A multi-probe method information fusion advanced geological prediction system

Abstract

The present application relates to the technical field of advanced geological prediction, in particular to a kind of advanced geological prediction system based on information fusion of multiple detection methods, comprising comprehensive analysis subsystem, advanced prediction module and notification and statistics module.The comprehensive analysis subsystem includes face analysis module, advanced drilling analysis module and geological radar analysis module.The present application combines stress-strain monitoring, geological radar detection, advanced drilling and other detection methods, improves the adaptability to complex construction environment, improves the intelligent level, realizes the rapid transmission and sharing of geological information;Through logical judgment program, data centralized analysis is carried out, geological abnormal conditions such as rock burst, discontinuous geological structure are found in time during drilling process, so as to output corresponding early warning signal, which provides more definite decision basis for construction personnel.

Description

Technical Field

[0001] This invention relates to the field of advanced geological prediction technology, specifically to an advanced geological prediction system based on information fusion of multiple detection methods. Background Technology

[0002] Advanced geological forecasting refers to a geological exploration method that, during tunnel construction, uses geological surveys, geophysical exploration, and other exploration techniques, based on existing geological data, to determine the engineering geology, hydrogeology, and adverse geological processes ahead of the tunnel face or corresponding ground location.

[0003] Multi-source data fusion originated from multi-source information fusion, first proposed in the 1970s primarily for military applications. By fusing data from multiple information sources, a better understanding of observed phenomena can be achieved. The results of multi-source data fusion fully exploit the usable value between data sources; information from various data sources is cross-verified, improving the accuracy of decision-making and reducing information errors and omissions, which is of significant value in many engineering fields. Currently, at the theoretical level, many domestic and international scholars have proposed various fusion methods for different data sources. These fusion methods can be divided into probabilistic methods and non-probabilistic methods.

[0004] However, existing advanced geological forecasting technologies still have many problems. First, different advanced geological forecasting methods have their specific applicable scope and limitations. In some remote areas or areas with extremely complex geological conditions, the application of advanced geological forecasting may be limited due to limitations in equipment and technical personnel.

[0005] Secondly, existing advanced geological prediction technologies lack a comprehensive observation system, and the processing and analysis capabilities of observation data need improvement. The results of advanced geological predictions largely depend on the experience and skill of the interpreters; different interpreters may arrive at different predictions based on the same observation data.

[0006] To address the aforementioned issues, it is necessary to propose an advanced geological prediction system based on information fusion from multiple detection methods. Summary of the Invention

[0007] The purpose of this invention is to solve the problems existing in the background technology and to propose an advanced geological prediction system based on information fusion of multiple detection methods.

[0008] An advanced geological prediction system based on information fusion from multiple detection methods includes a comprehensive analysis subsystem, an advanced forecasting module, and a notification and statistics module. The comprehensive analysis subsystem comprises a face analysis module, an advanced drilling analysis module, and a ground-penetrating radar analysis module.

[0009] The face analysis module acquires lithological characteristic data of the face at preset time intervals, specifically:

[0010] Obtain the mileage marker K of the working face. Obtain the rock type of the working face, including slate, limestone, phyllite, sandstone, schist, andesite, shale, marble, granite, and basalt, and match it to the preset lithology parameter C based on the lithology type. Obtain the dip angle of the rock strata at the working face. Rock layer thickness Joint group number Maximum joint gap and water output

[0011] As a preferred embodiment of the present invention, obtaining stress-strain characteristic data of the surrounding rock at the tunnel face includes:

[0012] Convergence value of surrounding rock sidewall Convergence rate of surrounding rock sidewall Convergence acceleration of surrounding rock sidewall vault settlement vault settlement rate vault subsidence acceleration Maximum principal stress value of surrounding rock Direction of maximum principal stress in surrounding rock and the dip angle of the maximum principal stress of the surrounding rock

[0013] Through formula Calculate the first cross-sectional characteristic parameters E11(k) and the second cross-sectional characteristic parameters E12(k) of the working face at mileage station K. Wherein... and All of these are preset weighting factors.

[0014] The first cross-section characteristic parameters and the second cross-section characteristic parameters are sent to the advanced prediction module.

[0015] The advanced drilling analysis module acquires information obtained by the horizontal drilling rig during horizontal geological drilling at the working face at kilometer marker K every preset time t, including the drilling power P, drilling speed V, and drilling depth ΔK of the horizontal drilling rig.

[0016] As a preferred embodiment of the present invention, lithological experimental parameters of the borehole core are obtained, including natural density. Dry density Saturation density Moisture content Harmony sound wave test longitudinal wave velocity

[0017] In a preferred embodiment of the present invention, mechanical experimental parameters of the borehole core are obtained, including the axial compressive strength of the borehole core. Cohesion elastic modulus Poisson's ratio and softening coefficient

[0018] With drilling power P as the vertical axis and time t as the horizontal axis, construct a drilling power-time graph and obtain its functional expression P = f1(t). With drilling speed V as the vertical axis and time t as the horizontal axis, construct a drilling speed-time graph and obtain its functional expression V = f2(t).

[0019] Through formula Calculate the first drilling characteristic parameter E21(K+△K) and the second drilling characteristic parameter E22(K+△K). Where T is the preset detection cycle. A set of preset weighting factors, Where μ is the first moment of the function g(t).

[0020] The first and second drilling characteristic parameters are sent to the advanced prediction module.

[0021] The ground-penetrating radar analysis module acquires the transient electromagnetic intensity ΔB of the electromagnetic waves detected at a depth ΔK along the tunnel face. An electromagnetic intensity transient variable-depth map is established with ΔB as the vertical axis and ΔK as the horizontal axis. The functional expression for the electromagnetic intensity transient variable-depth map is obtained as ΔB = h(ΔK).

[0022] As a preferred embodiment of the present invention, the electromagnetic intensity transient variable-mileage depth map is transformed to the frequency domain by Fourier transform to obtain the electromagnetic wave frequency spectrum.

[0023] The Fourier transform formula is: Where i is the imaginary unit and i×i=-1, v is the frequency obtained after decomposition, and S(v) is the electromagnetic wave frequency spectrum function obtained after Fourier transform. Feature extraction is performed on the electromagnetic wave frequency spectrum using the formula... Calculate the high-frequency occupancy rate ρ, where v1 is the preset high and low frequency cutoff frequencies. Send the high-frequency occupancy rate ρ to the advance prediction module.

[0024] The advanced prediction module acquires analysis data from the comprehensive analysis subsystem, including the first cross-section characteristic parameter E11(k), the second cross-section characteristic parameter E12(k), the first drilling characteristic parameter E21(K+△K), the second drilling characteristic parameter E22(K+△K), and the high-frequency occupancy rate ρ. This analysis data is then input into a logical judgment program.

[0025] As a preferred embodiment of the present invention, the logical judgment procedure is specifically as follows:

[0026] Extract the drilling mileage depth ΔK from the first drilling characteristic parameter E21(K+△K) and the second drilling characteristic parameter E22(K+△K), and proceed to the first and second numerical judgments.

[0027] First numerical judgment: Determine if the high-frequency occupancy rate ρ is less than the threshold ρMin. If yes, proceed to the third numerical judgment; if no, proceed to the fourth numerical judgment.

[0028] Second numerical judgment: Determine whether the second drilling characteristic parameter E22(K+△K) is greater than the threshold E22Ma. If yes, output a rockburst warning signal, and then proceed to the fourth numerical judgment; if no, directly proceed to the fourth numerical judgment.

[0029] The third numerical judgment: Determine whether the first drilling characteristic parameter E21(K+△K) is greater than the threshold E21Max. If yes, output a discontinuous geological warning signal, and then proceed to the fourth numerical judgment; if no, directly proceed to the fourth numerical judgment.

[0030] Fourth value judgment: Determine whether the mileage depth ΔK is less than the threshold KMax. If yes, proceed to the fifth value judgment; otherwise, end the entire logical judgment program.

[0031] Fifth value judgment: Determine whether the following conditions are met: the first section characteristic parameter E11(k) is greater than the threshold E11Max or the second section characteristic parameter E12(k) is greater than the threshold E12Max. If yes, output a stop check signal and then end the entire logic judgment program; if no, output a first-level alarm signal and then end the entire logic judgment program.

[0032] After the entire logic judgment program ends, all output signals are sent to the statistics and notification module.

[0033] The statistics and notification module outputs signals generated by the advanced forecasting module via a display screen, including discontinuous geological warning signals, rockburst warning signals, work stoppage inspection signals, or level one alarm signals. It also displays the corresponding alarm information and counts the number of times each signal is generated within a preset statistical period.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] (1) This invention provides a detailed geological information basis for tunnels or underground engineering by acquiring key geological parameters such as the mileage station number, rock type, rock stratum dip angle, rock stratum thickness, number of joint groups, maximum joint gap and water output of the tunnel face; by acquiring stress and strain characteristic data of the surrounding rock around the tunnel face, including convergence value, convergence rate, convergence acceleration, crown settlement, settlement rate, settlement acceleration, and the maximum principal stress value, direction and dip angle of the surrounding rock, the deformation and stress state of the surrounding rock can be monitored in real time; this is of great significance for timely detection of signs of surrounding rock instability and prevention of geological disasters.

[0036] (2) This invention combines multiple detection methods such as stress and strain monitoring, ground-penetrating radar detection, and advanced drilling, which improves the adaptability to complex construction environments. It sets up multiple links such as real-time monitoring, data analysis and early warning, which improves the level of intelligence. It acquires and processes geological information through digital means, and realizes the rapid transmission and sharing of geological information.

[0037] (3) This invention uses a logical judgment program to perform centralized data analysis, and promptly detects geological anomalies such as rock bursts and discontinuous geological structures during drilling, thereby outputting corresponding early warning signals; it adopts a multi-level numerical judgment mechanism, with each level of judgment based on specific conditions and thresholds; this design makes the output of early warning signals more accurate and avoids false alarms and missed alarms; at the same time, the program can output early warning signals of different levels according to different judgment results, providing construction personnel with clearer decision-making basis, which helps construction personnel to take preventive measures in advance and reduce construction risks; it also reduces the dependence of geological forecasting on the experience and level of interpreters. Attached Figure Description

[0038] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings:

[0039] Figure 1 This is a system block diagram of the present invention;

[0040] Figure 2 This is a flowchart of the logical judgment process of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0042] Advanced geological forecast.

[0043] During tunnel construction, a geological exploration method is a method that, based on existing geological data, uses geological surveys, geophysical exploration, and other exploration techniques to determine the engineering geology, hydrogeology, and adverse geological processes ahead of the tunnel at the tunnel face or corresponding ground location.

[0044] Please see Figure 1 As shown, an advanced geological prediction system based on information fusion from multiple detection methods includes a comprehensive analysis subsystem, an advanced forecasting module, and a notification and statistics module. The comprehensive analysis subsystem comprises a face analysis module, an advanced drilling analysis module, and a ground-penetrating radar analysis module.

[0045] The face analysis module acquires lithological characteristic data of the face at preset time intervals, specifically:

[0046] Obtain the mileage marker K of the working face. Obtain the rock type of the working face, including slate, limestone, phyllite, sandstone, schist, andesite, shale, marble, granite, and basalt, and match it to the preset lithology parameter C based on the lithology type. Obtain the dip angle of the rock strata at the working face. Rock layer thickness Joint group number Maximum joint gap and water output

[0047] Furthermore, obtain stress-strain characteristic data of the surrounding rock around the tunnel face, including:

[0048] Convergence value of surrounding rock sidewall Convergence rate of surrounding rock sidewall Convergence acceleration of surrounding rock sidewall vault settlement vault settlement rate vault subsidence acceleration Maximum principal stress value of surrounding rock Direction of maximum principal stress in surrounding rock and the dip angle of the maximum principal stress of the surrounding rock

[0049] Through formula Calculate the first cross-sectional characteristic parameters E11(k) and the second cross-sectional characteristic parameters E12(k) of the working face at mileage station K. Wherein... and All of these are preset weighting factors.

[0050] The first cross-section characteristic parameters and the second cross-section characteristic parameters are sent to the advanced prediction module.

[0051] It should be noted that the first section characteristic parameters quantify the lithological characteristics of the working face, including rock type, stratum occurrence, joint opening, and rock permeability. The second section characteristic parameters quantify the stress-strain characteristics of the surrounding rocks of the working face.

[0052] The advanced drilling analysis module acquires information obtained by the horizontal drilling rig during horizontal geological drilling at the working face at kilometer marker K every preset time t, including the drilling power P, drilling speed V, and drilling depth ΔK of the horizontal drilling rig.

[0053] Furthermore, lithological experimental parameters, including natural density, were obtained from the borehole core. Dry density Saturation density Moisture content Harmony sound wave test longitudinal wave velocity

[0054] Furthermore, mechanical experimental parameters of the borehole core were obtained, including the axial compressive strength of the borehole core. Cohesion elastic modulus Poisson's ratio and softening coefficient

[0055] With drilling power P as the vertical axis and time t as the horizontal axis, construct a drilling power-time graph and obtain its functional expression P = f1(t). With drilling speed V as the vertical axis and time t as the horizontal axis, construct a drilling speed-time graph and obtain its functional expression V = f2(t).

[0056] Through formula Calculate the first drilling characteristic parameter E21(K+△K) and the second drilling characteristic parameter E22(K+△K). Where T is the preset detection cycle. A set of preset weighting factors, Where μ is the first moment of the function g(t).

[0057] The first and second drilling characteristic parameters are sent to the advanced prediction module.

[0058] It should be noted that the first drilling characteristic parameter comprehensively reflects the fluctuation of the ratio of drilling power to drilling speed, and indirectly reflects the continuity of the formation encountered during advance drilling. If the rock encountered during advance drilling is uniform and continuous, the value of the first drilling characteristic parameter will approach 0. Conversely, if discontinuous formations such as faults, isolated boulders, or goafs are encountered during advance drilling, the value of the first drilling characteristic parameter will increase.

[0059] It should be further explained that the second drilling characteristic parameter comprehensively reflects the mechanical properties of the rocks encountered during the advanced drilling process.

[0060] The ground-penetrating radar analysis module acquires the transient electromagnetic intensity ΔB of the electromagnetic waves detected at a depth ΔK along the tunnel face. An electromagnetic intensity transient variable-depth map is established with ΔB as the vertical axis and ΔK as the horizontal axis. The functional expression for the electromagnetic intensity transient variable-depth map is obtained as ΔB = h(ΔK).

[0061] Furthermore, the electromagnetic intensity transient variable-mileage depth map is transformed into the frequency domain by Fourier transform to obtain the electromagnetic wave frequency spectrum.

[0062] The Fourier transform formula is: Where i is the imaginary unit and i×i=-1, v is the frequency obtained after decomposition, and S(v) is the electromagnetic wave frequency spectrum function obtained after Fourier transform. Feature extraction is performed on the electromagnetic wave frequency spectrum using the formula... Calculate the high-frequency occupancy rate ρ, where v1 is the preset high and low frequency cutoff frequencies. Send the high-frequency occupancy rate ρ to the advance prediction module.

[0063] It should be noted that the dielectric constant of rock masses fluctuates between 4 and 7, while the dielectric constant of water is 81 and that of air is 1, showing a significant difference among the three. Therefore, when electromagnetic waves pass through water-rich areas and goaf areas, their propagation speed in the medium decreases rapidly, resulting in a noticeable positive peak anomaly in the reflected wave. This is accompanied by strong reflection, diffraction, and scattering of electromagnetic waves, causing a shift in the electromagnetic frequency spectrum from high to low frequencies. Specifically, this is reflected in a decrease in the high-frequency occupancy rate (ρ).

[0064] The advanced prediction module acquires analysis data from the comprehensive analysis subsystem, including the first cross-section characteristic parameter E11(k), the second cross-section characteristic parameter E12(k), the first drilling characteristic parameter E21(K+△K), the second drilling characteristic parameter E22(K+△K), and the high-frequency occupancy rate ρ. This analysis data is then input into a logical judgment program.

[0065] Please see Figure 2 As shown, the logical judgment procedure is specifically as follows:

[0066] Extract the drilling mileage depth ΔK from the first drilling characteristic parameter E21(K+△K) and the second drilling characteristic parameter E22(K+△K), and proceed to the first and second numerical judgments.

[0067] First numerical judgment: Determine if the high-frequency occupancy rate ρ is less than the threshold ρMin. If yes, proceed to the third numerical judgment; if no, proceed to the fourth numerical judgment.

[0068] Second numerical judgment: Determine whether the second drilling characteristic parameter E22(K+△K) is greater than the threshold E22Ma. If yes, output a rockburst warning signal, and then proceed to the fourth numerical judgment; if no, directly proceed to the fourth numerical judgment.

[0069] The third numerical judgment: Determine whether the first drilling characteristic parameter E21(K+△K) is greater than the threshold E21Max. If yes, output a discontinuous geological warning signal, and then proceed to the fourth numerical judgment; if no, directly proceed to the fourth numerical judgment.

[0070] Fourth value judgment: Determine whether the mileage depth ΔK is less than the threshold KMax. If yes, proceed to the fifth value judgment; otherwise, end the entire logical judgment program.

[0071] Fifth value judgment: Determine whether the following conditions are met: the first section characteristic parameter E11(k) is greater than the threshold E11Max or the second section characteristic parameter E12(k) is greater than the threshold E12Max. If yes, output a stop check signal and then end the entire logic judgment program; if no, output a first-level alarm signal and then end the entire logic judgment program.

[0072] After the entire logic judgment program ends, all output signals are sent to the statistics and notification module.

[0073] The statistics and notification module outputs signals generated by the advanced forecasting module via a display screen, including discontinuous geological warning signals, rockburst warning signals, work stoppage inspection signals, or level one alarm signals. It also displays the corresponding alarm information and counts the number of times each signal is generated within a preset statistical period.

[0074] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0075] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims means any combination and all possible combinations of one or more of the associated listed items, and includes such combinations;

[0076] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A geological prediction system based on information fusion from multiple detection methods, comprising a comprehensive analysis subsystem and an advanced prediction module, characterized in that: The comprehensive analysis subsystem includes a face analysis module, a ground-penetrating radar analysis module, and an advanced drilling analysis module. The face analysis module acquires lithological characteristic data of the face at each preset time interval and calculates the first and second cross-sectional characteristic parameters of the face at mileage station K. The ground-penetrating radar analysis module acquires the transient electromagnetic intensity ΔB of the electromagnetic wave detected at a mileage depth ΔK along the tunneling direction at the face; an electromagnetic intensity transient variable-mileage depth map is established with the electromagnetic intensity transient variable ΔB as the vertical axis and the mileage depth ΔK as the horizontal axis; the functional expression of the electromagnetic intensity transient variable-mileage depth map, ΔB=h(ΔK), is obtained; the electromagnetic intensity transient variable-mileage depth map is transformed to the frequency domain through Fourier transform to obtain the electromagnetic wave frequency spectrum; feature extraction is performed on the electromagnetic wave frequency spectrum. Calculate the high-frequency occupancy rate ρ; The advanced drilling analysis module acquires drilling information obtained by the horizontal drilling rig at the working face of mileage station K every preset time t, and obtains the first and second drilling characteristic parameters through numerical calculations; it also acquires analysis data sent by the comprehensive analysis subsystem and inputs it into the logical judgment program to perform logical judgments. The logical judgment program is specifically as follows: Extract the drilling mileage depth ΔK from the first drilling characteristic parameter E21(K+ΔK) and the second drilling characteristic parameter E22(K+ΔK), and proceed to the first and second numerical judgments; First numerical judgment: Determine whether the high-frequency occupancy rate ρ is less than the threshold ρMin; if yes, proceed to the third numerical judgment; if no, proceed to the fourth numerical judgment. Second numerical judgment: Determine whether the second drilling characteristic parameter E22(K+ΔK) is greater than the threshold E22Ma; If yes, output a rockburst warning signal, then proceed to the fourth value judgment; if no, directly proceed to the fourth value judgment. The third numerical judgment: Determine whether the first drilling characteristic parameter E21(K+△K) is greater than the threshold E21Max; if yes, output a discontinuous geological warning signal, and then proceed to the fourth numerical judgment; if no, directly proceed to the fourth numerical judgment. Fourth value judgment: Determine whether the mileage depth ΔK is less than the threshold KMax; if yes, proceed to the fifth value judgment; if no, end the entire logic judgment program. Fifth value judgment: Determine whether the condition is met that the first section feature parameter E11(k) is greater than the threshold E11Max or the second section feature parameter E12(k) is greater than the threshold E12Max; if yes, output a stop check signal and then end the entire logic judgment program; if no, output a first-level alarm signal and then end the entire logic judgment program.

2. The advanced geological prediction system based on information fusion of multiple detection methods according to claim 1, characterized in that, It also includes a statistics and notification module; The statistics and notification module outputs the signals generated by the advanced forecasting module through the display screen, including discontinuous geological early warning signals, rock burst early warning signals, work stoppage inspection signals, and level one alarm signals; it also outputs the alarm information corresponding to the signals through the display screen and counts the number of times each signal is generated within a preset statistical period.

3. The advanced geological prediction system based on information fusion of multiple detection methods according to claim 1, characterized in that, The drilling information obtained from horizontal geological drilling includes: The drilling power P, drilling speed V, and drilling depth ΔK of the horizontal drilling rig; Lithological parameters from borehole cores, including natural density. Dry density saturation density Moisture content Harmony sound wave test longitudinal wave velocity ; Mechanical experimental parameters of borehole cores, including the axial compressive strength of the borehole cores. Cohesion Elastic modulus Poisson's ratio and softening coefficient .

4. The advanced geological prediction system based on information fusion of multiple detection methods according to claim 3, characterized in that, The specific process of obtaining the first and second drilling characteristic parameters through numerical calculation is as follows: With drilling power P as the vertical axis and time t as the horizontal axis, establish a drilling power-time graph and obtain its function expression P=f1(t); with drilling speed V as the vertical axis and time t as the horizontal axis, establish a drilling speed-time graph and obtain its function expression V=f2(t). Through formula Calculate the first drilling characteristic parameter E21(K+△K) and the second drilling characteristic parameter E22(K+△K); where T is the preset detection time period. A set of preset weighting factors, = , , , , , , , , , ; where μ is the first moment of the function g(t).

5. The advanced geological prediction system based on information fusion of multiple detection methods according to claim 2, characterized in that, The lithological characteristics of the tunnel face include: The mileage marker K of the working face; the rock type of the working face, including slate, limestone, phyllite, sandstone, schist, andesite, shale, marble, granite and basalt, and matched with the preset lithology parameter C according to the lithology type; Dip angle of rock strata at the working face Rock layer thickness Joint group number Maximum joint gap and water output ; Stress-strain characteristics of the surrounding rock at the tunnel face, including the convergence values ​​of the surrounding rock sidewalls. Convergence rate of surrounding rock sidewall Convergence acceleration of surrounding rock sidewalls , vault settlement , rate of arch settlement acceleration of arch subsidence Maximum principal stress value of surrounding rock , direction of maximum principal stress in surrounding rock and the dip angle of the maximum principal stress of the surrounding rock .

6. The advanced geological prediction system based on information fusion of multiple detection methods according to claim 5, characterized in that, The specific process for obtaining the first and second cross-sectional characteristic parameters of the working face at mileage station K through calculation is as follows: Through formula Calculate the first cross-sectional characteristic parameter E11(k) and the second cross-sectional characteristic parameter E12(k) of the working face at mileage station K; where , and All of these are preset weighting factors.

7. The advanced geological prediction system based on information fusion of multiple detection methods according to claim 2, characterized in that, The specific process of transforming the electromagnetic intensity transient variable-mileage depth map to the frequency domain and calculating the high-frequency occupancy rate using Fourier transform is as follows: The Fourier transform formula is: Where i is the imaginary unit and i×i=-1, v is the frequency obtained after decomposition, and S(v) is the electromagnetic wave frequency spectrum function obtained after Fourier transform; feature extraction is performed on the electromagnetic wave frequency spectrum using the formula Calculate the high-frequency occupancy rate ρ, where v1 is the preset high and low frequency cutoff frequencies; send the high-frequency occupancy rate ρ to the information fusion module.

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