A laser-based tunnel deformation monitoring method, system, medium, and product

Abstract

A kind of laser-based tunnel deformation monitoring method, system, medium and product.In the method, the data processing unit of system first determines the sampling point with slope value exceeding preset slope threshold as candidate target starting point, then utilizes skewness coefficient to filter out waveform window with required symmetry characteristics, and finally determines real target echo by comparing half-height time width of waveform window with standard pulse width ratio, to exclude scattering interference of non-standard pulse width.The data processing unit of system calculates propagation time and target distance of laser pulse based on real target echo, to ensure the accuracy of target distance, and the local deformation amount of tunnel based on the target distance can truly reflect the actual deformation state of tunnel, effectively solve the problem of inaccurate monitoring caused by scattering signal interference in high-concentration dust environment, and improve the accuracy of tunnel deformation monitoring.

Description

Technical Field

[0001] This application relates to the field of tunnel monitoring, and in particular to a laser-based method, system, medium, and product for monitoring tunnel deformation. Background Technology

[0002] With the continuous expansion of underground engineering projects such as subways and highways, the importance of tunnel structural safety monitoring is becoming increasingly prominent. During tunnel construction and operation, it is necessary to monitor the local deformation of the tunnel walls in real time to promptly identify potential safety hazards. Laser ranging technology, due to its non-contact and high-precision characteristics, has been widely applied in the field of tunnel deformation monitoring.

[0003] In related technologies, laser ranging sensors are deployed on the inner arc surface of the tunnel detection section. Taking the top of the inner arc surface of the tunnel or one end of the horizontal line as the reference measuring point, other measuring points are deployed at equal angles to both sides to achieve direct measurement of changes in the vertical or horizontal diameter of the tunnel.

[0004] However, in the tunnel construction environment, processes such as drilling, blasting, muck removal, and shotcreting inevitably generate large amounts of rock and cement dust in confined spaces, creating a persistent high-concentration dust environment. When a laser pulse propagates through a dust-filled medium, its energy is scattered by suspended particles along its path, and this scattered light is captured and converted into electrical signals. This causes the system to mistakenly interpret the scattered signals as target echoes, thus interfering with the accuracy of laser ranging and deformation monitoring. Summary of the Invention

[0005] This application provides a laser-based method, system, medium, and product for monitoring tunnel deformation, which addresses the technical problem that laser ranging signals are easily interfered with by scattering in high-concentration dust environments, thereby improving the accuracy of tunnel deformation monitoring.

[0006] In a first aspect, this application provides a laser-based method for monitoring tunnel deformation, comprising: receiving a returned mixed echo signal and converting the mixed echo signal into a digital signal to obtain a complete waveform data sequence arranged in a time series and containing amplitude data at multiple sampling time points, wherein the mixed echo signal includes target echo and dust scattering echo; determining sampling points in the complete waveform data sequence whose slope values ​​exceed a preset slope threshold as a set of candidate target starting points; taking the peak point of the waveform where each sampling point in the set of candidate target starting points is located as the center, extracting a waveform window data containing a preset number of sampling points, and calculating the skewness coefficient of the waveform window data, wherein the skewness coefficient is expressed as... The data distribution symmetry is demonstrated; waveform windows whose absolute values ​​of all skewness coefficients are less than a preset symmetry threshold are identified as a list of suspected target waveform windows; the time width at half-height of each waveform in the list of suspected target waveform windows is calculated, and suspected target waveforms whose difference between the time width and the preset standard pulse width is within a preset error range are identified as real target echoes; based on the candidate target starting point corresponding to the real target echo and the time position of the candidate target starting point in the complete waveform data sequence, the propagation time of the laser pulse is calculated, and the target distance is calculated based on the propagation time; the difference between the target distance at each monitoring point in the tunnel and the previous monitoring distance is identified as the local deformation of the tunnel.

[0007] By employing the above technical solution, the system's data processing unit first digitizes the received mixed echo signal to obtain a complete waveform data sequence, preserving the original characteristics of both the target echo signal and the dust scattering echo signal. Next, the system's data processing unit filters the complete waveform data sequence using a preset slope threshold, identifying sampling points with slope values ​​exceeding the threshold as candidate target starting points. This initially eliminates noise signals with gentle slopes. Then, the skewness coefficient is used to filter waveform windows with symmetrical characteristics. Because the target echo signal has a stronger waveform distribution symmetry, its skewness coefficient has a smaller absolute value, while the dust scattering echo has a more pronounced waveform distribution asymmetry, resulting in a larger absolute value of the skewness coefficient. This difference in characteristics allows for the initial differentiation between the effective target signal and the dust scattering interference signal. Finally, the system's data processing unit determines the true target echo by comparing the half-width at half-maximum (WHM) with the standard pulse width, eliminating scattering interference from non-standard pulse widths. The system's data processing unit calculates the laser pulse propagation time and target distance based on the true target echo, ensuring the accuracy of the target distance. The local deformation of the tunnel obtained based on the target distance can truly reflect the actual deformation state of the tunnel, effectively solving the problem of inaccurate monitoring caused by scattering signal interference in high-concentration dust environments, and improving the accuracy of tunnel deformation monitoring.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, taking the peak point of the waveform where each sampling point in the candidate target starting point set is located as the center, a waveform window data containing a preset number of sampling points is extracted, and the skewness coefficient of the waveform window data is calculated. Specifically, this includes: for each sampling point in the candidate target starting point set, determining a local maximum value in the complete waveform data sequence, and determining the location of the local maximum value as the waveform peak point; taking the time position of the waveform peak point as the center, extracting a corresponding number of data points in the directions before and after the center according to the preset number of sampling points to form a waveform window data sequence; calculating the arithmetic mean and standard deviation of the waveform window data sequence, and calculating the skewness coefficient of the waveform window data based on the arithmetic mean and standard deviation.

[0009] By adopting the above technical solution, the system's data processing unit first determines the waveform peak point corresponding to each candidate starting point, then extracts waveform window data centered on this peak point to ensure that the waveform window data contains key waveform features. Finally, it calculates the skewness coefficient based on the arithmetic mean and standard deviation to quantify the waveform symmetry. By accurately locating the core region of the waveform and calculating symmetry features, the system's data processing unit enables the skewness coefficient to more accurately reflect the essential differences between the target echo waveform and the dust scattering echo waveform, avoiding symmetry judgment errors caused by improper waveform window data extraction, and further improving the accuracy of target waveform window selection.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, taking the time position of the waveform peak point as the center, corresponding data points are intercepted in the direction before and after the center according to a preset number of sampling points to form a waveform window data sequence. Specifically, this includes: taking the time position of the waveform peak point as the center, determining whether the data points on both sides of the center are sufficient to form a window containing the preset number of sampling points; if so, then intercepting a waveform window data containing the preset number of sampling points to form a waveform window data sequence; if not, then taking the time position of the waveform peak point as the center, extending to both sides of the center, intercepting waveform window data containing all data points of the waveform where the sampling point is located to form a waveform window data sequence.

[0011] By adopting the above technical solution, the system's data processing unit first determines whether there is sufficient data on both sides of the peak point when truncating the waveform window. When there is sufficient data on both sides of the peak point, it truncates the standardized waveform window data according to the preset number of sampling points; when there is insufficient data, it truncates all data points of the waveform, avoiding the loss of effective waveform information due to forced standardization. This flexible waveform window data truncating method ensures that the waveform window data fully covers the key features of the waveform (such as peak height, half-width at half-maximum, etc.), avoids calculation errors introduced by data truncation or redundancy, and enables the subsequent skewness coefficient calculation to more realistically reflect the symmetry characteristics of the waveform, improving the applicability of different waveform window data extraction methods.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the process of determining the preset symmetry threshold specifically includes: merging the signal data of all background noise regions in the complete waveform data sequence into a dataset, and calculating the signal standard deviation of the dataset, wherein the background noise region is the signal reception time period in the complete waveform data sequence corresponding to the laser pulse round-trip distance being less than a preset minimum effective ranging and greater than a preset maximum effective ranging; extracting the peak amplitude of the waveform window data, and calculating the ratio of the peak amplitude to the signal standard deviation to obtain the signal-to-noise ratio of the waveform window data; and determining the preset symmetry threshold based on the signal-to-noise ratio through a preset mapping function, wherein the mapping function makes the preset symmetry threshold inversely proportional to the signal-to-noise ratio.

[0013] By adopting the above technical solution, the system's data processing unit first calculates the overall noise level (standard deviation) using the background noise signal, then calculates the signal-to-noise ratio (SNR) by combining it with the current waveform peak amplitude, and finally dynamically generates a preset symmetry threshold through a mapping function. This preset symmetry threshold is inversely proportional to the SNR. When the SNR is high, the preset symmetry threshold decreases, requiring higher waveform symmetry and eliminating weak interference; when the SNR is low, the preset symmetry threshold increases, allowing for a more relaxed standard to avoid missing weak target echo signals. Dynamic threshold adjustment enables the waveform symmetry screening standard to adapt to different noise environments, solving the problem that fixed thresholds cannot adequately handle both high and low SNR scenarios, and improving the accuracy of target echo signal screening in complex noise environments.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the propagation time of the laser pulse is calculated based on the candidate target starting point corresponding to the real target echo and the time position of the candidate target starting point in the complete waveform data sequence, and the target distance is calculated based on the propagation time. Specifically, this includes: extracting the timestamp of the candidate target starting point corresponding to the real target echo from the complete waveform data sequence; obtaining the laser pulse emission reference time that is time-synchronized with the mixed echo signal, and calculating the round-trip propagation time of the laser pulse in combination with the timestamp of the candidate target starting point; and calculating the target distance in combination with the round-trip propagation time and the speed of light.

[0015] By adopting the above technical solution, the system's data processing unit first extracts the precise timestamp corresponding to the real target echo from the complete waveform sequence, then calculates the round-trip propagation time by combining it with the synchronous laser pulse emission reference time, eliminating errors caused by time asynchrony. Finally, it calculates the target distance by combining it with the speed of light, ensuring the accuracy of the target distance calculation. This process, by accurately acquiring time parameters, reduces the impact of time measurement errors on the target distance calculation, making the obtained target distance more accurate and providing reliable basic data for subsequent deformation calculation.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, determining the difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel as the local deformation of the tunnel specifically includes: calculating the difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel to obtain a preliminary displacement dataset; calculating the median of the preliminary displacement dataset and determining the median as a general offset; and determining the difference between the preliminary displacement of each monitoring point and the general offset as the local deformation of the tunnel at the corresponding monitoring point.

[0017] By adopting the above technical solution, the system's data processing unit first calculates the preliminary displacement of each monitoring point to obtain a dataset. Then, the median of the dataset is determined as the universal offset. This universal offset reflects the error caused by changes in the laser sensor position due to tunnel settlement or convergence, resulting in a common offset in the distance measurements of all monitoring points. Finally, the universal offset is subtracted from the preliminary displacement to eliminate the impact of this error on each monitoring point. This collective error correction mechanism effectively solves the measurement deviation problem caused by changes in the laser sensor position, making the obtained local tunnel deformation more closely resemble the actual deformation of the tunnel structure and improving the accuracy of the calculation of local tunnel deformation.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel as the local deformation of the tunnel, the method further includes: comparing the local deformation of the tunnel with a preset safety range to determine whether the local deformation of the tunnel exceeds the preset safety range; if so, analyzing the hazard level of the data exceeding the preset safety range and generating an early warning signal based on the hazard level; transmitting the early warning signal to the cloud, generating a real-time monitoring report, and continuing monitoring; if not, uploading the local deformation of the tunnel at each monitoring point to the cloud, generating a periodic monitoring report according to a preset cycle, and continuing monitoring.

[0019] By adopting the above technical solution, the system's data processing unit first compares the local deformation with the preset safety range, identifies monitoring points outside the preset safety range, and then generates early warning signals based on the degree of exceedance of the data, uploading them to the cloud to generate real-time monitoring and detection reports, and promptly pushing risk information. Data within the limits are uploaded to the cloud and periodic monitoring and detection reports are generated periodically to determine deformation trends. This mechanism realizes the monitoring and risk-level response of tunnel deformation, ensuring that safety hazards can be detected and warned in a timely manner, and also achieving orderly management of deformation data through periodic reports, thereby improving the timeliness and systematic nature of tunnel safety monitoring.

[0020] In a second aspect, this application provides a tunnel deformation monitoring system, including one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the tunnel deformation monitoring system to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a tunnel deformation monitoring system, cause the tunnel deformation monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product that, when run on a tunnel deformation monitoring system, causes the tunnel deformation monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By employing multi-dimensional signal screening techniques, including slope screening of candidate starting points, waveform window skew analysis, and pulse width verification of mixed echo signals, the technology effectively solves the technical problem of scattering signals being easily misjudged as target echoes in high-concentration dust environments. This is achieved by gradually eliminating smooth noise, distinguishing between symmetrical waveforms and asymmetrical scattering interference, and determining the true target echo with a standard pulse width. As a result, accurate extraction of tunnel deformation monitoring data is achieved, improving the reliability of deformation monitoring results.

[0025] 2. By adopting a waveform window truncation technique centered on the waveform peak point, the sufficiency of data on both sides of the peak point is first determined. When the data is sufficient, a window of data with a preset number of sampling points is truncated; when the data is insufficient, the complete waveform data is truncated. This effectively solves the technical problem that the fixed window truncation method in the prior art is prone to waveform feature distortion due to missing or redundant data. As a result, the key features of different waveform shapes are completely preserved, improving the accuracy of waveform symmetry analysis and the adaptability to edge signals.

[0026] 3. By adopting a technology based on dynamically generating a preset symmetry threshold based on the signal-to-noise ratio, the signal-to-noise ratio is obtained by calculating the standard deviation of the background noise and the peak amplitude of the waveform. Then, the preset symmetry threshold is adjusted in real time through an inverse proportional mapping function. This effectively solves the technical problems in the existing technology where fixed thresholds are difficult to adapt to high and low signal-to-noise ratio scenarios, and are prone to missing weak targets or misjudging interference signals. In this way, the symmetry screening standard is adaptively adjusted to complex dust environments, and the accuracy of target echo signal screening under different noise intensities is improved. Attached Figure Description

[0027] Figure 1 This is a schematic flowchart of a laser-based tunnel deformation monitoring method in an embodiment of this application;

[0028] Figure 2 This is another schematic flowchart of the laser-based tunnel deformation monitoring method in the embodiments of this application;

[0029] Figure 3 This is a schematic diagram of the hardware structure of a tunnel deformation monitoring system in an embodiment of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] For ease of understanding, the method provided in this implementation is described in process below. Please refer to [link / reference]. Figure 1 This is a schematic flowchart of a laser-based tunnel deformation monitoring method in an embodiment of this application.

[0033] 101. Receive the returned mixed echo signal and convert it into a digital signal to obtain a complete waveform data sequence containing amplitude data at multiple sampling time points arranged in a time sequence. The mixed echo signal includes target echo and dust scattering echo.

[0034] Hybrid echo signals refer to signals that return after a laser pulse is emitted and contain multiple components, specifically including target echoes and dust scattering echoes. Target echoes are signals reflected back after a laser pulse illuminates a target detection point on the tunnel wall. Dust scattering echoes are signals returned after a laser pulse is scattered by dust particles inside the tunnel during propagation. Digital signals are signals represented in discrete digital form after analog signals are converted to digital signals. Time series arrangement refers to data arranged in an orderly manner according to the order of sampling time. Sampling time point amplitude data refers to data values ​​obtained by measuring the signal strength at each sampling moment. Complete waveform data sequence refers to a dataset composed of amplitude data from multiple sampling time points arranged in a time series, which can completely reflect the changes in the echo signal.

[0035] Specifically, the system's data processing unit first receives the mixed echo signal returned by the laser sensor (the signal received by the laser sensor is usually an analog signal), and then converts the mixed echo signal into a digital signal. During the conversion process, the system's data processing unit samples the analog signal at fixed time intervals, and each sampling time point corresponds to an amplitude data point reflecting the signal strength. These amplitude data points from multiple sampling time points arranged in chronological order constitute a complete waveform data sequence. This complete waveform data sequence fully records the entire change process of the mixed echo signal from its generation to its disappearance, including both the target echo information and the interfering dust scattering echo information.

[0036] 102. The sampling points in the complete waveform data sequence whose slope values ​​exceed the preset slope threshold are determined as the set of candidate target starting points.

[0037] The slope value of the data represents the rate of change of the waveform at that position; the preset slope threshold refers to the set slope value standard used to determine whether the waveform has begun to show a significant upward change; the sampling point refers to the position and amplitude data corresponding to each sampling time point when the signal is sampled; the candidate target starting point set refers to the set of sampling points that may be the starting point of the target echo selected from the complete waveform data sequence.

[0038] Specifically, the system's data processing unit first performs a first-order derivative operation on the complete waveform data sequence. By calculating the rate of change of amplitude between adjacent sampling points, it obtains a slope data sequence that corresponds one-to-one with the complete waveform data sequence. For each sampling point in the slope data sequence, the system's data processing unit extracts its corresponding slope value and compares this slope value with a preset slope threshold. The slope value is calculated by subtracting the amplitude data of the previous sampling point from the amplitude data of the current sampling point, and then dividing by the time interval between the two sampling points, thus quantifying the rate of change of the waveform at that position. When the slope value of a sampling point exceeds the preset slope threshold, it indicates that the waveform at that position has entered a significant rising phase from a flat state, which matches the waveform characteristics at the beginning of the target echo (target echoes usually have a steep rising edge). The system's data processing unit then identifies this sampling point as a candidate target starting point, and all sampling points that meet the conditions together constitute the set of candidate target starting points.

[0039] 103. Taking the peak point of the waveform where each sampling point in the candidate target starting point set is located as the center, extract a waveform window data containing a preset number of sampling points, and calculate the skewness coefficient of the waveform window data. The skewness coefficient represents the symmetry of the data distribution.

[0040] The peak point of the waveform where the sampling point is located refers to the sampling point in the waveform segment where the amplitude data reaches its maximum value within the starting point of the candidate target; the preset number of sampling points refers to the number of sampling points that are pre-set to constitute the waveform window data; the waveform window data refers to a continuous amplitude data segment containing the preset number of sampling points, centered on the peak point; the skewness coefficient is a statistic used to measure the symmetry of the waveform window data distribution; data distribution symmetry refers to whether the data distribution on both sides of the average value is uniform and symmetrical. A symmetrical data distribution has a skewness coefficient close to zero, while an asymmetrical data distribution has a skewness coefficient that is not zero.

[0041] Specifically, the system's data processing unit first identifies the peak point of the waveform segment containing each sampling point in the candidate target starting point set—that is, the sampling point with the largest amplitude in that waveform segment. Then, using this peak point as the center, a certain number of sampling points are extracted on both sides before and after it, ensuring that the number of extracted data points meets the preset number of sampling points, thus forming a waveform window data. Next, the system's data processing unit uses statistical methods to calculate the skewness coefficient of this waveform window data. During the calculation, the degree and direction of deviation of each data point within the window from the average value are comprehensively considered. The skewness coefficient, which reflects the symmetry of the data distribution, is obtained to distinguish the waveform characteristics of the target echo signal and the dust scattering echo signal. This is because the waveform distribution symmetry of the target echo signal differs from that of the dust scattering echo signal; the waveform distribution of the target echo signal is more symmetrical, and the absolute value of the skewness coefficient is lower.

[0042] 104. All waveform windows whose absolute value of the skewness coefficient is less than the preset symmetry threshold are identified as suspected target waveform windows.

[0043] The absolute value refers to the distance from the point corresponding to a number on the number line to the origin, which is used here to eliminate the influence of the positive and negative signs of the skewness coefficient; the preset symmetry threshold is a pre-set critical value used to determine whether the distribution of waveform window data is close to symmetry; the waveform window refers to a continuous amplitude data segment containing a preset number of sampling points, centered on the peak point; the suspected target waveform window list refers to the set of all waveform windows that may contain the real target echo waveform after filtering.

[0044] Specifically, the system's data processing unit takes the absolute value of the skewness coefficient for each waveform window and compares it with a preset symmetry threshold. The magnitude of this preset symmetry threshold reflects the requirement for waveform symmetry. When the absolute value of the skewness coefficient for a waveform window is less than the preset symmetry threshold, it indicates that the data distribution within that window is relatively symmetrical and matches the characteristics of the target echo waveform. The system's data processing unit then includes this waveform window in the list of suspected target waveform windows, preparing for further screening of the actual target echo signal.

[0045] 105. Calculate the time width at half height of each waveform in the suspected target waveform window list, and determine the suspected target waveforms whose difference between the time width and the preset standard pulse width is within the preset error range as the real target echoes.

[0046] The half-height time width of a waveform refers to the time interval between two positions in the waveform window where the waveform amplitude reaches half of its peak value; the preset standard pulse width is a pre-set standard time width of the target echo signal formed by the reflection of the target after the laser pulse is emitted; the preset error range is a pre-set maximum range of allowable time width differences from the standard pulse width; the real target echo refers to the signal filtered from the mixed echo signal that is indeed reflected back after the laser pulse irradiates the tunnel wall or other targets.

[0047] Specifically, the system's data processing unit first determines the peak amplitude of the waveform corresponding to each waveform window in the suspected target waveform window list. Then, it finds two positions where the amplitude is half of the peak amplitude, i.e., the half-height positions. These two positions correspond to two different sampling time points. The system's data processing unit calculates the time interval between these two sampling time points to obtain the time width of the waveform at the half-height position. Next, the system's data processing unit compares this time width with a preset standard pulse width (the preset standard pulse width is determined based on the inherent parameters of the laser emitter and the normal propagation characteristics of the target echo). When the difference between the two is within a preset error range, it indicates that the pulse width characteristics of the suspected target waveform match the real target echo. The system's data processing unit then identifies it as the real target echo, thereby effectively eliminating dust scattering echo interference due to inconsistent pulse width characteristics.

[0048] 106. Based on the candidate target starting point corresponding to the real target echo and the time position of the candidate target starting point in the complete waveform data sequence, calculate the propagation time of the laser pulse, and calculate the target distance based on the propagation time.

[0049] Time position refers to the sampling time corresponding to the starting point of the candidate target in the complete waveform data sequence; laser pulse propagation time refers to the time it takes for the laser pulse to travel from emission to reception after being reflected by the target; target distance refers to the distance between the laser ranging sensor and the monitored target such as the tunnel wall.

[0050] Specifically, the system's data processing unit first determines the candidate target starting point corresponding to the real target echo. This starting point is the sampling point where the corresponding real target echo waveform begins to rise, as determined in step 102. Then, it obtains the time position of this candidate target starting point in the complete waveform data sequence, which is the specific sampling time corresponding to that sampling point. Since the laser pulse emission time is known, the system's data processing unit subtracts the laser pulse emission time from the time position of the candidate target starting point to obtain the laser pulse propagation time. Finally, based on the laser pulse's propagation speed in air (a known constant), the system's data processing unit calculates the distance between the laser sensor and the target detection point on the tunnel wall using the formula: distance equals speed multiplied by time. That is, target distance = laser propagation speed × propagation time.

[0051] 107. The difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel is determined as the local deformation of the tunnel.

[0052] Each monitoring point inside the tunnel refers to a specific location point pre-deployed on the inner arc surface of the tunnel detection section for monitoring tunnel deformation; the target distance refers to the distance between the laser ranging sensor and the corresponding monitoring point at the current monitoring time; the previous monitoring distance refers to the distance between the laser ranging sensor and the monitoring point obtained when the monitoring point was last monitored; the difference refers to the numerical difference between the target distance and the previous monitoring distance; the local deformation of the tunnel refers to the amount of local positional change of the tunnel wall at each monitoring point, used to reflect the deformation of the tunnel.

[0053] Specifically, the system's data processing unit compares the target distance obtained from the current monitoring with the previous monitoring distance for each monitoring point within the tunnel. The difference between the two distances—target distance minus the previous monitoring distance (a negative result indicates a decrease in distance, potentially suggesting inward deformation of the tunnel wall, i.e., settlement and convergence)—is determined as the local deformation of the tunnel at that monitoring point. The system's data processing unit records the local deformation at each monitoring point. By analyzing this data, the deformation of various parts of the tunnel can be monitored in a timely manner, providing a basis for tunnel structural safety assessment.

[0054] The laser-based tunnel deformation monitoring method described in this application achieves accurate monitoring of tunnel deformation in high-concentration dust environments through a multi-dimensional processing flow involving slope screening of candidate starting points, waveform window skew analysis, and pulse width verification of the mixed echo signal. In this method, the system's data processing unit first converts the mixed signal containing target echo and dust scattering echo into a complete waveform data sequence. Candidate target starting points are screened using a preset slope threshold. Then, a waveform window is truncated with the peak point as the center to calculate the skew coefficient, screening out suspected windows with symmetry matching the target characteristics. Finally, the actual target echo signal is locked by comparing the half-height time width with the standard pulse width, effectively eliminating signal interference caused by dust scattering. After calculating the propagation time and target distance based on the actual echo, the system's data processing unit determines the local deformation amount by comparing it with historical distances. Compared to traditional methods that suffer from misjudgment due to scattering, this method significantly reduces the false recognition rate and achieves accurate monitoring of tunnel deformation in high-dust tunnel environments.

[0055] Based on the above, the following is a more detailed description of the process provided in this implementation. Please refer to [link / reference]. Figure 2 This is another schematic diagram of the laser-based tunnel deformation monitoring method in the embodiments of this application.

[0056] 201. Receive the returned mixed echo signal and convert it into a digital signal to obtain a complete waveform data sequence arranged in time sequence, containing amplitude data from multiple sampling time points. The mixed echo signal includes target echo and dust scattering echo. (This step has been explained in 101 and will not be repeated here.)

[0057] 202. The sampling points in the complete waveform data sequence whose slope values ​​exceed a preset slope threshold are identified as the set of candidate target starting points. (This step has been explained in 102 and will not be repeated here.)

[0058] 203. For each sampling point in the set of candidate target starting points, determine a local maximum value in the complete waveform data sequence, and determine the location of the local maximum value as the waveform peak point.

[0059] The candidate target starting point set refers to the set of sampling points selected from the complete waveform data sequence that may be the starting point of the target echo; the complete waveform data sequence is a dataset containing amplitude data at multiple sampling time points arranged in a time series; the local maximum value refers to the amplitude data in the local waveform segment where the candidate target starting point is located, which is greater than the amplitude data of the adjacent sampling points before and after it; the waveform peak point refers to the sampling point where the local maximum value is located, that is, the sampling point with the largest amplitude in the local waveform segment.

[0060] Specifically, this step is performed after the candidate target starting point set is determined, in preparation for subsequent waveform window data extraction. The system's data processing unit analyzes the local waveform segment containing each sampling point in the complete waveform data sequence. By comparing the amplitude data of adjacent sampling points before and after the sampling point, it finds the local maximum value with the largest amplitude in that local waveform segment, and then determines the sampling point position corresponding to this local maximum value as the waveform peak point.

[0061] 204. Taking the time position of the peak point of the waveform as the center, determine whether the data points on both sides of the center are sufficient to form a window containing the preset number of sampling points.

[0062] The time position of the waveform peak point refers to the sampling time corresponding to the waveform peak point in the complete waveform data sequence; the data points on both sides of the center refer to the sampling points before and after the waveform peak point; the preset number of sampling points is the total number of sampling points used to form the waveform window data; the window refers to a waveform segment composed of a continuous data point intercepted with the peak point as the center.

[0063] Specifically, this step is performed after the waveform peak point is determined and before the waveform window data is extracted. The system's data processing unit uses the time position of the waveform peak point as the center, counts the number of data points before (earlier in time) and after (later in time) the peak point, and determines whether the sum of the data points on both sides can meet the requirement of the preset number of sampling points, that is, whether there are enough data points to form a complete waveform window, providing a basis for subsequent extraction of window data.

[0064] 205. If so, then extract a segment of waveform window data containing the preset number of sampling points to form a waveform window data sequence.

[0065] Here, if the number of data points on both sides of the center is sufficient to form a window containing a preset number of sampling points; the preset number of sampling points is the total number of sampling points of the waveform window data set in advance; the waveform window data is a continuous amplitude data segment containing a preset number of sampling points, centered on the peak point; the waveform window data sequence is a dataset composed of the intercepted continuous data points in chronological order.

[0066] Specifically, this step is performed when the result of step 204 is "yes". The system's data processing unit, according to the preset number of sampling points, takes the time position of the waveform peak point as the center and evenly extracts a corresponding number of data points in front of and behind the peak point (for example, if the preset number of sampling points is 50, then 25 data points are extracted in front of the peak point and 25 data points are extracted behind the peak point). These continuous data points are combined to form a waveform window data sequence, ensuring that the window data can completely cover the key waveform features around the peak point.

[0067] 206. If not, then take the time position of the peak point of the waveform as the center, extend to both sides of the center, and extract the waveform window data containing all data points of the waveform where the sampling point is located to form a waveform window data sequence.

[0068] If not, it means that the number of data points on both sides of the center is insufficient to form a window containing the preset number of sampling points; the time position of the waveform peak point is the sampling time corresponding to the waveform peak point; extending to both sides of the center means expanding the interception range as much as possible from the peak point in the forward and backward directions; all data points of the waveform where the sampling point is located refer to all sampling points of the complete waveform segment corresponding to the starting point of the candidate target; the waveform window data sequence is a dataset composed of all intercepted data points in chronological order.

[0069] Specifically, this step is performed when the result of step 204 is "no". The system's data processing unit extends the truncation range forward and backward as much as possible, centered on the time position of the waveform peak point, until it includes all data points of the waveform segment where the candidate target's starting point is located. These data points are then combined to form a waveform window data sequence. In this case, although the number of data points contained in the waveform window is less than the preset number of sampling points, it ensures that all features of the waveform segment are completely preserved, avoiding the loss of key waveform information due to insufficient data.

[0070] 207. Calculate the arithmetic mean and standard deviation of the waveform window data sequence, and calculate the skewness coefficient of the waveform window data based on the arithmetic mean and standard deviation.

[0071] The arithmetic mean is the average of the sum of all amplitude data in the waveform window data sequence divided by the number of data points, used to reflect the central tendency of the data; the standard deviation is the square root of the average of the squares of the deviations of each amplitude data point from the arithmetic mean, used to measure the dispersion of the data; the skewness coefficient is a statistic calculated based on the arithmetic mean and standard deviation, used to measure the symmetry of the waveform window data distribution. The skewness coefficient of a symmetrical distribution is close to 0, a right-skewed distribution is positive, and a left-skewed distribution is negative.

[0072] Specifically, this step is performed after the waveform window data sequence is formed and before the suspected target waveform window is screened. It is a key feature extraction step to distinguish between target echo and dust scattering echo. The system's data processing unit first calculates the arithmetic mean of the waveform window data sequence, that is, summing all amplitude data in the window and dividing by the number of data points. For example, if the window data is [2, 3, 5, 7, 6, 4, 3], the sum is 30 and the arithmetic mean is 4.29. Then, the standard deviation is calculated. First, the difference between each data point and the mean is calculated (-2.29, -1.29, 0.71, 2.71, 1.71, -0.29, -1.29). Then, the sum of squares of the differences is calculated (5.24+1.66+0.50+7.34+2.92+0.08+1.66≈19.40). Dividing by the number of data points minus 1 (6) gives the variance as 3.23. The standard deviation is the square root of the variance. The skewness coefficient is approximately 1.79. The formula is the third central moment divided by the cube of the standard deviation. The third central moment is the sum of the cubes of the differences divided by the number of data points ([(-2.29)³ + (-1.29)³ + 0.71³ + 2.71³ + 1.71³ + (-0.29)³ + (-1.29)³] ÷ 7 ≈ 1.31). Therefore, the skewness coefficient is 1.31 ÷ (1.79³) ≈ 0.23. The skewness coefficient obtained through this calculation can be used to determine the waveform symmetry. Target echo signals, due to their stable reflection, typically have a smaller absolute value for the skewness coefficient, while dust scattering echoes have poor symmetry and a larger absolute value, thus achieving preliminary screening.

[0073] 208. All waveform windows whose absolute value of the skewness coefficient is less than the preset symmetry threshold are identified as suspected target waveform windows.

[0074] This step has been described in 104, where the process of determining the preset symmetry threshold is steps 2081 to 2083:

[0075] 2081. Merge the signal data of all background noise regions in the complete waveform data sequence into a dataset, and calculate the signal standard deviation of the dataset. The background noise region is the signal reception time period in the complete waveform data sequence that corresponds to the laser pulse round-trip distance being less than the preset minimum effective distance and greater than the preset maximum effective distance.

[0076] The background noise region refers to the signal reception time period in the complete waveform data sequence corresponding to the laser pulse round-trip distance being less than the preset minimum effective range and greater than the preset maximum effective range. The signal in this background noise region is mainly environmental noise rather than effective target echo or dust scattering echo. Signal data refers to the amplitude data at each sampling time point collected in the background noise region. The dataset refers to the collection formed by summarizing the signal data of all background noise regions. The signal standard deviation is a statistic used to measure the dispersion of signal data in the dataset, reflecting the fluctuation of background noise.

[0077] Specifically, the main purpose of this step is to provide a noise reference benchmark for subsequently determining the preset symmetry threshold. The system's data processing unit first delineates the background noise region within the complete waveform data sequence. This region is defined based on the round-trip distance of the laser pulse: when the round-trip distance is less than the preset minimum effective range, the laser has not yet reached any effective target; when the round-trip distance is greater than the preset maximum effective range, the laser has exceeded the monitoring range, and the signals in both of these regions are considered background noise. Next, the system's data processing unit merges all signal data within these two regions into a single dataset, and then quantifies the dispersion of the background noise by calculating the standard deviation of this dataset. For example, if the signal data in the background noise region is [1.2, 1.1, 1.3, 1.0, 1.4], then its standard deviation is approximately 0.16, reflecting the magnitude of the noise signal fluctuation.

[0078] 2082. Extract the peak amplitude of the waveform window data and calculate the ratio of the peak amplitude to the standard deviation of the signal to obtain the signal-to-noise ratio of the waveform window data.

[0079] Peak amplitude refers to the data value with the largest amplitude in the waveform window data, that is, the amplitude corresponding to the peak point of the waveform; the ratio is the value obtained by dividing the peak amplitude by the signal standard deviation; the signal-to-noise ratio is an indicator used to measure the comparison between the intensity of the effective signal (peak amplitude) and the background noise (signal standard deviation) in the waveform window data. The higher the value, the more prominent the effective signal is.

[0080] Specifically, this step quantifies the ratio of effective signal to noise within a waveform window. The system's data processing unit first extracts the peak amplitude from each waveform window's data, i.e., the largest amplitude data within that window. Then, it divides this peak amplitude by the signal standard deviation to obtain the signal-to-noise ratio (SNR) of the waveform window's data. For example, if the peak amplitude of a waveform window is 5.0 and the signal standard deviation is 0.16, then the SNR is 5.0 ÷ 0.16 = 31.25. The higher this value, the less noise interference the signal within that window experiences, and the more prominent the effective signal.

[0081] 2083. Based on the signal-to-noise ratio, the preset symmetry threshold is determined by a preset mapping function, which makes the preset symmetry threshold inversely proportional to the signal-to-noise ratio.

[0082] The preset mapping function is a pre-defined mathematical function used to convert the signal-to-noise ratio into a preset symmetry threshold; the preset symmetry threshold is a critical value used to determine whether the distribution of waveform window data is close to symmetry; the inverse relationship means that when the signal-to-noise ratio increases, the preset symmetry threshold decreases; when the signal-to-noise ratio decreases, the preset symmetry threshold increases.

[0083] Specifically, this step dynamically adjusts the preset symmetry threshold to adapt to different noise environments. The system's data processing unit calculates the preset symmetry threshold by substituting the signal-to-noise ratio (SNR) into a preset mapping function. Since the mapping function has an inverse relationship, when the SNR is high (strong effective signal, weak noise), the preset symmetry threshold decreases, imposing stricter requirements on waveform symmetry to ensure the selection of high-quality target echo signals; when the SNR is low (weak effective signal, strong noise), the preset symmetry threshold increases, appropriately relaxing the requirements on waveform symmetry to avoid missing effective target echo signals. For example, if the mapping function used to determine the preset symmetry threshold is 5 / SNR, when the SNR is 10, the preset symmetry threshold is 0.5; when the SNR is 5, the preset symmetry threshold is 1.0. This dynamic adjustment improves the accuracy of selection under different noise environments.

[0084] 209. Calculate the time width at half-height of each waveform in the suspected target waveform window list, and identify suspected target waveforms whose difference between the time width and the preset standard pulse width is within a preset error range as true target echoes. (This step has been explained in 105 and will not be repeated here.)

[0085] 210. Based on the candidate target starting point corresponding to the real target echo and the time position of the candidate target starting point in the complete waveform data sequence, calculate the propagation time of the laser pulse, and calculate the target distance based on the propagation time.

[0086] The method specifically includes steps 2101 to 2103, which are not shown in the figure.

[0087] 2101. Extract the timestamp of the candidate target starting point corresponding to the real target echo from the complete waveform data sequence.

[0088] The true target echo refers to the signal that is indeed reflected back after the target in the tunnel wall is irradiated by the laser, selected from the suspected target waveform window; the candidate target starting point refers to the sampling point in the complete waveform data sequence whose slope value exceeds the preset slope threshold and is initially determined to be the starting point of the target echo; the timestamp refers to the specific sampling time corresponding to the candidate target starting point in the complete waveform data sequence, used to mark the time position of the candidate target starting point.

[0089] Specifically, the system's data processing unit first locates the confirmed real target echo in the complete waveform data sequence, then finds the candidate target starting point corresponding to the real target echo, and then extracts the timestamp corresponding to the candidate target starting point from the time record of the complete waveform data sequence. For example, if the candidate target starting point is the 100th sampling point and the sampling interval is 1 nanosecond, then its timestamp is 100 nanoseconds, providing an accurate time basis for subsequent calculation of propagation time.

[0090] 2102. Obtain the laser pulse emission reference time that is synchronized with the mixed echo signal in time, and calculate the round-trip propagation time of the laser pulse by combining it with the timestamp of the candidate target's starting point.

[0091] The hybrid echo signal refers to a laser reflection signal that includes both target echo and dust scattering echo; temporal synchronization means that the laser pulse emission reference time and the hybrid echo signal reception time are based on the same time standard, ensuring consistency in time calculation; the laser pulse emission reference time refers to the precise time point at which the laser transmitter emits the laser pulse; the timestamp of the candidate target starting point is the starting sampling point time corresponding to the real target echo extracted in step 2101; the round-trip propagation time refers to the total time that the laser pulse takes from emission to reception after being reflected by the target.

[0092] Specifically, the system's data processing unit first acquires the laser pulse's emission reference time, which is synchronized with the sampling time of the mixed echo signal. Then, it subtracts the laser pulse's emission reference time from the candidate target's starting point's timestamp; the difference is the laser pulse's round-trip propagation time. For example, if the emission reference time is 0 nanoseconds and the candidate target's starting point timestamp is 200 nanoseconds, the round-trip propagation time is 200 nanoseconds. This time includes the entire duration from laser emission to reaching the target and then reflecting back to the receiving device.

[0093] 2103. Calculate the target distance by combining the round-trip propagation time and the speed of light.

[0094] The speed of light refers to the speed at which a laser beam travels through air, and is a known constant (approximately 3 × 10⁻⁶). 8 (m / s); target distance refers to the straight-line distance between the laser sensor and the monitoring point on the tunnel wall, that is, the actual distance from the monitoring point on the tunnel wall to the laser sensor.

[0095] Specifically, the system's data processing unit calculates the distance using the physical relationship between laser propagation distance and time: since the round-trip propagation time is the total time for the laser pulse to reach the monitoring point on the tunnel wall and return, the one-way propagation time is half of the round-trip propagation time. Multiplying the one-way propagation time by the speed of light yields the target distance. For example, if the round-trip propagation time is 200 nanoseconds (i.e., 2 × 10⁻⁶), the distance is calculated. -7 If the time is 1 second, then the one-way time is 1×10 -7 Seconds, target distance = 3 × 10 8 ×1×10 -7 =30 meters. This calculation yields the actual distance between the laser sensor and the monitoring point on the tunnel wall, providing basic data for subsequent deformation calculations.

[0096] 211. Calculate the difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel to obtain a preliminary displacement dataset.

[0097] Each monitoring point inside the tunnel refers to a specific location point pre-deployed on the inner arc surface of the tunnel detection section for monitoring tunnel deformation; the previous monitoring distance refers to the distance between the laser sensor and the monitoring point on the tunnel wall calculated using the same method when the monitoring point was last monitored; the preliminary displacement dataset refers to the set formed by summarizing the differences of all monitoring points inside the tunnel, which contains the initial deformation data of each tunnel wall monitoring point.

[0098] Specifically, the system's data processing unit, for each pre-defined monitoring point within the tunnel, retrieves the current target distance data and the stored previous monitoring distance data for that point. The difference between the two is calculated through subtraction, representing the initial displacement of each monitoring point. For example, if a monitoring point's current target distance is 50.2 meters and its previous monitoring distance was 50.0 meters, its initial displacement is 0.2 meters; another monitoring point's current target distance is 45.1 meters and its previous monitoring distance was 45.3 meters, resulting in an initial displacement of -0.2 meters. The system's data processing unit collects the initial displacement values ​​from all monitoring points, forming a preliminary displacement dataset to prepare for subsequent elimination of common errors.

[0099] 212. Calculate the median of the preliminary displacement dataset and determine the median as the universal offset.

[0100] The median is the value in the middle position after all the data in the preliminary displacement dataset are arranged in order of size. If the number of data is even, the average of the two middle values ​​is taken to reflect the central tendency of the data. The general offset is the median in the preliminary displacement dataset, which represents the common offset that may exist in all monitoring points.

[0101] Specifically, the system's data processing unit first arranges all data in the preliminary displacement dataset in ascending (or descending) order, and then determines the median based on the number of data points: if the number of data points is odd, the middle value is taken directly; if it is even, the arithmetic mean of the two middle values ​​is taken. For example, if the preliminary displacement dataset is [-0.3, -0.2, 0.1, 0.2, 0.3], and the middle value after sorting is 0.1, then the median is 0.1; if the dataset is [-0.4, -0.1, 0.2, 0.5], and the two middle values ​​are -0.1 and 0.2, then the median is (-0.1 + 0.2) ÷ 2 = 0.05. The system's data processing unit determines this median as the universal offset, which reflects the systematic changes common to all monitoring points, rather than the actual deformation of a local area within the tunnel.

[0102] 213. The difference between the preliminary displacement of each monitoring point and the general offset is determined as the local deformation of the tunnel at the corresponding monitoring point.

[0103] The difference refers to the value obtained by subtracting the general offset from the initial displacement of each monitoring point; the local deformation of the tunnel refers to the value obtained after eliminating the general offset, which can reflect the actual local position change of the tunnel wall at the monitoring point.

[0104] Specifically, the system's data processing unit subtracts the general offset from the initial displacement of each monitoring point within the tunnel, and the result is the local deformation of the tunnel at that monitoring point. For example, if the initial displacement of a monitoring point is 0.2 meters and the general offset is 0.1 meters, then its local tunnel deformation is 0.2 - 0.1 = 0.1 meters; if another monitoring point has an initial displacement of -0.2 meters and a general offset of 0.1 meters, then its local tunnel deformation is -0.2 - 0.1 = -0.3 meters. This method, by subtracting the general offset, eliminates the error caused by the overall offset of the laser sensor, making the calculation results more accurately reflect the true deformation at various local locations within the tunnel, and providing reliable data for tunnel safety assessment.

[0105] After step 213, there are steps 214 to 217, which are not shown in the figure.

[0106] 214. Compare the local deformation of the tunnel with the preset safety range to determine whether the local deformation of the tunnel exceeds the preset safety range.

[0107] The preset safety range refers to the maximum threshold range of local deformation of the tunnel that is allowed to be set in advance according to the tunnel engineering design standards, safety specifications and operation requirements. It is usually an interval that includes upper and lower limits. The comparison is to compare the local deformation of the tunnel at each monitoring point with the upper and lower limits of the preset safety range. Exceeding the preset safety range means that the local deformation of the tunnel is greater than the upper limit of the preset safety range or less than the lower limit of the preset safety range, indicating that there may be a safety hazard at the monitoring point.

[0108] Specifically, the system's data processing unit compares the local deformation of the tunnel at each monitoring point with a preset safety range. The preset safety range can be set, for example, to [-0.05 meters, 0.05 meters], indicating that a local deformation between -0.05 meters and 0.05 meters is considered safe. The system's data processing unit determines whether the deformation at each monitoring point is within this range. If the deformation at a monitoring point is 0.06 meters, exceeding the upper limit of 0.05 meters, it is determined to be outside the safety range; if the deformation is -0.03 meters, within the range, it is determined to be within the safety range. Through this judgment, monitoring point data that may pose a safety risk are filtered out.

[0109] 215. If so, analyze the hazard level of the data that exceeds the preset safety range, and generate an early warning signal based on the hazard level.

[0110] If it indicates that the local deformation of the tunnel exceeds the preset safety range; the data exceeding the preset safety range refers to the deformation data corresponding to the monitoring point where the local deformation of the tunnel is not within the preset safety range; the hazard level refers to the risk level classified according to factors such as the degree of exceeding the safety range, the deformation rate, and the importance of the location of the monitoring point in the tunnel, and is divided into three different levels: slight, moderate, and severe; the warning signal is a signal generated based on the hazard level to indicate that there are safety hazards in the tunnel, and includes key information such as the hazard level, the location of the monitoring point exceeding the standard, and the amount of deformation.

[0111] Specifically, the system's data processing unit analyzes all deformation data exceeding the safe range and, based on preset hazard level classification standards (e.g., deformation exceeding the upper limit by 0.05-0.1 meters is considered minor, 0.1-0.2 meters is moderate, and greater than 0.2 meters is severe), determines the hazard level of each exceeding monitoring point. It also considers whether the monitoring point is located in a weak area of ​​the tunnel structure (such as the arch or arch waist), appropriately increasing the hazard level for exceeding data at critical locations. Next, the system's data processing unit generates corresponding warning signals based on the determined hazard level; for example, a red warning signal corresponds to a severe level, a yellow warning signal to a moderate level, and a green warning signal to a minor level.

[0112] 216. Transmit the warning signal to the cloud, generate a real-time monitoring report, and continue monitoring.

[0113] The cloud refers to a remote data storage and processing platform used for centralized management of monitoring data and reports; real-time monitoring and detection reports refer to reports generated based on early warning signals and data exceeding the standard that are no longer within the safe range, reflecting the current safety status of the tunnel, including details of the monitoring points exceeding the standard, hazard level assessment, and recommended measures; continued monitoring means that after the early warning and report generation are completed, the system continues to monitor the tunnel deformation at the original time intervals to track the deformation trend.

[0114] Specifically, the system's data processing unit transmits the early warning signal to the cloud. Upon receiving the signal, the cloud integrates the corresponding data from monitoring points exceeding the standard, the hazard level, and basic tunnel information to generate a real-time monitoring report. The report clearly indicates the location of the exceeding standard, the amount of deformation, the hazard level, and the potential impact. Simultaneously, the system's data processing unit does not halt the monitoring process but continues to monitor the tunnel at a preset frequency (or increases the frequency based on the hazard level, such as changing from once per hour to once every 30 minutes) to continuously track whether the deformation further develops, ensuring timely understanding of changes in the safety situation.

[0115] 217. If not, upload the local deformation of the tunnel at each monitoring point to the cloud, generate a periodic monitoring and testing report according to the preset cycle, and continue monitoring.

[0116] If no, it indicates that the local deformation of the tunnel has not exceeded the preset safety range; the local deformation of the tunnel at each monitoring point refers to the deformation data of all monitoring points, including normal data within the safety range; the preset period refers to the time interval for generating periodic monitoring reports, such as daily, weekly, or monthly; the periodic monitoring and detection report refers to a report generated according to the preset period that summarizes the tunnel deformation over a period of time, including statistical information such as deformation trends, average values, and maximum values ​​at each monitoring point.

[0117] Specifically, the system's data processing unit compiles and summarizes the local deformation data of the tunnel from all monitoring points and uploads it to the cloud platform for storage, ensuring data traceability. The cloud platform performs statistical analysis on the uploaded historical data according to a preset cycle (e.g., daily), generating periodic monitoring reports. These reports display the deformation distribution at each monitoring point, deformation trends over a period of time (e.g., whether it is stable, whether there is any minor cumulative deformation), and other information. Simultaneously, the system's data processing unit continues to monitor the tunnel according to the original monitoring cycle to ensure timely detection of potential deformation problems.

[0118] The laser-based tunnel deformation monitoring method described in this application utilizes the differences in slope, symmetry, and time width between the target echo and the dust scattering echo. The system's data processing unit first converts the mixed signal containing both the target echo and dust scattering echo signals into a complete waveform data sequence. Candidate target starting points are selected based on waveform slope. A waveform window is then truncated centered on the peak point, and a skewness coefficient reflecting the symmetry of the data distribution is calculated. This coefficient, combined with a preset symmetry threshold dynamically generated based on the signal-to-noise ratio, filters out suspected target waveforms that meet symmetry characteristics. Subsequently, the actual target echo is further determined by comparing the half-height time width with the standard pulse width, effectively eliminating dust scattering interference. In target distance calculation, the target distance is accurately obtained based on the timestamp of the actual echo signal and the emission reference time, combined with the speed of light. Systematic errors are eliminated by subtracting a general offset from the difference between the current and historical distances, yielding the actual local tunnel deformation. The system's processing unit compares the local deformation of the tunnel with a preset safety range. For data exceeding the limit, it generates early warning signals based on the hazard level and uploads them to the cloud to generate real-time reports. For data within the limit, it uploads periodic reports for continuous monitoring. This approach, through multi-dimensional signal filtering, adaptive threshold adjustment, and error elimination, improves the accuracy of tunnel deformation monitoring. Furthermore, through tiered early warning and cloud-based data management, it achieves timely response to safety risks and efficient control of continuous monitoring, providing reliable protection for tunnel construction and operation safety.

[0119] The method provided in the above embodiments can be executed by the data processing unit of the tunnel deformation monitoring system. The tunnel deformation monitoring system in the embodiments of this invention is described below from a hardware processing perspective; please refer to [link to relevant documentation]. Figure 3This is a schematic diagram of the physical device structure of a tunnel deformation monitoring system in this application embodiment.

[0120] It should be noted that, Figure 3 The structure of the tunnel deformation monitoring system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0121] like Figure 3 As shown, the tunnel deformation monitoring system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0122] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0123] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0124] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0126] Specifically, the tunnel deformation monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the tunnel deformation monitoring system provided in the above embodiment.

[0127] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the tunnel deformation monitoring system described in the above embodiments; or it may exist independently and not assembled into the tunnel deformation monitoring system. The storage medium carries one or more computer programs, which, when executed by a processor of the tunnel deformation monitoring system, cause the tunnel deformation monitoring system to implement the tunnel deformation monitoring system provided in the above embodiments.

[0128] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0129] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0130] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0131] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A laser-based method for monitoring tunnel deformation, characterized in that, include: The returned mixed echo signal is received and converted into a digital signal to obtain a complete waveform data sequence containing amplitude data at multiple sampling time points, arranged in a time sequence. The mixed echo signal includes target echo and dust scattering echo. The sampling points in the complete waveform data sequence whose slope values ​​exceed a preset slope threshold are identified as the set of candidate target starting points; For each sampling point in the set of candidate target starting points, a local maximum value is determined in the complete waveform data sequence, and the location of the local maximum value is determined as the waveform peak point. Using the time position of the waveform peak point as the center, determine whether the data points on both sides of the center are sufficient to form a window containing the preset number of sampling points; If so, a segment of waveform window data containing a preset number of sampling points is extracted to form a waveform window data sequence; If not, then take the time position of the waveform peak point as the center, extend to both sides of the center, and extract waveform window data containing all data points of the waveform where the sampling point is located to form a waveform window data sequence; Calculate the arithmetic mean and standard deviation of the waveform window data sequence, and calculate the skewness coefficient of the waveform window data based on the arithmetic mean and standard deviation; Waveform windows whose absolute values ​​of all the aforementioned skewness coefficients are less than a preset symmetry threshold are identified as a list of suspected target waveform windows. Calculate the time width at half-height position of each waveform in the suspected target waveform window list, and determine the suspected target waveforms whose difference between the time width and the preset standard pulse width is within a preset error range as the real target echoes; Based on the candidate target starting point corresponding to the real target echo and the time position of the candidate target starting point in the complete waveform data sequence, the propagation time of the laser pulse is calculated, and the target distance is calculated based on the propagation time. The difference between the target distance and the previous monitoring distance at each monitoring point inside the tunnel is determined as the local deformation of the tunnel.

2. The method according to claim 1, characterized in that, The process of determining the preset symmetry threshold specifically includes: merging the signal data of all background noise regions in the complete waveform data sequence into a dataset, and calculating the signal standard deviation of the dataset. The background noise region is the signal reception time period in the complete waveform data sequence that corresponds to the laser pulse round-trip distance being less than the preset minimum effective distance and greater than the preset maximum effective distance. Extract the peak amplitude of the waveform window data and calculate the ratio of the peak amplitude to the signal standard deviation to obtain the signal-to-noise ratio of the waveform window data; Based on the signal-to-noise ratio, a preset symmetry threshold is determined by a preset mapping function, wherein the preset symmetry threshold is inversely proportional to the signal-to-noise ratio.

3. The method according to claim 1, characterized in that, Based on the candidate target starting point corresponding to the real target echo and the time position of the candidate target starting point in the complete waveform data sequence, the propagation time of the laser pulse is calculated, and the target distance is calculated based on the propagation time, specifically including: Extract the timestamp of the candidate target start point corresponding to the real target echo from the complete waveform data sequence; Obtain the laser pulse emission reference time that is time-synchronized with the mixed echo signal, and calculate the round-trip propagation time of the laser pulse by combining it with the timestamp of the candidate target starting point; The distance to the target is calculated by combining the round-trip propagation time and the speed of light.

4. The method according to claim 1, characterized in that, The step of determining the difference between the target distance and the previous monitoring distance at each monitoring point within the tunnel as the local deformation of the tunnel specifically includes: Calculate the difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel to obtain a preliminary displacement dataset; Calculate the median of the preliminary displacement dataset and determine the median as the universal offset; The difference between the preliminary displacement of each monitoring point and the general offset is determined as the local deformation of the tunnel at the corresponding monitoring point.

5. The method according to claim 1, characterized in that, After determining the difference between the target distance and the previous monitoring distance at each monitoring point in the tunnel as the local deformation of the tunnel, the method further includes: The local deformation of the tunnel is compared with a preset safety range to determine whether the local deformation of the tunnel exceeds the preset safety range. If so, analyze the hazard level of the data that exceeds the preset safety range, and generate a warning signal based on the hazard level; The warning signal is transmitted to the cloud to generate a real-time monitoring report, and monitoring continues. If not, the local deformation of the tunnel at each monitoring point will be uploaded to the cloud, a periodic monitoring and testing report will be generated according to the preset cycle, and monitoring will continue.

6. A tunnel deformation monitoring system, characterized in that, Includes one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors calling the computer instructions to cause the tunnel deformation monitoring system to perform the method as described in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the tunnel deformation monitoring system, the tunnel deformation monitoring system performs the method as described in any one of claims 1-5.

8. A computer program product, characterized in that, When the computer program product is run on the tunnel deformation monitoring system, the tunnel deformation monitoring system performs the method as described in any one of claims 1-5.

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