Tunnel monitoring and early warning ground-based radar data processing method

Abstract

The invention discloses a tunnel monitoring and early warning ground radar data processing method, and particularly relates to the technical field of radar signal processing, which comprises the steps of tunnel stratum partition division, tunnel layered signal feature acquisition, tunnel dynamic deformation inversion, stratum environment sensitive early warning, tunnel stratum deformation monitoring and tunnel stratum deformation risk area identification. By calculating the phase error change rate of each stratum and inverting the change of the tunnel angle, the tunnel deformation monitoring precision is improved, the requirement of safety evaluation is met, the correlation degree of the propagation speed error and the environmental parameter vector is further calculated, the environmental sensitive heterogeneous stratum monitoring sub-region is early warned, and the monitoring accuracy of the tunnel deformation is improved. Medium space distribution is verified in combination with environment parameters, environment disturbance false alarms are reduced, early warning accuracy is improved, through the phase error change rate and the comprehensive risk index, the tunnel stratum deformation risk coefficient is calculated, a high-risk area is identified, risk quantification is achieved, risk sources can be traced through a correlation signal spectrum, and monitoring efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and more specifically, to a method for processing ground-based radar data for tunnel monitoring and early warning. Background Technology

[0002] With the rapid development of urban rail transit and underground engineering, the need for safety monitoring of tunnel structures under complex geological conditions and long-term operational loads is becoming increasingly urgent. Most engineering accidents caused by tunnel stratum instability are related to the lag in deformation monitoring or data misjudgment. In particular, the accuracy of tunnel early warning is affected by the depth of the stratum.

[0003] However, in practical use, it still has some shortcomings. For example, the existing technology is more suitable for static or gradual deformation monitoring, and the sampling rate for dynamic deformation such as shield tunneling and train vibration is insufficient. It does not combine the characteristics of stratum homogeneity and environmental sensitivity for risk analysis, and its ability to monitor high-frequency dynamic deformation is insufficient, resulting in slow dynamic early warning response. When radar signals propagate in tunnels, in addition to the direct path, they are also reflected by obstacles such as tunnel walls and facilities. Current tunnel-based radar monitoring data processing does not consider the multipath effect caused by the heterogeneity of the strata. Wave velocity models based on the assumption of a homogeneous medium will result in phase errors when electromagnetic waves reach the receiver, which will affect the accuracy of subsequent analysis and thus lead to insufficient stability in tunnel monitoring. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method for processing ground-based radar data for tunnel monitoring and early warning, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for processing ground-based radar data for tunnel monitoring and early warning, comprising the following steps: Step S01: Tunnel stratigraphic zoning: This step is used to divide the tunnel monitoring area into several monitoring sub-regions according to stratigraphic type using the tunnel's three-dimensional model, and to number each monitoring sub-region.

[0006] Step S02: Tunnel Layered Signal Feature Acquisition: Using radar wave velocity testing equipment, the signal propagation characteristics of electromagnetic waves in each stratum monitoring sub-region are collected, and the signal propagation characteristics are correlated with spatial location to generate a signal monitoring map.

[0007] Step S03: Tunnel dynamic deformation inversion: By measuring the phase error in each stratum monitoring sub-region through electromagnetic wave propagation, the rate of change of phase error in each stratum monitoring sub-region is calculated, and the angular deformation trend of each stratum in the tunnel is evaluated.

[0008] Step S04: Early warning for environmentally sensitive formations: This includes a formation homogeneity assessment sub-step, an environmental parameter correction sub-step, and a tunnel deformation early warning sub-step. The formation homogeneity assessment sub-step is used to assess the spatial distribution of the formation medium, thereby locating heterogeneous areas. The environmental parameter correction sub-step is used to calculate the correlation between the propagation velocity error and the environmental parameter vector. The tunnel deformation early warning sub-step is used to provide early warning for environmentally sensitive heterogeneous formation monitoring sub-areas.

[0009] Step S05: Tunnel stratum deformation monitoring: Calculate multi-dimensional early warning indicators for tunnel stratum deformation risk coefficients in each stratum monitoring sub-region, including phase error change rate and comprehensive risk index.

[0010] Step S06: Identification of tunnel stratum deformation risk zones: This step is used to obtain the tunnel stratum deformation risk coefficients of each stratum monitoring sub-region in the tunnel monitoring area and to identify high-risk areas in the tunnel monitoring area.

[0011] Preferably, the specific analysis process for step S01: tunnel stratum zoning is as follows: Based on the tunnel geological survey report, a three-dimensional model of the tunnel is established. The three-dimensional model of the tunnel includes the tunnel's geometric parameters, inner wall material, and internal facilities, reflecting the actual physical structure of the tunnel. Using the 3D model of the tunnel, the tunnel monitoring area is divided into several stratum monitoring sub-regions according to the stratum type, and each stratum monitoring sub-region of the tunnel monitoring area is numbered sequentially as 1,2,...i,...n.

[0012] Preferably, the specific analysis process of step S02: tunnel layered signal feature acquisition is as follows: The signal propagation characteristics include wave velocity data and phase data. The wave velocity data is obtained by measuring the propagation velocity of each stratum monitoring sub-region using radar wave velocity testing equipment, comparing the measured propagation velocity with the reference propagation velocity, collecting the propagation velocity error of electromagnetic waves in each stratum monitoring sub-region, and correlating the propagation velocity error with spatial location. Using the spatial location of the tunnel as coordinates, a wave velocity error map is generated. The phase data is obtained by using the transmitter of the radar wave velocity testing equipment as the transmitting point to emit electromagnetic waves, receiving reflected signals from various strata, comparing the measured signal phase with the reference phase, collecting the phase error of the electromagnetic wave propagating in the monitoring sub-region of each stratum, and associating the phase error with the spatial location. Using the spatial location of the tunnel as coordinates, a phase error map is generated.

[0013] Preferably, the specific analysis process of step S03: tunnel dynamic deformation inversion is as follows: Step S31: Obtain the phase error of electromagnetic wave propagation in each stratum monitoring sub-region, establish a time and phase error sequence, and calculate the phase error change rate of each stratum monitoring sub-region; Step S32: Obtain the phase error change rate of each stratum monitoring sub-region of the tunnel and compare it with the preset phase error change rate. If the phase error change rate of a certain stratum monitoring sub-region is greater than the preset phase error change rate, it indicates that the deformation angle of the tunnel structure at that moment is abnormal. Then, establish an angle abnormal deformation early warning and push the early warning information to the monitoring center through the industrial Ethernet to simultaneously trigger the audible and visual alarm and emergency response process. Conversely, it indicates that there is no abnormal change in the deformation angle of the tunnel structure at that moment.

[0014] Preferably, the formation homogeneity assessment sub-step specifically involves: dividing each formation monitoring sub-region into three-dimensional grids according to an equal area division method, and specifically collecting the propagation speed error of electromagnetic waves in each grid within each formation monitoring sub-region; The dispersion of grid wave velocity error is evaluated using a formula for the difference in wave velocity error distribution. By calculating the stratigraphic homogeneity anomaly index of each grid in each stratigraphic monitoring sub-region, the stratigraphic homogeneity anomaly index is arranged in descending order to obtain the heterogeneous stratigraphic sequence of each grid in the stratigraphic monitoring sub-region. This sequence is then pushed to the monitoring center via industrial Ethernet for locating heterogeneous areas.

[0015] Preferably, the environmental parameter correction sub-step specifically involves: collecting environmental parameters of various strata monitoring sub-areas in the tunnel through a sensor network, and constructing an environmental parameter vector; The propagation speed error of electromagnetic waves in each stratum monitoring sub-region is obtained, along with the environmental parameter vector. The correlation coefficient between each parameter in the environmental parameter vector and the propagation speed error is calculated, and the degree of correlation between the propagation speed error and the environmental parameter vector is evaluated to obtain the environmental correlation vector.

[0016] Preferably, the tunnel deformation early warning sub-step specifically includes: obtaining the environmental correlation vector, extracting the correlation coefficient with the highest correlation degree, and calculating the comprehensive risk index of each stratum monitoring sub-area of ​​the tunnel; The comprehensive risk index of each stratum monitoring sub-region of the tunnel is obtained and compared with the preset comprehensive risk index. If the comprehensive risk index of a certain stratum monitoring sub-region is greater than the preset comprehensive risk index, it indicates that the stratum is an environmentally sensitive heterogeneous region, triggering a deformation warning. Conversely, it indicates that the medium spatial distribution of the stratum is uniform, the environmental parameter driving force is not significant, and the tunnel structure is stable. Warnings are pushed to the monitoring terminal for deformation warning areas, and the signal monitoring map of the warning area is linked.

[0017] Preferably, step S05: the specific analysis process for monitoring tunnel stratum deformation is as follows: Step S51: Obtain the phase error change rate of each stratum monitoring sub-region in the tunnel monitoring area at historical times, and extract the maximum and minimum phase error change rates of each stratum monitoring sub-region in the historical times; Step S52: Based on the phase error change rate, the maximum phase error change rate, the minimum phase error change rate, and the comprehensive risk index at the corresponding time, calculate the tunnel stratum deformation risk coefficient for each stratum monitoring sub-region.

[0018] Preferably, the specific analysis process for step S06: identification of tunnel stratum deformation risk zones is as follows: The tunnel stratum deformation risk coefficient of each stratum monitoring sub-region in the tunnel monitoring area is obtained and compared with the preset tunnel stratum deformation risk coefficient. If the tunnel stratum deformation risk coefficient of a certain stratum monitoring sub-region is greater than the preset tunnel stratum deformation risk coefficient, it indicates that there is an instability risk in that region. All stratum monitoring sub-regions with instability risk in the tunnel monitoring area are screened out and marked as high-risk areas, and the signal monitoring spectrum of that region is associated. Conversely, if the coefficient is less than the preset coefficient, it indicates that the region is a low-risk area.

[0019] The technical effects and advantages of this invention are as follows: 1. This invention provides a ground-based radar data processing method for tunnel monitoring and early warning. It calculates the rate of change of phase error in each monitoring sub-region of the strata by utilizing the phase error of electromagnetic wave propagation, and assesses the angular deformation trend of each stratum within the tunnel. This method achieves the inversion of tunnel angle changes through phase error, improving the accuracy of tunnel deformation monitoring and meeting the requirements of tunnel structural safety assessment. Through a stratum homogeneity assessment sub-step, it evaluates the spatial distribution of the stratum medium, thereby locating heterogeneous areas. Through an environmental parameter correction sub-step, it calculates the correlation between propagation velocity error and environmental parameter vectors. Through a tunnel deformation early warning sub-step, it calculates the comprehensive risk index of each monitoring sub-region of the tunnel strata, providing early warning for environmentally sensitive heterogeneous strata monitoring sub-regions. This method combines environmental parameters to verify the spatial distribution of the tunnel stratum medium. By screening environmental parameters, it reduces false alarms caused by environmental disturbances and improves the accuracy of deformation early warning. 2. This invention provides a method for processing ground-based radar data for tunnel monitoring and early warning. By acquiring the phase error change rate and comprehensive risk index of each stratum monitoring sub-region in the tunnel monitoring area, the method calculates the tunnel stratum deformation risk coefficient of each stratum monitoring sub-region and compares it with a preset tunnel stratum deformation risk coefficient. If the tunnel stratum deformation risk coefficient of a certain stratum monitoring sub-region is greater than the preset tunnel stratum deformation risk coefficient, it indicates that there is an instability risk in that region. All stratum monitoring sub-regions with instability risks in the tunnel monitoring area are screened out and marked as high-risk areas, and the signal monitoring spectrum of that region is associated. Conversely, if the risk coefficient is lower than the preset value, it indicates that the region is a low-risk area. This method achieves risk quantification, captures tunnel stratum risk trends, improves early warning timeliness, and allows for tracing the source of risk by associating with the signal spectrum, thereby improving the efficiency of tunnel risk monitoring. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a ground-based radar data processing method for tunnel monitoring and early warning according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, the present invention provides a method for processing ground-based radar data for tunnel monitoring and early warning, comprising the following steps: Step S01: Tunnel stratum partitioning: This step is used to divide the tunnel monitoring area into several monitoring sub-areas according to the stratum type using the tunnel 3D model, and to number each monitoring sub-area.

[0023] In one possible design, the specific analysis process for step S01: tunnel stratum partitioning is as follows: Based on the tunnel geological survey report, a three-dimensional model of the tunnel is established. The three-dimensional model of the tunnel includes the tunnel's geometric parameters, inner wall material, and internal facilities, reflecting the actual physical structure of the tunnel. Using the 3D model of the tunnel, the tunnel monitoring area is divided into several stratum monitoring sub-regions according to the stratum type, and each stratum monitoring sub-region of the tunnel monitoring area is numbered sequentially as 1,2,...i,...n.

[0024] Step S02: Tunnel Layered Signal Feature Acquisition: Using radar wave velocity testing equipment, the signal propagation characteristics of electromagnetic waves in each stratum monitoring sub-region are acquired, and the signal propagation characteristics are correlated with spatial location to generate a signal monitoring map; In one possible design, the specific analysis process for step S02: tunnel layered signal feature acquisition is as follows: The signal propagation characteristics include wave velocity data and phase data. The wave velocity data is obtained by measuring the propagation velocity of each stratum monitoring sub-region using radar wave velocity testing equipment, comparing the measured propagation velocity with the reference propagation velocity, collecting the propagation velocity error of electromagnetic waves in each stratum monitoring sub-region, and correlating the propagation velocity error with spatial location. Using the spatial location of the tunnel as coordinates, a wave velocity error map is generated. The phase data is obtained by using the transmitter of the radar wave velocity testing equipment as the transmitting point to emit electromagnetic waves, receiving reflected signals from various strata, comparing the measured signal phase with the reference phase, collecting the phase error of the electromagnetic wave propagating in the monitoring sub-region of each stratum, and associating the phase error with the spatial location. Using the spatial location of the tunnel as coordinates, a phase error map is generated.

[0025] Step S03: Tunnel dynamic deformation inversion: By measuring the phase error in each stratum monitoring sub-region through electromagnetic wave propagation, the rate of change of phase error in each stratum monitoring sub-region is calculated, and the angular deformation trend of each stratum in the tunnel is evaluated.

[0026] In one possible design, the specific analysis process of step S03: tunnel dynamic deformation inversion is as follows: Step S31: Obtain the phase error of electromagnetic wave propagation in each stratum monitoring sub-region, establish a time and phase error sequence, and calculate the phase error change rate of each stratum monitoring sub-region; Step S32: Obtain the phase error change rate of each stratum monitoring sub-region of the tunnel and compare it with the preset phase error change rate. If the phase error change rate of a certain stratum monitoring sub-region is greater than the preset phase error change rate, it indicates that the deformation angle of the tunnel structure at that moment is abnormal. Then, establish an angle abnormal deformation early warning and push the early warning information to the monitoring center through the industrial Ethernet to simultaneously trigger the audible and visual alarm and emergency response process. Conversely, it indicates that there is no abnormal change in the deformation angle of the tunnel structure at that moment.

[0027] In this embodiment, it should be specifically noted that the phase error change rate of each stratigraphic monitoring sub-region is calculated using the following formula: in, This represents the rate of change of phase error at time t in the i-th stratum monitoring sub-region. This represents the phase error at time t+1 in the i-th stratum monitoring sub-region. Let be the phase error at time t in the i-th stratum monitoring sub-region. Represented as a time interval; Specifically, the larger the value of the phase error change rate calculated by this formula, the more drastic the phase error change.

[0028] Step S04: Early warning for formation environment-sensitive areas: This includes a formation homogeneity assessment sub-step, an environmental parameter correction sub-step, and a tunnel deformation early warning sub-step. The formation homogeneity assessment sub-step is used to assess the spatial distribution of the formation medium, thereby locating heterogeneous areas. The environmental parameter correction sub-step is used to calculate the correlation between the propagation velocity error and the environmental parameter vector. The tunnel deformation early warning sub-step is used to provide early warning for monitoring sub-areas of environmentally sensitive heterogeneous formations.

[0029] In one possible design, the specific analysis process for step S04: early warning of formation environment sensitivity is as follows: Formation homogeneity assessment sub-step: Within each formation monitoring sub-region, a three-dimensional grid is divided according to the method of equal area division, and the propagation speed error of electromagnetic waves in each grid within each formation monitoring sub-region is collected. The dispersion of grid wave velocity error is evaluated using a formula for the difference in wave velocity error distribution. By calculating the stratigraphic homogeneity anomaly index of each grid in each stratigraphic monitoring sub-region, the stratigraphic homogeneity anomaly index is arranged in descending order to obtain the heterogeneous stratigraphic sequence of each grid in the stratigraphic monitoring sub-region, and then pushed to the monitoring center via industrial Ethernet for locating heterogeneous areas. Environmental parameter correction sub-step: Collect environmental parameters of each stratum monitoring sub-area in the tunnel through a sensor network, and construct an environmental parameter vector; The propagation speed error of electromagnetic waves in each stratum monitoring sub-region is obtained, along with the environmental parameter vector. The correlation coefficient between each parameter in the environmental parameter vector and the propagation speed error is calculated, and the degree of correlation between the propagation speed error and the environmental parameter vector is evaluated to obtain the environmental correlation vector. Tunnel deformation early warning sub-steps: Obtain the environmental correlation vector, extract the correlation coefficient with the highest degree of correlation, and calculate the comprehensive risk index of each stratum monitoring sub-area of ​​the tunnel; The comprehensive risk index of each stratum monitoring sub-region of the tunnel is obtained and compared with the preset comprehensive risk index. If the comprehensive risk index of a certain stratum monitoring sub-region is greater than the preset comprehensive risk index, it indicates that the stratum is an environmentally sensitive heterogeneous region, triggering a deformation warning. Conversely, it indicates that the medium spatial distribution of the stratum is uniform, the environmental parameter driving force is not significant, and the tunnel structure is stable. Warnings are pushed to the monitoring terminal for deformation warning areas, and the signal monitoring map of the warning area is linked.

[0030] In this embodiment, it should be specifically noted that the calculation formula for the difference in wave velocity error distribution is as follows: in, This represents the difference in wave velocity error distribution in the j-th grid of the i-th formation monitoring sub-region. This represents the propagation velocity error of the j-th grid in the i-th stratigraphic monitoring sub-region. Let represent the propagation velocity error of the i-th stratum monitoring sub-region, and m represent the total number of grids in that stratum; The formula for calculating the formation homogeneity anomaly index is as follows: in, It is represented as the stratigraphic homogeneity anomaly index of the j-th grid in the i-th stratigraphic monitoring sub-region; Specifically, if the propagation velocity error of a certain stratum fluctuates more, the stratum homogeneity anomaly index value will be larger, indicating that the spatial distribution of the medium in that stratum is more uneven, thereby locating the heterogeneous region and monitoring its deformation trend. The formula for calculating the comprehensive risk index is as follows: in, Let be the comprehensive risk index of the i-th stratigraphic monitoring sub-region. This is represented as the environmental relevance vector of the i-th stratigraphic monitoring sub-region. , These are respectively represented as correction factors for the formation homogeneity anomaly index and the environmental relevance vector; Specifically, the higher the comprehensive risk index value, the more significantly the wave velocity error dominated by formation heterogeneity is driven by environmental parameters, and the higher the deformation risk.

[0031] Step S05: Tunnel stratum deformation monitoring: Calculate multi-dimensional early warning indicators for tunnel stratum deformation risk coefficients in each stratum monitoring sub-region, including phase error change rate and comprehensive risk index.

[0032] In one possible design, step S05: the specific analysis process for monitoring tunnel stratum deformation is as follows: Step S51: Obtain the phase error change rate of each stratum monitoring sub-region in the tunnel monitoring area at historical times, and extract the maximum and minimum phase error change rates of each stratum monitoring sub-region in the historical times; Step S52: Based on the phase error change rate, the maximum phase error change rate, the minimum phase error change rate, and the comprehensive risk index at the corresponding time, calculate the tunnel stratum deformation risk coefficient for each stratum monitoring sub-region.

[0033] In this embodiment, it should be specifically noted that the formula for calculating the tunnel stratum deformation risk coefficient is as follows: in, Let represent the tunnel stratum deformation risk coefficient at time t in the i-th stratum monitoring sub-region. This represents the minimum historical phase error change rate for the i-th stratigraphic monitoring sub-region. This represents the maximum historical phase error rate of change for the i-th stratigraphic monitoring sub-region. This represents the rate of change of phase error at time t in the i-th stratum monitoring sub-region. It represents the comprehensive risk index at time t for the i-th stratigraphic monitoring sub-region.

[0034] Step S06: Identification of tunnel stratum deformation risk zone: This step is used to obtain the tunnel stratum deformation risk coefficient of each stratum monitoring sub-region in the tunnel monitoring area and to identify high-risk areas in the tunnel monitoring area.

[0035] In one possible design, the specific analysis process for step S06: identification of tunnel stratum deformation risk zones is as follows: The tunnel stratum deformation risk coefficient of each stratum monitoring sub-region in the tunnel monitoring area is obtained and compared with the preset tunnel stratum deformation risk coefficient. If the tunnel stratum deformation risk coefficient of a certain stratum monitoring sub-region is greater than the preset tunnel stratum deformation risk coefficient, it indicates that there is an instability risk in that region. All stratum monitoring sub-regions with instability risk in the tunnel monitoring area are screened out and marked as high-risk areas, and the signal monitoring spectrum of that region is associated. Conversely, if the coefficient is less than the preset coefficient, it indicates that the region is a low-risk area.

[0036] In this embodiment, it should be specifically explained that the present invention calculates the rate of change of phase error in each stratum monitoring sub-region by measuring the phase error of electromagnetic wave propagation in each stratum monitoring sub-region, and evaluates the angular deformation trend of each stratum in the tunnel. This realizes the inversion of tunnel angle changes through phase error, improves the accuracy of tunnel deformation monitoring, and meets the requirements of tunnel structural safety assessment. Through the stratum homogeneity assessment sub-step, the spatial distribution of the stratum medium is assessed, thereby locating heterogeneous areas. Through the environmental parameter correction sub-step, the correlation between propagation velocity error and environmental parameter vector is calculated. Through the tunnel deformation early warning sub-step, the comprehensive risk index of each stratum monitoring sub-region in the tunnel is calculated, and early warning is given for environmentally sensitive heterogeneous stratum monitoring sub-regions. This realizes the verification of the spatial distribution of the tunnel stratum medium by combining environmental parameters. By screening environmental parameters, false alarms caused by environmental disturbances are reduced, and the accuracy of deformation early warning is improved.

[0037] This invention obtains the phase error change rate and comprehensive risk index of each stratum monitoring sub-region within the tunnel monitoring area, calculates the tunnel stratum deformation risk coefficient for each stratum monitoring sub-region, and compares it with a preset tunnel stratum deformation risk coefficient. If the tunnel stratum deformation risk coefficient of a certain stratum monitoring sub-region is greater than the preset tunnel stratum deformation risk coefficient, it indicates that there is an instability risk in that region. All stratum monitoring sub-regions with instability risk within the tunnel monitoring area are screened out and marked as high-risk areas, and the signal monitoring spectrum of that region is associated. Conversely, if the risk coefficient is less than the preset value, it indicates that the region is a low-risk area. This achieves risk quantification, captures tunnel stratum risk trends, improves early warning timeliness, and allows for tracing the source of risk by associating with signal spectra, thereby improving the efficiency of tunnel risk monitoring.

[0038] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset values ​​in the formulas are set by those skilled in the art according to the actual situation.

[0039] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0040] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for processing ground-based radar data for tunnel monitoring and early warning, characterized in that, Includes the following steps: Step S01: Tunnel stratigraphic zoning: This step is used to divide the tunnel monitoring area into several monitoring sub-regions according to stratigraphic type using the tunnel's 3D model, and to number each monitoring sub-region. Step S02: Tunnel Layered Signal Feature Acquisition: Using radar wave velocity testing equipment, the signal propagation characteristics of electromagnetic waves in each stratum monitoring sub-region are acquired, and the signal propagation characteristics are correlated with spatial location to generate a signal monitoring map; Step S03: Tunnel dynamic deformation inversion: By measuring the phase error in each stratum monitoring sub-region through electromagnetic wave propagation, calculate the rate of change of phase error in each stratum monitoring sub-region, and evaluate the angular deformation trend of each stratum in the tunnel; Step S04: Early warning for environmentally sensitive formations: This includes a formation homogeneity assessment sub-step, an environmental parameter correction sub-step, and a tunnel deformation early warning sub-step. The formation homogeneity assessment sub-step is used to assess the spatial distribution of the formation medium, thereby locating heterogeneous areas. The environmental parameter correction sub-step is used to calculate the correlation between the propagation velocity error and the environmental parameter vector. The tunnel deformation early warning sub-step is used to provide early warning for environmentally sensitive heterogeneous formation monitoring sub-areas. Step S05: Tunnel stratum deformation monitoring: Calculate multi-dimensional early warning indicators for tunnel stratum deformation risk coefficients in each stratum monitoring sub-region, including phase error change rate and comprehensive risk index; Step S06: Identification of tunnel stratum deformation risk zones: This step is used to obtain the tunnel stratum deformation risk coefficients of each stratum monitoring sub-region in the tunnel monitoring area and to identify high-risk areas in the tunnel monitoring area.

2. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The specific analysis process for step S01: tunnel stratum partitioning is as follows: Based on the tunnel geological survey report, a three-dimensional model of the tunnel is established. The three-dimensional model of the tunnel includes the tunnel's geometric parameters, inner wall material, and internal facilities, reflecting the actual physical structure of the tunnel. Using the 3D model of the tunnel, the tunnel monitoring area is divided into several stratum monitoring sub-regions according to the stratum type, and each stratum monitoring sub-region of the tunnel monitoring area is numbered sequentially as 1,2,...i,...n.

3. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The specific analysis process for step S02, tunnel layered signal feature acquisition, is as follows: The signal propagation characteristics include wave velocity data and phase data. The wave velocity data is obtained by measuring the propagation velocity of each stratum monitoring sub-region using radar wave velocity testing equipment, comparing the measured propagation velocity with the reference propagation velocity, collecting the propagation velocity error of electromagnetic waves in each stratum monitoring sub-region, and correlating the propagation velocity error with spatial location. Using the spatial location of the tunnel as coordinates, a wave velocity error map is generated. The phase data is obtained by using the transmitter of the radar wave velocity testing equipment as the transmitting point to emit electromagnetic waves, receiving reflected signals from various strata, comparing the measured signal phase with the reference phase, collecting the phase error of the electromagnetic wave propagating in the monitoring sub-region of each stratum, and associating the phase error with the spatial location. Using the spatial location of the tunnel as coordinates, a phase error map is generated.

4. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The specific analysis process for step S03, the tunnel dynamic deformation inversion, is as follows: Step S31: Obtain the phase error of electromagnetic wave propagation in each stratum monitoring sub-region, establish a time and phase error sequence, and calculate the phase error change rate of each stratum monitoring sub-region; Step S32: Obtain the phase error change rate of each stratum monitoring sub-region of the tunnel and compare it with the preset phase error change rate. If the phase error change rate of a certain stratum monitoring sub-region is greater than the preset phase error change rate, it indicates that the deformation angle of the tunnel structure at that moment is abnormal. Then, establish an angle abnormal deformation early warning and push the early warning information to the monitoring center through the industrial Ethernet to simultaneously trigger the audible and visual alarm and emergency response process. Conversely, it indicates that there is no abnormal change in the deformation angle of the tunnel structure at that moment.

5. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The specific sub-step for assessing the homogeneity of the formation is as follows: within each formation monitoring sub-region, a three-dimensional grid is divided according to the method of equal area division, and the propagation speed error of electromagnetic waves in each grid within each formation monitoring sub-region is collected. The dispersion of grid wave velocity error is evaluated using a formula for the difference in wave velocity error distribution. By calculating the stratigraphic homogeneity anomaly index of each grid in each stratigraphic monitoring sub-region, the stratigraphic homogeneity anomaly index is arranged in descending order to obtain the heterogeneous stratigraphic sequence of each grid in the stratigraphic monitoring sub-region. This sequence is then pushed to the monitoring center via industrial Ethernet for locating heterogeneous areas.

6. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The environmental parameter correction sub-step specifically involves: collecting environmental parameters from various strata monitoring sub-areas in the tunnel through a sensor network, and constructing an environmental parameter vector; The propagation speed error of electromagnetic waves in each stratum monitoring sub-region is obtained, along with the environmental parameter vector. The correlation coefficient between each parameter in the environmental parameter vector and the propagation speed error is calculated, and the degree of correlation between the propagation speed error and the environmental parameter vector is evaluated to obtain the environmental correlation vector.

7. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The tunnel deformation early warning sub-step specifically includes: obtaining the environmental correlation vector, extracting the correlation coefficient with the highest degree of correlation, and calculating the comprehensive risk index of each stratum monitoring sub-area of ​​the tunnel. The comprehensive risk index of each stratum monitoring sub-region of the tunnel is obtained and compared with the preset comprehensive risk index. If the comprehensive risk index of a certain stratum monitoring sub-region is greater than the preset comprehensive risk index, it indicates that the stratum is an environmentally sensitive heterogeneous region, triggering a deformation warning. Conversely, it indicates that the medium spatial distribution of the stratum is uniform, the environmental parameter driving force is not significant, and the tunnel structure is stable. Warnings are pushed to the monitoring terminal for deformation warning areas, and the signal monitoring map of the warning area is linked.

8. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: Step S05: The specific analysis process for monitoring tunnel stratum deformation is as follows: Step S51: Obtain the phase error change rate of each stratum monitoring sub-region in the tunnel monitoring area at historical times, and extract the maximum and minimum phase error change rates of each stratum monitoring sub-region in the historical times; Step S52: Based on the phase error change rate, the maximum phase error change rate, the minimum phase error change rate, and the comprehensive risk index at the corresponding time, calculate the tunnel stratum deformation risk coefficient for each stratum monitoring sub-region.

9. The method for processing ground-based radar data for tunnel monitoring and early warning according to claim 1, characterized in that: The specific analysis process for step S06: identification of tunnel stratum deformation risk zones is as follows: The tunnel stratum deformation risk coefficient of each stratum monitoring sub-region in the tunnel monitoring area is obtained and compared with the preset tunnel stratum deformation risk coefficient. If the tunnel stratum deformation risk coefficient of a certain stratum monitoring sub-region is greater than the preset tunnel stratum deformation risk coefficient, it indicates that there is an instability risk in that region. All stratum monitoring sub-regions with instability risk in the tunnel monitoring area are screened out and marked as high-risk areas, and the signal monitoring spectrum of that region is associated. Conversely, if the coefficient is less than the preset coefficient, it indicates that the region is a low-risk area.

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