CN-121615088-B - Highway road surface non-uniform deformation identification method based on time sequence remote sensing data

CN121615088BCN 121615088 BCN121615088 BCN 121615088BCN-121615088-B

Abstract

The invention discloses a highway pavement non-uniform deformation identification method based on time sequence remote sensing data, and relates to the technical field of engineering measurement and deformation monitoring. The method comprises the steps of S1, laying a virtual measurement control point sequence along a design center line of a target highway according to preset measurement density to form a deformation measurement reference, obtaining multi-phase synthetic aperture radar image data and synchronous environment data, registering and resampling according to the deformation measurement reference to generate a standardized time sequence observation data sequence, S2, carrying out interference processing on the time sequence observation data sequence, analyzing a physical rule of phase change in combination with the environment data, and setting a dynamic stability index.

Inventors

CAO ZHENPING
LIAO RUI
HUANG YALEI
LIU XIAOTAO
ZHENG JIQIANG
SONG LEI
ZHANG ZE
Zha Man

Assignees

四川公路工程咨询监理有限公司
越西县交通建设工程质量安全站

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (7)

1. A highway pavement non-uniform deformation identification method based on time sequence remote sensing data is characterized by comprising the following steps: s1, laying a virtual measurement control point sequence along a design center line of a target highway according to preset measurement density to form a deformation measurement reference, acquiring multi-phase synthetic aperture radar image data and synchronous environment data, and registering and resampling according to the deformation measurement reference to generate a standardized time sequence observation data sequence; S2, carrying out interference processing on the time sequence observation data sequence, analyzing a physical rule of phase change by combining environment data, and setting a dynamic stability index; S3, based on road network topology constraint and preset road surface mechanical structure parameters, performing constraint unwrapping and filtering on time sequence phases of the measuring points, and resolving to obtain time sequence deformation sequences of the measuring points along the imaging geometric line-of-sight direction; S4, based on the time sequence deformation sequences of at least two different observation geometries, constructing a unified measurement adjustment model by taking the three-dimensional displacement of each measurement point as a parameter to be estimated, introducing a vertical dominant constraint and a space continuous smooth constraint, and adaptively solving the time sequence deformation sequences of each measurement point in the vertical direction through partition variance component estimation; S5, carrying out multi-scale sliding analysis and mutation detection on the time sequence deformation sequence in the vertical direction along the center line of the highway design, dividing uniform deformation sections, quantifying deformation differences among the sections, and outputting measurement results of non-uniform deformation according to a predefined risk assessment rule; the step S3 specifically comprises the following steps: Connecting adjacent measuring points based on the road network topology, and giving a composite weight of mechanical conduction constraint weight and observation quality weight to each connection to obtain a composite weight network; performing adaptive phase unwrapping of structural constraints based on the composite weight network; adopting a road surface mechanical response driving state model to carry out physical constraint filtering on the unwrapping phase; Converting the filtered phase estimation value into a time sequence deformation sequence along the imaging geometric line-of-sight direction, and synchronously outputting unwrapping reliability indexes; The self-adaptive phase unwrapping of the execution structure constraint comprises the steps of taking the space distribution of the mechanical conduction constraint weight as a main factor, screening out a deformation conduction main path in a composite weight network, triggering dynamic path reorganization when unwrapping along the main path is blocked due to poor local observation quality, automatically searching and switching to a parallel alternative conduction path in a topological network according to the similarity of the mechanical conduction constraint weight so as to maintain the unwrapping continuity; the construction mode of the road surface mechanical response driving state model comprises the following steps: Initializing a plurality of sub-mechanical response models respectively corresponding to the typical pavement structure types in parallel to form a competitive model set; In the filtering process, the matching likelihood of each sub-mechanical response model to the current time sequence phase is calculated, and weight is dynamically allocated or a dominant model is selected for state estimation.
2. The method for identifying the non-uniform deformation of the highway pavement based on the time sequence remote sensing data according to claim 1, wherein the step S1 of arranging the virtual measurement control point sequence according to the preset measurement density specifically comprises the following steps: Acquiring historical time sequence deformation measurement data of a covered target highway, extracting a historical deformation abnormal region and a deformation gradient region, and extracting a historical disease associated region based on a highway historical maintenance record; carrying out space fusion on the extracted characteristic areas, generating a comprehensive risk indication map along the highway line shape, and establishing a reference layout density model related to the risk degree of the map; Identifying a road design key section, and optimizing the density of the corresponding section in the reference layout density model by taking the road design key section as an optimization factor; And according to the optimized reference layout density model, self-adaptively determining a virtual measurement control point position sequence, and endowing each virtual measurement control point with an engineering attribute label, wherein the engineering attribute label at least comprises a road structure type and a design pile number.
3. The method for identifying the uneven deformation of the road surface based on the time sequence remote sensing data according to claim 1, wherein the method for setting the dynamic stability index in S2 specifically comprises the following steps: Performing differential interference processing on the time sequence observation data sequence to generate an original time sequence differential phase sequence of each space measurement point; the method comprises the steps of obtaining environment data, combining the environment data obtained synchronously, establishing a statistical response model of phase change and environment factors, removing periodic phase components caused by the environment factors from the original time sequence differential phase sequence by using the model, and obtaining a time sequence phase sequence after environment compensation; Based on the time sequence phase sequence after the environmental compensation, constructing a composite dynamic stability index of the consistency of the time dimension stability and the space dimension, wherein the evaluation threshold value of the dynamic stability index is differentially set according to the road structure type of the measuring point.
4. The method for identifying the uneven deformation of the road surface based on the time sequence remote sensing data, which is characterized in that the method for selecting the steady state deformation measuring points of the road surface in the step S2 comprises the following steps: Selecting measuring points with time dimension stability superior to a first threshold value to form a candidate point set, and removing isolated abnormal points which are inconsistent with deformation behaviors of direct topological neighborhood points of the measuring points according to space dimension consistency indexes in the candidate point set; mapping the candidate points subjected to topology optimization to the nearest virtual measurement control points according to the coordinates of the network nodes in the three-dimensional space where the candidate points are located; And binding engineering attribute labels corresponding to the virtual measurement control points mapped to each successfully mapped candidate point, and finally determining the engineering attribute labels as road surface steady-state deformation measurement points.
5. The method for identifying the non-uniform deformation of the highway pavement based on the time sequence remote sensing data is characterized in that the three-dimensional displacement comprises a vertical displacement component and a horizontal displacement component, the vertical dominant constraint is achieved by distributing priori weights higher than the horizontal displacement component to the vertical displacement component in parameters to be estimated, and the space continuous smooth constraint is used for applying a smooth punishment term to the differences of the three-dimensional displacement quantities of all adjacent point pairs based on the connection relation of adjacent measurement points in the highway network topology.
6. The method for identifying the non-uniform deformation of the highway pavement based on the time sequence remote sensing data according to claim 1 is characterized in that the partition basis of the partition variance component estimation is the source track of an observed value and the road structure type of a measuring point, in each iteration solution, the variance components of all the partitions are independently estimated according to the adjustment residual errors, and the weights of the observed values of the corresponding partitions in a measurement adjustment model are dynamically adjusted according to the variance components until the weights are converged.
7. The method for identifying the uneven deformation of the road surface based on the time-series remote sensing data according to claim 1, wherein the step S5 specifically comprises the following steps: Carrying out multi-scale sliding analysis on the time sequence deformation sequence in the vertical direction along the center line of the highway design, extracting deformation trend, fluctuation strength and frequency domain characteristics in each window, and fusing to generate a multi-dimensional characteristic sequence; calculating mutation confidence coefficient of each mileage point of the multi-dimensional feature sequence along a route by adopting probabilistic mutation detection independent of a fixed threshold value, determining a continuous mileage section with the mutation confidence coefficient continuously higher than a preset judgment threshold value as a deformation transition zone, and extracting the center mileage position as a boundary point of uneven deformation; dividing uniform deformation segments based on boundary points, identifying dominant deformation modes of each segment, and quantifying the average speed, trend and fluctuation difference between adjacent segments to form a difference vector; And inputting the difference vector and engineering attribute labels bound with measurement points forming each uniform deformation section into a predefined risk assessment rule base, outputting the comprehensive risk level, leading cause inference and treatment priority of each section through rule matching, and generating a measurement result comprising a design pile number and an assessment conclusion.

Description

Highway road surface non-uniform deformation identification method based on time sequence remote sensing data Technical Field The invention relates to the technical field of engineering measurement and deformation monitoring, in particular to a highway pavement non-uniform deformation identification method based on time sequence remote sensing data. Background The uneven deformation of the road surface is one of the main expression forms of road diseases, which affects the comfort and safety of driving, and the road surface structure is damaged when serious, thereby causing serious traffic accidents. Traditional deformation measurement methods (such as leveling) are limited by efficiency and cost, and it is difficult to achieve an efficient and continuous full line deformation survey of the highway. The time sequence synthetic aperture radar interferometry provides a new data source for wide area deformation monitoring, but the existing general method has obvious defects when being applied to strong continuity linear engineering such as highways, namely, the failure to fuse the network topology of the highways and the mechanical continuity constraint of the pavements, so that the deformation obtained by final calculation is insufficient in space continuity and engineering reliability, and is difficult to be used as an accurate engineering measurement result to define and quantify the uneven deformation section of the pavements. The invention patent with the publication number of CN120031392A discloses a deformation monitoring and controlling method, a system and a storage medium for road pavement. The sensor network is deployed to collect pavement data, the LSTM network is utilized to predict deformation risk and make grading strategy, and then the reinforced learning algorithm is utilized to automatically optimize traffic regulation and control scheme, so that intelligent monitoring and self-adaptive maintenance decision of pavement state are realized. However, the technical scheme and the similar technical scheme have the defects that the real-time or short-term response to the deformation is focused, the early identification and preventive early warning of the slow accumulated deformation are difficult to effectively realize, the monitoring capability is limited by the preset fixed sensor point position and the mobile inspection route, and the synchronous evaluation of the whole deformation state of the road network with wide area, full coverage and continuous space is difficult to realize. Disclosure of Invention The invention aims to provide a highway pavement non-uniform deformation identification method based on time sequence remote sensing data, so as to solve the problems in the background technology. In order to achieve the purpose, the invention provides the following technical scheme that the method for identifying the uneven deformation of the road surface based on the time sequence remote sensing data comprises the following steps: s1, laying a virtual measurement control point sequence along a design center line of a target highway according to preset measurement density to form a deformation measurement reference, acquiring multi-phase synthetic aperture radar image data and synchronous environment data, and registering and resampling according to the deformation measurement reference to generate a standardized time sequence observation data sequence; S2, carrying out interference processing on the time sequence observation data sequence, analyzing a physical rule of phase change by combining environment data, and setting a dynamic stability index; S3, based on road network topology constraint and preset road surface mechanical structure parameters, performing constraint unwrapping and filtering on time sequence phases of the measuring points, and resolving to obtain time sequence deformation sequences of the measuring points along the imaging geometric line-of-sight direction; S4, based on the time sequence deformation sequences of at least two different observation geometries, constructing a unified measurement adjustment model by taking the three-dimensional displacement of each measurement point as a parameter to be estimated, introducing a vertical dominant constraint and a space continuous smooth constraint, and adaptively solving the time sequence deformation sequences of each measurement point in the vertical direction through partition variance component estimation; s5, carrying out multi-scale sliding analysis and mutation detection on the time sequence deformation sequence in the vertical direction along the center line of the highway design, dividing uniform deformation sections, quantifying deformation differences among the sections, and outputting measurement results of non-uniform deformation according to a predefined risk assessment rule. Further, the step S1 of laying out the virtual measurement control point sequence according to the preset measurement density specifically