Laser scanning measurement method and system for thermal thawing settlement of frozen soil subgrade

Abstract

This invention discloses a laser scanning measurement method and system for thermal thawing settlement of frozen soil subgrade, belonging to the field of measurement technology. The method includes: acquiring gridded surface data and acquiring elevation data sequences for each grid point; acquiring elevation change indices for each grid point based on the elevation data sequences; acquiring multiple grid points adjacent to the i-th grid point and using them as multiple reference grid points for the i-th grid point, acquiring relative differences for each i-th grid point, and acquiring correction ratios for the i-th grid point based on the relative differences; acquiring target elevation data for each grid point based on the correction ratios and the measured elevation data at the current moment, and forming a measurement result including gridded surface data based on the target elevation data of each grid point. This invention has the advantages of identifying outliers, dynamically correcting measurements, and improving data reliability.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, specifically to a laser scanning measurement method and system for thermal thawing settlement of frozen soil subgrade. Background Technology

[0002] In the field of long-term health monitoring of transportation infrastructure (such as railway and highway subgrades) in permafrost regions, thermal thaw settlement is a key factor threatening the stability of the subgrade. Because permafrost is extremely sensitive to temperature changes, seasonal freeze-thaw cycles can cause uneven settlement on the subgrade surface. Traditional manual monitoring methods are inefficient and have limited coverage. While laser scanning technology exists and can acquire surface elevation data over a wide area, it still has shortcomings in practical applications.

[0003] Specifically, the monitoring environment for permafrost subgrades is complex (e.g., strong winds, drastic temperature changes, interference from surface water films), which can easily lead to measurement anomalies in local areas of laser scanning equipment. These anomalies manifest as significant isolated differences in elevation changes at individual grid points compared to adjacent points (e.g., sudden uplift or drop at a single point), contradicting the physical law of continuous and gradual change in permafrost settlement. If the original scanning data is directly used to generate settlement assessment results, it will mask the true settlement trend or introduce false deformation signals, thereby misleading engineering maintenance decisions—for example, misjudging noise caused by equipment vibration as local collapse, or ignoring the true settlement zone masked by anomalies. Summary of the Invention

[0004] To address the technical problem that existing technologies lack an automated mechanism for identifying and correcting anomalies in measurement data, making it difficult to improve data reliability while ensuring real-time monitoring, this invention provides a laser scanning measurement method and system for thermal thawing settlement of frozen soil subgrade.

[0005] A laser scanning measurement method for thermal thawing settlement of frozen soil subgrade includes: acquiring gridded surface data of the frozen soil subgrade monitoring area within the current scanning cycle based on laser scanning tracks deployed on both sides of the monitoring area; acquiring elevation data sequences of each grid point based on the gridded surface data; acquiring elevation change indices of each grid point based on the elevation data sequences of each grid point; acquiring multiple grid points adjacent to the i-th grid point and using them as multiple reference grid points for the i-th grid point; acquiring relative differences of each grid point based on the elevation change indices of the reference grid points and the elevation change indices of the i-th grid point; acquiring correction ratios of the i-th grid point based on the relative differences of the i-th grid point; acquiring target elevation data of each grid point based on the correction ratios of each grid point and the measured elevation data at the current moment; and forming a measurement result including gridded surface data based on the target elevation data of each grid point.

[0006] Optionally, obtaining the elevation change index of each grid point based on the elevation data sequence of each grid point includes: obtaining the data difference between the previous elevation data value and the subsequent elevation data value in the elevation data sequence of the i-th grid point; and summing all the data differences in the elevation data sequence of the i-th grid point to obtain the elevation change index of the i-th grid point.

[0007] Optionally, obtaining the relative differences of the i-th grid point based on the elevation change indices of each reference grid point and the elevation change index of the i-th grid point includes: subtracting the elevation change index of the j-th reference grid point of the i-th grid point from the elevation change index of the i-th grid point to obtain the j-th relative difference of the i-th grid point.

[0008] Optionally, obtaining the correction ratio of the i-th grid point based on multiple relative differences of the i-th grid point includes: dividing the sum of multiple relative differences of the i-th grid point by the number of relative differences of the i-th grid point to obtain the average difference of the i-th grid point; dividing the average difference of the i-th grid point by the elevation change index of the i-th grid point to obtain the correction ratio of the i-th grid point.

[0009] Optionally, obtaining the target elevation data of each grid point based on the correction ratio of each grid point and the measured elevation data at the current moment includes: subtracting the correction ratio of the i-th grid point from 1 to obtain the scaling ratio; multiplying the scaling ratio by the measured elevation data of the i-th grid point at the current moment to obtain the target elevation data of the i-th grid point.

[0010] Optionally, the measurement results including gridded surface data are generated based on the target elevation data of each grid point, including: generating corrected gridded surface data based on the target elevation data of each grid point and the corresponding location information of each grid point; and outputting the gridded surface data as the measurement results to represent the settlement state of the frozen soil subgrade monitoring area.

[0011] A laser scanning measurement system for thermal thawing settlement of frozen soil subgrade is also provided. The system includes: an acquisition module, used to acquire gridded surface data of the frozen soil subgrade monitoring area within the current scanning cycle based on laser scanning tracks deployed on both sides of the monitoring area, and to acquire elevation data sequences of each grid point based on the gridded surface data; a first data processing module, used to acquire elevation change indices of each grid point based on the elevation data sequences of each grid point; a second data processing module, used to acquire multiple grid points adjacent to the i-th grid point and use them as multiple reference grid points for the i-th grid point, and to acquire the relative differences of the i-th grid point based on the elevation change indices of the reference grid points and the elevation change indices of the i-th grid point, and to acquire the correction ratio of the i-th grid point based on the multiple relative differences; and a measurement module, used to acquire target elevation data of each grid point based on the correction ratio of each grid point and the measured elevation data at the current moment, and to form a measurement result including gridded surface data based on the target elevation data of each grid point.

[0012] Optionally, the first data processing module is further configured to: obtain the data difference between the previous elevation data value and the subsequent elevation data value in the elevation data sequence of the i-th grid point; and sum all the data differences in the elevation data sequence of the i-th grid point to obtain the elevation change index of the i-th grid point.

[0013] Optionally, the second data processing module is also used to: subtract the elevation change index of the j-th reference grid point from the elevation change index of the i-th grid point to obtain the j-th relative difference of the i-th grid point.

[0014] Optionally, the second data processing module is further configured to: divide the sum of multiple relative differences of the i-th grid point by the number of relative differences of the i-th grid point to obtain the average difference of the i-th grid point; divide the average difference of the i-th grid point by the elevation change index of the i-th grid point to obtain the correction ratio of the i-th grid point.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade, the spatial continuity characteristics of the physical laws of frozen soil settlement are utilized. By quantifying the consistency of the change trend of each grid point with its neighborhood (based on the spatial correlation of elevation change index), isolated anomalies (such as sudden rise / fall) caused by equipment vibration, water film reflection, or instantaneous strong winds are accurately located. Then, through an innovative correction ratio mechanism, the real-time measurement value is adaptively adjusted according to the spatial isolation degree of the anomaly point—the higher the anomaly degree, the larger the correction amplitude, and vice versa, the original data is retained. This can not only eliminate false deformation signals caused by local interference (such as misjudging the false signal of instrument vibration as collapse), but also restore the real settlement zone (such as a continuously gradually changing subsidence area) that is obscured by anomalies, thus outputting spatially continuous and physically consistent elevation data. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is a partial flowchart of the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade according to the present invention;

[0019] Figure 2 This is a schematic diagram of another part of the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade according to the present invention;

[0020] Figure 3 This is another schematic diagram of the process of laser scanning measurement method for thermal thawing settlement of frozen soil subgrade according to the present invention;

[0021] Figure 4 This is a schematic diagram illustrating the steps of the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade according to the present invention;

[0022] Figure 5 This is a schematic diagram of a portion of step S2 in the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade of the present invention;

[0023] Figure 6 This is a schematic diagram of a portion of step S3 in the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade of the present invention;

[0024] Figure 7 This is a schematic diagram of a portion of step S4 in the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade of the present invention;

[0025] Figure 8 This is a schematic diagram of another part of the steps in S4 of the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade in this invention. Detailed Implementation

[0026] 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.

[0027] 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.

[0028] 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.

[0029] like Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, a laser scanning method for measuring the thermal thawing settlement of frozen soil subgrade is provided. In one embodiment, the method includes:

[0030] S1. Based on the laser scanning tracks deployed on both sides of the frozen soil subgrade monitoring area, obtain the gridded surface data of the frozen soil subgrade monitoring area within the current scanning cycle, and obtain the elevation data sequence of each grid point based on the gridded surface data.

[0031] S2. Obtain the elevation change index of each grid point based on the elevation data sequence of each grid point;

[0032] S3. Obtain multiple grid points adjacent to the i-th grid point and use them as multiple reference grid points for the i-th grid point. Obtain the relative difference of the i-th grid point based on the elevation change index of each reference grid point and the elevation change index of the i-th grid point. Obtain the correction ratio of the i-th grid point based on the multiple relative difference of the i-th grid point.

[0033] S4. Obtain the target elevation data of each grid point based on the correction ratio of each grid point and the measured elevation data at the current moment, and form a measurement result including gridded surface data based on the target elevation data of each grid point.

[0034] In this embodiment, it should be noted that in S1, a high-precision, time-series spatial elevation dataset covering the permafrost subgrade monitoring area is constructed. The specific process is as follows: First, using mobile laser scanning equipment pre-installed on fixed tracks on both sides of the subgrade monitoring area, a high-density point cloud scan of the target permafrost subgrade surface is performed within a complete current scanning cycle (e.g., a complete inspection process). Considering that permafrost settlement monitoring needs to track changes over time, these tracks ensure the repeatability of each scanning path. After scanning, the large amount of unordered spatial point cloud data is spatially gridded. This processing is crucial; it discretizes the continuous three-dimensional space according to a preset resolution (e.g., a 10cm x 10cm grid), forming a regular two-dimensional grid covering the entire monitoring area. The center (or intersection) of each grid cell is a grid point. Then, based on the spatial coordinate correspondence, all elevation measurements (i.e., Z-coordinate values) of each grid point within the current scanning cycle (which may include multiple consecutive scans) are extracted and arranged in chronological order, forming a unique "elevation data sequence" for each grid point. This sequence records the raw data of the height change of the grid point over a period of time (such as the current scan cycle relative to the previous cycle or multiple consecutive scan points).

[0035] To illustrate further, consider a 50-meter-long section of frozen soil railway embankment. Parallel tracks are laid along both sides of the embankment, and a laser scanner moves along these tracks to scan the embankment surface. After scanning, the software divides the embankment surface area (assuming a length of 50 meters and a width of 6 meters) into 50,000 small square grid cells (assuming each grid cell has a side length of 10 cm). For a specific grid point near the centerline of the embankment (e.g., at coordinates (25m, 3m)), the software searches for all laser points that fall within or on the boundary of that grid cell during the scanner's operation, calculates or selects a representative point (e.g., an average value, an interpolation point), and records its elevation value. If the scanner performs several effective measurements on that point during the scan (e.g., due to equipment oscillation or multiple scans), then that point will obtain a data sequence containing multiple elevation values ​​(e.g., height values ​​H1, H2, H3...). All grid points underwent similar processing, ultimately establishing a data infrastructure that covers the entire monitoring area (grid point location information) and includes temporal variation characteristics (elevation data sequence of each point), providing input for subsequent analysis of the elevation variation trend of individual grid points over time (S2) and comparison of spatial correlation between points (S3).

[0036] In S2, the overall elevation change trend and magnitude of a single grid point relative to its own history (i.e., the starting time in the sequence) within the current scanning period are quantified. It follows the elevation data sequence generated in S1 (containing the continuously measured elevation values ​​of that grid point over a period of time). Specifically, for each grid point, the elevation difference between each adjacent measurement time in its elevation sequence is calculated one by one. This process traverses all consecutive time point pairs in the point's sequence. Then, the elevation differences generated at these adjacent times (including rising or falling trends) are algebraically accumulated. The result of this accumulation is the grid point's "elevation change index." This index represents the total accumulated elevation change of the point over a monitoring period. A positive value indicates overall uplift (e.g., frost heave), a negative value indicates overall subsidence, and a zero value indicates that the point's elevation has remained relatively stable. The absolute value of this index directly reflects the degree of movement (subsidence or uplift) of the point within the current monitoring period. This change index is the foundational data for crucial subsequent steps.

[0037] Furthermore, to illustrate with an example, consider the grid point located at (25m, 3m) mentioned in S1, whose elevation sequence is H1, H2, H3 (assuming they were measured in chronological order). Step S2 first calculates H2-H1 (the difference between the second and first measurements), then calculates H3-H2 (the difference between the third and second measurements). These two differences are then added together: (H2-H1) + (H3-H2), and this sum ultimately serves as the "elevation change index" for that point within this scanning cycle. If the location is continuously and slowly subsiding, for example, H1 > H2 > H3 (each subsequent elevation is lower than the previous one), then both (H2-H1) and (H3-H2) are negative, resulting in a large negative value after summing (representing the cumulative settlement). If a measurement is disturbed by birds, causing H2 to be abnormally high, it may temporarily make (H2-H1) positive. However, if the settlement is continuous, (H3-H2) may still be negative and have a large absolute value. The cumulative result may still be a significant negative value (because (H3-H2) cancels or partially cancels the positive deviation of (H2-H1), or its absolute negative value may be slightly smaller due to the disturbance. Crucially, this cumulative index is a single value; it is a "height summary" of the final displacement of the point at the end of the entire cycle relative to the beginning of the cycle. This summary value will be used in S3 to compare with neighboring points to determine whether the change at that point appears "isolated" or abnormal against the backdrop of the surrounding environment.

[0038] In S3, the i-th grid point represents any single grid point, where "i" is a positive integer less than or equal to the number of grid points. S3 assesses the "isolation" of each grid point's elevation change index relative to its surrounding area and calculates a "correction ratio" for data adjustment. This step is based on a key physical premise: real permafrost settlement is typically continuous and gradual in space, and the change trends of adjacent areas should have a certain similarity. Specifically, for a target grid point (e.g., point i), its neighboring grid points are first identified as a set of reference points (usually its direct neighbors in the vertical, horizontal, and even diagonal directions, forming a small neighborhood). Then, the elevation change index of the target point is compared with the change index of each reference point, calculating the "relative difference" of the target point relative to each reference point. This relative difference directly reflects the degree of difference in the target point's change compared to its neighboring points (the greater the difference, the more isolated). Finally, multiple relative differences between the target point and all its reference points are comprehensively analyzed. The key point is this: if a point's changes differ significantly from all its neighbors (all relative differences are significant in absolute value), it indicates that the point's behavior does not conform to the overall trend of the surrounding area, and it is highly likely to be an isolated point caused by measurement anomalies (such as a sudden drop or rise in a single point). Conversely, if its changes are similar to those of any of its neighbors (one or more relative differences are small in absolute value), its changes conform to the expected spatial continuity pattern, indicating that the data is reliable. Based on the overall magnitude and direction of multiple relative differences, a crucial "correction ratio" is calculated. The value and sign of this ratio comprehensively characterize the degree to which the point is considered anomalous in the current neighborhood (the larger the absolute value, the more anomalous it is, and the greater the correction required) and the direction of the deviation (positive or negative sign indicates the direction of correction).

[0039] Furthermore, to illustrate with an example, suppose there is a grid point (point A) in the center of a relatively flat area on the roadbed. Its S2 step result yields a significant negative change index (indicating large settlement). However, the change indices of the eight reference grid points surrounding point A are relatively close and have small absolute values ​​(indicating slight or stable changes). In this case, calculating the "relative difference" of point A relative to each of these eight neighbors will yield a large negative value (because the change index of point A is much smaller than any of the reference points), resulting in large absolute values ​​for all relative differences. Based on this, it would be determined that the change at point A is highly isolated, not conforming to the spatial continuity of frozen soil settlement, and point A is likely a false settlement anomaly caused by water droplet reflection or equipment vibration during a single scan. The final correction ratio will be a large negative value (because all differences are significant negative). Conversely, if point A is located at the center of a real subsidence zone, and the change indicators of its eight neighbors are also significantly negative (although the specific values ​​may vary slightly), then when calculating the relative difference between point A and each neighbor, some small values ​​(even close to zero, especially when the change of point A is very close to that of a neighbor) will appear, although there may be a few points with slightly larger differences. Based on this, it can be judged that the change of point A is supported by neighboring points, conforms to a continuous subsidence pattern, and the data is relatively reliable. In this case, the absolute value of the calculated correction ratio will be small, indicating low correction requirements. This correction ratio is designed to quantify the degree of deviation of a single point from the overall trend of change in its local neighborhood, providing a basis for subsequent targeted real-time data correction.

[0040] In S4, based on the "correction ratio" calculated in S3, the original elevation data of each grid point measured in real time at the current scanning moment is dynamically adjusted to generate more reliable "target elevation data," and finally outputs gridded surface measurement results that more realistically reflect the actual settlement state of the frozen soil subgrade. It uses the correction ratio obtained in step S3—which quantifies the relative degree of anomaly and direction of deviation of the data at that point in the current spatial neighborhood—to intervene in the real-time measurement values. Specifically, for each grid point, a "scaling ratio" is first calculated based on its unique correction ratio. The core meaning of this scaling ratio is: if the absolute value of the correction ratio is large (indicating that the change at that point is highly isolated and anomalous), the scaling ratio will deviate significantly from 1, meaning that a large correction (upward or downward adjustment) is needed for the original measurement value; if the absolute value of the correction ratio is small (indicating coordination with the surrounding area), the scaling ratio is close to 1, meaning that the original data is relatively reliable and only minor adjustments or no adjustment are needed. Then, the calculated scaling ratio is multiplied by the original elevation value directly measured by the scanner at that grid point at the current moment to obtain the "target elevation data" for that point. The target elevation data is a value corrected by intelligent spatial correlation analysis.

[0041] Furthermore, to illustrate with an example, suppose that at a certain scanning moment, a region on the roadbed surface is being scanned. A grid point (point B) is measured to have an elevation value by the laser scanner. If step S3 calculates that the correction ratio for point B is a negative number with a large absolute value (e.g., point A in the previous example showing "abnormal settlement" compared to surrounding stable points), then the calculated scaling ratio will be a number greater than 1. Multiplying this scaling ratio (greater than 1) by the current real-time measured elevation of point B will result in a target elevation value that is higher than the original measurement. This effectively corrects upwards the underestimated elevation value caused by abnormal interference (such as false low-level reflections caused by instantaneous water droplets), making it closer to the true height it should have when undisturbed, thus eliminating false "settlement pit" signals. Conversely, if point B is located at the center of a real settlement zone, also indicated by other points in the neighborhood, and its correction ratio has a small absolute value (because it is consistent with the nearby settlement trend), the scaling ratio will be very close to 1. The original measured elevation value, multiplied by the scaling ratio, remains essentially unchanged, and the true settlement information is preserved. Finally, by combining the target elevation data of all grid points after this spatial correlation judgment and correction with their location coordinates in the monitoring area, a complete, gridded roadbed surface elevation model is reconstructed.

[0042] In summary, the laser scanning measurement method for thermal thawing settlement of frozen soil subgrade utilizes the spatial continuity of the physical laws of frozen soil settlement. By quantifying the consistency of the change trend of each grid point with its neighborhood (based on the spatial correlation of elevation change indicators), it accurately locates isolated anomalies (such as sudden rises / falls in a single point) caused by equipment vibration, water film reflection, or instantaneous strong winds. Then, through an innovative correction ratio mechanism, it adaptively adjusts the real-time measurement value according to the spatial isolation degree of the anomaly point—the higher the anomaly degree, the larger the correction amplitude, and vice versa, the original data is retained. This can eliminate false deformation signals caused by local interference (such as misjudging false signals from instrument vibration as collapse) and restore the true settlement zone (such as continuously gradually changing subsidence areas) that are obscured by anomalies, thereby outputting spatially continuous and physically consistent elevation data.

[0043] like Figure 5 As shown, in one embodiment, obtaining the elevation change index of each grid point in S2 based on the elevation data sequence of each grid point includes:

[0044] S21. Obtain the difference between the previous elevation data value and the next elevation data value in the elevation data sequence of the i-th grid point;

[0045] S22. Add up all the data differences in the elevation data sequence of the i-th grid point and obtain the elevation change index of the i-th grid point.

[0046] In this embodiment, it should be noted that in S21, the difference between the elevation value measured at each subsequent moment and the elevation value measured at the previous moment in the sequence is calculated sequentially. This process traverses all consecutive pairs of time points in the grid point data sequence (for example, if the sequence has 4 elevation values ​​corresponding to times T1, T2, T3, and T4, then the differences between T2-T1, T3-T2, and T4-T3 are calculated). Each calculated difference (called a "data difference") is a signed quantity that explicitly indicates whether the point is rising (positive value) or falling (negative value) within this continuous time interval, and its specific change value. These data differences collectively constitute the core elements describing the refined, gradual elevation evolution trajectory of the grid point throughout the entire scanning cycle. They directly reflect the instantaneous or phased effects of environmental factors (such as frost heave and thaw settlement caused by temperature changes) or potential measurement interference at that point.

[0047] In S22, a single, summative index is synthesized to quantify the overall trend and cumulative effect of the height change of a grid point throughout the entire scanning cycle. Specifically, all data differences (positive or negative) are algebraically summed. This summation integrates the changes in discrete time segments. For example, if a point experiences a slight rise (positive data difference) in the early stage of the cycle, a significant subsidence (large negative data difference) in the middle stage, and a slight subsidence (small negative data difference) in the later stage, the final summation result will comprehensively reflect its overall subsidence trend. The resulting "elevation change index" represents the cumulative net height change of the point from the start to the end of the scanning cycle: a significant negative value indicates overall subsidence (settlement), a significant positive value indicates overall rise (frost heave), and a value close to zero indicates that the height is basically stable. The absolute value of this index intuitively reflects the total magnitude (severity) of the point's movement, while its sign clearly indicates the direction of movement (settlement or rise).

[0048] like Figure 6 As shown, in one embodiment, S3, obtaining the relative differences of the i-th grid point based on the elevation change indices of each reference grid point and the elevation change index of the i-th grid point includes:

[0049] S31. Subtract the elevation change index of the j-th reference grid point from the elevation change index of the i-th grid point to obtain the j-th relative difference of the i-th grid point.

[0050] In this embodiment, it should be noted that the j-th reference grid point of the i-th grid point represents any reference grid point of the i-th grid point, where "j" is a positive integer less than or equal to the number of reference grid points of the i-th grid point. In S31, the spatial variation consistency of the target grid point (i-th point) relative to each of its specific adjacent reference points (j-th point) is evaluated. Specifically, the elevation change index of the target point itself (calculated in S2) is subtracted from the elevation change index of the j-th reference point. The calculated result is called the "relative difference" of the target point relative to the j-th reference point. This difference is a signed numerical value. If the degree of change of the target point is much greater than that of the neighboring points (e.g., the target point sinks significantly while the neighboring points remain stable), the absolute value of the difference will be large and the sign depends on the direction of the target point's change (e.g., negative for settlement); if the change trends of the target point and a certain neighboring point are very similar (e.g., both slightly decreasing), the difference in their change indices is small, and the corresponding absolute value of the relative difference will be small or even close to zero. This relative difference directly quantifies the degree of abnormality or consistency of a target point compared to a specific neighboring point "control".

[0051] like Figure 6 As shown, in one embodiment, obtaining the correction ratio of the i-th grid point based on multiple relative differences in S3 includes:

[0052] S32. Divide the sum of the relative differences of the i-th grid point by the number of relative differences of the i-th grid point to obtain the average difference of the i-th grid point;

[0053] S33. Divide the average difference of the i-th grid point by the elevation change index of the i-th grid point to obtain the correction ratio of the i-th grid point.

[0054] In this embodiment, it should be noted that in S32, the arithmetic mean of the multiple relative differences between the target point and all its reference points (e.g., the eight surrounding points) calculated in step S31 is performed. This involves algebraically summing all these relative differences (each corresponding to a neighboring point) and then dividing by the total number of reference points to obtain the "average difference." This average difference incorporates information provided by all reference points within the neighborhood. Its key significance lies in the following: if the target point's change behavior is highly anomalous in the overall neighborhood (e.g., the differences from all neighbors are very significant), the average of multiple large relative differences will still result in an average difference with a large absolute value; conversely, if the target point's change can be supported by at least one or more neighbors (i.e., some relative differences have very small absolute values), the average will be lowered by these smaller differences, resulting in a smaller absolute value of the average difference. Therefore, the magnitude of the absolute value of the average difference characterizes the degree of macroscopic deviation of the target point from the overall trend of change in the entire neighboring region—the larger the value, the stronger the spatial isolation, and the more likely it is an anomaly caused by measurement noise or interference.

[0055] In S33, the average difference representing spatial isolation (result from S32) is transformed into an operational scaling factor (correction scale). This scaling factor guides the direction (sign) and intensity (absolute value) of subsequent corrections to real-time measurements. Specifically, the average difference of the target point is divided by its own elevation change index (result from S2). This "correction scale" has clear physical and mathematical meaning: its absolute value directly reflects the "relative degree of anomaly" (average deviation divided by its own change magnitude) of the target point's change relative to the overall trend of its neighborhood. For example, even if the absolute value of the average difference at a point is large, if the point's own change magnitude (absolute value of the elevation change index) is already large (e.g., located in a severe subsidence zone), its relative degree of anomaly (absolute value of the correction scale) may not be significant. The sign of the correction ratio precisely indicates the direction of the anomaly at the target point (related to the sign of the average difference): for example, if the target point shows subsidence in a generally stable region (resulting in a negative average difference), the correction ratio is also negative, meaning that the underestimated elevation value needs to be corrected upwards; if a point in a subsidence zone shows abnormal uplift (with a positive average difference), the correction ratio is positive, meaning that the overestimated elevation value needs to be corrected downwards. The larger the absolute value of the correction ratio, the higher the anomaly at that point, and the stronger the correction force needs to be applied to the real-time data.

[0056] like Figure 7 As shown, in one embodiment, S4, obtaining the target elevation data of each grid point based on the correction ratio of each grid point and the measured elevation data at the current moment includes:

[0057] S41. Subtract the correction ratio of the i-th grid point from 1 to obtain the scaling ratio;

[0058] S42. Multiply the scaling factor by the measured elevation data of the i-th grid point at the current moment to obtain the target elevation data of the i-th grid point.

[0059] In this embodiment, it should be noted that in S41, the correction ratio (characterizing the degree of anomaly and the direction of correction) generated in S3 is converted into an operational coefficient for actually modifying the measured value. Specifically, a simple operation is performed on the value 1—subtracting the correction ratio of the target grid point (the i-th point). This calculation result is called the "scaling ratio." The scaling ratio is significant: its deviation from 1 directly reflects the required correction intensity. If the absolute value of the correction ratio is large (indicating an anomaly in the target point's height), the scaling ratio will deviate significantly from 1 (greater than 1 or less than 1), indicating a need for substantial adjustment to the original elevation data. If the absolute value of the correction ratio is small (indicating that the target point change conforms to the neighborhood trend), the scaling ratio is very close to 1, meaning the data is basically reliable and requires only minimal or no modification. The sign of the scaling ratio is inherited from the sign of the correction ratio, jointly determining the direction of correction (raising or lowering).

[0060] In S42, the data correction is executed, directly affecting the latest measured elevation value at the target point at the current scan time. It utilizes a specific scaling factor calculated in S41: multiplying the scaling factor by the current real-time measured elevation value of that grid point. The result is the "target elevation data" for that point. This target elevation data is no longer the original, potentially disturbed reading, but a corrected value that has undergone intelligent spatial correlation analysis to remove (or significantly reduce) the influence of isolated local anomalies. Theoretically, the target elevation data should be closer to the true height of the point when unaffected by instantaneous disturbances (such as equipment vibration or water droplet reflections). The multiplication operation itself is a linear adjustment; its core logic lies in using the scaling factor to "neutralize" or "offset" the influence of identified anomalies on the current measurement value.

[0061] like Figure 8 As shown, in one embodiment, the measurement result including gridded surface data generated in S4 based on the target elevation data of each grid point includes:

[0062] S43. Based on the target elevation data of each grid point and combined with the location information corresponding to each grid point, generate the corrected gridded surface data.

[0063] S44. Output the gridded surface data as the measurement result to represent the settlement state of the frozen soil subgrade monitoring area.

[0064] In this embodiment, it should be noted that, based on the preset position of each grid point (i.e., its row and column coordinates in the grid, which was determined during the gridding in stage S1), the target elevation data calculated in S42 is accurately "placed" back to its corresponding spatial position. Since the target elevation data represents the corrected altitude of each point, when all points are assigned the corrected altitude value according to their coordinates, a regularly gridded three-dimensional digital surface model covering the entire permafrost subgrade monitoring area and reflecting the actual terrain at the current moment (after correction) is reconstructed. This model is called "corrected gridded surface data". Essentially, it restores a large number of discrete correction points into a continuous and complete set of surface elevation information through a regular grid structure and spatial indexing, preparing for the final evaluation.

[0065] S44 is the final delivery step of the entire method. It outputs the "corrected gridded surface data" constructed in S43 as the final, reliable measurement result. This output surface dataset minimizes local abrupt changes in elevation measurements caused by the complex monitoring environment of frozen soil subgrades (such as strong winds, temperature differences, and water films). The output data can be directly used to visualize the deformation of frozen soil subgrades. More importantly, it supports settlement analysis software in calculating assessment values ​​(such as settlement amount, settlement rate, and settlement area distribution maps), ultimately generating a settlement assessment report that reflects the true stability of the subgrade. These reports provide engineers with decision-making support (such as identifying specific settlement sections that need reinforcement), thereby guiding precise maintenance work and effectively avoiding erroneous judgments based on contaminated data (such as misjudging stable areas as dangerous areas or ignoring true subsidence zones masked by noise).

[0066] A laser scanning measurement system for thermal thawing settlement of frozen soil subgrade is also provided. The system includes:

[0067] The acquisition module is used to acquire gridded surface data of the frozen soil subgrade monitoring area within the current scanning cycle based on the laser scanning tracks deployed on both sides of the frozen soil subgrade monitoring area, and to acquire the elevation data sequence of each grid point based on the gridded surface data.

[0068] The first data processing module is used to obtain the elevation change index of each grid point based on the elevation data sequence of each grid point.

[0069] The second data processing module is used to acquire multiple grid points adjacent to the i-th grid point and use them as multiple reference grid points for the i-th grid point, and to acquire the relative difference of the i-th grid point based on the elevation change index of each reference grid point and the elevation change index of the i-th grid point, and to acquire the correction ratio of the i-th grid point based on the multiple relative difference of the i-th grid point.

[0070] The measurement module is used to acquire the target elevation data of each grid point based on the correction ratio of each grid point and the measured elevation data at the current moment, and to form a measurement result including gridded surface data based on the target elevation data of each grid point.

[0071] In one implementation, the first data processing module is further configured to: obtain the data difference between the previous elevation data value and the subsequent elevation data value in the elevation data sequence of the i-th grid point; and add up all the data differences in the elevation data sequence of the i-th grid point to obtain the elevation change index of the i-th grid point.

[0072] In one embodiment, the second data processing module is further configured to: subtract the elevation change index of the j-th reference grid point from the elevation change index of the i-th grid point to obtain the j-th relative difference of the i-th grid point.

[0073] In one embodiment, the second data processing module is further configured to: divide the sum of multiple relative differences of the i-th grid point by the number of relative differences of the i-th grid point to obtain the average difference of the i-th grid point; divide the average difference of the i-th grid point by the elevation change index of the i-th grid point to obtain the correction ratio of the i-th grid point.

[0074] In this embodiment, it should be noted that the specific operation method of the above-mentioned frozen soil subgrade thermal fusion settlement laser scanning measurement system has been described in detail in the embodiment of the frozen soil subgrade thermal fusion settlement laser scanning measurement method, and will not be elaborated here.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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 measuring thaw settlement of a frozen soil embankment by laser scanning, characterized in that, The method comprises the following steps: Based on the laser scanning track arranged on both sides of the permafrost roadbed monitoring area, the grid surface data sequence of the permafrost roadbed monitoring area in the current scanning period is obtained, and the elevation data sequence of each grid point is obtained based on the grid surface data sequence; According to the elevation data sequence of each grid point, the elevation change index of each grid point is obtained; A plurality of grid points adjacent to the i-th grid point are obtained as a plurality of reference grid points of the i-th grid point, and the relative difference amount of each reference grid point of the i-th grid point is obtained according to the elevation change index of each reference grid point of the i-th grid point and the elevation change index of the i-th grid point. The sum of the plurality of relative difference amounts of the i-th grid point is divided by the number of relative difference amounts of the i-th grid point to obtain the average difference amount of the i-th grid point; the average difference amount of the i-th grid point is divided by the elevation change index of the i-th grid point to obtain the correction ratio of the i-th grid point. According to the correction ratio of each grid point and the measured elevation data at the current time, the target elevation data of each grid point is obtained, and the measurement result including the grid surface data is formed according to the target elevation data of each grid point.

2. The method of claim 1, wherein, The method comprises the following steps: The data difference of the i-th grid point is obtained by subtracting the previous elevation data value from the next elevation data value in the elevation data sequence of the i-th grid point. The elevation change index of the i-th grid point is obtained by adding all the data differences in the elevation data sequence of the i-th grid point.

3. The method of claim 1, wherein, The method comprises the following steps: The j-th relative difference amount of the i-th grid point is obtained by subtracting the elevation change index of the j-th reference grid point of the i-th grid point from the elevation change index of the i-th grid point.

4. The method of claim 1, wherein, The method comprises the following steps: The scaling ratio is obtained by subtracting the correction ratio of the i-th grid point from 1. The target elevation data of the i-th grid point is obtained by multiplying the scaling ratio by the measured elevation data of the i-th grid point at the current time.

5. The method of claim 1, wherein, The method comprises the following steps: Based on the target elevation data of each grid point, the corrected grid surface data is generated by combining the position information corresponding to each grid point. The grid surface data is output as the measurement result to represent the settlement state of the permafrost roadbed monitoring area.

6. A frozen soil subgrade thaw settlement laser scanning measurement system, characterized in that, The permafrost roadbed thaw settlement laser scanning measurement system comprises: An acquisition module is configured to obtain the grid surface data sequence of the permafrost roadbed monitoring area in the current scanning period based on the laser scanning track arranged on both sides of the permafrost roadbed monitoring area, and obtain the elevation data sequence of each grid point based on the grid surface data sequence; A first data processing module is configured to obtain the elevation change index of each grid point according to the elevation data sequence of each grid point; A second data processing module is configured to obtain the relative difference amount of each reference grid point of the i-th grid point according to the elevation change index of each reference grid point of the i-th grid point and the elevation change index of the i-th grid point; The second data processing module is configured to obtain a plurality of grid points adjacent to the i-th grid point as a plurality of reference grid points of the i-th grid point, and obtain a plurality of relative difference amounts of the i-th grid point according to the elevation change index of each reference grid point of the i-th grid point and the elevation change index of the i-th grid point; The second data processing module is further configured to divide the sum of the plurality of relative difference amounts of the i-th grid point by the number of the relative difference amounts of the i-th grid point to obtain an average difference amount of the i-th grid point, and divide the average difference amount of the i-th grid point by the elevation change index of the i-th grid point to obtain a correction ratio of the i-th grid point; The measuring module is configured to obtain target elevation data of each grid point according to the correction ratio of each grid point and the measured elevation data at the current time, and form a measurement result including the gridded surface data according to the target elevation data of each grid point.

7. The thaw settlement laser scanning measurement system of claim 6, wherein, The first data processing module is further configured to: obtain a data difference between a previous elevation data value and a subsequent elevation data value in the elevation data sequence of the i-th grid point; add all the data differences in the elevation data sequence of the i-th grid point to obtain the elevation change index of the i-th grid point.

8. The frozen ground embankment thaw settlement laser scanning measurement system according to claim 6, characterized in that, The second data processing module is further configured to: obtain the j-th relative difference amount of the i-th grid point by subtracting the elevation change index of the j-th reference grid point of the i-th grid point from the elevation change index of the i-th grid point.

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