A multi-track InSAR deformation attribution reconstruction and coupling risk early warning method and system

Abstract

The application provides a kind of multi-track InSAR deformation attribution reconstruction and coupling risk early warning method and system;The application collects multi-temporal ascending / descending track SAR image, city wall structure, historical disease, peripheral disturbance and other multi-source data, constructs city wall structure semantic model;Through time series InSAR processing, stable scattering points are extracted, and scattering point attribution probability is calculated by combining space, radar, shielding and other factors to reconstruct three types of deformation field of wall body, wall base and peripheral disturbance;The vertical, along-wall and cross-wall deformation components are decomposed using multi-track data, the risk factors such as deformation gradient, coupling difference and disturbance time lag response are calculated, the comprehensive risk index is constructed, the dynamic early warning threshold is generated, and finally the risk level and disposal suggestion are outputted.The application solves the problems of traditional InSAR deformation attribution ambiguity, coupling deformation identification difficulty and early warning false alarm and missed alarm, and can realize global, long-term and automatic deformation monitoring and risk early warning of city wall, and is suitable for preventive protection of linear cultural relics.

Description

Technical Field

[0001] This invention belongs to the technical fields of cultural heritage protection, radar remote sensing deformation monitoring, and structural safety risk assessment; in particular, it relates to a multi-track InSAR deformation attribution reconstruction and coupling risk early warning method and system. Background Technology

[0002] As a typical linear cultural heritage, city walls are characterized by their long spatial scale, strong structural continuity, long history, complex wall materials, significant environmental disturbances, and high difficulty in protection and management. The structure of a city wall typically includes the wall itself, foundation, gates, watchtowers, ramparts, corners, slope protection, drainage facilities, and surrounding construction control zones. Affected by factors such as foundation settlement, changes in groundwater levels, traffic vibrations, surrounding construction, excavation of foundation pits, development of underground spaces, rainwater erosion, poor drainage, plant root erosion, material aging, and differences in historical maintenance, city walls may exhibit problems such as uneven settlement, wall cracking, local tilting, lateral displacement, wall bulging, foundation instability, and localized collapse.

[0003] Existing technologies for monitoring the safety of city walls mainly include manual inspection, crack gauge monitoring, leveling, total station surveying, GNSS monitoring, inclinometer monitoring, 3D laser scanning, UAV oblique photography, and close-range photogrammetry. These methods can obtain high-precision monitoring data in local areas, but they still have the following shortcomings:

[0004] First, the monitoring coverage is limited. City walls are usually distributed in a narrow strip, making it difficult to economically, continuously, and long-term cover the entire section of the city wall using traditional point-based monitoring methods.

[0005] Second, the ability to trace historical deformation is insufficient. Most ground-based monitoring equipment requires on-site deployment, making it difficult to obtain long-term deformation history prior to equipment deployment.

[0006] Third, the monitoring results lack semantic expression of the city wall structure. Traditional monitoring results are mostly expressed in the form of single points or local areas, which makes it difficult to directly correspond to specific protected objects such as the wall body, wall foundation, city gate, watchtower, corner, maintenance section, and damaged section.

[0007] Fourth, there is insufficient identification of the impact of external disturbances. Risks to city walls do not always manifest themselves first in the wall itself. Underground construction, traffic loads, abnormal drainage, or changes in groundwater often cause deformation in the surrounding area or the wall foundation before being transmitted to the wall structure. Traditional monitoring methods struggle to identify such disturbance transmission relationships at the regional scale.

[0008] Spaceborne InSAR technology possesses all-weather, non-contact, wide-area, and long-term observation capabilities, enabling the acquisition of millimeter- to centimeter-level deformation information of stable scattering points along the radar line of sight. Existing InSAR technology has been used to monitor urban land subsidence, landslides, mining subsidence, and deformation of bridges, roads, buildings, and large infrastructure. However, directly applying existing InSAR technology to linear cultural heritage sites such as city walls still has significant limitations:

[0009] First, InSAR output results are usually scattering points or grid deformation information. Its spatial representation is the radar scattering target, not the object protected by the wall. It is difficult to distinguish whether the scattering points represent the wall, the foundation of the wall, the surrounding roads, the buildings, or the surrounding disturbance area.

[0010] Second, existing InSAR building monitoring methods typically focus on average settlement rate, cumulative settlement, or single-point deformation anomalies, lacking identification of axial gradient, transverse wall offset, and differential settlement between adjacent segments for the narrow linear structure of city walls.

[0011] Third, existing methods typically lack coupled response analysis between the wall, the foundation, and the external disturbance, making it difficult to determine the source of deformation and its transmission path.

[0012] Fourth, existing methods often use uniform or empirical thresholds for early warning, without fully considering the city wall's protection level, the fragility of the wall material, the density of historical damage, radar observability, and differences in monitoring data quality, which can easily lead to false alarms or missed alarms.

[0013] Therefore, there is an urgent need for a dedicated InSAR deformation identification and risk warning method for linear cultural heritage such as city walls. This method should organically combine the semantic model of the city wall structure, multi-track InSAR deformation inversion, scattering point attribution probability reconstruction, wall-foundation-peripheral disturbance coupling analysis, risk sequence along the wall axis, and dynamic risk threshold to improve the structural interpretation ability, risk identification ability, and applicability of the city wall deformation monitoring results for protection and management. Summary of the Invention

[0014] The purpose of this invention is to provide a method and system for multi-track InSAR deformation attribution reconstruction and coupling risk early warning.

[0015] This invention is achieved through the following technical solution:

[0016] This invention relates to a method for multi-track InSAR deformation attribution reconstruction and coupling risk early warning, comprising the following steps:

[0017] Step S1, Data Acquisition: Acquire multi-temporal spaceborne SAR image data of the city wall to be monitored, three-dimensional structural data of the city wall, historical damage data of the city wall, historical maintenance data of the city wall, ground monitoring data, and data of external disturbance sources. The multi-temporal spaceborne SAR image data includes ascending orbit SAR images and / or descending orbit SAR images.

[0018] Step S2, Semantic Modeling: Based on the three-dimensional structural data of the city wall, the historical damage data of the city wall, and the historical maintenance data of the city wall, construct a semantic model of the city wall structure. The semantic model of the city wall structure includes at least semantic units of the wall body, semantic units of the wall foundation, semantic units of the city gate, semantic units of the watchtower, semantic units of the corner, semantic units of historical maintenance, semantic units of damage, and semantic units of external disturbance.

[0019] The semantic model of the city wall structure is represented by the following set:

[0020]

[0021] Where M represents the semantic model of the city wall structure, W represents the city wall axis, S represents the set of city wall segments, Z represents the set of structural semantic units, H represents the set of historical defects attributes, R represents the set of historical maintenance attributes, and E represents the set of external disturbance sources.

[0022] The set of city wall segments is represented as follows:

[0023]

[0024] Wherein, Sᵢ represents the i-th wall segment, and n represents the total number of wall segments; the wall segment Sᵢ is divided according to at least one of the following: fixed length, wall corner point, city gate node, enemy platform node, wall material change point, historical maintenance boundary, dense crack area boundary, terrain slope change point, or influence range of external disturbance source.

[0025] The set of structural semantic units is represented as follows:

[0026]

[0027] in, Represents a semantic unit of the wall. Represents the semantic unit of the wall foundation. This represents a semantic unit representing a city gate. Represents the semantic unit of enemy stations. Represents a corner semantic unit. This represents a semantic unit representing historical maintenance. Represents semantic units of disease. This represents a semantic unit representing an external perturbation.

[0028] Step S3, InSAR data processing: Perform time-series InSAR processing on multi-temporal spaceborne SAR image data, screen out stable scattering points, and extract the line-of-sight deformation time series, average deformation rate, coherence parameters, amplitude stability parameters, and phase residual parameters of the stable scattering points.

[0029] Step S4, Scattering point attribution determination: For each stable scattering point, the probability of the stable scattering point belonging to the wall semantic unit, the wall foundation semantic unit, and the peripheral disturbance semantic unit is calculated by combining its spatial location, projected mileage along the city wall axis, radar observation geometry, coherence parameters, phase residual parameters, wall height, occlusion probability, and structural semantic unit distance.

[0030] Step S5, Deformation field reconstruction: Combining the attribution probability of stable scattering points with the data reliability weight, the deformation time series of scattering points is reconstructed by weighting to obtain the wall deformation field, the wall foundation deformation field and the external disturbance deformation field respectively.

[0031] Step S6, Multi-dimensional Deformation Decomposition: Based on the time series of deformation along the ascending and descending orbits, the vertical deformation component and the east-west horizontal deformation component are obtained. Combined with the direction of the city wall axis, the horizontal deformation is further decomposed into the deformation component along the wall and the deformation component across the wall.

[0032] When both ascending and descending SAR images are acquired simultaneously, the vertical deformation component d U and the east-west horizontal deformation component d E Inversion is performed using the following observation equation:

[0033]

[0034] in, Indicates the deformation along the line of sight to the ascending orbit. This indicates the deformation along the line of sight to the descending orbit. and These represent the incident angles for the ascending and descending orbits, respectively. and These represent the azimuth angles for ascending and descending orbits, respectively.

[0035] The east-west horizontal deformation component Further based on the angle τ of the city wall axis i Decomposed into wall-direction deformation components and transverse wall deformation component :

[0036]

[0037] in, This represents the deformation component along the axis of the city wall. This represents the transverse deformation component of the wall perpendicular to its axis.

[0038] Step S7, Deformation characteristics and risk factors calculation: Convert the three types of deformation fields into a one-dimensional segmented deformation sequence along the city wall axis, and calculate the axial deformation gradient, differential settlement between adjacent segments, transverse wall offset, wall-foundation coupling difference, time delay response of external disturbance, and risk propagation direction.

[0039] The axial deformation gradient Calculate using the following formula:

[0040]

[0041] in, This represents the shape variable of the (i+1)th wall segment. This represents the shape variable of the i-1th wall segment. This represents the center mileage of the (i+1)th segment of the city wall. This represents the center mileage of the (i-1)th wall segment;

[0042] The adjacent segment differential settlement index Calculate using the following formula:

[0043]

[0044] in, Let represent the average deformation rate of the i-th wall segment. Let κ represent the cumulative deformation of the i-th wall segment at the end of the monitoring period, and let κ represent the cumulative deformation difference adjustment coefficient (κ∈[0.1, 2.0]).

[0045] Wall-foundation coupling difference index Calculate using the following formula:

[0046]

[0047] ξ represents the cumulative difference adjustment coefficient between the wall and the foundation (ξ∈[0.1, 2.0]);

[0048] External disturbance time delay response index L i Calculate using the following formula:

[0049]

[0050] in, This represents the time series of wall foundation deformation. This represents the deformation time series of the peripheral disturbance region after a lag of τ, where τ represents the time lag. maxThe maximum allowable lag time is represented by Corr, which represents the correlation coefficient function (Corr represents the Pearson correlation coefficient function, used to calculate the correlation between the time series of wall foundation deformation and the time series of lag deformation of the surrounding disturbance area). This represents the average deformation rate of the outer disturbance region. This represents the distance from the external disturbance source to the i-th wall segment. Indicates the distance attenuation parameter ( ∈[10m, 300m]);

[0051] Risk transmission direction F i Determine as follows:

[0052]

[0053] when When the value is greater than 0, the risk is determined to propagate along the direction of increasing city wall mileage; when... When <0, the risk is determined to propagate along the direction of decreasing city wall mileage; when =0 and When the risk exceeds a preset threshold, it is identified as a localized, concentrated risk.

[0054] Step S8, Construction of Comprehensive Risk Index: Based on axial deformation gradient, differential settlement between adjacent segments, transverse wall offset, wall-foundation coupling difference, time delay response to external disturbances, and historical disease factors, a comprehensive risk index for the city wall segments is constructed.

[0055] The comprehensive risk index R of the city wall segments i Calculate using the following formula:

[0056]

[0057] Among them, K i G represents the axial deformation gradient. i The differential settlement index (0-1) between adjacent segments is represented by d. T,i C represents the transverse wall deformation component. i L represents the wall-foundation coupling difference index (0-1). i H represents the time-delay response exponent to external disturbances (0-1). i Indicates historical disease factors (0-1), B i Let represent the vulnerability factor of the city wall (0-1), N represent the normalization function, and q1 to q7 be the weighting coefficients (0-1), satisfying the following:

[0058]

[0059] The normalization function N(x) uses a linear cutoff function:

[0060]

[0061] Among them, T x This represents the risk threshold corresponding to indicator x.

[0062] The historical disease factor H i Calculate using the following formula:

[0063]

[0064] Among them, I i F represents the historical crack strength factor (0-1). i W represents the historical maintenance frequency factor (0-1). i U represents the historical seepage or drainage anomaly factor (0-1). i c1 to c4 represent the underground space or construction disturbance factor (0-1), and c1 to c4 are weighting coefficients (0-1).

[0065] The vulnerability factor B of the city wall i Calculate using the following formula:

[0066]

[0067] Among them, M i A represents the vulnerability factor of the wall material ([0, 1]). i Represents the wall age factor ([0, 1]), P i T represents the protection level factor ([0, 1]). i The wall structure complexity factor is represented by ([0, 1]), and e1 to e4 are weighting coefficients ([0, 1]).

[0068] Step S9, Dynamic Threshold Generation: Based on the city wall protection level, wall material fragility, historical crack density, historical maintenance frequency, radar observability, and monitoring data reliability, a dynamic risk threshold is generated.

[0069] The dynamic risk threshold T dyn,i Generate according to the following formula:

[0070]

[0071] Where T0 represents the base threshold, O i Q represents the radar observability factor ([0, 1]). i H represents the monitoring data quality factor ([0, 1]). i Represents historical disease factors ([0, 1]), B i M represents the vulnerability factor of the city wall ([0, 1]). ib1 to b5 represent the multi-source verification confidence factor ([0, 1]), and b1 to b5 are adjustment coefficients ([0, 1]).

[0072] Risk levels are determined according to the following rules:

[0073] When R is satisfied i <T 1,i At that time, the i-th segment of the city wall is determined to be low-risk;

[0074] When T is satisfied 1,i ≤R i <T 2,i At that time, the i-th segment of the city wall is determined to be of medium risk;

[0075] When T is satisfied 2,i ≤R i <T 3,i At that time, the i-th segment of the city wall is determined to be high-risk;

[0076] When R is satisfied i ≥T 3,i At that time, the i-th segment of the city wall is determined to be an emergency risk;

[0077] Among them, T 1,i T 2,i and T 3,i Derived from dynamic risk threshold, and satisfies:

[0078] 0 <T 1,i <T 2,i <T 3,i

[0079] When any of the following mutation conditions are met, the risk level of the corresponding city wall segment will be increased by at least one level:

[0080] A. Transverse wall deformation component d T,i The number of consecutive p-periods increases;

[0081] B. Wall-foundation coupling difference index C i The coupling difference threshold is exceeded for p consecutive periods;

[0082] C. External disturbance time delay response index L i Exceeding the disturbance response threshold, and the wall foundation deformation rate V b,i Increase synchronously;

[0083] D. Axial deformation gradient K i The growth rate exceeds the gradient mutation threshold within a preset time window;

[0084] E. Differential settlement index of adjacent segments G i Exceeding the differential settlement threshold, and historical disease factor H i Exceeding the disease threshold.

[0085] Step S10, Risk Warning Output: Based on the comprehensive risk index and dynamic risk threshold, determine the risk level of each section of the city wall, and output the warning type and patrol and handling suggestions.

[0086] This invention also relates to a multi-track InSAR deformation attribution reconstruction and coupling risk early warning system, used to implement the aforementioned multi-track InSAR deformation attribution reconstruction and coupling risk early warning method, including:

[0087] The data acquisition module is used to acquire multi-temporal satellite-borne SAR image data, three-dimensional structural data of the city wall, historical damage data of the city wall, historical maintenance data of the city wall, ground monitoring data, and data of external disturbance sources;

[0088] The city wall structure semantic modeling module, connected to the data acquisition module, is used to construct a city wall structure semantic model that includes wall semantic units, wall foundation semantic units, city gate semantic units, enemy platform semantic units, corner semantic units, historical maintenance semantic units, disease semantic units, and external disturbance semantic units.

[0089] The temporal InSAR processing module, connected to the city wall structure semantic modeling module, is used to perform temporal InSAR processing on multi-temporal spaceborne SAR image data to obtain the line-of-sight deformation time series, average deformation rate, coherence parameters, amplitude stability parameters, and phase residual parameters of stable scattering points.

[0090] The scattering point attribution probability calculation module, connected to the time-series InSAR processing module, is used to calculate the attribution probability of stable scattering points belonging to wall semantic units, wall foundation semantic units, and peripheral disturbance semantic units, respectively.

[0091] The semantic deformation field reconstruction module is connected to the scattering point attribution probability calculation module. It is used to reconstruct the wall deformation field, the wall foundation deformation field, and the external disturbance deformation field based on the attribution probability and reliability weight.

[0092] The multi-track deformation decomposition module, connected to the semantic deformation field reconstruction module, is used to invert the vertical deformation component, the wall-directed deformation component, and the transverse wall-directed deformation component based on the ascending and descending track line-of-sight deformation time series.

[0093] The axial risk sequence calculation module, connected to the multi-track deformation decomposition module, is used to calculate the axial deformation gradient, differential settlement between adjacent segments, wall-foundation coupling difference, time delay response to external disturbances, and risk propagation direction along the city wall axis.

[0094] The risk assessment module, connected to the axial risk sequence calculation module, is used to construct a comprehensive risk index for city wall segments and generate dynamic risk thresholds.

[0095] The early warning output module, connected to the risk assessment module, is used to output the risk level of each section of the city wall, the type of early warning, the direction of risk propagation, and suggestions for inspection and handling.

[0096] Preferably, in step S1, the three-dimensional structural data of the city wall includes at least one of the following: city wall axis, city wall boundary, wall height, wall width, digital elevation model, three-dimensional laser point cloud data, and UAV oblique photography model; the data of the external disturbance sources includes at least one of the following: foundation pit construction data, underground tunnel data, road traffic load data, groundwater level change data, and surrounding building construction data.

[0097] The peripheral disturbance source data includes at least one of the following: foundation pit construction data, underground tunnel data, underground pipeline data, road traffic load data, groundwater level change data, surrounding building construction data, drainage anomaly data, slope hazard point data, and surface hardening change data.

[0098] Preferably, in step S3, the InSAR data processing uses the average coherence coefficient, amplitude deviation index, and phase residual standard deviation to jointly screen stable scattering points. When the average coherence coefficient of a scattering point is greater than a preset coherence coefficient threshold, the amplitude deviation index is less than a preset amplitude deviation index threshold, and the phase residual standard deviation is less than a preset phase residual standard deviation threshold, the scattering point is determined to be a stable scattering point.

[0099] The interference phase of the k-th stable scattering point in the image at period t satisfies the following decomposition relationship:

[0100]

[0101] in, This represents the interference phase of the k-th stable scattering point in the image at period t. Indicates the deformation phase. Indicates the phase of the terrain residual. Indicates atmospheric delay phase, Indicates the orbital error phase. Indicates the noise phase;

[0102] Deformation phase The following relationship exists between the line-of-sight deformation and the line-of-sight deformation:

[0103]

[0104] in, Let λ represent the line-of-sight deformation of the k-th stable scattering point in period t, and let λ represent the SAR radar wavelength.

[0105] Stable scattering points were selected by combining the average coherence coefficient, amplitude deviation index, and phase residual standard deviation. The selection criteria were as follows:

[0106]

[0107] Among them, Coh k Coh represents the average coherence coefficient at the k-th scattering point. k ∈[0,1], Coh0 represents the average coherence coefficient threshold, D A,k D represents the amplitude deviation index of the k-th scattering point. A,k ∈[0, +∞), D A,0 σ represents the amplitude deviation exponent threshold. φ,k σk represents the standard deviation of the phase residual at the k-th scattering point, and σ0 represents the threshold of the standard deviation of the phase residual.

[0108] Preferably, in step S4, the determination of the scattering point attribution uses the attribution energy function to calculate the attribution probability of a stable scattering point. The attribution energy function integrates multiple factors such as spatial distance, axis projection mileage deviation, radar geometric obstruction, coherence, phase residual, and radar observability, and configures corresponding weight coefficients for weighted calculation.

[0109] The probability P that the k-th stable scattering point belongs to the z-th structural semantic unit. z,k Calculate using the following formula:

[0110]

[0111] Among them, P z,k E represents the probability that the k-th stable scattering point belongs to the z-th structural semantic unit. z,k Z′ represents the energy function of the k-th stable scattering point relative to the z-th structural semantic unit, where Z′ includes at least the wall semantic unit, the foundation semantic unit, and the peripheral disturbance semantic unit.

[0112] The attribution energy function Ez,k is calculated according to the following formula:

[0113]

[0114] Where, d z,k l represents the spatial distance from the k-th stable scattering point to the z-th structural semantic unit. k Let l represent the projected mileage of the k-th stable scattering point along the axis of the city wall. z O represents the central mileage of the z-th structural semantic unit. z,k R represents the radar geometric obstruction factor ([0, 1]). k G represents the phase residual factor ([0, 1]). z,k Denotes the geometric inconsistency factor ([0, 1]), C k Denotes the coherence factor ([0, 1]), A z,kThe radar observability factor is represented by m1 to m7 ([0, 1]), and the weighting coefficients are m1 to m7 ([0, 1]).

[0115] Radar geometric obstruction factor O z,k Determine as follows:

[0116]

[0117] in, This represents the obstruction height of the k-th stable scattering point in the radar incident direction. Let ε represent the height of the wall corresponding to the i-th wall segment, and let ε be a constant to prevent the denominator from being zero (ε∈[0.01m, 0.10m]).

[0118] Radar observability factor Determined based on the segmental azimuth angle of the city wall, the radar incident angle, and the radar azimuth angle:

[0119]

[0120] Where θ represents the radar incident angle, α represents the radar azimuth angle, and τ i β represents the axial orientation angle of the i-th city wall segment. i The assumed angle represents the main deformation direction of the i-th wall segment.

[0121] Preferably, in step S5, the data reliability weight is calculated by weighting the time coherence factor, phase residual stability factor, amplitude stability factor, and spatial neighborhood consistency factor; the segmented deformation field is reconstructed by combining the attribution probability and reliability weight with the city wall segment as the unit.

[0122] The data reliability weight of the k-th stable scattering point Calculate using the following formula:

[0123]

[0124] in, The time coherence factor is represented by [0, 1] . This represents the phase residual stability factor ([0, 1]). The amplitude stability factor is represented as ([0, 1]). The spatial neighborhood consistency factor is represented as [0, 1];

[0125] The temporal coherence factor, phase residual stability factor, amplitude stability factor, and spatial neighborhood consistency factor are calculated according to the following formulas:

[0126]

[0127] in, This represents the maximum average coherence coefficient within the monitoring area. This represents the standard deviation of the deformation values ​​in the neighborhood of the k-th stable scattering point;

[0128] The semantic deformation field of the z-th structural semantic unit in the i-th city wall segment at period t. Reconstruct according to the following formula:

[0129]

[0130] Among them, Ω i Let i represent the set of stable scattering points within the i-th segment of the city wall. This represents the probability that the k-th stable scattering point belongs to the z-th structural semantic unit. This represents the data reliability weight for the k-th stable scattering point. This represents the deformation of the k-th stable scattering point in period t.

[0131] The deformation field of the wall was thus obtained. Deformation field of wall foundation and the external disturbance deformation field .

[0132] Preferably, in step S8, the historical disease factor is obtained by weighting four sub-factors: historical crack intensity, historical maintenance frequency, drainage anomaly, and construction disturbance; before constructing the comprehensive risk index, all input indicators are normalized using a linear truncation function.

[0133] Preferably, in step S9, the dynamic risk threshold is obtained by adaptively correcting the threshold based on the basic threshold and in combination with radar observability, monitoring data quality, historical defects, wall vulnerability, and multi-source verification credibility.

[0134] Preferably, the multi-track InSAR deformation attribution reconstruction and coupling risk early warning method further includes: after step S10, multi-source data correction and patrol priority calculation: using at least one type of ground monitoring data from leveling, total station, GNSS, 3D laser scanning, and UAV photogrammetry to correct the InSAR deformation results; combining the comprehensive risk index, cultural relic protection level, and anomaly duration to calculate patrol priority and generate a patrol list;

[0135] The correction formula is as follows:

[0136]

[0137] The correction item is:

[0138]

[0139] in, This represents the corrected deformation of the i-th segment of the city wall. This represents the deformation of the ground monitoring data after interpolation to the i-th city wall segment. This represents the shape variable of the i-th wall segment obtained from InSAR reconstruction. Indicates the correction term;

[0140] The inspection priority Pi is calculated according to the following formula:

[0141]

[0142] Among them, R i Z represents the comprehensive risk index. i X represents the cultural relic value or protection level factor ([0, 1]). i Represents the personnel or traffic exposure factor ([0, 1]), O i Y represents the abnormal duration factor ([0, 1]). i λ represents the on-site accessibility factor ([0, 1]), and λ1 to λ5 are the weighting coefficients ([0, 1]).

[0143] The present invention also relates to an electronic device, including a processor, a memory, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the aforementioned multi-track InSAR deformation attribution reconstruction and coupling risk warning method.

[0144] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a multi-track InSAR deformation attribution reconstruction and coupling risk early warning method.

[0145] Preferably, the above-mentioned multi-track InSAR deformation attribution reconstruction and coupling risk early warning system further includes a multi-source verification module. The multi-source verification module is connected to the semantic deformation field reconstruction module and the early warning output module, respectively, and is used to access leveling, total station, GNSS, crack meter, three-dimensional laser scanning and UAV photogrammetry data to correct and verify the InSAR deformation results and risk levels.

[0146] The present invention has the following advantages:

[0147] (1) Semantic constraints accurately distinguish the source of deformation: Construct a semantic model of the city wall’s exclusive structure, and calculate the probability of scattering point attribution through multi-factor joint calculation to solve the problem that traditional InSAR cannot distinguish the deformation of the wall, the foundation, and the surrounding disturbance. The deformation results can be directly corresponding to the cultural relic protection unit.

[0148] (2) Multidimensional deformation analysis of adaptable linear structure: Based on multi-track InSAR, three-dimensional deformation decomposition of vertical, wall-along and transverse wall directions is realized, and indicators such as axial deformation gradient and segmented differential settlement are designed to accurately identify the wall's uneven settlement, lateral displacement and local tilt.

[0149] (3) Realize the source analysis of coupled risks: Establish the wall-foundation coupling difference index and the external disturbance time delay response index to quantify the transmission law of external disturbance to the wall body, realize the source of risk inducement, and support preventive protection decision-making.

[0150] (4) Dynamic threshold reduces early warning error: abandoning the fixed threshold mode, the early warning threshold is generated adaptively by combining the wall's own attributes, historical defects and monitoring data quality, which effectively reduces false alarms and missed alarms.

[0151] (5) Integrated hardware and software system with strong engineering feasibility: The whole system is modularly designed, connecting satellite remote sensing data and ground monitoring data to achieve full automation of monitoring, analysis, evaluation, early warning and scheduling. The non-contact monitoring method will not damage the cultural relics and is suitable for long-term, high-frequency full-area monitoring of long-distance city walls.

[0152] (6) Dual protection layout: Simultaneously protect the core method and the dedicated system. The claims cover the core algorithm process and hardware and software architecture, providing a complete scope of patent protection and stronger infringement prevention capabilities. Detailed Implementation

[0153] The present invention will now be described in detail with reference to specific embodiments. It should be noted that the following embodiments are merely further illustrations of the present invention, but the scope of protection of the present invention is not limited to the following embodiments.

[0154] Example 1

[0155] This embodiment selects a section of ancient city wall as the monitoring object, and uses the method and system of the present invention to carry out deformation monitoring and risk warning. The specific implementation process is as follows:

[0156] 1. Multi-source data acquisition

[0157] The system accesses multi-temporal ascending and descending orbit satellite-borne SAR images covering the monitoring area through the data acquisition module; imports 3D structural data such as 3D laser point cloud of the city wall, UAV oblique photography model, axis coordinates, and wall dimensions; records the city wall's historical damage ledger, maintenance records, and crack distribution data; accesses ground monitoring data such as leveling, GNSS, and crack gauges along the line; and simultaneously collects data on external disturbance sources such as foundation pit construction, traffic load, groundwater level, and surrounding building construction in the area.

[0158] The three-dimensional structural data of the city wall includes at least one of the following: city wall axis, city wall boundary, wall height, wall width, digital elevation model, three-dimensional laser point cloud data, and UAV oblique photography model; the data of the external disturbance sources includes at least one of the following: foundation pit construction data, underground tunnel data, road traffic load data, groundwater level change data, and surrounding building construction data.

[0159] The peripheral disturbance source data includes at least one of the following: foundation pit construction data, underground tunnel data, underground pipeline data, road traffic load data, groundwater level change data, surrounding building construction data, drainage anomaly data, slope hazard point data, and surface hardening change data.

[0160] 2. Construct a semantic model of the city wall structure

[0161] The semantic modeling module for the city wall structure completes the segmentation of the city wall based on the city wall corners, city gates, watchtowers, material boundaries, areas with dense defects, and maintenance boundaries. Based on the segmentation, eight semantic units are labeled: wall body, wall foundation, city gate, watchtower, corner, historical maintenance area, defect area, and surrounding disturbance area, to complete the full-domain semantic modeling.

[0162] 3. Temporal InSAR processing

[0163] The temporal InSAR processing module performs preprocessing on SAR images, including registration, interferometry, deflating, and filtering, and performs temporal InSAR calculations. It also filters stable scattering points according to preset thresholds and extracts parameters such as line-of-sight deformation time series, average deformation rate, coherence coefficient, amplitude deviation, and phase residual for each point.

[0164] The InSAR data processing uses the average coherence coefficient, amplitude deviation index, and phase residual standard deviation to jointly screen stable scattering points. When the average coherence coefficient of a scattering point is greater than a preset coherence coefficient threshold, the amplitude deviation index is less than a preset amplitude deviation index threshold, and the phase residual standard deviation is less than a preset phase residual standard deviation threshold, the scattering point is determined to be a stable scattering point.

[0165] The interference phase of the k-th stable scattering point in the image at period t satisfies the following decomposition relationship:

[0166]

[0167] in, This represents the interference phase of the k-th stable scattering point in the image at period t. Indicates the deformation phase. Indicates the phase of the terrain residual. Indicates atmospheric delay phase, Indicates the orbital error phase. Indicates the noise phase;

[0168] Deformation phase The following relationship exists between the line-of-sight deformation and the line-of-sight deformation:

[0169]

[0170] in, Let λ represent the line-of-sight deformation of the k-th stable scattering point in period t, and let λ represent the SAR radar wavelength.

[0171] Stable scattering points were selected by combining the average coherence coefficient, amplitude deviation index, and phase residual standard deviation. The selection criteria were as follows:

[0172]

[0173] Among them, Coh k D represents the average coherence coefficient at the k-th scattering point, Coh0 represents the average coherence coefficient threshold, and D A,k D represents the amplitude deviation index of the k-th scattering point. A,0 σ represents the amplitude deviation exponent threshold. φ,k σk represents the standard deviation of the phase residual at the k-th scattering point, and σ0 represents the threshold of the standard deviation of the phase residual.

[0174] 4. Calculation of the probability of scattering point attribution

[0175] The scattering point attribution probability calculation module extracts parameters such as spatial coordinates, axis projection mileage, occlusion height, and semantic unit distance for each stable scattering point, substitutes them into the attribution energy function, and calculates the probability that the scattering point belongs to the wall, wall foundation, or surrounding disturbance area.

[0176] The determination of the scattering point attribution uses the attribution energy function to calculate the attribution probability of a stable scattering point. The attribution energy function integrates multiple factors such as spatial distance, axis projection mileage deviation, radar geometric obstruction, coherence, phase residual, and radar observability, and configures corresponding weight coefficients for weighted calculation.

[0177] The probability P that the k-th stable scattering point belongs to the z-th structural semantic unit. z,k Calculate using the following formula:

[0178]

[0179] Among them, P z,k E represents the probability that the k-th stable scattering point belongs to the z-th structural semantic unit. z,k Z′ represents the energy function of the k-th stable scattering point relative to the z-th structural semantic unit, where Z′ includes at least the wall semantic unit, the foundation semantic unit, and the peripheral disturbance semantic unit.

[0180] The attribution energy function Ez,k is calculated according to the following formula:

[0181]

[0182] Where, d z,k l represents the spatial distance from the k-th stable scattering point to the z-th structural semantic unit. k Let l represent the projected mileage of the k-th stable scattering point along the axis of the city wall. z O represents the central mileage of the z-th structural semantic unit. z,k R represents the radar geometric obstruction factor. k G represents the phase residual factor. z,k C represents the geometric inconsistency factor. k A represents the coherence factor. z,k This represents the radar observability factor, with m1 to m7 being weighting coefficients.

[0183] Radar geometric obstruction factor O z,k Determine as follows:

[0184]

[0185] in, This represents the obstruction height of the k-th stable scattering point in the radar incident direction. Let ε represent the height of the wall corresponding to the i-th wall segment, and let ε be a constant to prevent the denominator from being zero.

[0186] Radar observability factor Determined based on the segmental azimuth angle of the city wall, the radar incident angle, and the radar azimuth angle:

[0187]

[0188] Where θ represents the radar incident angle, α represents the radar azimuth angle, and τ i β represents the axial orientation angle of the i-th city wall segment. i The assumed angle represents the main deformation direction of the i-th wall segment.

[0189] 5. Semantic deformation field reconstruction

[0190] The semantic deformation field reconstruction module calculates the data reliability weight of each scattering point, and combines the attribution probability to weight and fuse the deformation of all scattering points in the segment to generate the wall deformation field, the wall foundation deformation field, and the external disturbance deformation field, respectively.

[0191] The data reliability weight is calculated by weighting the time coherence factor, phase residual stability factor, amplitude stability factor, and spatial neighborhood consistency factor; the segmented deformation field is reconstructed by combining the attribution probability and reliability weight with the city wall segment as the unit;

[0192] Data reliability weight of the kth stable scattering point Calculate using the following formula:

[0193]

[0194] in, Indicates the time coherence factor. This represents the phase residual stability factor. Indicates the amplitude stability factor. Indicates the spatial neighborhood consistency factor;

[0195] The temporal coherence factor, phase residual stability factor, amplitude stability factor, and spatial neighborhood consistency factor are calculated according to the following formulas:

[0196]

[0197] in, This represents the maximum average coherence coefficient within the monitoring area. This represents the standard deviation of the deformation values ​​in the neighborhood of the k-th stable scattering point;

[0198] The semantic deformation field of the z-th structural semantic unit in the i-th city wall segment at period t. Reconstruct according to the following formula:

[0199]

[0200] Among them, Ω i Let i represent the set of stable scattering points within the i-th segment of the city wall. This represents the probability that the k-th stable scattering point belongs to the z-th structural semantic unit. This represents the data reliability weight for the k-th stable scattering point. This represents the deformation of the k-th stable scattering point in period t.

[0201] The deformation field of the wall was thus obtained. Deformation field of wall foundation and the external disturbance deformation field .

[0202] 6. Multidimensional Deformation Decomposition

[0203] The multi-track deformation decomposition module uses the line-of-sight deformation data from ascending and descending tracks and radar observation parameters to establish observation equations, and inverts the vertical deformation component and the east-west horizontal deformation component; combined with the orientation angle of the city wall segment axis, the horizontal deformation is decomposed into the deformation component along the wall and the deformation component across the wall.

[0204] 7. Deformation characteristics and risk factor calculation

[0205] The axial risk sequence calculation module converts the two-dimensional deformation field into a one-dimensional segmented deformation sequence, calculates the axial deformation gradient, differential settlement between adjacent segments, transverse wall offset, wall-foundation coupling difference, external disturbance time delay response index, and determines the risk propagation direction.

[0206] 8. Calculation of Comprehensive Risk Index

[0207] The risk assessment module calculates historical disease factors, normalizes all risk indicators, and then performs a weighted sum to obtain the comprehensive risk index for each section of the city wall. It also generates dynamic risk thresholds by combining factors such as the protection level of the city wall, materials, and data quality.

[0208] 9. Risk warning and multi-source verification

[0209] The early warning output module compares the comprehensive risk index with the dynamic threshold to classify the risk level and outputs the early warning type, risk analysis and disposal suggestions; the multi-source verification module accesses ground measured data to correct and verify the InSAR deformation results and risk level; the system calculates the patrol priority based on the risk index, cultural relic level and abnormal duration, generates a standardized patrol list and pushes it to the management terminal.

[0210] Multi-source data correction and patrol priority calculation: Correct InSAR deformation results using at least one type of ground monitoring data from leveling, total station, GNSS, 3D laser scanning, and UAV photogrammetry; calculate patrol priority and generate patrol list by combining comprehensive risk index, cultural relic protection level, and anomaly duration;

[0211] The correction formula is as follows:

[0212]

[0213] The correction item is:

[0214]

[0215] in, This represents the corrected deformation of the i-th segment of the city wall. This represents the deformation of the ground monitoring data after interpolation to the i-th city wall segment. This represents the shape variable of the i-th wall segment obtained from InSAR reconstruction. Indicates the correction term;

[0216] The patrol priority Pi is calculated according to the following formula:

[0217]

[0218] Among them, R i Z represents the comprehensive risk index. iX represents the value or protection level of a cultural relic. i Indicates the exposure factor for people or traffic, O i Y represents the abnormal duration factor. i λ represents the on-site accessibility factor, and λ1 to λ5 are weighting coefficients.

[0219] Example 2

[0220] Normal working conditions (ordinary section of the ancient city wall, without strong disturbance or minor damage).

[0221] 1. Monitoring Targets and Data Collection

[0222] Monitoring object: Section 3 of a national-level ancient city wall (section length 50m, wall material is blue brick, built about 600 years ago).

[0223] Data collection:

[0224] ① 20 scenes each of multi-temporal rising / falling orbit Sentinel-1 SAR images;

[0225] ② Three-dimensional data of the city wall: three-dimensional laser point cloud, UAV tilt model, axis coordinates, wall height 7.2m, width 4.5m;

[0226] ③Historical data: Minor cracks in some areas, no major repairs in the past 5 years;

[0227] ④ External disturbances: No foundation pit or underground construction, and normal traffic load;

[0228] ⑤ Ground monitoring: 60 consecutive days of monitoring data from leveling, GNSS, and crack gauges;

[0229] 2. Construct a semantic model of the city wall structure.

[0230] According to the segmentation rules, the area is divided into independent 50m segments, labeled with semantic units: wall unit, wall foundation unit, small drainage facility unit, and no city gate / enemy platform / outer strong disturbance unit.

[0231] 3. Temporal InSAR processing and stable scattering point selection

[0232] (1) Image preprocessing: registration, interferometry, Goldstein filtering, removal of flat terrain effect, atmospheric phase compensation;

[0233] (2) Screening rules: Coh > 0.7, DA < 0.3, σ φ <0.1 rad;

[0234] (3) Screening results: A total of 126 effective stable scattering points were extracted in this segment;

[0235] (4) Extract parameters: Output the line-of-sight deformation time series, average deformation rate, coherence coefficient, and phase residual point by point.

[0236] 4. Calculation of the probability of scattering point attribution

[0237] Substituting into the attribution energy function, and using preset weights m1 to m7, the calculation is as follows:

[0238] (1) Average probability of belonging to the semantic unit of the wall: 0.82;

[0239] (2) Average probability of belonging to the semantic unit of wall foundation: 0.15;

[0240] (3) Average probability of belonging to the peripheral perturbation semantic unit: 0.03.

[0241] 5. Data reliability weighting and deformation field reconstruction

[0242] (1) Calculate the reliability weight of each scattering point based on a 5m spatial neighborhood;

[0243] (2) Weighted reconstruction results:

[0244] Average wall deformation rate: -0.21 mm / month;

[0245] Average deformation rate of the wall foundation: -0.18 mm / month;

[0246] Deformation rate of the outer region: -0.05 mm / month;

[0247] 6. Multidimensional Deformation Decomposition

[0248] By combining the Sentinel-1 incident angle and azimuth angle, the deformation is inverted and decomposed.

[0249] Vertical deformation: -0.21 mm / month; Deformation along the wall: -0.04 mm / month; Transverse deformation: -0.02 mm / month;

[0250] 7. Deformation characteristics and risk factor calculation

[0251] (1) Axial deformation gradient: 0.002 mm / month;

[0252] (2) Differential settlement between adjacent segments: 0.03 mm / month;

[0253] (3) Wall-foundation coupling difference index: 0.02;

[0254] (4) External disturbance time delay response index: 0.01 (no obvious disturbance transmission);

[0255] (5) Risk propagation direction: no targeted propagation, determined to be a stable segment.

[0256] 8. Calculation of Comprehensive Risk Index

[0257] Historical disease factor H i =0.12, Wall vulnerability factor B i =0.28;

[0258] Substituting into the comprehensive risk index formula, we can calculate R. i =0.16.

[0259] 9. Dynamic risk thresholds and risk classification

[0260] The base threshold T0 = 0.4; after multi-factor correction, the dynamic threshold for this segment is 0.42.

[0261] T 1,i =0.168, T 2,i =0.294, T 3,i =0.42;

[0262] According to the judgment rule, R i <T 1,I Low risk.

[0263] 10. Multi-source calibration and inspection priority

[0264] (1) After correction of the ground leveling data, the wall deformation rate was corrected to -0.20 mm / month, with an error of <5%.

[0265] (2) Patrol priority P i =0.19, the lowest priority, included in the regular monthly inspection list.

[0266] 11. Output Results

[0267] This section of the city wall is classified as a low-risk area, with minimal overall deformation, stable structure, and no signs of disease development; routine inspections are sufficient.

[0268] Example 3

[0269] External disturbance conditions (construction of the city wall near the foundation pit, with significant coupled deformation).

[0270] 1. Monitoring Targets and Data Collection

[0271] Monitoring object: 50m of the 8th section of an ancient city wall. There is a foundation pit under construction 25m outside the section, which is a typical external disturbance scenario.

[0272] Data collected: In addition to standard SAR imagery and city wall data, supplementary data include the construction sequence of the foundation pit, groundwater level monitoring, and surface settlement data;

[0273] 2. Semantic Model Construction

[0274] It includes three core semantic units: wall, wall foundation, and external construction disturbance;

[0275] 3. InSAR Scattering Point Screening

[0276] There are 118 effective stable scattering points, and the overall coherence is slightly lower than that under normal operating conditions.

[0277] 4. Probability of scattering point attribution

[0278] Wall unit: 0.65; foundation unit: 0.27; external disturbance unit: 0.08.

[0279] 5. Deformation field reconstruction

[0280] Average wall deformation rate: -1.32 mm / month;

[0281] Average deformation rate of the wall foundation: -1.65 mm / month;

[0282] Deformation rate in the outer region: -2.87 mm / month;

[0283] 6. Multidimensional Deformation Decomposition

[0284] Vertical deformation: -1.32 mm / month; Transverse deformation: -0.36 mm / month;

[0285] 7. Deformation characteristics and risk factors

[0286] (1) Axial deformation gradient: 0.08 mm / month;

[0287] (2) Differential settlement between adjacent segments: 0.42 mm / month;

[0288] (3) Wall-foundation coupling difference index: 0.31 (significant coupling deformation);

[0289] (4) External disturbance time delay response index: 0.68 (the disturbance is obviously transmitted to the wall);

[0290] (5) Risk propagation direction: propagation along the direction of increasing mileage of the city wall.

[0291] 8. Calculation of Comprehensive Risk Index

[0292] Historical disease factor H i =0.25, Vulnerability Factor B i =0.30;

[0293] Substituting into the comprehensive risk index formula, we can calculate R. i =0.59.

[0294] 9. Dynamic risk thresholds and risk classification

[0295] Dynamic threshold = 0.41;

[0296] T 1,i =0.164, T 2,i =0.287, T 3,i =0.41;

[0297] According to the judgment rule, T 2,i <R i <T 3,i It is considered high-risk.

[0298] 10. Recommendations for Multi-Source Calibration and Handling

[0299] (1) After correction of the ground GNSS data, the deformation data error is <6%;

[0300] (2) Patrol priority P i =0.87 (highest priority);

[0301] (3) Output early warning: The foundation pit construction caused wall foundation settlement and deformation was transmitted to the wall. It is recommended to suspend construction near the wall, increase monitoring, and conduct on-site special investigation.

[0302] Example 4

[0303] Complex obstruction and damaged condition (mountain city wall, vegetation obstruction and weathering damage).

[0304] 1. Monitoring Targets and Data Collection

[0305] Monitoring object: Section 12 of the ancient city wall in the mountainous area. The wall is largely weathered and damaged, and the top and outer side are covered by dense shrubs. The SAR image shows localized decrease in coherence. There is no human construction disturbance.

[0306] 2. Semantic Model Construction

[0307] Added semantic units for weathering and damage and vegetation occlusion;

[0308] 3. InSAR Scattering Point Screening

[0309] Due to the shading effect, there are only 73 effective stable scattering points (a significant reduction in number).

[0310] 4. Attribution probability and deformation field

[0311] Average probability of wall unit: 0.71; wall base unit: 0.22; shading area: 0.07; wall deformation rate: -0.45 mm / month;

[0312] 5. Risk Factors and Composite Index

[0313] Axial gradient and coupling differences are both at a moderate level, and the overall risk index R is [missing information]. i =0.33.

[0314] 6. Dynamic thresholds and grading

[0315] Dynamic threshold = 0.40;

[0316] According to the judgment rule, T 1,i <R i <T 2,i It is classified as medium risk.

[0317] The following conclusions can be drawn from the comparison of the above embodiments, as shown in Table 1.

[0318] Table 1

[0319]

[0320] This invention, through structural semantic constraints, multi-track deformation decomposition, and coupled risk analysis, is comprehensively superior to traditional InSAR monitoring schemes and effectively solves existing technical problems.

[0321] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for multi-track InSAR deformation attribution reconstruction and coupling risk early warning, characterized in that, Includes the following steps: Step S1, Data Acquisition: Acquire multi-temporal spaceborne SAR image data of the city wall to be monitored, three-dimensional structural data of the city wall, historical damage data of the city wall, historical maintenance data of the city wall, ground monitoring data, and data of external disturbance sources; among which, the multi-temporal spaceborne SAR image data includes ascending orbit SAR images and / or descending orbit SAR images. Step S2, Semantic Modeling: Based on the three-dimensional structural data of the city wall, the historical damage data of the city wall, and the historical maintenance data of the city wall, construct a semantic model of the city wall structure; the semantic model of the city wall structure includes at least semantic units of the wall body, semantic units of the wall foundation, semantic units of the city gate, semantic units of the watchtower, semantic units of the corner, semantic units of historical maintenance, semantic units of damage, and semantic units of external disturbance. Step S3, InSAR data processing: Perform time-series InSAR processing on multi-temporal spaceborne SAR image data, screen out stable scattering points, and extract the line-of-sight deformation time series, average deformation rate, coherence parameters, amplitude stability parameters, and phase residual parameters of the stable scattering points. Step S4, Scattering point attribution determination: For each stable scattering point, the probability of the stable scattering point belonging to the wall semantic unit, the wall foundation semantic unit, and the peripheral disturbance semantic unit is calculated by combining its spatial location, projected mileage along the city wall axis, radar observation geometry, coherence parameters, phase residual parameters, wall height, occlusion probability, and structural semantic unit distance. The determination of the scattering point attribution adopts the attribution energy function to calculate the attribution probability of a stable scattering point. The attribution energy function integrates multiple factors such as spatial distance, axis projection mileage deviation, radar geometric obstruction, coherence, phase residual, and radar observability, and configures corresponding weight coefficients for weighted calculation. Step S5, Deformation field reconstruction: Combining the attribution probability of stable scattering points with the data reliability weight, the deformation time series of scattering points is reconstructed by weighting to obtain the wall deformation field, the wall foundation deformation field and the external disturbance deformation field respectively. The data reliability weight is calculated by weighting the time coherence factor, phase residual stability factor, amplitude stability factor, and spatial neighborhood consistency factor; the segmented deformation field is reconstructed by combining the attribution probability and reliability weight with the city wall segment as the unit. Step S6, Multi-dimensional Deformation Decomposition: Based on the time series of deformation along the ascending and descending orbits, the vertical deformation component and the east-west horizontal deformation component are obtained. Combined with the direction of the city wall axis, the horizontal deformation is further decomposed into the deformation component along the wall and the deformation component across the wall. Step S7, Deformation characteristics and risk factors calculation: Convert the three types of deformation fields into a one-dimensional segmented deformation sequence along the city wall axis, and calculate the axial deformation gradient, differential settlement between adjacent segments, transverse wall offset, wall-foundation coupling difference, time delay response of external disturbance, and risk propagation direction. Step S8, Construction of Comprehensive Risk Index: Based on axial deformation gradient, differential settlement between adjacent segments, transverse wall offset, wall-foundation coupling difference, time delay response to external disturbances, and historical disease factors, a comprehensive risk index for the city wall segments is constructed. Step S9, Dynamic Threshold Generation: Based on the city wall protection level, wall material fragility, historical crack density, historical maintenance frequency, radar observability, and monitoring data reliability, a dynamic risk threshold is generated. Step S10, Risk Warning Output: Based on the comprehensive risk index and dynamic risk threshold, determine the risk level of each section of the city wall, and output the warning type and patrol and handling suggestions.

2. The multi-track InSAR deformation attribution reconstruction and coupling risk early warning method as described in claim 1, characterized in that, In step S1, the three-dimensional structural data of the city wall includes at least one of the following: city wall axis, city wall boundary, wall height, wall width, digital elevation model, three-dimensional laser point cloud data, and UAV oblique photography model. The peripheral disturbance source data includes at least one of the following: foundation pit construction data, underground tunnel data, underground pipeline data, road traffic load data, groundwater level change data, surrounding building construction data, drainage anomaly data, slope hazard point data, and surface hardening change data.

3. The multi-track InSAR deformation attribution reconstruction and coupling risk early warning method as described in claim 1, characterized in that, In step S3, the InSAR data processing uses the average coherence coefficient, amplitude deviation index, and phase residual standard deviation to jointly screen stable scattering points. When the average coherence coefficient of a scattering point is greater than a preset coherence coefficient threshold, the amplitude deviation index is less than a preset amplitude deviation index threshold, and the phase residual standard deviation is less than a preset phase residual standard deviation threshold, the scattering point is determined to be a stable scattering point.

4. The multi-track InSAR deformation attribution reconstruction and coupling risk early warning method as described in claim 1, characterized in that, In step S8, the historical disease factor is obtained by weighting four sub-factors: historical crack intensity, historical maintenance frequency, drainage anomaly, and construction disturbance. Before constructing the comprehensive risk index, all input indicators are normalized using a linear truncation function.

5. The multi-track InSAR deformation attribution reconstruction and coupling risk early warning method as described in claim 1, characterized in that, In step S9, the dynamic risk threshold is obtained by adaptively correcting the threshold based on the basic threshold and in combination with radar observability, monitoring data quality, historical defects, wall vulnerability, and multi-source verification credibility.

6. The multi-track InSAR deformation attribution reconstruction and coupling risk early warning method as described in claim 1, characterized in that, It also includes: after step S10, multi-source data correction and patrol priority calculation: using at least one type of ground monitoring data from leveling, total station, GNSS, 3D laser scanning, and UAV photogrammetry to correct InSAR deformation results; combining comprehensive risk index, cultural relic protection level, and anomaly duration to calculate patrol priority and generate patrol list.

7. A multi-track InSAR deformation attribution reconstruction and coupling risk early warning system, characterized in that, To implement the method of claim 1, the method comprises: The data acquisition module is used to acquire multi-temporal satellite-borne SAR image data, three-dimensional structural data of the city wall, historical damage data of the city wall, historical maintenance data of the city wall, ground monitoring data, and data of external disturbance sources; The city wall structure semantic modeling module, connected to the data acquisition module, is used to construct a city wall structure semantic model that includes wall semantic units, wall foundation semantic units, city gate semantic units, enemy platform semantic units, corner semantic units, historical maintenance semantic units, disease semantic units, and external disturbance semantic units. The temporal InSAR processing module, connected to the city wall structure semantic modeling module, is used to perform temporal InSAR processing on multi-temporal spaceborne SAR image data to obtain the line-of-sight deformation time series, average deformation rate, coherence parameters, amplitude stability parameters, and phase residual parameters of stable scattering points. The scattering point attribution probability calculation module, connected to the time-series InSAR processing module, is used to calculate the attribution probability of stable scattering points belonging to wall semantic units, wall foundation semantic units, and peripheral disturbance semantic units, respectively. The semantic deformation field reconstruction module is connected to the scattering point attribution probability calculation module. It is used to reconstruct the wall deformation field, the wall foundation deformation field, and the external disturbance deformation field based on the attribution probability and reliability weight. The multi-track deformation decomposition module, connected to the semantic deformation field reconstruction module, is used to invert the vertical deformation component, the wall-directed deformation component, and the transverse wall-directed deformation component based on the ascending and descending track line-of-sight deformation time series. The axial risk sequence calculation module, connected to the multi-track deformation decomposition module, is used to calculate the axial deformation gradient, differential settlement between adjacent segments, wall-foundation coupling difference, time delay response to external disturbances, and risk propagation direction along the city wall axis. The risk assessment module, connected to the axial risk sequence calculation module, is used to construct a comprehensive risk index for city wall segments and generate dynamic risk thresholds. The early warning output module, connected to the risk assessment module, is used to output the risk level of each section of the city wall, the type of early warning, the direction of risk propagation, and suggestions for inspection and handling.

8. The multi-track InSAR deformation attribution reconstruction and coupling risk early warning system as described in claim 7, characterized in that, It also includes a multi-source verification module, which is connected to the semantic deformation field reconstruction module and the early warning output module respectively. It is used to access leveling, total station, GNSS, crack meter, 3D laser scanning and UAV photogrammetry data to correct and verify the InSAR deformation results and risk level.

Images