Road investigation intelligent design method and system based on satellite images

Abstract

The invention discloses a highway investigation intelligent design method and system based on satellite images, relates to the technical field of remote sensing, and corrects errors caused by atmosphere, illumination and satellite orbit factors through geometric calibration and atmospheric correction of satellite image data Ir to form a corrected influence data set Ip; and the influence of environmental factors on the data precision is effectively eliminated. According to the method, the abnormal rate S is corrected in combination with different geological information, a corrected abnormal rate Sadj is generated, the corrected abnormal rate Sadj is combined with a structural sensitivity function fstruct, risk assessment values R of different areas are calculated, and on this basis, a road overall safety score Rtotal is obtained through statistical analysis. A feasible reference basis is provided for later construction and maintenance of the road, and early recognition and early warning of potential safety hazards in the road construction process are ensured through abnormal trend analysis, dynamic monitoring and geological correction means.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and in particular to a method and system for intelligent design of highway survey based on satellite images. Background Art

[0002] Satellite images are widely used in many fields such as land resources, environmental monitoring, and urban planning. In the process of highway construction, geological surveys, route selection, and construction quality monitoring are one of the key tasks to ensure the safety and quality of the project. With the continuous expansion of the scale of the project and the continuous advancement of technology, remote sensing technology based on satellite images has been gradually introduced into the entire process of highway survey and design, playing an increasingly important role. Especially in the face of complex factors such as geological disasters, ecological impacts, and construction environment, technical means based on remote sensing images can provide accurate data support and decision-making basis for survey and design. Specifically in the later stage of highway construction, especially in terms of roadbed stability and bridge bearing capacity, dynamic monitoring technology has become an important research field.

[0003] Although remote sensing technology has been introduced in the field of modern highway construction for environmental monitoring and geological surveys, there are still many limitations in dynamic monitoring after highway construction, especially in long-term tracking of surface subsidence. Existing monitoring methods mostly rely on ground field surveys and measuring instruments. Although they can accurately obtain subsidence data, there are certain timeliness issues. Usually, ground monitoring requires more frequent on-site surveys, and it is difficult to monitor a large area. It is impossible to grasp the trend of subsidence changes in real time, especially for complex terrain and difficult-to-reach areas. Monitoring is difficult. In addition, traditional settlement monitoring methods are not sensitive enough to detect early settlement, and it is easy to miss some minor deformations, causing safety hazards in the later stages of construction.

[0004] The lack of efficiency and accuracy of this traditional monitoring method has led to great pressure on highway construction projects during the operation phase, especially on bridges, tunnels and other facilities in high-risk areas. As settlement gradually accumulates, it may cause unnecessary safety problems and even cause structural damage in extreme cases. Therefore, relying on ground monitoring cannot fully control geological changes, nor can it respond to settlement changes in a timely manner, which limits the accurate prediction and real-time adjustment of structural safety during highway construction. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligent design of highway survey based on satellite images, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a highway survey intelligent design method based on satellite images, comprising the following steps:

[0007] S1, by performing geometric calibration and atmospheric correction on the collected satellite image data Ir, the errors caused by the atmosphere, illumination and satellite orbit factors are corrected to form the corrected impact data set Ip;

[0008] S2, extracting surface subsidence features from the corrected impact data set Ip, including cracks and elevation changes, and integrating the extracted features to form a surface anomaly feature set F;

[0009] S3, performing anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation is based on the feature set F of surface settlement at different time points to calculate the change amount and rate of surface anomalies, and obtain the anomaly rate S;

[0010] S4, correct the abnormal rate S according to different geological information, obtain the influence of different geological factors on the abnormal rate S, and mark it as the corrected abnormal rate Sadj;

[0011] S5. Based on the obtained corrected abnormal rate Sadj and the structural sensitivity function F, the settlement impact is evaluated, the risk assessment value R of different areas is calculated, and the risk warning is triggered according to the risk assessment value R. When the risk warning is triggered, the overall safety score Rtotal of the highway is obtained through statistical analysis of the risk assessment values ​​R of different areas, and compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger the execution measures according to the overall abnormal assessment result of the highway.

[0012] Preferably, said S1 includes S11 and S12;

[0013] S11, geometrically calibrating the collected satellite image data Ir to correct the spatial errors caused by satellite orbit errors, image tilt and ground undulation factors in the satellite image data Ir, so that the geographic coordinates in the satellite image data Ir can be consistent with the actual ground coordinates, wherein the geometric calibration is specifically performed by matching the ground control points with the corresponding points in the satellite image data Ir to obtain the calibrated target geographic coordinates (xt, yt), and by integrating the target geographic coordinates (xt, yt), the output image data set Ig after geometric calibration is obtained;

[0014] The target geographic coordinates (xt, yt) are obtained by the following calibration formula:

[0015] (xt, yt)=Mgeo*(xr, yr)+b;

[0016] Wherein, (xr, yr) represents the coordinate position in the satellite image data Ir, specifically represents the horizontal axis value and the vertical axis value of the coordinate position, Mgeo represents the geometric transformation matrix, and b represents the translation variable.

[0017] Preferably, S12, by performing atmospheric correction on the acquired output image dataset Ig, correcting the optical signal received by the satellite sensor affected by the atmosphere, illumination, dust and water vapor factors, and by correcting each target geographic coordinate (xt, yt) in the output image dataset Ig, to obtain a corrected impact dataset Ip;

[0018] The calculation formula for correcting the target geographic coordinates (xt, yt) in the output image dataset Ig is as follows:

[0019]

[0020] Where Padj(xt, yt) represents the corrected target geographic coordinates, Pgeo represents the reflectivity after geometric correction, and Tatm represents the atmospheric effect correction coefficient, which indicates the degree of influence of the atmosphere on light propagation.

[0021] 5. Preferably, S2 includes S21 and S22;

[0022] S21, extracting surface settlement features from the corrected impact data set Ip, including cracks and elevation changes, and specifically analyzing the differences in cracks and elevation changes by comparing the impact data sets Ip in different time periods;

[0023] Among them, the elevation change extraction obtains the elevation change value △H of the area by affecting the lifting and settlement information of the corrected target geographic coordinates Padj (xt, yt) in the data set Ip in different time periods, and compares it with the preset settlement uplift threshold Thigh to obtain the extraction result of the corrected target geographic coordinates Padj (xt, yt), and marks the corrected target geographic coordinates Padj (xt, yt) according to the extraction result to form the settlement geographic coordinate set Cjj and the uplift geographic coordinate set Lqj;

[0024] The elevation change value △H is obtained by the following calculation formula:

[0025] ΔH=Padj(xt,yt,Hc)-Padj(xt,yt,Hr);

[0026] Where, Padj(xt, yt, Hc) and Padj(xt, yt, Hr) represent the elevation values ​​of the corrected target geographic coordinates Padj(xt, yt) before and after the fixed period, respectively;

[0027] When the elevation change value △H>1.5 times of the settlement uplift threshold value Thigh, the extraction result of the corrected target geographic coordinate Padj (xt, yt) is obtained as the settlement result, indicating that the corrected target geographic coordinate Padj (xt, yt) is a settlement geographic coordinate point, which is added to the settlement geographic coordinate set Cjj;

[0028] When the elevation change value △H is less than 0.5 times of the settlement uplift threshold value Thigh, the extraction result of the corrected target geographic coordinate Padj (xt, yt) is obtained as an uplift result, indicating that the corrected target geographic coordinate Padj (xt, yt) is an uplift geographic coordinate point, which is added to the uplift geographic coordinate set Lqj;

[0029] Among them, crack extraction extracts crack information through the height difference map, obtains the gradient fluctuation value △I of the image gradient by comparing the impact data set Ip of different time periods, and compares it with the preset gradient fluctuation threshold Tcrack to obtain the extraction result of the corrected target geographic coordinate Padj (xt, yt), and marks the corrected target geographic coordinate Padj (xt, yt) according to the extraction result to form the crack geographic coordinate set Lfj;

[0030] The gradient fluctuation value ΔI is obtained by the following calculation formula:

[0031]

[0032] In the formula, and They represent the local change rates of the corrected target geographic coordinates Padj(xt, yt) in the xt horizontal axis direction and the yt vertical axis direction, respectively. It represents the gradient value of the corrected target geographic coordinate Padj(xt, yt) calculated by combining the local change rate in the xt horizontal axis direction and the yt vertical axis direction;

[0033] When the gradient fluctuation value △I>gradient fluctuation threshold Tcrack, the corrected target geographic coordinate Padj(xt, yt) is extracted as the crack result, indicating that the corrected target geographic coordinate Padj(xt, yt) is the crack geographic coordinate point, which is added to the crack geographic coordinate set Lfj.

[0034] Preferably, S22, replace the same corrected target geographic coordinates Padj (xt, yt) in the corrected impact data set Ip according to the acquired settlement geographic coordinate set Cjj, uplift geographic coordinate set Lqj and fracture geographic coordinate set Lfj, and obtain the surface anomaly feature set F by integrating the settlement geographic coordinate set Cjj, uplift geographic coordinate set Lqj, fracture geographic coordinate set Lfj and impact data set Ip.

[0035] Preferably, said S3 includes S31;

[0036] S31, performing anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation is based on the feature set F of surface settlement at different time points to calculate the change amount and rate of surface anomalies, and obtain the anomaly rate S;

[0037] The abnormal change rate S is obtained by the following calculation formula:

[0038]

[0039] Wherein, △Cj represents the amount of settlement change, △Lq represents the amount of uplift change, △Lf represents the amount of crack change, △t represents the time interval, β represents the adjustment coefficient, SCj represents the settlement change rate, SLq represents the uplift change rate, and SLf represents the crack change rate.

[0040] Preferably, the settlement change amount △Cj is obtained by the calculation formula △Cj=F(Cjj, t+1)-F(Cjj, t), wherein t represents time, and F(Cjj, t+1) represents the settlement geographic coordinate set in the surface anomaly feature set F at time t+1;

[0041] The uplift change ΔLq is obtained by the calculation formula ΔLq=F(Lqj, t+1)-F(Lqj, t), wherein F(Lqj, t+1) represents the uplift geographic coordinate set in the surface anomaly feature set F at time t+1;

[0042] The crack change amount △Lf is obtained by the calculation formula △Lf=F(Lfj, t+1)-F(Lfj, t), wherein F(Lfj, t+1) represents the crack geographic coordinate set in the surface anomaly feature set F at time t+1;

[0043] The sedimentation change rate SCj is expressed by Obtain calculation formula;

[0044] The elevation change rate SLq is expressed by Obtain calculation formula;

[0045] The crack change rate SLf is expressed by Obtain the calculation formula.

[0046] Preferably, the S4 includes S41;

[0047] S41, correcting the abnormal rate S according to different geological information, obtaining the influence of different geological factors on the abnormal rate S, and marking it as the corrected abnormal rate Sadj;

[0048] The modified abnormal rate Sadj is obtained by the following calculation formula:

[0049]

[0050] Where n represents the total number of geological factors, λ(i) represents the correction coefficient of the i-th geological factor, and G(i) represents the influence degree of the i-th geological factor.

[0051] Preferably, the S5 includes S51;

[0052] S51, based on the obtained modified abnormal rate Sadj and the structural sensitivity function F, the settlement impact assessment is performed, the risk assessment value R of different areas is calculated, and the risk warning is triggered according to the risk assessment value R;

[0053] The risk assessment value R is obtained by the following calculation formula:

[0054]

[0055] Where R(t) represents the risk assessment value at time t, γ represents the scaling factor, exp represents the exponential function, Sadj(t) represents the corrected abnormal rate at time t, f(m) represents the sensitivity of highway facilities in region m to ground changes, L represents the total length of highways in region m, and dm represents the integral variable, specifically representing a small change to region m;

[0056] When the risk assessment value R>1, a risk warning is triggered, the overall highway survey mechanism is executed, the current area m is marked as a maintenance area, and a notification is sent to the relevant road maintenance department for processing;

[0057] S52. When executing the overall highway survey mechanism, the overall highway safety score Rtotal is obtained by statistically analyzing the risk assessment values ​​R of different areas, and compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger the execution measures according to the overall abnormal assessment result of the highway;

[0058] The overall safety score of the highway is Rtotal The calculation formula is obtained, where Q represents the total number of assessment areas, and R(t, m) represents the risk assessment value of area m at time t;

[0059] The overall abnormality assessment result of the highway is obtained by the following comparison method:

[0060] When the highway overall safety score Rtotal is less than the overall abnormal assessment threshold Tsafe, the highway overall abnormal assessment result is obtained as a normal result;

[0061] When the overall highway safety score Rtotal ≥ the overall abnormal assessment threshold Tsafe, the overall abnormal assessment result of the highway is obtained as an abnormal result, triggering execution measures, including prompting the current area m to be abnormal, and replanning and diverting the current highway.

[0062] A highway survey intelligent design system based on satellite images, including a satellite data calibration module, a data feature extraction module, a data analysis module, a correction module and an evaluation and decision module;

[0063] The satellite data calibration module performs geometric calibration and atmospheric correction on the collected satellite image data Ir to correct the errors caused by the atmosphere, illumination and satellite orbit factors to form a corrected impact data set Ip;

[0064] The data feature extraction module extracts surface settlement features, including cracks and elevation changes, from the corrected impact data set Ip, and integrates the extracted features to form a surface anomaly feature set F;

[0065] The data analysis module performs anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation calculates the change amount and rate of the surface anomaly based on the feature set F of the surface settlement at different time points to obtain the anomaly rate S;

[0066] The correction module corrects the abnormal rate S according to different geological information, obtains the influence of different geological factors on the abnormal rate S, and marks it as the corrected abnormal rate Sadj;

[0067] The evaluation decision module evaluates the impact of settlement based on the obtained corrected abnormal rate Sadj and the structural sensitivity function F, calculates the risk assessment value R of different areas, and determines whether to trigger a risk warning based on the risk assessment value R. When a risk warning is triggered, the overall safety score Rtotal of the highway is obtained through statistical analysis of the risk assessment values ​​R of different areas, and is compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger execution measures based on the overall abnormal assessment result of the highway.

[0068] The present invention provides a method and system for intelligent design of highway survey based on satellite images, which has the following beneficial effects:

[0069] (1) Through geometric calibration and atmospheric correction of satellite image data Ir, the errors caused by atmospheric, lighting and satellite orbit factors are corrected to form the corrected impact data set Ip, which effectively eliminates the impact of environmental factors on data accuracy. The anomaly rate S is corrected in combination with different geological information to generate the corrected anomaly rate Sadj, which is combined with the structural sensitivity function fstruct to calculate the risk assessment value R of different regions, and on this basis, the overall highway safety score Rtotal is obtained through statistical analysis. It provides a practical reference for the later construction and maintenance of highways, helps relevant departments make accurate decisions, and ensures the early identification and early warning of safety hazards during highway construction through abnormal trend analysis, dynamic monitoring and geological correction. Through precise calculation and analysis, the settlement risk can be predicted in the early stage of highway construction, providing data support for subsequent design, avoiding the lag in the discovery of hidden geological problems in traditional survey methods, and significantly improving design efficiency and safety.

[0070] (2) By calculating the change in settlement △Cj, uplift △Lq and crack △Lf, the change rate of various types of surface changes can be obtained. By adjusting and integrating these rates, the method can comprehensively evaluate the changing trend of the highway geological environment and timely discover potential safety hazards. It not only improves the accuracy and real-time performance of highway settlement monitoring, but also makes scientific assessments based on different types of surface changes. Compared with traditional survey methods, the method can capture the evolution of complex geological phenomena such as settlement, uplift and cracks in real time, providing a more reliable and data-driven decision-making basis for highway design and safety monitoring, and further improving the safety and emergency response capabilities of highway construction.

[0071] (3) The risk assessment value R(t) of different areas is calculated, and the set threshold is used to determine whether a risk warning is triggered. The sensitivity of the highway facilities in the area is also combined to make the risk assessment more comprehensive and precise. The overall safety score Rtotal of the highway is obtained, and compared with the preset safety threshold Tsafe to ensure the accuracy of the overall abnormal assessment results of the highway. If the overall safety score exceeds the threshold, the system will automatically trigger further measures, including marking the abnormal area and replanning the highway route. This multi-level and multi-dimensional assessment system not only improves the safety of the highway, but also enhances the flexibility and response speed of emergency management, provides an effective decision-making support tool for the road maintenance department, and greatly improves the accuracy and timeliness of highway maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the steps of a method for intelligent design of highway survey based on satellite images according to the present invention;

[0073] Figure 2 The present invention is a schematic diagram of a highway survey intelligent design system based on satellite images. DETAILED DESCRIPTION

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0075] Example 1

[0076] The present invention provides a method for intelligent highway survey and design based on satellite images. Figure 1 , including the following steps:

[0077] S1, by performing geometric calibration and atmospheric correction on the collected satellite image data Ir, the errors caused by the atmosphere, illumination and satellite orbit factors are corrected to form the corrected impact data set Ip;

[0078] S2, extracting surface subsidence features from the corrected impact data set Ip, including cracks and elevation changes, and integrating the extracted features to form a surface anomaly feature set F;

[0079] S3, performing anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation is based on the feature set F of surface settlement at different time points to calculate the change amount and rate of surface anomalies, and obtain the anomaly rate S;

[0080] S4, correct the abnormal rate S according to different geological information, obtain the influence of different geological factors on the abnormal rate S, and mark it as the corrected abnormal rate Sadj;

[0081] S5. Based on the obtained corrected abnormal rate Sadj and the structural sensitivity function F, the settlement impact is evaluated, the risk assessment value R of different areas is calculated, and the risk warning is triggered according to the risk assessment value R. When the risk warning is triggered, the overall safety score Rtotal of the highway is obtained through statistical analysis of the risk assessment values ​​R of different areas, and compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger the execution measures according to the overall abnormal assessment result of the highway.

[0082] In this embodiment, through precise satellite image data processing and multi-level analysis, intelligent improvement in the highway survey and design process is achieved. First, through geometric calibration and atmospheric correction of satellite image data Ir, the errors caused by atmospheric, lighting and satellite orbit factors are corrected to form a corrected impact data set Ip, which effectively eliminates the impact of environmental factors on data accuracy. Next, the surface settlement features are extracted, including cracks and elevation changes. By integrating these features, a surface anomaly feature set F is constructed, which provides a reliable data basis for further analysis. Through abnormal trend analysis and change rate calculation, based on time series analysis and surface settlement data at different time points, the change amount and rate of surface anomalies are calculated, and the abnormal rate S is obtained. The calculation of this abnormal rate combines the data changes at different time nodes, which can timely reflect the abnormal dynamics in the highway construction process and provide a scientific basis for predicting potential risks. In order to further improve the accuracy of prediction, the abnormal rate S was corrected in combination with different geological information to generate the corrected abnormal rate Sadj, which was combined with the structural sensitivity function fstruct to calculate the risk assessment value R of different regions. On this basis, the overall safety score Rtotal of the highway was obtained through statistical analysis. After comparing with the preset overall abnormal assessment threshold Tsafe, it can effectively determine whether the highway has potential risks and trigger early warning in time. This process provides a practical reference for the later construction and maintenance of the highway, helps relevant departments make accurate decisions, and ensures the early identification and early warning of safety hazards during highway construction through abnormal trend analysis, dynamic monitoring and geological correction. Through precise calculation and analysis, the settlement risk can be predicted in the early stage of highway construction, providing data support for subsequent design, avoiding the lag in the discovery of hidden geological problems in traditional survey methods, and significantly improving the design efficiency and safety.

[0083] Example 2

[0084] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0085] S11, geometrically calibrating the collected satellite image data Ir to correct the spatial errors caused by satellite orbit errors, image tilt and ground undulation factors in the satellite image data Ir, so that the geographic coordinates in the satellite image data Ir can be consistent with the actual ground coordinates, wherein the geometric calibration is specifically performed by matching the ground control points with the corresponding points in the satellite image data Ir to obtain the calibrated target geographic coordinates (xt, yt), and by integrating the target geographic coordinates (xt, yt), the output image data set Ig after geometric calibration is obtained;

[0086] The target geographic coordinates (xt, yt) are obtained by the following calibration formula:

[0087] (xt, yt)=Mgeo*(xr, yr)+b;

[0088] In the formula, (xr, yr) represents the coordinate position in the satellite image data Ir, specifically represents the horizontal axis value and the vertical axis value of the coordinate position, Mgeo represents the geometric transformation matrix, specifically represents the spatial transformation of the satellite image data Ir to the actual ground coordinates, including the use of the affine transformation matrix for spatial transformation, and b represents the translation variable, specifically represents the displacement in the coordinate transformation.

[0089] S12, by performing atmospheric correction on the acquired output image dataset Ig, correcting the optical signal received by the satellite sensor affected by the atmosphere, illumination, dust and water vapor factors, and by correcting each target geographic coordinate (xt, yt) in the output image dataset Ig, obtaining a corrected impact dataset Ip;

[0090] The calculation formula for correcting the target geographic coordinates (xt, yt) in the output image dataset Ig is as follows:

[0091]

[0092] Wherein, Padj(xt, yt) represents the corrected target geographic coordinates after correction, Pgeo represents the reflectivity after geometric correction, which is usually calculated by the spectral reflectivity collected by the sensor and obtained after geometric transformation processing, Tatm represents the atmospheric effect correction coefficient, which indicates the degree of influence of the atmosphere on the propagation of light. Specifically, different components in the atmosphere will absorb or scatter the light signal received by the satellite sensor, thereby affecting the measured value of the reflectivity. The atmospheric effect correction coefficient Tatm can be calculated through the atmospheric transmission model MODTRAN and the atmospheric transmission model 6S to compensate for the influence of the atmosphere on the propagation of light.

[0093] 6. S2 includes S21 and S22;

[0094] S21, extracting surface settlement features from the corrected impact data set Ip, including cracks and elevation changes, and specifically analyzing the differences in cracks and elevation changes by comparing the impact data sets Ip in different time periods;

[0095] Among them, the elevation change extraction obtains the elevation change value △H of the area by affecting the lifting and settlement information of the corrected target geographic coordinates Padj (xt, yt) in the data set Ip in different time periods, and compares it with the preset settlement uplift threshold Thigh to obtain the extraction result of the corrected target geographic coordinates Padj (xt, yt), and marks the corrected target geographic coordinates Padj (xt, yt) according to the extraction result to form the settlement geographic coordinate set Cjj and the uplift geographic coordinate set Lqj;

[0096] The elevation change value △H is obtained by the following calculation formula:

[0097] ΔH=Padj(xt,yt,Hc)-Padj(xt,yt,Hr);

[0098] Where, Padj(xt, yt, Hc) and Padj(xt, yt, Hr) represent the elevation values ​​of the corrected target geographic coordinates Padj(xt, yt) before and after the fixed period, respectively;

[0099] When the elevation change value △H>1.5 times of the settlement uplift threshold value Thigh, the extraction result of the corrected target geographic coordinate Padj (xt, yt) is obtained as the settlement result, indicating that the corrected target geographic coordinate Padj (xt, yt) is a settlement geographic coordinate point, which is added to the settlement geographic coordinate set Cjj;

[0100] When the elevation change value △H is less than 0.5 times of the settlement uplift threshold value Thigh, the extraction result of the corrected target geographic coordinate Padj (xt, yt) is obtained as an uplift result, indicating that the corrected target geographic coordinate Padj (xt, yt) is an uplift geographic coordinate point, which is added to the uplift geographic coordinate set Lqj;

[0101] Among them, crack extraction extracts crack information through the height difference map. Specifically, the morphology and deformation of the settlement area will be more prominent after atmospheric correction. The gradient fluctuation value △I of the image gradient is obtained by comparing the impact data sets Ip of different time periods, and compared with the preset gradient fluctuation threshold Tcrack to obtain the extraction result of the corrected target geographic coordinates Padj (xt, yt), and the corrected target geographic coordinates Padj (xt, yt) are marked according to the extraction results to form the crack geographic coordinate set Lfj;

[0102] The gradient fluctuation value ΔI is obtained by the following calculation formula:

[0103]

[0104] In the formula, and They represent the local change rates of the corrected target geographic coordinates Padj(xt, yt) in the xt horizontal axis direction and the yt vertical axis direction, respectively. Specifically, it represents the partial derivative of the corrected target geographic coordinate Padj(xt, yt) with respect to the horizontal axis of xt, which reflects the rate of change of the pixel value in the image in the horizontal direction. In most cases, the edge or significant structural changes of the image will lead to The value of becomes larger, Specifically, it represents the partial derivative of the corrected target geographic coordinate Padj (xt, yt) with respect to the vertical axis, which reflects the rate of change of the pixel value in the image in the vertical direction. The edge and important structural changes of the image usually lead to The changes are large, It represents the gradient value of the corrected target geographic coordinate Padj(xt, yt) calculated by combining the local change rate in the xt horizontal axis direction and the yt vertical axis direction, reflecting the overall change degree of the point;

[0105] When the gradient fluctuation value △I>gradient fluctuation threshold Tcrack, the corrected target geographic coordinate Padj(xt, yt) is extracted as the crack result, indicating that the corrected target geographic coordinate Padj(xt, yt) is the crack geographic coordinate point, which is added to the crack geographic coordinate set Lfj.

[0106] In this embodiment, the spatial errors caused by satellite orbit error, image tilt and ground undulation are corrected by geometric calibration of the collected satellite image data Ir, ensuring the consistency of the (xt, yt) coordinates in the image data with the actual ground coordinates, and providing accurate geographic data for subsequent analysis. In addition, the method corrects the influence of the atmosphere, light and environmental factors on the satellite sensor optical signal through atmospheric correction, forming a corrected impact data set Ip, further improving the reliability and accuracy of the data. By extracting the surface settlement features of the corrected impact data set Ip, the method can accurately identify abnormal phenomena such as settlement, uplift and cracks, ensuring that different types of geological changes are discovered in time. In the elevation change analysis, by comparing the elevation changes of the coordinates at different time points, the settlement and uplift areas are accurately judged, and the settlement geographic coordinate set Cjj and the uplift geographic coordinate set Lqj are formed respectively, effectively identifying the surface anomalies in the highway construction process. The extraction of cracks is judged by the fluctuation value of the image gradient, forming a crack geographic coordinate set Lfj, ensuring the comprehensive monitoring of the surface deformation details, and providing a more reliable basis for highway design and safety assessment. Especially in the identification of potential risk areas such as surface subsidence and cracks, the method not only improves the timeliness of monitoring, but also provides data support for the adjustment of design plans and safety warnings, avoiding engineering accidents caused by geological changes and effectively improving the safety and sustainability of highway construction.

[0107] Example 3

[0108] This embodiment is explained in Example 2. Please refer to Figure 1 Specifically: S22, replace the same corrected target geographic coordinates Padj (xt, yt) in the corrected impact data set Ip according to the acquired settlement geographic coordinate set Cjj, uplift geographic coordinate set Lqj and fracture geographic coordinate set Lfj, and obtain the surface anomaly feature set F by integrating the settlement geographic coordinate set Cjj, uplift geographic coordinate set Lqj, fracture geographic coordinate set Lfj and impact data set Ip.

[0109] The S3 includes S31;

[0110] S31, performing anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation is based on the feature set F of surface settlement at different time points to calculate the change amount and rate of surface anomalies, and obtain the anomaly rate S;

[0111] The abnormal change rate S is obtained by the following calculation formula:

[0112]

[0113] Wherein, △Cj represents the amount of settlement change, △Lq represents the amount of uplift change, △Lf represents the amount of crack change, △t represents the time interval, β represents the adjustment coefficient, SCj represents the settlement change rate, SLq represents the uplift change rate, and SLf represents the crack change rate.

[0114] The settlement change △Cj is obtained by the calculation formula △Cj=F(Cjj, t+1)-F(Cjj, t), wherein t represents time, and F(Cjj, t+1) represents the settlement geographic coordinate set in the surface anomaly feature set F at time t+1;

[0115] The uplift change ΔLq is obtained by the calculation formula ΔLq=F(Lqj, t+1)-F(Lqj, t), wherein F(Lqj, t+1) represents the uplift geographic coordinate set in the surface anomaly feature set F at time t+1;

[0116] The crack change amount △Lf is obtained by the calculation formula △Lf=F(Lfj, t+1)-F(Lfj, t), wherein F(Lfj, t+1) represents the crack geographic coordinate set in the surface anomaly feature set F at time t+1;

[0117] The sedimentation change rate SCj is expressed by Obtain calculation formula;

[0118] The elevation change rate SLq is expressed by Obtain calculation formula;

[0119] The crack change rate SLf is expressed by Obtain the calculation formula.

[0120] In this embodiment, a comprehensive and systematic highway survey and safety assessment solution is provided through accurate surface anomaly feature extraction and change rate calculation. By extracting surface anomaly features such as settlement, uplift and cracks in the corrected impact data set Ip, the method can identify different types of geological changes, and replace them by correcting the target geographic coordinates Padj (xt, yt), integrating them into a surface anomaly feature set F, laying a solid foundation for subsequent abnormal trend analysis and change rate calculation. In the calculation of the abnormal change rate S, by comparing the surface settlement feature set F at different time points, the method can dynamically monitor the changes in settlement, uplift and cracks, and calculate the settlement change △Cj, uplift change △Lq and crack change △Lf respectively, thereby obtaining the change rate of various types of surface changes. By adjusting and integrating these rates, the method can comprehensively evaluate the changing trend of the highway geological environment and timely discover potential safety hazards, which not only improves the accuracy and real-time performance of highway settlement monitoring, but also can make scientific assessments based on different types of surface changes. Compared with traditional survey methods, this method can capture the evolution process of complex geological phenomena such as subsidence, uplift and cracks in real time, providing a more reliable, data-driven decision-making basis for highway design and safety monitoring, and further improving the safety and emergency response capabilities of highway construction.

[0121] Example 4

[0122] This embodiment is explained in Example 3, please refer to Figure 1 Specifically: S4 includes S41;

[0123] S41, correcting the abnormal rate S according to different geological information, obtaining the influence of different geological factors on the abnormal rate S, and marking it as the corrected abnormal rate Sadj;

[0124] The modified abnormal rate Sadj is obtained by the following calculation formula:

[0125]

[0126] Where n represents the total number of geological factors, which specifically reflects the diversity of the geological factors we consider, such as soil type, rock type and groundwater. λ(i) represents the correction coefficient of the i-th geological factor, which specifically indicates the degree of influence of the geological factor on the abnormal rate. The correction coefficient is used to quantify the contribution of each geological factor to the overall change rate. G(i) represents the degree of influence of the i-th geological factor, which specifically represents the specific influence of the i-th geological factor on the abnormal change of the surface. For example, soil type may have a greater impact on settlement, so its influence G1 may be relatively large; groundwater flow may have a greater impact on uplift, so the impact of G2 on uplift will be quantified to a certain extent.

[0127] The S5 includes S51;

[0128] S51, based on the obtained modified abnormal rate Sadj and the structural sensitivity function F, the settlement impact assessment is performed, the risk assessment value R of different areas is calculated, and the risk warning is triggered according to the risk assessment value R;

[0129] The risk assessment value R is obtained by the following calculation formula:

[0130]

[0131] Where R(t) represents the risk assessment value at time t, γ represents the scaling factor, exp represents the exponential function, Sadj(t) represents the corrected anomaly rate at time t, f(m) represents the sensitivity of the highway facilities in region m to ground changes, f(m) is close to 1, indicating that the infrastructure at this location is very sensitive to surface deformation; if f(m) is much less than 1, it means that the infrastructure at this location is not very sensitive, L represents the total length of the highway in region m, and dm represents the integral variable, which specifically represents the small changes in region m;

[0132] When the risk assessment value R>1, a risk warning is triggered, the overall highway survey mechanism is executed, the current area m is marked as a maintenance area, and a notification is sent to the relevant road maintenance department for processing;

[0133] S52. When executing the overall highway survey mechanism, the overall highway safety score Rtotal is obtained by statistically analyzing the risk assessment values ​​R of different areas, and compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger the execution measures according to the overall abnormal assessment result of the highway;

[0134] The overall safety score of the highway is Rtotal The calculation formula is obtained, where Q represents the total number of assessment areas, and R(t, m) represents the risk assessment value of area m at time t;

[0135] The overall abnormality assessment result of the highway is obtained by the following comparison method:

[0136] When the highway overall safety score Rtotal is less than the overall abnormal assessment threshold Tsafe, the highway overall abnormal assessment result is obtained as a normal result;

[0137] When the highway overall safety score Rtotal ≥ the overall abnormal assessment threshold Tsafe, the highway overall abnormal assessment result is obtained as an abnormal result, triggering execution measures, including prompting the current area m to be abnormal, replanning the current highway and rerouting prompts;

[0138] Among them, replanning of the current highway includes the possibility of replanning the highway based on the current abnormal characteristics and risk assessment. This process includes re-determining the best route or designing a detour for the road to ensure smooth traffic and avoid risk areas as much as possible. For example, different highway design plans can be proposed for areas with significant settlement or cracks, including measures such as improving the road structure strength, adding support or changing the pavement material to improve the road's ability to resist deformation;

[0139] At the same time, based on geological assessment and settlement impacts, it may be necessary to reconstruct or reinforce the roadbed in the affected sections, including strengthening the base soil layer and adding a drainage layer, so as to improve the road bearing capacity and reduce the risk of future settlement or deformation;

[0140] Rerouting tips include temporary detour route planning: temporary rerouting guidance for the current road, especially in areas with severe subsidence, cracks or other geological changes, calculating the safest temporary detour route based on the impact range, and publishing relevant information on the traffic monitoring platform; traffic diversion measures: different traffic diversion measures are taken according to different degrees of abnormalities, such as restricting the passage of heavy vehicles, partially closing traffic, setting up warning signs, etc., to reduce the impact of abnormal sections on traffic flow.

[0141] In this embodiment, by combining the correction of abnormal rate S with the assessment of settlement impact by different geological factors, accurate and comprehensive decision support is provided for highway safety assessment. By correcting the geological factors, the corrected abnormal rate Sadj is obtained, and the influence of various geological factors such as soil type, rock type and groundwater on surface changes is considered. The correction coefficient λ(i) and the influence degree G(i) of various factors are quantified, so that the calculation of abnormal rate is more accurate and can effectively reflect the change trend under different geological conditions. By combining the corrected abnormal rate Sadj with the structural sensitivity function f(m), the settlement impact assessment is performed, and the risk assessment value R(t) of different regions is calculated, and the set threshold is used to determine whether the risk warning is triggered. This step not only takes into account the geological characteristics of the region, but also combines the sensitivity of the highway facilities in the region, so that the risk assessment is more comprehensive and precise. When the risk assessment value exceeds the predetermined threshold, the system can trigger the corresponding highway overall survey mechanism and start the maintenance program simultaneously, ensuring the timeliness and effectiveness of highway safety management. By statistically analyzing the risk assessment values ​​of different areas, the overall highway safety score Rtotal is obtained, and by comparing it with the preset safety threshold Tsafe, the accuracy of the overall abnormal assessment results of the highway is ensured. If the overall safety score exceeds the threshold, the system will automatically trigger further measures, including marking abnormal areas and replanning highway routes. This multi-level and multi-dimensional evaluation system not only improves the safety of highways, but also enhances the flexibility and response speed of emergency management, provides effective decision-making support tools for road maintenance departments, and greatly improves the accuracy and timeliness of highway maintenance.

[0142] Example 5

[0143] A highway survey intelligent design system based on satellite images, please refer to Figure 2 ,Specifically: including satellite data calibration module, data feature extraction module, data analysis module, correction module and evaluation decision module;

[0144] The satellite data calibration module performs geometric calibration and atmospheric correction on the collected satellite image data Ir to correct the errors caused by the atmosphere, illumination and satellite orbit factors to form a corrected impact data set Ip;

[0145] The data feature extraction module extracts surface settlement features, including cracks and elevation changes, from the corrected impact data set Ip, and integrates the extracted features to form a surface anomaly feature set F;

[0146] The data analysis module performs anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation calculates the change amount and rate of the surface anomaly based on the feature set F of the surface settlement at different time points to obtain the anomaly rate S;

[0147] The correction module corrects the abnormal rate S according to different geological information, obtains the influence of different geological factors on the abnormal rate S, and marks it as the corrected abnormal rate Sadj;

[0148] The evaluation decision module evaluates the impact of settlement based on the obtained corrected abnormal rate Sadj and the structural sensitivity function F, calculates the risk assessment value R of different areas, and determines whether to trigger a risk warning based on the risk assessment value R. When a risk warning is triggered, the overall safety score Rtotal of the highway is obtained through statistical analysis of the risk assessment values ​​R of different areas, and is compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger execution measures based on the overall abnormal assessment result of the highway.

[0149] In this embodiment,

[0150] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent highway survey and design based on satellite images, characterized in that: The following steps are involved: S1, by performing geometric calibration and atmospheric correction on the collected satellite image data Ir, the errors caused by the atmosphere, illumination and satellite orbit factors are corrected to form the corrected impact data set Ip; S2, extracting surface subsidence features from the corrected impact data set Ip, including cracks and elevation changes, and integrating the extracted features to form a surface anomaly feature set F; S3, performing anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation is based on the feature set F of surface settlement at different time points to calculate the change amount and rate of surface anomalies, and obtain the anomaly rate S; S4, correct the abnormal rate S according to different geological information, obtain the influence of different geological factors on the abnormal rate S, and mark it as the corrected abnormal rate Sadj; S5. Based on the obtained corrected abnormal rate Sadj and the structural sensitivity function F, the settlement impact is evaluated, the risk assessment value R of different areas is calculated, and the risk warning is triggered according to the risk assessment value R. When the risk warning is triggered, the overall safety score Rtotal of the highway is obtained through statistical analysis of the risk assessment values ​​R of different areas, and compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger the execution measures according to the overall abnormal assessment result of the highway.

2. The method for intelligent design of highway survey based on satellite images according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, geometrically calibrating the collected satellite image data Ir to correct the spatial errors caused by satellite orbit errors, image tilt and ground undulation factors in the satellite image data Ir, so that the geographic coordinates in the satellite image data Ir can be consistent with the actual ground coordinates, wherein the geometric calibration is specifically performed by matching the ground control points with the corresponding points in the satellite image data Ir to obtain the calibrated target geographic coordinates (xt, yt), and by integrating the target geographic coordinates (xt, yt), the output image data set Ig after geometric calibration is obtained; The target geographic coordinates (xt, yt) are obtained by the following calibration formula: (xt, yt)=Mgeo*(xr, yr)+b; Wherein, (xr, yr) represents the coordinate position in the satellite image data Ir, specifically represents the horizontal axis value and the vertical axis value of the coordinate position, Mgeo represents the geometric transformation matrix, and b represents the translation variable.

3. The method for intelligent design of highway survey based on satellite images according to claim 2 is characterized in that: S12, by performing atmospheric correction on the acquired output image dataset Ig, correcting the optical signal received by the satellite sensor affected by the atmosphere, illumination, dust and water vapor factors, and by correcting each target geographic coordinate (xt, yt) in the output image dataset Ig, obtaining a corrected impact dataset Ip; The calculation formula for correcting the target geographic coordinates (xt, yt) in the output image dataset Ig is as follows: Where Padj(xt, yt) represents the corrected target geographic coordinates, Pgeo represents the reflectivity after geometric correction, and Tatm represents the atmospheric effect correction coefficient, which indicates the degree of influence of the atmosphere on light propagation.

4. The method for intelligent design of highway survey based on satellite images according to claim 3 is characterized by: The S2 includes S21 and S22; S21, extracting surface settlement features from the corrected impact data set Ip, including cracks and elevation changes, and specifically analyzing the differences in cracks and elevation changes by comparing the impact data sets Ip in different time periods; Among them, the elevation change extraction obtains the elevation change value △H of the area by affecting the lifting and settlement information of the corrected target geographic coordinates Padj (xt, yt) in the data set Ip in different time periods, and compares it with the preset settlement uplift threshold Thigh to obtain the extraction result of the corrected target geographic coordinates Padj (xt, yt), and marks the corrected target geographic coordinates Padj (xt, yt) according to the extraction result to form the settlement geographic coordinate set Cjj and the uplift geographic coordinate set Lqj; The elevation change value △H is obtained by the following calculation formula: ΔH=Padj(xt,yt,Hc)-Padj(xt,yt,Hr); Where, Padj(xt, yt, Hc) and Padj(xt, yt, Hr) represent the elevation values ​​of the corrected target geographic coordinates Padj(xt, yt) before and after the fixed period, respectively; When the elevation change value △H>1.5 times of the settlement uplift threshold value Thigh, the extraction result of the corrected target geographic coordinate Padj (xt, yt) is obtained as the settlement result, indicating that the corrected target geographic coordinate Padj (xt, yt) is a settlement geographic coordinate point, which is added to the settlement geographic coordinate set Cjj; When the elevation change value △H is less than 0.5 times of the settlement uplift threshold value Thigh, the extraction result of the corrected target geographic coordinate Padj (xt, yt) is obtained as an uplift result, indicating that the corrected target geographic coordinate Padj (xt, yt) is an uplift geographic coordinate point, which is added to the uplift geographic coordinate set Lqj; Among them, crack extraction extracts crack information through the height difference map, obtains the gradient fluctuation value △I of the image gradient by comparing the impact data set Ip of different time periods, and compares it with the preset gradient fluctuation threshold Tcrack to obtain the extraction result of the corrected target geographic coordinate Padj (xt, yt), and marks the corrected target geographic coordinate Padj (xt, yt) according to the extraction result to form the crack geographic coordinate set Lfj; The gradient fluctuation value ΔI is obtained by the following calculation formula: In the formula, and They represent the local change rates of the corrected target geographic coordinates Padj(xt, yt) in the xt horizontal axis direction and the yt vertical axis direction, respectively. It represents the gradient value of the corrected target geographic coordinate Padj(xt, yt) calculated by combining the local change rate in the xt horizontal axis direction and the yt vertical axis direction; When the gradient fluctuation value △I>gradient fluctuation threshold Tcrack, the corrected target geographic coordinate Padj(xt, yt) is extracted as the crack result, indicating that the corrected target geographic coordinate Padj(xt, yt) is the crack geographic coordinate point, which is added to the crack geographic coordinate set Lfj.

5. The method for intelligent design of highway survey based on satellite images according to claim 4 is characterized in that: S22. Replace the same corrected target geographic coordinates Padj (xt, yt) in the corrected impact data set Ip according to the acquired settlement geographic coordinate set Cjj, uplift geographic coordinate set Lqj and fracture geographic coordinate set Lfj, and obtain the surface anomaly feature set F by integrating the settlement geographic coordinate set Cjj, uplift geographic coordinate set Lqj, fracture geographic coordinate set Lfj and impact data set Ip.

6. The method for intelligent design of highway survey based on satellite images according to claim 5 is characterized by: The S3 includes S31; S31, performing anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation is based on the feature set F of surface settlement at different time points to calculate the change amount and rate of surface anomalies, and obtain the anomaly rate S; The abnormal change rate S is obtained by the following calculation formula: Wherein, △Cj represents the amount of settlement change, △Lq represents the amount of uplift change, △Lf represents the amount of crack change, △t represents the time interval, β represents the adjustment coefficient, SCj represents the settlement change rate, SLq represents the uplift change rate, and SLf represents the crack change rate.

7. The method for intelligent design of highway survey based on satellite images according to claim 6 is characterized by: The settlement change △Cj is obtained by the calculation formula △Cj=F(Cjj, t+1)-F(Cjj, t), wherein t represents time, and F(Cjj, t+1) represents the settlement geographic coordinate set in the surface anomaly feature set F at time t+1; The uplift change ΔLq is obtained by the calculation formula ΔLq=F(Lqj, t+1)-F(Lqj, t), wherein F(Lqj, t+1) represents the uplift geographic coordinate set in the surface anomaly feature set F at time t+1; The crack change amount △Lf is obtained by the calculation formula △Lf=F(Lfj, t+1)-F(Lfj, t), wherein F(Lfj, t+1) represents the crack geographic coordinate set in the surface anomaly feature set F at time t+1; The sedimentation change rate SCj is expressed by Obtain calculation formula; The elevation change rate SLq is expressed by Obtain calculation formula; The crack change rate SLf is expressed by Obtain the calculation formula.

8. The method for intelligent design of highway survey based on satellite images according to claim 1, characterized in that: The S4 includes S41; S41, correcting the abnormal rate S according to different geological information, obtaining the influence of different geological factors on the abnormal rate S, and marking it as the corrected abnormal rate Sadj; The modified abnormal rate Sadj is obtained by the following calculation formula: Where n represents the total number of geological factors, λ(i) represents the correction coefficient of the i-th geological factor, and G(i) represents the influence degree of the i-th geological factor.

9. The method for intelligent design of highway survey based on satellite images according to claim 1, characterized in that: The S5 includes S51; S51, based on the obtained modified abnormal rate Sadj and the structural sensitivity function F, the settlement impact assessment is performed, the risk assessment value R of different areas is calculated, and the risk warning is triggered according to the risk assessment value R; The risk assessment value R is obtained by the following calculation formula: Where R(t) represents the risk assessment value at time t, γ represents the scaling factor, exp represents the exponential function, Sadj(t) represents the corrected abnormal rate at time t, f(m) represents the sensitivity of highway facilities in region m to ground changes, L represents the total length of highways in region m, and dm represents the integral variable, specifically representing a small change to region m; When the risk assessment value R>1, a risk warning is triggered, the overall highway survey mechanism is executed, the current area m is marked as a maintenance area, and a notification is sent to the relevant road maintenance department for processing; S52. When executing the overall highway survey mechanism, the overall highway safety score Rtotal is obtained by statistically analyzing the risk assessment values ​​R of different areas, and compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger the execution measures according to the overall abnormal assessment result of the highway; The overall safety score of the highway is Rtotal The calculation formula is obtained, where Q represents the total number of assessment areas, and R(t, m) represents the risk assessment value of area m at time t; The overall abnormality assessment result of the highway is obtained by the following comparison method: When the highway overall safety score Rtotal is less than the overall abnormal assessment threshold Tsafe, the highway overall abnormal assessment result is obtained as a normal result; When the overall highway safety score Rtotal ≥ the overall abnormal assessment threshold Tsafe, the overall abnormal assessment result of the highway is obtained as an abnormal result, triggering execution measures, including prompting the current area m to be abnormal, and replanning and diverting the current highway.

10. A highway survey intelligent design system based on satellite images, applied to a highway survey intelligent design method based on satellite images as claimed in any one of claims 1 to 9, characterized in that: It includes satellite data calibration module, data feature extraction module, data analysis module, correction module and evaluation and decision module; The satellite data calibration module performs geometric calibration and atmospheric correction on the collected satellite image data Ir to correct the errors caused by the atmosphere, illumination and satellite orbit factors to form a corrected impact data set Ip; The data feature extraction module extracts surface settlement features, including cracks and elevation changes, from the corrected impact data set Ip, and integrates the extracted features to form a surface anomaly feature set F; The data analysis module performs anomaly trend analysis and change rate calculation based on the formed surface anomaly feature set F, wherein the anomaly trend analysis includes time series analysis, and the change rate calculation calculates the change amount and rate of the surface anomaly based on the feature set F of the surface settlement at different time points to obtain the anomaly rate S; The correction module corrects the abnormal rate S according to different geological information, obtains the influence of different geological factors on the abnormal rate S, and marks it as the corrected abnormal rate Sadj; The evaluation decision module evaluates the impact of settlement based on the obtained corrected abnormal rate Sadj and the structural sensitivity function F, calculates the risk assessment value R of different areas, and determines whether to trigger a risk warning based on the risk assessment value R. When a risk warning is triggered, the overall safety score Rtotal of the highway is obtained through statistical analysis of the risk assessment values ​​R of different areas, and is compared with the preset overall abnormal assessment threshold Tsafe to obtain the overall abnormal assessment result of the highway, and trigger execution measures based on the overall abnormal assessment result of the highway.

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