CN114427869B - Mining inclinometer abnormal calibration data judging and processing method - Google Patents

Mining inclinometer abnormal calibration data judging and processing method Download PDF

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CN114427869B
CN114427869B CN202111615438.7A CN202111615438A CN114427869B CN 114427869 B CN114427869 B CN 114427869B CN 202111615438 A CN202111615438 A CN 202111615438A CN 114427869 B CN114427869 B CN 114427869B
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宫浩
王信文
张鹏
汪凯斌
刘京科
李渊
石岩
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Xian Research Institute Co Ltd of CCTEG
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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Abstract

The invention discloses a mining inclinometer abnormal calibration data judging and processing method, which adopts an inclinometer to determine a rotation interval and a given position, adopts a sensor to acquire and preprocess data at each given position to obtain triaxial geomagnetic field data and triaxial acceleration data, and eliminates the problem that the deviation between certain acquired data and normal data is larger due to human reading errors in calibration by judging and screening the inclinometer calibration original acquired data, thereby improving the error compensation precision and the gesture resolving precision of the inclinometer; the fitting interpolation is carried out on the judged acquired data, so that the reliability of the original data is improved, repeated calibration of the inclinometer due to the abnormality of the original acquired data is avoided, and the problem of reduced measurement accuracy of the inclinometer due to the artificial reading error is solved; the technical problems of low measurement precision and large workload caused by large deviation between the acquired data of the sensor and the normal data in the error correction process of the inclinometer are solved.

Description

Mining inclinometer abnormal calibration data judging and processing method
Technical Field
The invention belongs to the field of data processing, relates to an abnormal data judging and processing method, and particularly relates to a mining inclinometer abnormal calibration data judging and processing method.
Background
In the exploration of hidden disaster-causing factors such as underground coal mine gas and water damage, a drilling method is generally used for exploration, and the purpose of gas drainage and water exploration and drainage is realized through construction and drilling. How to judge whether the actual drilling track is drilled according to the designed track and whether the final hole point reaches the design target point is a key point for limiting the drilling effect. At present, the measurement of the drilling track is mostly realized by a drilling inclinometer, and three angles in the drilling are determined by a measurement nipple in the inclinometer by a triaxial geomagnetic field sensor and a triaxial acceleration sensor: inclination angle, azimuth angle, and facing angle. Because of the special use environment of the underground coal mine, the inclinometer needs to calibrate the instrument before being used, so that higher measurement accuracy is achieved.
At present, a plurality of error correction methods for inclinometers based on magnetic sensors are adopted, and error correction is carried out on the measuring nipple by adopting methods such as a least square method, an elliptical false method and the like, so that a good effect is obtained. However, the methods are all carried out on the premise that the original data of the sensor collected during calibration are not interfered, and in the calibration process, the sensor is influenced by factors such as calibration environment, errors of a manual rotation non-magnetic turntable and the like, so that the actual collected data of the sensor at certain points in space have larger difference from the normal data. The abnormal data cannot be found in time when the calibration is usually carried out, and in this case, when the subsequent error modeling and error coefficient calculation are carried out, the calculated sensor error correction coefficient is not a real correction coefficient, so that the measurement accuracy of the inclinometer is reduced. When this happens, the inclinometer probe is usually recalibrated on the nonmagnetic turntable, resulting in a great deal of manpower and time waste.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a mining inclinometer abnormal calibration data judging and processing method, which solves the technical problems of low measurement precision and large workload caused by large deviation between acquired data and normal data of a sensor in the error correction process of the inclinometer in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a mining inclinometer abnormal calibration data judging and processing method specifically comprises the following steps:
step one, determining a rotation interval and a given position by using an inclinometer, acquiring data at each given position by using a sensor, and preprocessing acquired data to obtain triaxial geomagnetic field data H of the given position 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]；
The rotation interval and the given position are determined by the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable;
calculating to obtain a triaxial geomagnetic field component theoretical value H of the given position according to the geomagnetic field intensity and the gravitational field intensity 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]；
Wherein: i represents the sequence number of a given location;
step three, calculating actual collection average values of triaxial geomagnetic field data in each rotation interval respectively
Figure BDA0003436267170000021
And the actual acquisition average value of triaxial acceleration data +.>
Figure BDA0003436267170000022
Calculating theoretical average value of triaxial geomagnetic field in each rotation interval>
Figure BDA0003436267170000023
And triaxial acceleration theoryTheoretical average>
Figure BDA0003436267170000024
And find the triaxial magnetic field difference delta H j Triaxial acceleration difference ΔG j ；
Wherein: j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
step four, respectively judging magnetic field difference values |delta H of three axes j Difference in sum acceleration Δg j Whether the I meets the formula (6), if so, performing a step seven, and if not, performing a step five;
Figure BDA0003436267170000031
wherein:
H m a determination threshold for the magnetic field;
G m determining a threshold value for acceleration;
fifthly, eliminating triaxial geomagnetic field data H corresponding to axes which do not meet the formula (6) 1i And head-tail data in the triaxial acceleration data G1i, and recalculate the actual acquisition average value of the triaxial geomagnetic field of the sensor
Figure BDA0003436267170000032
And the actual acquisition average value of triaxial acceleration +.>
Figure BDA0003436267170000033
And find the magnetic field difference |DeltaH j ' and acceleration difference |ΔG j ′|；
Step six, judging the magnetic field difference value |delta H j ' and acceleration difference |ΔG j Whether the' |meets the formula (9), if yes, acquiring three-axis geomagnetic field data and three-axis acceleration data without abnormality, and entering a step seven; if not, repeating the steps five to six until the formula (9) is established;
Figure BDA0003436267170000034
step seven, adding the head and tail data removed in the step five except the data removed at present back to the three-axis geomagnetic field data and the three-axis acceleration data without abnormality obtained in the step six again, and repeating the step four until the judgment of the three-axis geomagnetic field data and the three-axis acceleration data acquired by the magnetic sensor and the acceleration sensor is completed;
and step eight, performing polynomial fitting on the abnormal triaxial geomagnetic field data and triaxial acceleration data obtained in the step six, interpolating the removed abnormal data to obtain updated triaxial geomagnetic field data and triaxial acceleration data, and performing error correction processing on the updated triaxial geomagnetic field data and triaxial acceleration data to obtain corrected inclination angle, azimuth angle, facing angle and error correction coefficient.
The invention also comprises the following technical characteristics:
in the first step, the specific process of data acquisition and preprocessing is as follows:
step 1.1, placing and fixing a probe tube of an inclinometer on a high-precision triaxial nonmagnetic rotary table, and then rotating the nonmagnetic rotary table to a given position in a space;
step 1.2, acquiring and performing median average filtering processing through a magnetic sensor and an acceleration sensor to obtain triaxial geomagnetic field data H 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]。
The specific method for determining the given position through the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable comprises the following steps:
s1, selecting azimuth angles of a high-precision triaxial nonmagnetic rotary table, and respectively rotating inclination angles to-20 degrees, -53 degrees, -75 degrees, -90 degrees, -0 degrees, +20 degrees, +53 degrees, +75 degrees and +90 degrees to obtain rotation intervals;
s2, rotating the facing angle of the tool at intervals of 10 degrees within the range of 0-360 degrees under each inclination angle to obtain 36 given positions.
The theoretical value H of the triaxial geomagnetic field component 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]The calculation is carried out according to the formula (1) and the formula (2) respectively:
Figure BDA0003436267170000041
Figure BDA0003436267170000042
wherein:
H 0 is geomagnetic field strength;
G 0 gravity field strength;
alpha is the magnetic inclination angle;
Figure BDA0003436267170000051
azimuth for a given location;
θ is the tilt angle of a given position;
gamma is the facing angle of a given position;
the third step specifically comprises the following steps:
step 3.1, calculating the actual acquisition average value of the triaxial geomagnetic field data in each rotation interval through a formula (3)
Figure BDA0003436267170000052
And the actual acquisition average value of triaxial acceleration data +.>
Figure BDA0003436267170000053
Figure BDA0003436267170000054
Wherein:
j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
m is the number of given positions in each rotation interval;
step 3.2, calculating the theoretical average value of the triaxial geomagnetic field in each rotation interval through a formula (4)
Figure BDA0003436267170000055
And the theoretical average value of triaxial acceleration +.>
Figure BDA0003436267170000056
Figure BDA0003436267170000057
Step 3.3, calculating the theoretical average value of the triaxial geomagnetic field through a formula (5)
Figure BDA0003436267170000058
Mean value of actual acquisition of triaxial geomagnetic field +.>
Figure BDA0003436267170000059
Magnetic field difference |ΔH of (2) j I and triaxial acceleration theoretical average value +.>
Figure BDA00034362671700000510
Mean value of actual acquisition of triaxial acceleration +.>
Figure BDA00034362671700000511
Acceleration difference |Δg of (a) j |。
Figure BDA00034362671700000512
The fifth step specifically comprises the following steps:
step 5.1, eliminating the triaxial geomagnetic field data H of the axes of which the magnetic field difference value or acceleration difference value does not satisfy the formula (3) 1i ＝[H x1i ，H y1i ，H z1i ]And degree of triaxial accelerationAccording to G 1i ＝[G x1i ，G y1i ，G z1i ]After the head and tail data of (2), calculating the actual acquisition average value of the triaxial geomagnetic field of the sensor according to a formula (4)
Figure BDA0003436267170000061
And the actual acquisition average value of triaxial acceleration +.>
Figure BDA0003436267170000062
Figure BDA0003436267170000063
Step 5.2, calculating the theoretical average value of the triaxial geomagnetic field according to the formula (5)
Figure BDA0003436267170000064
Mean value of actual acquisition of triaxial geomagnetic field +.>
Figure BDA0003436267170000065
Magnetic field difference |ΔH' j I and triaxial acceleration theoretical average value +.>
Figure BDA0003436267170000066
With the actual collection average value of triaxial acceleration
Figure BDA0003436267170000067
Acceleration difference |Δg of (a) j ′|。
Figure BDA0003436267170000068
Compared with the prior art, the invention has the beneficial technical effects that:
according to the invention, the original acquired data of the inclinometer calibration are judged and screened, so that the problem of larger deviation between certain acquired data and normal data caused by human reading errors in the calibration process is solved, and the error compensation precision and the attitude resolving precision of the inclinometer are improved; by fitting interpolation on the judged acquired data, the reliability of the original data is further improved, repeated calibration of the inclinometer due to the abnormality of the original acquired data is avoided, and the problem of reduced measurement accuracy of the inclinometer due to artificial reading errors is solved; the method solves the technical problems of low measurement precision and large workload caused by large deviation between the acquired data of the sensor and the normal data in the error correction process of the inclinometer in the prior art.
Drawings
FIG. 1 is a flow chart of a method for discriminating calibration anomaly data of an inclinometer according to the present invention;
FIG. 2 is a graph of inclinometer calibration tilt error after interference;
FIG. 3 is a graph of inclinometer calibration inclination error after anomaly data determination processing;
FIG. 4 is a graph of the calibration azimuth error of the inclinometer after being disturbed;
FIG. 5 is a graph of the inclinometer calibration azimuth error after anomaly data determination processing.
The following examples illustrate the invention in further detail.
Detailed Description
It should be noted that, the normal data in the present invention refers to the actual collected data without human error.
All parts in the present invention are known in the art, unless otherwise specified.
The following specific embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solutions of the present application fall within the protection scope of the present invention.
The invention provides a mining inclinometer abnormal calibration data judging and processing method, which specifically comprises the following steps:
step one, determining a rotation interval and given positions by using an inclinometer, acquiring data at each given position by using a sensor, and preprocessing acquired data to obtain three given positionsAxial geomagnetic field data H 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]；
The rotation interval and the given position are determined by the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable;
calculating to obtain a triaxial geomagnetic field component theoretical value H of the given position according to the geomagnetic field intensity and the gravitational field intensity 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]；
Wherein: i represents the sequence number of a given location;
step three, calculating actual collection average values of triaxial geomagnetic field data in each rotation interval respectively
Figure BDA0003436267170000081
And the actual acquisition average value of triaxial acceleration data +.>
Figure BDA0003436267170000082
Calculating theoretical average value of triaxial geomagnetic field in each rotation interval>
Figure BDA0003436267170000083
And the theoretical average value of triaxial acceleration +.>
Figure BDA0003436267170000084
And find out the triaxial magnetic field difference value |DeltaH j I and triaxial acceleration difference |Δg j |；
Wherein: j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
step four, respectively judging magnetic field difference values |delta H of three axes j Difference in sum acceleration Δg j Whether the I meets the formula (6), if so, performing a step seven, and if not, performing a step five;
Figure BDA0003436267170000085
wherein:
H m the value range is 200-500 for the magnetic field judgment threshold value;
G m the value range of the acceleration judgment threshold value is 0.03-0.05;
fifthly, eliminating triaxial geomagnetic field data H corresponding to axes which do not meet the formula (6) 1i And head-tail data in the triaxial acceleration data G1i, and recalculate the actual acquisition average value of the triaxial geomagnetic field of the sensor
Figure BDA0003436267170000086
And the actual acquisition average value of triaxial acceleration +.>
Figure BDA0003436267170000087
And find the magnetic field difference |DeltaH j ' and acceleration difference |ΔG j ′|；
Step six, judging the magnetic field difference value |delta H j ' and acceleration difference |ΔG j Whether the' |meets the formula (9), if yes, acquiring three-axis geomagnetic field data and three-axis acceleration data without abnormality, and entering a step seven; if not, repeating the steps five to six until the formula (9) is established;
Figure BDA0003436267170000088
step seven, adding the head and tail data removed in the step five except the data removed at present back to the three-axis geomagnetic field data and the three-axis acceleration data without abnormality obtained in the step six again, and repeating the step four until the judgment of the three-axis geomagnetic field data and the three-axis acceleration data acquired by the magnetic sensor and the acceleration sensor is completed;
and step eight, performing polynomial fitting on the abnormal triaxial geomagnetic field data and triaxial acceleration data obtained in the step six, interpolating the removed abnormal data to obtain updated triaxial geomagnetic field data and triaxial acceleration data, and performing error correction processing on the updated triaxial geomagnetic field data and triaxial acceleration data to obtain corrected inclination angle, azimuth angle, facing angle and error correction coefficient.
In the above technical solution, in step seven, if the formula (9) is satisfied after N times of rejection, the N-1 times of head-tail data rejected in step five is added back to the three-axis geomagnetic field data and the three-axis acceleration data without abnormality obtained in step six, and step four is repeated until the three-axis geomagnetic field data and the three-axis acceleration data collected by the magnetic sensor and the acceleration sensor are judged to be complete;
by judging and screening the original acquired data of the inclinometer calibration, the problem that the deviation between certain acquired data and normal data is large due to human reading errors in the calibration process is solved, and the error compensation precision and the gesture resolving precision of the inclinometer are improved; by fitting interpolation on the judged acquired data, the reliability of the original data is further improved, repeated calibration of the inclinometer due to the abnormality of the original acquired data is avoided, and the problem of reduced measurement accuracy of the inclinometer due to artificial reading errors is solved; the technical problems of low measurement precision and large workload caused by large deviation between collected data of a sensor and normal data in the error correction process of the inclinometer in the prior art are solved.
Specifically, in the first step, the specific process of data acquisition and preprocessing is as follows:
step 1.1, placing and fixing a probe tube of an inclinometer on a high-precision triaxial nonmagnetic rotary table, and then rotating the nonmagnetic rotary table to a given position in a space;
step 1.2, acquiring and performing median average filtering processing through a magnetic sensor and an acceleration sensor to obtain triaxial geomagnetic field data H 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]。
Specifically, the specific method for determining the given position through the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable comprises the following steps:
s1, selecting azimuth angles of a high-precision triaxial nonmagnetic rotary table, and respectively rotating inclination angles to-20 degrees, -53 degrees, -75 degrees, -90 degrees, -0 degrees, +20 degrees, +53 degrees, +75 degrees and +90 degrees to obtain rotation intervals;
s2, rotating the facing angle of the tool at intervals of 10 degrees within the range of 0-360 degrees under each inclination angle to obtain 36 given positions.
Triaxial geomagnetic field component theoretical value H 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]The calculation is carried out according to the formula (1) and the formula (2) respectively:
Figure BDA0003436267170000101
Figure BDA0003436267170000102
wherein:
H 0 is geomagnetic field strength;
G 0 gravity field strength;
alpha is the magnetic inclination angle;
Figure BDA0003436267170000103
azimuth for a given location;
θ is the tilt angle of a given position;
gamma is the facing angle of a given position;
specifically, the third step specifically includes the following steps:
step 3.1, calculating the actual acquisition average value of the triaxial geomagnetic field data in each rotation interval through a formula (3)
Figure BDA0003436267170000104
And the actual acquisition average value of triaxial acceleration data +.>
Figure BDA0003436267170000105
Figure BDA0003436267170000111
Wherein:
j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
m is the number of given positions in each rotation interval;
step 3.2, calculating the theoretical average value of the triaxial geomagnetic field in each rotation interval through a formula (4)
Figure BDA0003436267170000112
And the theoretical average value of triaxial acceleration +.>
Figure BDA0003436267170000113
Figure BDA0003436267170000114
Step 3.3, calculating the theoretical average value of the triaxial geomagnetic field through a formula (5)
Figure BDA0003436267170000115
Mean value of actual acquisition of triaxial geomagnetic field +.>
Figure BDA0003436267170000116
Magnetic field difference |ΔH of (2) j I and triaxial acceleration theoretical average value +.>
Figure BDA0003436267170000117
Mean value of actual acquisition of triaxial acceleration +.>
Figure BDA0003436267170000118
Acceleration difference |Δg of (a) j |。
Figure BDA0003436267170000119
Specifically, the fifth step specifically includes the following steps:
step 5.1, eliminating the triaxial geomagnetic field data H of the axes of which the magnetic field difference value or acceleration difference value does not satisfy the formula (3) 1i ＝[H x1i ，Hy 1 i，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]After the head and tail data of (2), calculating the actual acquisition average value of the triaxial geomagnetic field of the sensor according to a formula (4)
Figure BDA00034362671700001110
And the actual acquisition average value of triaxial acceleration +.>
Figure BDA00034362671700001111
Figure BDA00034362671700001112
Step 5.2, calculating the theoretical average value of the triaxial geomagnetic field according to the formula (5)
Figure BDA0003436267170000121
Mean value of actual acquisition of triaxial geomagnetic field +.>
Figure BDA0003436267170000122
Magnetic field difference |ΔH' j I and triaxial acceleration theoretical average value +.>
Figure BDA0003436267170000123
With the actual collection average value of triaxial acceleration
Figure BDA0003436267170000124
Acceleration difference |Δg of (a) j ′|。
Figure BDA0003436267170000125
Examples:
the embodiment provides a mining inclinometer abnormal calibration data judging and processing method, as shown in fig. 1, which specifically comprises the following steps:
step one, determining a rotation interval and a given position by using an inclinometer, acquiring data at each given position by using a sensor, and preprocessing acquired data to obtain triaxial geomagnetic field data H of the given position 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]；
The rotation interval and the given position are determined by the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable;
in this embodiment, 9 intervals are determined by azimuth angle, inclination angle and facing angle, the given positions at 324 are included in the 9 intervals, the given positions at 36 are added in total in the rotation interval with the inclination angle of 0 °, and 3 abnormal points are manually set at the 1 st, 10 th and 20 th positions in the x-axis data of the acceleration sensor and the 1 st, 10 th and 20 th positions in the x-axis data of the magnetic sensor respectively, and the rest are normal, as shown in table 1. The inclination angle measurement error curve after the calibration of the inclinometer is finished is shown in fig. 2, and the absolute value of the inclination angle error is maximally more than 1 degree; the azimuth error curve is shown in fig. 4, and the absolute value of the azimuth error exceeds 9 ° at maximum.
TABLE 1 sensor data acquisition at 0℃Tilt
Figure BDA0003436267170000126
Figure BDA0003436267170000131
/>
Figure BDA0003436267170000141
Step twoCalculating to obtain a triaxial geomagnetic field component theoretical value H of a given position according to geomagnetic field intensity and gravitational field intensity 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]；
Wherein: i represents the sequence number of a given location;
in the present embodiment, theoretical values of the triaxial geomagnetic field component and triaxial acceleration component at 36 given positions when the inclination angle is 0 ° are calculated as shown in table 2.
TABLE 2 theoretical values of triaxial geomagnetic field component and triaxial acceleration component at 0 degree of tilt angle
Figure BDA0003436267170000142
/>
Figure BDA0003436267170000151
Step three, calculating actual collection average values of triaxial geomagnetic field data in each rotation interval respectively
Figure BDA0003436267170000152
And the actual acquisition average value of triaxial acceleration data +.>
Figure BDA0003436267170000153
Calculating theoretical average value of triaxial geomagnetic field in each rotation interval>
Figure BDA0003436267170000154
And the theoretical average value of triaxial acceleration +.>
Figure BDA0003436267170000155
And find out the triaxial magnetic field difference value |DeltaH j I and triaxial acceleration difference |Δg j |；
Wherein: j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
in the present embodiment, the actual collection average value of the three-axis geomagnetic field data of 36 given positions within the rotation interval of 0 ° is the
Figure BDA0003436267170000161
Triaxial acceleration data actual acquisition average value +.>
Figure BDA0003436267170000162
Theoretical average value of triaxial geomagnetic field>
Figure BDA0003436267170000163
Theoretical mean value of triaxial acceleration>
Figure BDA0003436267170000164
Triaxial magnetic field difference |DeltaH j I and triaxial acceleration difference |Δg j I, as shown in table 3.
TABLE 3 data collected at 0℃inclination, theoretical data and corresponding differences
Figure BDA0003436267170000165
Step four, respectively judging magnetic field difference values |delta H of three axes j Difference in sum acceleration Δg j Whether the I meets the formula (6), if so, performing a step seven, and if not, performing a step five;
Figure BDA0003436267170000166
wherein:
H m the value of the magnetic field judgment threshold value is 200;
G m the value of the acceleration judgment threshold value is 0.03;
in this embodiment, referring to the data in table 3, it is determined whether the determination condition of formula (6) is satisfied, and the acceleration x-axis and geomagnetic field x-axis data obtained by the determination do not satisfy the determination condition of formula (6), so that the acceleration sensor and geomagnetic field sensor x-axis data are substituted into the fifth step; the y-axis and z-axis data of the acceleration sensor and the geomagnetic field sensor meet the judgment condition of the formula (6), and are substituted into the seventh step;
fifthly, eliminating triaxial geomagnetic field data H corresponding to axes which do not meet the formula (6) 1i And head-tail data in the triaxial acceleration data G1i, and recalculate the actual acquisition average value of the triaxial geomagnetic field of the sensor
Figure BDA0003436267170000167
And the actual acquisition average value of triaxial acceleration +.>
Figure BDA0003436267170000168
And find the magnetic field difference |DeltaH j ' and acceleration difference |ΔG j ′|；
In this embodiment, after first removing the first and last data of the acceleration x-axis and the geomagnetic field x-axis, the geomagnetic field x-axis actually collects the average value
Figure BDA0003436267170000171
And acceleration x-axis actual acquisition mean +.>
Figure BDA0003436267170000172
Geomagnetic field x-axis difference |Δh x ' and acceleration x-axis difference |ΔG x 'the table 4 shows the'.
TABLE 4 geomagnetic field after first and last data rejection, acceleration x-axis average and corresponding difference
Figure BDA0003436267170000173
Step six, judging the magnetic field difference value |delta H j ' and acceleration difference |ΔG j Whether the' |meets the formula (9), if yes, acquiring three-axis geomagnetic field data and three-axis acceleration data without abnormality, and entering a step seven; if not, repeating the steps five to six until the formula (9) is established;
Figure BDA0003436267170000174
referring to the data shown in the table 4, after the data are removed, the data of the x axis of the geomagnetic field sensor and the data of the x axis of the acceleration sensor still do not meet the formula (9), so the steps five to six are continuously repeated until the data of the x axis of the geomagnetic field sensor and the data of the x axis of the acceleration sensor meet the formula (9);
step seven, adding the head and tail data removed in the step five except the data removed at present back to the three-axis geomagnetic field data and the three-axis acceleration data without abnormality obtained in the step six again, and repeating the step four until the judgment of the three-axis geomagnetic field data and the three-axis acceleration data acquired by the magnetic sensor and the acceleration sensor is completed;
in this embodiment, after the geomagnetic field x-axis and acceleration x-axis data are rejected for 3 times, the formula (9) is established, and then the data rejected for the previous two times are added back to the non-abnormal triaxial geomagnetic field data and triaxial acceleration data obtained in the step six, and the step four is repeated until the geomagnetic field x-axis data and acceleration x-axis data are judged to be complete.
And step eight, performing polynomial fitting on the abnormal triaxial geomagnetic field data and triaxial acceleration data obtained in the step six, interpolating the removed abnormal data to obtain updated triaxial geomagnetic field data and triaxial acceleration data, and performing error correction processing on the updated triaxial geomagnetic field data and triaxial acceleration data to obtain corrected inclination angle, azimuth angle, facing angle and error correction coefficient.
In this embodiment, polynomial fitting interpolation is performed on the abnormal geomagnetic field-free x-axis data and the acceleration-free x-axis data obtained in the step seven, and the fitting power is 8 th power. Obtaining updated geomagnetic field x-axis data and acceleration x-axis data as shown in Table 5
TABLE 5 fitting the interpolated final geomagnetic field and acceleration x-axis data
Figure BDA0003436267170000181
/>
Figure BDA0003436267170000191
FIG. 3 is a graph of the error in tilt angle measurement after the processing of the anomaly data determination and processing method provided by the present invention, it can be seen that the absolute value of tilt angle can be controlled within 0.3 ° after the determination processing; FIG. 5 is a graph of azimuth angle measurement error after the abnormal data is determined and processed by the method provided by the invention, and the absolute value of the azimuth angle error after the processing can be controlled within 1.3 degrees, which proves that the method solves the technical problems of low measurement precision and large workload caused by larger deviation between the acquired data of the sensor and the normal data in the error correction process of the inclinometer in the prior art.
As a preferred scheme of this embodiment, in the first step, the specific process of data acquisition and preprocessing is:
step 1.1, placing and fixing a probe tube of an inclinometer on a high-precision triaxial nonmagnetic rotary table, and then rotating the nonmagnetic rotary table to a given position in a space;
step 1.2, acquiring and performing median average filtering processing through a magnetic sensor and an acceleration sensor to obtain triaxial geomagnetic field data H 1i ＝[H x1i， H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]。
As a preferred aspect of the present embodiment, the specific method for determining the given position by the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable includes the steps of:
s1, selecting azimuth angles of a high-precision triaxial nonmagnetic rotary table, and respectively rotating inclination angles to-20 degrees, -53 degrees, -75 degrees, -90 degrees, -0 degrees, +20 degrees, +53 degrees, +75 degrees and +90 degrees to obtain rotation intervals;
s2, rotating the facing angle of the tool at intervals of 10 degrees within the range of 0-360 degrees under each inclination angle to obtain 36 given positions.
Triaxial geomagnetic field component theoretical value H 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial accelerationComponent theory value G 2i ＝[G x2i ，G y2i ，G z2i ]The calculation is carried out according to the formula (1) and the formula (2) respectively:
Figure BDA0003436267170000201
Figure BDA0003436267170000202
wherein:
H 0 is geomagnetic field strength;
G 0 gravity field strength;
alpha is the magnetic inclination angle;
Figure BDA0003436267170000211
azimuth for a given location;
θ is the tilt angle of a given position;
gamma is the facing angle of a given position;
as a preferable scheme of the present embodiment, the third step specifically includes the following steps:
step 3.1, calculating the actual acquisition average value of the triaxial geomagnetic field data in each rotation interval through a formula (3)
Figure BDA0003436267170000212
And the actual acquisition average value of triaxial acceleration data +.>
Figure BDA0003436267170000213
Figure BDA0003436267170000214
Wherein:
j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
m is the number of given positions in each rotation interval;
step 3.2, calculating the theoretical average value of the triaxial geomagnetic field in each rotation interval through a formula (4)
Figure BDA0003436267170000215
And the theoretical average value of triaxial acceleration +.>
Figure BDA0003436267170000216
Figure BDA0003436267170000217
Step 3.3, calculating the theoretical average value of the triaxial geomagnetic field through a formula (5)
Figure BDA0003436267170000218
Mean value of actual acquisition of triaxial geomagnetic field +.>
Figure BDA0003436267170000219
Magnetic field difference |ΔH of (2) j I and triaxial acceleration theoretical average value +.>
Figure BDA00034362671700002110
Mean value of actual acquisition of triaxial acceleration +.>
Figure BDA00034362671700002111
Acceleration difference |Δg of (a) j |。
Figure BDA00034362671700002112
/>
As a preferred solution of this embodiment, the fifth step specifically includes the following steps:
step 5.1, eliminating the triaxial geomagnetic field data H of the axes of which the magnetic field difference value or acceleration difference value does not satisfy the formula (3) 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]After the head and tail data of (2), calculating the actual acquisition average value of the triaxial geomagnetic field of the sensor according to a formula (4)
Figure BDA0003436267170000221
And the actual acquisition average value of triaxial acceleration +.>
Figure BDA0003436267170000222
Figure BDA0003436267170000223
Step 5.2, calculating the theoretical average value of the triaxial geomagnetic field according to the formula (5)
Figure BDA0003436267170000224
Mean value of actual acquisition of triaxial geomagnetic field +.>
Figure BDA0003436267170000225
Magnetic field difference |ΔH' j I and triaxial acceleration theoretical average value +.>
Figure BDA0003436267170000226
With the actual collection average value of triaxial acceleration
Figure BDA0003436267170000227
Acceleration difference |Δg of (a) j |。
Figure BDA0003436267170000228
/>

Claims (4)

1. The mining inclinometer abnormal calibration data judging and processing method is characterized by comprising the following steps of:
step one, determining a rotation interval and a given position by using an inclinometer, acquiring data at each given position by using a sensor, and preprocessing acquired data to obtain triaxial geomagnetic field data of the given positionH 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]；
The rotation interval and the given position are determined by the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable;
calculating to obtain a triaxial geomagnetic field component theoretical value H of the given position according to the geomagnetic field intensity and the gravitational field intensity 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]；
Wherein: i represents the sequence number of a given location;
step three, calculating actual collection average values of triaxial geomagnetic field data in each rotation interval respectively
Figure FDA0004126506790000011
And the actual acquisition average value of triaxial acceleration data +.>
Figure FDA0004126506790000012
Calculating theoretical average value of triaxial geomagnetic field in each rotation interval>
Figure FDA0004126506790000013
And the theoretical average value of triaxial acceleration +.>
Figure FDA0004126506790000014
And find the triaxial magnetic field difference delta H j Triaxial acceleration difference ΔG j ；
The third step specifically comprises the following steps:
step 3.1, calculating the actual acquisition average value of the triaxial geomagnetic field data in each rotation interval through a formula (3)
Figure FDA0004126506790000015
And the actual acquisition average value of triaxial acceleration data +.>
Figure FDA0004126506790000016
Figure FDA0004126506790000017
Wherein:
j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
m is the number of given positions in each rotation interval;
step 3.2, calculating the theoretical average value of the triaxial geomagnetic field in each rotation interval through a formula (4)
Figure FDA0004126506790000021
And the theoretical average value of triaxial acceleration +.>
Figure FDA0004126506790000022
Figure FDA0004126506790000023
Step 3.3, calculating the theoretical average value of the triaxial geomagnetic field through a formula (5)
Figure FDA0004126506790000024
And the actual collection average value of the triaxial geomagnetic field
Figure FDA0004126506790000025
Magnetic field difference |ΔH of (2) j I and triaxial acceleration theoretical average value +.>
Figure FDA0004126506790000026
Mean value of actual acquisition of triaxial acceleration +.>
Figure FDA0004126506790000027
Acceleration difference |Δg of (a) j |；
Figure FDA0004126506790000028
Wherein: j is x, y and z, wherein x, y and z respectively represent the x axis, the y axis and the z axis of the sensor;
step four, respectively judging magnetic field difference values |delta H of three axes j Difference in sum acceleration Δg j Whether the I meets the formula (6), if so, performing a step seven, and if not, performing a step five;
Figure FDA0004126506790000029
wherein:
H m a determination threshold for the magnetic field;
G m determining a threshold value for acceleration;
fifthly, eliminating triaxial geomagnetic field data H corresponding to axes which do not meet the formula (6) 1i And triaxial acceleration data G 1i The head and tail data in the sensor are recalculated to obtain the actual acquisition average value of the triaxial geomagnetic field of the sensor
Figure FDA00041265067900000210
And the actual acquisition average value of triaxial acceleration +.>
Figure FDA00041265067900000211
And find the magnetic field difference |DeltaH j ' and acceleration difference |ΔG j ’|；
Step six, judging the magnetic field difference value |delta H j ' and acceleration difference |ΔG j Whether the' |meets the formula (9), if yes, acquiring three-axis geomagnetic field data and three-axis acceleration data without abnormality, and entering a step seven; if not, repeating the steps five to six until the formula (9) is established;
Figure FDA0004126506790000031
step seven, adding the head and tail data removed in the step five except the data removed at present back to the three-axis geomagnetic field data and the three-axis acceleration data without abnormality obtained in the step six again, and repeating the step four until the judgment of the three-axis geomagnetic field data and the three-axis acceleration data acquired by the magnetic sensor and the acceleration sensor is completed;
and step eight, performing polynomial fitting on the abnormal triaxial geomagnetic field data and triaxial acceleration data obtained in the step six, interpolating the removed abnormal data to obtain updated triaxial geomagnetic field data and triaxial acceleration data, and performing error correction processing on the updated triaxial geomagnetic field data and triaxial acceleration data to obtain corrected inclination angle, azimuth angle, facing angle and error correction coefficient.
2. The method for judging and processing the abnormal calibration data of the mining inclinometer as claimed in claim 1, wherein in the first step, the specific process of data acquisition and preprocessing is as follows:
step 1.1, placing and fixing a probe tube of an inclinometer on a high-precision triaxial nonmagnetic rotary table, and then rotating the nonmagnetic rotary table to a given position in a space;
step 1.2, acquiring and performing median average filtering processing through a magnetic sensor and an acceleration sensor to obtain triaxial geomagnetic field data H 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1i ＝[G x1i ，G y1i ，G z1i ]。
The specific method for determining the given position through the azimuth angle, the inclination angle and the facing angle of the high-precision triaxial nonmagnetic turntable comprises the following steps:
s1, selecting azimuth angles of a high-precision triaxial nonmagnetic rotary table, and respectively rotating inclination angles to-20 degrees, -53 degrees, -75 degrees, -90 degrees, -0 degrees, +20 degrees, +53 degrees, +75 degrees and +90 degrees to obtain rotation intervals;
s2, rotating the facing angle of the tool at intervals of 10 degrees within the range of 0-360 degrees under each inclination angle to obtain 36 given positions.
3. The method for determining and processing abnormal calibration data of mining inclinometer as set forth in claim 1, wherein the theoretical value H of the triaxial geomagnetic field component is 2i ＝[H x2i ，H y2i ，H z2i ]And triaxial acceleration component theoretical value G 2i ＝[G x2i ，G y2i ，G z2i ]The calculation is carried out according to the formula (1) and the formula (2) respectively:
Figure FDA0004126506790000041
/>
Figure FDA0004126506790000042
wherein:
H 0 is geomagnetic field strength;
G 0 gravity field strength;
alpha is the magnetic inclination angle;
Figure FDA0004126506790000043
azimuth for a given location;
θ is the tilt angle of a given position;
gamma is the facing angle at a given location.
4. The mining inclinometer abnormal calibration data judging and processing method as set forth in claim 1, wherein the fifth step specifically includes the following steps:
step 5.1, eliminating the triaxial geomagnetic field data H of the axes of which the magnetic field difference value or acceleration difference value does not satisfy the formula (3) 1i ＝[H x1i ，H y1i ，H z1i ]And triaxial acceleration data G 1o ＝[G x1i ，G y1i ，G z1i ]After the head and tail data of (2), calculating the actual acquisition average value of the triaxial geomagnetic field of the sensor according to a formula (4)
Figure FDA0004126506790000051
And the actual acquisition average value of triaxial acceleration +.>
Figure FDA0004126506790000052
Figure FDA0004126506790000053
Step 5.2, calculating the theoretical average value of the triaxial geomagnetic field according to the formula (5)
Figure FDA0004126506790000054
And the actual collection average value of the triaxial geomagnetic field
Figure FDA0004126506790000055
Magnetic field difference |ΔH' j I and triaxial acceleration theoretical average value +.>
Figure FDA0004126506790000056
Mean value of actual acquisition of triaxial acceleration +.>
Figure FDA0004126506790000057
Acceleration difference |Δg of (a) j ’|；
Figure FDA0004126506790000058
/>
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