Method and device for monitoring bridge deformation risk coupled with temperature by PS-InSAR

Abstract

The present application belongs to the technical field of highway bridge safety monitoring and analysis, and discloses a bridge deformation risk monitoring method and equipment coupled with PS-InSAR and temperature, acquires multi-temporal spaceborne SAR image data of a research area, obtains a differential interferogram, calculates an original line-of-sight total deformation sequence, constructs a deformation-temperature-load coupling nonlinear model, calculates a coupling dynamic weight and a temperature-load cross-coupling threshold value, further calculates a bridge overall deformation risk score, a single body risk level, a cluster spatial risk density, and a cluster overall risk level and outputs and displays them. The present application constructs a deformation-temperature-load coupling model, solves the confusion problem of traditional PS-InSAR temperature variation and structure deformation, and achieves millimeter-level precision in deformation calculation; without the need of pre-embedded sensors on site, the present application relies on spaceborne SAR large-scale surface monitoring, and greatly reduces the construction and operation and maintenance cost of massive small and medium-sized bridges.

Description

Technical Field

[0001] This invention relates to the field of highway bridge safety monitoring and analysis technology, specifically to a method and equipment for monitoring bridge deformation risk by coupling PS-InSAR with temperature. Background Technology

[0002] Spaceborne permanent scatterer synthetic aperture radar (PS-InSAR) can utilize stable scatterers such as bridge railings, streetlights, and piers to achieve non-contact, large-area, millimeter-level deformation monitoring, becoming a core technology for bridge cluster surveys. However, existing PS-InSAR bridge monitoring technology has three insurmountable drawbacks: 1. Existing technology only uses the average deformation rate for linear analysis, completely ignoring the nonlinear deformation characteristics of bridges caused by seasonal temperature and traffic load. Permanent deformation is confused with temperature-induced deformation, resulting in large deformation calculation errors and failure to obtain the true structural deformation. 2. There is a lack of PS point deformation threshold and weight system for the characteristics of bridge components such as main beams, piers and towers. It can only output single-point deformation data and cannot realize automated risk classification and accurate screening of large-scale bridge clusters. 3. No joint elimination model for atmospheric errors, orbital errors and temperature deformation has been established. Relying solely on spaceborne remote sensing data is not reliable enough to meet the engineering accuracy requirements for highway bridge safety assessment.

[0003] Currently, no technical solution can simultaneously achieve accurate decomposition of nonlinear deformation of bridges, elimination of temperature interference, and automated risk screening of large-scale bridge clusters, severely hindering the engineering application of spaceborne remote sensing technology in highway bridge cluster monitoring. Addressing the shortcomings of existing PS-InSAR bridge monitoring technologies, such as linear analysis, lack of component weighting systems, and incomplete error elimination, this invention proposes a nonlinear deformation decomposition method coupling temperature and load. It constructs a component-level weighted evaluation system to achieve adaptive risk screening of large-scale bridge clusters, overcoming all the deficiencies of existing technologies and possessing outstanding substantive features and significant progress. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and device for monitoring bridge deformation risk by coupling PS-InSAR with temperature.

[0005] The above-mentioned objectives of the present invention are achieved through the following technical means: A PS-InSAR-temperature coupled method for monitoring bridge deformation risk includes the following steps: Step 1: Acquire multi-temporal spaceborne SAR image data of the study area. Perform image registration, radiometric calibration, and differential interferometry on the multi-temporal spaceborne SAR image data to obtain differential interferograms. Based on the differential interferograms, determine the effective PS measurement points and calculate the original line-of-sight total deformation sequence of the effective PS measurement points. , Step 2: Construct a deformation-temperature-load coupled nonlinear model based on the following formula: ; in, For the first The original line-of-sight total deformation sequence of the effective PS measurement points For time-series traffic loads, This is the time series term for atmospheric delay-orbit error. For the first Random noise components at each effective PS measurement point The solution of the first problem is obtained by fitting a deformation-temperature-load coupled nonlinear model. Structural permanent deformation components at each effective PS measurement point Temperature deformation amplitude Temperature phase Temperature phase Load response coefficient Load deformation amplitude Load angular frequency Load phase and error correction coefficient Calculate the change in traffic load response. , Step 3, calculate the first... The coupling dynamic weight of each effective PS measurement point and the Temperature-load cross-coupling threshold at each effective PS measuring point Further calculate the overall deformation risk score of the bridge. , Step 4: Calculate the individual risk level Cluster spatial risk density and the overall risk level of the cluster. And display the output.

[0006] As described above, determining the effective PS measurement points based on the differential interferogram includes the following steps: On the differential interferogram, PS candidate points are selected. The PS candidate points with amplitude deviation index DIA < 0.25 and temporal coherence coefficient > 0.75 are selected as effective PS measurement points.

[0007] As described above, the original line of sight to the total deformation sequence Based on the following formula: , in, To correspond to the fixed radar wavelength of spaceborne SAR satellites, For a moment The single-time phase differential interferometry phase corresponding to the effective PS measurement point.

[0008] As described above, traffic load response change Based on the following formula: , in, This represents the average traffic load.

[0009] As mentioned above, the first The coupling dynamic weight of each effective PS measurement point Based on the following formula: , The first Temperature-load cross-coupling threshold at each effective PS measuring point Based on the following formula: , The bridge's overall deformation risk score Based on the following formula: , in, For the first The final coupling weight of each effective PS measurement point; For the first The basic weight of the component type to which each valid PS measurement point belongs; For the first Criticality correction factor for the component type to which each valid PS measurement point belongs; For the first Temperature deformation sensitivity coefficient of each effective PS measuring point; For the first Load response sensitivity coefficient of each effective PS measuring point; For the first Stability weights for each effective PS measurement point; For the first Temperature-load cross-coupling threshold for each effective PS measuring point; For the first The basic deformation threshold of the component type to which each valid PS measurement point belongs; , , All are temperature-load correction factors; Assign a score to the overall deformation risk of the bridge; For the first Risk attenuation coefficient of the component to which each valid PS measuring point belongs; This is the absolute value operator.

[0010] As mentioned above, the first The temperature deformation sensitivity coefficient of each effective PS measuring point is based on the following formula: ; in, This represents the maximum value of the temperature deformation amplitude at all valid PS measuring points across the entire bridge. No. Load response sensitivity coefficient of each effective PS measuring point Based on the following formula: ; in, For the first Traffic load response change at each effective PS measurement point The maximum value of the traffic load response change at all valid PS measuring points; The first Stability weight of each valid PS measurement point Calculated based on the following formula: ; in, For the first Amplitude deviation index of each effective PS measurement point , For the first Temporal coherence coefficient of each effective PS measurement point .

[0011] As described above, the individual risk level Based on the following formula: ; ; ; in, Assign a score to the overall deformation risk of the bridge; and These are the safety-level adaptive dynamic threshold and the attention-level adaptive dynamic threshold, respectively. The average dynamic threshold for all bridge components; and All are risk threshold correction coefficients; The average component weight of effective PS measurement points, Cluster spatial risk density Based on the following formula: ; in, The geometric plane coordinates of the bridge cluster center points are: This represents the total number of bridges. This refers to the bridge's serial number; For the first Individual risk level of each bridge; For the first The weights of the bridge's components; It is a globally fixed reference constant; Radius of impact of basic risks; For the first Geometric center point of the bridge and bridge cluster center point Spatial planar distance between them; For the first Seasonal temperature-induced deformation components at all effective PS measuring points inside the bridge The arithmetic mean; Overall risk level of the cluster Based on the following formula: ; in, The overall risk level of the cluster; Bridge cluster center The area of ​​the cluster region corresponding to the cluster; The overall deformation risk score for all bridges within the cluster. The arithmetic mean; The total area corresponding to the bridges in the study area; To calculate the overall deformation risk score for all bridges in the study area. The arithmetic mean.

[0012] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described method.

[0013] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0014] A computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0015] Compared with the prior art, the present invention has the following advantages: 1. Precise Deformation Decomposition: Construct a deformation-temperature-load coupled model to distinguish permanent structural deformation components, seasonal temperature-induced deformation components, traffic load dynamic deformation components, traffic load daily periodic deformation components, atmospheric-orbit joint error components, and random noise components. This solves the problem of confusion between temperature variation and structural deformation in traditional PS-InSAR, and the deformation calculation achieves millimeter-level accuracy. 2. Component Differentiation Assessment: Based on the key stress characteristics of the bridge, the effective PS measuring points are divided into core stress-bearing components, important stress-bearing components, and general components. The temperature-load cross-coupling threshold is dynamically corrected with temperature and load, thus overcoming the defect of fixed thresholds that cannot adapt to seasonal fluctuations. 3. Massive Cluster Intelligent Screening: A dual-layer algorithm of individual risk level + overall cluster risk level enables fully automatic risk zoning of hundreds or thousands of bridges, adapting to large-scale bridge surveys. 4. Multi-error joint elimination: Unified elimination of five types of interference: terrain, track, atmosphere, temperature, and load, combined with on-site GNSS benchmarking and verification, to meet the standards for highway bridge safety assessment engineering. 5. Low-cost non-contact monitoring: No need to pre-embed sensors on the bridge site, relying on spaceborne SAR for large-area monitoring, which greatly reduces the construction and operation and maintenance costs of a large number of small and medium-sized bridges. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0017] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to embodiments. The embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] Example 1: A PS-InSAR-temperature coupled method for monitoring bridge deformation risk includes the following steps: Step 1: Preprocessing of multi-temporal spaceborne SAR data.

[0019] Multi-temporal spaceborne SAR imagery and digital elevation data (DEM) of the study area were acquired. Image registration, radiometric calibration, and differential interferometry were performed on the multi-temporal spaceborne SAR imagery to obtain differential interferograms. Based on the differential interferograms, effective PS measurement points were determined, and the original line-of-sight total deformation sequence of the effective PS measurement points was calculated. The specific steps are as follows.

[0020] Step 1.1: Acquisition and quality screening of multi-source data.

[0021] Acquire multi-temporal spaceborne SAR imagery data from Sentinel-1 / TerraSAR-X / Cosmo-SkyMed covering the study area of ​​the bridge cluster, and simultaneously collect 30m resolution digital elevation data (DEM) for the study area; perform quality screening on the multi-temporal spaceborne SAR imagery data, remove low-quality images with cloud obstruction, orbital anomalies, and strong noise, and retain effective multi-temporal spaceborne SAR imagery data with continuous temporal coverage and complete spatial coverage.

[0022] Step 1.2: Perform high-precision registration of multi-temporal spaceborne SAR image data.

[0023] Using the master image of the multi-temporal spaceborne SAR image data as the benchmark, coarse registration and fine registration processing are performed on all slave images of the multi-temporal spaceborne SAR image data: coarse registration between the master image and slave images is completed by image feature point matching, and then sub-pixel level fine registration between the master image and slave images is achieved based on the complex cross-correlation algorithm. The registration accuracy is controlled within 1 / 8 pixel, ensuring that the pixel positions of SAR images of different temporal phases are strictly aligned and eliminating spatial offset errors.

[0024] Step 1.3, Radiation calibration process.

[0025] Radiometric calibration is performed on the registered multi-temporal spaceborne SAR image data to convert the raw DN values ​​of the multi-temporal spaceborne SAR image data into standardized backscattering coefficients. This further eliminates radiation distortion caused by sensor gain, antenna pattern, propagation path, etc., ensuring that the radiation values ​​of images from different time phases and orbits are uniform and comparable.

[0026] Step 1.4: Terrain phase removal and differential interferogram generation.

[0027] The original interferometric phase is obtained from the radiometrically calibrated multi-temporal spaceborne SAR image data. The simulated terrain phase is generated using digital elevation data (DEM). The simulated terrain phase is then subtracted from the original interferometric phase. Combined with precise orbit data, the phase deviation introduced by orbital error is removed, and a differential interferogram containing only deformation phase, atmospheric phase, and noise phase is generated, thus completing the differential interferometric processing.

[0028] Step 1.5: Extraction and stability screening of permanent scatterer PS point candidates.

[0029] On the differential interferogram, for rigid structural areas such as bridge railings, street light bases, piers, main beams, and cable towers, high-amplitude and high-coherence PS candidate points are extracted as effective PS measurement points. Stability screening is performed using a dual threshold of amplitude deviation index DIA < 0.25 and temporal coherence coefficient > 0.75. The PS candidate points retained by the stability screening are effective PS measurement points, forming bridge-specific PS candidate points.

[0030] Step 1.6: Extraction and storage of raw time-series phase data at PS points.

[0031] For the selected valid PS measurement points, the corresponding temporal differential interferometry phases are extracted from the differential interferograms phase by phase. The temporal differential interferometry phases of the valid PS measurement points are sorted, denoised, and normalized according to time. The original line-of-sight total deformation sequence of the valid PS measurement points is obtained from the temporal differential interferometry phases. .

[0032] , in, To correspond to the fixed radar wavelength of the spaceborne SAR satellite, the original line-of-sight total deformation sequence of each effective PS measurement point was obtained by time-by-time phase conversion. , For a moment The single-phase differential interferometric phase corresponding to the effective PS measurement point is a scalar phase value.

[0033] The temporal differential interferometric phase is a set of time series; Single phase (time) in time-series differential interferometry phase A single phase sample.

[0034] Step 2: Construction of the deformation-temperature-load coupling model and nonlinear deformation decomposition.

[0035] Construct a deformation-temperature-load coupled nonlinear model to obtain the original total deformation sequence of effective PS measurement points. The deformation is precisely decomposed into structural permanent deformation components, seasonal temperature-induced deformation components, traffic load dynamic deformation components, traffic load daily periodic deformation components, atmospheric-track joint error components, and random noise components. An adaptive weighted least squares method is used for iterative fitting and separation, automatically assigning temperature and load deformation weights, and simultaneously eliminating temperature interference, load interference, and atmospheric and track errors to obtain the bridge's true permanent deformation data.

[0036] The expression for the deformation-temperature-load coupled nonlinear model is: ; In the formula: For the first The original line-of-sight total deformation sequence of the effective PS measurement points.

[0037] For the first The structural permanent deformation components of each effective PS measurement point.

[0038] For the first The seasonal temperature cycle-induced deformation component of each effective PS measuring point For the first Temperature deformation amplitude of each effective PS measuring point For the first Temperature angular frequency at each effective PS measurement point For the first Temperature phase of each effective PS measurement point.

[0039] For the first Traffic load dynamic deformation components at each effective PS measuring point For the first Load response coefficient of each effective PS measuring point For time-series traffic loads.

[0040] For the first The daily periodic deformation components of traffic load at each effective PS measuring point For the first Load deformation amplitude values ​​of one effective PS measuring point For the first The load angular frequency of one effective PS measurement point For the first Load phase of each effective PS measuring point.

[0041] For the first Atmospheric-orbit joint error components of each effective PS measurement point For the first Error correction coefficient for each effective PS measurement point This is the time series term for atmospheric delay-orbit error.

[0042] For the first The random noise components at each effective PS measurement point represent the random noise from both the measurement and the environment.

[0043] Temporal traffic load The temporal traffic load was obtained from external measurements: ETC gantries, traffic flow monitoring equipment, and highway toll stations along the bridge.

[0044] Atmospheric delay-orbit error time series term It is obtained from atmospheric phase and precise orbit data separated by time-series differential interferometry and belongs to the preprocessing derivative data of step 1.

[0045] Time variable Timestamps and standardized time sequences are taken from multi-temporal spaceborne SAR images.

[0046] , , , , , , , ,as well as These are the error correction coefficients; these parameters are the coefficients to be determined in the model. For the observed values, and Given the independent variables, an adaptive weighted least squares iterative fitting method is used to solve the problem. , , , , , , , ,as well as .

[0047] The permanent deformation component of the structure is obtained by solving the deformation-temperature-load coupled nonlinear model. As permanent deformation data of effective PS measuring points, the traffic load response change is calculated. This reflects the sensitivity of effective PS measuring points to vehicle load deformation.

[0048] Traffic load response change Calculations based on the following steps: Time-series traffic load Calculate the average traffic load , .

[0049] Step 3: Establishment of a graded weighted evaluation system for bridge structure PS points coupled with dynamic threshold.

[0050] Based on the key stress characteristics of bridges, effective PS measuring points are divided into three component types: core load-bearing components (main beams, cable towers), important load-bearing components (piers and abutments), and general components (bridge railings, street light bases). The basic deformation thresholds of each type of effective PS measuring point are determined in conjunction with the "Technical Condition Assessment Standard for Highway Bridges" (JTG / T H21-2011), and the thresholds are dynamically corrected by coupling temperature and load deformation parameters. The component deformation risk coefficient and the overall bridge deformation assessment score are calculated by weighted formula, and a deformation assessment system for effective PS measuring points specific to bridge structures is established.

[0051] No. The coupling dynamic weight of each effective PS measurement point formula: ; No. Temperature-load cross-coupling threshold at each effective PS measuring point Correction formula: ; Bridge overall deformation risk score The formula: ; In the formula: For the first The final coupling weight of each effective PS measurement point.

[0052] For the first The basic weight of the component type to which each valid PS measurement point belongs.

[0053] For the first The criticality correction factor for the component type to which each valid PS measurement point belongs ( and (Refer to the corresponding preset table to retrieve the value for the main beam / pier / guardrail).

[0054] For the first Temperature deformation sensitivity coefficient of the effective PS measurement points, the first The temperature deformation sensitivity coefficient of each effective PS measuring point is based on the following formula: ; in, For the first Temperature deformation amplitude values ​​of each effective PS measuring point; This represents the maximum temperature deformation amplitude at all valid PS measuring points across the entire bridge; its physical meaning is: normalizing the temperature deformation amplitude to... The larger the range, the higher the temperature sensitivity coefficient, and the greater the weight of the measuring point in the risk assessment.

[0055] For the first The load response sensitivity coefficient of the effective PS measuring point, the th Load response sensitivity coefficient of each effective PS measuring point Based on the following formula: ; in, For the first Traffic load response change at each effective PS measurement point This represents the maximum value of the traffic load response change across all valid PS measuring points. The load response sensitivity coefficient is used to quantify the sensitivity of the measuring point to traffic load deformation; the stronger the load response, the greater the weight amplification.

[0056] For the first The stability weight of the effective PS measurement point is determined by the first... Amplitude deviation index of each effective PS measurement point and temporal coherence coefficient Calculated; the first Stability weight of each valid PS measurement point Calculated based on the following formula: ; The smaller The closer the value is to 1, the more stable the effective PS measurement points are. The larger the value, the higher the weight.

[0057] For the first Temperature-load cross-coupling threshold for each effective PS measuring point; For the first The basic deformation threshold of each effective PS measuring point is determined by combining the basic deformation threshold of the component type to which the effective PS measuring point belongs with the "Technical Condition Assessment Standard for Highway Bridges" (JTG / T H21-2011). , , All are temperature-load correction factors.

[0058] Assign a score to the overall deformation risk of the bridge; For the first The risk attenuation coefficient of the component to which each valid PS measuring point belongs is determined according to the component type.

[0059] This is the absolute value operator.

[0060] Step 4: Automated adaptive risk screening and spatial clustering classification of large-scale bridge clusters.

[0061] An original automated screening method is adopted, which comprehensively assesses the risk level of individual bridges, the spatial risk density of bridge clusters, and the risk of large-scale bridge clusters. Based on the separated real permanent deformation data, component weighted scores and dynamic thresholds, the method completes the risk level determination of individual bridges through an original formula; spatial risk density clustering is performed on large-scale bridge clusters to identify high-risk contiguous sections; and the risk distribution map of bridge clusters is automatically divided and output according to the comprehensive risk level of the clusters, thus completing the rapid screening of the study area.

[0062] Individual risk level Based on the following formula: ; ; ; in, The overall deformation risk score of the bridge is calculated (by step 3). and These are the safety-level adaptive dynamic threshold and the attention-level adaptive dynamic threshold, respectively. The average dynamic threshold for all bridge components is determined by taking the temperature-load cross-coupling threshold of all valid PS measuring points on the current bridge. arithmetic mean , This represents the total number of valid PS measurement points on the bridge. The serial number of the valid PS measurement point; and All are risk threshold correction coefficients; The average component weight of effective PS measuring points is taken as the coupled dynamic weight of all effective PS measuring points of the current bridge. arithmetic mean .

[0063] Cluster spatial risk density Based on the following formula: ; in, For cluster spatial risk density, The geometric plane coordinates of the bridge cluster center points are obtained based on the following steps: First, extract the CGCS2000 plane coordinates of all valid PS measurement points within the range of each bridge, calculate the plane coordinates of the geometric center point of a single bridge, and then use the DBSCAN spatial density clustering algorithm to automatically generate several clusters for the geometric center points of all bridges in the study area. The average plane coordinates of the geometric center points of each bridge in each cluster are the geometric plane coordinates of the bridge cluster center points.

[0064] This represents the total number of bridges. This refers to the bridge's serial number.

[0065] For the first The risk level of each individual bridge is (0 safe / 1 concern / 2 dangerous).

[0066] For the first The component weights of the bridge are the first... The coupling dynamic weights of all effective PS measurement points of the bridge Take the arithmetic mean.

[0067] It is a globally fixed benchmark constant, which can be the maximum value of the basic weight of the core component, and is fixed at 1.0.

[0068] The basic risk impact radius is a fixed constant preset in the project (2000m in the example, which can be adjusted according to the scale of the monitoring area).

[0069] For the first Geometric center point of the bridge and bridge cluster center point The spatial plane distance between them.

[0070] For the first Seasonal temperature-induced deformation components at all effective PS measuring points inside the bridge The arithmetic mean.

[0071] Overall risk level of the cluster Based on the following formula: ; in, The value represents the overall risk level of the bridge cluster (0~100% percentage). The higher the value, the higher the overall risk of the entire bridge cluster.

[0072] Bridge cluster center The cluster area of ​​the corresponding cluster is automatically calculated from the GIS vector polygon features.

[0073] The overall deformation risk score for all bridges within the cluster. The arithmetic mean.

[0074] The total area corresponding to the bridges in the study area is obtained from the boundary vector statistics of the previous study area.

[0075] To calculate the overall deformation risk score for all bridges in the study area. The arithmetic mean.

[0076] Step 5: Data output and verification.

[0077] Structural permanent deformation components based on effective PS measurement points of bridges Generate the structural permanent deformation curves of the effective PS measurement points of the bridge.

[0078] Bridge overall deformation risk score based on individual bridges and individual risk level Generate a single-bridge risk assessment report.

[0079] Based on cluster spatial risk density Based on the clustering results of the clustering, a risk distribution map of the bridge cluster is generated.

[0080] Output the permanent structural deformation curves of the effective PS measurement points of the bridge, the single-bridge risk assessment report, the risk distribution map of the bridge cluster, and all parameters calculated in the aforementioned steps.

[0081] By comparing and verifying the data with the above data using ground-based GNSS monitoring, the accuracy of the permanent structural deformation monitoring reached the millimeter level, and the risk identification results met the accuracy requirements of highway bridge safety assessment projects.

[0082] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0083] Example 2: In this embodiment, a computer device is also provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0084] Example 3: In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0085] Example 4: In this embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0086] It should be noted that the embodiments described in this invention are merely illustrative examples of the spirit of the invention. Those skilled in the art to which this invention pertains can make various modifications or additions to the described embodiments or use similar methods to substitute them, but without departing from the spirit of the invention or exceeding the scope of the invention.

Claims

1. A bridge deformation risk monitoring method coupled with PS-InSAR and temperature, characterized in that, Includes the following steps: Step 1: Acquire multi-temporal spaceborne SAR image data of the study area. Perform image registration, radiometric calibration, and differential interferometry on the multi-temporal spaceborne SAR image data to obtain differential interferograms. Based on the differential interferograms, determine the effective PS measurement points and calculate the original line-of-sight total deformation sequence of the effective PS measurement points. , Step 2: Construct a deformation-temperature-load coupled nonlinear model based on the following formula: ； in, For the first The original line-of-sight total deformation sequence of the effective PS measurement points For time-series traffic loads, This is the time series term for atmospheric delay-orbit error. For the first Random noise components at each effective PS measurement point The solution of the first problem is obtained by fitting a deformation-temperature-load coupled nonlinear model. Structural permanent deformation components at each effective PS measurement point Temperature deformation amplitude Temperature phase Temperature phase Load response coefficient Load deformation amplitude Load angular frequency Load phase and error correction coefficient Calculate the change in traffic load response. , Step 3, calculate the first... The coupling dynamic weight of each effective PS measurement point and the Temperature-load cross-coupling threshold at each effective PS measuring point Calculate the overall deformation risk score of the bridge. , Step 4: Calculate the individual risk level Cluster spatial risk density and the overall risk level of the cluster. And display the output.

2. The bridge deformation risk monitoring method coupled with PS-InSAR and temperature according to claim 1, characterized in that, The determination of effective PS measurement points based on differential interferograms includes the following steps: On the differential interferogram, PS candidate points are selected. PS candidate points with amplitude deviation index DIA < 0.25 and temporal coherence coefficient > 0.75 are selected as effective PS measurement points.

3. The bridge deformation risk monitoring method coupled with PS-InSAR and temperature according to claim 1, characterized in that, The original line-of-sight total deformation sequence Based on the following formula: ， in, To correspond to the fixed radar wavelength of spaceborne SAR satellites, For a moment The single-time phase differential interferometry phase corresponding to the effective PS measurement point.

4. The bridge deformation risk monitoring method coupled with PS-InSAR and temperature as described in claim 1, characterized in that, The traffic load response change Based on the following formula: ， in, This represents the average traffic load.

5. The bridge deformation risk monitoring method coupled with PS-InSAR and temperature according to claim 4, characterized in that, The first The coupling dynamic weight of each effective PS measurement point Based on the following formula: ， The first Temperature-load cross-coupling threshold at each effective PS measuring point Based on the following formula: ， The bridge's overall deformation risk score Based on the following formula: ， in, For the first The final coupling weight of each effective PS measurement point; For the first The basic weight of the component type to which each valid PS measurement point belongs; For the first Criticality correction factor for the component type to which each valid PS measurement point belongs; For the first Temperature deformation sensitivity coefficient of each effective PS measuring point; For the first Load response sensitivity coefficient of each effective PS measuring point; For the first Stability weights for each effective PS measurement point; For the first Temperature-load cross-coupling threshold for each effective PS measuring point; For the first The basic deformation threshold of the component type to which each valid PS measurement point belongs; , , All are temperature-load correction factors; Assign a score to the overall deformation risk of the bridge; For the first Risk attenuation coefficient of the component to which each valid PS measuring point belongs; This is the absolute value operator.

6. The bridge deformation risk monitoring method coupled with PS-InSAR and temperature according to claim 5, characterized in that, The first The temperature deformation sensitivity coefficient of each effective PS measuring point is based on the following formula: ； in, This represents the maximum value of the temperature deformation amplitude at all valid PS measuring points across the entire bridge. No. Load response sensitivity coefficient of each effective PS measuring point Based on the following formula: ； in, For the first Traffic load response change at each effective PS measurement point The maximum value of the traffic load response change at all valid PS measuring points; The first Stability weight of each effective PS measurement point Calculated based on the following formula: ； in, For the first Amplitude deviation index of each effective PS measurement point , For the first Temporal coherence coefficient of each effective PS measurement point .

7. The bridge deformation risk monitoring method coupled with PS-InSAR and temperature as described in claim 6, characterized in that, The individual risk level Based on the following formula: ； ； ； in, Assign a score to the overall deformation risk of the bridge; and These are the safety-level adaptive dynamic threshold and the attention-level adaptive dynamic threshold, respectively. The average dynamic threshold for all bridge components; and All are risk threshold correction coefficients; The average component weight of effective PS measurement points, Cluster spatial risk density Based on the following formula: ； in, The geometric plane coordinates of the bridge cluster center points are: This represents the total number of bridges. This refers to the bridge's serial number; For the first Individual risk level of the bridge; For the first The weights of the bridge's components; It is a globally fixed reference constant; Radius of impact of basic risks; For the first Geometric center point of the bridge and bridge cluster center point Spatial planar distance between them; For the first Seasonal temperature-induced deformation components at all effective PS measuring points inside the bridge The arithmetic mean; Overall risk level of the cluster Based on the following formula: ； in, The overall risk level of the cluster; Bridge cluster center The area of ​​the cluster region corresponding to the cluster; The overall deformation risk score for all bridges within the cluster. The arithmetic mean; The total area corresponding to the bridges in the study area; To calculate the overall deformation risk score for all bridges in the study area. The arithmetic mean.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the PS-InSAR coupled temperature bridge deformation risk monitoring method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the PS-InSAR coupled temperature bridge deformation risk monitoring method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the PS-InSAR coupled temperature-based bridge deformation risk monitoring method as described in any one of claims 1 to 7.

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