CN117027765B - Mine drilling equipment accurate punching control method based on electromagnetic force detection - Google Patents
Mine drilling equipment accurate punching control method based on electromagnetic force detection Download PDFInfo
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- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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Abstract
The invention discloses an accurate perforation control method of mine drilling equipment based on electromagnetic force detection, which relates to the technical field of perforation, and comprises the following steps: establishing a trigger electric field in a target perforation area; establishing a spherical coordinate system by taking the detection probe as an origin, and marking the direction and the magnitude of the detected electromagnetic force in the spherical coordinate system; determining the moving direction and path of the detection probe by utilizing a polar angle axis and an azimuth angle axis in a spherical coordinate system; combining magnetic field positioning with visual positioning and acoustic positioning to estimate the punching position of the target; introducing a positioning error model to evaluate the influence of magnetic field interference on the punching position of the target; optimizing a punching path planning algorithm based on the positioning error model, and calculating a confidence interval of a target punching position by using the punching path planning algorithm; and (5) performing punching work along the optimal path by using a drilling device. According to the invention, by combining different positioning technologies, the accuracy of positioning the punching position can be improved.
Description
Technical Field
The invention relates to the technical field of punching, in particular to an accurate punching control method of mine drilling equipment based on electromagnetic force detection.
Background
Electromagnetic force detection is a technique that uses electromagnetic force as a detection means. The electromagnetic force detection principle is that electromagnetic waves are emitted by a detection device, and when the electromagnetic waves meet the target object, reflection, scattering or transmission occurs, so that the electromagnetic field distribution around the detection device is changed
Mine drilling equipment is specialized equipment used in mine exploration and exploitation processes for performing drilling operations. The drilling equipment is a tool for drilling or enlarging a well hole or cave to acquire underground resources or conduct geological investigation, and the accurate drilling control of the mine drilling equipment is to ensure that the drilling equipment realizes accurate position and direction control in the drilling process by a control system and a technical means so as to realize accurate drilling operation.
However, in the mine drilling equipment accurate drilling control method using electromagnetic force detection, electromagnetic guidance is used for positioning and guiding depending on a magnetic field signal generated by detecting a target position, when an external magnetic field interference source exists, disturbance is caused on the detected magnetic field signal, so that deviation is generated on detection of the target drilling position, influence of magnetic field interference on the target drilling position cannot be accurately estimated, drilling precision errors cannot be filtered in the target drilling position, multiple times of adjustment and correction of the drilling position are needed, more time and resources are consumed, and further drilling work efficiency is reduced.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
The invention mainly aims to provide an accurate perforation control method for mine drilling equipment based on electromagnetic force detection, so as to solve the technical problems existing in the prior art.
For this purpose, the invention adopts the following specific technical scheme:
an accurate perforation control method of mine drilling equipment based on electromagnetic force detection comprises the following steps:
s1, a trigger electric field is established in a target punching area, and a charge assembly is arranged on a detection probe to generate detection electromagnetic force representing the target punching area;
s2, using the detection probe as an origin to establish a spherical coordinate system, and marking the direction and the magnitude of the detected electromagnetic force in the spherical coordinate system;
s3, determining the moving direction and path of the detection probe by utilizing a polar angle axis and an azimuth angle axis in the spherical coordinate system, and detecting and updating the spherical coordinate information in real time;
s4, combining magnetic field positioning with visual positioning and acoustic positioning, estimating a target punching position, and performing cross verification on the target punching position;
s5, a positioning error model is introduced to evaluate the influence of magnetic field interference on the target punching position, and punching precision errors are filtered in the target punching position;
s6, optimizing a punching path planning algorithm based on the positioning error model, calculating a confidence interval of a target punching position by using the punching path planning algorithm, and adjusting a punching strategy according to the confidence interval;
s7, punching work is carried out along an optimal path by using drilling equipment, and vector change of the detection electromagnetic force is detected in real time, so that closed-loop control and track tracking are realized.
Optionally, the method for establishing a spherical coordinate system by using the detection probe as an origin, and marking the direction and the magnitude of the detected electromagnetic force in the spherical coordinate system comprises the following steps:
s21, placing a detection probe in the triggering electric field and taking the detection probe as an origin of a spherical coordinate system;
s22, determining a radial axis, a polar angle axis and an azimuth angle axis of the spherical coordinate system;
the radial axis points to a target punching position, the polar angle axis corresponds to latitude, and the azimuth angle axis corresponds to longitude;
s23, measuring the electromagnetic force in the trigger electric field by using a measuring device, and determining radial coordinates according to the measured electromagnetic force;
s24, measuring the direction angle of electromagnetic force in the trigger electric field by using measuring equipment, and converting the measured direction angle into a polar angle and an azimuth angle in a spherical coordinate system;
s25, marking the electromagnetic force and the direction angle in a spherical coordinate system, and respectively representing the electromagnetic force and the direction angle by using a vector and a scale.
Optionally, the combining magnetic field positioning with visual positioning and acoustic positioning, estimating the target perforation location, and cross-verifying the target perforation location includes the steps of:
s41, detecting the magnetic field distribution condition in a target punching area in real time by using a tesla meter, and acquiring magnetic field positioning data;
s42, acquiring visual positioning data in the target perforation area by using a visual sensor, and determining the position and the posture of the target perforation area in the visual field by using an image processing technology and a computer visual algorithm;
s43, transmitting an acoustic wave signal by using an acoustic wave sensor, and receiving acoustic wave positioning data in a target punching area;
s44, carrying out multi-source data fusion on the magnetic field positioning data, the visual positioning data and the acoustic positioning data, and estimating the target punching position based on the data fusion result.
Optionally, the multi-source data fusion of the magnetic field positioning data, the visual positioning data and the acoustic positioning data, and the estimation of the target perforation position based on the data fusion result include the following steps:
s441, preprocessing magnetic field positioning data, visual positioning data and acoustic positioning data respectively;
s442, performing time alignment on the magnetic field positioning data, the visual positioning data and the acoustic positioning data to ensure that the same target punching position is corresponding to the same time point;
s443, constructing a fuzzy judgment matrix, and respectively calculating weight vectors of magnetic field positioning data, visual positioning data and acoustic positioning data;
s444, establishing a data fusion model, and carrying out weighted fusion on the magnetic field positioning data, the visual positioning data and the acoustic positioning data according to the weight vector to obtain an estimation result of the target punching position.
Optionally, the constructing the fuzzy judgment matrix and calculating weight vectors of the magnetic field positioning data, the visual positioning data and the acoustic positioning data respectively includes the following steps:
s4431, constructing a fuzzy judgment matrix, and calculating a triangle fuzzy number complementary judgment matrix set;
s4432, calculating a fuzzy comprehensive judgment matrix based on the judgment matrix set;
s4433, respectively calculating fuzzy comprehensive evaluation values of magnetic field positioning data, visual positioning data and acoustic positioning data, and carrying out normalization processing to respectively obtain fuzzy relative weight vectors of the positioning data;
s4434, comparing the triangular fuzzy numbers corresponding to the positioning data in pairs, and sequencing the comparison results by using a sequencing algorithm of a fuzzy complementary judgment matrix to obtain the actual relative weight vector of the positioning data.
Optionally, the introducing a positioning error model evaluates the influence of the magnetic field disturbance on the target perforation location and filtering the perforation accuracy error in the target perforation location comprises the steps of:
s51, respectively extracting data in a normal working state and data in the presence of magnetic field interference from magnetic field positioning data;
s52, performing frequency domain analysis on the extracted magnetic field positioning data to obtain a frequency spectrum function;
s53, constructing a positioning error model according to the extracted magnetic field positioning data, and combining the positioning error model with a frequency spectrum function to evaluate the accuracy error of the magnetic field interference on the target punching position;
s54, analyzing the size and distribution characteristics of the precision errors of the target punching positions;
s55, filtering out accuracy errors caused by magnetic field interference in the target punching position, and correcting the target punching position.
Optionally, the performing frequency domain analysis on the extracted magnetic field positioning data, and obtaining a spectrum function includes the following steps:
s521, selecting main parameters in the front of a magnetic field time domain waveform period from the extracted magnetic field positioning data as the input of the neural network, and outputting the main parameters as the trained neural network;
s522, respectively initializing weight coefficients and bias of an input layer, a hidden layer and an output layer according to the relation characteristics of Fei Bona odd columns and golden section in the Ainity wave theory;
s523, inputting samples in the training sample set and expected output, and respectively calculating output errors of each layer of units;
s524, judging whether iteration conditions are met or not based on the output error of each layer of units, if yes, ending the algorithm, otherwise, continuing to execute step S525;
s525, taking the corrected component coefficients as output parameters, and obtaining corresponding spectrum functions;
s526, converting the frequency spectrum function into a corresponding time domain waveform, and evaluating the accuracy error of the magnetic field interference to the target punching position according to the rising edge and the falling edge of the time domain waveform.
Optionally, the calculation formula of the output error of each layer unit is:
in the method, in the process of the invention,represent the firstkLayer numberjThe outputs of the individual cells;
representation oftTime of day (time)jOffset of individual units;
representation oftTime and the firstjWeight coefficients among the units;
representation oftTime of day (time)k−1Layer of the first layeriThe outputs of the individual cells;
represent the firstk−1Number of neurons in a layer.
Optionally, the optimizing the perforation path planning algorithm based on the positioning error model calculates a confidence interval of the target perforation position by using the perforation path planning algorithm, and adjusts the perforation strategy according to the confidence interval, including the following steps:
s61, determining a punching path by using a path planning algorithm according to the position of the drilling equipment and the target punching position, and calculating a confidence interval of the target punching position;
s62, acquiring each node on the punching path, calculating the distance from the adjacent node of each node to the punching path, and sequencing the boundary nodes of the target confidence interval;
s63, calculating the intersection point position of the perpendicular bisector corresponding to the adjacent node and the punching path by using a perpendicular bisector equation;
s64, determining airspace boundary nodes according to the intersection point positions, and calculating airspace boundary node sets by using a convex hull algorithm;
s65, judging whether nodes in the airspace boundary node set are on convex hull boundaries or not, and carrying out intersection test on each node and the convex hull boundaries to judge whether the airspace boundary needs to be crossed or not;
and S66, adjusting the original planning path according to the airspace boundary node set to obtain a new punching path passing through the confidence interval, and dynamically adjusting the confidence interval and the punching path in real time.
Optionally, the obtaining each node on the perforation path, calculating a distance from a neighboring node of each node to the perforation path, and sequencing the boundary nodes of the target confidence interval includes the following steps:
s621, sequencing the adjacent nodes according to a anticlockwise sequence, and judging whether the adjacent nodes are in a local minimum state or not;
s622, defining angles formed by two adjacent nodes and each node as adjacent angles, and extracting the adjacent node with the largest adjacent angle;
s623, if the range of the current node comprises an area outside the coverage range of the current node, the node is indicated to be a potential local minimum point;
s624, exchanging boundary information between the local minimum point and the adjacent node with the largest adjacent angle, and distributing the local minimum point information by using a hole-surrounding algorithm set.
The beneficial effects of the invention are as follows:
1. the invention can mark the magnitude of the detected electromagnetic force by establishing the spherical coordinate system, thereby providing the intensity information of the electromagnetic force encountered by the drilling equipment, being beneficial to the drilling equipment to adjust the power or apply a proper control strategy, and simultaneously being capable of conveniently converting the detected electromagnetic force information into other coordinate systems, thereby being capable of being better integrated with the control system of the drilling equipment, realizing accurate control and operation and being beneficial to ensuring the accuracy and the safety of the drilling equipment in mine operation.
2. According to the invention, the accuracy of punching position positioning can be improved by combining different positioning technologies, the magnetic field positioning can provide a larger range of positioning information, the visual positioning can provide high-precision target detection and positioning, the acoustic positioning can provide three-dimensional position positioning information, the more accurate target punching position can be obtained by combining the positioning technologies, a complex geological structure and an interference source usually exist in a mine environment, a single positioning technology can not cope with all conditions, and multiple positioning technologies can comprehensively consider different environmental factors and provide more reliable punching positioning results.
3. The invention introduces the positioning error model to help evaluate the influence degree of the magnetic field interference on the target punching position, so that the relation between the magnetic field data and the positioning error can be analyzed, the influence of the magnetic field interference on the target punching position can be quantitatively evaluated, the positioning error can be predicted according to the magnetic field data and corrected into the target punching position by applying the positioning error model to the actual positioning data, and the accuracy and the precision of punching can be further improved.
4. According to the invention, the drilling path can be planned more accurately by optimizing the path planning algorithm, the confidence interval and the drilling path can be dynamically adjusted in real time, the deviation can be reduced as much as possible by optimizing the path planning algorithm and comprehensively considering the positioning error, so that the drilling position is closer to the target position, meanwhile, the confidence interval represents the possible drilling position range within the positioning error range, and the operator of the drilling equipment can be helped to know the uncertainty of the drilling position and make a corresponding decision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a mine drilling equipment precise punching control method based on electromagnetic force detection according to an embodiment of the invention.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. As described in the background art, in the prior art, the punching precision error cannot be filtered in the target punching position, and in order to solve the problems, the invention provides an accurate punching control method for mine drilling equipment based on electromagnetic force detection.
The invention will be further described with reference to the accompanying drawings and detailed description, as shown in fig. 1, a mine drilling equipment accurate drilling control method based on electromagnetic force detection according to an embodiment of the invention, the drilling control method comprises the following steps:
s1, a trigger electric field is established in a target punching area, and a charge assembly is arranged on a detection probe to generate detection electromagnetic force representing the target punching area.
It should be noted that, the triggering electric field may be created by placing electric charges or applying voltages around the target perforation area, the electric charge component may be an electronic element such as a charge distribution or a capacitor, when the electric field interacts with the target perforation area, a detection electromagnetic force is generated, and the position and the characteristic of the target perforation area may be determined by measuring the magnitude and the direction of the detection electromagnetic force.
S2, using the detection probe as an origin to establish a spherical coordinate system, and marking the direction and the magnitude of the detected electromagnetic force in the spherical coordinate system.
The method for marking the direction and the magnitude of the detection electromagnetic force in the spherical coordinate system by taking the detection probe as an origin point comprises the following steps of:
s21, placing a detection probe in the triggering electric field and taking the detection probe as an origin of a spherical coordinate system;
s22, determining a radial axis, a polar angle axis and an azimuth angle axis of the spherical coordinate system.
The radial axis points to the target punching position, the polar angle axis corresponds to latitude, and the azimuth angle axis corresponds to longitude.
It should be noted that the spherical coordinate system is a three-dimensional coordinate system for describing the positions of points in space, and in the spherical coordinate system, the positions of the points are determined by three parameters of radial distance, polar angle, and azimuth angle.
The radial axis (r axis) is a straight line of the spherical coordinate system, which intersects the center of the sphere and points to the position where the point is located, and extends along the direction from the center of the sphere to the point, and the length thereof indicates the distance from the point to the center of the sphere, which is also referred to as the radial distance.
The polar axis (θ axis) is a ray in the spherical coordinate system, and points from the center of the sphere along the radial axis are located at a point, the angle of this axis is called the polar angle, and it usually ranges from 0 to pi, and indicates positivezThe included angle of the axes.
The azimuth axis (phi axis) is a ray in the spherical coordinate system, from the center of the sphere to the point along the polar axis, and the angle of this axis is called azimuth, and is usually in the range of 0 to 2 pi, representing the azimuth and the azimuthxThe included angle of the axes.
S23, measuring the electromagnetic force in the trigger electric field by using a measuring device, and determining radial coordinates according to the measured electromagnetic force.
The measuring device in this step includes a force sensor, a magnetic induction sensor, and the like.
S24, measuring the direction angle of the electromagnetic force in the trigger electric field by using a measuring device, and converting the measured direction angle into a polar angle and an azimuth angle in a spherical coordinate system.
The measuring device in this step includes a three-axis magnetometer, a gyroscope, a magnetic compass, and the like.
S25, marking the electromagnetic force and the direction angle in a spherical coordinate system, and respectively representing the electromagnetic force and the direction angle by using a vector and a scale.
S3, determining the moving direction and path of the detection probe by utilizing a polar angle axis and an azimuth angle axis in the spherical coordinate system, and detecting and updating the spherical coordinate information in real time;
s4, combining magnetic field positioning, visual positioning and acoustic positioning, estimating a target punching position, and performing cross verification on the target punching position.
Wherein, combine magnetic field location with vision location, acoustic wave location, estimate the target punching position, and cross-verify the target punching position includes the following steps:
s41, detecting the magnetic field distribution condition in the target perforation area in real time by using a tesla meter, and acquiring magnetic field positioning data.
A tesla meter is an instrument for measuring the intensity of a magnetic field, and is capable of detecting intensity component values of the magnetic field in three axial directions (x, y, and z axes) to obtain the distribution of the magnetic field in space. And establishing a uniform or non-uniform static magnetic field in the target punching area, installing teslameter in the detection area in an equidistant mode, measuring triaxial magnetic field intensity values of all positions in real time by the teslameter, and reconstructing the distribution condition of the magnetic field in the area by utilizing a numerical calculation or fitting method according to the magnetic field intensity values.
S42, acquiring visual positioning data in the target perforation area by using a visual sensor, and determining the position and the posture of the target perforation area in the visual field by using an image processing technology and a computer visual algorithm.
It should be noted that, the flow of this step includes: acquiring an image or video of a target perforation area; extracting features of the target perforation area from the image by using image processing technologies such as edge detection, feature extraction and the like; the extracted features are processed and analyzed using computer vision algorithms, such as feature matching, object tracking, three-dimensional reconstruction, etc., to determine the position and pose of the object-perforated region in the field of view.
S43, transmitting an acoustic wave signal by using an acoustic wave sensor, and receiving acoustic wave positioning data in the target perforation area.
S44, carrying out multi-source data fusion on the magnetic field positioning data, the visual positioning data and the acoustic positioning data, and estimating the target punching position based on the data fusion result.
The multi-source data fusion is carried out on the magnetic field positioning data, the visual positioning data and the acoustic wave positioning data, and the target punching position is estimated based on the data fusion result, and the method comprises the following steps:
s441, preprocessing magnetic field positioning data, visual positioning data and acoustic positioning data respectively;
s442, performing time alignment on the magnetic field positioning data, the visual positioning data and the acoustic positioning data to ensure that the same target punching position is corresponding to the same time point.
It should be noted that, when different positioning sensors work, original positioning data can be generated, but the data acquisition time may not be completely synchronous, a unified clock signal needs to be set as a time reference for data acquisition, and the time points of the data are matched by comparing the difference between the data acquisition time of each sensor and the clock signal and using methods such as linear or higher-order interpolation according to the difference value.
S443, constructing a fuzzy judgment matrix, and respectively calculating weight vectors of the magnetic field positioning data, the visual positioning data and the acoustic positioning data.
The construction of the fuzzy judgment matrix and the calculation of weight vectors of magnetic field positioning data, visual positioning data and acoustic wave positioning data respectively comprise the following steps:
s4431, constructing a fuzzy judgment matrix, and calculating a triangle fuzzy number complementary judgment matrix set;
s4432, calculating a fuzzy comprehensive judgment matrix based on the judgment matrix set;
s4433, respectively calculating fuzzy comprehensive evaluation values of magnetic field positioning data, visual positioning data and acoustic positioning data, and carrying out normalization processing to respectively obtain fuzzy relative weight vectors of the positioning data;
s4434, comparing the triangular fuzzy numbers corresponding to the positioning data in pairs, and sequencing the comparison results by using a sequencing algorithm of a fuzzy complementary judgment matrix to obtain the actual relative weight vector of the positioning data.
S444, establishing a data fusion model, and carrying out weighted fusion on the magnetic field positioning data, the visual positioning data and the acoustic positioning data according to the weight vector to obtain an estimation result of the target punching position.
It should be noted that, performing time alignment processing on the magnetic field positioning data, the visual positioning data and the acoustic positioning data, and establishing a data fusion model, such as a Bayesian model or a Kalman filtering model, based on the weight vectors calculated in the step model of each positioning data; and carrying out weighted fusion on the three groups of positioning data according to the weight vectors.
S5, a positioning error model is introduced to evaluate the influence of magnetic field interference on the target punching position, and punching precision errors are filtered in the target punching position.
Wherein, the introduced positioning error model evaluates the influence of magnetic field interference on the target punching position, and filters punching precision errors in the target punching position, comprising the following steps:
s51, respectively extracting data in a normal working state and data in the presence of magnetic field interference from magnetic field positioning data;
s52, performing frequency domain analysis on the extracted magnetic field positioning data to obtain a frequency spectrum function.
The frequency domain analysis is performed on the extracted magnetic field positioning data, and the spectrum function acquisition comprises the following steps:
s521, selecting main parameters in the front of a magnetic field time domain waveform period from the extracted magnetic field positioning data as the input of the neural network, and outputting the main parameters as the trained neural network;
s522, respectively initializing the weight coefficients and the bias of the input layer, the hidden layer and the output layer according to the relation characteristics of the Fei Bona odd columns and the golden section in the Ainit wave theory.
For the bias coefficient, a value in the feebner odd number column is used as the weight coefficient, that is, each number in the Fei Bona odd number column is used as an initial value of the weight coefficient.
Note that the Fei Bona odd sequence (Fibonacci sequence) is an infinite sequence, with 0 and 1 as the start values, and each of the subsequent numbers is the sum of the first two numbers.
For the bias term, the initial value is obtained by multiplying the weight coefficient by the golden section ratio.
S523, inputting samples in the training sample set and expected outputs, and respectively calculating output errors of each layer of units.
The calculation formula of the output error of each layer unit is as follows:
in the method, in the process of the invention,represent the firstkLayer numberjThe outputs of the individual cells;
representation oftTime of day (time)jOffset of individual units;
representation oftTime and the firstjWeight coefficients among the units;
representation oftTime of day (time)k−1Layer of the first layeriThe outputs of the individual cells;
represent the firstk−1Number of neurons in a layer.
S524, judging whether iteration conditions are met or not based on the output error of each layer of units, if yes, ending the algorithm, otherwise, continuing to execute step S525;
s525, taking the corrected component coefficients as output parameters, and obtaining corresponding spectrum functions;
s526, converting the frequency spectrum function into a corresponding time domain waveform, and evaluating the accuracy error of the magnetic field interference to the target punching position according to the rising edge and the falling edge of the time domain waveform.
S53, constructing a positioning error model according to the extracted magnetic field positioning data, and combining the positioning error model with a frequency spectrum function to evaluate the accuracy error of the magnetic field interference on the target punching position.
It should be noted that, a sample set of magnetic field positioning data is extracted, including magnetic field strength and a corresponding target punching position, and a positioning error model is established by regression analysis, gaussian process regression, and the like according to the extracted data sample.
S54, analyzing the size and distribution characteristics of the precision errors of the target punching positions;
s55, filtering out accuracy errors caused by magnetic field interference in the target punching position, and correcting the target punching position.
And S6, optimizing a punching path planning algorithm based on the positioning error model, calculating a confidence interval of the target punching position by using the punching path planning algorithm, and adjusting the punching strategy according to the confidence interval.
The positioning error model-based optimization perforation path planning algorithm is used for calculating a confidence interval of a target perforation position, and the perforation strategy is adjusted according to the confidence interval, and the positioning error model-based optimization perforation path planning algorithm comprises the following steps:
s61, determining a punching path by using a path planning algorithm according to the position of the drilling equipment and the target punching position, and calculating a confidence interval of the target punching position.
It should be noted that, by using an a-x algorithm, dijkstra algorithm, or genetic algorithm, an optimal drilling path is calculated according to the position of the drilling device and the target drilling position, the path planning algorithm may consider the terrain and other constraints to find the optimal path, and calculate the confidence interval of the target drilling position on the basis of the path planning, which may be implemented by estimating the position error of the drilling device, the sensor measurement error, and the uncertainty of the path planning algorithm.
S62, obtaining each node on the punching path, calculating the distance between the adjacent node of each node and the punching path, and sequencing the boundary nodes of the target confidence interval.
The method for acquiring each node on the perforation path, calculating the distance from the adjacent node of each node to the perforation path, and sequencing the boundary nodes of the target confidence interval comprises the following steps:
s621, sequencing the adjacent nodes according to a anticlockwise sequence, and judging whether the adjacent nodes are in a local minimum state or not;
s622, defining angles formed by two adjacent nodes and each node as adjacent angles, and extracting the adjacent node with the largest adjacent angle;
s623, if the range of the current node comprises an area outside the coverage range of the current node, the node is indicated to be a potential local minimum point;
s624, exchanging boundary information between the local minimum point and the adjacent node with the largest adjacent angle, and distributing the local minimum point information by using a hole-surrounding algorithm set.
S63, calculating the intersection point position of the perpendicular bisector corresponding to the adjacent node and the punching path by using a perpendicular bisector equation;
s64, determining airspace boundary nodes according to the intersection point positions, and calculating airspace boundary node sets by using a convex hull algorithm;
s65, judging whether nodes in the airspace boundary node set are on convex hull boundaries or not, and carrying out intersection test on each node and the convex hull boundaries to judge whether the airspace boundary needs to be crossed or not;
and S66, adjusting the original planning path according to the airspace boundary node set to obtain a new punching path passing through the confidence interval, and dynamically adjusting the confidence interval and the punching path in real time.
S7, punching work is carried out along an optimal path by using drilling equipment, and vector change of the detection electromagnetic force is detected in real time, so that closed-loop control and track tracking are realized.
It should be noted that, when the drilling equipment is running, the change of the detected electromagnetic force is detected and recorded in real time, the motion of the drilling equipment is corrected by adjusting the transmission mechanism, the driving force or other control parameters, and the drilling equipment can perform the punching work along the optimal punching path and realize the track tracking by real-time detection and closed-loop control.
In summary, by means of the above technical scheme of the present invention, the detected electromagnetic force can be marked by establishing the spherical coordinate system, so that the strength information of the electromagnetic force encountered by the drilling equipment can be provided, the drilling equipment is facilitated to adjust power or apply a proper control strategy, and meanwhile, the spherical coordinate system can conveniently convert the detected electromagnetic force information into other coordinate systems, so that the drilling equipment can be integrated with the control system of the drilling equipment better, accurate control and operation are realized, and further, the accuracy and safety of the drilling equipment in mine operation are facilitated to be ensured.
According to the invention, the accuracy of punching position positioning can be improved by combining different positioning technologies, the magnetic field positioning can provide a larger range of positioning information, the visual positioning can provide high-precision target detection and positioning, the acoustic positioning can provide three-dimensional position positioning information, the more accurate target punching position can be obtained by combining the positioning technologies, a complex geological structure and an interference source usually exist in a mine environment, a single positioning technology can not cope with all conditions, and multiple positioning technologies can comprehensively consider different environmental factors and provide more reliable punching positioning results.
The invention introduces the positioning error model to help evaluate the influence degree of the magnetic field interference on the target punching position, so that the relation between the magnetic field data and the positioning error can be analyzed, the influence of the magnetic field interference on the target punching position can be quantitatively evaluated, the positioning error can be predicted according to the magnetic field data and corrected into the target punching position by applying the positioning error model to the actual positioning data, and the accuracy and the precision of punching can be further improved.
According to the invention, the drilling path can be planned more accurately by optimizing the path planning algorithm, the confidence interval and the drilling path can be dynamically adjusted in real time, the deviation can be reduced as much as possible by optimizing the path planning algorithm and comprehensively considering the positioning error, so that the drilling position is closer to the target position, meanwhile, the confidence interval represents the possible drilling position range within the positioning error range, and the operator of the drilling equipment can be helped to know the uncertainty of the drilling position and make a corresponding decision.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (8)
1. The mine drilling equipment accurate punching control method based on electromagnetic force detection is characterized by comprising the following steps of:
s1, a trigger electric field is established in a target punching area, and a charge assembly is arranged on a detection probe to generate detection electromagnetic force representing the target punching area;
s2, using the detection probe as an origin to establish a spherical coordinate system, and marking the direction and the magnitude of the detected electromagnetic force in the spherical coordinate system;
s3, determining the moving direction and path of the detection probe by utilizing a polar angle axis and an azimuth angle axis in the spherical coordinate system, and detecting and updating the spherical coordinate information in real time;
s4, combining magnetic field positioning with visual positioning and acoustic positioning, estimating a target punching position, and performing cross verification on the target punching position;
s5, a positioning error model is introduced to evaluate the influence of magnetic field interference on the target punching position, and punching precision errors are filtered in the target punching position;
s6, optimizing a punching path planning algorithm based on the positioning error model, calculating a confidence interval of a target punching position by using the punching path planning algorithm, and adjusting a punching strategy according to the confidence interval;
s7, punching along an optimal path by using drilling equipment, and detecting vector change of the detected electromagnetic force in real time to realize closed-loop control and track tracking;
the introduced positioning error model evaluates the influence of magnetic field interference on the target punching position and filters punching precision errors in the target punching position, and the method comprises the following steps:
s51, respectively extracting data in a normal working state and data in the presence of magnetic field interference from magnetic field positioning data;
s52, performing frequency domain analysis on the extracted magnetic field positioning data to obtain a frequency spectrum function;
s53, constructing a positioning error model according to the extracted magnetic field positioning data, and combining the positioning error model with a frequency spectrum function to evaluate the accuracy error of the magnetic field interference on the target punching position;
s54, analyzing the size and distribution characteristics of the precision errors of the target punching positions;
s55, filtering out accuracy errors caused by magnetic field interference in the target punching position, and correcting the target punching position;
the frequency domain analysis is carried out on the extracted magnetic field positioning data, and the spectrum function acquisition comprises the following steps:
s521, selecting main parameters in the front of a magnetic field time domain waveform period from the extracted magnetic field positioning data as the input of the neural network, and outputting the main parameters as the trained neural network;
s522, respectively initializing weight coefficients and bias of an input layer, a hidden layer and an output layer according to the relation characteristics of Fei Bona odd columns and golden section in the Ainity wave theory;
s523, inputting samples in the training sample set and expected output, and respectively calculating output errors of each layer of units;
s524, judging whether iteration conditions are met or not based on the output error of each layer of units, if yes, ending the algorithm, otherwise, continuing to execute step S525;
s525, taking the corrected component coefficients as output parameters, and obtaining corresponding spectrum functions;
s526, converting the frequency spectrum function into a corresponding time domain waveform, and evaluating the accuracy error of the magnetic field interference to the target punching position according to the rising edge and the falling edge of the time domain waveform.
2. The method for controlling accurate perforation of mine drilling equipment based on electromagnetic force detection according to claim 1, wherein the method for establishing a spherical coordinate system by using the detection probe as an origin, and marking the direction and the magnitude of the detected electromagnetic force in the spherical coordinate system comprises the following steps:
s21, placing a detection probe in the triggering electric field and taking the detection probe as an origin of a spherical coordinate system;
s22, determining a radial axis, a polar angle axis and an azimuth angle axis of the spherical coordinate system;
the radial axis points to a target punching position, the polar angle axis corresponds to latitude, and the azimuth angle axis corresponds to longitude;
s23, measuring the electromagnetic force in the trigger electric field by using a measuring device, and determining radial coordinates according to the measured electromagnetic force;
s24, measuring the direction angle of electromagnetic force in the trigger electric field by using measuring equipment, and converting the measured direction angle into a polar angle and an azimuth angle in a spherical coordinate system;
s25, marking the electromagnetic force and the direction angle in a spherical coordinate system, and respectively representing the electromagnetic force and the direction angle by using a vector and a scale.
3. The mine drilling equipment accurate punching control method based on electromagnetic force detection according to claim 2, wherein the combination of magnetic field positioning, visual positioning and acoustic positioning, estimation of a target punching position and cross-validation of the target punching position comprises the following steps:
s41, detecting the magnetic field distribution condition in a target punching area in real time by using a tesla meter, and acquiring magnetic field positioning data;
s42, acquiring visual positioning data in the target perforation area by using a visual sensor, and determining the position and the posture of the target perforation area in the visual field by using an image processing technology and a computer visual algorithm;
s43, transmitting an acoustic wave signal by using an acoustic wave sensor, and receiving acoustic wave positioning data in a target punching area;
s44, carrying out multi-source data fusion on the magnetic field positioning data, the visual positioning data and the acoustic positioning data, and estimating the target punching position based on the data fusion result.
4. The mine drilling equipment accurate punching control method based on electromagnetic force detection according to claim 3, wherein the multi-source data fusion of magnetic field positioning data, visual positioning data and acoustic positioning data is performed, and the target punching position is estimated based on the data fusion result, comprising the following steps:
s441, preprocessing magnetic field positioning data, visual positioning data and acoustic positioning data respectively;
s442, performing time alignment on the magnetic field positioning data, the visual positioning data and the acoustic positioning data to ensure that the same target punching position is corresponding to the same time point;
s443, constructing a fuzzy judgment matrix, and respectively calculating weight vectors of magnetic field positioning data, visual positioning data and acoustic positioning data;
s444, establishing a data fusion model, and carrying out weighted fusion on the magnetic field positioning data, the visual positioning data and the acoustic positioning data according to the weight vector to obtain an estimation result of the target punching position.
5. The method for controlling accurate perforation of mine drilling equipment based on electromagnetic force detection according to claim 4, wherein the constructing the fuzzy judgment matrix and calculating weight vectors of magnetic field positioning data, visual positioning data and acoustic wave positioning data respectively comprises the following steps:
s4431, constructing a fuzzy judgment matrix, and calculating a triangle fuzzy number complementary judgment matrix set;
s4432, calculating a fuzzy comprehensive judgment matrix based on the judgment matrix set;
s4433, respectively calculating fuzzy comprehensive evaluation values of magnetic field positioning data, visual positioning data and acoustic positioning data, and carrying out normalization processing to respectively obtain fuzzy relative weight vectors of the positioning data;
s4434, comparing the triangular fuzzy numbers corresponding to the positioning data in pairs, and sequencing the comparison results by using a sequencing algorithm of a fuzzy complementary judgment matrix to obtain the actual relative weight vector of the positioning data.
6. The mine drilling equipment accurate punching control method based on electromagnetic force detection according to claim 5, wherein the calculation formula of the output error of each layer of unit is as follows:
;
in the method, in the process of the invention,represent the firstkLayer numberjThe outputs of the individual cells;
representation oftTime of day (time)jOffset of individual units;
representation oftTime and the firstjWeight coefficients among the units;
representation oftTime of day (time)k−1Layer of the first layeriThe outputs of the individual cells;
represent the firstk−1Number of neurons in a layer.
7. The mine drilling equipment accurate punching control method based on electromagnetic force detection according to claim 6, wherein the positioning error model-based optimization punching path planning algorithm is used for calculating a confidence interval of a target punching position, and the punching strategy is adjusted according to the confidence interval, and the method comprises the following steps:
s61, determining a punching path by using a path planning algorithm according to the position of the drilling equipment and the target punching position, and calculating a confidence interval of the target punching position;
s62, acquiring each node on the punching path, calculating the distance from the adjacent node of each node to the punching path, and sequencing the boundary nodes of the target confidence interval;
s63, calculating the intersection point position of the perpendicular bisector corresponding to the adjacent node and the punching path by using a perpendicular bisector equation;
s64, determining airspace boundary nodes according to the intersection point positions, and calculating airspace boundary node sets by using a convex hull algorithm;
s65, judging whether nodes in the airspace boundary node set are on convex hull boundaries or not, and carrying out intersection test on each node and the convex hull boundaries to judge whether the airspace boundary needs to be crossed or not;
and S66, adjusting the original planning path according to the airspace boundary node set to obtain a new punching path passing through the confidence interval, and dynamically adjusting the confidence interval and the punching path in real time.
8. The method for precisely drilling and controlling the mine drilling equipment based on electromagnetic force detection according to claim 7, wherein the steps of obtaining each node on the drilling path, calculating the distance from the neighboring node of each node to the drilling path, and sequencing the boundary nodes of the target confidence interval comprise the following steps:
s621, sequencing the adjacent nodes according to a anticlockwise sequence, and judging whether the adjacent nodes are in a local minimum state or not;
s622, defining angles formed by two adjacent nodes and each node as adjacent angles, and extracting the adjacent node with the largest adjacent angle;
s623, if the range of the current node comprises an area outside the coverage range of the current node, the node is indicated to be a potential local minimum point;
s624, exchanging boundary information between the local minimum point and the adjacent node with the largest adjacent angle, and distributing the local minimum point information by using a hole-surrounding algorithm set.
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