Laser scanning measurement method and system for hot melt settlement of frozen soil roadbed

Abstract

The invention discloses a frozen soil roadbed hot melt settlement laser scanning measurement method and system, and relates to the technical field of measurement, the method comprises the following steps: obtaining gridding surface data, and obtaining an elevation data sequence of each grid point; acquiring an elevation change index of each grid point according to the elevation data sequence of each grid point; acquiring a plurality of grid points adjacent to the ith grid point and taking the plurality of grid points as a plurality of reference grid points of the ith grid point, acquiring each relative difference quantity of the ith grid point, and acquiring a correction proportion of the ith grid point according to the plurality of relative difference quantities of the ith grid point; and obtaining target elevation data of each grid point according to the correction proportion of each grid point and the measured elevation data at the current moment, and forming a measurement result including the gridding surface data according to the target elevation data of each grid point. The method has the advantages of identifying abnormal values, dynamically correcting measurement and improving data reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of measurement, in particular to a frozen soil roadbed thaw settlement laser scanning measurement method and system. BACKGROUND

[0002] In the field of long-term health monitoring of traffic infrastructure (such as railway and highway roadbeds) in permafrost regions, thaw settlement is a key factor threatening the stability of roadbeds. Due to the extreme sensitivity of permafrost to temperature changes, seasonal freeze-thaw cycles can cause uneven settlement of the roadbed surface. Traditional manual monitoring methods are inefficient and have limited coverage. Although existing laser scanning technology can obtain large-scale surface elevation data, it still has defects in practical application.

[0003] Specifically, the monitoring environment of the frozen soil roadbed is complex (such as strong wind, large temperature difference, and surface water film interference), which can easily cause measurement outliers in local areas of the laser scanning equipment. These outliers show significant isolated differences in the elevation changes of individual grid points compared to their adjacent points (such as sudden single-point uplift or sudden drop), which is contrary to the overall continuous and gradual physical law of frozen soil settlement. If the original scanning data is directly used to generate settlement evaluation results, it will mask the true settlement trend or introduce false deformation signals, thereby misleading engineering maintenance decisions - for example, mistaking noise caused by equipment shaking as local collapse, or ignoring the true settlement zone masked by outliers. SUMMARY

[0004] To address the technical problem of the lack of automatic identification and correction mechanism for outliers in measurement data in the prior art, which makes it difficult to ensure real-time monitoring while improving data reliability, the present application provides a frozen soil roadbed thaw settlement laser scanning measurement method and system.

[0005] A frozen soil roadbed thaw settlement laser scanning measurement method, comprising: based on the laser scanning tracks arranged on both sides of the frozen soil roadbed monitoring area, obtaining the gridded surface data of the frozen soil roadbed monitoring area in the current scanning period, and based on the gridded surface data, obtaining the elevation data sequence of each grid point; obtaining the elevation change index of each grid point according to the elevation data sequence of each grid point; obtaining 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 obtaining 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, and obtaining a correction ratio of the i-th grid point according to the plurality of relative difference amounts of the i-th grid point; obtaining the 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 forming a measurement result including the gridded surface data according to the target elevation data of each grid point.

[0006] Optionally, the obtaining the elevation variation index of each grid point according to the sequence of the elevation data of each grid point comprises: obtaining a data difference of a later elevation data value minus an earlier elevation data value in the sequence of the elevation data of the i-th grid point; and adding all the data differences in the sequence of the elevation data of the i-th grid point to obtain the elevation variation index of the i-th grid point.

[0007] Optionally, the obtaining the relative difference amount of each reference grid point of the i-th grid point according to the elevation variation index of each reference grid point of the i-th grid point and the elevation variation index of the i-th grid point comprises: subtracting the elevation variation index of the j-th reference grid point of the i-th grid point from the elevation variation index of the i-th grid point to obtain the j-th relative difference amount of the i-th grid point.

[0008] Optionally, the obtaining the correction ratio of the i-th grid point according to the plurality of relative difference amounts of the i-th grid point comprises: dividing a sum of the plurality of relative difference amounts of the i-th grid point by a 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 dividing the average difference amount of the i-th grid point by the elevation variation index of the i-th grid point to obtain the correction ratio of the i-th grid point.

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

[0010] Optionally, the forming the measurement result comprising the gridded surface data according to the target elevation data of each grid point comprises: generating the corrected gridded surface data based on the target elevation data of each grid point and the position information corresponding to each grid point; and outputting the gridded surface data as the measurement result to represent the settlement state of the permafrost roadbed monitoring area.

[0011] The application further provides a frozen soil roadbed thaw settlement laser scanning measurement system, which comprises: an acquisition module, which is used for acquiring grid surface data of a frozen soil roadbed monitoring area in a current scanning period based on laser scanning tracks arranged on both sides of the frozen soil roadbed monitoring area, and acquiring elevation data sequences of each grid point based on the grid surface data; a first data processing module, which is used for acquiring an elevation change index of each grid point according to the elevation data sequences of the grid points; a second data processing module, which is used for acquiring a plurality of grid points adjacent to an i-th grid point as a plurality of reference grid points of the i-th grid point, and acquiring a plurality of relative difference amounts of the i-th grid point according to the elevation change indexes of the reference grid points of the i-th grid point and the elevation change index of the i-th grid point, and acquiring a correction ratio of the i-th grid point according to the plurality of relative difference amounts of the i-th grid point; and a measurement module, which is used for acquiring target elevation data of each grid point according to the correction ratio of each grid point and measurement elevation data at a current time, and forming a measurement result comprising the grid surface data according to the target elevation data of each grid point.

[0012] Optionally, the first data processing module is further used for: acquiring a data difference of a later elevation data value minus an earlier elevation data value in the elevation data sequence of the i-th grid point; and adding 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 further used for: 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 amount of the i-th grid point.

[0014] Optionally, the second data processing module is further used for: dividing a sum of the plurality of relative difference amounts of the i-th grid point by a 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 dividing the average difference amount 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 application has the following beneficial effects: In the whole laser scanning measurement method for thaw settlement of permafrost subgrade, the spatial continuity feature of the physical law of permafrost settlement is utilized, the consistency of the change trend of each grid point and its neighborhood (the spatial correlation of the elevation change index is taken as the criterion) is quantified, and the isolated abnormal points (such as single-point sudden rise / drop) caused by device jitter, water film reflection or instantaneous strong wind are accurately positioned; then, through the innovative correction ratio mechanism, the real-time measurement value of the abnormal point is adaptively adjusted according to the spatial isolation degree of the abnormal point - the higher the abnormality degree, the greater the correction amplitude, and vice versa, the original data is retained, which can not only eliminate the false deformation signal (such as instrument jitter false signal misjudged as collapse) caused by local interference, but also restore the real settlement zone (such as continuous gradual sinking area) covered by the abnormal value, so that the spatial continuous and physically consistent elevation data are output. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the specific embodiments or prior art of the present application, the drawings needed in the specific embodiment or prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual proportion.

[0017] Figure 1 Part of the flowchart of the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 2 Another part of the flowchart of the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 3 Another part of the flowchart of the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 4 Step diagram of the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 5 Part of the step diagram of S2 in the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 6 Part of the step diagram of S3 in the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 7 Part of the step diagram of S4 in the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application; Figure 8 Another part of the step diagram of S4 in the laser scanning measurement method for thaw settlement of permafrost subgrade of the present application. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0020] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0021] As shown in Figure 1 , Figure 2 , Figure 3 and Figure 4 , a frozen soil subgrade thermal melting settlement laser scanning measurement method is provided, in one embodiment, the method comprises: S1, based on the laser scanning tracks laid on both sides of the frozen soil subgrade monitoring area, obtaining the gridded surface data of the frozen soil subgrade monitoring area in the current scanning period, and obtaining the elevation data sequence of each grid point based on the gridded surface data; S2, obtaining the elevation change index of each grid point according to the elevation data sequence of each grid point; S3, obtaining 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 obtaining 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, and obtaining a correction ratio of the i-th grid point according to the plurality of relative difference amounts of the i-th grid point; S4, obtaining the 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 forming a measurement result including the gridded surface data according to the target elevation data of each grid point.

[0022] In this embodiment, it needs to be noted that in S1, a high-precision, time-series spatial elevation dataset covering the permafrost roadbed monitoring area is constructed. The specific process is as follows: first, a mobile laser scanning device installed on the fixed track on both sides of the roadbed monitoring area is used to perform high-density point cloud scanning on the target permafrost roadbed surface within a complete current scanning period (for example, a complete inspection process). Considering that permafrost settlement monitoring needs to track changes over time, these tracks ensure the repeatability of the scanning path each time. After scanning is completed, a large amount of unordered spatial point cloud data obtained is subjected to spatial gridding processing. This processing is key, which discretizes continuous three-dimensional space according to a preset resolution (for example, a 10cmx10cm grid) to form a regular two-dimensional grid covering the entire monitoring area. The center (or intersection point) of each grid cell is a grid point. Then, according to the spatial coordinate correspondence, all elevation measurements (i.e., Z coordinate values) of each grid point position within the current scanning period (which can include multiple continuous scans) are extracted, arranged in time order, and a "elevation data sequence" exclusive to each grid point is formed. This sequence records the height change raw data of the grid point within a period of time (such as the current scanning period relative to the previous period or multiple continuous scanning points).

[0023] Further, for example, the monitoring object is a 50-meter-long permafrost railway roadbed. Parallel tracks are laid along the rails on both sides, and the laser scanner moves on the tracks to sweep the roadbed surface. After scanning is completed, the software divides the roadbed surface area (assuming 50 meters long and 6 meters wide) into 50,000 small square grid cells (assuming each grid has a side length of 10cm). For a specific grid point near the center line of the roadbed (for example, at coordinates (25m, 3m)), all laser points falling within the grid cell or its boundary during the current scanner operation are found, and one representative point (such as the average value, interpolation point) is calculated or selected, and its elevation value is recorded. If the scanner performs several effective measurements on the point during the current scanning process (for example, due to device swinging or multiple scans), the point will obtain a data sequence containing multiple elevation values (such as height values H1, H2, H3...). All grid points are subjected to similar processing, and finally a data infrastructure covering the entire monitoring area (grid point position information) and containing time-series change characteristics (elevation data sequence of each point) is established, providing input for subsequent analysis of the elevation change trend of a single grid point over time (S2) and comparison of the spatial correlation between points (S3).

[0024] In S2, the overall trend and magnitude of the elevation change of each grid point in the current scanning period relative to its own history (i.e. the starting time in the sequence) is quantified. It takes the elevation data sequence generated in S1 (containing the elevation values of the grid point measured continuously over time) as input. Specifically, for each grid point, the height difference between each pair of adjacent measurement time points in its elevation sequence is calculated. This process traverses all pairs of consecutive time points in the sequence. Then, the height difference values (containing the rising or falling trend) generated by adjacent time points are algebraically accumulated. The result of this accumulation is the "elevation change indicator" of the grid point. This indicator represents the total elevation change accumulated by the point in the monitoring period. A positive value indicates that the point has an overall uplift (e.g. frost heaving), a negative value indicates that it has an overall subsidence, and a zero value indicates that the height of the point has remained basically stable. The absolute value of the indicator value directly reflects the intensity of the movement (subsidence or uplift) of the point in the current monitoring period. This change indicator is the basis data for the subsequent steps.

[0025] Further, for example, consider the grid point at (25m, 3m) mentioned in S1, whose elevation sequence is H1, H2, H3 (assuming measured in time order). The S2 step first calculates H2-H1 (i.e. the difference between the second measurement and the first measurement), and then calculates H3-H2 (i.e. the difference between the third and the second). Then, the two difference values are added: (H2-H1) + (H3-H2), and the sum is finally taken as the "elevation change indicator" of the point in this scanning period. If the location is continuously and slowly subsiding, such as H1 > H2 > H3 (the height at the later time is lower than the previous one), then (H2-H1) and (H3-H2) are both negative, and the cumulative subsidence amount is large after accumulation (a large negative value). If a bird disturbance causes H2 to be abnormally high, although (H2-H1) will be temporarily positive, if the subsidence is continuous, (H3-H2) may still be negative and have a large absolute value, and the result of the accumulation may still be a significant negative value (because (H3-H2) offsets or partially offsets the positive deviation of (H2-H1)), or the absolute value of the negative value may be slightly smaller due to the disturbance. The key is that this accumulation indicator is a single numerical value, which is a "summary" of the final displacement of the point at the end of the period relative to the start of the period. This summary value will be used in S3 to compare with neighboring points to determine whether the change of the point is "isolated" or abnormal in the context of the surrounding environment.

[0026] In S3, the i-th grid point represents any one of the grid points, "i" is a positive integer, and is less than or equal to the number of grid points. Among them, S3 evaluates the "isolation degree" of the change of the elevation change index of each grid point relative to the change of its surrounding area, and calculates a "correction ratio" for data correction based on this. This step is based on a key physical premise: the real permafrost settlement is generally continuous and gradual in space, and the change trend of adjacent areas should have certain similarity. The specific process is: for the target grid point (for example, point i), first determine its adjacent grid points as a set of reference points (usually its directly adjacent points in the up, down, left, right and even diagonal directions, forming a small neighborhood). Then, the difference between the elevation change index of the target point and the change index of each reference point will be compared one by one, and the "relative difference amount" of the target point relative to each reference point will be calculated. This relative difference amount intuitively reflects the difference degree of the change of the target point "highlighted" by a neighboring point (the greater the difference, the more isolated). Then, multiple relative difference amounts between the target point and all its reference points will be comprehensively analyzed. The key point is: if a point has a large difference with the changes of all its adjacent points (the absolute values of all relative difference amounts are significant), it means that the change behavior of the point does not conform to the overall trend of the surrounding area, and it is likely to be an isolated point caused by measurement anomalies (such as a single point sudden drop or sudden rise); on the contrary, if the change is similar to that of any adjacent point (there is one or more relative difference amounts with small absolute values), it means that the change conforms to the expected spatial continuity pattern, indicating that the data is reliable. Based on the overall size and direction of multiple relative difference amounts, a key "correction ratio" will be calculated. The value and sign of the ratio represent the degree of abnormality of the point in the current neighborhood (the larger the absolute value, the more abnormal, the greater the correction amplitude required) and the direction of deviation (the positive and negative signs indicate the correction direction).

[0027] Further, for example, assume that in the center of a relatively flat area on the embankment, there is a grid point (point A). Its S2 step results in a significant negative change indicator (indicating a large amount of settlement). But the change indicators of the eight reference grid points around point A are all close and have small absolute values (indicating slight or stable changes). At this time, the calculation of the "relative difference amount" of point A relative to each of the eight neighbors will result in a large negative value (because the change indicator of point A is much smaller than that of any reference point), resulting in a large absolute value of all relative difference amounts. Accordingly, it is determined that the change of point A is highly isolated and does not conform to the spatial continuity rule of frozen soil settlement, and point A is likely to be a false settlement anomaly point caused by water droplet reflection or equipment vibration during a single scan. The final correction ratio will be a large negative absolute value (because all differences are significant negative differences). Conversely, if point A is located at the center of a real settlement zone, and the change indicators of the eight neighbors around point A are also significant negative values (although the specific values may have slight differences), then when calculating the relative difference amount of point A relative to each neighbor, some small values (even close to zero difference amount, especially when the change amount of point A is very close to that of a neighbor) will appear, although there may be a few points with a slightly larger difference. Accordingly, it is determined that the change of point A is supported by neighboring points and conforms to the continuous settlement pattern, and the data is relatively reliable. At this time, the absolute value of the correction ratio calculated will be smaller, indicating that the correction requirement is low. This correction ratio is designed to quantify the deviation of a single point from the overall trend of the local neighborhood, providing a basis for targeted real-time data correction in the next step.

[0028] 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 time is dynamically adjusted to generate more reliable "target elevation data", and finally the grid surface measurement result which can more truly reflect the actual settlement state of the frozen soil embankment is output. It uses the correction ratio obtained in S3, which quantifies the relative abnormality and deviation direction of the point data in the current spatial neighborhood, to intervene in the real-time measurement value. The specific process is: for each grid point, first calculate a "scaling ratio" according to its exclusive correction ratio. The core meaning of this scaling ratio is: if the absolute value of the correction ratio is large (indicating that the change of the point is highly isolated and abnormal), the scaling ratio will deviate significantly from 1, meaning that the original measurement value needs to be corrected by a large margin (up or down); if the absolute value of the correction ratio is small (indicating coordination with the surroundings), the scaling ratio is close to 1, meaning that the original data is reliable and only needs to be fine-tuned or not. Then, multiply the calculated scaling ratio with the original elevation value directly measured by the scanner at the grid point at the current time to obtain the "target elevation data" of the point. This target elevation data is the value after intelligent spatial correlation analysis and correction.

[0029] 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.

[0030] 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.

[0031] 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: 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; 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.

[0032] 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.

[0033] 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).

[0034] 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: 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.

[0035] 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 anomalousness or consistency of a target point compared to a specific neighboring point "control".

[0036] 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: 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; 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.

[0037] 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.

[0038] In S33, the average difference quantity (S32 result) representing the spatial isolation is converted into an operational scaling factor (correction scaling) that will guide the direction (sign) and strength (absolute value) of the subsequent correction of the real-time measurement values. The specific method is: divide the average difference quantity of the target point by its own elevation change indicator (S2 result). The "correction scaling" generated in this way has a clear physical and mathematical meaning: the absolute value of the scaling directly reflects the "relative abnormality degree" of the target point change relative to the overall trend of its neighborhood (average deviation size divided by its own change scale). For example, even if the absolute value of the average difference quantity of a certain point is large, but if the change amplitude of the point itself (the absolute value of the elevation change indicator) is large (such as in a serious subsidence zone), the relative abnormality degree (the absolute value of the correction scaling) may not be large. The sign of the correction scaling accurately indicates the direction of the abnormality of the target point (related to the sign of the average difference quantity): for example, if the target point shows subsidence in a generally stable area (resulting in a negative average difference quantity), the correction scaling is also negative, meaning that the underestimated elevation value needs to be corrected upwards; if a point in the subsidence zone abnormally rises (the average difference quantity is positive), the correction scaling is positive, meaning that the overestimated elevation value needs to be corrected downwards. The larger the absolute value of the correction scaling, the higher the abnormality of the point, and the stronger the correction force that needs to be applied in the real-time data.

[0039] As shown in Figure 7 In one embodiment, obtaining the target elevation data of each grid point in S4 according to the correction scaling of each grid point and the measured elevation data at the current time includes: S41, subtracting 1 from the correction scaling of the i-th grid point to obtain a scaling scaling; S42, multiplying the scaling scaling by the measured elevation data of the i-th grid point at the current time to obtain the target elevation data of the i-th grid point.

[0040] 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).

[0041] 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.

[0042] like Figure 8 As shown, in one embodiment, the measurement result in S4, which includes gridded surface data, is generated based on the target elevation data of each grid point. 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. S44. Output the gridded surface data as the measurement result to represent the settlement state of the frozen soil subgrade monitoring area.

[0043] In the present embodiment, it is to be noted that the target elevation data calculated in S42 is accurately "placed" back to its corresponding spatial position according to the pre-set position of each grid point (i.e. its row and column coordinates in the grid, which is determined when the grid is formed in S1). Since the target elevation data represents the corrected elevation of each point, when all points are assigned with the corrected height value according to their coordinates, a regular gridded 3D digital surface model covering the entire permafrost embankment monitoring area and reflecting the actual terrain at the current time (after correction) is reconstructed. This model is referred to as "corrected gridded surface data". It essentially restores a large number of discrete correction points into a continuous and complete set of ground elevation information through a regular grid structure and spatial indexing, preparing for the final evaluation.

[0044] In S44, it is the result delivery step of the entire method. It outputs the "corrected gridded surface data" constructed in S43 as the final and reliable measurement result. This output surface data set maximally suppresses the local mutation abnormal values of elevation measurement caused by the complex monitoring environment of permafrost embankment (such as strong wind, temperature difference, water film, etc.). The output data can be directly used to visualize the deformation condition of the permafrost embankment, and more importantly, it can support the settlement analysis software to calculate evaluation values (such as settlement amount, settlement rate, settlement area distribution map, etc.), and finally form a settlement evaluation report that can reflect the true stable state of the embankment. These reports provide decision basis for engineers (such as determining the specific settlement section that needs to be reinforced), thereby guiding accurate maintenance work and effectively avoiding incorrect judgments based on contaminated data (such as misjudging a stable area as a dangerous area, or ignoring a real subsidence zone covered by noise).

[0045] Also provided is a permafrost embankment thaw settlement laser scanning measurement system, comprising: The acquisition module is configured to acquire gridded surface data of the permafrost embankment monitoring area in a current scanning period based on the laser scanning tracks arranged on both sides of the permafrost embankment monitoring area, and acquire elevation data sequences of the grid points based on the gridded surface data; The first data processing module is configured to acquire elevation change indicators of the grid points according to the elevation data sequences of the grid points; The second data processing module is configured to acquire 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, acquire a plurality of relative difference amounts of the i-th grid point according to the elevation change indicators of the reference grid points of the i-th grid point and the elevation change indicator of the i-th grid point, and acquire a correction ratio of the i-th grid point according to the plurality of relative difference amounts 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.

[0046] In one embodiment, the first data processing module is further configured to: obtain data differences between the previous elevation data value and the previous elevation data value in the elevation data sequence of the i-th grid point; and 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.

[0047] In one embodiment, the second data processing module is further configured to: subtract 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 amount of the i-th grid point.

[0048] In one embodiment, the second data processing module is further configured to: divide the sum of the 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 the 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 the correction ratio of the i-th grid point.

[0049] In the present embodiment, it should be noted that the specific manner of performing the operation of the above-mentioned permafrost subgrade thaw settlement laser scanning measurement system has been described in detail in the embodiments of the permafrost subgrade thaw settlement laser scanning measurement method, and will not be described in detail here.

[0050] The preferred embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited to the specific details in the above-described embodiments, and various simple modifications can be made to the technical solutions of the present application within the technical concept of the present application, and these simple modifications all belong to the protection scope of the present application.

[0051] In addition, it should be noted that each specific technical feature described in the above-described specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present application will not describe various possible combinations again.

[0052] In addition, various different embodiments of the present application can also be combined in any appropriate manner, as long as they do not deviate from the idea of the present application, and they should also be considered as disclosed by the present application.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A method for measuring thaw settlement of a frozen soil embankment by laser scanning, characterized in that, The method comprises the steps of: Based on the laser scanning track arranged on both sides of the permafrost roadbed monitoring area, the grid surface data 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; 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 each relative difference amount 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, and the correction ratio of the i-th grid point is obtained according to the plurality of relative difference amounts 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 steps of: 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 steps of: 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 steps of: The average difference amount of the i-th grid point is obtained by dividing the sum of the plurality of relative difference amounts of the i-th grid point by the number of relative difference amounts of the i-th grid point. The correction ratio of the i-th grid point is obtained by dividing the average difference amount of the i-th grid point by the elevation change index of the i-th grid point.

5. The method of claim 1, wherein, The method comprises the steps of: 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.

6. The method of claim 1, wherein, The method comprises the steps of: 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.

7. 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 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. The first data processing module is configured to obtain an elevation change index of each grid point according to an elevation data sequence of each grid point. The second data processing module is configured to obtain a plurality of reference grid points adjacent to the i-th grid point and serving as the 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, and obtain a correction ratio of the i-th grid point according to the plurality of relative difference amounts 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.

8. The frozen ground embankment thaw settlement laser scanning measurement system according to claim 7, characterized in that, 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.

9. The thaw settlement laser scanning measurement system for permafrost roadbeds of claim 7, wherein, 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.

10. The frozen ground embankment thaw settlement laser scanning measurement system of claim 7, wherein, The second data processing module is further configured to: obtain the average difference amount of the i-th grid point by dividing 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; obtain the correction ratio of the i-th grid point by dividing the average difference amount of the i-th grid point by the elevation change index of the i-th grid point.

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