Urban building group safety risk assessment method and system based on insar and fuzzy mathematics theory

Abstract

This invention provides a method and system for safety risk assessment of urban building clusters based on InSAR and fuzzy mathematics theory, belonging to the technical field of urban building cluster safety risk assessment. This invention acquires temporal deformation monitoring data of buildings within a target area using PS-InSAR technology; it traces back to engineering specifications to determine eight risk deformation assessment indicators and their classification thresholds; and calculates the standardized risk index of each assessment indicator through the relative relationship between deformation data and risk level thresholds. Addressing the differences in indicator contribution and the fuzziness of threshold boundaries, it introduces the analytic hierarchy process to determine weights, and combines fuzzy comprehensive evaluation theory to construct an assessment model, outputting the comprehensive risk level of the buildings. This method constructs a systematic and quantifiable risk assessment process, realizing automated assessment across the entire chain of "deformation monitoring - specification benchmarking - fuzzy decision-making," providing reliable technical support for safety early warning of urban building clusters.

Description

Technical Field

[0001] This invention belongs to the field of urban building cluster safety risk assessment technology, specifically involving a method and system for urban building cluster safety risk assessment based on InSAR and fuzzy mathematics theory. Background Technology

[0002] Over long-term use, buildings may experience varying degrees of deformation, such as settlement and tilting, due to changes in geological conditions, construction quality, and the natural environment. This not only affects the normal use of the building but may also pose serious safety hazards, even causing casualties and property damage. Therefore, effective safety risk prevention and control for urban building complexes has become a core challenge for urban public safety management.

[0003] Traditional building safety assessments primarily rely on manual inspections and limited-point sensor monitoring, which suffers from limitations such as low efficiency, high cost, and strong subjectivity. Especially for large-scale building complexes, achieving high-frequency, automated, and high-precision safety monitoring is difficult, making it hard to provide timely warnings of potential risks.

[0004] Interferometric Synthetic Aperture Radar (InSAR) measurement technology has opened up a new technical path for building cluster safety early warning due to its large-scale, all-weather, and high-precision deformation monitoring capabilities. InSAR technology is a space-based Earth observation technology developed based on Synthetic Aperture Radar (SAR) technology and interferometric measurement technology. SAR is an active microwave remote sensing method that records the amplitude and phase information of a resolution cell on the ground by transmitting microwave pulses and receiving reflected signals. By performing complex conjugate multiplication on two SAR images of the same area at different time points, the phase difference between the two echo signals corresponding to the same target is extracted. This difference is then combined with orbital data and imaging geometry models to obtain the target's three-dimensional spatial position and deformation information.

[0005] However, existing research on deformation monitoring and risk assessment of urban buildings using InSAR technology largely focuses on regional macro-settlement trend analysis based on a single deformation index. Risk assessment systems for individual buildings or building complexes remain incomplete and fail to comprehensively assess building risks. The main bottlenecks are as follows: (1) Data conversion barrier: The massive deformation data acquired by InSAR has weak spatial correlation with building entities. There is a lack of efficient algorithms to accurately map discrete monitoring points to individual building units, making it difficult for deformation information to directly serve building safety assessment. (2) Lack of evaluation standards: The safety risk level of buildings needs to be quantified in conjunction with engineering specifications, but the existing specifications lack a systematic definition of the mapping relationship between InSAR deformation index and risk threshold, making it difficult for the assessment results to be aligned with industry standards. (3) The traditional threshold method is highly subjective in the allocation of weights for multiple indicators, and the risk level jump at the threshold boundary (such as the sudden change from "safe" to "dangerous") is contrary to the gradual risk characteristics in engineering practice, which leads to a decrease in the credibility of the evaluation results.

[0006] Therefore, it is necessary to provide a method and system for assessing the safety risks of urban building complexes to solve the above problems. Summary of the Invention

[0007] This invention discloses a method and system for safety risk assessment of urban building clusters. The method constructs a systematic and quantifiable risk assessment process, realizing automated assessment of the entire chain of "deformation monitoring - standard benchmarking - fuzzy decision-making", providing reliable technical support for safety early warning of urban building clusters, and thus effectively solving at least one of the technical problems involved in the background art.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: A method for safety risk assessment of urban building clusters based on InSAR and fuzzy mathematics theory includes the following steps: Step S1: Obtain temporal deformation monitoring data of buildings within the target area based on PS-InSAR technology; Step S2: Construct a risk indicator system, select building risk assessment indicators and set threshold limits for each assessment indicator under different risk levels, and calculate the standardized risk index of buildings in the target area under different assessment indicators based on the risk indicator system. Step S3: Construct a fuzzy membership function, treating each risk level of the evaluation index as a fuzzy set, and calculate the membership degree of the standardized risk index relative to different fuzzy sets; Step S4: The analytic hierarchy process (AHP) is introduced to determine the initial weights of different evaluation indicators, and the overweighting method is used to optimize the initial weights to obtain the standardized weights of each evaluation indicator. Step S5: Construct a weight vector using the standardized weights of all evaluation indicators, construct a fuzzy membership matrix using the membership degrees of all evaluation indicators under different risk levels, synthesize the weight vector and the fuzzy membership matrix to construct a comprehensive risk fuzzy evaluation model, and use the comprehensive risk fuzzy evaluation model to calculate the comprehensive risk assessment score of the building.

[0009] As a preferred improvement, step S1 specifically includes the following steps: Based on PS-InSAR technology, an interferometric phase model including temperature phase is used to estimate the deformation of PS points within the target area; The deformation estimation results of the interferometric phase model are verified using GNSS monitoring data or leveling data; By utilizing the spatial relationship between the latitude and longitude coordinates of PS points and the urban building outline vector data, the external environment of buildings is eliminated, and only PS points within the building polygons are retained; The phase time series of each PS point is decomposed, the amplitude of the seasonal periodic term is extracted, and points dominated by temperature effects are marked or removed. Using the elevation information of PS points and ground elevation information, PS points at the top of high-rise buildings are eliminated; The deformation characteristic index of the building is calculated based on the temporal deformation information of the retained PS points.

[0010] As a preferred improvement, the risk index system is constructed based on existing engineering specifications, literature, and historical experience. The evaluation indicators include cumulative settlement, differential settlement, settlement gradient, settlement rate, differential settlement rate, settlement rate gradient, tilt rate, and tilt angle.

[0011] As a preferred improvement, the calculation process of the standardized risk index is expressed as follows: In the formula, To evaluate the standardized risk index of the indicator, X represents the actual deformation index of the building obtained from the deformation data of point PS. These are the threshold values ​​for different risk levels of the assessment indicators; < < .

[0012] As a preferred improvement, a trapezoidal fuzzy function is used to construct the mapping relationship between risk levels and fuzzy sets. The standardized risk index classifies risk levels as fuzzy at the risk level boundary points, with a membership degree of 0.5; when the standardized risk index is between the two boundary points, the risk level classification is clear, with a membership degree of 1.0.

[0013] As a preferred improvement, the standardized weights of the evaluation indicators are calculated as follows: In the formula, The optimized weight for the i-th evaluation metric; This represents the standardized risk index of the assessment indicator; is the initial weight of the i-th evaluation indicator; n is the number of evaluation indicators that can actually be calculated for the building; The standardized weight of the i-th evaluation indicator is, The result after normalization.

[0014] As a preferred improvement, the comprehensive risk fuzzy evaluation model Represented as: In the formula, Represents the weight vector; Represents a fuzzy membership matrix; This represents a weighted average type fuzzy matrix transformation; Representation matrix The elements in, where: In the formula, This represents the membership degree of the i-th evaluation indicator under the j-th risk level. , ; Then the overall risk assessment score Represented as: In the formula, Represents the score set vector, ; This indicates the matrix transpose.

[0015] A safety risk assessment system for urban building clusters based on InSAR and fuzzy mathematics theory includes: The data acquisition module is used to acquire time-series deformation monitoring data of buildings within the target area based on PS-InSAR technology; The risk indicator construction module is used to build a risk indicator system, select building risk assessment indicators and set threshold limits for each assessment indicator under different risk levels, and calculate the standardized risk index of buildings in the target area under different assessment indicators based on the risk indicator system. The fuzzy membership calculation module is used to construct fuzzy membership functions, treating each risk level of the evaluation index as a fuzzy set, and calculating the membership degree of the standardized risk index relative to different fuzzy sets. The weight optimization module is used to introduce the analytic hierarchy process to determine the initial weights of different evaluation indicators, and to optimize the initial weights using the overweighting method to obtain the standardized weights of each evaluation indicator. The assessment module is used to construct a weight vector with standardized weights for all assessment indicators, and to construct a fuzzy membership matrix with the membership degrees of all assessment indicators under different risk levels. The weight vector and the fuzzy membership matrix are then combined to construct a comprehensive risk fuzzy evaluation model. This comprehensive risk fuzzy evaluation model is used to calculate the building's comprehensive risk assessment score. The beneficial effects of this invention are as follows: (1) Fully explore InSAR deformation information and dynamically extract multi-scale deformation risk indicators: Combine the spatial positional relationship between PS points and building vector contours to screen building-related PS points, which can accurately calculate the deformation of individual building units. Based on the number of building-related PS points, adaptively select evaluation indicators to realize progressive risk assessment from single indicators to spatial gradient indicators, and fully explore the multi-dimensional information value of limited PS point data; (2) Integrating norm-driven quantitative assessment with fuzzy decision-making to improve the accuracy and reliability of safety risk assessment for urban building clusters: This invention establishes a norm-driven multi-dimensional deformation index threshold mapping system, transforming PS-InSAR deformation monitoring data into eight standardized building risk indices, thus opening up the transformation path from remote sensing monitoring data to engineering safety standards. By integrating the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation theory, the contribution of multiple indicators to the overall risk of buildings is objectively quantified, eliminating the defect of abrupt level changes in the critical area of ​​the traditional threshold method, realizing a gradual division of risk levels, making the assessment results more consistent with the actual gradual change law of engineering risks, and improving the accuracy of early warning. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A flowchart illustrating the safety risk assessment method for urban building clusters based on InSAR and fuzzy mathematics theory provided by this invention. Figure 2 This is a schematic diagram of the membership function distribution of building risk levels in Example 1; Figure 3 This is a schematic diagram of the discrete deformation field formed by the PS points retained after screening in a certain city in Example 1; Figure 4 This is a distribution map of the number of associated PS points within the building area of ​​a certain area in the study area of ​​Example 1; Figure 5 This is a schematic diagram of the fitted plan of the example building in Example 1; Figure 6This is a diagram showing the results of the comprehensive risk assessment of buildings in a certain area within the study area in Example 1. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Please refer to the following: Figures 1-6 This embodiment provides a method for assessing the safety risks of urban building clusters based on InSAR and fuzzy mathematics theory, including the following steps: Step S1: Obtain temporal deformation monitoring data of buildings within the target area based on PS-InSAR technology.

[0019] InSAR uses two SAR images of the same area taken by two satellites (or two flights of the same satellite) to calculate their interferometric phase difference and generate an interferogram. The phase information in the interferogram includes various components such as surface deformation, topographic relief, and atmospheric delay. PS points refer to point targets that maintain stable and strong scattering characteristics in long-term SAR image series. By processing long-term SAR images, PS-InSAR technology can identify these PS points scattered throughout the observation area and accurately estimate the surface deformation rate, elevation correction value, and atmospheric delay phase at each PS point. By combining the spatial relationship between the three-dimensional spatial coordinates of PS points and the spatial vector data of urban building outlines, PS point data belonging to the building area and their temporal deformation information are filtered and extracted to calculate the deformation characteristics of individual buildings.

[0020] Specifically, in this embodiment, an interferometric phase model incorporating temperature phase is used to estimate the deformation of the PS point within the target area. This reduces interference from building temperature deformation and accurately obtains the long-period deformation of the building. The estimation process is expressed as follows: In the formula, This represents the interferometric phase difference between the same target PS point in two SAR images; Indicates the wavelength of electromagnetic waves; , These represent the temporal baseline and spatial baseline between the two images, respectively. This represents the difference in deformation rate between the same target PS point in two SAR images; Indicates the slant distance between the satellite and the observed target; Indicates the angle of incidence of the radar wave; This represents the elevation residual between the same target PS point on two SAR images; This indicates the temperature difference corresponding to the time when the two images were acquired; This represents the difference in temperature deformation coefficient between the same target PS point in two SAR images; This represents the interference phase residual.

[0021] After estimating the deformation of all PS points within the target area using PS-InSAR technology, the deformation estimation results of the interferometric phase model are verified using GNSS monitoring data or leveling data to ensure the accuracy of the deformation inversion results and the reliability of subsequent evaluation results. Taking the GNSS monitoring data verification process as an example: PS points and GNSS measurement points are matched according to the principle of spatial proximity. When the planar distance between the two is less than 10 m, they are considered comparable. The LOS-direction deformation variables obtained from InSAR are uniformly converted to the same reference direction and time base for comparison. The root mean square error (RMSE) and correlation coefficient (R) of the difference between the two are calculated as verification indicators. When the RMSE is less than 5 mm and the R is greater than 0.7, the verification is considered successful, and the inversion results are used for subsequent evaluation. When the verification fails, the reference point selection, atmospheric delay correction parameters, and low-coherence PS point rejection rules are re-optimized, and the abnormal areas are inverted and verified again until the accuracy requirements are met.

[0022] The target area typically contains a large number of PS points, which include a significant amount of non-target interference. (1) External environment of buildings: roads, open spaces, vegetation, etc.

[0023] (2) Non-structural thermal deformation: Temperature deformation of building surfaces (especially metal roofs and glass curtain walls) caused by sunlight; (3) Dynamic response deformation: the periodic oscillation of the top of a high-rise building under wind load.

[0024] These interfering points can severely "pollute" the dataset, masking foundation settlement or structural deformation. Therefore, it is necessary to filter the PS points. The filtering method is as follows: (1) Use the spatial relationship between the latitude and longitude coordinates of PS points and the urban building outline vector data to remove the external environment of buildings and retain only the PS points within the building polygons; (2) Decompose the phase time series of each PS point, extract the amplitude of the seasonal periodic term, and mark or remove points where the temperature effect dominates (the amplitude of the seasonal periodic term is greater than the preset threshold); (3) Using the elevation information of PS points and ground elevation information, PS points at the top of high-rise buildings are eliminated (for example, PS points at the top 1 / 3 or higher floors of the building are eliminated) to significantly reduce the impact of wind vibration and extreme temperature deformation.

[0025] After the screening is completed, the retained PS points are the PS points within the building area. The deformation characteristic index of the building is calculated based on the temporal deformation information of the retained PS points.

[0026] Step S2: Construct a risk indicator system, select building risk assessment indicators and set threshold limits for each assessment indicator under different risk levels, and calculate the standardized risk index of buildings in the target area under different assessment indicators based on the risk indicator system.

[0027] The risk indicator system is constructed based on existing engineering specifications, literature, and historical experience. In this embodiment, the assessment indicators include cumulative settlement, differential settlement, settlement gradient, settlement rate, differential settlement rate, settlement rate gradient, tilt rate, and tilt angle.

[0028] In the calculation of the above evaluation indicators, it is first necessary to calculate the LOS deformation of the PS point within the building area at each time node. Transformed into vertical deformation (i.e., the settlement of the building), forming a time-series deformation data set. Within any time window, the derivative of the deformation with respect to time is calculated to obtain the deformation rate within that time window. The conversion process is expressed as follows: The calculation process for the above evaluation indicators is as follows: The cumulative settlement D and the settlement rate V are the maximum values ​​in the time series deformation data set and the time series deformation rate set of all PS points in the building, respectively. Differential settlement ∆ and differential settlement rate ∆V are the maximum absolute differences in settlement amount and settlement rate between any two PS points within the building area, respectively; Settlement gradient β and settlement rate gradient These are the ratios of the maximum absolute difference between the settlement amount and settlement rate between any two PS points within the building area to the geometric distance between the two PS points, respectively. The tilt rate and tilt angle are determined by fitting the plane and the horizontal plane using the plane geometric coordinate information of point PS and the vertical deformation rate and deformation value using the Lagrange multiplier method, respectively. The specific formulas are as follows: In the formula, , The first in the building The planar geometric x and y coordinates of point PS; For the first in the building The vertical deformation value of each PS point, where N is the number of PS points within the building area, and N≥3; , , These represent the spatial centroid coordinates of the PS point set; Let P represent the covariance matrix of the point set PS after centering; M is the eigenvector corresponding to the smallest eigenvalue of matrix A. , , Represents the feature elements in the feature vector M; Indicates the degree of tilt; This indicates the tilt rate.

[0029] Based on the source analysis of existing engineering specifications and literature, as well as the total value of historical experience, the threshold limits for various assessment indicators and their corresponding risk levels are set as shown in Table 1.

[0030] Table 1. Building Risk Assessment Indicators and Thresholds The relationship between the calculability of the above risk assessment indicators and the number of PS points n within the building area is shown in Table 2.

[0031] Table 2. Relationship between the computability of risk assessment indicators and the number of PS points within the building area. With cumulative settlement D and tilt rate For example: only one PS point is needed within the building area to calculate the cumulative settlement D, while the tilt rate... At least 3 PS points are required to complete the calculation.

[0032] To better quantify the risk of a building, the building's deformation is converted into a standardized risk index based on the threshold values ​​of different risk levels for various assessment indicators. The calculation process is as follows: In the formula, To evaluate the standardized risk index of the indicator, X represents the actual deformation index of the building obtained from the deformation data of point PS. These are the threshold values ​​for different risk levels of the assessment indicators. Taking cumulative settlement as an example, The values ​​are 25, 50, and 100 respectively.

[0033] Step S3: Construct a fuzzy membership function, treating each risk level of the evaluation index as a fuzzy set, and calculate the membership degree of the standardized risk index relative to different fuzzy sets.

[0034] Specifically, this implementation uses a trapezoidal fuzzy function to construct the mapping relationship between risk levels and fuzzy sets, wherein: Membership of Category A risk level Determined by the membership function of the partial trapezoid, as shown below: In the formula, x is the standardized risk index; a, b, c, and d are preset parameters.

[0035] Membership of risk levels B and C Determined by the membership function of the intermediate trapezoid, as shown below: Membership of Category D risk level Determined by the membership function of the larger half-trapezoidal shape, as shown below: In the above membership functions, the values ​​of a, b, c, and d follow these rules: when the standardized risk index is at the risk level boundary point, the risk level division is fuzzy, and the membership degree is 0.5; when the standardized risk index is between the two boundary points, the risk level division is clear, and the membership degree is 1.0. Therefore, the values ​​of a, b, c, and d are shown in Table 4, and the final membership function distribution is as follows: Figure 2 As shown.

[0036] Table 4. Preset Parameter Values ​​for Fuzzy Membership Functions Step S4: The analytic hierarchy process (AHP) is introduced to determine the initial weights of different evaluation indicators, and the overweighting method is used to optimize the initial weights to obtain the standardized weights of each evaluation indicator.

[0037] The Analytic Hierarchy Process (AHP), as a multi-criteria decision-making method, determines the relative importance of each factor through pairwise comparisons and quantifies subjective judgments using a 1-9 scale, providing a basis for selecting the optimal solution. The different scales and their meanings are shown in Table 5. Table 5. Scales and Meanings of the Analytic Hierarchy Process (AHP) The relative weights of the various evaluation indicators determined in this implementation method are shown in Table 6.

[0038] Table 6. Relative weights and initial weights of various evaluation indicators. Taking cumulative settlement and differential settlement as examples, the relative importance scale between the two is 1 / 5, indicating that differential settlement is significantly more important than cumulative settlement.

[0039] Calculate the geometric mean of all relative weights for each evaluation indicator, normalize it, and obtain the initial weight of each evaluation indicator, which is also recorded in Table 6.

[0040] To better reflect the differences in importance among various evaluation indicators, this invention introduces an overweighting method to optimize the initial weights of each indicator. Specifically, a "penalty" mechanism is applied to indicators with higher risk indices to amplify their weights, resulting in a more reasonable weight distribution across all indicators. The specific execution process is as follows: In the formula, The optimized weight for the i-th evaluation metric; This represents the standardized risk index of the assessment indicator; is the initial weight of the i-th evaluation indicator; n is the number of evaluation indicators that can actually be calculated for the building; The standardized weight of the i-th evaluation indicator is, The result after normalization.

[0041] Step S5: Construct a weight vector using the standardized weights of all evaluation indicators, construct a fuzzy membership matrix using the membership degrees of all evaluation indicators under different risk levels, synthesize the weight vector and the fuzzy membership matrix to construct a comprehensive risk fuzzy evaluation model, and use the comprehensive risk fuzzy evaluation model to calculate the comprehensive risk assessment score of the building.

[0042] Weight vector Represented as: Fuzzy membership matrix Represented as: In the formula, This represents the membership degree of the i-th evaluation indicator under the j-th risk level. , .

[0043] The comprehensive risk fuzzy evaluation model Represented as: In the formula, This represents a weighted average type fuzzy matrix transformation; Representation matrix The elements in, where: Then the overall risk assessment score Represented as: In the formula, Represents the score set vector, ; This indicates the matrix transpose.

[0044] Example 1 This embodiment selects a region within a city as the target area, acquiring 75 up-orbit images from the Sentinel-1A satellite from February 13, 2020 to December 23, 2022. Following a standardized PS-InSAR data processing workflow, and using temperature data as a temperature parameter input into an interferometric phase model incorporating temperature phase, temporal deformation monitoring data for the region is obtained, as shown below. Figure 3 The discrete deformation field composed of PS points shown is obtained after screening as follows: Figure 4 The distribution of associated PS points within the building area is shown.

[0045] The planar geometric coordinates and vertical deformation information of point PS are unified into the same Cartesian coordinate system (including units) to ensure the feasibility of the calculation process and the reliability of the results.

[0046] Based on the number of PS points within the building area, corresponding examples are selected to obtain the calculation results of the corresponding building deformation index and standardized risk index. When the number of associated PS points within the building area n≥3, the results are as follows: Figure 5 The diagram shows a partial plan view of the buildings.

[0047] The initial weights of various building types obtained based on the analytic hierarchy process (AHP) are used. The weight allocation of example buildings is optimized according to a high-calculation-risk "penalty" mechanism. A fuzzy membership matrix is ​​introduced to synthesize the risk assessment matrix, resulting in a comprehensive building risk score. The final deformation indices of the example buildings, the optimized weight allocation of these indices, and the standardized risk index calculation results are shown in Table 7. The comprehensive fuzzy evaluation results are shown in Table 8. The resulting comprehensive risk level map of buildings in the target area is shown in Table 8. Figure 6 As shown.

[0048] Table 7. Calculation results of deformation indices, optimized weight allocation, and standardized risk index for example buildings. Table 8. Comprehensive Fuzzy Evaluation Results of Example Buildings The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of the present invention without departing from the spirit of the present invention, and all of these modifications are within the protection scope of the present invention.

Claims

1. A method for assessing the safety risks of urban building clusters based on InSAR and fuzzy mathematics theory, characterized in that, Includes the following steps: Step S1: Obtain temporal deformation monitoring data of buildings within the target area based on PS-InSAR technology; Step S2: Construct a risk indicator system, select building risk assessment indicators and set threshold limits for each assessment indicator under different risk levels, and calculate the standardized risk index of buildings in the target area under different assessment indicators based on the risk indicator system. Step S3: Construct a fuzzy membership function, treating each risk level of the evaluation index as a fuzzy set, and calculate the membership degree of the standardized risk index relative to different fuzzy sets; Step S4: The analytic hierarchy process (AHP) is introduced to determine the initial weights of different evaluation indicators, and the overweighting method is used to optimize the initial weights to obtain the standardized weights of each evaluation indicator. Step S5: Construct a weight vector using the standardized weights of all evaluation indicators, construct a fuzzy membership matrix using the membership degrees of all evaluation indicators under different risk levels, synthesize the weight vector and the fuzzy membership matrix to construct a comprehensive risk fuzzy evaluation model, and use the comprehensive risk fuzzy evaluation model to calculate the comprehensive risk assessment score of the building.

2. The urban building cluster safety risk assessment method based on InSAR and fuzzy mathematics theory according to claim 1, characterized in that, Step S1 specifically includes the following steps: Based on PS-InSAR technology, an interferometric phase model including temperature phase is used to estimate the deformation of PS points within the target area; The deformation estimation results of the interferometric phase model are verified using GNSS monitoring data or leveling data; By utilizing the spatial relationship between the latitude and longitude coordinates of PS points and the urban building outline vector data, the external environment of buildings is eliminated, and only PS points within the building polygons are retained; The phase time series of each PS point is decomposed, the amplitude of the seasonal periodic term is extracted, and points dominated by temperature effects are marked or removed. Using the elevation information of PS points and ground elevation information, PS points at the top of high-rise buildings are eliminated; The deformation characteristic index of the building is calculated based on the temporal deformation information of the retained PS points.

3. The urban building cluster safety risk assessment method based on InSAR and fuzzy mathematics theory according to claim 1, characterized in that, The risk indicator system is constructed based on existing engineering specifications, literature, and historical experience. The assessment indicators include cumulative settlement, differential settlement, settlement gradient, settlement rate, differential settlement rate, settlement rate gradient, tilt rate, and tilt angle.

4. The urban building cluster safety risk assessment method based on InSAR and fuzzy mathematics theory according to claim 3, characterized in that, The calculation process for the standardized risk index is as follows: In the formula, To evaluate the standardized risk index of the indicator, X represents the actual deformation index of the building obtained from the deformation data of point PS. These are the threshold values ​​for different risk levels of the assessment indicators; < < .

5. The urban building cluster safety risk assessment method based on InSAR and fuzzy mathematics theory according to claim 1, characterized in that, A trapezoidal fuzzy function is used to construct the mapping relationship between risk levels and fuzzy sets. The standardized risk index classifies risk levels as fuzzy at the risk level boundary points, with a membership degree of 0.

5. When the standardized risk index is between the two boundary points, the risk level classification is clear, with a membership degree of 1.

0.

6. The urban building cluster safety risk assessment method based on InSAR and fuzzy mathematics theory according to claim 4, characterized in that, The standardized weights of the evaluation indicators are calculated as follows: In the formula, The optimized weight for the i-th evaluation metric; This represents the standardized risk index of the assessment indicator; Let be the initial weight of the i-th evaluation indicator; n represents the number of evaluation indicators that can actually be calculated for the building; The standardized weight of the i-th evaluation indicator is, The result after normalization.

7. The urban building cluster safety risk assessment method based on InSAR and fuzzy mathematics theory according to claim 6, characterized in that, The comprehensive risk fuzzy evaluation model Represented as: In the formula, Represents the weight vector; Represents a fuzzy membership matrix; This represents a weighted average type fuzzy matrix transformation; Representation matrix The elements in, where: In the formula, This represents the membership degree of the i-th evaluation indicator under the j-th risk level. , ; Then the overall risk assessment score Represented as: In the formula, Represents the score set vector, ; This indicates the matrix transpose.

8. A safety risk assessment system for urban building clusters based on InSAR and fuzzy mathematics theory, characterized in that, include: The data acquisition module is used to acquire time-series deformation monitoring data of buildings within the target area based on PS-InSAR technology; The risk indicator construction module is used to build a risk indicator system, select building risk assessment indicators and set threshold limits for each assessment indicator under different risk levels, and calculate the standardized risk index of buildings in the target area under different assessment indicators based on the risk indicator system. The fuzzy membership calculation module is used to construct fuzzy membership functions, treating each risk level of the evaluation index as a fuzzy set, and calculating the membership degree of the standardized risk index relative to different fuzzy sets. The weight optimization module is used to introduce the analytic hierarchy process to determine the initial weights of different evaluation indicators, and to optimize the initial weights using the overweighting method to obtain the standardized weights of each evaluation indicator. The assessment module is used to construct a weight vector with the standardized weights of all assessment indicators, construct a fuzzy membership matrix with the membership degrees of all assessment indicators under different risk levels, synthesize the weight vector and the fuzzy membership matrix to construct a comprehensive risk fuzzy evaluation model, and use the comprehensive risk fuzzy evaluation model to calculate the comprehensive risk assessment score of the building.

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