A method for magnetic needle induction positioning of cave paths

Abstract

The present invention relates to the field of construction and underground engineering technology, and in particular to a method for magnetic needle induction positioning of a cave path. The method comprises the following steps: acquiring geomagnetic characteristic data; preprocessing the geomagnetic characteristic data to generate geomagnetic cave preprocessing data; acquiring rock formation parameter data; calculating magnetic field influence factors on the rock formation parameter data to generate rock formation magnetic field influence factors; and generating a cave path sequence from the geomagnetic cave preprocessing data based on the rock formation magnetic field influence factors to generate a magnetic field intensity integrated path. Therefore, the present invention solves the problems of low accuracy and significant environmental interference in traditional cave path detection by comprehensively utilizing geomagnetic characteristic data, rock formation parameter correction, and three-dimensional path model construction, thereby improving the accuracy and reliability of cave path positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of construction and underground engineering, and in particular to a method for magnetic needle induction positioning of a cave path. Background Art

[0002] Geotechnical investigations often use a combination of drilling and geophysical exploration to identify the unique engineering geology and hydrogeology of a construction site. Geophysical exploration is a commonly used indirect method, and its results require interpretation. The accuracy of identifying underground geological anomalies such as caves is low, leading to misjudgments. While drilling is direct and reliable, it suffers from a limited understanding of the specifics. The layout of the exploration line requires spacing between boreholes, generally no less than 10-15 meters. Connectivity between caves and the direction of groundwater flow within them cannot be determined. Understanding the distribution of caves and the direction of groundwater flow during the exploration phase is crucial for underground engineering construction and emergency response to groundwater surges. There are few existing methods for detecting the connectivity and paths of caves. The main methods used are contrast detection, tracer method, and water injection method. The underground cave contrast detection method injects soluble metal compounds into salt contrast holes and measures the resistivity difference between underground caves and surrounding limestone to detect caves in limestone areas. First, it is necessary to assume that the upstream direction of groundwater is known, which is difficult to do in advance. The method of injecting tracers into caves causes the concentration of tracers dissolved in abundant groundwater to become very low, making accurate identification difficult. Summary of the Invention

[0003] Based on this, it is necessary to provide a cave path magnetic needle induction positioning method to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for magnetic needle induction positioning of a cave path is provided, the method comprising the following steps:

[0005] Step S1: Acquire geomagnetic characteristic data; perform data preprocessing on the geomagnetic characteristic data to generate geomagnetic cave preprocessing data;

[0006] Step S2: Acquire rock formation parameter data; calculate the magnetic field influence factor of the rock formation parameter data to generate the rock formation magnetic field influence factor; generate a cave path sequence for the geomagnetic cave preprocessing data based on the rock formation magnetic field influence factor to generate a magnetic field intensity integration path;

[0007] Step S3: Generate spatial coordinate path points using the magnetic field intensity integration path to generate three-dimensional path point data of the cave; construct a three-dimensional trajectory path model based on the three-dimensional path point data of the cave to generate a three-dimensional path model of the cave;

[0008] Step S4: perform magnetic needle path accuracy assessment on the karst cave three-dimensional path model to generate magnetic needle path accuracy assessment data; generate a feedback report based on the magnetic needle path accuracy assessment data to generate a karst cave path magnetic needle induction positioning report, thereby completing the karst cave path magnetic needle induction positioning operation.

[0009] The present invention has the beneficial effect of removing noise and interference by acquiring and preprocessing geomagnetic characteristic data, generating high-quality geomagnetic cave preprocessed data that provides reliable input for subsequent path inference. This stage of data cleaning and preprocessing ensures the accuracy and representativeness of the magnetic field data and reduces data deviations caused by environmental factors. Secondly, rock formation parameter data is acquired and rock formation magnetic field influencing factors are calculated. By combining geological parameters to modify the magnetic field intensity, the influence of different rock formations on the geomagnetic field is accurately simulated. This step not only improves the accuracy of the magnetic field model but also makes the geomagnetic data more consistent with the actual geological environment, providing data support for the precise identification of cave paths. Based on this, the integrated path generated by the rock formation magnetic field influencing factors further lays the foundation for the generation of cave path sequences. During the path generation process, the integrated path of magnetic field intensity is used to generate spatial coordinate points. By establishing three-dimensional path point data, three-dimensional spatial distribution information of the cave path can be provided. The three-dimensional cave path point data generated in this step not only ensures the spatial accuracy of the path but also provides a solid data foundation for the subsequent construction of the three-dimensional trajectory model, thereby improving the positioning accuracy of the cave path. Finally, by evaluating the magnetic needle accuracy of the cave path model and combining it with the feedback report generated by the evaluation data, it is helpful to quantify and correct the path accuracy, thereby ensuring the reliability and effectiveness of the positioning results. In summary, the present invention effectively improves the accuracy of cave path positioning through multi-level data processing and optimization methods, and can provide real-time feedback on path changes and correction results, thereby significantly improving the efficiency and accuracy of cave detection, and solving the problem that traditional cave detection methods are greatly affected by the environment and difficult to ensure accuracy. Therefore, the present invention solves the problems of low accuracy and greater environmental interference in traditional cave path detection by comprehensively utilizing geomagnetic characteristic data, rock layer parameter correction and three-dimensional path model construction, thereby improving the accuracy and reliability of cave path positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the steps of a method for magnetic needle induction positioning of a cave path;

[0011] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0012] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0013] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0014] Figure 5 The bionic drift bottle floats in the cave along with the direction of water flow;

[0015] Figure 6 It is a simulated drift bottle;

[0016] Figure 7 It is a bionic drift bottle structure;

[0017] Figure 8 To place a magnetic needle between adjacent cave boreholes on the ground;

[0018] Figure 9 To guide the tail cover;

[0019] Figure 10 This is the sample of guide fin A;

[0020] Figure 11 This is the principle diagram of the guide fin folding;

[0021] Figure 12 Open the schematic for the guide fins;

[0022] Figure 13 This is the principle diagram for calculating the strength of the ground magnetic field using the bionic drift bottle magnetic needle;

[0023] In the figure: 1-cave; 2-cave drilling hole; 3-drifting bottle; 301-hook; 302-guide tail; 303-hook; 304-guide tail outer cover; 305-guide fin; 306-guide cap; 4-magnetic needle.

[0024] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0027] It should be understood that although the terms "first," "second," and the like are used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 4 A method for magnetic needle induction positioning of a cave path, comprising the following steps:

[0029] Step S1: Acquire geomagnetic characteristic data; perform data preprocessing on the geomagnetic characteristic data to generate geomagnetic cave preprocessing data;

[0030] Step S2: Acquire rock formation parameter data; calculate the magnetic field influence factor of the rock formation parameter data to generate the rock formation magnetic field influence factor; generate a cave path sequence for the geomagnetic cave preprocessing data based on the rock formation magnetic field influence factor to generate a magnetic field intensity integration path;

[0031] Step S3: Generate spatial coordinate path points using the magnetic field intensity integration path to generate three-dimensional path point data of the cave; construct a three-dimensional trajectory path model based on the three-dimensional path point data of the cave to generate a three-dimensional path model of the cave;

[0032] Step S4: perform magnetic needle path accuracy assessment on the karst cave three-dimensional path model to generate magnetic needle path accuracy assessment data; generate a feedback report based on the magnetic needle path accuracy assessment data to generate a karst cave path magnetic needle induction positioning report, thereby completing the karst cave path magnetic needle induction positioning operation.

[0033] The present invention has the beneficial effect of removing noise and interference by acquiring and preprocessing geomagnetic characteristic data, generating high-quality geomagnetic cave preprocessed data that provides reliable input for subsequent path inference. This stage of data cleaning and preprocessing ensures the accuracy and representativeness of the magnetic field data and reduces data deviations caused by environmental factors. Secondly, rock formation parameter data is acquired and rock formation magnetic field influencing factors are calculated. By combining geological parameters to modify the magnetic field intensity, the influence of different rock formations on the geomagnetic field is accurately simulated. This step not only improves the accuracy of the magnetic field model but also makes the geomagnetic data more consistent with the actual geological environment, providing data support for the precise identification of cave paths. Based on this, the integrated path generated by the rock formation magnetic field influencing factors further lays the foundation for the generation of cave path sequences. During the path generation process, the integrated path of magnetic field intensity is used to generate spatial coordinate points. By establishing three-dimensional path point data, three-dimensional spatial distribution information of the cave path can be provided. The three-dimensional cave path point data generated in this step not only ensures the spatial accuracy of the path but also provides a solid data foundation for the subsequent construction of the three-dimensional trajectory model, thereby improving the positioning accuracy of the cave path. Finally, by evaluating the magnetic needle accuracy of the cave path model and combining it with the feedback report generated by the evaluation data, it is helpful to quantify and correct the path accuracy, thereby ensuring the reliability and effectiveness of the positioning results. In summary, the present invention effectively improves the accuracy of cave path positioning through multi-level data processing and optimization methods, and can provide real-time feedback on path changes and correction results, thereby significantly improving the efficiency and accuracy of cave detection, and solving the problem that traditional cave detection methods are greatly affected by the environment and difficult to ensure accuracy. Therefore, the present invention solves the problems of low accuracy and greater environmental interference in traditional cave path detection by comprehensively utilizing geomagnetic characteristic data, rock layer parameter correction and three-dimensional path model construction, thereby improving the accuracy and reliability of cave path positioning.

[0034] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for magnetically inductively locating a karst cave path according to the present invention. In this example, the method for magnetically inductively locating a karst cave path includes the following steps:

[0035] Step S1: Acquire geomagnetic characteristic data; perform data preprocessing on the geomagnetic characteristic data to generate geomagnetic cave preprocessing data;

[0036] In an embodiment of the present invention, after obtaining geomagnetic characteristic data, the data first needs to be preprocessed to generate geomagnetic cave preprocessed data. Specific technical means include performing noise removal, outlier detection, normalization, and data smoothing on the geomagnetic characteristic data. In practical applications, geomagnetic characteristic data contains a large amount of noise and interference, which are caused by environmental noise, electromagnetic interference, equipment errors, etc. Therefore, noise removal is the first key operation. A filtering algorithm (such as Kalman filtering or median filtering) is used to denoise the geomagnetic data to remove high-frequency noise and retain valid signals. Next, outlier detection is required. Statistical methods such as box plots or Z-score analysis are used to identify and remove abnormal data points that do not conform to the normal distribution to ensure the reliability and representativeness of the remaining data. In addition, there are deviations in geomagnetic data due to fluctuations in equipment performance or changes in measurement conditions. For this reason, data normalization is widely used. By standardizing the maximum and minimum values ​​of geomagnetic data, the deviations caused by different sensors and measurement environments can be eliminated, so that the data is in a unified scale range, which is convenient for subsequent analysis and comparison. Finally, to improve data smoothness and reduce the impact of noise, the processed data is smoothed using methods such as smooth interpolation or spline curve fitting, thereby generating stable and continuous geomagnetic cave preprocessing data. This processed data provides more accurate input for subsequent cave path estimation, magnetic field intensity calculation, and path optimization, ensuring high accuracy and reliability in subsequent calculations and model building.

[0037] Step S2: Acquire rock formation parameter data; calculate the magnetic field influence factor of the rock formation parameter data to generate the rock formation magnetic field influence factor; generate a cave path sequence for the geomagnetic cave preprocessing data based on the rock formation magnetic field influence factor to generate a magnetic field intensity integration path;

[0038] In embodiments of the present invention, rock formation parameter data is acquired. This data includes rock physical properties such as density, magnetic susceptibility, porosity, and permeability, which directly influence variations in the geomagnetic field. This rock formation parameter data provides essential information for the subsequent calculation of magnetic field influencing factors. Specific technical approaches include calculating magnetic field influencing factors based on rock formation parameter data to quantify the rock formation's impact on the geomagnetic field. The calculation of magnetic field influencing factors utilizes physical models and mathematical formulas, such as the Gaussian model of the geomagnetic field or Maxwell's equations for magnetic susceptibility. Through analytical or numerical simulation, the local perturbation effect of the rock formation on the geomagnetic field is inferred based on the rock formation's physical properties (such as magnetic susceptibility and porosity). This process involves detailed modeling of the magnetic field distribution, thickness, and uniformity of each rock formation layer, and calculation of the variations in magnetic field intensity at different spatial points for each rock formation. These variations constitute key data for the magnetic field influencing factors. By calculating the rock formation magnetic field influencing factors, the extent of each rock formation's impact on the geomagnetic field can be determined, providing accurate magnetic field perturbation information for subsequent geomagnetic data analysis. Next, the pre-processed geomagnetic cave data is used to generate a cave path sequence based on the rock formation magnetic field influencing factors, generating an integrated magnetic field intensity path. Specifically, this process combines the rock formation magnetic field influencing factors with the pre-processed geomagnetic data, analyzes the intensity distribution and changing trends of the geomagnetic field, and uses algorithms (such as the shortest path algorithm or path search algorithm) to generate an integrated magnetic field intensity path for the cave path. The integrated path not only takes into account the magnetic field disturbance of the rock formation, but also integrates the characteristics of the geomagnetic data. Through path simulation and optimization algorithms, a more realistic cave path sequence is generated.

[0039] Step S3: Generate spatial coordinate path points using the magnetic field intensity integration path to generate three-dimensional path point data of the cave; construct a three-dimensional trajectory path model based on the three-dimensional path point data of the cave to generate a three-dimensional path model of the cave;

[0040] In the embodiment of the present invention, the magnetic field intensity integration path is utilized to generate spatial coordinate path points. The core technical means of this step is to convert the magnetic field intensity integration path into spatial coordinate points through spatial analysis and computational geometry methods. Specifically, the magnetic field intensity integration path provides the magnetic field intensity variation data of the cave path, but the data itself does not contain clear spatial coordinates. Therefore, it is necessary to utilize the mapping relationship between magnetic field data and geographic coordinate system to associate the magnetic field intensity with the coordinate points in three-dimensional space. This process involves the magnetic field intensity inversion technology based on the spatial model. By setting the transmission model of the earth's magnetic field (such as by wave equation or inversion algorithm), the actual position in three-dimensional space is calculated according to the magnetic field intensity distribution of the cave path. Interpolation algorithm (such as Lagrange interpolation, spline interpolation, etc.) is adopted to refine the spatial path to generate the three-dimensional coordinate data of the path points. These spatial coordinate points reflect the geometric distribution of the cave path at different positions and can accurately characterize the spatial structure of the cave. Next, based on the generated cave three-dimensional path point data, it is necessary to construct a three-dimensional trajectory path model to generate a cave three-dimensional path model. The technical means of this process include curve fitting, surface construction, and 3D modeling technologies. Common methods include Bezier curve fitting, spline curve fitting, B-spline surface modeling, etc. These methods can generate a continuous 3D trajectory path model based on discrete 3D path point data.

[0041] Step S4: perform magnetic needle path accuracy assessment on the karst cave three-dimensional path model to generate magnetic needle path accuracy assessment data; generate a feedback report based on the magnetic needle path accuracy assessment data to generate a karst cave path magnetic needle induction positioning report, thereby completing the karst cave path magnetic needle induction positioning operation.

[0042] In the embodiment of the present invention, data from magnetic needle observation points along the cave path must first be collected. This data includes information such as the magnetic field strength, direction, and measurement error at each observation point during the measurement process. Next, error analysis methods, such as statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), or correlation coefficient (R), are used to evaluate the accuracy difference between the three-dimensional cave path model and the actual observation data. These evaluation indicators can quantitatively describe the accuracy of the magnetic needle path positioning, providing a basis for subsequent path correction and optimization. Furthermore, methods such as regression analysis and error propagation models can be used to conduct a more detailed analysis of the path errors, further identifying the sources of model errors and implementing targeted improvements. After completing the magnetic needle path accuracy assessment, a feedback report is generated based on the assessment results. Specifically, the feedback report needs to include detailed assessment results, such as path error analysis, path accuracy assessment results, and error source analysis, and visually display the specific details of the magnetic needle path accuracy through charts and graphs. To improve the report's readability, the feedback report also needs to provide an in-depth interpretation of the assessment data, including suggestions for path optimization, error source investigation, and recommendations for improving path accuracy through model correction or adjusting magnetic field intensity. Report generation is completed through an automated report generation system, which generates the final report file through data integration and formatting.

[0043] Preferably, step S1 includes the following steps:

[0044] Step S11: obtaining geomagnetic characteristic data;

[0045] Step S12: removing abnormal noise from the geomagnetic characteristic data to generate geomagnetic abnormal noise-removed data;

[0046] Step S13: performing data standardization processing on the geomagnetic anomaly noise removal data to generate geomagnetic cave preprocessing data, wherein the geomagnetic cave preprocessing data includes geomagnetic horizontal component data and geomagnetic vertical component data.

[0047] In an embodiment of the present invention, real-time measurement of geomagnetic field data is performed using a geomagnetic sensor or magnetometer. Geomagnetic characteristic data includes information on the strength, direction, and components of the geomagnetic field, expressed as horizontal components (e.g., east-west) and vertical components (e.g., up-down). These data provide preliminary information about the structure of underground rock formations and caves, and are the basis for locating cave paths. Abnormal noise is removed from the acquired geomagnetic characteristic data. Specific technical means for this process include using filtering algorithms (e.g., Gaussian filtering, mean filtering, bandpass filtering, etc.) to remove noise generated during the measurement process due to equipment errors, environmental factors (e.g., weather changes, electromagnetic interference, etc.), or human factors. In addition, statistical methods such as the Z-score method and the Mad method (median absolute deviation) can be used to detect outliers in the data and remove data points that do not meet the standards. Through this process, noise that affects the accuracy of the magnetic field data can be effectively removed, ensuring data quality, thereby providing more reliable input data for subsequent analysis. The data after the geomagnetic anomaly noise is removed is subjected to data normalization processing to generate geomagnetic cave preprocessing data. The specific technical means of this processing step adopts normalization or standardization methods to convert data of different dimensions into a unified scale. For example, by subtracting the mean of the data set from each data point and then dividing it by the standard deviation of the data set to perform standardization, ensure that the data are in the same range (for example, between [-1, 1] or [0, 1]). The standardized data can eliminate the interference caused by the difference in the magnitude of each variable, which helps to improve the accuracy of subsequent analysis and modeling. In particular, in the geomagnetic cave preprocessing data, the geomagnetic horizontal and vertical component data involved are more balanced through this processing method, thereby avoiding the bias of magnetic field data in different directions on the analysis results.

[0048] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes

[0049] Step S21: obtaining rock formation parameter data;

[0050] Step S22: Calculate the magnetic field influence factor of the rock formation parameter data to generate the rock formation magnetic field influence factor;

[0051] Step S23: performing magnetic field intensity correction calculation on the geomagnetic cave preprocessing data based on the rock formation magnetic field influencing factor to generate cave magnetic field intensity correction data; generating a cave path sequence based on the cave magnetic field intensity correction data to generate a magnetic field intensity integrated path;

[0052] In an embodiment of the present invention, geological exploration techniques are used to obtain the physical parameters of relevant rock formations, such as rock density, magnetic susceptibility, and permeability. The rock formation parameter data provides the laws governing the influence of rock formations on the geomagnetic field, providing a basis for magnetic field intensity correction and path generation. Accurately acquiring rock formation parameters can provide a scientific basis for subsequent calculations of rock formation magnetic field influence factors and magnetic field intensity correction. A magnetic field influence factor calculation method is employed for the rock formation parameter data. Specific technical approaches include utilizing the physical properties of the rock formation (such as magnetic susceptibility and density) and its position and shape in the geomagnetic field, using a magnetic field transfer model to calculate the interference and influence of the rock formation on the geomagnetic field. Common methods include using a magnetic field distribution model (such as a simplified Gaussian distribution model or finite element method simulation) to derive the magnetic field influence factor, or employing geomagnetic field inversion technology to perform inversion analysis based on known rock formation distribution characteristics. The calculated rock formation magnetic field influence factor is expressed in numerical form and can be used for subsequent magnetic field intensity correction calculations. The rock formation magnetic field influence factor is used to perform magnetic field intensity correction calculations on geomagnetic cave preprocessed data. The specific technical approach involves correcting the geomagnetic field intensity based on the cave path data using calculated rock formation magnetic field influencing factors, using a weighted average method or interpolation method. This process combines the magnetic field effects of the rock formation with the original geomagnetic data, and uses a correction algorithm to correct for any deviations in the original data. Common correction methods include numerical simulations based on physical models (such as the Lorentz force model and magnetic field intensity correction models) to ensure the accuracy and consistency of the geomagnetic data within the cave path region. Based on the cave magnetic field intensity correction data, a cave path sequence is further generated. The data used in this process includes the integrated magnetic field intensity path, the geomagnetic field data of the cave path, and geographic coordinate information. A path fitting algorithm, such as the least squares method or Bezier curve fitting, is combined with the corrected magnetic field intensity data to generate a cave path sequence. This process optimizes the path smoothness and accuracy to ensure the generated path is highly operational and accurate, ultimately forming an integrated magnetic field intensity path.

[0053] Preferably, the rock formation magnetic field influence factor is calculated by the following formula:

[0054]

[0055] Among them, h i is the thickness of the i-th rock or soil layer, δ i is the skin depth of the i-th rock or soil layer;

[0056] The skin depth of the i-th rock or soil layer is calculated using the following formula:

[0057]

[0058] Where ω is the angular frequency. For static magnetic fields such as magnetic needles, ω = 0, and μ0 is the vacuum permeability (μ0 = 4π×10 ―7 Tesla meter / ampere), the coordinates of the ground magnetic needle in the spherical coordinate system are ρ i represents the resistivity of the i-th rock and soil layer, then the influence coefficient of each rock and soil layer on the magnetic field intensity can be simplified to the following formula:

[0059]

[0060] In the embodiment of the present invention, the magnetic field influence factor S of the rock and soil layer is defined as i , its expression is e^(-h i / δ i ), where h i represents the thickness of the i-th layer of rock and soil, δ i The skin depth of the layer is the depth at which the amplitude of an electromagnetic wave decays to 1 / e (about 37%) of the surface intensity when the electromagnetic wave propagates in a conductive medium. i The calculation formula is √(2ρ i / ωμ ri μ0), ρ i represents the resistivity of the i-th layer of rock and soil, ω is the angular frequency, μ i is the relative permeability of the layer, and μ0 is the vacuum permeability. In particular, for the static magnetic field, the angular frequency ω is set to 0, which simplifies the skin depth formula to Considering the characteristics of the static magnetic field, the angular frequency ω is taken as 0, which makes the skin depth tend to infinity, and then causes the exponential part of the initial influence factor formula to approach 0. Therefore, in order to avoid mathematical singularity, this method directly uses the magnetic field influence factor S of the rock and soil layer by deriving the formula. i Simplified to This actually derives an attenuation model that is only related to the thickness, resistivity and permeability of the rock and soil layer, thus bypassing the concept of skin depth. This simplified formula more clearly reflects the attenuation effect of different rock and soil layer parameters on the geomagnetic field strength. Among them, the higher the resistivity and the lower the permeability, the weaker the attenuation effect of the rock and soil layer on the magnetic field; conversely, the lower the resistivity and the higher the permeability, the stronger the attenuation effect. In practical applications, this model can be used to calculate the influence factor of the rock and soil layer on the ground magnetic field strength through known data such as the thickness, resistivity and permeability of the rock and soil layer, thereby realizing quantitative analysis and prediction of the geomagnetic field distribution. In addition, the model is based on the earth coordinate system and uses r, θ, Three parameters are used to identify the position of the ground magnetic needle, thereby achieving an accurate description of the influence of the magnetic field in three-dimensional space.

[0061] Preferably, the cave magnetic field strength correction data is calculated by the following formula:

[0062]

[0063] Among them, α is the correction coefficient, S i is the influence coefficient of each rock and soil layer on the magnetic field intensity. Due to the averaging process, the correction coefficient α is empirically taken as 0.9-1.1; N is the magnetic field intensity not affected by the rock and soil layer, as shown in the following formula:

[0064]

[0065] Where m is the magnetic dipole moment of the magnetic needle placed in the drift bottle, N is the number of drift bottles put into the cave, |r| is the distance from the ground observation point to the midpoint of the magnetic axis of the drift bottle in the cave, |θ| is the angle between the line connecting the observation point and the midpoint of the magnetic axis and z, and are radial unit vector and angular unit vector in the spherical coordinate system respectively, μ0 is the vacuum permeability (μ0=4π×10 ―7 Tesla meter / ampere). Taking the midpoint of the magnetic axis of the bottle in the cave as the coordinate zero point, the coordinates in the spherical coordinate system are Then |r|cosθ=∑ n h i , h i is the thickness of the i-th rock or soil layer, the vector and Can be expressed as:

[0066]

[0067] in, It is expressed as the vector of the cave path and the three-dimensional direction of the magnetic field.

[0068] In the embodiment of the present invention, the cave magnetic field intensity B f It is determined by a combination of factors, including the correction coefficient α, the influence coefficient S of the rock and soil layer on the magnetic field intensity i , and the superposition effect of the cave itself on the magnetic field B N The value range of α is based on experimental experience and is between 0.9 and 1.1. It is used to correct errors caused by simplified models or assumptions to ensure the credibility of the results. N , which is further decomposed into the vector component form by the magnetic dipole moment of the drift bottle in the cave (m), the number of drift bottles (N), and the geometric relationship between the observation point and the magnetic axis of the cave (including distance (r), angle In the formula, the magnetic field formula generated by the magnetic dipole is used, combined with the unit vector of the spherical coordinate system The decomposition expression of clarifies the directional component of the magnetic field. Here, (|r|) is the distance from the observation point to the center of the cave's magnetic axis, and through the rock layer thickness h i The model also considers the physical parameters of magnetic constant and magnetic dipole moment (m), which are combined with the spherical coordinate position of the observation point. A quantitative prediction of the magnetic field strength was formed. In addition, the distribution number (N) and magnetization intensity (m) of the drifting bottles were controlled experimentally to further optimize the applicability of the model.

[0069] Preferably, step S23 includes the following steps:

[0070] Step S231: obtaining geomagnetic needle data; performing magnetic field intensity correction calculation on the geomagnetic cave preprocessed data based on the rock formation magnetic field influence factor to generate cave magnetic field intensity correction data;

[0071] Step S232: Calculating the magnetic field torque sensitivity based on the geomagnetic needle data to generate geomagnetic torque sensitivity data;

[0072] Step S233: Continuously measure the magnetic field gradient change of the cave magnetic field intensity correction data, and perform branch point gradient distribution processing to generate cave magnetic field gradient change data; generate a cave path sequence based on the cave magnetic field gradient change data and the geomagnetic moment sensitivity data to generate a magnetic field intensity integrated path.

[0073] In the embodiments of the present invention, geomagnetic needle data is acquired. This process relies on a high-precision geomagnetic needle sensor to perform real-time measurements of magnetic field direction and intensity. By measuring the direction and intensity of the geomagnetic needle, the spatial distribution of the geomagnetic field can be obtained in real time. Based on this, combined with the previously calculated results of the rock formation magnetic field influencing factors, the pre-processed geomagnetic cave data is subjected to magnetic field intensity correction calculations. This correction is primarily performed using physical models (such as magnetic field models or numerical simulations based on geomagnetic field equations). This process aims to eliminate the influence of rock formation magnetic field effects on the measurement results to obtain more accurate geomagnetic intensity data. The generated cave magnetic field intensity correction data provides a corrected geomagnetic field intensity value. The key technical approach to calculating geomagnetic moment sensitivity is to utilize geomagnetic needle data and the characteristics of the geomagnetic field to calculate the needle's torque response based on physical principles. Torque sensitivity refers to the sensitivity of the needle's response intensity to the geomagnetic field torque. It is commonly expressed as M = γ * B, where M is the needle's torque, γ is the needle's sensitivity factor, and B is the geomagnetic field intensity. During the calculation process, the needle's response to geomagnetic field variations is calculated using magnetic field equations, taking into account variations in the geomagnetic field and the needle's geometry. This generates geomagnetic moment sensitivity data. The cave magnetic field intensity correction data is obtained through continuous magnetic field gradient measurement. The specific technical approach involves using gradient measurement techniques to capture subtle differences in magnetic field variations within the cave region. The magnetic field gradient represents the rate of change of the magnetic field in space. Numerical difference methods (such as the finite difference method) or magnetic field inversion algorithms are used to calculate the variation in magnetic field intensity at different locations. To improve measurement accuracy, branch point gradient distribution processing is also required. An algorithm is used to analyze magnetic field variation trends, particularly at key nodes along the cave path. The gradient distribution is optimized to generate accurate cave magnetic field gradient variation data. This data provides critical spatial variation information for the subsequent generation of cave path sequences. Finally, the cave path sequence is generated based on the cave magnetic field gradient variation data and geomagnetic moment sensitivity data. The specific technical approach involves combining magnetic field moment and gradient with a path planning algorithm (such as a shortest path algorithm or an optimization algorithm) to calculate the optimal cave path trajectory.

[0074] Preferably, the geomagnetic moment sensitivity data is calculated by the following formula:

[0075] vector With vector When the direction angle is greater than 3°, the magnetic moment generated by the magnetic field on the ground magnetic needle is greater than the minimum moment κ min , which satisfies the following formula:

[0076]

[0077] B f To correct the magnetic field strength of the cave, we can substitute the above formula:

[0078]

[0079] Wherein, N is the number of drift bottles and m is the magnetic dipole moment of the magnetic needle in the drift bottle, wherein the magnetic dipole moment m of the magnetic needle in the drift bottle is determined by the magnetic needle model.

[0080] In the embodiment of the present invention, the technology provides that the magnetic dipole moment vector and the ground magnetic field vector When the angle is greater than 3 degrees, the torque generated by the magnetic field on the ground magnetic needle needs to be greater than a minimum torque κ min Then, this minimum torque is quantified by the physical formula, where represents the magnitude of the magnetic dipole moment, The physical meaning of this formula is that when the angle deviation exceeds 3 degrees, the torque on the magnetic needle must be greater than a minimum value determined by the size of the magnetic dipole moment, the magnetic field strength, and the sine value of the 3-degree angle. The formula is further expanded to describe the complex distribution of the magnetic field inside the cave, which includes multiple parameters such as the number of drift bottles N, the magnetic dipole moment m of the magnetic needle in the drift bottle, magnetic permeability, distance r, angle, etc. These parameters together determine the magnetic field distribution inside the cave.

[0081] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0082] Step S31: extracting the spatial coordinates of the path points of the magnetic field intensity integration path to generate the spatial coordinate data of the cave path points; performing coordinate smoothing interpolation processing based on the spatial coordinate data of the cave path points to generate the coordinate smoothing interpolation data of the cave;

[0083] Step S32: generating spatial coordinate path points based on the cave coordinate smoothing interpolation data to generate three-dimensional cave path point data;

[0084] Step S33: constructing a three-dimensional trajectory path model based on the three-dimensional path point data of the cave to generate a three-dimensional path model of the cave.

[0085] In an embodiment of the present invention, the spatial coordinates of the path points on the integrated magnetic field intensity path are extracted. This step primarily relies on the spatial position of the data points. High-precision positioning technologies, such as the Global Positioning System (GPS) or Inertial Measurement Unit (IMU), are used in conjunction with the magnetic field characteristics of the cave path to calculate the three-dimensional spatial coordinates of each point on the path. After obtaining these path point spatial coordinates, they need to be subjected to coordinate smoothing interpolation to eliminate noise, reduce irregularities, and reduce errors. This process uses interpolation algorithms, such as cubic interpolation or spline interpolation, to smooth the coordinates between the cave path points, generating smoothed interpolated cave coordinate data that is smooth and closer to the actual path. The application of this technology effectively improves the continuity and visualization of the path, reducing irregular fluctuations caused by equipment errors or environmental factors during the actual measurement process. Based on the smoothed interpolated cave coordinate data, spatial coordinate path point generation is further performed. This technical approach relies on a path reconstruction algorithm based on spatial data, using mathematical methods such as Bezier curves or B-spline curves to connect the smoothly interpolated coordinate points in series to generate three-dimensional path point data for the cave. Bezier curves and B-spline curves can effectively connect a set of discrete data points into smooth curves, and have strong spatial adaptability and high computational efficiency. Therefore, they are widely used in the generation of three-dimensional path points. In this way, the three-dimensional morphology of the cave can be accurately depicted. The key technical means of constructing a three-dimensional trajectory path model based on the three-dimensional path point data of the cave is to use a three-dimensional modeling algorithm, such as a surface reconstruction algorithm or a voxel modeling method, to convert the obtained three-dimensional path point data into an actual three-dimensional trajectory path model. Specifically, by calculating the surface or volume model between the path points, the detailed three-dimensional structure of the cave can be generated. In this process, the spatial relationship between the data points is combined, the least squares method is applied to optimize the fitting accuracy of the path model, and the three-dimensional visualization technology is used to present the modeling results as a realistic three-dimensional path model.

[0086] Preferably, step S33 includes the following steps:

[0087] Step S331: performing three-dimensional curve fitting on the three-dimensional path point data of the cave using a Bezier curve to generate three-dimensional path curve data of the cave;

[0088] Step S332: performing a horizontal spatial structure analysis of the cave based on the three-dimensional path curve data and the geomagnetic horizontal component data to generate cave path-geomagnetic horizontal component data; performing a vertical spatial structure analysis of the cave based on the three-dimensional path curve data and the geomagnetic vertical component data to generate cave path-geomagnetic vertical component data;

[0089] Step S333: construct a three-dimensional trajectory path model based on the cave path-geomagnetic horizontal component data and the cave path-geomagnetic vertical component data to generate a cave three-dimensional path model.

[0090] In an embodiment of the present invention, a Bezier curve is used to fit the three-dimensional path point data of the cave. The Bezier curve is a mathematical tool that describes the shape of a curve using control points. Its advantage is that it can flexibly adjust the curve shape while maintaining smoothness, making it particularly suitable for path modeling in three-dimensional space. In this step, appropriate control points are first selected based on the spatial coordinate data of the cave path points. These control points are then fitted using the Bezier curve algorithm to generate a three-dimensional curve model that conforms to the path characteristics. This model can accurately reflect the three-dimensional shape and direction of the cave and provide reliable data support for subsequent spatial analysis and path modeling. Based on the three-dimensional path curve data and geomagnetic horizontal component data, a cave horizontal spatial structure analysis is performed. The main purpose of this analysis is to study the distribution and direction of the cave path on the horizontal plane, particularly the changes in magnetic field intensity at different locations. By combining the three-dimensional curve of the cave path with the geomagnetic horizontal component data, spatial interpolation methods such as Kriging interpolation or nearest neighbor interpolation are used to analyze the horizontal component of the magnetic field around the cave path to generate cave path-geomagnetic horizontal component data. These data reveal the horizontal magnetic field characteristics of the cave path, helping to analyze path variation trends and potential sources of magnetic field disturbances. Similarly, vertical spatial structure analysis based on the three-dimensional cave path curve data and geomagnetic vertical component data utilizes similar spatial analysis methods to explore the vertical variations in the magnetic field characteristics of the cave path, generating cave path-geomagnetic vertical component data. This data provides insights into the vertical magnetic field variations of the cave path, further enriching the spatial analysis of the cave. A three-dimensional trajectory model is constructed based on the cave path-geomagnetic horizontal component data and the cave path-geomagnetic vertical component data. This step combines the horizontal and vertical magnetic field component information with the cave path data to construct a more complete three-dimensional trajectory model. By comprehensively considering the geomagnetic horizontal and vertical component data, a complete three-dimensional model of the cave path is constructed using three-dimensional modeling algorithms such as least squares fitting, surface reconstruction, or voxel modeling.

[0091] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0092] Step S41: acquiring cave detection radar data; simulating the results of the cave three-dimensional path model to generate path data of the magnetic needle observation points;

[0093] Step S42: Calculating the deviation between the path data of the magnetic needle observation point and the cave detection radar data, and evaluating them according to a preset path magnetic needle deviation index threshold to generate magnetic needle path accuracy evaluation data;

[0094] Step S43: Generate a feedback report based on the magnetic needle path accuracy evaluation data, and generate a cave path magnetic needle induction positioning report, thereby completing the cave path magnetic needle induction positioning operation.

[0095] In an embodiment of the present invention, simulation calculations are performed using cave detection radar data and a constructed three-dimensional path model. The cave detection radar provides actual measured magnetic field data. When combined with the three-dimensional cave path model, path data for magnetic needle observation points along the path can be generated. Specifically, based on the spatial structure of the cave path and the magnetic field variation model, the radar-measured data is matched with the path model to simulate the actual exploration process and generate a set of path data for magnetic needle observation points. This process utilizes the principle of magnetic torque, combining the three-dimensional coordinates of the path with the actual magnetic field distribution to calculate the corresponding observation point data. The observation point positions and expected magnetic field strengths are then inferred based on the path model. By comparing the path magnetic needle observation point data with the actual cave detection radar data, the deviation between the two is calculated. This calculation can employ various error analysis methods, such as root mean square error (RMSE) or maximum deviation. These calculation results reflect the error between the path model and the actual measurement results, and are compared with a preset deviation index threshold to evaluate the accuracy of the current magnetic needle path. The evaluation results can quantify the accuracy of the path and convert them into quantitative accuracy data, further helping to optimize the path model. By processing the initial accuracy assessment data, the system can automatically generate an accuracy feedback report based on the assessment results. This report generation technology relies on data visualization and automated reporting tools. Through charts and data analysis, it generates a report that includes accuracy deviations, evaluation indicators, and improvement suggestions.

[0096] The present invention has the beneficial effect of removing noise and interference by acquiring and preprocessing geomagnetic characteristic data, generating high-quality geomagnetic cave preprocessed data that provides reliable input for subsequent path inference. This stage of data cleaning and preprocessing ensures the accuracy and representativeness of the magnetic field data and reduces data deviations caused by environmental factors. Secondly, rock formation parameter data is acquired and rock formation magnetic field influencing factors are calculated. By combining geological parameters to modify the magnetic field intensity, the influence of different rock formations on the geomagnetic field is accurately simulated. This step not only improves the accuracy of the magnetic field model but also makes the geomagnetic data more consistent with the actual geological environment, providing data support for the precise identification of cave paths. Based on this, the integrated path generated by the rock formation magnetic field influencing factors further lays the foundation for the generation of cave path sequences. During the path generation process, the integrated path of magnetic field intensity is used to generate spatial coordinate points. By establishing three-dimensional path point data, three-dimensional spatial distribution information of the cave path can be provided. The three-dimensional cave path point data generated in this step not only ensures the spatial accuracy of the path but also provides a solid data foundation for the subsequent construction of the three-dimensional trajectory model, thereby improving the positioning accuracy of the cave path. Finally, by evaluating the magnetic needle accuracy of the cave path model and combining it with the feedback report generated by the evaluation data, it is helpful to quantify and correct the path accuracy, thereby ensuring the reliability and effectiveness of the positioning results. In summary, the present invention effectively improves the accuracy of cave path positioning through multi-level data processing and optimization methods, and can provide real-time feedback on path changes and correction results, thereby significantly improving the efficiency and accuracy of cave detection, and solving the problem that traditional cave detection methods are greatly affected by the environment and difficult to ensure accuracy. Therefore, the present invention solves the problems of low accuracy and greater environmental interference in traditional cave path detection by comprehensively utilizing geomagnetic characteristic data, rock layer parameter correction and three-dimensional path model construction, thereby improving the accuracy and reliability of cave path positioning.

[0097] like Figures 5 to 13 As shown, the present invention also provides a cave path magnetic needle induction positioning method, comprising the following steps:

[0098] The karst boreholes revealed during drilling at the construction site are enlarged using a drill bit with a diameter of not less than 150mm. At least 10 bionic drift bottles equipped with magnetic needles are connected with ropes and placed into the water in the cave through the enlarged borehole. The bionic drift bottles placed in the cave one after another float along the direction of the water flow. At least one row of magnetic needles are placed between the karst boreholes where the bionic drift bottles are placed and the surrounding adjacent karst boreholes. The ground magnetic needles and the magnetic needles in the underground caves are inductively coupled. The position of the ground magnetic needles is moved to find the position of the maximum deflection angle of the magnetic needle pointer. The positions of the maximum deflection angles of the magnetic needles are connected with smooth curves to obtain the water flow direction and path of the underground cave.

[0099] The specific implementation includes the following steps:

[0100] like Figure 5 As shown, no less than two karst cave boreholes 2 are selected on the planar drilling distribution map of the karst cave 1 revealed by drilling at the construction site, and a drill bit with a diameter of no less than 150 mm is used for hole expansion.

[0101] like Figure 5 As shown, at least 10 bionic drift bottles 3 equipped with magnetic needles 4 are connected by ropes and placed into the water in the cave through an enlarged drill hole. The distance between the bionic drift bottles 3 is no more than 2.0m.

[0102] Specifically, the magnetic field induction distance of the magnetic needle 4 in the rock and soil stratum is greater than the burial depth of the cave 1 .

[0103] like Figure 5 As shown, the bionic drift bottles 3 put into the cave 1 one after another float along the direction of the water flow. Due to the connection of the thin rope, the bionic drift bottles 3 are arranged in a row and float in sequence.

[0104] like Figure 5 、 Figure 6 As shown, at least one row of magnetic needles 4 is placed between the ground and the surrounding adjacent cave boreholes 2. The distance between the ground magnetic needles 4 is no more than 2.0 m. The ground magnetic needles and the magnetic needles in the underground caves are mutually inductive. The positions of the ground magnetic needles 4 are moved to find the position of the maximum deflection angle of the pointer of the magnetic needle 4. The positions of the maximum deflection angles of the magnetic needles are connected with smooth curves to obtain the water flow direction and path of the underground cave 1.

[0105] Furthermore, if Figure 13 As shown, a method for magnetic needle induction positioning of a cave path, and a method for calculating the magnetic field strength induced by a magnetic needle in the cave to a ground magnetic needle include the following steps:

[0106] Stratum modeling: According to the geological survey report, the average thickness of each rock and soil layer in the stratum is determined, and the stratum is modeled. From the top of cave 1 upwards, each stratum is marked as i, i = 1...n, and the thickness of each stratum is marked as h i , i=1…n.

[0107] Determine the resistivity and relative permeability of each rock and soil layer: Determine the resistivity and relative permeability of each rock and soil layer based on the properties of each rock and soil layer provided in the geological survey report. Alternatively, rock and soil samples can be collected to test the resistivity and relative permeability in the laboratory. Alternatively, field testing methods such as magnetotellurics can be used to obtain the resistivity and relative permeability of each rock and soil layer. The resistivity and relative permeability of each rock and soil layer are marked as ρ. i and μ ri .

[0108] Calculation of ground magnetic field strength: Without considering the influence of various rock and soil layers on the magnetic field strength, assume that the magnetic dipole moment of the magnetic needle 4 placed in the drift bottle 3 is m, the number of drift bottles 3 in the cave 1 is N, and the magnetic field strength B formed by all the drift bottles 3 on the ground N Calculate as follows:

[0109]

[0110] Where m is the magnetic dipole moment of the magnetic needle placed in the drift bottle, N is the number of drift bottles put into the cave, |r| is the distance from the ground observation point to the midpoint of the magnetic axis of the drift bottle in the cave, |θ| is the angle between the line connecting the observation point and the midpoint of the magnetic axis and z, and are the radial unit vector and angular unit vector in the spherical coordinate system, μ0 is the vacuum permeability, μ0=4π×10 ―7 Tesla meter / ampere. Figure 13 As shown, the midpoint of the magnetic axis of the drift bottle in the cave is taken as the coordinate zero point, and the coordinates in the spherical coordinate system are r, θ, Then |r|cosθ=∑ n h i ,vector and Can be expressed as:

[0111] in, It is expressed as the vector of the cave path and the three-dimensional direction of the magnetic field.

[0112] The influence coefficient of each rock and soil layer on the magnetic field intensity is calculated using the following formula:

[0113]

[0114] Where h i is the thickness of the i-th rock or soil layer, δ i is the skin depth of the i-th rock layer or soil layer. The skin depth of the i-th rock layer or soil layer is calculated using the following formula:

[0115]

[0116] Where ω is the angular frequency. For static magnetic fields such as magnetic needles, ω = 0, and μ0 is the vacuum permeability (μ0 = 4π×10 ―7 Tesla meter / ampere), the coordinates of the ground magnetic needle in the spherical coordinate system are ρi represents the resistivity of the i-th rock and soil layer, and the influence coefficient of each rock and soil layer on the magnetic field intensity is simplified to the following formula:

[0117]

[0118] Considering the influence of each rock and soil layer on the magnetic field strength, the ground magnetic field strength is calculated as follows:

[0119]

[0120] Where α is the correction coefficient, S i is the influence coefficient of the i-th layer on the magnetic field intensity. Since the model is an average process, the modeling effect is not significant, and the correction coefficient α is empirically set to 0.9-1.1.

[0121] It is known that the magnetic dipole moment of the ground magnetic needle 4 is m0. The magnetic torque generated by the ground magnetic needle 4 under the action of the magnetic field is calculated as follows:

[0122]

[0123] Where, and are the unit vector of the magnetic axis of the ground magnetic needle and the unit vector of the magnetic field generated by the drift bottle at the position of the ground magnetic needle.

[0124] In order to ensure that the ground magnetic needle 4 points accurately, it is necessary to ensure that the vector With vector When the direction angle is greater than 3°, the magnetic moment generated by the magnetic field on the ground magnetic needle is greater than the minimum moment κ min , which satisfies the following formula:

[0125]

[0126] Where, κ min is the minimum torque for the magnetic needle to rotate freely. min Determined by the steering sensitivity of the ground magnetic needle 4, B f Calibration data for the magnetic field intensity of the cave.

[0127] The calculation formula is:

[0128]

[0129] Substituting into the above formula we get:

[0130]

[0131] As can be seen, there are two variables in this inequality: the number N of drift bottles 3 and the magnetic dipole moment m of the magnetic needle 4 inside the bottle. The magnetic dipole moment m of the magnetic needle inside the bottle is determined by the needle model. If the needle model is selected, the number of drift bottles 3 required in the cave can be calculated. If the number of drift bottles in the cave is predetermined, the formula can be used to determine the magnetic dipole moment of the required needle, thereby determining the model of the needle 4.

[0132] Furthermore, if Figure 7 、 Figure 8 As shown, a method for magnetic needle induction positioning of a cave path is shown. A guide fin 305 is attached to each side of the bottle body of the bionic drift bottle 3, and a guide tail 302 is installed at the bottle tail of the bionic drift bottle.

[0133] Furthermore, a method for magnetic needle induction positioning of a cave path is characterized in that the guide fins 305 of the bionic drift bottle are retracted when drifting along the direction of the water flow. Figure 11 , open when drifting against the direction of water flow, see Figure 12 , hindering the bionic drift bottle 3 from drifting in the opposite direction, forcing the bionic drift bottle 3 to turn around, and ensuring that the head of the bionic drift bottle is always in front when drifting downstream.

[0134] Furthermore, if Figure 11 、 Figure 12 As shown, a method for magnetic needle induction positioning of a cave path is shown. The guide tail 302 of the bionic drift bottle is retracted when drifting along the direction of the water flow, and is opened to the outer cover edge 304 of the guide tail when drifting against the direction of the water flow. Figure 9 , preventing the bionic drift bottle from drifting in the opposite direction, the guide tail 302 and the bilateral guide fins 305 jointly control the moving direction of the bionic drift bottle 3, ensuring that the head of the bionic drift bottle is always in front when drifting downstream.

[0135] Furthermore, a method for magnetic needle induction positioning of a cave path is provided, wherein a guide cap is installed at the front end of the guide fin of the bionic drift bottle. Figure 10 The guide cap 306 allows the guide fin 305 to open into an acute angle. The guide fin 305 is made of fan-shaped plastic sheets strung together by nylon lines, and the fan-shaped plastic sheets on the guide fins are connected by no less than 3 nylon lines.

[0136] Furthermore, a method for magnetic needle induction positioning of a cave path is provided, wherein the directions of the magnetic needles 4 in the bionic drift bottle are all consistent, and the compass is directed toward the bottle mouth. Figure 5 .

[0137] Furthermore, a method for magnetic needle induction positioning of a cave path is provided, wherein the bionic drift bottle 3 has a wide bottle mouth. Figure 7 The inner diameter of the bottle mouth is slightly smaller than the inner diameter of the bottle body. The bottle mouth is tightly closed with a bottle cap, and water does not enter the bottle. After a magnetic needle is installed in the drift bottle, it can float on the water.

[0138] Furthermore, the size of the magnetic needles 4 in the bionic drift bottles is selected based on the calculation that the magnetic field strength generated on the ground by the magnetic needles 4 in a row of bionic drift bottles is greater than the strength required to deflect the ground magnetic needles.

[0139] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for magnetic needle induction positioning of a cave path, characterized in that: The following steps are involved: Step S1: Acquire geomagnetic characteristic data; perform data preprocessing on the geomagnetic characteristic data to generate geomagnetic cave preprocessing data; Step S2: obtaining rock formation parameter data; Calculate the magnetic field influence factor of the rock formation parameter data to generate the rock formation magnetic field influence factor; generate the cave path sequence of the geomagnetic cave preprocessing data based on the rock formation magnetic field influence factor to generate the magnetic field intensity integration path; Step S3: Generate spatial coordinate path points using the magnetic field intensity integration path to generate three-dimensional path point data of the cave; Construct a three-dimensional trajectory path model based on the three-dimensional path point data of the cave to generate a three-dimensional path model of the cave; Step S4: perform magnetic needle path accuracy assessment on the karst cave three-dimensional path model to generate magnetic needle path accuracy assessment data; generate a feedback report based on the magnetic needle path accuracy assessment data to generate a karst cave path magnetic needle induction positioning report, thereby completing the karst cave path magnetic needle induction positioning operation.

2. The method for magnetic needle induction positioning of a cave path according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining geomagnetic characteristic data; Step S12: removing abnormal noise from the geomagnetic characteristic data to generate geomagnetic abnormal noise-removed data; Step S13: performing data standardization processing on the geomagnetic anomaly noise removal data to generate geomagnetic cave preprocessing data, wherein the geomagnetic cave preprocessing data includes geomagnetic horizontal component data and geomagnetic vertical component data.

3. The cave path magnetic needle induction positioning method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining rock formation parameter data; Step S22: Calculate the magnetic field influence factor of the rock formation parameter data to generate the rock formation magnetic field influence factor; Step S23: performing magnetic field intensity correction calculation on the geomagnetic cave preprocessing data based on the rock formation magnetic field influencing factor to generate cave magnetic field intensity correction data; generating a cave path sequence based on the cave magnetic field intensity correction data to generate a magnetic field intensity integrated path.

4. The method for magnetic needle induction positioning of a cave path according to claim 3, characterized in that: The rock formation magnetic field influence factor is calculated by the following formula: in, is the thickness of the i-th rock or soil layer, is the skin depth of the i-th rock or soil layer; The skin depth of the i-th rock layer or soil layer is calculated by the following formula: in, is the angular frequency, for the static magnetic field of the magnetic needle, take , is the vacuum permeability, Tesla meter / ampere, the coordinates of the ground magnetic needle in the spherical coordinate system are , , , ρᵢ represents the resistivity of the i-th rock and soil layer, and the influence coefficient of each rock and soil layer on the magnetic field intensity can be obtained by simplifying the following formula: 。 5. The method for magnetic needle induction positioning of a cave path according to claim 3, characterized in that: The cave magnetic field intensity correction data is calculated using the following formula: in, is the correction factor, is the influence coefficient of each rock and soil layer on the magnetic field intensity. Due to the averaging process, the correction coefficient According to experience, the value is 0.9-1.1; is the magnetic field intensity not affected by the rock and soil layer, which is calculated by the following formula: Among them, m is the magnetic dipole moment of the magnetic needle placed in the drift bottle, N is the number of drift bottles put into the cave, is the distance from the ground observation point to the midpoint of the magnetic axis of the drift bottle in the cave, The line connecting the observation point to the midpoint of the magnetic axis and Angle, and are the radial unit vector and angular unit vector in the spherical coordinate system, respectively. is the vacuum permeability, Tesla meter / ampere; Taking the midpoint of the magnetic axis of the bottle in the cave as the coordinate zero point, the coordinates in the spherical coordinate system are , , , then , is the thickness of the i-th rock or soil layer, the vector and Can be expressed as: in, 、 、 It is expressed as the vector of the cave path and the three-dimensional direction of the magnetic field.

6. The method for magnetic needle induction positioning of a cave path according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: obtaining geomagnetic needle data; performing magnetic field intensity correction calculation on the geomagnetic cave preprocessed data based on the rock formation magnetic field influence factor to generate cave magnetic field intensity correction data; Step S232: Calculating the magnetic field torque sensitivity based on the geomagnetic needle data to generate geomagnetic torque sensitivity data; Step S233: Continuously measure the magnetic field gradient change of the cave magnetic field intensity correction data, and perform branch point gradient distribution processing to generate cave magnetic field gradient change data; generate a cave path sequence based on the cave magnetic field gradient change data and the geomagnetic moment sensitivity data to generate a magnetic field intensity integrated path.

7. The method for magnetic needle induction positioning of a cave path according to claim 6, characterized in that: The geomagnetic moment sensitivity data is calculated using the following formula: vector With vector When the direction angle is greater than 3°, the magnetic moment generated by the magnetic field on the ground magnetic needle is greater than the minimum moment , which satisfies the following formula: To correct the magnetic field strength of the cave, we can substitute the above formula: in, is the number of drift bottles and is the magnetic dipole moment of the magnetic needle in the drift bottle, where the magnetic dipole moment of the magnetic needle in the drift bottle Determined by the magnetic needle model.

8. The method for magnetic needle induction positioning of a cave path according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting the spatial coordinates of the path points of the magnetic field intensity integration path to generate the spatial coordinate data of the cave path points; performing coordinate smoothing interpolation processing based on the spatial coordinate data of the cave path points to generate the coordinate smoothing interpolation data of the cave; Step S32: generating spatial coordinate path points based on the cave coordinate smoothing interpolation data to generate three-dimensional cave path point data; Step S33: constructing a three-dimensional trajectory path model based on the three-dimensional path point data of the cave to generate a three-dimensional path model of the cave.

9. The method for magnetic needle induction positioning of a cave path according to claim 8, characterized in that: Step S33 includes the following steps: Step S331: performing three-dimensional curve fitting on the three-dimensional path point data of the cave using a Bezier curve to generate three-dimensional path curve data of the cave; Step S332: performing a horizontal spatial structure analysis of the cave based on the three-dimensional path curve data and the geomagnetic horizontal component data to generate cave path-geomagnetic horizontal component data; performing a vertical spatial structure analysis of the cave based on the three-dimensional path curve data and the geomagnetic vertical component data to generate cave path-geomagnetic vertical component data; Step S333: construct a three-dimensional trajectory path model based on the cave path-geomagnetic horizontal component data and the cave path-geomagnetic vertical component data to generate a cave three-dimensional path model.

10. The method for magnetic needle induction positioning of a cave path according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: acquiring cave detection radar data; simulating the results of the cave three-dimensional path model to generate path data of the magnetic needle observation points; Step S42: Calculating the deviation between the path data of the magnetic needle observation point and the cave detection radar data, and evaluating them according to a preset path magnetic needle deviation index threshold to generate magnetic needle path accuracy evaluation data; Step S43: Generate a feedback report based on the magnetic needle path accuracy evaluation data, and generate a cave path magnetic needle induction positioning report, thereby completing the cave path magnetic needle induction positioning operation.

Images