CN111257954B - Vehicle-mounted array type detection method and system based on feature inversion - Google Patents
Vehicle-mounted array type detection method and system based on feature inversion Download PDFInfo
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- CN111257954B CN111257954B CN202010125052.7A CN202010125052A CN111257954B CN 111257954 B CN111257954 B CN 111257954B CN 202010125052 A CN202010125052 A CN 202010125052A CN 111257954 B CN111257954 B CN 111257954B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J18/00—Arms
- B25J18/02—Arms extensible
- B25J18/025—Arms extensible telescopic
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
- G01V3/088—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices operating with electric fields
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
- G01V3/10—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices using induction coils
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/15—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for use during transport, e.g. by a person, vehicle or boat
- G01V3/165—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for use during transport, e.g. by a person, vehicle or boat operating with magnetic or electric fields produced or modified by the object or by the detecting device
Abstract
The invention discloses a vehicle-mounted array type detection method and system based on feature inversion, wherein the method comprises the following steps: step 1: carrying out first detection in a working area to obtain a precise measurement area and a first detection response, wherein the precise measurement area is contained in the working area; step 2: performing second detection in the accurate measurement area obtained in the step 1 to obtain a second detection response; and step 3: performing iterative inversion according to the first detection response obtained in the step 1 and the second detection response obtained in the step 2, and outputting the position, the inclination and the inclination of the central point of the target body after the iteration is completed; the system uses vehicle-mounted detection to enhance the anti-interference capability to various complex climates or environments and improve the precision in detection; the detection speed is improved by utilizing a plurality of arrays; in the aspect of processing interpretation, a combination of a characteristic database and an inversion method is utilized to improve the processing speed and meet the requirement of interpretation accuracy.
Description
Technical Field
The invention belongs to the field of geophysical transient electromagnetic methods, and particularly relates to a vehicle-mounted array type detection method and system based on feature inversion.
Background
Geophysical methods have been of great importance in the exploration field as a powerful exploration method. Among them, the magnetic method and the electric method are two methods widely used for a metal target. The magnetic prospecting is a widely used prospecting means, but because the magnetic method uses the earth magnetic field and belongs to a passive source, when magnetic impurities exist in a detected region, magnetic abnormal signals of a low-resistance target body can be covered by other interference sources, and the position of the low-resistance target body is difficult to judge through a contour map. In addition, under the condition that the geomagnetic environment is complex, the detection false alarm rate is greatly improved. With the development of society, the exploration precision of the magnetic sensor is required to be higher and higher, and the magnetic method obviously cannot meet the requirement of fine exploration.
The transient electromagnetic method has high detection efficiency, strong identification capability to a target body, small influence of complex terrain, and high requirement on the sensitivity of the probe arrangement direction and position. In the traditional method, unmanned aerial vehicle detection is adopted, on the one hand, the precision of the arrangement direction and the position cannot be guaranteed, and complicated posture correction is required in data acquisition, and on the other hand, the precision requirement of fine exploration cannot be met; in terms of process interpretation, there are currently methods of direct inversion, apparent resistivity definition, and feature database comparison. The time required for directly carrying out inversion is too long, and the method is not suitable for on-site low-resistance target body detection; the precision requirement is difficult to guarantee by the definition of the apparent resistivity; the simple characteristic database contrast has high requirements on professionals and is not suitable for complex geological conditions.
Disclosure of Invention
The invention aims to provide a vehicle-mounted array type detection method and system based on feature inversion, which are used for solving the problem that the detection method and device in the prior art cannot meet the precision requirement of fine exploration.
In order to realize the task, the invention adopts the following technical scheme:
a vehicle-mounted array type detection method based on feature inversion comprises the following steps:
step 1: carrying out first detection in a working area to obtain a precise measurement area and a first detection response, wherein the precise measurement area is contained in the working area;
step 2: performing second detection in the accurate measurement area obtained in the step 1 to obtain a second detection response;
and step 3: performing iterative inversion according to the first detection response obtained in the step 1 and the second detection response obtained in the step 2, and outputting characteristic parameters of an inverted model after the iteration is completed, wherein the characteristic parameters comprise the central point position, the inclination and the dip angle of a target body;
the iterative inversion comprises the following substeps:
step a: calculating characteristic parameters according to the first detection response, and searching a database of numerical simulation responses according to the characteristic parameters to obtain forward responses, wherein the database of the numerical simulation responses comprises a plurality of groups of responses and characteristic parameters which correspond one to one;
step c: inputting the forward response into a neural network model, and outputting a simulated actual measurement response;
step b: setting initial characteristic parameters of the inversion model according to the characteristic parameters obtained in the step a, inputting the simulated actual measurement response obtained in the step c and the second detection response obtained in the step 2 into the inversion model, and performing iterative updating on the characteristic parameters of the inversion model;
and calculating the iteration error and the iteration times of each iteration, finishing the iteration if the iteration error is more than or equal to the iteration error threshold value or the iteration times is more than or equal to the maximum iteration times, and otherwise, continuing the iteration updating.
Further, the first detection response, the second detection response and the simulated measured response comprise physical quantities of magnetic induction along the z-axis direction and a first derivative thereof with respect to time at each time point.
Further, the inversion model in step b is as shown in formula i:
wherein the content of the first and second substances,
roughness operators in x, y, z directions, respectively, and λ is LagrangeDaily multiplier, m is the model parameter, mk+1Represents the model after the (k + 1) th iteration update, mkRepresents the model updated at the k-th iteration and each mkAll correspond to a group of characteristic parameters, W is a diagonal matrix, F (-) is a simulated measured response value, d is a measured response value, JkIs a forward function.
Further, the iteration error threshold is 0.005, and the maximum iteration number interval is [10,20 ].
A vehicle-mounted array type detection system based on characteristic inversion comprises an unmanned vehicle, a mechanical arm, a transmitting and receiving device, a wireless transmission device and a processing terminal;
the unmanned vehicle is used for moving in a working area, and a mechanical arm, a transmitting and receiving device and a wireless transmission device are arranged in the unmanned vehicle; the mechanical arm is used for controlling the receiving and transmitting device to realize detection and transmitting measured data to the terminal in real time through the wireless transmission device;
the processing terminal obtains measured data, and then the central point position, the inclination and the inclination of the target body are obtained according to any one of the characteristic inversion-based vehicle-mounted array detection methods, so that detection is completed.
Furthermore, the mechanical arm is also used for adjusting the posture of the rectangular transmitting coil, so that the normal direction of the rectangular transmitting coil is perpendicular to the horizontal plane.
Furthermore, the receiving and transmitting device comprises a transmitting coil and an array type receiving probe, wherein 6 receiving probes are distributed in the transmitting coil at equal intervals and can measure 6 measuring points simultaneously, the array type receiving probe and the transmitting coil are fixedly connected through a non-metal material, the distance between the array type receiving probe and the transmitting coil is 2 to 3 measuring points, and the distance between the receiving coils is 2 measuring points.
Furthermore, the measuring speed of the mechanical arm in the detection process is more than 0.5m/s and less than 1 m/s.
Furthermore, the measuring speed of the mechanical arm is reduced to 1/3-1/5 of the speed of the first detection in the second detection.
Compared with the prior art, the invention has the following technical characteristics:
(1) the anti-interference capability is strong: in terms of data acquisition, the device uses a vehicle-mounted type and a mechanical arm, has low measurement height and high signal-to-noise ratio, is slightly influenced by the change of the climate environment, and can keep the whole measurement process relatively stable;
(2) the speed is high: in terms of data acquisition, on one hand, due to the use of the mechanical arm, a measuring point does not need to be temporarily paved, and only a measuring area needs to be divided, and on the other hand, the device utilizes a multi-array receiving device, so that the efficiency is far higher than that of the traditional single-channel receiving; in terms of processing and explanation, the response diagram is directly observed during scanning, and approximate information of a target body can be obtained without inversion and apparent resistivity definition; during fine exploration, although inversion is used, the forward part in the inversion method is replaced by using the contrast of a database, so that the inversion speed is greatly improved;
(3) the precision is high: in terms of data acquisition, the movement measurement of the measuring points is controlled by a mechanical arm, so that uniform sampling and horizontal sampling can be realized. Sampling at a constant speed, wherein the measuring point position is more accurate than a manually pulled measuring point, and the error caused by the direction and the posture of the probe is reduced by horizontal sampling; in the aspect of processing and explanation, the characteristic inversion method is used, the definition is more accurate than that of directly using forward response or apparent resistivity, and the inversion method utilizes a neural network to compare actual measurement, so that the result is more consistent with the actual situation.
Drawings
FIG. 1 is a schematic illustration of a work area implementation;
FIG. 2 is a schematic diagram of a modified six-channel receiving and transmitting device;
in fig. 2, the asterisks represent the receiving coils and the dots represent the measurement points;
FIG. 3(a) is a schematic diagram of a horizontally disposed low-resistance target;
FIG. 3(b) is a diagram of the forward response of a horizontally positioned low-resistance target;
FIG. 4(a) is a schematic illustration of a low resistance target that is prone to placement;
4(b) to 4(g) are forward response graphs of low-resistance targets that tend to be placed at different times;
FIG. 5 is a schematic illustration of a precision measurement zone delineation;
fig. 6 is a schematic flow chart of a method.
Detailed Description
The invention aims to use vehicle-mounted detection to enhance the anti-interference capability to various complex climates or environments and improve the detection precision, then utilize a multi-array to improve the detection speed, and utilize the combination of a characteristic database and an inversion method to improve the processing speed and meet the requirement of interpretation precision. The terms mentioned in the invention are explained first:
numerical simulation response characteristics database: a database for numerical simulation calculations using theoretical models.
Measured response characteristics database: a database is created by collecting data in a laboratory or in a work area of known condition before detection and then obtaining a large amount of data through an actual physical model, wherein the parameters and physical quantities contained in the database are the same as those in a numerical simulation response characteristic database.
Iterative inversion: an initial model is determined according to known geological and geophysical information, and then the field effect of the model is calculated in a forward mode. The initial model is modified by the difference (residual value) between the calculated value and the observed value, then the calculated field value is calculated, and model modification is performed according to the comparison result. And repeating iteration until the difference (or mean square error) between the calculated value and the observed value reaches preset precision, and finally obtaining an inversion result.
And (3) inversion modeling: the model is obtained by iterative calculation in an inversion algorithm, the parameter type of the model is not changed in the iterative process, only the value is updated, the updated quantity in the invention comprises the resistivity, the position, the inclination direction and the inclination angle of a target body, and the next iterative model covers the previous iterative model.
The embodiment discloses a vehicle-mounted array type detection method based on feature inversion, which comprises the following steps:
a vehicle-mounted array type detection method based on feature inversion comprises the following steps:
step 1: carrying out first detection in a working area to obtain a precise measurement area and a first detection response, wherein the precise measurement area is contained in the working area;
step 2: performing second detection in the accurate measurement area obtained in the step 1 to obtain a second detection response;
and step 3: performing iterative inversion according to the first detection response obtained in the step 1 and the second detection response obtained in the step 2, and outputting characteristic parameters of an inverted model after the iteration is completed, wherein the characteristic parameters comprise the central point position, the inclination and the dip angle of a target body;
the iterative inversion comprises the following substeps:
step a: calculating characteristic parameters according to the first detection response, and searching a database of numerical simulation responses according to the characteristic parameters to obtain forward responses, wherein the database of the numerical simulation responses comprises a plurality of groups of responses and characteristic parameters which correspond one to one;
step c: inputting the forward response into a neural network model, and outputting a simulated actual measurement response;
step b: setting initial characteristic parameters of the inversion model according to the characteristic parameters obtained in the step a, inputting the simulated actual measurement response obtained in the step c and the second detection response obtained in the step 2 into the inversion model, and performing iterative updating on the characteristic parameters of the inversion model;
and calculating the iteration error and the iteration times of each iteration, finishing the iteration if the iteration error is more than or equal to the iteration error threshold value or the iteration times is more than or equal to the maximum iteration times, and otherwise, continuing the iteration updating.
Specifically, the establishing process of the neural network model in the step a is as follows: the forward response of the numerical simulation response database is used as the input of the neural network, the actual measurement data of the corresponding model in the actual measurement response database is used as the output of the neural network, and a trained neural network for inputting a theoretical value and outputting a simulation measured value can be obtained through a large amount of data input and output.
In particular, the numerical simulation response database typically records the resistivity (ρ) of the surrounding rock at each measurementbIn units of Ω/m), resistivity of the target body (ρ)aIn the unit ofOmega/m), the coordinate position (x, y, z, unit is m) of the target body in the measuring area, and the inclination angle (which is the included angle between the projection of the target body on the xoz plane and the positive direction of the z axis)
The unit is DEG, the range is 0-90 DEG, the inclination direction of the target body (the included angle theta between the projection of the target body on the horizontal plane and the positive direction of the y axis is shown, the unit is DEG, the range is 0-360 DEG, the clockwise rotation is increased), the coordinate position (x, y, z, the unit is m) of the measuring point in the work area, the measuring time point t (the unit is s, the range is 10 DEG, and the unit is m)-5～10-2s, s represents seconds) and B corresponding to each time pointz(magnetic induction in the z-direction, in T) and
(Bzthe first derivative with respect to time, in T/s).
Specifically, the typical theoretical model and the actual physical model corresponding to the numerical simulation response database and the actual measurement response database need to be ensured in actual use: 1. ensuring that the resistivities of surrounding rocks of the two models are approximately the same; 2. ensuring that the resistivities of the target bodies are the same; 3. the coordinate positions of the two model objects in the measuring area are the same, and the inclination directions and the angles of the two model objects are also the same.
Specifically, the forward response refers to the magnitude of three components of an electric field and a magnetic field obtained by establishing a model and calculating according to a numerical method after the resistivity of surrounding rocks in the underground medium, the resistivity of a target body and the morphological position are given or known. In this context, forward responses include each point in time of calculation and the corresponding for each point in time of calculation
And Bz。
Specifically, the method for acquiring the characteristic parameters and the target surface according to the first detection response comprises the following steps: and drawing the response of the scanned measured data into a plan view, and roughly calculating the central point position, the inclination and the inclination angle of the low-resistance target body according to the plan view with abnormal response. If the abnormal response form is an ellipse and hardly changes along with the change of time, the abnormal response form can be preliminarily determined as a low-resistance target body which is horizontally or vertically placed, at the moment, the abnormal extreme point is the central point of the low-resistance target body, the long axis of the ellipse is the long edge of the low-resistance target body, the short axis of the ellipse is the short axis of the low-resistance target body, a square is further drawn by using the central point of the low-resistance target body, the side length of the square is 1.2 times of the long edge of the low-resistance target body, and the square area is the target surface; if the abnormal response form is an ellipse in the early stage, but stretches towards a certain direction along with time, the low-resistance target body at the position is inclined, the moving direction of the extreme value is the inclined direction of the low-resistance target body, the central point in the early stage ellipse form is the central point of the low-resistance target body, the long axis of the ellipse is the long edge of the low-resistance target body, the short axis of the ellipse is the short axis of the low-resistance target body, the central point of the low-resistance target body is further used for drawing a square, the side length of the square is 1.2 times of the long edge of the low-resistance target body, and the area of the square is the target surface.
Specifically, the abnormality means that a low-resistance or high-resistance response is shown at the position of a target body due to the electrical difference between the target body and surrounding rocks, when tau/r is less than or equal to 16, the abnormality is called as an early stage, and when tau/r is less than or equal to 16>16, referred to as late, where r is the distance from the center point of the transmitting loop to the center point of the receiving probe, where τ is
ρ is background resistivity, t is calculation time, μ0Is the permeability under vacuum.
Specifically, the first probe response further includes the ability to calculate the depth of burial of the top surface of the low-resistance target, the earliest time t at which an anomaly will occur0And substituting the background conductivity sigma into the time-depth change relational expression, and calculating to obtain the approximate top surface buried depth h.
Where π is the circumference ratio, μ0Is a vacuumLower magnetic permeability.
Specifically, the size and resistivity of the low-resistance target volume are known and given during inversion iteration, and the resistivity of the background can be sampled and measured, so that the inversion characteristic parameters are only: the center point position, inclination and inclination of the low resistance target. First, an objective function U (m) is constructed:
where W is a diagonal matrix that normalizes the data, R is a model smoothness constraint term,
is roughness operator in x, y and z directions, lambda is Lagrange multiplier, m is model parameter, F (m) is response value of corresponding model obtained from database, d is actually measured response value,
to observe the expected value, when the observed data is accurate data, it can be ordered
W ═ I, I identity matrix.
F (m) at mkThe Taylor formula is expanded, and after two or more high-order terms are cut off, the following steps are carried out:
F(mk+1)＝F(mk+δm)≈F(mk)+Jk(mk+1-mk) (4)
wherein m isk+1Represents the model after the (k + 1) th iterative update, Jk(mk+1-mk) Is the partial derivative matrix of the forward function to the model parameters in the model mkThe value of (a) is (b),
order to
An updated formula for the model can be derived:
taking the obtained updated model as input, repeating the above steps until the target function reaches the threshold requirement or the iteration number requirement, and each mkAll correspond to a group of characteristic parameters, and finally, the characteristic parameters of the result model are output.
Specifically, the accurate measurement area is used as a measurement area for secondary detection, namely, during fine detection, the speed of the accurate measurement area is reduced to 1/3-1/5 of the speed during scanning according to the final precision required by the work area.
Preferably, the iteration error threshold is 0.005, and the maximum iteration number interval is [10,20 ].
A vehicle-mounted array type detection system based on characteristic inversion comprises an unmanned vehicle, a mechanical arm, a transmitting and receiving device, a wireless transmission device and a processing terminal;
the unmanned vehicle is used for moving in a working area, and a mechanical arm, a transmitting and receiving device and a wireless transmission device are arranged in the unmanned vehicle; the mechanical arm is used for controlling the receiving and transmitting device to realize detection and transmitting measured data to the terminal in real time through the wireless transmission device;
the processing terminal obtains measured data, and then the central point position, the inclination and the inclination of the target body are obtained according to any one of the characteristic inversion-based vehicle-mounted array detection methods, so that detection is completed.
When the unmanned vehicle is operated in a work area by remote control, the unmanned vehicle is driven to the work area, the whole detection process is controlled by the mechanical arm, when the normal direction of the receiving and transmitting coil is not perpendicular to the ground due to uneven terrain, the included angle between the mechanical arm and the coil is mechanically adjusted to ensure that the included angle between the normal direction of the coil and the ground is 90 +/-5 degrees, the advancing speed of the mechanical arm is set to perform scanning and fine exploration, data transmission is performed through a wireless device, and a plane response diagram is used for determining a precise measurement area on a terminal or a characteristic inversion program based on database comparison is used for performing data processing and data interpretation.
Specifically, the terminal has the functions of processing and analyzing the scanning and fine exploration data. After the scan data is obtained and the data is further subjected to smooth denoising treatment, displaying the data in a planar response graph of each time period; when data of fine exploration is obtained, the data is input into a characteristic parameter inversion program based on a database, so that required characteristic parameters are obtained through inversion.
The mechanical arm is further used for adjusting the posture of the rectangular transmitting coil to enable the normal direction of the rectangular transmitting coil to be perpendicular to the horizontal plane.
Preferably, the receiving and transmitting device comprises a transmitting coil and an array type receiving probe, 6 receiving probes are distributed in the transmitting coil at equal intervals and can measure 6 measuring points simultaneously, the array type receiving probe and the transmitting coil are fixedly connected through a non-metal material, the distance between the array type receiving probe and the transmitting coil is 2 to 3 measuring points, and the distance between the receiving coils is 2 measuring points.
The receiving and transmitting coil is fixed on a handle of the mechanical arm, and the mechanical arm is used for controlling the movement of the improved receiving probe and the transmitting coil to perform multipoint simultaneous measurement.
Preferably, the measuring speed of the mechanical arm in the detection process is more than 0.5m/s and less than 1 m/s.
The measurement process is as follows: when the measurement is started, the central points of the receiving probes of the multiple arrays are all positioned right above the measurement point of the left (right) boundary of the measurement area; and further controlling the mechanical arm to move rightwards (leftwards) at a constant speed along the line measuring direction, and when the central point of the receiving probe reaches the right (left) boundary of the measuring area, moving the mechanical arm downwards by the distance of one measuring point, and further controlling the mechanical arm to move leftwards (rightwards) at a constant speed along the line measuring direction until the central point of the receiving probe reaches the left (right) boundary of the line measuring, wherein the last step is a measuring period. And after one period of measurement is finished, controlling the mechanical arm to move 12 measuring points downwards to carry out next scanning measurement. And wirelessly transmitting the data obtained by scanning to a terminal, drawing a plane response diagram on different time points, and quickly delineating a target surface.
Claims (9)
1. A vehicle-mounted array type detection method based on feature inversion is characterized by comprising the following steps:
step 1: carrying out first detection in a working area to obtain a precise measurement area and a first detection response, wherein the precise measurement area is contained in the working area;
step 2: performing second detection in the accurate measurement area obtained in the step 1 to obtain a second detection response;
and step 3: performing iterative inversion according to the first detection response obtained in the step 1 and the second detection response obtained in the step 2, and outputting characteristic parameters of an inverted model after the iteration is completed, wherein the characteristic parameters comprise the central point position, the inclination and the dip angle of a target body;
the iterative inversion comprises the following substeps:
step a: calculating characteristic parameters according to the first detection response, and searching a database of numerical simulation responses according to the characteristic parameters to obtain forward responses, wherein the database of the numerical simulation responses comprises a plurality of groups of responses and characteristic parameters which correspond one to one;
step c: inputting the forward response into a neural network model, and outputting a simulated actual measurement response;
step b: setting initial characteristic parameters of the inversion model according to the characteristic parameters obtained in the step a, inputting the simulated actual measurement response obtained in the step c and the second detection response obtained in the step 2 into the inversion model, and performing iterative updating on the characteristic parameters of the inversion model;
and calculating the iteration error and the iteration times of each iteration, finishing the iteration if the iteration error is more than or equal to the iteration error threshold value or the iteration times is more than or equal to the maximum iteration times, and otherwise, continuing the iteration updating.
2. The on-board array type detection method based on feature inversion of claim 1, wherein the first detection response, the second detection response and the simulated measured response comprise physical quantities of magnetic induction intensity along the z-axis direction and a first derivative thereof with respect to time at each time point.
3. The on-board array detection method based on feature inversion of claim 1, wherein the inversion model in step b is as follows:
wherein the content of the first and second substances,
roughness operators in x, y and z directions, lambda is Lagrange multiplier, m is model parameter, m isk+1Represents the model after the (k + 1) th iteration update, mkRepresents the model updated at the k-th iteration and each mkAll correspond to a group of characteristic parameters, W is a diagonal matrix, F (-) is a simulated measured response value, d is a measured response value, JkIs a forward function.
4. The on-board array detection method based on feature inversion of claim 3, wherein the iteration error threshold is 0.005 and the maximum iteration number interval is [10,20 ].
5. A vehicle-mounted array type detection system based on characteristic inversion is characterized by comprising an unmanned vehicle, a mechanical arm, a transmitting and receiving device, a wireless transmission device and a processing terminal;
the unmanned vehicle is used for moving in a working area, and a mechanical arm, a transmitting and receiving device and a wireless transmission device are arranged in the unmanned vehicle; the mechanical arm is used for controlling the receiving and transmitting device to realize detection and transmitting measured data to the terminal in real time through the wireless transmission device;
the processing terminal obtains measured data, and then the center point position, the inclination and the inclination of the target body are obtained according to the vehicle-mounted array type detection method based on the characteristic inversion of any one of claims 1 to 4, so that the detection is completed.
6. The on-board array detection system based on feature inversion of claim 5, wherein the robotic arm is further configured to adjust the attitude of the rectangular transmitting coil such that a normal direction thereof is perpendicular to a horizontal plane.
7. The vehicle-mounted array type detection system based on feature inversion as claimed in claim 5, wherein the receiving and transmitting device comprises a transmitting coil and an array type receiving probe, 6 receiving probes are distributed in the transmitting coil at equal intervals and can measure 6 measuring points simultaneously, the array type receiving probe and the transmitting coil are fixedly connected through a non-metal material, the distance between the array type receiving probe and the transmitting coil is 2 to 3 measuring points, and the distance between the receiving coils is 2 measuring points.
8. The on-board array type detection system based on feature inversion of claim 5, wherein a measurement speed of the robot during the detection process is greater than 0.5m/s and less than 1 m/s.
9. The on-vehicle array detection system based on feature inversion of claim 5, wherein the measured speed of the mechanical arm is reduced to 1/3-1/5 of the speed of the first detection in the second detection.
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