CN110513120B - Self-adaptive positioning system and method for cutting head of heading machine - Google Patents
Self-adaptive positioning system and method for cutting head of heading machine Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/003—Arrangement of measuring or indicating devices for use during driving of tunnels, e.g. for guiding machines
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- E—FIXED CONSTRUCTIONS
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- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/10—Making by using boring or cutting machines
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
- G01V3/081—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices the magnetic field is produced by the objects or geological structures
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Abstract
The invention discloses a heading machine cutting head self-adaptive positioning system and method based on optical and magnetic field positioning. The magnetic field positioning part firstly traverses a working space through a cutting head with a fixed permanent magnet, an optical positioning module records the three-dimensional space position in real time, a magnetic field sensor records the magnetic induction intensity of the corresponding position to obtain training data, and then an initial magnetic field positioning model is constructed through a deep learning algorithm. In the working process of the magnetic field positioning model, the optical auxiliary positioning system continues to work, the acquisition of the position information of the cutting head is kept when the cutting head of the heading machine is not buried, the magnetic field positioning model is updated and trained through the high-precision position information acquired by the optical auxiliary positioning system, in the updating and training process, data provided by an optical auxiliary positioning result has time weight, and the learning rate of the data closer to the current time in the model updating and training process is higher. By continuously updating and training the magnetic field positioning model, the magnetic field positioning model is automatically adaptive to environmental changes, and the influence of geomagnetic changes on magnetic field positioning accuracy after a magnetic source demagnetizes due to oscillation and a heading machine moves for a long distance is avoided.
Description
Technical Field
The invention relates to a positioning system and a positioning method for a cutting head of a heading machine. The invention particularly relates to a heading machine cutting head positioning system and method which are mainly based on magnetic field positioning of deep learning, assisted by optical positioning and capable of automatically adapting to environmental changes.
Background
The magnetic field positioning method is a feasible method for positioning the cutting head of the heading machine in a high-dust low-visibility environment. But the magnetic field localization method also has its limitations.
Aiming at the problem that the precision of a magnetic field positioning system of a cutting head of a heading machine is reduced along with the use time, namely the problem that the geomagnetic condition is changed along with the long-time large-range movement of the heading machine and the problem that the magnetic field positioning precision is reduced due to the fact that the magnetism of a magnetic source positioned at the rear part of the cutting head of the heading machine is weakened due to long-time vibration, optical positioning is introduced to carry out auxiliary calibration on the magnetic field positioning system, dynamic improvement is carried out on a magnetic field positioning model, automatic calibration and adjustment of the cutting head positioning system of the heading machine are achieved, the cutting head positioning system can automatically adapt to the environmental change, the positioning precision is kept in a dynamic environment, and the stability of.
Disclosure of Invention
The invention discloses a self-adaptive positioning system and a self-adaptive positioning method for a cutting head of a heading machine, which solve the problem that the positioning accuracy is reduced along with the change of a positioning system along with the environment and the attenuation of the strength of a magnetic source in the process of positioning the heading machine by using a magnetic field positioning method, and greatly improve the stability of the positioning system for the cutting head of the heading machine. The self-adaptive positioning system comprises a magnetic field positioning module, a magnetic source capable of being fixed behind the cutting head, an optical auxiliary positioning module and a terminal processor containing a self-adaptive positioning model. As the geomagnetic condition can be changed along with the large-scale movement and the time lapse of the heading machine, the strength of a magnetic source positioned at the rear part of the cutting head can be influenced due to the vibration influence of the cutting head, and the accuracy of a magnetic field positioning model established on the initial geomagnetic environment and the strength of the magnetic source is inevitably reduced or even loses efficacy. According to the method, a magnetic field positioning part firstly traverses a working space through a cutting head with a fixed permanent magnet, an optical auxiliary positioning module records the position of a three-dimensional space in real time, a magnetic field sensor records the magnetic induction intensity of a corresponding position to obtain training data, and then a magnetic field positioning model is constructed through a machine learning algorithm. And then in the working process of the magnetic field positioning model, the optical auxiliary positioning system continues working, the acquisition of the position information of the cutting head is kept when the cutting head of the heading machine is not buried, the magnetic field positioning model is updated and trained through the high-precision position information acquired by the optical auxiliary positioning system, in the updating and training process, the data provided by the optical auxiliary positioning result has time weight, and the learning rate of the data closer to the current time in the model updating and training process is higher. By continuously updating and training the magnetic field positioning model, the magnetic field positioning model is automatically adaptive to environmental changes, and the influence of geomagnetic changes on magnetic field positioning accuracy after a magnetic source demagnetizes due to oscillation and a heading machine moves for a long distance is avoided.
The specific technical content provided by the invention is as follows:
a self-adaptive positioning system for a cutting head of a heading machine comprises a magnetic field positioning module, a magnetic source capable of being fixed behind the cutting head, an optical auxiliary positioning module and a terminal processor containing a self-adaptive positioning model. The magnetic field positioning module is used for carrying out magnetic field positioning on the cutting head; the magnetic source is used for transmitting the position data of the cutting head to the magnetic field positioning module; the optical auxiliary positioning module is used for providing high-precision data required by calibration adjustment; the terminal processor containing the self-adaptive positioning model is used for processing the obtained position data and automatically adjusting the magnetic field positioning model according to the environmental change so as to keep the positioning precision of the magnetic field positioning model; the position data comprises high-precision position data obtained by the auxiliary positioning module and magnetic field position data obtained by the magnetic field positioning module.
Preferably, the magnetic field positioning module comprises at least two triaxial magnetic field sensors which are respectively arranged on two sides of the machine body of the heading machine or on a sliding rail and a support which move synchronously with the machine body.
Preferably, the optical auxiliary positioning module comprises at least two optical cameras and a matched data acquisition and transmission device, the at least two optical cameras are located behind the cutting head, and after the relative position relation between the at least two optical cameras and the machine body is fixed, the optical cameras are not changed in the positioning process, the optical cameras are not limited to visible light wave bands, and cameras with various wave bands such as far infrared and near infrared can be used according to specific environments.
Preferably, the magnetic source is fixed behind the cutting head and moves together with the cutting head, and the magnetic field intensity obtained by the magnetic field sensor is influenced by the movement of the magnetic source. The magnetic source is a permanent magnet or an electromagnet.
The positioning method disclosed by the invention comprises the following steps: the magnetic field positioning method is characterized in that a positioning model is constructed after a sample is trained by utilizing a machine learning algorithm in a processor, and the cutting head is positioned in a magnetic field by utilizing the positioning model; the machine learning algorithm is a deep learning algorithm, the positioning model is a deep learning positioning model, the deep learning positioning model is obtained after training is carried out by utilizing sample data, the deep learning positioning model is used for carrying out magnetic field positioning on the cutting head, magnetic field intensity data corresponding to magnetic sources at different positions are obtained through a magnetic field sensor, and the deep learning positioning model obtains the space position of the cutting head by utilizing the magnetic field intensity data. The optical auxiliary positioning method is used for positioning the cutting head based on an image recognition method and a geometric optical method, wherein the image recognition method can be based on the imaging characteristics of the cutting head under the condition of visible light and also can be based on the infrared imaging characteristics generated by friction heating of the working cutting head; the automatic calibration method carries out updating training on the model used for magnetic field positioning according to the optical auxiliary positioning result, in the updating training process, data provided by the optical auxiliary positioning result have time weight, and the learning rate of the data closer to the current time is higher in model updating. The magnetic field positioning is used for providing cutting head positioning information in a global full-time period in the positioning process of the cutting head, the optical auxiliary positioning system works only when the cutting head of the heading machine is not shielded by the machine body or is not buried in slag, high-precision position data are provided for automatically adjusting and calibrating a magnetic field positioning model, and the influence of geomagnetic variation on magnetic field positioning precision after a magnetic source demagnetizes due to oscillation and the heading machine moves in a long distance is avoided.
Preferably, the automatic calibration method comprises the steps of:
firstly, an optical auxiliary positioning system acquires position information of a cutting head;
step two, collecting magnetic field data by a magnetic field sensor;
and step three, judging whether the optical positioning system is in a normal working state. If yes, executing the step four; if not, executing the step ten;
step four, storing the optical positioning result into historical data, namely a data serial number i;
step five, storing the magnetic field data into historical data, and a data serial number i;
step six, i = i + 1;
step seven, updating the time weight of the historical data, wherein the larger the serial number i is, the larger the weight is;
step eight, adjusting the learning rate of the data at different times when the training model is updated according to the time weight;
step nine, using historical data to update the training magnetic field positioning model;
step ten, bringing the magnetic field data acquired by the magnetic field sensor into a magnetic field positioning model;
and step eleven, outputting a magnetic field positioning result.
Drawings
FIG. 1 is a schematic view of a simulation model;
FIG. 2 is a schematic diagram of magnetic field signals and corresponding positioning coordinates;
FIG. 3 is a schematic diagram of the variation of the magnetic field;
FIG. 4 is a flow chart of an adaptive positioning algorithm;
FIG. 5 is a diagram of the effect of the ordinary magnetic field positioning model on the positioning of original data;
FIG. 6 is a diagram of the positioning effect of a common magnetic field positioning model on data introducing magnetic field changes;
FIG. 7 is a graph of the localization effect of an adaptive model on data that introduces magnetic field variations.
Detailed Description
The invention will be further explained in detail by means of embodiments in the following with reference to the drawings, without in any way limiting the scope of the invention.
The positioning effect of the self-adaptive positioning model is displayed by using simulation data in the embodiment of the invention.
As shown in FIG. 1, in the simulation model, the magnetic source is a 500-turn circular electromagnet with an inner diameter of 55cm and an outer diameter of 60cm, and the operating current is 1A. Located 500cm above the plane of the sensor. The magnetic field sensor is located in the center of the plane of the sensor.
The invention provides a heading machine cutting head composite positioning system based on infrared and magnetic field positioning.
The invention relates to a positioning system of a cutting head of a heading machine. The positioning method of the present invention will be described in detail below.
In the embodiment, a positioning model needs to be trained in advance, a cutting head with a fixed permanent magnet traverses a working space, an infrared positioning module records a three-dimensional space position at any time, and a magnetic field sensor records magnetic induction intensity of a corresponding position to acquire training data. In the simulation model, the actual position of the magnetic source and the magnetic field correspondingly acquired by the magnetic field sensor can be directly acquired and used, and the data obtained by simulation is shown in fig. 2. The coordinate information obtained through simulation is used for replacing positioning information of optical auxiliary positioning, and the magnetic field information obtained through simulation is used for replacing magnetic field information collected by a magnetic field sensor in actual positioning. We simulate the change of the earth magnetism by introducing a magnetic field change amount changing with the positioning sequence, so as to show the magnetic field change amount introduced by the positioning effect of the adaptive positioning model and the common model under the condition of the geomagnetic change, as shown in fig. 3. In the adaptive positioning model, besides initial training, the positioning model is trained in real time to ensure the positioning accuracy after the magnetic field is introduced to change.
The flow chart of the adaptive positioning algorithm is shown in fig. 4. The optimization algorithm comprises the following steps:
firstly, an optical auxiliary positioning system acquires position information of a cutting head;
step two, collecting magnetic field data by a magnetic field sensor;
and step three, judging whether the optical positioning system is in a normal working state. If yes, executing the step four; if not, executing the step ten;
step four, storing the optical positioning result into historical data, namely a data serial number i;
step five, storing the magnetic field data into historical data, and a data serial number i;
step six, i = i + 1;
step seven, updating the time weight of the historical data, wherein the larger the serial number i is, the larger the weight is;
step eight, adjusting the learning rate of the data at different times when the training model is updated according to the time weight;
step nine, using historical data to update the training magnetic field positioning model;
step ten, bringing the magnetic field data acquired by the magnetic field sensor into a magnetic field positioning model;
and step eleven, outputting a magnetic field positioning result.
The relative positioning error of the original data, which is obtained by positioning the original data without using the general positioning model of the adaptive positioning algorithm, is shown in fig. 5, and it can be seen from fig. 5 that the relative error is less than 1.6%, and when the magnetic field change is not introduced, that is, the geomagnetism is static, the precision of the general positioning model is very high.
The relative positioning error of the general positioning model without using the adaptive positioning algorithm for positioning the data after the magnetic field change is introduced is shown in fig. 6, and it can be known from fig. 6 that after the magnetic field change is introduced, that is, when the geomagnetism is dynamic, the relative positioning error of the general positioning model gradually increases along with the time lapse, and after the serial number is greater than 2000, the relative positioning error of the x axis already exceeds 100%, and the general positioning model fails.
The relative positioning error of the positioning model using the adaptive positioning algorithm for positioning the data after the magnetic field change is introduced is shown in fig. 7, and it can be seen from fig. 7 that after the magnetic field change is introduced, that is, when the geomagnetism is dynamic, the relative positioning error of the adaptive positioning model is always kept below 2.5% along with the time lapse, and the phenomenon that the relative positioning error is increased along with the time lapse is not shown, which proves that the adaptive positioning model can be well adapted to the environment in which the geomagnetic environment gradually changes, and keep higher positioning accuracy.
It is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (6)
1. A self-adaptive positioning method for a cutting head of a heading machine is provided, which utilizes a self-adaptive positioning system to position the cutting head of the heading machine, and the self-adaptive positioning system comprises: the device comprises a magnetic field positioning module, a magnetic source fixed behind a cutting head, an optical auxiliary positioning module and a terminal processor containing a self-adaptive positioning model; the magnetic field positioning module is used for carrying out magnetic field positioning on the cutting head; the magnetic source is used for transmitting the position data of the cutting head to the magnetic field positioning module; the optical auxiliary positioning module is used for providing high-precision data required by calibration adjustment; the terminal processor containing the self-adaptive positioning model is used for processing the obtained position data and automatically adjusting the magnetic field positioning model according to the environmental change so as to keep the positioning precision of the magnetic field positioning model; the position data comprises high-precision position data obtained by an optical auxiliary positioning module and magnetic field position data obtained by a magnetic field positioning module; the method is characterized in that: the self-adaptive positioning method comprises magnetic field positioning, optical auxiliary positioning and automatic calibration; the magnetic field positioning method is characterized in that a positioning model is constructed after a sample is trained by utilizing a machine learning algorithm in a processor, and the cutting head is positioned in a magnetic field by utilizing the positioning model; the machine learning algorithm is a deep learning algorithm, the positioning model is a deep learning positioning model, the deep learning positioning model is obtained after training is carried out by utilizing sample data, the deep learning positioning model is used for carrying out magnetic field positioning on the cutting head, magnetic field intensity data corresponding to magnetic sources at different positions are obtained through a magnetic field sensor, and the deep learning positioning model obtains the spatial position of the cutting head by utilizing the magnetic field intensity data; the optical auxiliary positioning method is used for positioning the cutting head based on an image recognition method and a geometric optical method, wherein the image recognition method is based on the imaging characteristics of the cutting head under the condition of visible light or based on the infrared imaging characteristics generated by friction heating of the working cutting head; the automatic calibration method carries out updating training on the model used for magnetic field positioning according to the optical auxiliary positioning result, in the updating training process, data provided by the optical auxiliary positioning result has time weight, and the learning rate of the data closer to the current time is higher in model updating; the magnetic field positioning is used for providing cutting head positioning information in a global full-time period in the positioning process of the cutting head, the optical auxiliary positioning module works only when the cutting head of the heading machine is not shielded by the machine body or is not buried in slag, high-precision position data are provided for automatically adjusting and calibrating a magnetic field positioning model, and the influence of geomagnetic variation after a magnetic source demagnetizes due to oscillation and the heading machine moves in a long distance on the magnetic field positioning precision is avoided; the self-adaptive positioning method specifically comprises the following steps:
firstly, an optical auxiliary positioning module acquires position information of a cutting head;
step two, collecting magnetic field data by a magnetic field sensor;
judging whether the optical auxiliary positioning module is in a normal working state; if yes, executing the step four; if not, executing the step ten;
step four, storing the optical positioning result into historical data, namely a data serial number i;
step five, storing the magnetic field data into historical data, and a data serial number i;
step six, i is i + 1;
step seven, updating the time weight of the historical data, wherein the larger the serial number i is, the larger the weight is;
step eight, adjusting the learning rate of the data at different times when the training model is updated according to the time weight;
step nine, using historical data to update the training magnetic field positioning model;
step ten, bringing the magnetic field data acquired by the magnetic field sensor into a magnetic field positioning model;
and step eleven, outputting a magnetic field positioning result.
2. The positioning method according to claim 1, wherein the magnetic field positioning module comprises at least two triaxial magnetic field sensors respectively arranged on two sides of the body of the heading machine or on a sliding rail and a bracket which move synchronously with the body.
3. The positioning method according to claim 1, wherein the optical auxiliary positioning module comprises at least two optical cameras and a data acquisition and transmission device, and the at least two optical cameras are located behind the cutting head and are not changed in the positioning process after the relative position relationship with the machine body is fixed.
4. The positioning method according to claim 3, wherein the magnetic source is fixed behind the cutting head and moves together with the cutting head, and the magnetic field intensity obtained by the magnetic field sensor is influenced by the movement of the magnetic source; the magnetic source is a permanent magnet or an electromagnet.
5. The positioning method according to claim 1, characterized in that: the optical auxiliary positioning method is composed of an image recognition algorithm and a three-dimensional positioning algorithm of a geometric optical method.
6. The positioning method according to claim 1, characterized in that: the automatic calibration refers to adjusting the magnetic field positioning model obtained through deep learning according to high-precision position information provided by the optical auxiliary positioning, the magnetic field positioning model obtained through the deep learning is adjusted to be retrained again on the magnetic field positioning model, the retraining process is carried out in real time under the normal working condition of the optical auxiliary positioning, in the retraining process, the high-precision position information provided by the optical auxiliary positioning closer to the current time has higher weight, the weight is used for determining the learning rate in the retraining process, and the higher the weight is, the higher the learning rate is.
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- 2019-08-17 CN CN201910761257.1A patent/CN110513120B/en not_active Expired - Fee Related
Patent Citations (7)
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| US5208538A (en) * | 1989-06-30 | 1993-05-04 | Kabushiki Kaisha Komatsu Seisakusho | Apparatus having a pair of magnetic field generating cables for measuring position of an underground excavator |
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