to train a complex nonlinear mapping model from the high-dimensional signal parameter feature space to the actual structural deformation parameters. The actual structural deformation parameters are the three-dimensional displacements, cross-sectional convergence values, settlement values, and strain distributions of each monitoring point required for the application. Data for training the model can come from large-scale laboratory simulations, high-precision numerical simulations combining electromagnetic and structural mechanics simulations, and accumulated data from long-term field monitoring and simultaneous traditional high-precision measurements. The steps involved in model training, such as feature selection, dimensionality reduction, and hyperparameter optimization, can be directly applied to existing mature technologies and will not be elaborated here.

[0061] As a preferred feature of this embodiment, when collecting key parameters for radio electromagnetic wave signal transmission, this embodiment also performs multipath effect suppression and environmental factor compensation. Tunnels are typical multipath propagation environments, and the superposition of multipath signals can seriously interfere with the accurate measurement of signal strength indication, signal phase difference, and signal propagation delay. This embodiment suppresses multipath effects through various methods, including: Use directional antennas to reduce received reflected signals; Use broadband signals and Rake receivers and other related receiving technologies to distinguish and combine multipath components; Using channel estimation techniques, such as CIR-based channel impulse response analysis, to estimate channel characteristics by sending known probing sequences to identify and compensate for parameter deviations caused by multipath; Space-time processing technologies such as beamforming and MIMO are used to distinguish the direct path and the reflected path in the spatial and temporal dimensions.

[0062] At the same time, the monitoring system integrates environmental sensors such as temperature and humidity to monitor changes in environmental parameters in real time, and uses these environmental parameters as input features through the established compensation model or in the machine learning model to correct their impact on the wireless signal propagation characteristics and the performance of electronic devices in the wireless signal monitoring unit due to oscillator frequency drift.

[0063] 5. Deformation Results Analysis and Visualization The data acquisition and processing terminal integrates the deformation data of each monitoring point or section to form the deformation field of the entire tunnel or the deformation time history curve of the key parts.

[0064] The remote monitoring and early warning platform visualizes these calculated deformation data, such as changes in the 3D coordinates of each monitoring point, changes in cross-sectional shape, and settlement / uplift curves along the tunnel axis, in an intuitive and interactive manner. Examples include 2D graphs in the form of time-displacement and rate-time curves; dynamically updated tunnel cross-sectional convergence diagrams, 3D deformation cloud maps, and deformation rate contour maps. The 3D deformation cloud map can be precisely aligned with the tunnel's BIM or GIS model and displayed as an overlay, with color and arrows indicating deformation magnitude and direction. This allows users to comprehensively and dynamically understand the tunnel structure's deformation state and historical evolution at any given moment.

[0065] VI. Early Warning and Response The monitoring system compares the deformation values calculated in real time, including absolute displacement, relative displacement, convergence value, settlement value, and other data, as well as the corresponding deformation rates, with pre-set multi-level alarm thresholds. In this embodiment, the multi-level alarm thresholds can be set as follows: Level 1 Blue Warning (Concern Level), Level 2 Yellow Warning (Alert Level), Level 3 Orange Warning (Danger Level), and Level 4 Red Warning (Emergency Level). These alarm thresholds can be set based on regulations such as the Railway Tunnel Design Code and the Highway Tunnel Design Code, as well as design requirements for the monitoring tunnel and safety management regulations during the operation period.

[0066] Once the monitoring value exceeds the threshold of any level, the remote monitoring and early warning platform will automatically trigger the alarm of the corresponding level immediately, and send the alarm information including alarm time, location, monitoring value, over-limit level, preliminary judgment of possible deformation cause, etc. to the preset user management personnel, technical leaders and emergency response teams accurately and promptly through various channels such as platform interface pop-up windows, sound and light alarms, mobile phone text messages, emails, APP message push, telephone voice notifications, etc.

[0067] The monitoring system also features intelligent adaptive monitoring capabilities. For example, if the deformation rate in a specific area is detected to be accelerating or approaching a warning threshold, the data acquisition and processing terminal can automatically adjust the monitoring frequency of the relevant wireless signal monitoring units in that area from the conventional once an hour to once every 10 minutes or even higher. This allows for more intensified and enhanced monitoring of key deformation areas, enabling more timely capture of deformation dynamics and providing more comprehensive data support for risk assessment and decision-making. Furthermore, based on historical data analysis, the monitoring system can appropriately reduce the monitoring frequency in areas with stable deformation to conserve energy and communication resources.

[0068] Finally, the monitoring system also optimizes data management. All raw data and processed results from tunnel deformation monitoring are stored long-term for historical tracing, deformation trend prediction, and structural health assessment. The system also dynamically adjusts baseline parameter sets based on long-term monitoring data, optimizing machine learning model parameters to adapt to gradual changes in the tunnel environment and improve monitoring accuracy.

Claims

1. A tunnel deformation monitoring system based on wireless signals, characterized in that: include: Multiple wireless signal monitoring units, each of which is arranged inside the tunnel in a preset manner, each of which is equipped with a radio frequency transceiver module and an antenna system, and each of which is used to transmit and receive radio electromagnetic wave signals of a preset frequency; a data acquisition and processing terminal, the data acquisition and processing terminal being communicatively connected to the plurality of wireless signal monitoring units, the data acquisition and processing terminal being configured to receive at least one or more combinations of key parameters of the radio electromagnetic wave signals during propagation, collected by each of the wireless signal monitoring units; The data acquisition and processing terminal is also used to calculate the relative position changes between the wireless signal monitoring units and / or the changes in the signal propagation path characteristics caused by the deformation of the tunnel structure based on the changes in the key parameters between consecutive monitoring cycles and / or relative to the preset initial baseline value, combined with the preset electromagnetic wave propagation model and deformation solution algorithm, to achieve monitoring of tunnel deformation.

2. The tunnel deformation monitoring system according to claim 1, characterized in that: The key parameters include signal strength indication, signal phase difference and signal propagation delay.

3. The tunnel deformation monitoring system according to claim 1, characterized in that: The radio frequency transceiver module is used to transmit and receive the radio electromagnetic wave signal at multiple different preset frequency points; the data acquisition and processing terminal is also used to calculate the change amount in the key parameters of the radio electromagnetic wave signal obtained at multiple preset frequency points through differential processing, joint solution and / or phase unwrapping.

4. The tunnel deformation monitoring system according to any one of claims 1 to 3, characterized in that: The deformation solution algorithm is built into the data acquisition and processing terminal, or is obtained by the data acquisition and processing terminal through external call; the deformation solution algorithm includes one or more pre-trained machine learning models, which use the change in the key parameters and preset optional environmental parameters as input and output the deformation amount and / or deformation mode classification result of the tunnel structure; The machine learning model is obtained after training with historical monitoring data, laboratory simulation data and / or numerical simulation data.

5. The tunnel deformation monitoring system according to claim 1, characterized in that: The system further includes a remote monitoring and early warning platform; the remote monitoring and early warning platform is connected to the data acquisition and processing terminal via a network communication, and is configured to receive, store, and analyze relative position changes and / or changes in signal propagation path characteristics from the data acquisition and processing terminal, integrate these into deformation monitoring data, and present the tunnel deformation status and trends to the user in a graphical and / or digital manner; The remote monitoring and early warning platform is equipped with an early warning module, which is used to trigger an early warning action and notify the user through multiple preset methods when the tunnel deformation amount and / or tunnel deformation rate represented by the deformation monitoring data exceeds preset multi-level thresholds.

6. A tunnel deformation monitoring method based on wireless signals, characterized in that: The hardware basis of the method is the tunnel deformation monitoring system according to claim 1, and the method comprises the following steps: S1. In the initial stable state of the tunnel structure, deploy and calibrate multiple wireless signal monitoring units, and establish a mapping relationship model between the reference signal parameter set and key parameters and the tunnel space geometry; S2. During the monitoring process, periodically collect key parameters of radio electromagnetic wave signal transmission between different wireless signal monitoring units, including signal strength indication, signal phase difference and signal propagation delay; S3, calculating the variation of the acquired key parameters relative to the established reference signal parameter set; S4. Based on the calculated changes in key parameters, tunnel deformation data is calculated using a mapping relationship model between the key parameters and the tunnel spatial geometry; S5. Based on the calculated tunnel deformation data, trigger external warning operations and adaptive monitoring strategy adjustment operations through preset thresholds.

7. The tunnel deformation monitoring method according to claim 6, characterized in that: In step S4, the process of calculating the tunnel deformation data is performed based on one or more combinations of changes in signal strength indication, changes in signal phase difference, and changes in signal propagation delay.

8. The tunnel deformation monitoring method according to claim 7, characterized in that: When calculating tunnel deformation data based on signal strength indicator changes, a theoretical model is established to correlate signal strength indicator changes with distance changes between wireless signal monitoring units. Alternatively, a preliminarily estimated distance change between different wireless signal monitoring unit nodes is generated based on a pre-trained empirical model or lookup table. When solving tunnel deformation data based on signal phase difference changes, for the preset frequency When the propagation path length between two corresponding wireless signal monitoring units changes When the value of , is the wavelength of the radio electromagnetic wave signal; by measuring the phase difference change , the change in propagation path length can be obtained by inversion Based on multi-frequency measurement and differential phase method, combined with the change of signal strength indication and signal propagation delay, the tunnel deformation data is estimated and solved; When solving tunnel deformation data based on signal propagation delay changes, the propagation time changes of radio electromagnetic wave signals are used to calculate the tunnel deformation data. To reflect the change in the length of its propagation path Using TDOA technology, by measuring the time difference between the radio electromagnetic wave signal emitted by any wireless signal monitoring unit and the arrival of at least three other different wireless signal monitoring units, the relative position change from the signal transmission point to the corresponding signal receiving point can be determined, thereby reflecting the change in the propagation path length.

9. The tunnel deformation monitoring method according to claim 7, characterized in that: The changes in signal strength indication, signal phase difference, and signal propagation delay are combined with auxiliary information to form an input feature vector, which is then input into a mapping relationship model between key parameters and tunnel spatial geometry for solution. The training data for this mapping relationship model comes from laboratory simulations and / or numerical simulations. It uses a model with the same three-dimensional structure as the tunnel to be monitored and the corresponding three types of key data: signal strength indication changes, signal phase difference changes, and signal propagation delay changes. This forms a complex nonlinear mapping model from the high-dimensional signal parameter feature space to the actual structural deformation parameters.

10. The tunnel deformation monitoring method according to claim 6, characterized in that: When collecting key parameters of radio electromagnetic wave signal transmission, multipath effects are suppressed through various methods, including: configuring the antenna system of the wireless signal monitoring unit as a directional antenna; using broadband signal reception technology to distinguish and combine multipath components; and using channel estimation technology to identify and compensate for multipath parameter deviations.

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