A method for continuously monitoring the settlement of a highway pavement

Abstract

The present application belongs to the technical field of highway pavement monitoring, and relates to a highway pavement settlement continuous monitoring method, system, computer equipment, computer readable storage medium and computer program product, comprising: acquiring a target area grid first settlement sequence and a reference area grid second and benchmark point third settlement sequence; fitting a target area fourth settlement sequence based on the second and third sequences, and subtracting the first sequence to obtain a net settlement sequence; setting virtual points along the transverse direction at each cross section and assigning the net settlement, calculating the settlement gradient of adjacent virtual points in real time, determining the risk mileage by threshold value and connecting to form a risk settlement zone; performing similarity matching on the adjacent time risk settlement zone to form a settlement zone migration track, and arranging a virtual cross section and multiple actual monitoring points at the center of the track mileage to continuously monitor the net settlement. The present application can realize dynamic identification of the benchmark reference, so that the monitoring network always covers the maximum deformation gradient area, and avoids missing the high-risk settlement area.

Description

Technical Field

[0001] This invention belongs to the field of highway pavement monitoring technology, specifically relating to a method for continuous monitoring of highway pavement settlement. Background Technology

[0002] In areas such as goaf, soft soil foundations, and areas with over-extraction of groundwater, surface subsidence often has the following characteristics: First, there is a large-scale, long-term regional overall subsidence background; second, with the advancement of coal mining faces, the expansion of groundwater funnels, and the movement of the soft soil consolidation front, the boundaries and maximum deformation zones of subsidence basins will slowly drift in space.

[0003] The existing methods for continuous monitoring of highway pavement settlement generally involve selecting a relatively stable reference zone near the target monitoring section, establishing a reference reference system by setting up a set of benchmark points within the reference zone, and then setting up several monitoring cross-sections at predetermined mileage intervals along the highway, with monitoring points set up on each cross-section to form a monitoring network along the route. By periodically or continuously acquiring the elevation or displacement changes of the monitoring points relative to the reference reference system, it is determined whether settlement has occurred in the target monitoring section and the magnitude of the settlement.

[0004] However, under the background of surface subsidence with the above characteristics: on the one hand, the selected reference area itself may subside with the regional overall subsidence, which makes the actual subsidence of the road surface underestimated or even present a false impression of stability, that is, a pseudo-subsidence result of "the benchmark following the disease" occurs; on the other hand, since the monitoring cross-section is laid out statically, if the boundary of the subsidence basin and the maximum deformation zone migrate over time, the area covered by the monitoring network will change from the original high-risk subsidence area to the subsided area, while the new boundary that is undergoing strong gradient deformation is in the monitoring blind zone.

[0005] In summary, in areas such as mining subsidence zones, soft soil foundations, and areas with over-extraction of groundwater, the continuous monitoring method for road surface settlement based on a fixed reference system and a static monitoring network cannot identify the pseudo-settlement phenomenon caused by the sinking of the reference system as the region settles. It is also difficult to track the settlement basin boundaries and the maximum deformation zone that migrate over time, thus resulting in a systematic underestimation or omission of road surface settlement risk. Summary of the Invention

[0006] To address the aforementioned technical issues, this invention proposes a continuous monitoring scheme for highway pavement settlement. This scheme enables the automatic identification and elimination of the influence of overall regional deformation by the reference system, avoiding false settlement monitoring results caused by the reference system sinking with regional settlement. Furthermore, it enables the monitoring network to adaptively adjust as the settlement basin boundary and the maximum deformation zone migrate, ensuring that the monitoring network always covers the maximum deformation gradient area and avoiding the omission of high-risk settlement areas.

[0007] This invention is achieved through the following technical solution:

[0008] Firstly, a method for continuous monitoring of highway pavement settlement is proposed, comprising the following steps: acquiring a first settlement value sequence for each grid point within the target monitoring area and a second settlement value sequence for each grid point within a reference area based on radar remote sensing imagery; acquiring a third settlement value sequence for each benchmark point within the reference area based on ground measurement; wherein, the benchmark points are independently deployed ground measurement points within the reference area, and the benchmark points and grid points use different data acquisition methods; fitting a fourth settlement value sequence for each grid point within the target monitoring area based on the second settlement value sequence for each grid point within the reference area and the third settlement value sequence for each benchmark point; obtaining the difference between the first and fourth settlement value sequences for each grid point within the target monitoring area to obtain the net settlement value sequence for each grid point within the target monitoring area; on each cross section: along the cross section direction Multiple virtual points are set at intervals; based on the spatial correspondence, the net settlement value sequence of the grid points is assigned to each virtual point; at each monitoring time: the settlement gradient values ​​of all adjacent virtual points are obtained; the mileage points corresponding to the cross-sections where the absolute value of the maximum settlement gradient is greater than the gradient threshold are marked as risk mileage points; adjacent risk mileage points are connected in series to form risk settlement zones; for all two adjacent monitoring times: each risk settlement zone at the previous monitoring time is matched with each risk settlement zone at the next monitoring time; the mileage centers of all successfully matched risk settlement zones are connected to form one or more settlement zone migration trajectories; a virtual cross-section is established at each mileage center of each settlement zone migration trajectory; on each virtual cross-section: multiple actual monitoring points are set along the cross-section direction; the net settlement value of the highway pavement is monitored using multiple actual monitoring points.

[0009] Secondly, a continuous monitoring system for highway pavement settlement is proposed, including:

[0010] The settlement value sequence acquisition module is used to acquire the first settlement value sequence of each grid point in the target monitoring area and the second settlement value sequence of each grid point in the reference area based on radar remote sensing images; and to acquire the third settlement value sequence of each benchmark point in the reference area based on ground measurement methods; wherein, the benchmark point is a ground measurement point independently set up in the reference area, and the benchmark point and the grid point adopt different data acquisition methods;

[0011] The first settlement field acquisition module is used to fit the fourth settlement value sequence of each grid point in the target monitoring area based on the second settlement value sequence of each grid point in the reference area and the third settlement value sequence of each benchmark point.

[0012] The second settlement field acquisition module is used to obtain the difference between the first settlement value sequence and the fourth settlement value sequence of each grid point in the target monitoring area, so as to obtain the net settlement value sequence of each grid point in the target monitoring area.

[0013] The virtual point assignment module is used to: set multiple virtual points at intervals along the cross section direction on each cross section; and assign the net settlement value sequence of the grid points to each virtual point according to the spatial position correspondence.

[0014] The settlement zone generation module is used at each monitoring time to: obtain the settlement gradient values ​​of all adjacent virtual points; mark the mileage points corresponding to the cross-sections where the absolute value of the maximum settlement gradient is greater than the gradient threshold as risk mileage points; and connect adjacent risk mileage points to form a risk settlement zone.

[0015] The similarity matching module is used to perform similarity matching between each risk subsidence zone at the previous monitoring time and each risk subsidence zone at the next monitoring time for all two adjacent monitoring times.

[0016] The migration trajectory generation module is used to connect the mileage centers of all successfully matched risk settlement zones to form one or more settlement zone migration trajectories.

[0017] The virtual cross-section construction module is used to create a virtual cross-section at the center of each mileage of each settlement zone migration trajectory.

[0018] The monitoring point setting module is used to set multiple actual monitoring points along the cross-sectional direction on each virtual cross-section.

[0019] The net settlement monitoring module is used to monitor the net settlement value of the highway pavement using multiple actual monitoring points.

[0020] Thirdly, a computer device is proposed, comprising a memory, a processor, and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive data, and the processor is used to read the computer program and execute a method for continuous monitoring of road surface settlement as described in the first aspect.

[0021] Fourthly, a computer-readable storage medium is proposed, on which instructions are stored, which, when executed on a computer, perform a method for continuous monitoring of road surface settlement as described in any of the first aspects.

[0022] Fifthly, a computer program product containing instructions is proposed, which, when executed on a computer, causes the computer to perform a method for continuous monitoring of road surface settlement as described in the first aspect; the computer includes: a general-purpose computer, a special-purpose computer, or a programmable device.

[0023] Compared with existing technologies, this invention has the following advantages and beneficial effects: Based on the grid settlement data and benchmark point data of the target monitoring area and reference area, a regional background settlement field evolving over time is constructed. Background subtraction is performed on the observed settlement of the target monitoring area to obtain a net settlement value that only reflects the abnormal deformation of the highway itself, fundamentally eliminating the benchmark drift and settlement underestimation problems easily caused by relying on "absolutely stable benchmark points." Furthermore, by setting virtual points on each cross-section and calculating the lateral settlement gradient based on the net settlement distribution, high-gradient risk mileage sections are identified. Then, risk settlement zones at different times are further... Similarity matching and the formation of settlement zone migration trajectories not only dynamically pinpoint the current and evolving locations of high-risk settlement zones, but also enable continuous characterization of the spatial evolution of settlement. Furthermore, by establishing virtual cross-sections centered on the settlement zone migration trajectories, and accordingly deploying or utilizing actual monitoring points for targeted monitoring of net settlement, the monitoring network can adaptively adjust as the risk zone migrates. This allows the monitoring focus to shift from static, fixed positions to dynamic tracking along with the movement of the danger zone, thereby significantly improving the accuracy, relevance, and timeliness of highway pavement settlement monitoring, and reducing the systematic underestimation and under-judgment of settlement risks. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0025] Figure 1 This is a flowchart of a method for continuous monitoring of road surface settlement provided in Embodiment 1 of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. The embodiments described below are some, but not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] In the following description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other embodiments, well-known structures, materials, or methods are not specifically described to avoid obscuring the invention. Unless otherwise specified, the materials, instruments, and reagents used in the following embodiments are commercially available. Unless otherwise specified, the techniques used in the embodiments are conventional methods well known to those skilled in the art.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0029] Example 1: A method for continuous monitoring of highway pavement settlement is provided, applicable to highway sections in areas with slow overall deformation, such as mining subsidence areas, soft soil foundations, and groundwater over-extraction areas, where the boundaries of settlement basins and the maximum deformation zone migrate over time. This method is based on a dynamic benchmark and adaptive tracking of migrating settlement zones, and includes three execution stages: the first stage is to construct a regional dynamic benchmark to obtain the background settlement field; the second stage is to subtract the background to obtain the net settlement field and identify risk settlement zones on the cross-section; the third stage is to track the migration of risk settlement zones and deploy measured monitoring points based on the migration trajectory. Through these three stages, an integrated monitoring process is achieved, encompassing dynamic reference benchmarks, visualization of risk settlement zones, and adaptive adjustment of the monitoring network. Specifically, this method includes... Figure 1 The following steps are shown:

[0030] Step 1: Obtain the first settlement value sequence of each grid point in the target monitoring area and the second settlement value sequence of each grid point in the reference area; obtain the third settlement value sequence of each benchmark point in the reference area.

[0031] The purpose of this step is to prepare a complete and comparable original settlement data foundation for the subsequent construction of the dynamic background settlement field and net settlement field, and to perform constraint correction on the grid data using benchmark points.

[0032] Among them, the first settlement value sequence of each grid point in the target monitoring area is the original settlement data of the target monitoring area, which includes the results of the change of settlement data measured at each grid point location of the highway pavement and its surrounding area over time; the second settlement value sequence of each grid point in the reference area is used to characterize the overall settlement pattern and evolution trend at the regional scale, and is the direct data source for subsequent fitting of the background settlement field; the third settlement value sequence of the benchmark point has high accuracy and strong reliability. The third settlement value sequence is introduced into the background settlement fitting process to constrain and correct the grid settlement data of the reference area, avoiding systematic deviations in the background field caused by simply relying on grid data.

[0033] The first and second settlement value sequences were obtained through the following steps:

[0034] Step 1.1: Collect multiple radar images of the complete monitoring area.

[0035] The complete monitoring area consists of the target monitoring area and the reference area.

[0036] Radar imagery can record road surface settlement data for the entire monitoring area. By periodically capturing radar images of the entire monitoring area, a multi-period radar image set is formed over time. By analyzing the phase difference of radar images at different sampling times within the multi-period radar image set, the deformation of the road surface is calculated, thus reflecting the vertical settlement of the road surface. The periodic capturing refers to the frequency at which radar satellites capture radar images. The time interval between radar satellite image captures can be set to 6-12 days (different satellites have different revisit periods; for example, the Sentinel-1 satellite can achieve a 12-day revisit). It should be noted that the frequency of radar satellite image capture is a result of balancing monitoring timeliness and data validity. This implementation sets a capturing interval of 6-12 days, which can capture the slow settlement trend while avoiding data redundancy due to too short an interval or the omission of key deformation information due to too long an interval.

[0037] Step 1.2: Based on the regional plan, divide each phase of radar imagery into a first radar imagery of the target monitoring area and a second radar imagery of the reference area.

[0038] The regional planning refers to the pre-defined target monitoring area and reference area within the complete monitoring area.

[0039] Since the absolute value of settlement data in the target monitoring area is usually greater than that in the reference area, the purpose of dividing the first radar image into the second radar image is to distinguish the settlement data of different areas in the same radar image, so as to make it easier to fit the background settlement of the target monitoring area using the second radar image of the reference area.

[0040] Step 1.3: Perform gridding processing on each phase of the first radar image to obtain the first gridded image sequence of the target monitoring area; perform gridding processing on each phase of the second radar image to obtain the second gridded image sequence of the reference area.

[0041] Interferometric processing of the image data set is performed using InSAR processing software. The first radar image of the target monitoring area and the second radar image of the reference area are divided into grid cells (i.e., grid points). This allows the settlement information at each location in the target monitoring area and the reference area to be expressed as a sequence of settlement values ​​in units of grid points, providing a unified spatial coordinate system and data structure for subsequent calculations.

[0042] Step 1.4: For the first gridded image sequence: obtain the deformation of each grid point in two adjacent gridded images to obtain the first deformation sequence; For the second gridded image sequence: obtain the deformation of each grid point in two adjacent gridded images to obtain the second deformation sequence.

[0043] Deformation is the change in surface displacement corresponding to each grid point. According to the time sequence of radar image acquisition (as mentioned above, every 6 to 12 days as a time node), the deformation of each grid point at different times is recorded to form a data chain of deformation changes over time, i.e., deformation sequence.

[0044] Step 1.5: Based on the angle between the radar line of sight and the vertical direction, obtain the vertical displacement component of each deformation in the first deformation sequence to obtain the first settlement value sequence of each grid point in the target monitoring area; based on the angle between the radar line of sight and the vertical direction, obtain the vertical displacement component of each deformation in the second deformation sequence to obtain the second settlement value sequence of each grid point in the reference area.

[0045] Deformation is the displacement along the radar's line-of-sight. When radar satellites monitor surface deformation, they do not directly measure vertical subsidence, but rather the change in the straight-line distance between the satellite and the surface target. The direction of this straight-line distance is the radar's line-of-sight direction. Since there is an angle of incidence between the radar's line-of-sight direction and the vertical direction, the radar's line-of-sight direction is tilted towards the surface. Therefore, it is necessary to extract the vertical subsidence from the displacement along the radar's line-of-sight direction. The vertical subsidence is equal to the quotient of the displacement along the radar's line-of-sight direction and the cosine of the angle of incidence.

[0046] Furthermore, the third settling value sequence was obtained through the following steps:

[0047] Step 1.6: Collect the three-dimensional coordinates of each reference point within the reference area at each sampling time, and establish the three-dimensional coordinate sequence of each reference point.

[0048] The three-dimensional coordinates of the reference point are collected by GNSS base stations deployed at intervals along the highway. The GNSS base stations continuously collect satellite data (including information such as time, position, and signal error) at preset sampling time intervals (such as every 60 minutes), forming a time-series satellite data set. The satellite data set is then processed by post-processing software (such as RTKLIB, HG-Monitor, etc.) to output a three-dimensional coordinate sequence that changes with the sampling time.

[0049] Step 1.7: For each reference point: obtain the difference in normal coordinates between two adjacent sampling times to obtain the third settlement value sequence.

[0050] Step 2: Based on the second settlement value sequence of each grid point in the reference area and the third settlement value sequence of each benchmark point, fit the fourth settlement value sequence of each grid point in the target monitoring area.

[0051] The purpose of this step is to correlate and fit the grid settlement sequence within the reference area with the benchmark settlement sequence, and construct a background settlement sequence (fourth settlement value sequence) that evolves over time for each grid point within the target monitoring area, thereby forming a dynamic background settlement benchmark field and providing an accurate reference benchmark for subsequent net settlement calculations.

[0052] Before using the second and third settlement value sequences to determine the background settlement field of the target monitoring area, it is necessary to perform spatiotemporal alignment on the second and third settlement value sequences. This will enable consistent fusion of the grid settlement data of the reference area with the high-precision benchmark settlement data within a unified time reference and spatial coordinate framework. This will allow the two types of data to correctly correspond to the actual deformation state at the same time and spatial location during the background settlement field fitting process, thereby improving the fitting accuracy of the fourth settlement value sequence and avoiding background field deviations caused by temporal or spatial misalignments.

[0053] Spatiotemporal alignment includes two parts: temporal alignment and spatial alignment.

[0054] 1. Time alignment

[0055] The time alignment described in this embodiment refers to: using each radar image acquisition time of the second settlement value sequence as a reference, unifying the time dimension of the third settlement sequence to the time dimension of the second settlement value sequence. This is achieved by setting a time window of a certain length before and after each radar image acquisition time, i.e., setting a time window centered on the radar image acquisition time. The three-dimensional coordinate data and settlement displacement data corresponding to each radar image acquisition time within the time window are extracted. The equivalent three-dimensional coordinate data and equivalent settlement displacement data corresponding to each radar image acquisition time are calculated using the averaging method or interpolation method, thus aligning the deformation time series of each grid point with the three-dimensional coordinate time series of each reference point at the same time point.

[0056] The specific method is as follows: Steps 2.1 to 2.4 are executed at each radar image acquisition time.

[0057] Step 2.1.1: Generate a time window containing multiple observation times, with the radar image acquisition time as the center time.

[0058] Within the time window, the time interval between adjacent observation times is equal to the time interval between adjacent 3D coordinate data acquisition times.

[0059] Taking the radar image acquisition time of 14:00 on May 10, 2024 as an example: set a time window of 12 hours before and after 14:00 on May 10, 2024, then the total time window range is from 02:00 on May 10, 2024 to 02:00 on May 11, 2024, and the time interval between adjacent observation times is 60 minutes (corresponding to the sampling time interval of the above three-dimensional coordinates).

[0060] Step 2.1.2: For each benchmark point: extract the settlement value at each observation time from the third settlement value sequence to obtain the settlement value array.

[0061] There are two possible scenarios:

[0062] Case 1: Each observation moment within the time window has a corresponding third settlement value.

[0063] Continuing with the example of the radar image acquisition time of 14:00 on May 10, 2024: 24 third-order settlement values ​​(unit: mm) can be extracted within the time window, resulting in a settlement value array. Example data is as follows:

[0064] May 10, 2024: 02:00 (3.2), 03:00 (3.3), 04:00 (3.1), 05:00 (3.2), 06:00 (3.3), 07:00 (3.2), 08:00 (3.4), 09:00 (3.3), 10:00 (3.5), 11:00 (3.4), 12:00 (3.6), 13:00 (3.5) 14:00 (3.7), 15:00 (3.6), 16:00 (3.5), 17:00 (3.4), 18:00 (3.3), 19:00 (3.2), 20:00 (3.1), 21:00 (3.0), 22:00 (2.9), 23:00 (2.8); May 11, 2024: 00:00 (2.9), 01:00 (3.0).

[0065] Scenario 2: Within the time window, there are one or more observation times where the third settlement value is missing.

[0066] For example, among the 24 observation times corresponding to the above time window, the third settlement value is missing at 10:00 and 16:00, but can be extracted at the remaining 22 observation times. The resulting settlement values ​​are as follows:

[0067] May 10, 2024: 02:00 (3.2), 03:00 (3.3), 04:00 (3.1), 05:00 (3.2), 06:00 (3.3), 07:00 (3.2), 08:00 (3.4), 09:00 (3.3), 10:00 (missing), 11:00 (3.4), 12:00 (3.6), 13:00 (3.5) 14:00 (3.7), 15:00 (3.6), 16:00 (missing), 17:00 (3.4), 18:00 (3.3), 19:00 (3.2), 20:00 (3.1), 21:00 (3.0), 22:00 (2.9), 23:00 (2.8); May 11, 2024: 00:00 (2.9), 01:00 (3.0).

[0068] The settlement value array corresponding to each benchmark point can be established by following the method provided in the example above.

[0069] Step 2.1.3: For the settlement value array that has a third settlement value at each observation time: calculate the average value of all third settlement values ​​in the settlement value array to obtain the equivalent third settlement value at the time of radar image acquisition.

[0070] This step corresponds to situation 1 above: Since the settlement value array has a corresponding third settlement value at each observation time within the time window, the equivalent data of the third settlement value is obtained by calculating the average value of the settlement value array. That is, the average value of the 24 third settlement values ​​in the settlement value array is the equivalent third settlement value at the time of radar image acquisition. After calculation, the equivalent third settlement value at the time of radar image acquisition is ≈3.27, and finally the third settlement value at 14:00 on May 10, 2024 (the time of radar image acquisition) is 3.3 (rounded to one decimal place).

[0071] Step 2.1.4: For a settlement value array with missing data at one or more observation times: fill in each missing third settlement value by interpolation to obtain a new settlement value array, calculate the average of all third settlement values ​​in the new settlement value array, and obtain the equivalent third settlement value at the radar image acquisition time.

[0072] This step corresponds to scenario 2 above: The third settlement value is missing at 10:00 and 16:00. Interpolation must first be performed on these two times to obtain a settlement value array with complete data. Then, the equivalent third settlement value at the radar image acquisition time is calculated using the method described in scenario 1 above. Specifically:

[0073] For the missing third settlement value at 10:00: take the average of the third settlement values ​​corresponding to the adjacent observation times to obtain a new third settlement value - take the average of the third settlement value at 09:00 (3.3) and the third settlement value at 11:00 (3.4) (3.35) to obtain the third settlement value at 10:00.

[0074] For the missing third settlement value at 16:00: the average of the third settlement values ​​corresponding to the adjacent observation times is used to obtain a new third settlement value - the average of the third settlement value at 15:00 (3.6) and the third settlement value at 17:00 (3.4) (3.5) is taken to obtain the third settlement value at 16:00.

[0075] After interpolation, a new settlement value array (containing 24 settlement displacement data points) is obtained. The sum of the 24 third settlement values ​​in the new settlement value array is divided by 24, and the average value of the resulting third settlement values ​​is the equivalent third settlement value at the time of radar image acquisition. Calculation shows that the equivalent third settlement value at the time of radar image acquisition is ≈3.28. Finally, the settlement displacement data at 14:00 on May 10, 2024 (the time of radar image acquisition) is 3.3 (rounded to one decimal place).

[0076] 2. Spatial alignment

[0077] The spatial alignment described in this embodiment refers to mapping the second settlement value sequence of each grid point and the third settlement value sequence of each reference point to the geodetic coordinate system.

[0078] After the above spatiotemporal alignment, based on the second settlement value sequence of each grid point in the reference area and the third settlement value sequence of each benchmark point, the fourth settlement value sequence of each grid point in the target monitoring area is fitted. The specific method is as follows:

[0079] Step 2.2.1: Select points with data integrity greater than the integrity threshold from all grid points and reference points in the reference area to form a candidate point set.

[0080] Data integrity greater than the integrity threshold means that the ratio of the number of time nodes with valid data to the total number of time nodes in the second settlement value sequence of grid points and / or the third settlement value sequence of benchmark points is greater than the integrity threshold (e.g., 80%), thereby avoiding the situation of long-term data loss for the selected grid points and / or benchmark points.

[0081] The selected candidate point set forms the basis for constructing the background subsidence field. Regional subsidence is a slow, continuous, and overall deformation, requiring long-term data series to capture its deformation trend. Therefore, only by ensuring the integrity of the candidate point set can the overall slow deformation trend of the entire monitoring area be accurately reflected. If there are many missing data points in the candidate point set, it will lead to a distorted fit of the background subsidence field, making it unreliable as a dynamic benchmark.

[0082] For example:

[0083] Assuming a monitoring period of one year and a radar image data acquisition interval of 12 days, there are a total of 30 time nodes within a complete monitoring period. If a certain grid point has valid data in 27 out of the 30 time nodes, then the data completeness of the deformation time series of that point is 90% (>80%), and it can be included in the candidate point set.

[0084] Based on the above example method, all grid points and benchmark points with data integrity greater than 80% are selected to form a candidate point set.

[0085] Step 2.2.2: Remove candidate points from the candidate point set whose settlement change rate is greater than the change rate threshold.

[0086] The purpose of this step is to initially eliminate points that are affected by local anomalies.

[0087] The rate of settlement change refers to the rate of change of settlement value between two adjacent time points. For example:

[0088] Assuming the monitoring period is 48 days (for ease of explanation, the monitoring period is shortened from 1 year to 48 days in this example), and the time interval for collecting radar image data of a certain grid point in the candidate point set is 12 days, then there are a total of 5 time nodes in a complete monitoring period, and the second settlement value sequence is: 2.0mm (day 0), 2.3mm (day 12), 2.5mm (day 24), 2.7mm (day 36), and 8.9mm (day 48). The settlement change rate for days 0-12 is approximately (2.3-2.0) ÷ 12 days ≈ 0.025 mm / day; for days 12-24 it is approximately (2.5-2.3) ÷ 12 days ≈ 0.017 mm / day; for days 24-36 it is approximately (2.7-2.5) ÷ 12 days ≈ 0.017 mm / day; and for days 36-48 it is approximately (8.9-2.7) ÷ 12 days ≈ 0.517 mm / day. It can be seen that the settlement change rate for days 36-48 is much higher than the data change gradients for the other time periods. This can be determined as a sudden change in data caused by local construction, equipment failure, etc. Therefore, this grid point is removed from the candidate point set and will not participate in the subsequent background settlement field fitting. In addition, a change rate threshold can be set based on the rate of change of historical settlement values ​​without data mutations. This change rate threshold can be the maximum value of the historical data change gradient. For example, if the change rate threshold is 0.03, then the settlement change rate from day 36 to 48 is greater than 0.03, and the settlement change rate in other time periods is less than 0.03. In this case, the grid point is removed from the candidate point set and will not participate in the subsequent background settlement field fitting.

[0089] Step 2.2.3: Obtain the similarity between each remaining candidate point and every other remaining candidate point, and divide all remaining candidate points into multiple clusters based on the similarity.

[0090] By obtaining the similarity between candidate points and selecting a set of points with coordinated motion based on this, the background settlement field is fitted. This aims to eliminate local anomalies and noise interference, ensuring that the constructed fourth settlement value sequence truly reflects the overall settlement trend of the region, thereby improving the accuracy and reliability of the dynamic benchmark system and net settlement analysis.

[0091] The remaining candidate points refer to the candidate points retained after the initial screening in step 2.2.2. These candidate points have complete data and no obvious short-term mutations, which are the basis for subsequent time series clustering.

[0092] By calculating the similarity between any two remaining candidate points, we can determine whether the data change trends of the two candidate points are consistent. If the similarity is closer to 1, it means that the data change trends of the two remaining candidate points are more similar. Conversely, if the similarity is less than 1, it means that the data change trends of the two remaining candidate points are more different.

[0093] For example:

[0094] Assuming that after the screening in step 2.2.2, there are still 6 remaining candidate points in the candidate point set, the settlement time series of these 6 remaining candidate points at 4 time points are as follows:

[0095] Point A: 2.0mm, 2.3mm, 2.5mm, 2.7mm;

[0096] Point B: 1.8mm, 2.1mm, 2.4mm, 2.6mm;

[0097] Point C: 2.1mm, 2.2mm, 5.8mm, 6.0mm;

[0098] Point D: 2.2mm, 2.5mm, 2.8mm, 3.1mm;

[0099] Point E: 2.0mm, 2.4mm, 2.3mm, 2.7mm;

[0100] Point F: 1.9mm, 2.5mm, 3.2mm, 4.0mm.

[0101] Calculate the Pearson correlation coefficient between the two points:

[0102] The Pearson correlation coefficient between point A and point B is approximately 0.998.

[0103] The Pearson correlation coefficient between point A and point C is approximately 0.650.

[0104] The Pearson correlation coefficient between point A and point D is approximately 0.999.

[0105] The Pearson correlation coefficient between point A and point E is approximately 0.932.

[0106] The Pearson correlation coefficient between point A and point F is approximately 0.915.

[0107] The Pearson correlation coefficient between point B and point C is approximately 0.630.

[0108] The Pearson correlation coefficient between point B and point D is approximately 0.997.

[0109] The Pearson correlation coefficient between point B and point E is approximately 0.928.

[0110] The Pearson correlation coefficient between point B and point F is approximately 0.921.

[0111] The Pearson correlation coefficient between point C and point D is approximately 0.642.

[0112] The Pearson correlation coefficient between point C and point E is approximately 0.668.

[0113] The Pearson correlation coefficient between point C and point F is approximately 0.876.

[0114] The Pearson correlation coefficient between point D and point E is approximately 0.930.

[0115] The Pearson correlation coefficient between point D and point F is approximately 0.912.

[0116] The Pearson correlation coefficient between point E and point F is approximately 0.945.

[0117] Based on the similarity calculation results, clustering algorithms (such as hierarchical clustering and K-means clustering) were used to divide the remaining candidate points into multiple clusters. Points within each cluster exhibited highly similar settlement trends (if points in the same cluster were affected by the same factor, their settlement data all showed a slow and uniform settlement trend with similar settlement rates). Points in different clusters were affected by local anomalies, resulting in significant differences in settlement trends. By using clustering to remove local anomalies, a set of points reflecting common regional settlement was accurately selected, laying the foundation for constructing a smooth and reliable background settlement field.

[0118] Using hierarchical clustering or K-means clustering algorithms to cluster similarity coefficients is an existing technology. As those skilled in the art know, they know how to use hierarchical clustering or K-means clustering algorithms to cluster the above 15 similarity values.

[0119] Taking hierarchical clustering algorithm as an example:

[0120] Hierarchical clustering was performed on the above 15 similarity values:

[0121] S1. For the above 6 candidate points A, B, C, D, E, F, each candidate point is treated as an initial cluster, resulting in a total of 6 clusters: C0={A}, C1={B}, C2={C}, C3={D}, C4={E}, C5={F}.

[0122] S2. Hierarchical clustering is a stepwise merging clustering method based on maximum similarity:

[0123] First merger:

[0124] Calculate the pairwise similarity of all initial clusters (taking the maximum similarity coefficient r between point pairs): r = 0.998 for A and B, r = 0.999 for A and D → the maximum r between A, B, and D is 0.999; r < 0.999 for all other clusters. Then merge A and D into a new cluster C6 = {A, D}, and the clusters are now: {C6, C1, C2, C4, C5}.

[0125] Second merger:

[0126] Calculate the similarity between the new cluster and other clusters: C6(A,D) and B have r=0.998 (A and B) and 0.997 (D and B) → maximum r=0.998; then merge C6 and B into a new cluster C7={A,B,D}, and the clusters are: {C7,C2,C4,C5}.

[0127] Third merger:

[0128] Calculate the similarity between C7 and other clusters: C7 and E have r = 0.932 (A and E), 0.928 (B and E), 0.930 (D and E) → maximum r = 0.932; then merge C7 and E into a new cluster C8 = {A, B, D, E}, and the cluster is now {C8, C2, C5}.

[0129] Fourth merger:

[0130] Calculate the similarity between C8 and F: r = 0.915 (A and F), 0.921 (B and F), 0.912 (D and F), 0.945 (E and F) → maximum r = 0.945; then merge C8 and F into a new cluster C9 = {A, B, D, E, F}, and the cluster is now {C9, C2}.

[0131] 5th merger:

[0132] Merging C9 and C, the maximum value of r between them is 0.668 (E and C), resulting in a cluster {A, B, D, E, F, C}.

[0133] S3. Draw a hierarchical clustering tree (tree diagram) based on the merging order and similarity.

[0134] First, clusters A and D, with a Pearson correlation coefficient of 0.999 due to their settlement trends, were merged into one cluster. Next, this cluster containing A and D was merged with B with a high correlation coefficient of 0.998, forming a stable cluster consisting of A, B, and D. Subsequently, this cluster was merged with E with a correlation coefficient of 0.932. After that, the merged cluster was integrated with F with a correlation coefficient of 0.945. Finally, the cluster consisting of all points was merged with C with a moderate correlation coefficient of 0.668.

[0135] S4. By setting a similarity threshold, the final cluster is obtained by cutting from the clustering tree.

[0136] When the threshold r = 0.9: it is divided into 2 classes, class 1 = {A, B, D, E, F} (r ≥ 0.912) and class 2 = {C} (r = 0.668 < 0.9).

[0137] Step 2.2.4: Select a cluster whose coverage area is greater than the coverage threshold and whose overall change range is within the threshold range as a candidate cluster.

[0138] This step uses two indicators, coverage area and overall change magnitude, to filter background candidate clusters from multiple clusters after clustering. Coverage area refers to the area covered by all candidate points within a cluster. This is calculated by extracting the spatial coordinates of all points within the cluster, generating the smallest bounding polygon of the point set using GIS software, and then calculating the area of ​​this polygon. Overall change magnitude refers to the difference between the settlement value of the last time node and the settlement value of the first time node in the settlement sequence of each candidate point. A negative result indicates settlement, while a positive result indicates uplift.

[0139] The selection logic in this step is as follows: background subsidence is a large-scale overall deformation, and only clusters with large coverage areas can represent the common subsidence trend of the entire region; furthermore, background subsidence is a slow deformation of the entire region, with an intensity typically ranging from "several millimeters to tens of millimeters per year." Excessive variation may indicate a severely localized subsidence area, while insufficient variation may indicate a special stable point (lacking universality). Therefore, a cluster covering at least 80% of the complete detection area and whose overall variation falls within a preset threshold range is selected as a candidate background cluster. The threshold variation range can be an absolute threshold range—which can be determined based on industry survey data (such as the definition of slow regional subsidence in the "Specifications for Geological Investigation of Highway Engineering"). For example, for mined-out areas, the absolute threshold range for overall variation can be set to [-20, -5]; for soft soil foundations, it can be set to [-15, -3]; and for groundwater over-extraction areas, it can be set to [-10, -2].

[0140] By screening background candidate clusters, settlement data that is not affected by local anomalies and reflects the slow deformation of the region is provided for subsequent fitting of a reliable background settlement field.

[0141] Step 2.2.5: Using the spatial coordinates and settlement value sequence of all remaining candidate points in the candidate cluster, fit the background settlement surface of the complete monitoring area at each monitoring time.

[0142] Background settlement changes over time, therefore it needs to be processed step-by-step according to time nodes. A separate background settlement surface needs to be fitted for each monitoring time, ultimately forming a series of background settlement surfaces that evolve over time, such as the surface at time t1, the surface at time t2, and so on. n The time-matter surface is used to match the net settlement calculation requirements at different subsequent time points.

[0143] The samples used to generate the background settlement surface are derived from the spatial coordinates of all remaining candidate points within the background candidate cluster and the settlement value of each remaining candidate point at each time-aligned monitoring moment.

[0144] The background settlement surface is a mathematical model (such as a multivariate binomial model or a Kriging interpolation model). The independent variable of this mathematical model is the spatial coordinates of the remaining candidate points, and the dependent variable is the background settlement value. The purpose of fitting is to determine the parameters in the model using sample data, enabling the mathematical model to accurately predict the background settlement value at any location within the complete monitoring area at any time. The fitted background settlement surface covers the entire monitoring area, thus ensuring that all monitoring points within the road corridor (including grid points, reference points, and observation points) can find their corresponding background settlement values ​​on the surface.

[0145] Background settlement is caused by regional factors, and the amount of settlement varies slowly with spatial location (x, y coordinates). The settlement at any location is related to the settlement of the surrounding area. This characteristic determines that the spatial distribution of background settlement can be described by a continuous mathematical surface. The general form of the multivariate binomial is: z(x,y)= a 0+ a 1x+ a 2y+ a 3x 2 + a 4xy+ a 5y 2 Its function graph is a smooth quadratic surface. After substituting any x and y coordinates, the change in settlement value S is continuous and gradual (the first term describes linear tilting, and the second term describes nonlinear bending), which matches the core characteristics of background settlement: "no sudden changes and gradual changes".

[0146] The construction of the background settlement surface using multivariate binomial fitting is essentially "solving for function coefficients using discrete point data, and then using the function model to deduce the global settlement," as detailed below:

[0147] Step 1: Select discrete sample points. The discrete sample points are derived from the candidate clusters obtained in step 2.2.4.

[0148] Step 2: Solve for the polynomial coefficients. Substitute the (x, y, z) values ​​of each sample point into the multivariate binomial formula to construct an overdetermined system of equations. Solve for the optimal combination of coefficients using the least squares method. a 0~ a 5.

[0149] Step 3: Derive the global background settlement surface. Once the coefficients are determined, the multivariate binomial formula becomes the "universal model" describing the background settlement of the area. At this point, for any location within the target monitoring area (regardless of whether there is a monitoring point), simply inputting its x and y coordinates allows the calculation of the background settlement value z at that location using the formula. Integrating the calculation results from all locations forms a continuous background settlement surface covering the entire road corridor.

[0150] Examples are given below:

[0151] Assume there are 6 remaining candidate points and their spatial locations within the background candidate cluster: point A (5230, 3156), point B (5240, 3156), point C (5250, 3156), point D (5230, 3157), point E (5240, 3157), and point F (5250, 3157). At a certain radar image acquisition time (e.g., day 12), the acquired settlement values ​​are: point A 1.2 mm, point B 1.5 mm, point C 1.8 mm, point D 1.3 mm, point E 1.6 mm, and point F 1.9 mm.

[0152] Substituting into the multivariate binomial formula, the coefficients are obtained by solving using the least squares method: a 0 = -471.3 a 1 = 0.03 a 2 = 0.1, a 3=0, a 4=0, a 5=0, and the final fitting formula simplifies to: z(x,y)=-471.3+0.03x+0.1y.

[0153] Step 2.2.6: Based on the background settlement surface and the spatial coordinates of each grid point in the target monitoring area, obtain the settlement value of each grid point in the target monitoring area at each monitoring time, and obtain the fourth settlement value sequence of each grid point in the target monitoring area.

[0154] Based on the example in step 2.2.5, the background settlement surface on day 12 can be fitted using the aforementioned background settlement surface model. This background settlement surface can then be used to predict the settlement value at any monitoring point within the complete monitoring area on day 12. For example, for any location within the road corridor (e.g., x=5245, Y=3156.5), substituting the values ​​into the formula yields a background settlement of 1.7 mm.

[0155] By plotting the settlement values ​​at all locations, a continuous background settlement surface map can be obtained, clearly showing the linear gradual settlement trend of the area from southwest to northeast.

[0156] By following the method described in this example, the settlement value of each grid point in the target monitoring area at each monitoring time is obtained, resulting in the fourth settlement value sequence of each grid point in the target monitoring area.

[0157] Step 3: Obtain the difference between the first and fourth settlement value sequences of each grid point within the target monitoring area to obtain the net settlement value sequence of each grid point within the target monitoring area.

[0158] The purpose of this step is to extract the background settlement value from the settlement observation values ​​of the target monitoring area, thereby obtaining the true settlement of the target monitoring area.

[0159] For example, for one grid point P2 within the target monitoring area, assuming the coordinates of P2 are (5247, 3158), the actual monitored settlement value of point P2 on the 12th day is 2.0 mm. Based on the mathematical model z(x,y)=-471.3+0.03x+0.1y constructed in step 2 above, the background settlement value of monitoring point P2 can be calculated to be 1.91. Further calculation yields the net settlement value at monitoring point P2: 2.0-1.91=0.09 mm. The net settlement value of monitoring point P2 represents the true deformation caused by the road engineering itself (such as the roadbed and structures) or local anomalies, and is a core indicator for assessing road safety. For each grid point within the target monitoring area, the corresponding net settlement value can be calculated using the method described in the example above, resulting in a sequence of net settlement values ​​for each grid point within the target monitoring area.

[0160] Step 4: On each cross section: set multiple virtual points at intervals along the cross section direction; according to the spatial correspondence, assign the net settlement value sequence of the grid points to each virtual point.

[0161] The purpose of this step is to transform the net settlement field of each grid point within the target monitoring area into a series of ordered settlement points along each highway cross section, thereby preparing for subsequent calculations of lateral settlement gradients and dynamic identification of risk cross sections and risk settlement zones without being limited by the actual sensor placement.

[0162] It should be noted that this step requires a pre-constructed cross-section. Each cross-section corresponds to a kilometer point set along the centerline of the highway. The cross-section direction is perpendicular to the tangent direction of the highway centerline at the kilometer point, and the cross-section covers all lateral positions from the left shoulder to the right shoulder.

[0163] On each cross section, multiple virtual points are set at intervals along the cross section direction. For example, from the left shoulder (-30m) to the right shoulder (+30m) of the cross section, a virtual point is set every 10m, namely: (s, -30), (s, -20), (s, -10), (s, 0), (s, +10), (s, +20), (s, +30). Here, s represents the mileage.

[0164] By using the net settlement value sequence of each grid point in the entire target monitoring area obtained from step 3, we find the grid point that has a corresponding spatial coordinate position relationship with each virtual point, and directly assign the net settlement value sequence of the found grid point to the corresponding virtual point to obtain the net settlement value sequence of each virtual point.

[0165] Step 5: At each monitoring time: obtain the settlement gradient values ​​of all adjacent virtual points; mark the mileage points corresponding to the cross-sections where the absolute value of the maximum settlement gradient is greater than the gradient threshold as risk mileage points; connect adjacent risk mileage points to form a risk settlement zone.

[0166] The purpose of this step is to automatically identify and spatially cluster the road sections with the most significant lateral differential settlement along the current route, so as to provide clear high-risk area targets for subsequent settlement zone migration trajectory tracking and settlement zone-based monitoring network optimization.

[0167] On each cross section, each virtual point has net settlement values ​​at multiple monitoring times.

[0168] First, for each monitoring time point: obtain the settlement gradient values ​​of all adjacent virtual points on that cross section to obtain the net settlement distribution curve of a single mileage. For example, at the monitoring time (day 12), on the cross section corresponding to mileage point (K1+250), the net settlement values ​​of each virtual point are as follows: (K1+250, -30) = 1.5mm, (K1+250, -20) = 1.3mm, (K1+250, -10) = 1.2mm, (K1+250, 0) = 0.7mm, (K1+250, +10) = 0.9mm, (K1+250, +20) = 1.0mm, (K1+250, +30) = 1.2mm.

[0169] Then, the mileage points corresponding to the cross-sections where the maximum settlement gradient value is greater than the gradient threshold are marked as risk mileage points. In this embodiment, the settlement gradient is calculated as the difference in net settlement value ÷ the difference in lateral distance. For example, based on the net settlement values ​​of each virtual point on the cross-section corresponding to mileage point (K1+250) on day 12, a series of net settlement value differences can be calculated as follows: -0.02mm / m, -0.01mm / m, -0.05mm / m, +0.02mm / m, +0.01mm / m, +0.01mm / m. The absolute value of the maximum settlement gradient is 0.05mm / m. Furthermore, the gradient threshold is a pre-determined "safety threshold," which can be set according to the limits for uneven settlement in the "Highway Subgrade Design Specifications." For example, the net settlement gradient threshold for expressways / Class I highways can be set to 0.03~0.04mm / m. If the absolute value of the maximum settlement gradient is 0.05 mm / m and the net settlement gradient threshold is 0.04 mm / m, then the mileage point (K1+250) is marked as a risk mileage point.

[0170] Finally, adjacent risk mileage points are linked together to form risk settlement zones. After comparing gradient thresholds, multiple scattered risk mileage points can be selected. These risk settlement points may be dispersed or continuous. They are then arranged according to the chronological order of road mileage markers (from start to end) to form risk settlement points. Adjacent risk settlement points are determined based on their mileage markers (adjacent means the mileage interval between two risk settlement points is less than or equal to a preset mileage interval; for example, if the preset mileage interval is 10 meters, then two risk settlement points within 10 meters of each other are considered adjacent). Continuously adjacent risk mileage points are integrated into a single risk mileage zone, while non-adjacent risk mileage points are treated as independent risk settlement zones, ultimately resulting in a series of risk settlement zones. For example, the risk settlement point sequence after being arranged according to the chronological order of road mileage markers is: K1+280, K1+2... Given the sequence of risk settlement points K1+280, K1+300, K1+350, K1+360, K1+420, K1+430, and K1+440, the adjacent relationships in this sequence include: K1+280 and K1+290, K1+290 and K1+300, K1+350 and K1+360, K1+420 and K1+430, and K1+430 and K1+440. This results in three risk settlement zones: K1+280~K1+290~K1+300, K1+350~K1+360, and K1+420~K1+430~K1+440.

[0171] Step 6: For all two adjacent monitoring times: perform similarity matching between each risk settlement zone at the previous monitoring time and each risk settlement zone at the next monitoring time; connect the mileage centers of all successfully matched risk settlement zones to form one or more settlement zone migration trajectories.

[0172] The purpose of this step is to establish a stable one-to-one correspondence between risky subsidence zones in the time dimension, identify the spatial location of the same physical subsidence zone at different times, thereby forming a continuous subsidence zone migration trajectory, and providing a reliable analytical basis for subsequent dynamic monitoring and evolution risk assessment based on the subsidence zone trajectory.

[0173] The similarity matching refers to: finding the risk settlement zones identified at two adjacent monitoring times, and determining whether the risk settlement zones at the two monitoring times belong to the same dynamically changing settlement zone by judging whether the mileage distance between the two risk settlement zones is close and whether the lateral settlement difference is similar. Specifically, it includes the following steps:

[0174] Step 6.1: Obtain the difference in center mileage between the two risk settlement zones. If the difference in center mileage is less than the distance threshold, establish a matching relationship between the two risk settlement zones and obtain the settlement amplitude of the two risk settlement zones along the cross section.

[0175] For example, the risk subsidence zones at monitoring time t1 include: A (K1+280~K1+300) and B (K1+350~K1+360), and the risk subsidence zones at monitoring time t2 include: A' (K1+290~K1+310) and C (K1+420~K1+440). Risk subsidence zone A (K1+280~K1+300) needs to be matched with risk subsidence zones A' (K1+290~K1+310) and C (K1+420~K1+440), respectively. Similarly, risk subsidence zone B (K1+350~K1+360) needs to be matched with risk subsidence zones A' (K1+290~K1+310) and C (K1+420~K1+440), respectively. The matching process involves determining whether the difference in the center mileage distance between the two risk subsidence zones is less than a distance threshold. For example, matching risk settlement zone A (K1+280~K1+300) with risk settlement zone A' (K1+290~K1+310): the center mileage of risk settlement zone A (K1+280~K1+300) is (280+300) / 2 = 290 meters, and the center mileage of risk settlement zone A' is (290+310) / 2 = 300 meters. The difference in their center mileage distances is 10 meters (less than a preset distance threshold, such as 20 meters). Therefore, risk settlement zone A (K1+280~K1+300) and risk settlement zone A' (K1+290~K1+310) are spatially matched. Furthermore, given the spatial matching relationship between the two risk settlement zones, the settlement amplitude of each risk settlement zone is obtained along the cross-sectional direction. The settlement amplitude refers to the difference between the maximum and minimum settlement values. The overall settlement trend of the risk settlement zone is reflected by calculating the settlement amplitude of the risk settlement zone.

[0176] Step 6.2: If the difference in settlement amplitude between the two risk settlement zones is less than the settlement amplitude threshold, the two risk settlement zones are determined to be successfully matched.

[0177] If the difference in settlement amplitude between two risk settlement zones is less than the settlement amplitude threshold, it indicates that the settlement trends of the risk settlement zones at two adjacent monitoring times are similar, meaning that the risk settlement zone at the later monitoring time is a continuation of the risk settlement zone at the previous monitoring time. For example, if the settlement amplitude of risk settlement zone A (K1+280~K1+300) is 0.2 mm / m and the settlement amplitude of risk settlement zone A' (K1+290~K1+310) is 0.23 mm / m, and the settlement amplitude of both is 0.03 mm / m (less than the preset settlement amplitude threshold, such as 0.01 mm / m), then the two risk settlement zones can be determined to be successfully matched.

[0178] Step 7: Connect the mileage centers of all successfully matched risk settlement zones to form one or more settlement zone migration trajectories.

[0179] The purpose of this step is to establish a connection between two risk settlement zones in the time dimension, given that the risk settlement zones at two adjacent monitoring times have a spatial connection.

[0180] Step 8: Establish a virtual cross-section at the center of each mileage of the migration trajectory of each settlement zone.

[0181] The purpose of this step is to transform the settlement zone migration trajectory into a series of dynamic monitoring cross-sections that can be continuously analyzed, directly evaluated, and used to guide the placement of measurement points, enabling the monitoring cross-sections to adaptively adjust as the settlement basin boundary and the zone of maximum deformation migrate. The method for establishing virtual cross-sections is the same as the current method for establishing cross-sections at mileage points.

[0182] Step 9: On each virtual cross section: Multiple actual monitoring points will be set along the cross section direction; the net settlement value of the highway pavement will be monitored using multiple actual monitoring points.

[0183] The purpose of this step is to set up multiple actual monitoring points on each virtual cross section, centered on the migration trajectory of the settlement zone, and to use these actual monitoring points to measure and track the net settlement of the road surface at that location. This allows the monitoring network to dynamically focus as the risk settlement zone migrates, ensuring that the monitoring network composed of actual monitoring points always covers the area with the maximum deformation gradient, thus avoiding the omission of high-risk settlement areas.

[0184] Example 2: Corresponding to Example 1, this example provides a continuous monitoring system for highway pavement settlement, including:

[0185] The settlement value sequence acquisition module is used to acquire the first settlement value sequence of each grid point within the target monitoring area and the second settlement value sequence of each grid point within the reference area; and to acquire the third settlement value sequence of each benchmark point within the reference area.

[0186] The first settlement field acquisition module is used to fit the fourth settlement value sequence of each grid point in the target monitoring area based on the second settlement value sequence of each grid point in the reference area and the third settlement value sequence of each benchmark point.

[0187] The second settlement field acquisition module is used to obtain the difference between the first settlement value sequence and the fourth settlement value sequence of each grid point in the target monitoring area, so as to obtain the net settlement value sequence of each grid point in the target monitoring area.

[0188] The virtual point assignment module is used to: set multiple virtual points at intervals along the cross section direction on each cross section; and assign the net settlement value sequence of the grid points to each virtual point according to the spatial position correspondence.

[0189] The settlement zone generation module is used at each monitoring time to: obtain the settlement gradient values ​​of all adjacent virtual points; mark the mileage points corresponding to the cross-sections where the absolute value of the maximum settlement gradient is greater than the gradient threshold as risk mileage points; and connect adjacent risk mileage points to form a risk settlement zone.

[0190] The similarity matching module is used to perform similarity matching between each risk subsidence zone at the previous monitoring time and each risk subsidence zone at the next monitoring time for all two adjacent monitoring times.

[0191] The migration trajectory generation module is used to connect the mileage centers of all successfully matched risk settlement zones to form one or more settlement zone migration trajectories.

[0192] The virtual cross-section construction module is used to create a virtual cross-section at the center of each mileage of each settlement zone migration trajectory.

[0193] The monitoring point setting module is used to set multiple actual monitoring points along the cross-sectional direction on each virtual cross-section.

[0194] The net settlement monitoring module is used to monitor the net settlement value of the highway pavement using multiple actual monitoring points.

[0195] Furthermore, the highway pavement settlement continuous monitoring system also includes:

[0196] The radar image acquisition module is used to acquire multiple radar images of the entire monitoring area.

[0197] The first radar image processing module is used to divide each phase of radar imagery into a first radar image of the target monitoring area and a second radar image of the reference area according to the regional planning.

[0198] The second radar image processing module is used to perform gridding processing on each phase of the first radar image to obtain a first gridded image sequence of the target monitoring area; and to perform gridding processing on each phase of the second radar image to obtain a second gridded image sequence of the reference area.

[0199] The image sequence processing module is used to: obtain the deformation of each grid point in two adjacent gridded images for the first gridded image sequence, and obtain the first deformation sequence for the second gridded image sequence;

[0200] The deformation sequence processing module is used to obtain the vertical displacement component of each deformation in the first deformation sequence based on the angle between the radar line of sight and the vertical direction, so as to obtain the first settlement value sequence of each grid point in the target monitoring area; and to obtain the vertical displacement component of each deformation in the second deformation sequence based on the angle between the radar line of sight and the vertical direction, so as to obtain the second settlement value sequence of each grid point in the reference area.

[0201] Furthermore, the highway pavement settlement continuous monitoring system also includes:

[0202] The three-dimensional coordinate acquisition module is used to acquire the three-dimensional coordinates of each reference point within the reference area at each sampling time, and to establish a three-dimensional coordinate sequence for each reference point.

[0203] The coordinate processing module is used to obtain the difference in normal coordinates between two adjacent sampling times for each reference point, thus obtaining the third settlement value sequence.

[0204] The spatiotemporal alignment module is used to spatially and temporally align the second and third settlement value sequences.

[0205] The spatiotemporal alignment module includes:

[0206] The coordinate system mapping unit is used to uniformly map the second and third settlement value sequences to the geodetic coordinate system;

[0207] The time window generation unit is used to generate a time window containing multiple observation times, with the radar image acquisition time as the center time; within the time window, the time interval between adjacent observation times is equal to the time interval between adjacent three-dimensional coordinate data acquisition times;

[0208] The settlement value extraction unit is used to extract the settlement value at each observation time from the third settlement value sequence for each benchmark point, thereby obtaining a settlement value array.

[0209] The first equivalent value acquisition unit is used to calculate the average value of all third settlement values ​​in the settlement value array that has a third settlement value at each observation time, and obtain the equivalent third settlement value at the time of radar image acquisition.

[0210] The second equivalent value acquisition unit is used to fill in each missing third settlement value in a settlement value array that has missing data at one or more observation times by interpolation, to obtain a new settlement value array, and to calculate the average value of all third settlement values ​​in the new settlement value array to obtain the equivalent third settlement value at the time of radar image acquisition.

[0211] Furthermore, the first settlement field acquisition module includes:

[0212] The point selection and filtering unit is used to select points with a data integrity greater than the integrity threshold from all grid points and reference points in the reference area, forming a candidate point set;

[0213] The candidate point elimination unit is used to eliminate candidate points from the candidate point set whose settlement change rate is greater than the change rate threshold.

[0214] Candidate point clustering unit is used to obtain the similarity between each remaining candidate point and every other remaining candidate point, and to divide all remaining candidate points into multiple clusters based on the similarity.

[0215] The candidate cluster generation unit is used to select a cluster whose coverage area is greater than the coverage range threshold and whose overall change range is within the threshold range as a candidate cluster.

[0216] The background settlement fitting unit is used to fit the background settlement surface of the complete monitoring area at each monitoring time by utilizing the spatial coordinates and settlement value sequence of all remaining candidate points in the candidate cluster.

[0217] The settlement value sequence generation unit is used to obtain the settlement value of each grid point in the target monitoring area at each monitoring time based on the background settlement surface and the spatial coordinates of each grid point in the target monitoring area, and obtain the fourth settlement value sequence of each grid point in the target monitoring area.

[0218] Furthermore, the similarity matching module includes:

[0219] The distance difference acquisition unit is used to obtain the difference in the center mileage distance between two risk settlement zones;

[0220] The matching relationship establishment unit is used to establish a matching relationship between two risk settlement zones when the difference in center mileage distance is less than a distance threshold.

[0221] The settlement amplitude acquisition unit is used to acquire the settlement amplitude of the two risk settlement zones along the cross-sectional direction.

[0222] The matching result determination unit is used to determine that the two risk settlement zones are successfully matched when the difference in settlement amplitude between the two risk settlement zones is less than the sixth threshold.

[0223] Example 3: Based on the method provided in Example 1 and the system provided in Example 2, this example provides a computer device that executes the method described in Example 1 or any other method that may involve the method described in Example 1. The device includes a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program and executes the method described in Example 1 or any other method that may involve the method described in Example 1. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or last-in-first-out (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0224] The working process, working details and technical effects of the aforementioned computer device provided in this embodiment can be found in the method described in Embodiment 1 or any method that may involve the method described in Embodiment 1, and will not be repeated here.

[0225] Example 4: This example provides a computer-readable storage medium that stores instructions that include the method described in Example 1 or any other method that may involve the method described in Example 1. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the method described in Example 1 or any other method that may involve the method described in Example 1. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0226] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in this embodiment can be found in the method described in Embodiment 1 or any method that may be related to Embodiment 1, and will not be repeated here.

[0227] Example 5: This example provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the method described in Example 1 or any method that may involve the method described in Example 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0228] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0229] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0230] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0231] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the scope of the invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness and purpose of the invention, should still fall within the scope of the disclosed technical content. Furthermore, terms such as "upper," "lower," "left," "right," and "middle" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

Claims

1. A method for continuous monitoring of highway pavement settlement, characterized in that, Includes the following steps: The first settlement value sequence of each grid point within the target monitoring area and the second settlement value sequence of each grid point within the reference area are obtained based on radar remote sensing imagery. The third settlement value sequence of each benchmark point within the reference area is obtained based on ground-based measurements. The benchmark points are independently deployed ground measurement points within the reference area, and different data acquisition methods are used for the benchmark points and grid points. Based on the second settlement value sequence of each grid point within the reference area and the third settlement value sequence of each benchmark point, a fourth settlement value sequence of each grid point within the target monitoring area is fitted. The difference between the first and fourth settlement value sequences of each grid point within the target monitoring area is obtained to obtain the net settlement value sequence of each grid point within the target monitoring area. On each cross section: multiple virtual points are set at intervals along the cross section direction; according to the spatial correspondence, the net settlement value sequence of the grid points is assigned to each virtual point; At each monitoring time: acquire the settlement gradient values ​​of all adjacent virtual points; mark the mileage points corresponding to the cross-sections where the absolute value of the maximum settlement gradient is greater than the gradient threshold as risk mileage points; and connect adjacent risk mileage points to form a risk settlement zone. For all two adjacent monitoring times: perform similarity matching between each risk settlement zone at the previous monitoring time and each risk settlement zone at the next monitoring time; connect the mileage centers of all successfully matched risk settlement zones to form one or more settlement zone migration trajectories; A virtual cross-section is established at the center of each mileage of the migration trajectory of each settlement zone; on each virtual cross-section, multiple actual monitoring points are set along the cross-section direction; the net settlement value of the highway pavement is monitored using multiple actual monitoring points.

2. The method for continuous monitoring of highway pavement settlement according to claim 1, characterized in that, Before obtaining the first and second settlement value sequences, the following steps are also included: Collect multiple phases of radar imagery of the complete monitoring area; According to the regional plan, each phase of radar imagery is divided into the first radar imagery of the target monitoring area and the second radar imagery of the reference area. Each phase of the first radar image is processed into a grid to obtain the first gridded image sequence of the target monitoring area; each phase of the second radar image is processed into a grid to obtain the second gridded image sequence of the reference area. For the first gridded image sequence: obtain the deformation of each grid point in two adjacent gridded images to obtain the first deformation sequence; for the second gridded image sequence: obtain the deformation of each grid point in two adjacent gridded images to obtain the second deformation sequence. Based on the angle between the radar line of sight and the vertical direction, the vertical displacement component of each deformation in the first deformation sequence is obtained, resulting in the first settlement value sequence for each grid point within the target monitoring area; based on the angle between the radar line of sight and the vertical direction, the vertical displacement component of each deformation in the second deformation sequence is obtained, resulting in the second settlement value sequence for each grid point within the reference area.

3. A method for continuous monitoring of highway pavement settlement according to claim 1 or 2, characterized in that, Before obtaining the third settlement value sequence for each benchmark point within the reference area, the following steps are included: collecting the three-dimensional coordinates of each benchmark point within the reference area at each sampling time, and establishing a three-dimensional coordinate sequence for each benchmark point; for each benchmark point: obtaining the difference in normal coordinates between two adjacent sampling times to obtain the third settlement value sequence; After obtaining the third settlement value sequence for each benchmark point within the reference area, the following steps are included: spatially aligning and temporally aligning the second and third settlement value sequences; Spatial alignment includes the following steps: mapping the second and third settlement value sequences to a unified geodetic coordinate system; Time alignment includes the following steps: S1 to S4 are executed at each radar image acquisition time; S1: Using the radar image acquisition time as the center time, a time window containing multiple observation times is generated; within the time window, the time interval between adjacent observation times is equal to the time interval between adjacent 3D coordinate data acquisition times; S2: For each benchmark point: extract the settlement value at each observation time from the third settlement value sequence to obtain a settlement value array; S3: For a settlement value array that has a third settlement value at each observation time: calculate the average of all third settlement values ​​in the settlement value array to obtain the equivalent third settlement value at the time of radar image acquisition; S4: For a settlement value array with missing data at one or more observation times: fill in each missing third settlement value by interpolation to obtain a new settlement value array, calculate the average of all third settlement values ​​in the new settlement value array, and obtain the equivalent third settlement value at the time of radar image acquisition.

4. The method for continuous monitoring of highway pavement settlement according to claim 1, characterized in that, Fitting the fourth settlement value sequence for each grid point within the target monitoring area includes the following steps: From all grid points and reference points within the reference area, points with data integrity greater than the integrity threshold are selected to form a candidate point set; Remove candidate points from the candidate point set whose settlement change rate is greater than the change rate threshold; Obtain the similarity between each remaining candidate point and every other remaining candidate point, and divide all remaining candidate points into multiple clusters based on the similarity. Select a cluster whose coverage area is greater than the coverage threshold and whose overall change is within the threshold range as a candidate cluster; Using the spatial coordinates and settlement value sequence of all remaining candidate points within the candidate cluster, the background settlement surface of the complete monitoring area at each monitoring time is fitted. Based on the background settlement surface and the spatial coordinates of each grid point within the target monitoring area, the settlement value of each grid point within the target monitoring area at each monitoring time is obtained, resulting in the fourth settlement value sequence for each grid point within the target monitoring area.

5. The method for continuous monitoring of highway pavement settlement according to claim 1, characterized in that, Similarity matching includes the following steps: Obtain the difference in center mileage between two risk settlement zones. If the difference in center mileage is less than the distance threshold, establish a matching relationship between the two risk settlement zones and obtain the settlement amplitude of the two risk settlement zones along the cross section. If the difference in settlement amplitude between two risk settlement zones is less than the sixth threshold, the two risk settlement zones are considered to be successfully matched.

6. A continuous monitoring system for highway pavement settlement, characterized in that, include: The settlement value sequence acquisition module is used to acquire the first settlement value sequence of each grid point in the target monitoring area and the second settlement value sequence of each grid point in the reference area based on radar remote sensing images; and to acquire the third settlement value sequence of each benchmark point in the reference area based on ground measurement methods; wherein, the benchmark point is a ground measurement point independently set up in the reference area, and the benchmark point and the grid point adopt different data acquisition methods; The first settlement field acquisition module is used to fit the fourth settlement value sequence of each grid point in the target monitoring area based on the second settlement value sequence of each grid point in the reference area and the third settlement value sequence of each benchmark point. The second settlement field acquisition module is used to obtain the difference between the first settlement value sequence and the fourth settlement value sequence of each grid point in the target monitoring area, so as to obtain the net settlement value sequence of each grid point in the target monitoring area. The virtual point assignment module is used to: set multiple virtual points at intervals along the cross section direction on each cross section; and assign the net settlement value sequence of the grid points to each virtual point according to the spatial position correspondence. The settlement zone generation module is used at each monitoring time to: obtain the settlement gradient values ​​of all adjacent virtual points; mark the mileage points corresponding to the cross-sections where the absolute value of the maximum settlement gradient is greater than the gradient threshold as risk mileage points; and connect adjacent risk mileage points to form a risk settlement zone. The similarity matching module is used to perform similarity matching between each risk subsidence zone at the previous monitoring time and each risk subsidence zone at the next monitoring time for all two adjacent monitoring times. The migration trajectory generation module is used to connect the mileage centers of all successfully matched risk settlement zones to form one or more settlement zone migration trajectories. The virtual cross-section construction module is used to create a virtual cross-section at the center of each mileage of each settlement zone migration trajectory. The monitoring point setting module is used to set multiple actual monitoring points along the cross-sectional direction on each virtual cross-section. The net settlement monitoring module is used to monitor the net settlement value of the highway pavement using multiple actual monitoring points.

7. A continuous monitoring system for highway pavement settlement according to claim 6, characterized in that, Also includes: The radar image acquisition module is used to acquire multiple radar images of the entire monitoring area. The first radar image processing module is used to divide each phase of radar imagery into a first radar image of the target monitoring area and a second radar image of the reference area according to the regional planning. The second radar image processing module is used to perform gridding processing on each phase of the first radar image to obtain a first gridded image sequence of the target monitoring area; and to perform gridding processing on each phase of the second radar image to obtain a second gridded image sequence of the reference area. The image sequence processing module is used to: obtain the deformation of each grid point in two adjacent gridded images for the first gridded image sequence, and obtain the first deformation sequence for the second gridded image sequence; The deformation sequence processing module is used to obtain the vertical displacement component of each deformation in the first deformation sequence based on the angle between the radar line of sight and the vertical direction, so as to obtain the first settlement value sequence of each grid point in the target monitoring area; and to obtain the vertical displacement component of each deformation in the second deformation sequence based on the angle between the radar line of sight and the vertical direction, so as to obtain the second settlement value sequence of each grid point in the reference area.

8. A continuous monitoring system for road surface settlement according to claim 6 or 7, characterized in that, Also includes: The three-dimensional coordinate acquisition module is used to acquire the three-dimensional coordinates of each reference point within the reference area at each sampling time, and to establish a three-dimensional coordinate sequence for each reference point. The coordinate processing module is used to obtain the difference in normal coordinates between two adjacent sampling times for each reference point, thus obtaining the third settlement value sequence. The spatiotemporal alignment module is used to spatially and temporally align the second and third settlement value sequences. The spatiotemporal alignment module includes: The coordinate system mapping unit is used to uniformly map the second and third settlement value sequences to the geodetic coordinate system; The time window generation unit is used to generate a time window containing multiple observation times, with the radar image acquisition time as the center time; within the time window, the time interval between adjacent observation times is equal to the time interval between adjacent three-dimensional coordinate data acquisition times; The settlement value extraction unit is used to extract the settlement value at each observation time from the third settlement value sequence for each benchmark point, thereby obtaining a settlement value array. The first equivalent value acquisition unit is used to calculate the average value of all third settlement values ​​in the settlement value array that has a third settlement value at each observation time, and obtain the equivalent third settlement value at the time of radar image acquisition. The second equivalent value acquisition unit is used to fill in each missing third settlement value in a settlement value array that has missing data at one or more observation times by interpolation, to obtain a new settlement value array, and to calculate the average value of all third settlement values ​​in the new settlement value array to obtain the equivalent third settlement value at the time of radar image acquisition.

9. A continuous monitoring system for road surface settlement according to claim 6 or 7, characterized in that, The first settlement field acquisition module includes: The point selection and filtering unit is used to select points with a data integrity greater than the integrity threshold from all grid points and reference points in the reference area, forming a candidate point set; The candidate point elimination unit is used to eliminate candidate points from the candidate point set whose settlement change rate is greater than the change rate threshold. Candidate point clustering unit is used to obtain the similarity between each remaining candidate point and every other remaining candidate point, and to divide all remaining candidate points into multiple clusters based on the similarity. The candidate cluster generation unit is used to select a cluster whose coverage area is greater than the coverage range threshold and whose overall change range is within the threshold range as a candidate cluster. The background settlement fitting unit is used to fit the background settlement surface of the complete monitoring area at each monitoring time by utilizing the spatial coordinates and settlement value sequence of all remaining candidate points in the candidate cluster. The settlement value sequence generation unit is used to obtain the settlement value of each grid point in the target monitoring area at each monitoring time based on the background settlement surface and the spatial coordinates of each grid point in the target monitoring area, and obtain the fourth settlement value sequence of each grid point in the target monitoring area.

10. A continuous monitoring system for road surface settlement according to claim 6 or 7, characterized in that, The similarity matching module includes: The distance difference acquisition unit is used to obtain the difference in the center mileage distance between two risk settlement zones; The matching relationship establishment unit is used to establish a matching relationship between two risk settlement zones when the difference in center mileage distance is less than a distance threshold. The settlement amplitude acquisition unit is used to acquire the settlement amplitude of the two risk settlement zones along the cross-sectional direction. The matching result determination unit is used to determine that the two risk settlement zones are successfully matched when the difference in settlement amplitude between the two risk settlement zones is less than the sixth threshold.

11. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive data, and the processor is used to read the computer program and execute a method for continuous monitoring of road surface settlement as described in any one of claims 1-5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, perform a method for continuous monitoring of road surface settlement as described in any one of claims 1-5.

13. A computer program product containing instructions, characterized in that, When the instructions are executed on a computer, the computer performs a method for continuous monitoring of road surface settlement as described in any one of claims 1-5; the computer includes: a general-purpose computer, a special-purpose computer, or a programmable device.

Images