Dike hidden danger detection and early warning method and system based on electric field and electromagnetic field coupling

Abstract

The invention discloses an embankment hidden danger detection and early warning method and system based on electric field and electromagnetic field coupling, and the method comprises the following steps: obtaining and processing the resistivity and dielectric constant of an embankment dam body, constructing a joint inversion objective function based on a processing result, and carrying out the detection and early warning of the embankment hidden danger. A joint inversion model is established, a minimum joint inversion objective function is used for solving, a multi-physics field joint inversion result is obtained and used for delineating an abnormal area, a time-shifting resistivity monitoring network is arranged in the area for detection, the seepage velocity is obtained, soil mass mechanical parameters are defined, and the seepage velocity is calculated. And constructing an embankment hidden danger numerical model, using seepage velocities as boundary condition input, simulating embankment dam body stability safety coefficients under different seepage velocities, setting an early warning threshold value, and giving an alarm when the embankment dam body stability safety coefficients reach the early warning threshold value. Through coupling analysis of the earth electric field, the seepage velocity and the like, data multiplicity is effectively eliminated, the defect resolution is remarkably improved, and the misjudgment risk is greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy project safety monitoring, and specifically relates to a method and system for detecting and warning hidden dangers of levees based on the coupling of electric fields and electromagnetic fields. The method and system are suitable for the accurate detection and dynamic warning of hidden diseases such as leakage, cracks, and cavities of water conservancy facilities such as levees and earth-rock dams. Background Art

[0002] The problem of levee hidden danger detection and early warning under the background of low detectable physics is a technical problem that needs to be solved urgently in the process of levee engineering safety management. Traditional levee hidden danger detection methods mainly rely on single physical field technology (such as high-density resistivity method and geological radar method), which has the following defects: First, it has strong multi-solution, and the existing technology lacks a multi-physical field collaborative inversion mechanism, and a single physical field cannot distinguish between hidden danger targets of different causes (such as leakage channels and clay layers), resulting in insufficient accuracy in locating hidden dangers; second, there is a contradiction between resolution and detection depth. The geological radar has high shallow resolution but insufficient penetration depth, and the resistivity method has deep penetration but slightly lower resolution. For example, the high-density resistivity method is susceptible to shallow interference when detecting deep seepage, while the geological radar is sensitive to shallow cracks but cannot effectively detect deep loose areas; third, the dynamic monitoring capability is weak. The existing technology lacks dynamic, time-shifted, and continuous observations, and cannot track the temporal and spatial evolution trend of hidden dangers in real time, assess the risk of hidden danger disasters, and implement dynamic early warnings. Therefore, it is necessary to develop a detection technology and system based on the coupling of geoelectric field and electromagnetic field to improve the ability of accurate detection and dynamic early warning of levee hidden dangers. Summary of the invention

[0003] In order to solve the problems in the related art, the present application provides a dike hidden danger detection and early warning method and system based on the coupling of electric field and electromagnetic field, which solves the problems mentioned in the background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a dike hidden danger detection and early warning method based on electric field and electromagnetic field coupling, comprising the following steps: Step S1: Obtain the resistivity and dielectric constant of the embankment body; Step S2: Process the resistivity and dielectric constant, and construct a joint inversion objective function based on the processed results; Step S3: establishing a joint inversion model, solving the joint inversion model by minimizing the joint inversion objective function, and obtaining a multi-physics joint inversion result; delineating the abnormal area based on the multi-physics joint inversion result; Step S4: using a time-lapse resistivity monitoring network to perform periodic resistivity detection on the abnormal area to obtain time series resistivity data; Step S5: converting the time series resistivity data into a spatiotemporal continuous three-dimensional resistivity field using the Kriging interpolation method, and calculating the seepage velocity based on the spatiotemporal continuous three-dimensional resistivity field; Step S6: Based on the results of the multi-physical field joint inversion, establish a numerical model of the hidden dangers of the dike using the COMSOL stress-seepage coupling module; define the soil mechanical parameters, and input the seepage velocity as the boundary condition into the numerical model of the hidden dangers of the dike to simulate the stability safety factor of the dike dam under different seepage velocities; Step S7: Set the warning threshold, and issue an alarm when the stability safety factor of the dike dam reaches the warning threshold.

[0005] Furthermore, in step S2, the joint inversion objective function is constructed, and the specific process is as follows: First, process the collected resistivity and dielectric constant, and construct the joint inversion objective function based on the processed results; among them, the joint inversion objective function consists of a fitting difference term, a model regularization term, and a cross-gradient constraint term; Minimize the joint inversion objective function, which is expressed as: (1); In the formula, represents the resistivity; represents the dielectric constant; represents minimizing the joint inversion objective function; and are the fitting difference terms of the resistivity and dielectric constant respectively; is the model regularization term; is the cross-gradient constraint term; and are the weight coefficients of the fitting difference terms of the resistivity and dielectric constant respectively; is the weight coefficient of the model regularization term; is the weight coefficient of the cross-gradient constraint term; Among them, F ERT and F GPR are calculated using equations (2) and (3) respectively, which are expressed as: (2); (3); In the formula, and are the observed resistivity and observed dielectric constant of the i-th measurement point respectively; and are the calculated resistivity and calculated dielectric constant of the i-th measurement point respectively; represents the error weight of the resistivity of the i-th measurement point; represents the error weight of the dielectric constant of the i-th measurement point; n represents the total number of measurement points; The model regularization term is calculated using equation (4), which is expressed as: (4); In the formula, are the joint inversion model parameters including resistivity and dielectric constant; The cross-gradient constraint term is calculated by Equation (5) and expressed as: (5); In the formula, , , are the gradients of resistivity in the x, y, and z coordinates respectively; , , are the gradients of dielectric constant in the x, y, and z coordinates respectively.

[0006] Furthermore, the joint inversion result of multiple physical fields obtained in step S3 is as follows. The specific process is: Build a joint inversion model based on the Bayesian framework, solve the joint inversion model parameters by minimizing the joint inversion objective function, and use the gradient descent method in the optimization process to iteratively update the joint inversion model parameters, which is expressed as: (6); In the formula, is the joint inversion model parameter at the k-th iteration; represents the joint inversion model parameter at the (k + 1)-th iteration; η is the learning rate; When the change rate of iteratively updating the joint inversion model parameters by minimizing the joint inversion objective function is less than 1% or the number of iterations reaches the maximum value, the iteration terminates, and the joint inversion result of multiple physical fields is obtained.

[0007] Furthermore, the time series resistivity data obtained in step S4 is as follows. The specific process is: Arrange a time-lapse resistivity monitoring network to conduct periodic resistivity detection on the abnormal area to obtain time series resistivity data ρ(x, y, z, t); where x, y, z are the coordinates of the embankment soil body in x, y, z; t is the detection time.

[0008] Furthermore, the seepage velocity is calculated based on the spatio-temporal continuous three-dimensional resistivity field in step S5. The specific process is: Use the Kriging interpolation method to convert the time series resistivity data ρ(x, y, z, t) into a spatio-temporal continuous three-dimensional resistivity field; Calculate the seepage velocity based on the spatio-temporal continuous three-dimensional resistivity field, which is expressed as: (7); In the formula, v represents the seepage velocity, and Δx is the displacement of the seepage front within Δt; represents the time interval; represents the resistivity at time t; represents the change in resistivity; represents the change rate of resistivity with time t; represents the change rate of resistivity with coordinates .

[0009] Furthermore, in step S6, the stability safety factor of the dike dam under different seepage velocities is simulated. The specific process is as follows: According to the multi - physical - field joint inversion result in step S3, use the COMSOL stress - seepage coupling module to establish a numerical model of dike hidden dangers, and define the soil mechanical parameters; the soil mechanical parameters include elastic modulus E, Poisson's ratio , permeability coefficient K, effective cohesion c′ of the soil, and effective internal friction angle ϕ′ of the soil; and input the seepage velocity as the boundary condition into the numerical model of dike hidden dangers; use the COMSOL stress - seepage coupling module to simulate the stability safety factor of the dike dam under different seepage velocities based on the soil mechanical parameters and the numerical model of dike hidden dangers.

[0010] Furthermore, the specific process of step S7 is as follows: When 1.5>F s ≥1.0, an alarm is issued, and a warning message is sent through the data - processing PC and transmitted to the upper computer; F s represents the stability safety factor of the dike dam.

[0011] Furthermore, the stability safety factor of the dike dam is calculated by the simplified Bishop method in the limit equilibrium method and is expressed by the following formula (8): (8); In the formula, W is the weight of the soil strip; θ is the inclination angle of the slip surface; l is the length of the divided slip surface strip.

[0012] A dike hidden - danger detection and warning system based on the coupling of electric field and electromagnetic field, which is applied to the above - mentioned dike hidden - danger detection and warning method based on the coupling of electric field and electromagnetic field, includes: A multi - physical - field synchronous acquisition module: used to obtain the resistivity and dielectric constant of the dike dam; A first data - processing and analysis unit: used to process the resistivity and dielectric constant, and construct a joint inversion objective function based on the processed results; A second data - processing and analysis unit: used to establish a joint inversion model, solve the joint inversion model by minimizing the joint inversion objective function, and obtain the multi - physical - field joint inversion result; circle the abnormal area based on the multi - physical - field joint inversion result; A third data - processing and analysis unit: used to perform periodic resistivity detection on the abnormal area using a time - lapse resistivity monitoring network to obtain time - series resistivity data; Fourth data processing and analysis unit: It is used to convert the time-series resistivity data into a spatio-temporally continuous three-dimensional resistivity field by using Kriging interpolation method, and calculate the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field; Fifth data processing and analysis unit: It is used to establish a numerical model of hidden dangers in dikes based on the results of multi-physical field joint inversion by using the COMSOL stress-seepage coupling module; define the soil mechanical parameters, and input the seepage velocity as the boundary condition into the numerical model of hidden dangers in dikes to simulate the stability safety factor of the dike dam under different seepage velocities; Sixth data processing and analysis unit: It is used to set the warning threshold and issue an alarm when the stability safety factor of the dike dam reaches the warning threshold.

[0013] Compared with the existing technologies, the present invention has the following beneficial effects: (1) By breaking through the limitations of traditional single-physical-field detection, through coupled analysis of the geoelectric field, seepage velocity, etc., the present invention effectively eliminates the multi-solution nature of data, significantly improves the defect resolution, and greatly reduces the risk of misjudgment.

[0014] (2) By adopting the Bayesian framework and time-lapse resistivity monitoring network technology, and combining with the COMSOL stress-seepage coupling module for real-time modeling, the present invention realizes the leap from static detection to dynamic tracking, can monitor the evolution trend of defects (such as the leakage expansion rate) in real time, and shortens the warning response time. Brief Description of the Drawings

[0015] Figure 1 It is the method flow chart of the present invention.

[0016] Figure 2 It is the structural schematic diagram of the dike dam of the present invention.

[0017] Reference numerals: 1. Dike dam; 2. Crawler multi-frequency geological radar vehicle; 3. High-density electrical method survey line; 4. Radar data transmission cable; 5. Electrical method data transmission cable; 6. Geological radar host; 7. High-density electrical method host; 8. Data processing PC terminal. Detailed Embodiment

[0018] As Figure 1 shown, the present invention provides a technical solution: A method for detecting and warning hidden dangers in dikes based on the coupling of electric field and electromagnetic field, including the following steps: Step S1: Obtain the resistivity and dielectric constant of the dike dam; Step S2: Process the resistivity and dielectric constant, and construct a joint inversion objective function based on the processed results; Step S3: Establish a joint inversion model, solve the joint inversion model by minimizing the joint inversion objective function to obtain the results of multi-physical field joint inversion; delineate the abnormal area based on the results of multi-physical field joint inversion; Step S4: Use a time-lapse resistivity monitoring network to conduct periodic resistivity detection on the abnormal area to obtain time-series resistivity data; Step S5: Use the Kriging interpolation method to convert the time-series resistivity data into a spatio-temporally continuous three-dimensional resistivity field, and calculate the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field; Step S6: Based on the multi-physical field joint inversion results, use the COMSOL stress-seepage coupling module to establish a numerical model for hidden dangers of the levee; define the soil mechanical parameters, and input the seepage velocity as a boundary condition into the numerical model for hidden dangers of the levee to simulate the stability safety factor of the levee dam under different seepage velocities; Step S7: Set an early warning threshold, and issue an alarm when the stability safety factor of the levee dam reaches the early warning threshold.

[0019] Among them, the specific process of obtaining the resistivity and dielectric constant of the levee dam is as follows: Figure 2 In the figure, 1 is the levee dam, 2 is the crawler multi-frequency geological radar vehicle, 3 is the high-density electrical sounding line, 4 is the radar data transmission cable, 5 is the electrical sounding data transmission cable, 6 is the geological radar host, 7 is the high-density electrical sounding host, and 8 is the data processing PC; Specifically, by arranging a high-density electrical sounding line 3 parallel to the axis of the levee dam 1, use the high-density electrical sounding line 3 to obtain the geoelectric field data (resistivity) of the levee dam 1, and transmit the geoelectric field data (resistivity) to the high-density electrical sounding host 7 through the electrical sounding data transmission cable 5; at the same time, use the crawler multi-frequency geological radar vehicle 2 to collect the electromagnetic field data (dielectric constant) of the levee dam 1, and transmit the electromagnetic field data (dielectric constant) to the geological radar host 6 through the radar data transmission cable 4; after preprocessing, the resistivity and dielectric constant are uniformly transmitted to the data processing PC 8 through 5G / wireless; Specifically, the detection of hidden dangers of the levee is divided into two stages: a general survey and a detailed survey; in the general survey stage, the key is to delineate the suspected hidden danger locations, and in the detailed survey stage, for the suspected hidden danger locations delineated in the general survey stage, further confirm the spatial distribution of the hidden dangers and track their spatio-temporal changes; In the general survey stage, mainly use the crawler multi-frequency geological radar vehicle, and use a 100MHz antenna to continuously scan the levee dam, with a detection depth of 8-10m; at the same time, use a high-density electrical sounding line (high-density resistivity method) to collect data along the levee dam, with an electrode spacing of 5m and a detection depth of 20m; in the detailed survey stage, conduct densified detection on the abnormal areas found in the general survey stage. On the basis of the previous electrode spacing of 5m and a detection depth of 20m, the resistivity measurement point spacing is densified to 2m, and the crawler multi-frequency geological radar vehicle uses a 400MHz antenna to improve the resolution; and arrange a time-lapse resistivity monitoring network in the abnormal areas (suspected leakage areas) to continuously collect geoelectric field data and analyze the dynamic changes of seepage.

[0020] Among them, in step S2, the joint inversion objective function is constructed, and the specific process is as follows: First, the collected resistivity and dielectric constant are processed, and the joint inversion objective function is constructed based on the processed results; among them, the joint inversion objective function consists of a fitting difference term, a model regularization term, and a cross-gradient constraint term; Minimizing the joint inversion objective function is expressed as: (1); In the formula, represents the resistivity; represents the dielectric constant; represents minimizing the joint inversion objective function; and are the fitting difference terms of the resistivity and the dielectric constant respectively; is the model regularization term; is the cross-gradient constraint term; and are the weight coefficients of the fitting difference terms of the resistivity and the dielectric constant respectively; is the weight coefficient of the model regularization term; is the weight coefficient of the cross-gradient constraint term; Among them, F ERT and F GPR are calculated by equations (2) and (3) respectively, and are expressed as: (2); (3); In the formula, and are the observed resistivity and the observed dielectric constant of the i-th measurement point respectively; and are the calculated resistivity and the calculated dielectric constant of the i-th measurement point respectively; represents the error weight of the resistivity of the i-th measurement point; represents the error weight of the dielectric constant of the i-th measurement point; n represents the total number of measurement points; The model regularization term adopts sparse constraint (L1 norm) and is calculated by equation (4), and is expressed as: (4); In the formula, are the parameters of the joint inversion model including resistivity and dielectric constant; Among them, the cross-gradient constraint term is calculated by equation (5), and is expressed as: (5); In the formula, , , They are the gradients of the resistivity in the x, y, and z directions respectively; , , They are the gradients of the permittivity in the x, y, and z directions respectively.

[0021] Among them, in step S3, the multi-physical-field joint inversion result is obtained. The specific process is as follows: Based on the Bayesian framework, a joint inversion model is constructed. The parameters of the joint inversion model are solved by minimizing the joint inversion objective function. The gradient descent method is used in the optimization process to iteratively update the parameters of the joint inversion model; when the change rate of the iterative update of the parameters of the joint inversion model by minimizing the joint inversion objective function is less than 1% or the number of iterations reaches the maximum value, the iteration terminates, and the multi-physical-field joint inversion result is obtained; The iterative update of the parameters of the joint inversion model is expressed as: (6); In the formula, is the parameter of the joint inversion model at the k-th iteration; represents the parameter of the joint inversion model at the (k + 1)-th iteration; η is the learning rate, and the learning rate is generally taken as 0.01; Through the above multi-physical-field joint inversion method, the multi-solution problem of a single physical field can be eliminated, the inversion resolution can be improved, and potential hazards can be accurately located.

[0022] Among them, in step S4, the time-series resistivity data is obtained. The specific process is as follows: A time-lapse resistivity monitoring network is arranged to conduct periodic resistivity detection on the abnormal area, and the time-series resistivity data ρ(x, y, z, t) is obtained; where; x, y, z are the coordinates of the embankment soil body in the x, y, and z directions; t is the detection time.

[0023] Among them, in step S5, the seepage velocity is calculated based on the spatio-temporal continuous three-dimensional resistivity field. The specific process is as follows: The time-series resistivity data ρ(x, y, z, t) is converted into a spatio-temporal continuous three-dimensional resistivity field by using the Kriging interpolation method; Based on the spatio-temporal continuous three-dimensional resistivity field, the seepage velocity is calculated, which is expressed as: (7); In the formula, v represents the seepage velocity, Δx is the displacement of the seepage front within Δt; represents the time interval; represents the resistivity at time t; represents the change in resistivity; represents the rate of change of resistivity with time t; represents the rate of change of resistivity with the coordinate of.

[0024] Among them, in step S6, the stability safety factor of the levee dam body under different seepage velocities is simulated. The specific process is as follows: According to the multi-physical field joint inversion results in step S3, use the COMSOL stress-seepage coupling module to establish a numerical model of levee hidden dangers, and define the soil mechanical parameters; the soil mechanical parameters include elastic modulus E, Poisson's ratio , permeability coefficient K, effective cohesion c' of the soil, and effective internal friction angle ϕ' of the soil; and input the seepage velocity as a boundary condition into the numerical model of levee hidden dangers; use the COMSOL stress-seepage coupling module to simulate the stability safety factor of the levee dam body under different seepage velocities based on the soil mechanical parameters and the numerical model of levee hidden dangers.

[0025] Among them, the specific process of step S7 is as follows: Set three-level warning thresholds: when F s ≥1.5 is the yellow warning threshold, when 1.5 > F s ≥1.0 is the orange warning threshold, when F s <1.0 is the red warning threshold; F s represents the stability safety factor of the levee dam body; When 1.5 > F s ≥1.0, an alarm is issued, and a warning message is sent through the data processing PC and transmitted to the upper computer to remind the staff.

[0026] Among them, the stability safety factor of the levee dam body is calculated by the simplified Bishop method in the limit equilibrium method and is expressed by the following formula (8): (8); In the formula, W is the weight of the soil strip; θ is the inclination angle of the slip surface; l is the length of the slip surface sub-strip.

[0027] A levee hidden danger detection and warning system based on the coupling of electric field and electromagnetic field is applied to the above-mentioned levee hidden danger detection and warning method based on the coupling of electric field and electromagnetic field, and includes: Multi-physical field synchronous acquisition module: used to obtain the resistivity and dielectric constant of the levee dam body; First data processing and analysis unit: used to process the resistivity and dielectric constant, and construct a joint inversion objective function based on the processed results; Second data processing and analysis unit: used to establish a joint inversion model, solve the joint inversion model by minimizing the joint inversion objective function, and obtain the multi-physical field joint inversion results; circle the abnormal area based on the multi-physical field joint inversion results; Third data processing and analysis unit: used to perform periodic resistivity detection on the abnormal area using a time-lapse resistivity monitoring network to obtain time-series resistivity data; The fourth data processing and analysis unit: used to convert time series resistivity data into a spatio-temporally continuous three-dimensional resistivity field by Kriging interpolation method, and calculate the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field; The fifth data processing and analysis unit: used to establish a numerical model of hidden dangers in dikes by using the COMSOL stress-seepage coupling module based on the multi-physics joint inversion results; define soil mechanical parameters, and input the seepage velocity as a boundary condition into the numerical model of hidden dangers in dikes to simulate the stability safety factor of the dike dam under different seepage velocities; The sixth data processing and analysis unit: used to set an early warning threshold and issue an alarm when the stability safety factor of the dike dam reaches the early warning threshold.

[0028] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields, characterized in that Including the following steps: Step S1: Obtain the resistivity and dielectric constant of the levee dam body; Step S2: Process the resistivity and dielectric constant, and construct a joint inversion objective function based on the processed results; Step S3: Establish a joint inversion model, solve the joint inversion model by minimizing the joint inversion objective function, and obtain the multi-physical field joint inversion result; delineate the abnormal area based on the multi-physical field joint inversion result; Step S4: Use a time-lapse resistivity monitoring network to conduct periodic resistivity detection on the abnormal area to obtain time-series resistivity data; Step S5: Use the Kriging interpolation method to convert the time-series resistivity data into a spatio-temporally continuous three-dimensional resistivity field, and calculate the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field; Step S6: Based on the multi-physical field joint inversion result, use the COMSOL stress-seepage coupling module to establish a numerical model of levee hidden dangers; define the soil mechanical parameters, and input the seepage velocity as the boundary condition into the numerical model of levee hidden dangers to simulate the stability safety factor of the levee dam body under different seepage velocities; Step S7: Set an early warning threshold, and issue an alarm when the stability safety factor of the levee dam body reaches the early warning threshold.

2. The method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 1, wherein: In step S2, the process of constructing the joint inversion objective function is as follows: First, process the collected resistivity and dielectric constant, and construct a joint inversion objective function based on the processed results; among them, the joint inversion objective function consists of a fitting error term, a model regularization term, and a cross-gradient constraint term; Minimizing the joint inversion objective function means: （1）； Wherein, represents the resistivity; represents the dielectric constant; represents minimizing the joint inversion objective function; and are the fitting difference terms of the resistivity and the dielectric constant respectively; is the model regularization term; is the cross-gradient constraint term; and are the weight coefficients of the fitting difference terms of the resistivity and the dielectric constant respectively; is the weight coefficient of the model regularization term; is the weight coefficient of the cross-gradient constraint term; Among them, F ERT and F GPR are calculated by formulas (2) and (3) respectively, indicating: （2）； （3）； Wherein, and are the observed resistivity and the observed dielectric constant of the i-th measurement point, respectively; and are the calculated resistivity and the calculated dielectric constant of the i-th measurement point, respectively; represents the error weight of the resistivity of the i-th measurement point; represents the error weight of the dielectric constant of the i-th measurement point; n represents the total number of measurement points; The model regularization term is calculated using equation (4), which means: （4）； In the formula, is the joint inversion model parameter including resistivity and permittivity; The cross-gradient constraint term is calculated using equation (5), which means: （5）； In the formula, , , are the gradients of the resistivity in the x, y, and z coordinates, respectively; , , are the gradients of the permittivity in the x, y, and z coordinates, respectively.

3. The method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 2, wherein: In step S3, the process of obtaining the multi-physical field joint inversion result is as follows: Construct a joint inversion model based on the Bayesian framework, solve the joint inversion model parameters by minimizing the joint inversion objective function, and use the gradient descent method for the optimization process to iteratively update the joint inversion model parameters, which means: （6）； wherein, is the joint inversion model parameter of the k-th iteration; represents the joint inversion model parameter of the (k + 1)-th iteration; η is the learning rate; When the change rate of iteratively updating the joint inversion model parameters by minimizing the joint inversion objective function is less than 1% or the number of iterations reaches the maximum value, the iteration terminates, and the multi-physical field joint inversion result is obtained.

4. A method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 3, characterized in that: In step S4, the process of obtaining the time-series resistivity data is as follows: Arrange a time-lapse resistivity monitoring network to conduct periodic resistivity detection on the abnormal area to obtain time-series resistivity data ρ(x, y, z, t); where; x, y, z are the coordinates of the levee soil space in x, y, z; t is the detection time.

5. A method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 4, characterized in that: In step S5, the process of calculating the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field is as follows: Use the Kriging interpolation method to convert the time-series resistivity data ρ(x, y, z, t) into a spatio-temporally continuous three-dimensional resistivity field; Calculate the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field, which means: （7）； Wherein, v represents the seepage velocity, and Δx is the displacement of the seepage front within Δt; represents the time interval; represents the resistivity at time t; represents the change in resistivity; represents the rate of change of resistivity with respect to time t; represents the rate of change of resistivity with respect to the coordinate of.

6. The method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 5, wherein: In step S6, the process of simulating the stability safety factor of the levee dam body under different seepage velocities is as follows: According to the multi-physical field joint inversion result in step S3, use the COMSOL stress-seepage coupling module to establish a numerical model of levee hidden dangers and define the soil mechanical parameters; The soil mechanical parameters include the elastic modulus E, Poisson's ratio , permeability coefficient K, effective cohesive force c' of the soil, and effective internal friction angle ϕ' of the soil; And input the seepage velocity as a boundary condition into the numerical model of hidden dangers in the dike; use the COMSOL stress-seepage coupling module to simulate the stability safety factor of the dike dam under different seepage velocities based on the soil mechanical parameters and the numerical model of hidden dangers in the dike.

7. A method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 6, characterized in that: The specific process of step S7 is as follows: When 1.5 > F s ≥ 1.0, an alarm is issued, and a warning message is sent through the data - processing PC side and transmitted to the host computer; F s represents the stability safety factor of the dike dam body.

8. A method for detecting and warning hidden dangers of dikes based on the coupling of electric fields and electromagnetic fields according to claim 7, characterized in that: The stability safety factor of the dike dam is calculated by the simplified Bishop method in the limit equilibrium method and is expressed by the following formula (8): （8）； In the formula, W is the weight of the soil strip; θ is the inclination angle of the slip surface; l is the length of the divided slip surface strip.

9. A dike hidden danger detection and early warning system based on the coupling of electric field and electromagnetic field, which is applied to a dike hidden danger detection and early warning method based on the coupling of electric field and electromagnetic field according to any one of claims 1-8, and is characterized in that It includes: Multi-physical field synchronous acquisition module: used to obtain the resistivity and dielectric constant of the dike dam; First data processing and analysis unit: used to process the resistivity and dielectric constant and construct a joint inversion objective function based on the processed results; Second data processing and analysis unit: used to establish a joint inversion model, solve the joint inversion model by minimizing the joint inversion objective function, and obtain the multi-physical field joint inversion result; delineate the abnormal area based on the multi-physical field joint inversion result; Third data processing and analysis unit: used to conduct periodic resistivity detection on the abnormal area using a time-lapse resistivity monitoring network to obtain time-series resistivity data; Fourth data processing and analysis unit: used to convert the time-series resistivity data into a spatio-temporally continuous three-dimensional resistivity field using the Kriging interpolation method and calculate the seepage velocity based on the spatio-temporally continuous three-dimensional resistivity field; Fifth data processing and analysis unit: used to establish a numerical model of hidden dangers in the dike using the COMSOL stress-seepage coupling module based on the multi-physical field joint inversion result; define the soil mechanical parameters, and input the seepage velocity as a boundary condition into the numerical model of hidden dangers in the dike to simulate the stability safety factor of the dike dam under different seepage velocities; Sixth data processing and analysis unit: used to set an early warning threshold and issue an alarm when the stability safety factor of the dike dam reaches the early warning threshold.

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