Method and system for dynamically measuring deformation of tunnel structure

Abstract

The invention discloses a tunnel structure deformation dynamic measurement method and system, and relates to the technical field of data measurement, and the method comprises the steps: obtaining a reference axis according to the head end and the tail end of a tunnel, obtaining a to-be-measured region and a to-be-processed reference point, and obtaining a distance data sequence, comparing the distance data sequences of the to-be-processed reference point to obtain an associated fluctuation parameter of the to-be-processed reference point; obtaining the reference point to be processed with the minimum associated fluctuation parameter as a target reference point, and obtaining the dynamic measurement interval duration of the target reference point between the adjacent measurement time points in the current measurement period according to the associated fluctuation parameter of the target reference point; and obtaining a measurement strategy of the to-be-measured area based on the target reference point and the corresponding dynamic measurement interval duration, and completing deformation measurement of the to-be-measured area. The method has the advantages of stable and reliable measurement, optimized resource efficiency and dynamic adjustment of measurement frequency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data measurement, in particular to a tunnel structure deformation dynamic measurement method and system. BACKGROUND

[0002] In the field of tunnel engineering structure health monitoring, especially for long-term deformation monitoring of tunnel sections susceptible to geological activities or construction disturbances, existing monitoring methods usually adopt a static measurement strategy with a fixed frequency, which raises some problems.

[0003] Specifically, a fixed measurement interval is difficult to adapt to the dynamic changes in tunnel deformation rate. When sudden rock layer displacement or external vibration (such as adjacent construction, traffic load) causes accelerated deformation, low-frequency measurement will miss the key deformation inflection point, increasing the risk of structural instability. Conversely, high-frequency measurement during the stable deformation period results in resource waste. At the same time, existing methods rely on a single reference point or ignore the differences in different reference points due to local environmental interference (such as uneven rock microseismicity and equipment vibration). If the selected reference point itself produces abnormal fluctuations due to disturbances (such as instrument micro-displacement and local rock loosening), the reference axis distance data provided by the reference point will contain noise of non-true structural deformation, resulting in a decrease in the overall credibility of the multi-point measurement results based on this data. In addition, the static strategy cannot respond in real time to changes in the deformation trend, and the adjustment of the measurement interval relies on human experience, lacking a data-driven mechanism, resulting in a lag in risk warning. SUMMARY

[0004] To solve the technical problem in the prior art that it is difficult to efficiently capture true, continuous and small structural deformation information under the premise of ensuring data reliability, the present application provides a tunnel structure deformation dynamic measurement method and system, which can automatically identify stable reference points, dynamically optimize measurement frequency and effectively suppress local interference.

[0005] A tunnel structure deformation dynamic measurement method, comprising: obtaining a reference axis according to the first end and the tail end of a tunnel, and obtaining a to-be-measured region in the tunnel and a plurality of to-be-processed reference points arranged in the to-be-measured region, and obtaining distance data sequences between each to-be-processed reference point and the reference axis in a previous measurement period before a current measurement period, wherein the distance data sequences are formed by a plurality of distance values obtained at different measurement time points in the measurement period; comparing the distance data sequences of each to-be-processed reference point with each other and obtaining correlation fluctuation parameters of each to-be-processed reference point; obtaining a to-be-processed reference point with the smallest correlation fluctuation parameter as a target reference point, and obtaining a dynamic measurement interval duration between each adjacent measurement time point in the current measurement period according to the correlation fluctuation parameter of the target reference point; obtaining a measurement strategy of the to-be-measured region based on the target reference point and the corresponding dynamic measurement interval duration, and completing deformation measurement of the to-be-measured region.

[0006] Optionally, the comparing the distance data sequence of each to-be-processed reference point with each other and obtaining the correlation fluctuation parameter of each to-be-processed reference point comprises: obtaining a difference between a next distance value and a previous distance value of adjacent distance values in the distance data sequence of each to-be-processed reference point, and dividing the difference by the previous distance value to obtain a change ratio in a time period corresponding to the adjacent distance values; and obtaining the correlation fluctuation parameter of each to-be-processed reference point according to the change ratios of all to-be-processed reference points in each time period.

[0007] Optionally, the obtaining the correlation fluctuation parameter of each to-be-processed reference point according to the change ratios of all to-be-processed reference points in each time period comprises: obtaining an average value of the change ratios of all to-be-processed reference points in a same time period and recording the average value as a reference ratio in the time period; obtaining an absolute value of a difference between the change ratio of the i-th to-be-processed reference point in each time period and the reference ratio and recording the absolute value as an absolute deviation; obtaining a number of absolute deviations of the i-th to-be-processed reference point that exceed a preset deviation threshold and recording the number as a fluctuation number; and dividing the fluctuation number by a total number of absolute deviations of the i-th to-be-processed reference point to obtain the correlation fluctuation parameter of the i-th to-be-processed reference point.

[0008] Optionally, the obtaining the dynamic measurement interval duration between each adjacent measurement time point of the target reference point in the current measurement period according to the correlation fluctuation parameter of the target reference point comprises: obtaining a distance value at an i-2-th measurement time point and a distance value at an i-1-th measurement time point of the target reference point in the current measurement period, and taking an absolute value of a difference between the distance value at the i-1-th measurement time point and the distance value at the i-2-th measurement time point as a real-time change amount of an i-th measurement time point, wherein i is a positive integer and i≥3; obtaining a reference measurement interval according to the correlation fluctuation parameter of the target reference point; and correcting the reference measurement interval according to the real-time change amount of the i-th measurement time point to obtain a dynamic measurement interval duration between the i-th measurement time point and an i+1-th measurement time point.

[0009] Optionally, the process of correcting the baseline measurement interval based on the real-time change at the i-th measurement time point and obtaining the dynamic measurement interval duration between the i-th and (i+1)-th measurement time points includes: obtaining a preset change threshold; if the real-time change at the i-th measurement time point exceeds the preset change threshold, then dividing the difference between the real-time change at the i-th measurement time point and the preset change threshold by the preset change threshold, and using this difference as the processing ratio for the i-th measurement time point, and setting the maximum value of the processing ratio to 1; if the real-time change at the i-th measurement time point does not exceed the preset change threshold, then using 0 as the processing ratio for the i-th measurement time point; multiplying half of the baseline measurement interval by the processing ratio for the i-th measurement time point to obtain the correction amount for the i-th measurement time point; subtracting the correction amount for the i-th measurement time point from the baseline measurement interval to obtain the dynamic measurement interval duration between the i-th and (i+1)-th measurement time points.

[0010] Optionally, the measurement strategy for obtaining the area to be measured based on the target reference point and the corresponding dynamic measurement interval includes: determining the next measurement time point according to the dynamic measurement interval of the target reference point, and updating the dynamic measurement interval in real time after each measurement of the target reference point; and generating the deformation measurement result of the area to be measured after the measurement is executed.

[0011] A dynamic measurement system for tunnel structure deformation is also provided. The system includes: an acquisition module, used to acquire a reference axis based on the tunnel's beginning and end, acquire the area to be measured within the tunnel and multiple reference points to be processed set within the area to be measured, and acquire distance data sequences between each reference point to be processed and the reference axis in the previous measurement cycle, wherein the distance data sequence is formed by sequentially arranging multiple distance values ​​acquired at different measurement time points within the measurement cycle; a first measurement processing module, used to compare the distance data sequences of each reference point to be processed and acquire the associated fluctuation parameters of each reference point to be processed; a second measurement processing module, used to acquire the reference point to be processed with the smallest associated fluctuation parameter as the target reference point, and acquire the dynamic measurement interval duration between each adjacent measurement time point of the target reference point within the current measurement cycle based on the associated fluctuation parameter of the target reference point; and a target measurement module, used to acquire the measurement strategy for the area to be measured based on the target reference point and the corresponding dynamic measurement interval duration, and complete the deformation measurement of the area to be measured.

[0012] Optionally, the first measurement processing module is further configured to: obtain the difference between the next distance value and the previous distance value in the distance data sequence of each reference point to be processed, and divide the difference by the previous distance value to obtain the change ratio of the adjacent distance value in the time period; and obtain the associated fluctuation parameters of each reference point to be processed according to the change ratio of all reference points to be processed in each time period.

[0013] Optionally, the first measurement processing module is further configured to: obtain the average value of the change ratio of all reference points to be processed in the same time period and record it as the reference ratio in that time period; obtain the absolute value of the difference between the change ratio of the i-th reference point to be processed and the reference ratio in each time period and record it as the absolute deviation; obtain the number of absolute deviations of the i-th reference point to be processed that exceed the preset deviation threshold and record it as the fluctuation number; divide the fluctuation number by the number of absolute deviations of the i-th reference point to be processed and obtain the associated fluctuation parameter of the i-th reference point to be processed.

[0014] Optionally, the second measurement processing module is further configured to: obtain the distance value of the target reference point at the (i-2)th measurement time point and the distance value at the (i-1)th measurement time point within the current measurement cycle, and use the absolute value of the difference between the distance value at the (i-1)th measurement time point and the distance value at the (i-2)th measurement time point as the real-time change at the i-th measurement time point, where i is a positive integer and i≥3; obtain the reference measurement interval according to the associated fluctuation parameters of the target reference point; correct the reference measurement interval according to the real-time change at the i-th measurement time point and obtain the dynamic measurement interval duration between the i-th measurement time point and the (i+1)th measurement time point.

[0015] The beneficial effects of this invention are reflected in: In the dynamic measurement method for tunnel structure deformation, firstly, by comparing and analyzing the historical distance data sequences of multiple benchmark points within the measurement area, the associated fluctuation parameter of each point is calculated. This parameter quantifies the resistance to local interference based on the deviation of the relative change ratio of each point from the regional average change ratio and the frequency of exceeding the standard. This allows for the objective selection of the most stable target benchmark point from the candidate points, avoiding noise introduced by relying on a single benchmark point that may be affected by local disturbances (such as instrument micro-displacement or rock micro-cracks), thus improving the reliability of subsequent deformation measurement reference data. Secondly, using the selected most stable target benchmark point as the center, the initial benchmark measurement interval is set using its historical stability (associated fluctuation parameter), and the real-time change is calculated based on the two latest consecutive measurements of that point. The interval of the next measurement is then dynamically adjusted in real time—when an accelerating deformation trend is detected (large real-time change), the measurement interval is automatically shortened to densely capture key deformation inflection points and reduce the risk of structural instability; conversely, when the deformation stabilizes (small real-time change), the interval is extended to reduce unnecessary resource consumption. Furthermore, this closed-loop dynamic adjustment mechanism is entirely data-driven, responding in real time to changes in structural state without relying on human experience or judgment. It adapts to dynamic changes in deformation rate and optimizes the efficiency of monitoring resource allocation. Ultimately, by combining highly reliable benchmark data with an adaptive acquisition frequency, it more efficiently acquires continuous, minute deformation information that reflects the true structural changes of the tunnel while ensuring data authenticity. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a schematic diagram illustrating the steps of the dynamic measurement method for tunnel structure deformation of the present invention; Figure 2 This is a schematic diagram of part of step S2 in the dynamic measurement method for tunnel structure deformation of the present invention; Figure 3 This is a schematic diagram of part of step S22 in the dynamic measurement method of tunnel structure deformation of the present invention; Figure 4 This is a schematic diagram of part of step S3 in the dynamic measurement method of tunnel structure deformation of the present invention; Figure 5 This is a schematic diagram of part of step S33 in the dynamic measurement method for tunnel structure deformation of the present invention; Figure 6 This is a schematic diagram of part of step S4 in the dynamic measurement method for tunnel structure deformation of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] like Figure 1As shown, a method for dynamically measuring the deformation of a tunnel structure is provided. In one embodiment, the method includes: S1. Obtain the reference axis based on the tunnel's beginning and end, and obtain the area to be measured in the tunnel and multiple reference points to be processed set in the area to be measured. Obtain the distance data sequence between each reference point to be processed and the reference axis in the previous measurement cycle of the current measurement cycle. The distance data sequence is formed by arranging multiple distance values ​​obtained at different measurement time points in the measurement cycle in sequence. S2. Compare the distance data sequences of each reference point to be processed and obtain the associated fluctuation parameters of each reference point to be processed. S3. Obtain the reference point to be processed with the smallest associated fluctuation parameter and use it as the target reference point. Based on the associated fluctuation parameter of the target reference point, obtain the dynamic measurement interval duration between each adjacent measurement time point of the target reference point in the current measurement cycle. S4. Based on the target reference point and the corresponding dynamic measurement interval, obtain the measurement strategy for the area to be measured, and complete the deformation measurement of the area to be measured.

[0022] In this embodiment, it should be noted that in S1, the spatial reference system, monitoring objects, and historical data foundation required for subsequent analysis are established. First, a spatial reference axis is determined by the relatively fixed beginning (entrance) and end (exit) of the tunnel structure. This axis serves as the spatial reference baseline for the entire measurement, and theoretically, the beginning and end are considered to be less affected by the overall structural deformation or to change relatively slowly, thus providing a relatively stable benchmark. Based on this reference axis, the method needs to identify specific sections within the tunnel that require key monitoring, i.e., the areas to be measured. The division of these areas is based on the fact that the internal geological activity characteristics of the rock strata and externally induced vibrations (such as nearby construction, vehicle traffic, etc.) are considered to be roughly uniform within the area, ensuring that each point within the same area to be measured is mainly subjected to external environmental excitations of the same source and intensity. Within each identified area to be measured, multiple reference points are arranged, which are usually equipped with precision measuring equipment (such as prisms, sensor mounting bases) to measure their distance from the aforementioned reference axis (usually the vertical distance or the distance in the normal direction).

[0023] Finally, step S1 requires acquiring historical measurement data from a complete measurement cycle prior to the start of the current measurement cycle. Specifically, this involves obtaining a series of distance values ​​to the reference axis measured at different specific time points within that historical cycle for each reference point to be processed. These distance values ​​are arranged chronologically according to the measurement time, forming a distance data sequence for each reference point. These sequences record the trajectory of the reference point's positional change relative to the reference axis over a past period, serving as the primary data source for subsequent reference point stability analysis and associated fluctuation calculations.

[0024] Furthermore, the stability of the reference axis, formed by the fixed points at both ends, directly affects the accuracy of the entire measurement, avoiding the risk of error propagation caused by using potentially changing internal points as references. Precise division of the measurement area is a crucial prerequisite, ensuring that multiple reference points selected within the same area experience essentially the same external disturbances (rock activity and artificial vibrations). Thus, in subsequent step S2, when comparing and correlating the distance data sequences of these reference points, their differences can be more significantly attributed to local instability factors within the points themselves (such as minor loosening of the installation point, local rock fissures, slight drift of the equipment itself, or local stress release), rather than regional overall environmental disturbances. The multi-point deployment strategy of the reference points provides opportunities for data redundancy and mutual verification, laying the foundation for selecting the most stable target reference point from the candidate points.

[0025] Furthermore, the complete distance data sequence of the previous period contains the measurement values ​​of each point at multiple discrete time points. It not only provides the positional change process of the point in the historical time dimension (the amount of change between consecutive measurement time points can be used to calculate the rate of change), but more importantly, the differences in the measurement values ​​of these points at the same time point and the differences in their respective evolution patterns over time provide the necessary comparative basis for calculating the associated fluctuation parameters reflecting the stability of the point itself through the S2 step.

[0026] In S2, the distance data sequences recorded by each pre-set reference point in the previous complete measurement cycle (obtained from step S1) are analyzed and compared in depth to quantitatively evaluate the signal stability or reliability of each reference point itself—that is, to what extent its distance change is affected by the overall real deformation of the tunnel area, and to what extent it is mixed with abnormal fluctuations (noise) caused by local factors of the point itself.

[0027] To achieve this assessment, S2 employed a statistical analysis approach based on comparing population changes over time periods. The process began by processing the raw distance sequences of each benchmark point: for each benchmark point, the distance differences between adjacent measurement time points were analyzed, and these differences were compared to the distance values ​​at earlier time points to calculate the relative change ratio within adjacent time periods. This change ratio aims to reflect the rate or magnitude of change of the point's location between two measurement time points relative to its previous location, helping to mitigate the impact of differences in absolute distance values ​​between different points. It is noteworthy that these change ratio calculations were performed simultaneously over the respective time periods of multiple benchmark points, laying the data foundation for subsequent population comparisons.

[0028] Then, for each specific measurement time period (i.e., the time interval connecting two adjacent measurement time points), the method focuses on examining the aforementioned change ratio data of all the reference points to be processed within this time period. Since these points are intentionally deployed in the same measurement area (the characteristics of this area have been clearly defined in S1), theoretically, they experience the same intensity of external environmental effects (such as rock movement and construction vibration). Therefore, under ideal conditions without local disturbances, the relative change ratios of each point within the same time period should be relatively close, reflecting a common external influence.

[0029] Furthermore, based on the aforementioned group data, S2 obtains the associated fluctuation parameters for each benchmark point as a quantitative indicator of its stability. For each specific time period, the arithmetic mean of the change proportions of all benchmark points within that time period is first calculated; this mean is defined as the benchmark proportion for that time period. The benchmark proportion represents the relative level or typical rate of overall distance change exhibited by the measured area under the influence of the common environment within that time period.

[0030] Next, for each individual reference point in the sequence (e.g., the i-th point), in each time period, its own calculated change ratio for that time period is compared with the reference ratio for the same time period. The absolute value of the difference between the two is calculated to obtain an absolute deviation. This absolute deviation intuitively quantifies the degree to which the change pattern of that specific reference point deviates from the common behavior of the group in that time period. The greater the deviation, the greater the inconsistency between the signal of that point and the overall signal of the region during that time period.

[0031] Finally, to comprehensively evaluate the overall stability of a benchmark point across all historical time periods, the method counts the number of times the absolute deviation value of that point exceeded a preset deviation threshold (representing an acceptable range of normal fluctuations) across all time periods, obtaining the number of fluctuations. Dividing this number of fluctuations by the total number of analyzable time periods for that point (i.e., the total number of absolute deviations it possesses) yields the final output of the benchmark point—the correlation fluctuation parameter (a value between 0 and 1). The smaller this parameter, the more consistent the distance change pattern of that point is with the overall performance of the same region for most time periods throughout the historical cycle, with few large abnormal fluctuations. This suggests that the location measurement data of that point is relatively less affected by abnormal interference from its own factors (such as equipment loosening, local rock instability, etc.), meaning that the point's location is relatively more stable and more likely to accurately reflect the overall deformation of the region. Conversely, a large correlation fluctuation parameter indicates that the measurement results are unstable and unreliable.

[0032] In S3, a dynamic measurement frequency strategy is developed for the current measurement cycle based on the selection of the most stable target reference point. This aims to replace the existing fixed-interval mode, enabling the data acquisition frequency to adaptively adjust according to the actual activity level of tunnel deformation.

[0033] First, a benchmark measurement interval is set using the associated fluctuation parameters of the target benchmark point calculated by S2. This benchmark interval reflects a preliminary and reasonable default measurement frequency recommendation based on the historical stability of that point. A target benchmark point with high stability (small associated fluctuation parameters) has highly reliable monitoring data, and using this point to guide frequency decisions carries lower risk, thus potentially corresponding to a relatively long benchmark interval. On the other hand, a target benchmark point that, while more stable than other points, still exhibits some fluctuations (relatively larger associated fluctuation parameters) requires a relatively shorter benchmark interval to address its inherent uncertainties and provide more timely data updates.

[0034] Next, S3 introduces a real-time feedback mechanism to dynamically and finely adjust this reference interval. At each specific measurement time point within the current measurement cycle of the target reference point (denoted as the i-th time point), the distance values ​​of the two earlier consecutive measurement time points within this cycle (i.e., time points i-2 and i-1) are obtained. The absolute value of the distance change between these two points is calculated as the real-time change at time point i. This real-time change, using only the two most recent data points in the current cycle, aims to capture the rate or activity of tunnel deformation reflected by the target reference point within the most recent short time period (i.e., between time points i-2 and i-1).

[0035] Furthermore, after obtaining the real-time change, S3 dynamically corrects the baseline measurement interval for that point to determine when the next measurement point (point i+1) should be executed. The core logic of the correction is: the larger the most recently observed deformation (i.e., the larger the real-time change), the more likely the deformation is in an acceleration phase or a critical change stage, requiring subsequent measurements to follow up more quickly (i.e., reducing the measurement interval) to avoid missing important dynamic processes; conversely, if the most recently observed change is small (small real-time change), it indicates the deformation may be in a stable phase or a slow accumulation phase, allowing the next measurement to be appropriately postponed (i.e., increasing the measurement interval) to save measurement resources. The correction process is controlled by a preset change threshold. If the real-time change exceeds this threshold, it indicates a significant change, requiring the interval to be shortened based on the degree of excess (real-time change minus the threshold, then divided by the threshold to obtain a ratio). To prevent excessive shortening, this ratio is set to an upper limit of 1.

[0036] Then, this ratio is multiplied by half the baseline interval to obtain a correction amount. The final dynamic measurement interval duration is obtained by subtracting this correction amount from the baseline measurement interval. This means that the larger the change, the larger the correction amount, and thus the more it is subtracted, resulting in a shorter final measurement interval. If the real-time change does not exceed the threshold, the correction amount is zero, and the baseline interval is directly used as the next dynamic measurement interval duration. In this way, after each new measurement is completed (generating data for point i), the dynamic interval for the next segment can be calculated based on the two most recently acquired data points (point i-1 and point i), truly realizing dynamic adjustment of frequency and response to the latest deformation trends. This ensures a sufficiently high data acquisition density during critical change periods, while reducing the measurement frequency during stable periods.

[0037] In S4, the dynamic measurement interval duration output from S3 is transformed into specific operation plans and control commands. First, based on the target reference point at the current time point (e.g., the i-th measurement time point), the next dynamic measurement interval duration (i.e., the interval between point i and point i+1) is calculated in real-time, or the operator can accurately determine the time point when the next measurement action (i.e., the next distance measurement of the target reference point) should occur. This process is not a one-time planning, but a continuous, real-time closed-loop control: after each new distance measurement is executed at the target reference point, the newly acquired distance data at that point (i.e., the distance value of point i) immediately participates in the real-time update calculation of step S3.

[0038] Specifically, this new data point, along with a previous data point (e.g., point i-1), is used to calculate the next new real-time change, and subsequently update the next dynamic measurement interval between points i+1 and i+2. This makes the management of the entire measurement frequency adaptive, closely following the latest tunnel deformation state (activity level) reflected by the target reference point, truly responding to the objective need for real-time changes in the tunnel deformation rate. In other words, the timing of the measurement work is no longer fixed, but driven by the dynamic behavior of the tunnel structure itself through selected stable target reference points.

[0039] Furthermore, when measurements are performed according to dynamically defined time points, the core operation is to obtain the actual distance value from the target reference point to the reference axis at the current moment (i+1th point in the next measurement time). This newly acquired data not only serves the dynamic update of subsequent measurement intervals (as mentioned above), but more importantly, it is the foundation and key input for calculating the overall deformation of the area to be measured. The target reference point is selected precisely because it exhibits the lowest correlation fluctuation parameter compared to other points in the same area during historical periods, meaning that the distance data it provides has higher stability and reliability, is less affected by its own local noise, and is more likely to truly reflect the overall displacement or deformation trend of the tunnel relative to the reference axis in this area. Therefore, by continuously monitoring the changes in the distance value of this target reference point over time (e.g., calculating the difference between the current value and the initial value, or the difference between the current value and the previous measurement value), the overall deformation information of the area to be measured can be directly and stably deduced. After the measurement is performed, the final deformation measurement results will be generated based on these changes.

[0040] This measurement strategy achieves a dual objective by intelligently selecting the most representative stable reference points and applying dynamically optimized monitoring frequencies to them: on the one hand, the dynamic frequency ensures that sufficiently dense data points are acquired during periods of active deformation to capture potential risks (avoiding the risk of missing key changes by using fixed low frequencies), while reducing the frequency during stable periods to save resources (avoiding the waste of fixed high frequencies); on the other hand, relying on the most stable reference points significantly reduces the negative impact of local disturbances on the reliability of the final measurement results, improves the authenticity and credibility of deformation information, and provides more solid data support for structural safety assessment.

[0041] In summary, the dynamic measurement method for tunnel structure deformation firstly involves comparing and analyzing the historical distance data sequences of multiple benchmark points within the measurement area to calculate the associated fluctuation parameter for each point. This parameter quantifies the resistance to local interference based on the deviation of the relative change ratio of each point from the regional average change ratio and the frequency of exceeding the standard. This objectively selects the most stable target benchmark point from the candidate points, avoiding noise introduced by relying on a single benchmark point that may be affected by local disturbances (such as instrument micro-displacement or rock micro-cracks), thus improving the reliability of subsequent deformation measurement reference data. Secondly, using the selected most stable target benchmark point as the center, the initial benchmark measurement interval is set using its historical stability (associated fluctuation parameter). The real-time change is calculated based on the two latest consecutive measurements of this point, and the interval for the next measurement is dynamically adjusted in real time. When an accelerating deformation trend is detected (large real-time change), the measurement interval is automatically shortened to densely capture key deformation inflection points and reduce the risk of structural instability; conversely, when the deformation stabilizes (small real-time change), the interval is extended to reduce unnecessary resource consumption. Furthermore, this closed-loop dynamic adjustment mechanism is entirely data-driven, responding in real time to changes in structural state without relying on human experience or judgment. It adapts to dynamic changes in deformation rate and optimizes the efficiency of monitoring resource allocation. Ultimately, by combining highly reliable benchmark data with an adaptive acquisition frequency, it more efficiently acquires continuous, minute deformation information that reflects the true structural changes of the tunnel while ensuring data authenticity.

[0042] like Figure 2 As shown, in one embodiment, S2 involves comparing the distance data sequences of each reference point to be processed and obtaining the associated fluctuation parameters of each reference point to be processed, including: S21. Obtain the difference between the next distance value and the previous distance value in the distance data sequence of each reference point to be processed, and divide the difference by the previous distance value to obtain the change ratio of the adjacent distance value under the time period. S22. Obtain the associated fluctuation parameters of each benchmark point under each time period based on the change ratio of all benchmark points to be processed.

[0043] In this embodiment, it should be noted that in S21, for each reference point, a series of distance values ​​obtained in chronological order are obtained, and the distance difference between every two consecutive measurement time points (i.e., within a time period) is calculated sequentially. This difference represents the absolute displacement amplitude of the point relative to the reference axis within this time period.

[0044] However, since different reference points may have different initial positions and distances from the reference axis, the same absolute displacement means a greater relative deformation for points with closer initial distances. Therefore, simply looking at the absolute difference is insufficient for a fair comparison of the signal quality of different points. To address this issue, S21 divides the calculated distance difference by the distance value at the previous time point to obtain a dimensionless relative value—the change ratio. This change ratio essentially measures the relative rate of change or amplitude of the reference point's position relative to its previous state within a specific time period. This effectively eliminates or weakens the impact of differences in the initial installation positions of different measuring points on the perceived amplitude of change. This allows subsequent comparisons of the changing behavior of numerous reference points to be based on a fairer foundation that better reflects the relative deformation intensity, providing more homogeneous data preparation for the next step of group data comparative analysis (S22).

[0045] In S22, based on the standardized change ratios calculated for each reference point and its corresponding time period in S21, these scattered change ratio data are integrated and deeply mined. The goal is to ultimately generate a single index that can quantitatively reflect the overall stability and signal reliability of each reference point throughout the entire historical measurement period—the associated fluctuation parameter. It introduces the concept of group reference: although the change ratio of each reference point describes its own change behavior, when they are examined together with the numerous change ratios of all points in the same measurement area (assuming uniform environmental excitation within the area) in the same time period, these ratios should exhibit a certain commonality or consistency pattern (ideally tending towards a typical value representing the overall deformation of the area). Any significant deviation of any single point from this consistency pattern may originate from the interference or instability unique to that point itself.

[0046] Furthermore, S22 uses a series of statistical and comparative analyses to identify and measure the degree and frequency of deviation between the change ratio sequence of each benchmark point and the reference standard calculated based on the change ratio of the population. Finally, it compresses this deviation information into a comprehensive score. The lower the score, the more the behavior of the point fits the overall trend of the region throughout the entire historical period, and the less susceptible it is to interference from local factors, i.e., the higher its stability.

[0047] like Figure 3 As shown, in one embodiment, obtaining the associated fluctuation parameters of each reference point in S22 based on the change ratio of all reference points to be processed in each time period includes: S221. Obtain the average value of the change ratio of all the reference points to be processed in the same time period and record it as the reference ratio in that time period. S222. Obtain the absolute value of the difference between the change ratio of the i-th reference point to be processed and the reference ratio in each time period and record it as the absolute deviation. S223. Obtain the number of absolute deviations exceeding the preset deviation threshold among all absolute deviations of the i-th reference point to be processed and record it as the fluctuation number. Divide the fluctuation number by the number of all absolute deviations of the i-th reference point to be processed and obtain the associated fluctuation parameter of the i-th reference point to be processed.

[0048] In this embodiment, it should be noted that in S221, within each specific time period defined by adjacent measurement time points, step S21 has already calculated a change ratio reflecting the relative change of its own position for each reference point to be processed within that time period. The task of S221 is to summarize and analyze the change ratios of all reference points to be processed within this specific time period and calculate their arithmetic mean. This mean is given a specific name—the reference ratio. The reference ratio represents the average relative change in distance of the reference points in the entire area to be measured as a group within that specific time period. It is considered a statistical characterization or reference signal of the overall deformation response of the area under the influence of a common external environment (such as rock activity or vibration) during that time period. This reference ratio provides a core comparative benchmark for the next step of evaluating whether the behavior of each individual reference point within that time period is gregarious.

[0049] In S222, after establishing the baseline proportion for each time period in S221, the focus shifts to evaluating the consistency of each individual benchmark point (e.g., denoted as point Pi) across different time periods. For point Pi, for each time period (e.g., time period k) in the historical measurement cycle, S222 extracts the change proportion of that point calculated by S21 within that time period k, and also extracts the baseline proportion of the region calculated by S221 for the same time period k.

[0050] Next, the absolute value of the difference between the change ratio of point Pi in time period k and the baseline ratio for that time period is calculated. This result is called the absolute deviation of point Pi in time period k. The absolute deviation is a very intuitive indicator: its value directly quantifies how much the actual change behavior of point Pi deviates from the regional baseline average behavior within time period k. The larger the absolute deviation value, the greater the difference between the change pattern of point Pi and the overall performance of the region in that specific time period k. This may suggest that point Pi was subjected to some local special disturbances in time period k that other points did not experience (such as minor instability of local rock mass near the point or occasional vibration of monitoring equipment), and the resulting distance data contained more noise unrelated to the actual deformation of the overall structure in that time period.

[0051] In S223, a reasonable preset deviation threshold is obtained. This threshold represents the acceptable normal fluctuation range—that is, the reasonable upper limit of the deviation of the proportion of change caused by measurement random errors and slight environmental disturbances from the reference proportion. Specifically, when determining the preset deviation threshold, the absolute deviation of all reference points in the measurement area under all time periods of the previous measurement cycle is first extracted (calculated by S222). The 90th percentile of all absolute deviation values ​​is calculated (i.e., 90% of the deviation values ​​are below this value). Finally, the percentile value is multiplied by a safety factor of 0.8 to avoid oversensitivity leading to misjudging stable points as outliers. The resulting value is used as the preset deviation threshold.

[0052] Next, all N absolute deviations of point Pi are iterated, and the number of those exceeding a preset deviation threshold is counted. This number is defined as the fluctuation count of point Pi. The fluctuation count reflects the number of times the signal of point Pi exhibits significant abnormal fluctuations throughout the entire historical observation period.

[0053] Finally, dividing this number of fluctuations by the total number of time periods N of point Pi yields the associated fluctuation parameter of point Pi. This is a value between 0 and 1 (0 ≤ associated fluctuation parameter ≤ 1). Its core meaning is: the frequency (proportion of the number of times) of significant abnormal fluctuations in the signal of point Pi throughout the entire historical measurement period. The closer the associated fluctuation parameter is to 0, the more closely point Pi's behavior closely resembles the regional baseline proportion (i.e., conforms to the overall trend) for the vast majority of time periods (the higher the proportion), with few large, identifiable abnormal deviations. This strongly suggests that the point is minimally affected by local interference, has strong inherent stability, and its data is more representative of the true deformation signal of the region. Conversely, the larger the associated fluctuation parameter, the higher the frequency of abnormal fluctuations in the point's historical performance, indicating that it may be a problem point, highly affected by local factors (not overall deformation factors), and its data reliability is low.

[0054] like Figure 4 As shown, in one embodiment, S3, obtaining the dynamic measurement interval duration between adjacent measurement time points of the target reference point within the current measurement cycle based on the associated fluctuation parameters of the target reference point includes: S31. Obtain the distance value of the target reference point at the (i-2)th measurement time point and the distance value at the (i-1)th measurement time point within the current measurement cycle, and take the absolute value of the difference between the distance value at the (i-1)th measurement time point and the distance value at the (i-2)th measurement time point as the real-time change at the ith measurement time point, where i is a positive integer and i≥3; S32. Obtain the benchmark measurement interval based on the associated fluctuation parameters of the target benchmark point; S33. Correct the baseline measurement interval based on the real-time change at the i-th measurement time point and obtain the dynamic measurement interval duration between the i-th measurement time point and the (i+1)-th measurement time point.

[0055] In this embodiment, it should be noted that in S31, after the selected target reference point enters the current measurement cycle (the cycle in which monitoring is being carried out), at each actual measurement point within this cycle (for example, referred to as the i-th measurement time point, where i≥3), a real-time change index for judging the recent activity level of deformation is prepared. S31 first reads the data of the two time points immediately preceding the current time point (the i-th point) from the data already obtained by the target reference point in the current cycle—that is, the distance value of the (i-2)-th measurement time point and the distance value of the (i-1)-th measurement time point.

[0056] Then, the absolute value of the difference between the distance value at point i-1 and the distance value at point i-2 is calculated. This absolute value is the final real-time change required at time point i. This real-time change utilizes only the results of the two most recent consecutive measurements, representing the absolute change in the distance to the target reference point within the most recent and shortest time segment from time point i-2 to time point i-1. It discards historical period data (already used for stability assessment) and focuses on capturing the latest and most immediate deformation rate or change magnitude information of the target point within the current period, close to the current decision moment. The purpose of this indicator is to provide the most direct feedback on the dynamic response of the tunnel structure at this moment, providing an immediate basis for subsequent adaptive adjustment of the measurement frequency (determining when the next time point arrives).

[0057] In step S32, the time value of the baseline measurement interval is obtained. This baseline measurement interval is not a fixed global setting, but is closely related to the historical performance stability of the specific point selected as the target baseline. The input data is the associated fluctuation parameter calculated for the target point in step S2. Based on engineering practice and risk control logic, this method sets a relatively long baseline measurement interval for points with smaller associated fluctuation parameters (meaning stable and reliable historical performance, and less susceptible to local disturbances), because the risk of relying on their data for frequency decisions is lower; while for points with the smallest associated fluctuation parameters in the region but still relatively large values ​​(meaning there is still some potential fluctuation or uncertainty), a relatively short baseline measurement interval is set. In this way, the more stable the target point, the higher the confidence in the trend of its data changes, and a slightly longer observation period is acceptable; conversely, when the stability of the target point is only relatively better but the absolute level still needs to be improved, its data needs to be checked more frequently, and the interval shortened, in order to more promptly detect changing trends and deal with potential risks (even if such risks may partly stem from the slight instability characteristics that still exist at the point itself).

[0058] In S33, the more drastic and significant the recent changes in structural deformation detected in real time (manifested as a large amount of real-time change calculated in S31), the more urgent it is to remeasure after a shorter period of time to increase the data acquisition density, so as to accurately capture the possible accelerating deformation process that may be taking place, and prevent the omission of key information due to excessively long measurement intervals, which could lead to delayed or incorrect judgments on structural safety. Conversely, if the recent deformation changes are slight (small amount of real-time change), it indicates that the structural behavior may be relatively stable, allowing the next measurement to be appropriately postponed to reduce unnecessary resource consumption (manpower, equipment, energy consumption).

[0059] S33 uses an algorithm to determine the direction (shortening) and adjust the magnitude (how much to shorten) of the baseline measurement interval (representing the basic rhythm) set by S32 according to the magnitude of real-time changes. Finally, it outputs a specific, dynamically adjusted dynamic measurement interval duration, which is the length of time that should be waited between the i-th measurement time point and the (i+1)-th measurement time point. This interval value determines the execution time of the next measurement action and ensures that the monitoring frequency can match the actual dynamic situation of tunnel deformation in real time, achieving on-demand measurement.

[0060] like Figure 5 As shown, in one embodiment, step S33, which involves correcting the reference measurement interval based on the real-time change at the i-th measurement time point and obtaining the dynamic measurement interval duration between the i-th and (i+1)-th measurement time points, includes: S331. Obtain a preset change threshold. If the real-time change at the i-th measurement time point exceeds the preset change threshold, then divide the difference between the real-time change at the i-th measurement time point and the preset change threshold by the preset change threshold, and use it as the processing ratio at the i-th measurement time point, and set the maximum value of the processing ratio to 1. S332. If the real-time change at the i-th measurement time point does not exceed the preset change threshold, then 0 is taken as the proportion to be processed at the i-th measurement time point. S333. Multiply half of the reference measurement interval by the processing ratio of the i-th measurement time point to obtain the correction amount of the i-th measurement time point. Subtract the correction amount of the i-th measurement time point from the reference measurement interval to obtain the dynamic measurement interval duration between the i-th measurement time point and the (i+1)-th measurement time point.

[0061] In this embodiment, it should be noted that in S331, S331 defines how to calculate the shortening intensity coefficient (processing ratio) when a large real-time change is detected (considered to require shortening the interval). It sets a preset change threshold, which represents an engineering judgment—when the real-time change is less than or equal to this threshold, the structural deformation is considered to be in a stable or slowly changing state, requiring no frequency intervention (maintaining the reference interval is sufficient); only when the real-time change exceeds this threshold is the deformation considered to exhibit a certain degree of activity, requiring the triggering of a frequency increase mechanism. Specifically, when determining the preset change threshold, firstly, the maximum natural fluctuation amplitude of the target reference point during a finite number of historical stable periods (e.g., when the deformation rate is stable), then, based on the maximum daily deformation rate allowed by the tunnel design (e.g., 0.1 mm / day), 30% to 50% of this is taken as the engineering safety limit. Finally, the smaller value between the natural fluctuation amplitude and the engineering safety limit is taken as the preset change threshold.

[0062] When the real-time change at the i-th measurement time point exceeds a preset change threshold, S331 calculates a ratio: (real-time change - change threshold) / change threshold. The numerator of this ratio is the additional change exceeding the threshold, and the denominator is the threshold itself. Therefore, this ratio quantifies the degree to which the real-time change exceeds the safe / stable range (multiple relationship). To prevent the ratio from becoming too large or the interval from being excessively shortened in extreme cases (such as drastic deformation) (potentially causing excessive frequent measurements or exceeding the instrument's capacity), S331 sets the maximum value of this ratio to 1 (i.e., when the ratio > 1, it is calculated as 1). This ratio is the processing ratio, ranging from 0 to 1. It represents the assessment of the required shortening of the baseline measurement interval; a larger value indicates a higher degree of need for intensive monitoring due to active deformation.

[0063] In S332, when the real-time change at the i-th measurement time point calculated in S31 does not exceed the preset change threshold in S331, it indicates that in the recent short period of time (from point i-2 to i-1), the structural deformation rate / amplitude is in an acceptable stable state or below the set activity threshold. Therefore, it is determined that no intervention to strengthen (shorten the interval) the measurement frequency is needed based on active deformation at the current moment. At this time, S332 explicitly sets the processing ratio to 0. This means that in the next step, S333, when calculating the actual interval correction, the correction amount used for shortening will be 0 (because the processing ratio is 0), and the final output dynamic measurement interval duration will be directly equal to the baseline measurement interval set in S32, maintaining the original rhythm.

[0064] S333 is the step that calculates the specific amount of interval reduction and determines the final dynamic interval duration. It requires three inputs: the baseline measurement interval (I_base) obtained in S32, the ratio to be processed (Ratio_adjust, range 0-1) determined in S331 or S332, and the understanding that the maximum amount of reduction of the baseline interval I_base is limited (here, there is an implicit engineering constraint that the upper limit of the allowed reduction does not exceed I_base / 2).

[0065] S333 first calculates the correction amount: Correction amount = (I_base * 0.5) * Ratio_adjust. Here, I_base * 0.5 represents the upper limit of the maximum allowable shortening of the reference interval (i.e., half of the reference interval). Ratio_adjust represents the required shortening ratio (or intensity). Multiplying the two together yields the precise amount of time to be shortened from the reference interval—the correction amount—determined based on the current level of active deformation (Ratio_adjust). The larger the Ratio_adjust (1 corresponds to the most active period), the larger the correction amount (tending towards I_base / 2), meaning more shortening is required. When Ratio_adjust is 0 (stationary period), the correction amount is 0.

[0066] Ultimately, the dynamic measurement interval duration = I_base - correction amount. This formula clearly reflects the core purpose of the rule design: when the deformation is significantly active (Ratio_adjust>0, especially when it tends towards 1), the interval is shortened by subtracting a positive correction amount, and the higher the activity, the more the interval duration is shortened (I_base - a larger positive number), and the next measurement will arrive earlier; during the stationary period (Ratio_adjust=0), the interval remains unchanged at I_base.

[0067] like Figure 6 As shown, in one embodiment, the measurement strategy in S4 for obtaining the area to be measured based on the target reference point and the corresponding dynamic measurement interval includes: S41. Determine the next measurement time point based on the dynamic measurement interval of the target benchmark point, and update the dynamic measurement interval in real time after each measurement of the target benchmark point. S42. After the measurement is executed, the deformation measurement results of the area to be measured are generated.

[0068] In this embodiment, it should be noted that in S41, the abstract time value (dynamic measurement interval duration) output by S3 is transformed into a specific measurement operation plan. After the target reference point completes measurement at the i-th measurement time point, S33 (specifically calculated by S333) provides the precise time length (denoted as ΔT_i) that the next measurement time point (i+1) should wait after the i-th point. The task of S41 is to mark the i+1-th time point as the time to perform the next target reference point distance measurement at [the time of the i-th point + ΔT_i]. This ensures that each subsequent measurement action is executed at the optimal time based on the latest dynamically generated evaluation.

[0069] S41 is not just static scheduling, but more importantly, it realizes closed-loop dynamic updates of the measurement cycle. After each new measurement is executed as planned (such as obtaining a new distance value at point i+1), the new data point (data at point i+1) will immediately trigger the restart of the entire feedback loop. Based on the latest data point and the data of its previous point (point i), the processes S31, S32 (possibly), and S33 (including S331-S333) are re-executed to calculate the updated ΔT_{i+1} value used to determine the interval between point i+1 and point i+2. Then, S41 immediately plans the time of point i+2 based on this.

[0070] Therefore, the entire measurement rhythm is constantly and dynamically adjusted based on the latest two data provided by the target reference point. Its measurement frequency can continuously and in real time match the current deformation activity of the tunnel structure, forming a fully adaptive closed-loop control.

[0071] In S42, when the time point determined by S41 is reached and a measurement operation is performed on the target reference point (e.g. at point i+1), the measuring instrument (such as a total station or a distance sensor) will accurately acquire a new distance value from the target reference point to the preset reference axis. This newly acquired distance value is the key measurement result.

[0072] Furthermore, the task of S42 is to calculate and generate the final measurement result expressing the deformation of the area to be measured, based on all distance data obtained in its measurement sequence after the target reference point is selected (especially the comparison between the current value and the initial reference value or adjacent values). Since the target reference point has been rigorously statistically screened by S2 to prove that it is the most stable point in the entire area with the most consistent response to the common environment and the least affected by local disturbances, the change in distance from this point to the fixed reference axis can largely be considered to reflect the true overall displacement or deformation of the tunnel structure in the area relative to the reference axis. This result clearly shows the change in the position of the target point and outputs this change as a reliable measure of the overall deformation of the structure in the monitored area to the user for safety status assessment and decision support.

[0073] A dynamic measurement system for tunnel structure deformation is also provided, the system comprising: The acquisition module is used to acquire the reference axis based on the tunnel's beginning and end, acquire the area to be measured in the tunnel and multiple reference points to be processed set in the area to be measured, and acquire the distance data sequence between each reference point to be processed and the reference axis in the previous measurement cycle of the current measurement cycle. The distance data sequence is formed by arranging multiple distance values ​​acquired at different measurement time points in the measurement cycle in sequence. The first measurement and processing module is used to compare the distance data sequences of each reference point to be processed and obtain the associated fluctuation parameters of each reference point to be processed. The second measurement processing module is used to obtain the reference point to be processed with the smallest associated fluctuation parameter and use it as the target reference point, and to obtain the dynamic measurement interval duration between each adjacent measurement time point of the target reference point in the current measurement cycle based on the associated fluctuation parameter of the target reference point. The target measurement module is used to obtain the measurement strategy of the area to be measured based on the target reference point and the corresponding dynamic measurement interval, and to complete the deformation measurement of the area to be measured.

[0074] In one embodiment, the first measurement processing module is further configured to: obtain the difference between the next distance value and the previous distance value in the distance data sequence of each reference point to be processed, and divide the difference by the previous distance value to obtain the change ratio of the adjacent distance value in the time period; and obtain the associated fluctuation parameters of each reference point to be processed according to the change ratio of all reference points to be processed in each time period.

[0075] In one embodiment, the first measurement processing module is further configured to: obtain the average value of the change ratio of all reference points to be processed in the same time period and record it as the reference ratio in that time period; obtain the absolute value of the difference between the change ratio of the i-th reference point to be processed and the reference ratio in each time period and record it as the absolute deviation; obtain the number of absolute deviations of the i-th reference point to be processed that exceed a preset deviation threshold and record it as the fluctuation number; divide the fluctuation number by the number of absolute deviations of the i-th reference point to be processed and obtain the associated fluctuation parameter of the i-th reference point to be processed.

[0076] In one embodiment, the second measurement processing module is further configured to: obtain the distance value of the target reference point at the (i-2)th measurement time point and the distance value at the (i-1)th measurement time point within the current measurement cycle, and use the absolute value of the difference between the distance value at the (i-1)th measurement time point and the distance value at the (i-2)th measurement time point as the real-time change at the i-th measurement time point, where i is a positive integer and i≥3; obtain the reference measurement interval according to the associated fluctuation parameters of the target reference point; correct the reference measurement interval according to the real-time change at the i-th measurement time point and obtain the dynamic measurement interval duration between the i-th measurement time point and the (i+1)th measurement time point.

[0077] In this embodiment, it should be noted that the specific method of performing the above-mentioned dynamic measurement system for tunnel structure deformation has been described in detail in the embodiments of the dynamic measurement method for tunnel structure deformation, and will not be elaborated here.

[0078] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0079] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0080] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention 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 or all 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 the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for dynamically measuring deformation of a tunnel structure, characterized by, The method comprises the following steps: obtaining a reference axis according to a tunnel head and a tunnel tail, and obtaining a to-be-measured region in the tunnel and a plurality of to-be-processed reference points arranged in the to-be-measured region, and obtaining distance data sequences between each to-be-processed reference point and the reference axis in a previous measurement period before a current measurement period, wherein the distance data sequences are formed by a plurality of distance values obtained at different measurement time points in the measurement period; comparing the distance data sequences of each to-be-processed reference point with each other to obtain a correlation fluctuation parameter of each to-be-processed reference point; obtaining a to-be-processed reference point with the smallest correlation fluctuation parameter as a target reference point, and obtaining a dynamic measurement interval length between each adjacent measurement time point in the current measurement period of the target reference point according to the correlation fluctuation parameter of the target reference point; obtaining a measurement strategy of the to-be-measured region based on the target reference point and the corresponding dynamic measurement interval length, and completing deformation measurement of the to-be-measured region.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining a difference value between a subsequent distance value and a previous distance value of adjacent distance values in the distance data sequence of each to-be-processed reference point, and dividing the difference value by the previous distance value to obtain a change ratio in a time period corresponding to the adjacent distance values; obtaining the correlation fluctuation parameter of each to-be-processed reference point according to the change ratios of all to-be-processed reference points in each time period.

3. The method of claim 1, wherein, The method comprises the following steps: obtaining an average value of the change ratios of all to-be-processed reference points in the same time period as a reference ratio in the time period; obtaining an absolute value of a difference between the change ratio of the i-th to-be-processed reference point in each time period and the reference ratio as an absolute deviation; obtaining a number of absolute deviations of the i-th to-be-processed reference point that exceed a preset deviation threshold as a fluctuation number, and dividing the fluctuation number by a total number of absolute deviations of the i-th to-be-processed reference point to obtain the correlation fluctuation parameter of the i-th to-be-processed reference point.

4. The method of claim 1, wherein, The method comprises the following steps: obtaining a distance value at an i-2-th measurement time point and a distance value at an i-1-th measurement time point of the target reference point in the current measurement period, and taking an absolute value of a difference between the distance value at the i-1-th measurement time point and the distance value at the i-2-th measurement time point as a real-time change amount of an i-th measurement time point, wherein i is a positive integer and i≥3; obtaining a reference measurement interval according to the correlation fluctuation parameter of the target reference point; correcting the reference measurement interval according to the real-time change amount of the i-th measurement time point to obtain a dynamic measurement interval length between the i-th measurement time point and an i+1-th measurement time point.

5. The method of claim 1, wherein, The method comprises the following steps: obtaining a preset variation threshold, if the real-time variation at the i-th measurement time point exceeds the preset variation threshold, then the difference between the real-time variation at the i-th measurement time point and the preset variation threshold divided by the preset variation threshold is taken as a to-be-processed ratio at the i-th measurement time point, and the maximum value of the to-be-processed ratio is taken as 1; if the real-time variation at the i-th measurement time point does not exceed the preset variation threshold, then 0 is taken as the to-be-processed ratio at the i-th measurement time point; multiplying one-half of the reference measurement interval by the to-be-processed ratio at the i-th measurement time point to obtain a correction amount at the i-th measurement time point, and subtracting the correction amount at the i-th measurement time point from the reference measurement interval to obtain a dynamic measurement interval length between the i-th measurement time point and the i+1-th measurement time point.

6. The method of claim 1, wherein, The measurement strategy of the to-be-measured region based on the target reference point and the corresponding dynamic measurement interval length comprises: determining a next measurement time point according to the dynamic measurement interval length of the target reference point, and updating the dynamic measurement interval length in real time after each measurement of the target reference point; generating a deformation measurement result of the to-be-measured region after measurement execution.

7. A system for dynamic measurement of deformation of a tunnel structure, characterized by The system comprises: an acquisition module configured to acquire a reference axis according to a tunnel head and a tail, and to acquire a to-be-measured region in the tunnel and a plurality of to-be-processed reference points arranged in the to-be-measured region, and to acquire a distance data sequence between each to-be-processed reference point and the reference axis in a previous measurement period before a current measurement period, wherein the distance data sequence is formed by a plurality of distance values acquired at different measurement time points in the measurement period; a first measurement processing module configured to compare the distance data sequences of the to-be-processed reference points with each other and to acquire correlation fluctuation parameters of the to-be-processed reference points; a second measurement processing module configured to acquire a to-be-processed reference point with the smallest correlation fluctuation parameter as a target reference point, and to acquire a dynamic measurement interval length between each adjacent measurement time point of the target reference point in the current measurement period according to the correlation fluctuation parameter of the target reference point; a target measurement module configured to acquire a measurement strategy of the to-be-measured region based on the target reference point and the corresponding dynamic measurement interval length, and to complete deformation measurement of the to-be-measured region.

8. The tunnel structure deformation dynamic measurement system according to claim 7, characterized in that, The first measurement processing module is further configured to: obtain a difference between a latter distance value and a former distance value of adjacent distance values in the distance data sequence of each to-be-processed reference point, and divide the difference by the former distance value to obtain a variation ratio in a time period corresponding to the adjacent distance values; acquire correlation fluctuation parameters of the to-be-processed reference points according to the variation ratios of all the to-be-processed reference points in each time period.

9. The tunnel structure deformation dynamic measurement system according to claim 7, wherein, The first measurement processing module is further configured to: obtain an average value of the variation ratios of all the to-be-processed reference points in the same time period as a reference ratio in the time period; obtain an absolute value of a difference between the variation ratio and the reference ratio of the i-th to-be-processed reference point in each time period as an absolute deviation; The number of absolute deviations exceeding the preset deviation threshold in all absolute deviations of the i-th to-be-processed reference point is obtained and recorded as a fluctuation number, and the fluctuation number is divided by the number of all absolute deviations of the i-th to-be-processed reference point to obtain a correlation fluctuation parameter of the i-th to-be-processed reference point.

10. The tunnel structure deformation dynamic measurement system according to claim 7, wherein, The second measurement processing module is further configured to: obtain a distance value at an i-2-th measurement time point and a distance value at an i-1-th measurement time point of the target reference point in a current measurement period, and take an absolute value of a difference between the distance value at the i-1-th measurement time point and the distance value at the i-2-th measurement time point as a real-time change amount of the i-th measurement time point, wherein i is a positive integer and i≥3; obtain a reference measurement interval according to the correlation fluctuation parameter of the target reference point; correct the reference measurement interval according to the real-time change amount of the i-th measurement time point to obtain a dynamic measurement interval length between the i-th measurement time point and an i+1-th measurement time point.

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