Karst cave path magnetic needle induction positioning method

Abstract

The invention relates to the technical field of building and underground engineering, in particular to a karst cave path magnetic needle induction positioning method. The method comprises the following steps: acquiring geomagnetic characteristic data; performing data preprocessing on the geomagnetic characteristic data to generate geomagnetic karst cave preprocessing data; acquiring rock stratum parameter data; performing magnetic field influence factor calculation on the rock stratum parameter data to generate a rock stratum magnetic field influence factor; carrying out karst cave path sequence generation on the geomagnetic karst cave preprocessing data based on the rock stratum magnetic field influence factors, and generating a magnetic field intensity integration path; therefore, by comprehensively using geomagnetic characteristic data, rock stratum parameter correction and three-dimensional path model construction, the problems that traditional karst cave path detection is low in precision, large in environmental interference and the like are solved, and the precision and reliability of karst cave path positioning are improved.

Description

Technical Field

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

[0002] In geotechnical exploration, a method combining drilling and geophysical prospecting is commonly used to investigate the special engineering geological and hydrogeological conditions of the construction site. Geophysical prospecting is a commonly used indirect method, and the results of geophysical prospecting need to be interpreted. The identification accuracy of underground geological anomalies such as karst caves is not high, resulting in misjudgment. The drilling method is directly reliable, but there is the problem of limited perspective. When arranging exploration lines, there are requirements for the drilling spacing, generally not less than 10m - 15m. Whether there is connectivity between karst caves and the flow direction of groundwater in the karst caves cannot be determined. It is crucial to clarify the distribution path of karst caves and the flow direction of groundwater during the exploration stage for underground engineering construction and emergency rescue of groundwater outburst. There are few existing methods for detecting the connectivity and path of karst caves, mainly including contrast detection method, tracer method, water injection effect method, etc. For the underground karst cave contrast detection method, soluble metal compounds are put into the salt injection contrast holes, and the resistivity electrical differences between the underground karst caves and the surrounding limestone are measured to detect the karst caves in the limestone area. First, it is necessary to assume the known upstream direction of groundwater, but it is difficult to achieve this in advance; for the method of putting tracers in karst caves, the concentration of the tracer becomes very low when dissolved in abundant groundwater, and it is also difficult to accurately distinguish. Summary of the Invention

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

[0004] To achieve the above object, a method for inductive positioning of a karst cave path by a magnetic needle includes the following steps:

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

[0006] Step S2: Obtain rock formation parameter data; calculate the magnetic field influence factor for the rock formation parameter data to generate a rock formation magnetic field influence factor; generate an integrated magnetic field strength path by generating a karst cave path sequence based on the rock formation magnetic field influence factor for the preprocessed geomagnetic karst cave data;

[0007] Step S3: Use the integrated magnetic field strength path to generate spatial coordinate path points to generate three-dimensional karst cave path point data; construct a three-dimensional trajectory path model based on the three-dimensional karst cave path point data to generate a three-dimensional karst cave path model;

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

[0009] The beneficial effects of the present invention are as follows. By acquiring geomagnetic characteristic data and performing preprocessing, noise and interference can be removed, and high-quality preprocessed geomagnetic karst cave data can be generated, providing reliable input for subsequent path speculation. The data cleaning and preprocessing at this stage ensure the accuracy and representativeness of the magnetic field data, reducing data deviation caused by environmental factors. Secondly, by acquiring rock formation parameter data and calculating the influence factor of the rock formation magnetic field, the influence of different rock formations on the geomagnetic field is accurately simulated through the correction of the magnetic field intensity by combining geological parameters. This link not only improves the accuracy of the magnetic field model but also makes the geomagnetic data more conform to the actual geological environment, providing data support for the accurate identification of karst cave paths. Based on this, the integrated path generated by the influence factor of the rock formation magnetic field further lays a foundation for the generation of the karst cave path sequence. During the path generation process, the magnetic field intensity integrated path is used to generate spatial coordinate points. By establishing three-dimensional path point data, three-dimensional spatial distribution information can be provided for the karst cave path. The three-dimensional karst 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 karst cave path. Finally, through the magnetic needle accuracy evaluation of the karst cave path model and the feedback report generated by combining the evaluation data, it is helpful to quantitatively analyze and correct the path accuracy, ensuring the reliability and effectiveness of the positioning result. In summary, through multi-level data processing and optimization methods, the present invention effectively improves the accuracy of karst cave path positioning, can provide real-time feedback on path changes and correction results, thereby significantly improving the detection efficiency and accuracy of karst caves, and solving the problems that traditional karst cave detection methods are greatly affected by the environment and it is difficult to guarantee accuracy. Therefore, by comprehensively using geomagnetic characteristic data, rock formation parameter correction, and three-dimensional path model construction, the present invention solves the problems of low accuracy and large environmental interference in traditional karst cave path detection, and improves the accuracy and reliability of karst cave path positioning. Brief Description of the Drawings

[0010] Figure 1 It is a schematic diagram of the step flow of a magnetic needle induction positioning method for a karst cave path;

[0011] Figure 2 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in

[0012] Figure 3 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in

[0013] Figure 4 is Figure 1 a detailed implementation step flow diagram of step S4 in

[0014] Figure 5 the bionic drift bottle floating in the karst cave along the water flow direction;

[0015] Figure 6 is a simulation drift bottle;

[0016] Figure 7 is the bionic drift bottle structure;

[0017] Figure 8 is placing magnetic needles between adjacent karst cave drill holes on the ground;

[0018] Figure 9 is the guide tail outer cover;

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

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

[0021] Figure 12 is the principle diagram of guide fin opening;

[0022] Figure 13 is the principle diagram of the bionic drift bottle magnetic needle calculating the ground magnetic field intensity;

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

[0024] The realization, functional features and advantages of the purpose of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific implementation manners

[0025] The technical method of the present invention patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

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

[0028] To achieve the above object, please refer to Figures 1 to 4 , a method for magnet needle induction positioning of a karst cave path, the method comprising the following steps:

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

[0030] Step S2: Obtain rock formation parameter data; calculate the magnetic field influence factor for the rock formation parameter data to generate a rock formation magnetic field influence factor; generate an integrated magnetic field strength path by generating a karst cave path sequence based on the rock formation magnetic field influence factor for the preprocessed geomagnetic karst cave data;

[0031] Step S3: Use the integrated magnetic field strength path to generate spatial coordinate path points to generate three-dimensional karst cave path point data; construct a three-dimensional trajectory path model based on the three-dimensional karst cave path point data to generate a three-dimensional karst cave path model;

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

[0033] The beneficial effects of the present invention are as follows. By obtaining geomagnetic characteristic data and performing preprocessing, noise and interference can be removed, and high-quality geomagnetic cave preprocessing data can be generated, providing reliable input for subsequent path speculation. The data cleaning and preprocessing at this stage ensure the accuracy and representativeness of the magnetic field data, reducing data deviation caused by environmental factors. Secondly, by obtaining rock formation parameter data and calculating the influence factor of the rock formation magnetic field, through the correction of the magnetic field intensity by combining geological parameters, the influence of different rock formations on the geomagnetic field is accurately simulated. This link not only improves the accuracy of the magnetic field model but also makes the geomagnetic data more conform to the actual geological environment, providing data support for the accurate identification of cave paths. Based on this, the integrated path generated by the influence factor of the rock formation magnetic field further lays a foundation for the generation of the cave path sequence. During the path generation process, the magnetic field intensity integrated path is used to generate spatial coordinate points. By establishing three-dimensional path point data, three-dimensional spatial distribution information can be provided for the cave path. 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, through the evaluation of the magnetic needle accuracy of the cave path model and the feedback report generated by combining the evaluation data, it helps to quantitatively analyze and correct the path accuracy, ensuring the reliability and effectiveness of the positioning result. In summary, through multi-level data processing and optimization methods, the present invention effectively improves the accuracy of cave path positioning, can real-time feedback path changes and correction results, thus significantly improving the cave detection efficiency and accuracy, and solving the problem that traditional cave detection methods are greatly affected by the environment and it is difficult to guarantee the accuracy. Therefore, by comprehensively using geomagnetic characteristic data, rock formation parameter correction, and three-dimensional path model construction, the present invention solves the problems of low accuracy and large environmental interference in traditional cave path detection, and improves the accuracy and reliability of cave path positioning.

[0034] In an embodiment of the present invention, with reference to Figure 1 shown in the following figure is a schematic flow chart of the steps of a method for magnetic needle induction positioning of a cave path according to the present invention. In this example, the method for magnetic needle induction positioning of a cave path includes the following steps:

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

[0036] In the embodiments of the present invention, after obtaining the geomagnetic characteristic data, it is first necessary to preprocess the data to generate preprocessed geomagnetic karst cave data. The specific technical means include operations such as noise removal, outlier detection, normalization processing, and data smoothing on the geomagnetic characteristic data. In practical applications, the geomagnetic characteristic data contains a large amount of noise and interference, and these interference factors come from environmental noise, electromagnetic interference, equipment errors, etc. Therefore, noise removal is the first key operation. The geomagnetic data is denoised using a filtering algorithm (such as Kalman filtering or median filtering) to remove high-frequency noise and retain the effective signal. Next, outlier detection is required. Through statistical methods such as box plots or Z-score analysis, etc., identify and remove those 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 the geomagnetic data due to fluctuations in equipment performance or changes in measurement conditions. For this reason, data normalization processing is widely used. By standardizing the maximum and minimum values of the geomagnetic data, the deviations brought by different sensors and measurement environments can be eliminated, making the data within a unified scale range, which is convenient for subsequent analysis and comparison. Finally, in order to improve the smoothness of the data and reduce the influence of noise, smoothing interpolation or spline curve fitting and other methods are used to smooth the processed data, thereby generating stable and continuous preprocessed geomagnetic karst cave data. These processed data can provide more accurate inputs for subsequent karst cave path speculation, magnetic field intensity calculation, and path optimization, ensuring high precision and reliability in the calculations and model construction in the subsequent steps.

[0037] Step S2: Obtain the rock formation parameter data; calculate the magnetic field influence factor for the rock formation parameter data to generate the rock formation magnetic field influence factor; generate the cave path sequence based on the rock formation magnetic field influence factor for the preprocessed geomagnetic karst cave data to generate the integrated magnetic field intensity path;

[0038] In the embodiments of the present invention, by obtaining formation parameter data, which includes the physical properties of rocks, such as density, magnetic susceptibility, porosity, permeability, etc., these characteristics directly affect the variation of the geomagnetic field. The formation parameter data provides the necessary information for the subsequent calculation of the magnetic field influence factor. The specific technical means includes calculating the magnetic field influence factor based on the formation parameter data to quantify the influence of the formation on the geomagnetic field. The calculation of the magnetic field influence factor uses physical models and mathematical formulas, such as the Gaussian model of the geomagnetic field or the Maxwell equation of magnetic susceptibility. Through analytical or numerical simulation methods, according to the physical properties of the formation (such as magnetic susceptibility, porosity, etc.), the local disturbance influence of the formation on the geomagnetic field is deduced. This process involves detailed modeling of the magnetic field distribution of each formation layer, the thickness and uniformity of the formation, etc., and calculates the change of the magnetic field intensity at different spatial points of each formation layer. These changes constitute the key data of the magnetic field influence factor. By calculating the magnetic field influence factor of the formation, the influence degree of each formation on the geomagnetic field can be obtained, providing accurate magnetic field disturbance information for the subsequent geomagnetic data analysis. Next, based on the magnetic field influence factor of the formation, a cave path sequence is generated for the preprocessed geomagnetic cave data to generate an integrated path of magnetic field intensity. Specifically, this process combines the magnetic field influence factor of the formation with the preprocessed geomagnetic data, analyzes the intensity distribution and its change trend of the geomagnetic field, and uses algorithms (such as the shortest path algorithm or the path search algorithm) to generate an integrated path of magnetic field intensity for the cave path. The integrated path not only considers the magnetic field disturbance of the formation, but also comprehensively considers the characteristics of the geomagnetic data, and generates a more realistic cave path sequence through path simulation and optimization algorithms.

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

[0040] In the embodiments of the present invention, the magnetic field intensity integration path is used 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 change data of the karst cave path, but this data itself does not contain clear spatial coordinates. Therefore, it is necessary to use the mapping relationship between the magnetic field data and the geographical coordinate system to associate the magnetic field intensity with the coordinate points in the three-dimensional space. This process involves the magnetic field intensity inversion technology based on the spatial model. By setting the transmission model of the geomagnetic field (such as through the wave equation or inversion algorithm), the actual position of the karst cave path in the three-dimensional space is deduced according to the magnetic field intensity distribution of the karst cave path. The interpolation algorithm (such as Lagrange interpolation, spline interpolation, etc.) is used to refine the spatial path and generate the three-dimensional coordinate data of the path points. These spatial coordinate points reflect the geometric distribution of the karst cave path at different positions and can accurately characterize the spatial structure of the karst cave. Next, based on the generated three-dimensional path point data of the karst cave, it is necessary to construct a three-dimensional trajectory path model to generate a three-dimensional path model of the karst cave. The technical means of this process include curve fitting, surface construction, and three-dimensional modeling technology. Common methods include Bezier curve fitting, spline curve fitting, B-spline surface modeling, etc. These methods can generate a continuous three-dimensional trajectory path model based on the discrete three-dimensional path point data.

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

[0042] In the embodiments of the present invention, first, it is necessary to collect the data of the magnetic needle observation points on the karst cave path. These data include information such as the magnetic field strength, direction, and measurement error of each observation point during the measurement process. Then, error analysis methods are used, such as statistical indicators like root mean square error (RMSE), mean absolute error (MAE), or correlation coefficient (R), to evaluate the accuracy difference between the three-dimensional path model of the karst cave and the actual observation data. Through these evaluation indicators, the accuracy of the magnetic needle path positioning can be quantitatively described, providing a basis for subsequent path correction and optimization. In addition, methods such as regression analysis and error propagation models can be used to conduct a more detailed analysis of the path error, further identifying the sources of model errors and making targeted improvements. After completing the accuracy evaluation of the magnetic needle path, a feedback report is generated based on the evaluation results. Specifically, the feedback report needs to contain detailed data of the evaluation results, such as path error analysis, path accuracy evaluation results, error source analysis, etc., and visually display the specific situation of the magnetic needle path accuracy in the form of charts. To improve the readability of the report, the feedback report also needs to conduct an in-depth interpretation of the evaluation data, including suggestions for path optimization, investigation of error sources, and suggestions on how to improve the path accuracy by model correction or adjusting the magnetic field strength. The generation of the report 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: Obtain geomagnetic characteristic data;

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

[0046] Step S13: Perform data standardization processing on the geomagnetic abnormal noise-removed data to generate geomagnetic karst cave preprocessing data, where the geomagnetic karst cave preprocessing data includes geomagnetic horizontal component data and geomagnetic vertical component data.

[0047] In the embodiments of the present invention, real-time measurement of geomagnetic field data is carried out through a geomagnetic sensor or a magnetometer. The geomagnetic characteristic data includes the intensity, direction, and component information of the geomagnetic field, which is represented by the horizontal component (such as the east-west direction) and the vertical component (such as the up-down direction) of the geomagnetic field. These data provide preliminary information about the underground rock formations and karst cave structures and are the basis for locating the karst cave path. For the obtained geomagnetic characteristic data, abnormal noise is removed. The specific technical means for this process include using filtering algorithms (such as Gaussian filtering, mean filtering, band-pass filtering, etc.) to remove the noise generated during the measurement process due to equipment errors, environmental factors (such as 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 also be used to detect outliers in the data and remove the data points that do not meet the standards. Through this process, the noise affecting the accuracy of the magnetic field data can be effectively removed, ensuring the quality of the data, and thus providing more reliable input data for subsequent analysis. After removing the abnormal geomagnetic noise from the data, data normalization processing is performed to generate preprocessed geomagnetic karst cave data. The specific technical means for this processing step adopt normalization or standardization methods to convert data with different dimensions into a unified scale. For example, standardization is performed by subtracting the mean of the data set from each data point and then dividing by the standard deviation of the data set to ensure that the data is within the same range (for example, between [-1,1] or [0,1]). The standardized data can eliminate the interference caused by the differences in the magnitudes of various variables and help improve the accuracy of subsequent analysis and modeling. Especially in the preprocessed geomagnetic karst cave data, the horizontal and vertical component data of the geomagnetic field involved become more balanced through this processing method, thus avoiding the bias of the magnetic field data in different directions on the analysis results.

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

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

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

[0051] Step S23: Based on the rock formation magnetic field influence factor, perform magnetic field intensity correction calculation on the preprocessed geomagnetic karst cave data to generate corrected karst cave magnetic field intensity data; based on the corrected karst cave magnetic field intensity data, generate a karst cave path sequence to generate an integrated magnetic field intensity path;

[0052] In the embodiments of the present invention, physical parameters of relevant rock formations are obtained through geological exploration techniques, such as the density, magnetic susceptibility, permeability, etc. of rocks. The rock formation parameter data provides the influence law of the rock formation on the geomagnetic field, and provides a basis for the correction of the magnetic field intensity and the generation of the path. By accurately obtaining the rock formation parameters, a scientific basis can be provided for the subsequent calculation of the magnetic field influence factor of the rock formation and the correction of the magnetic field intensity. For the rock formation parameter data, a calculation method of the magnetic field influence factor is adopted. Specific technical means include using the physical properties of the rock formation (such as magnetic susceptibility and density) and factors such as its position and shape in the geomagnetic field, and calculating the interference and influence of the rock formation on the geomagnetic field through a magnetic field transfer model. Common methods include using a magnetic field distribution model (such as a simplified Gaussian distribution model or a finite element method simulation) to deduce the magnetic field influence factor, or adopting a geomagnetic inversion technique to perform inversion analysis based on the known rock formation distribution characteristics. The calculated magnetic field influence factor of the rock formation is represented in numerical form and can be used for subsequent magnetic field intensity correction calculations. The magnetic field intensity correction calculation is performed on the preprocessed geomagnetic cave data through the magnetic field influence factor of the rock formation. The specific technical means is to use the calculated magnetic field influence factor of the rock formation on the basis of the cave path data, and correct the geomagnetic field intensity by using the weighted average method or the interpolation method. This process combines the magnetic field effect of the rock formation with the original geomagnetic data and corrects the deviation in the original data through a correction algorithm. Common correction methods include numerical simulations based on physical models (such as using the Lorentz force model, the magnetic field intensity correction model, etc.) to ensure the accuracy and consistency of the geomagnetic data in the cave path area. Based on the corrected data of the cave magnetic field intensity, the generation of the cave path sequence is further carried out. At this time, the data used includes the magnetic field intensity integration path, the geomagnetic data of the cave path, and the geographical coordinate information. Through a path fitting algorithm, such as the least squares method or the Bezier curve fitting, etc., combined with the data after the magnetic field intensity correction, the path sequence of the cave is generated. This process optimizes the smoothness and accuracy of the path to ensure that the generated path has high operability and accuracy, and finally forms a magnetic field intensity integration path.

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

[0054]

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

[0056] The skin depth of the i-th rock formation or soil layer is calculated by 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 permeability of free space (μ 0 = 4π×10 ―7 Tesla·m / A), and the coordinates of the ground magnetic needle in the spherical coordinate system are ρ i represents the resistivity of the i-th layer of rock and soil, then the influence coefficient of each rock and soil layer on the magnetic field strength is simplified to be calculated by 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 i , and its expression is e^(-h i / δ i ), where h i represents the thickness of the i-th layer of rock and soil, and δ i represents the skin depth of this layer. The skin depth is the depth at which the amplitude of the electromagnetic wave decays to 1 / e (about 37%) of the surface intensity when propagating in a conductive medium. Here, the calculation formula of the skin depth δ i is √(2ρ i / ωμ ri μ 0 ), ρ i represents the resistivity of the i-th layer of rock and soil, ω is the angular frequency, μ i is the relative magnetic permeability of this layer, and μ 0 is the permeability of free space. In particular, for a static magnetic field, the angular frequency ω is set to 0, which results in the simplification of the skin depth formula to Considering the characteristics of the static magnetic field, the angular frequency ω is taken as 0, so that the skin depth tends to infinity, and then the exponential part in the initial influence factor formula approaches 0. Therefore, to avoid mathematical singularities, this method simplifies the magnetic field influence factor S of the rock and soil layer directly through formula derivation i to This actually derives an attenuation model that is only related to the thickness, resistivity, and magnetic 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 magnetic permeability, the weaker the attenuation effect of the rock and soil layer on the magnetic field; on the contrary, the lower the resistivity and the higher the magnetic permeability, the stronger the attenuation effect. In practical applications, this model can calculate the influence factor of the known rock and soil layer on the ground magnetic field strength through data such as the thickness, resistivity, and magnetic permeability of the rock and soil layer, so as to realize the quantitative analysis and prediction of the geomagnetic field distribution. And this model is based on the earth coordinate system and uses r, θ, three parameters to identify the position of the ground magnetic needle, thus realizing the accurate description of the magnetic field influence in three-dimensional space.

[0061] Preferably, the corrected data of the karst cave magnetic field intensity is calculated by the following formula:

[0062]

[0063] where α is the correction coefficient, and 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; where B 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 thrown into the karst cave, |r| is the distance from the ground observation point to the midpoint of the magnetic axis of the drift bottle in the karst 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 the angular direction unit vector in the spherical coordinate system respectively, and μ 0 is the magnetic permeability of vacuum (μ 0 = 4π×10 ―7 tesla·m / A). Taking the midpoint of the magnetic axis of the drift bottle in the karst cave as the coordinate origin, the coordinates in the spherical coordinate system are then |r|cosθ = ∑ n h i where h i is the thickness of the i-th rock layer or soil layer, and the vectors and can be respectively expressed as:

[0066]

[0067] where represents the vector of the karst cave path and the three-dimensional direction of the magnetic field.

[0068] In the embodiment of the present invention, the karst cave magnetic field intensity B f is determined by a combination of multiple factors, including the correction coefficient α, the influence coefficient S i of the rock and soil layer on the magnetic field intensity, and the superposition effect B N of the karst cave itself on the magnetic field. Among them, the value range of α is based on experimental experience, and the range is 0.9 - 1.1, which is used to correct the errors caused by simplified models or assumptions to ensure the credibility of the results. For B N , it is further decomposed in the form of vector components and consists of the magnetic dipole moment (m) of the drift bottle in the karst cave, the number of drift bottles (N), and the geometric relationship between the observation point and the magnetic axis of the karst cave (including the distance (r), the angle The magnetic field strength calculated comprehensively (etc.). In the formula, using the magnetic field formula generated by a magnetic dipole and combining with the decomposition expression of the unit vector in the spherical coordinate system clarifies the directional components of the magnetic field. Here, (|r|) is the distance from the observation point to the center of the magnetic axis of the karst cave, and is obtained by cumulative summation through the rock layer thickness h i The physical parameters of the magnetic constant and the magnetic dipole moment (m) are also considered in the model. These parameters, combined with the spherical coordinate position of the observation point form a quantitative prediction of the magnetic field strength. In addition, the distribution quantity (N) of the drift bottles and the magnetization intensity (m) can be controlled through experiments to further optimize the applicability of the model.

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

[0070] Step S231: Obtain geomagnetic needle data; perform magnetic field strength correction calculation on the preprocessed data of the geomagnetic karst cave based on the magnetic field influence factor of the rock layer to generate corrected data of the karst cave magnetic field strength;

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

[0072] Step S233: Measure the continuous magnetic field gradient change of the corrected data of the karst cave magnetic field strength, and perform branch point gradient distribution processing to generate karst cave magnetic field gradient change data; generate a magnetic field strength integration path according to the karst cave magnetic field gradient change data and the geomagnetic torque sensitivity data.

[0073] In the embodiments of the present invention, by obtaining geomagnetic needle data, this process relies on a high-precision geomagnetic needle sensor to perform real-time measurement of the magnetic field direction and intensity. By measuring the direction and intensity of the geomagnetic needle, the spatial distribution information of the geomagnetic field can be obtained in real time. On this basis, combined with the previous calculation results of the rock formation magnetic field influence factor, magnetic field intensity correction calculation is performed on the preprocessed geomagnetic cave data, mainly through physical models (such as magnetic field models or numerical simulations based on geomagnetic field equations) for correction. This process aims to eliminate the influence of the rock formation magnetic field effect on the measurement results to obtain more accurate geomagnetic intensity data. The generated cave magnetic field intensity correction data provides the corrected geomagnetic field intensity value. For the calculation of the geomagnetic torque sensitivity, the key technical means is to utilize the geomagnetic needle data and the characteristics of the geomagnetic field, and calculate the torque response of the needle according to physical principles. The torque sensitivity refers to the sensitivity of the response intensity of the needle under the action of the geomagnetic field torque, and is commonly expressed by the formula M = γ * B, where M is the torque of the needle, γ is the sensitivity factor of the needle, and B is the geomagnetic field intensity. During the calculation process, considering the changes in the geomagnetic field and the geometric shape of the needle, the reaction of the needle to the changes in the geomagnetic field is calculated through the magnetic field equation, thereby generating geomagnetic torque sensitivity data. The cave magnetic field intensity correction data undergoes continuous magnetic field gradient change measurement. The specific technical means is to adopt gradient measurement technology to obtain the small differences in the magnetic field changes within the cave area. The magnetic field gradient represents the rate of change of the magnetic field in space. The numerical difference method (such as the finite difference method) or the magnetic field inversion algorithm is used to calculate the changes in the magnetic field intensity at different positions. In order to improve the measurement accuracy, branch point gradient distribution processing is also required. By analyzing the change trend of the magnetic field through algorithms, especially at the key nodes of the cave path, gradient distribution optimization is performed to generate accurate cave magnetic field gradient change data. This data can provide key spatial change information for the subsequent generation of the cave path sequence. Finally, the cave path sequence is generated based on the cave magnetic field gradient change data and the geomagnetic torque sensitivity data. The specific technical means is to combine the magnetic field torque and gradient, and adopt a path planning algorithm (such as the shortest path algorithm or optimization algorithm) to calculate the optimal trajectory of the cave path.

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

[0075] vector and vector When the included angle between the directions is greater than 3°, the magnetic torque generated by the ground needle under the action of the magnetic field is greater than the minimum torque κ min , that is, the following formula is satisfied:

[0076]

[0077] B f is the cave magnetic field intensity correction data, substituting it into the above formula gives:

[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, and the magnetic dipole moment m of the magnetic needle in the drift bottle is determined by the model of the magnetic needle.

[0080] In the embodiment of the present invention, this technology stipulates that when the included angle between the magnetic dipole moment vector and the ground magnetic field vector is greater than 3 degrees, the torque generated by the magnetic field acting on the ground magnetic needle needs to be greater than a minimum torque κ min . Then, this minimum torque is quantified through physical formulas, where represents the magnitude of the magnetic dipole moment, and represents the corrected data of the karst cave magnetic field intensity. The physical meaning of this formula is that when the angle deviation exceeds 3 degrees, the torque received by the magnetic needle must be greater than a minimum value determined by the magnitude of the magnetic dipole moment, the magnetic field intensity, and the sine value of the 3-degree angle. Immediately afterwards, this technology further expands the corrected data of the karst cave magnetic field intensity with a formula, which describes the complex distribution of the magnetic field inside the karst cave and includes multiple parameters such as the number N of drift bottles, the magnetic dipole moment m of the magnetic needle in the drift bottle, the magnetic permeability, the distance r, and the angle. These parameters jointly determine the magnetic field distribution inside the karst cave.

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

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

[0083] Step S32: Generate the three-dimensional path points of the karst cave according to the smoothed interpolation data of the karst cave coordinates to generate the three-dimensional path point data of the karst cave;

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

[0085] In the embodiments of the present invention, the spatial coordinates of path points on the magnetic field intensity integration path are extracted. This step mainly relies on the spatial positions of data points. Through high-precision positioning technologies such as the Global Positioning System (GPS) or Inertial Measurement Unit (IMU), combined with the magnetic field characteristics of the karst cave path, the three-dimensional spatial coordinates of each point on the path are calculated. After obtaining the spatial coordinates of these path points, coordinate smoothing interpolation processing needs to be performed on them to eliminate noise, reduce irregularities and errors. This process uses interpolation algorithms such as cubic interpolation or spline interpolation to smooth the coordinates between the karst cave path points, generating smooth and more realistic karst cave coordinate smoothing interpolation data. The application of this technology effectively improves the continuity and visualization effect of the path, and reduces the irregular fluctuations caused by equipment errors or environmental factors during the actual measurement process. Based on the karst cave coordinate smoothing interpolation data, spatial coordinate path points are further generated. This technical means relies on path reconstruction algorithms based on spatial data, and uses mathematical methods such as Bezier Curve or B-Spline Curve to connect the smoothed interpolation coordinate points in series to generate three-dimensional path point data of the karst cave. Bezier curves and B-spline curves can effectively connect a set of discrete data points into a smooth curve, and have strong spatial adaptability and high calculation efficiency, so they are widely used in the generation of three-dimensional path points. Through this method, the three-dimensional shape of the karst cave can be accurately depicted. Based on the three-dimensional path point data of the karst cave, a three-dimensional trajectory path model is constructed. The key technical means is to use three-dimensional modeling algorithms such as surface reconstruction algorithms or voxel modeling methods 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 path points, a detailed three-dimensional structure of the karst cave can be generated. During this process, the spatial relationship between data points is combined, the least squares method is used to optimize the fitting accuracy of the path model, and three-dimensional visualization technology is used to present the modeling result as a realistic three-dimensional path model.

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

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

[0088] Step S332: Based on the three-dimensional path curve data of the karst cave and the horizontal component data of the geomagnetic field, perform an analysis of the horizontal spatial structure of the karst cave to generate karst cave path - horizontal component data of the geomagnetic field; based on the three-dimensional path curve data of the karst cave and the vertical component data of the geomagnetic field, perform an analysis of the vertical spatial structure of the karst cave to generate karst cave path - vertical component data of the geomagnetic field;

[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 three-dimensional cave path model.

[0090] In the embodiment of the present invention, the Bezier curve is used to perform three-dimensional curve fitting on the three-dimensional cave path point data. The Bezier curve is a mathematical tool that describes the shape of a curve through control points. Its advantage is that it can flexibly adjust the curve shape while maintaining smoothness, and is particularly suitable for path modeling in three-dimensional space. In this step, first, according to the spatial coordinate data of the cave path points, appropriate control points are selected, and the Bezier curve algorithm is applied to fit these control points to generate a three-dimensional curve model that conforms to the path characteristics. This model can accurately reflect the three-dimensional shape and trend of the cave and provide reliable data support for subsequent spatial analysis and path modeling. Based on the three-dimensional cave path curve data and the geomagnetic horizontal component data, a horizontal spatial structure analysis of the cave is carried out. The main purpose of this analysis is to study the distribution and trend of the cave path on the horizontal plane, especially the change of magnetic field intensity at different positions. By combining the three-dimensional curve of the cave path with the geomagnetic horizontal component data and using spatial interpolation methods such as Kriging interpolation or nearest neighbor interpolation algorithm, the horizontal component of the magnetic field around the cave path is analyzed to generate the cave path - geomagnetic horizontal component data. These data can reveal the magnetic field characteristics of the cave path in the horizontal direction, helping to analyze the change trend of the path and potential magnetic field disturbance sources. Similarly, the vertical spatial structure analysis based on the three-dimensional cave path curve data and the geomagnetic vertical component data uses a similar spatial analysis method to explore the change of the magnetic field characteristics of the cave path in the vertical direction and generate the cave path - geomagnetic vertical component data. These data provide the magnetic field change law of the cave path in the vertical direction, further enriching the spatial analysis of the cave. A three-dimensional trajectory path model is constructed according to 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 path model. By comprehensively considering the data of the geomagnetic horizontal and vertical components and using three-dimensional modeling algorithms such as least squares fitting, surface reconstruction or voxel modeling method, a complete three-dimensional model of the cave path is constructed.

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

[0092] Step S41: Obtain cave detection radar data; perform result simulation on the three-dimensional cave path model to generate path magnetic needle observation point path data;

[0093] Step S42: Calculate the deviation of the path data of the path magnetic needle observation point and the cave detection radar data, and evaluate them according to the 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, generate a cave path magnetic needle induction positioning report, and thus complete the cave path magnetic needle induction positioning operation.

[0095] In the embodiment of the present invention, the cave detection radar data and the constructed three-dimensional path model are used for simulation calculation. The cave detection radar provides the actual measured magnetic field data, which can generate the path data of the path magnetic needle observation point after being combined with the cave three-dimensional path model. Specifically, based on the spatial structure of the cave path and the magnetic field change model, the data measured by the radar is matched with the path model to simulate the actual detection process and generate a set of path data of the magnetic needle observation point. This process needs to use the magnetic field torque principle, combine the three-dimensional coordinate points of the path with the actual magnetic field distribution, calculate the corresponding observation point data, and calculate the observation point position and expected magnetic field intensity according to 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. The calculation can adopt a variety of error analysis methods, such as root mean square error (RMSE) or maximum deviation value (Max Deviation). These calculation results reflect the error between the path model and the actual measurement result, and evaluate the accuracy of the current magnetic needle path by comparing with the preset deviation index threshold. The evaluation result can quantify the accuracy of the path and convert it into quantitative accuracy data, which further helps to optimize the path model. By processing the preliminary accuracy assessment data, the system can automatically generate an accuracy feedback report based on the assessment results. The report generation technology relies on data visualization and automated reporting tools to generate reports including accuracy deviation, assessment indicators, and improvement suggestions through charts and data analysis.

[0096] The beneficial effects of the present invention are as follows. By acquiring geomagnetic characteristic data and performing preprocessing, noise and interference can be removed, and high-quality preprocessed geomagnetic karst cave data can be generated, providing reliable input for subsequent path speculation. The data cleaning and preprocessing at this stage ensure the accuracy and representativeness of the magnetic field data, reducing data deviation caused by environmental factors. Secondly, by acquiring rock layer parameter data and calculating the rock layer magnetic field influence factor, through the correction of the magnetic field intensity by combining geological parameters, the influence of different rock layers on the geomagnetic field is accurately simulated. This link not only improves the accuracy of the magnetic field model but also makes the geomagnetic data more conform to the actual geological environment, providing data support for the accurate identification of karst cave paths. Based on this, the integrated path generated by the rock layer magnetic field influence factor further lays a foundation for the generation of the karst cave path sequence. During the path generation process, the magnetic field intensity integrated path is used to generate spatial coordinate points. By establishing three-dimensional path point data, three-dimensional spatial distribution information of the karst cave path can be provided. The three-dimensional karst 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 karst cave path. Finally, through the evaluation of the magnetic needle accuracy of the karst cave path model and the feedback report generated by combining the evaluation data, it is helpful to quantitatively analyze and correct the path accuracy, ensuring the reliability and effectiveness of the positioning result. In summary, through multi-level data processing and optimization methods, the present invention effectively improves the accuracy of karst cave path positioning, can provide real-time feedback on path changes and correction results, thereby significantly improving the efficiency and accuracy of karst cave detection, and solving the problems that traditional karst cave detection methods are greatly affected by the environment and it is difficult to guarantee accuracy. Therefore, by comprehensively applying geomagnetic characteristic data, rock layer parameter correction, and three-dimensional path model construction, the present invention solves the problems of low accuracy and large environmental interference in traditional karst cave path detection, and improves the accuracy and reliability of karst cave path positioning.

[0097] As Figures 5 to 13 shown, the present invention also provides a method for magnetic needle induction positioning of karst cave paths, including the following steps:

[0098] When drilling a karst cave borehole exposed during the drilling of a construction site, use a drill bit with a diameter of not less than 150 mm to ream the hole. Connect no less than 10 bionic drift bottles equipped with magnetic needles with ropes and put them into the water in the karst cave through the enlarged borehole. The bionic drift bottles placed in the karst cave float along the water flow direction. Place no less than one row of magnetic needles between the karst cave boreholes corresponding to the bionic drift bottles placed on the ground and the adjacent karst cave boreholes around. The magnetic needles on the ground and the magnetic needles in the underground karst cave interact inductively. Move the position of the magnetic needles on the ground to find the position with the largest deflection angle of the magnetic needle pointer. Connect the positions of the maximum deflection angles of each magnetic needle with a smooth curve to obtain the water flow direction and path of the underground karst cave.

[0099] The specific implementation includes the following steps:

[0100] As shown Figure 5 in the plan drilling distribution map of karst caves 1 revealed by drilling at the construction site, select no less than 2 karst cave drill holes 2, and use a drill bit with a diameter of not less than 150 mm for reaming.

[0101] As shown Figure 5 in the figure, connect no less than 10 bionic drift bottles 3 equipped with magnetic needles 4 with ropes and put them into the water in the karst cave through the enlarged drill holes. The distance between the bionic drift bottles 3 is not greater than 2.0 m.

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

[0103] As shown Figure 5 in the figure, the bionic drift bottles 3 put into the karst cave 1 successively float along the water flow direction. Due to the connection of the thin ropes, the bionic drift bottles 3 are arranged in a row and float in sequence.

[0104] As shown Figure 5 and Figure 6 in the figure, place no less than one row of magnetic needles 4 between the ground and the adjacent karst cave drill holes 2 around. The distance between the ground magnetic needles 4 is not greater than 2.0 m. The ground magnetic needles and the magnetic needles in the underground karst cave are inductively coupled. Move the positions of the ground magnetic needles 4, find the positions with the maximum deflection angle of the pointers of the magnetic needles 4, and connect the positions of the maximum deflection angles of each magnetic needle with a smooth curve to obtain the water flow direction and path of the underground karst cave 1.

[0105] Furthermore, as shown Figure 13 in the figure, for a method for magnetic needle induction positioning of karst cave paths, the calculation method for the magnetic field intensity of the magnetic needles in the karst cave that is inductive to the ground magnetic needles includes the following steps:

[0106] Stratigraphic modeling: According to the geological exploration report, determine the average thickness of each rock and soil layer in the stratum, model the stratum, and mark each stratum as i from the roof of the karst cave 1 upwards, where i = 1…n, and mark the thickness of each stratum as h i , i = 1…n.

[0107] Determine the resistivity and relative magnetic permeability of each rock and soil layer: According to the properties of each rock and soil layer provided in the geological exploration report, determine the resistivity and relative magnetic permeability of each rock and soil layer. It is also possible to collect rock and soil samples and test the resistivity and relative magnetic permeability in the laboratory, or use on-site testing methods such as magnetotellurics to obtain the resistivity and relative magnetic permeability of each rock and soil layer. The resistivity and relative magnetic permeability of each rock and soil layer are respectively marked as ρ i and μ ri .

[0108] Calculation of the ground magnetic field intensity: Without considering the influence of each rock and soil layer on the magnetic field intensity, assuming that the magnetic dipole moment of the magnetic needle 4 placed in the drift bottle 3 is m, the number of drift bottles 3 put into the karst cave 1 is N, and the magnetic field intensity B formed by all the drift bottles 3 on the ground N is calculated according to the following formula:

[0109]

[0110] In the formula, 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 karst cave, |r| is the distance from the ground observation point to the midpoint of the magnetic axis of the drift bottle in the karst 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 the angular direction unit vector in the spherical coordinate system respectively, μ 0 is the magnetic permeability of vacuum, μ 0 = 4π×10 ―7 Tesla·m / A. As Figure 13 shown, with the midpoint of the magnetic axis of the drift bottle in the karst cave as the coordinate origin, the coordinates in the spherical coordinate system are r, θ, then |r|cosθ = ∑ n h i , and the vectors and can be respectively expressed as:

[0111] Among them, represents the vector of the karst 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 by the following formula:

[0113]

[0114] In the formula, h i is the thickness of the i-th rock layer 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 by the following formula:

[0115]

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

[0117]

[0118] Considering the influence of each rock and soil layer on the magnetic field intensity, the magnetic field intensity on the ground is calculated by the following formula:

[0119]

[0120] In the formula, α is the correction coefficient, and S i is the influence coefficient of the i-th layer on the magnetic field intensity. Since the model is an average treatment and the modeling influence is not significant, the correction coefficient α is empirically taken as 0.9 - 1.1.

[0121] It is known that the magnetic dipole moment of the ground magnetic needle 4 is m 0 , and the magnetic torque generated by the ground magnetic needle 4 under the action of the magnetic field is calculated by the following formula:

[0122]

[0123] In the formula, and are the unit vectors 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, respectively.

[0124] To ensure the accurate pointing of the ground magnetic needle 4, it is necessary to ensure that when the included angle between the vector and the vector is greater than 3°, the magnetic torque generated by the ground magnetic needle under the action of the magnetic field is greater than the minimum torque κ min , that is, the following formula is satisfied:

[0125]

[0126] In the formula, κ min is the minimum torque for the free rotation of the magnetic needle. κ min is determined according to the turning sensitivity of the ground magnetic needle 4, and B f is the correction data of the magnetic field intensity of the karst cave.

[0127] The calculation formula can be obtained as:

[0128]

[0129] Substituting into the above formula gives:

[0130]

[0131] It can be seen that there are two variables in the inequality, namely the number N of the drift bottles 3 and the magnetic dipole moment m of the magnetic needle 4 in the drift bottle. Among them, the magnetic dipole moment m of the magnetic needle in the drift bottle is determined by the magnetic needle model. If the magnetic needle model is selected, the number of drift bottles 3 required in the karst cave can be calculated and determined; if the number of drift bottles in the karst cave is preset in advance, the magnetic dipole moment of the required magnetic needle can be determined using the formula, and thus the model of the selected magnetic needle 4 can be determined.

[0132] Furthermore, as Figure 7 、 Figure 8 shown, for a magnetic needle induction positioning method for karst cave paths, a guiding fin 305 is pasted on each side of the bottle body of the bionic drift bottle 3, and a guiding tail 302 is installed at the bottle tail of the bionic drift bottle.

[0133] Furthermore, a magnetic needle induction positioning method for karst cave paths is characterized in that when the guiding fin 305 of the bionic drift bottle drifts along the water flow direction, it folds up, as shown in Figure 11 , and when it drifts against the water flow direction, it opens, as shown in Figure 12 , hindering the reverse drift of the bionic drift bottle 3, forcing the bionic drift bottle 3 to turn around, and ensuring that the head of the bionic drift bottle is always in the front when drifting along with the water.

[0134] Furthermore, as Figure 11 、 Figure 12 shown, for a magnetic needle induction positioning method for karst cave paths, when the guiding tail 302 of the bionic drift bottle drifts along the water flow direction, it folds up, and when it drifts against the water flow direction, it opens to the edge of the guiding tail outer cover 304, as shown in Figure 9 , hindering the reverse drift of the bionic drift bottle, and the guiding tail 302 and the bilateral guiding 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 the front when drifting along with the water.

[0135] Furthermore, for a magnetic needle induction positioning method for karst cave paths, a guiding cap is installed at the front end of the guiding fin on the bottle body of the bionic drift bottle, as shown in Figure 10 , the guiding cap 306 allows the guiding fin 305 to open into an acute angle, the guiding fin 305 is strung by nylon threads from fan-shaped plastic sheets, and the fan-shaped plastic sheets on the guiding fin are connected by no less than 3 nylon threads.

[0136] Furthermore, for a magnetic needle induction positioning method for karst cave paths, the directions of the magnetic needles 4 in the bionic drift bottle are all the same, and the compass points towards the bottle mouth, as shown in Figure 5 .

[0137] Furthermore, for a magnetic needle induction positioning method for karst cave paths, the bionic drift bottle 3 has a wide bottle mouth, as shown in 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 sealed with a bottle cap, no water enters the bottle, and the drift bottle can float on the water surface after being filled with magnetic needles.

[0138] Furthermore, according to the calculation that the magnetic field intensity generated by the magnetic needles 4 in a row of bionic drift bottles on the ground is greater than the intensity required to deflect the ground magnetic needle, select the size specification of the magnetic needles 4 in the bionic drift bottle.

[0139] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope 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 for 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 removal 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 method for magnetic needle induction positioning of a cave path 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: Based on the rock formation magnetic field influence factor, the magnetic field strength correction calculation is performed on the geomagnetic cave preprocessing data to generate cave magnetic field strength correction data; based on the cave magnetic field strength correction data, a cave path sequence is generated to generate a magnetic field strength 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: 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; The skin depth of the i-th rock layer or soil layer is calculated by the following formula: Where ω is the angular frequency. For static magnetic fields such as magnetic needles, ω = 0, 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 r, θ, 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:

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 by the following formula: 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 average processing, the correction coefficient α is empirically taken as 0.9-1.1; N is the magnetic field strength not affected by the rock and soil layer, which is calculated by the following formula: 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 to the midpoint of the magnetic axis and z, and are the radial unit vector and the angular unit vector in the spherical coordinate system, μ0 is the vacuum permeability, μ0=4π×10 ―7 Tesla·meter / ampere. Taking the midpoint of the magnetic axis of the drifting bottle in the cave as the coordinate zero point, the coordinates in the spherical coordinate system are r, θ, 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: 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 strength correction calculation on the geomagnetic cave preprocessing data based on the rock formation magnetic field influence factor to generate cave magnetic field strength correction data; Step S232: Calculate the magnetic field moment sensitivity according to the geomagnetic needle data to generate geomagnetic moment 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 the cave magnetic field gradient change data; generate the cave path sequence according to the cave magnetic field gradient change data and the geomagnetic moment sensitivity data to generate the 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 by 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 κ min , that is, it satisfies the following formula: B f The correction data of the magnetic field intensity of the cave is substituted into the above formula to obtain: Among them, 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.

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: Generate spatial coordinate path points according to the cave coordinate smoothing interpolation data to generate three-dimensional path point data of the cave; 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 the Bezier curve to generate three-dimensional path curve data of the cave; Step S332: Perform horizontal spatial structure analysis of the cave based on the three-dimensional path curve data of the cave and the geomagnetic horizontal component data to generate cave path-geomagnetic horizontal component data; perform vertical spatial structure analysis of the cave based on the three-dimensional path curve data of the cave 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 three-dimensional cave 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 path magnetic needle observation point; Step S42: Calculate the deviation of the path data of the path magnetic needle observation point and the cave detection radar data, and evaluate them according to the 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, generate a cave path magnetic needle induction positioning report, and thus complete the cave path magnetic needle induction positioning operation.

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