CN118462050B - Vehicle-mounted reverse circulation drilling machine and method suitable for sampling loose stratum in shallow coverage area - Google Patents
Vehicle-mounted reverse circulation drilling machine and method suitable for sampling loose stratum in shallow coverage area Download PDFInfo
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- 238000005553 drilling Methods 0.000 title claims abstract description 159
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- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B7/00—Special methods or apparatus for drilling
- E21B7/02—Drilling rigs characterised by means for land transport with their own drive, e.g. skid mounting or wheel mounting
- E21B7/027—Drills for drilling shallow holes, e.g. for taking soil samples or for drilling postholes
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- 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 application relates to a vehicle-mounted reverse circulation drilling machine and a method suitable for sampling loose strata in a shallow coverage area. It comprises the following steps: the drilling machine comprises a power system, an electric control system, a drilling machine, a vacuum pump and a drilling tower, and is characterized in that the drilling tower is suitable for being installed on an automobile chassis and used for adjusting the height of the drilling machine; the vacuum pump is used for maintaining negative pressure in a drilling hole to prevent mud from entering the drilling hole, and the electric control system is used for automatically adjusting parameters of the drilling machine; the power system is used for providing power for the drilling machine. Thus, the vehicle-mounted reverse circulation drilling machine suitable for sampling the loose stratum in the shallow coverage area combines the high mobility of the vehicle-mounted platform and the high efficiency of the reverse circulation drilling technology, and can realize the high-efficiency sampling in the loose stratum in the shallow coverage area.
Description
Technical Field
The application relates to the technical field of geological exploration, in particular to a vehicle-mounted reverse circulation drilling machine and method suitable for sampling loose strata in a shallow coverage area.
Background
In the field of geological exploration, sampling work of shallow coverage areas has been a challenge in geological research and resource development. Currently, air circulation drilling machines are mainly used for sampling in these areas in China. The working principle of the air circulation drilling machine is that the medium in the deep part of the drill rod is conveyed to the ground surface through the outer wall of the drill rod by utilizing air pressure, so that sampling is realized. However, this approach has some significant limitations and drawbacks.
First, air circulation drilling machines have shortcomings in the representativeness of the sampling media that cannot be met. In addition, due to the effect of air pressure, the medium may be affected by air flow in the rising process, so that the physical and chemical properties of the sample are changed, and the chemical characteristics of the deep stratum cannot be truly reflected. The distortion of the sampling medium can cause adverse effects on geological analysis and mining judgment, and the accuracy and efficiency of exploration are reduced.
Secondly, air circulation drilling machines also have problems in terms of maneuverability. At present, although the sampling efficiency is improved to a certain extent, the domestic crawler type RC drilling machine (air reverse circulation drilling machine) has poor maneuverability and is difficult to adapt to complex and changeable geological environments and working conditions. This limits the rapid deployment and flexible application of the drilling rig under different terrain and conditions, affecting continuity and timeliness of the exploration work.
Accordingly, an on-board reverse circulation drilling rig suitable for shallow coverage area unconsolidated formation sampling is desired.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present application provides a vehicle-mounted reverse circulation drilling machine suitable for sampling loose strata in a shallow coverage area, the vehicle-mounted reverse circulation drilling machine comprising: the drilling machine comprises a power system, an electric control system, a drilling machine, a vacuum pump and a drilling tower, and is characterized in that the drilling tower is suitable for being installed on an automobile chassis and used for adjusting the height of the drilling machine; the vacuum pump is used for maintaining negative pressure in a drilling hole to prevent mud from entering the drilling hole, and the electric control system is used for automatically adjusting parameters of the drilling machine; the power system is used for providing power for the drilling machine.
Optionally, the electronic control system includes: the data acquisition module is used for acquiring the time sequence of the bit load data acquired by the sensor and the time sequence of the pressure in the drill rod; the drilling machine data time sequence autocorrelation module is used for respectively constructing a time sequence of the drill bit load data and a spatial covariance matrix of the time sequence of the pressure in the drill rod so as to obtain a drill bit load time sequence correlation matrix and a pressure in the drill rod time sequence correlation matrix; the drilling machine data time sequence mode feature extraction module is used for respectively carrying out feature extraction on the drill bit load time sequence incidence matrix and the drill rod internal pressure time sequence incidence matrix through a time sequence mode feature extractor based on a deep neural network model so as to obtain a drill bit load time sequence incidence feature vector and a drill rod internal pressure time sequence incidence feature vector; the drill bit load-drill rod internal pressure time sequence characteristic interaction module is used for inputting the drill bit load time sequence association characteristic vector and the drill rod internal pressure time sequence association characteristic vector into the nonlinear coupling interaction module to obtain a drill bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; and the drill propulsion speed control module is used for determining that the propulsion speed at the current time point should be increased, decreased or kept unchanged based on the drill bit load-internal pressure time sequence nonlinear interactive coupling representation vector.
Optionally, the drilling machine data timing autocorrelation module includes: the data normalization unit is used for performing data normalization on the time sequence of the drill bit load data and the time sequence of the pressure in the drill rod according to the time dimension to obtain a drill bit load time sequence input vector and a drill rod pressure time sequence input vector; the space covariance calculation unit is used for calculating the space covariance matrix of the bit load time sequence input vector and the drill rod internal pressure time sequence input vector to obtain the bit load time sequence incidence matrix and the drill rod internal pressure time sequence incidence matrix.
Optionally, the drilling machine data time sequence mode feature extraction module is used for respectively enabling the drill bit load time sequence correlation matrix and the intra-drill rod pressure time sequence correlation matrix to pass through a time sequence mode feature extractor based on a cavity convolutional neural network model so as to obtain the drill bit load time sequence correlation feature vector and the intra-drill rod pressure time sequence correlation feature vector.
Optionally, the drill bit load-intra-drill rod pressure time sequence characteristic interaction module comprises: the drill bit load-drill rod internal pressure time sequence response unit is used for calculating position-by-position response between the drill rod internal pressure time sequence correlation characteristic vector and the drill bit load time sequence correlation characteristic vector so as to obtain a drill bit load-drill rod internal pressure time sequence interaction characteristic vector; and the drill bit load-drill rod internal pressure time sequence interactive coupling representation unit is used for carrying out nonlinear response optimization processing on the drill bit load-drill rod internal pressure time sequence interactive characteristic vector so as to obtain the drill bit load-drill rod internal pressure time sequence nonlinear interactive coupling representation vector.
Optionally, the bit load-internal pressure time sequence interactive coupling representation unit is used for calculating the product between the square value of each position characteristic value in the bit load-internal pressure time sequence interactive characteristic vector and a first preset adjustment super-parameter to obtain a first bit load-internal pressure time sequence interactive nonlinear factor quadratic term adjustment parameter; calculating the product of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a second preset adjustment super parameter to obtain a first drill bit load-drill rod internal pressure time sequence interaction nonlinear factor primary item adjustment parameter; calculating the sum of the first bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter and the first bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter to obtain a first bit load-drill rod internal pressure time sequence interaction nonlinear factor; calculating the product of the square value of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a third preset adjustment super-parameter to obtain a second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter; calculating the product of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a fourth preset adjustment super parameter to obtain a second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor primary term adjustment parameter; calculating the sum of the second bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter and the second bit load-drill rod internal pressure time sequence interaction nonlinear factor once term adjustment parameter, and then adding a constant term and modulating to obtain a second bit load-drill rod internal pressure time sequence interaction nonlinear factor; calculating a division between the first drill bit load-drill rod internal pressure time sequence interaction nonlinear factor and the second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor to obtain drill bit load-drill rod internal pressure time sequence interaction nonlinear response values, and performing vectorization normalization on the plurality of drill bit load-drill rod internal pressure time sequence interaction nonlinear response values according to a time dimension to obtain the drill bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector.
Optionally, the drill propulsion speed control module is configured to input the drill bit load-in-drill rod pressure time sequence nonlinear interaction coupling representation vector into a classifier-based propulsion speed controller to obtain a control instruction, where the control instruction is used to represent that the propulsion speed at the current time point should be increased, decreased or kept unchanged.
Optionally, the device further comprises a training module for training the time sequence pattern feature extractor based on the cavity convolutional neural network model, the nonlinear coupling interaction module and the propulsion speed controller based on the classifier.
Optionally, the training module includes: the training data acquisition unit is used for acquiring a time sequence of training bit load data acquired by the sensor and a time sequence of pressure in the training drill rod; the training drill data time sequence autocorrelation unit is used for respectively constructing a time sequence of the training drill bit load data and a spatial covariance matrix of the time sequence of the pressure in the training drill rod so as to obtain a training drill bit load time sequence correlation matrix and a training drill rod pressure time sequence correlation matrix; the training drill data time sequence pattern feature extraction unit is used for extracting features of the training drill bit load time sequence correlation matrix and the training drill rod internal pressure time sequence correlation matrix through a time sequence pattern feature extractor based on the deep neural network model so as to obtain a training drill bit load time sequence correlation feature vector and a training drill rod internal pressure time sequence correlation feature vector; the training bit load-internal pressure time sequence characteristic interaction unit is used for inputting the training bit load time sequence related characteristic vector and the training internal pressure time sequence related characteristic vector into the nonlinear coupling interaction module to obtain a training bit load-internal pressure time sequence nonlinear interaction coupling representation vector; the training classification unit is used for inputting the training bit load-internal pressure time sequence nonlinear interactive coupling representation vector in the drill rod into the classifier-based propulsion speed controller to obtain a training control instruction; a loss function calculation unit for calculating a cross entropy loss function value between the training control instruction and the real instruction to obtain a classification loss function value; and the training unit is used for training the time sequence mode feature extractor based on the cavity convolutional neural network model, the nonlinear coupling interaction module and the propulsion speed controller based on the classifier by using the classification loss function value.
In a second aspect, the present application provides a method for sampling a loose formation suitable for a shallow coverage area, the method comprising: adjusting the height of a drilling machine by using a drilling tower, wherein the drilling tower is mounted on an automobile chassis; maintaining negative pressure in a drilling hole by using a vacuum pump to prevent mud from entering the drilling hole, and automatically adjusting parameters of the drilling machine by using an electric control system; providing power to the drilling machine using a power system; wherein, the electrical control system includes: acquiring a time sequence of drill bit load data acquired by a sensor and a time sequence of pressure in a drill rod; respectively constructing a space covariance matrix of the time sequence of the bit load data and the time sequence of the pressure in the drill rod to obtain a bit load time sequence correlation matrix and a drill rod pressure time sequence correlation matrix; the time sequence pattern feature extractor based on the deep neural network model is used for extracting features of the drill bit load time sequence correlation matrix and the drill rod internal pressure time sequence correlation matrix respectively so as to obtain a drill bit load time sequence correlation feature vector and a drill rod internal pressure time sequence correlation feature vector; inputting the bit load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector into a nonlinear coupling interaction module to obtain a bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; based on the bit load-intra-drill pipe pressure time sequence nonlinear cross coupling representation vector, the propulsion speed at the current time point is determined to be increased, decreased or kept unchanged.
By adopting the technical scheme, the drilling machine comprises a power system, an electric control system, a drilling machine, a vacuum pump and a drilling tower, and is characterized in that the drilling tower is suitable for being arranged on an automobile chassis and used for adjusting the height of the drilling machine; the vacuum pump is used for maintaining negative pressure in a drilling hole to prevent mud from entering the drilling hole, and the electric control system is used for automatically adjusting parameters of the drilling machine; the power system is used for providing power for the drilling machine. Thus, the vehicle-mounted reverse circulation drilling machine suitable for sampling the loose stratum in the shallow coverage area combines the high mobility of the vehicle-mounted platform and the high efficiency of the reverse circulation drilling technology, and can realize the high-efficiency sampling in the loose stratum in the shallow coverage area.
Additional features and advantages of the application will be set forth in the detailed description which follows.
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The above and other features, advantages and aspects of embodiments of the present application will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
In the drawings: FIG. 1 is a schematic diagram illustrating an on-board reverse circulation rig suitable for shallow coverage area unconsolidated formation sampling, according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating the electrical control system of an on-board reverse circulation drilling rig suitable for shallow coverage area unconsolidated formation sampling, according to an exemplary embodiment.
FIG. 3 is a flow chart illustrating a method for sampling a loose formation for a shallow coverage area, according to an exemplary embodiment.
Fig. 4 is a block diagram of an electronic device, according to an example embodiment.
FIG. 5 is a diagram illustrating an application scenario of a vehicle-mounted reverse circulation drilling rig suitable for shallow coverage area unconsolidated formation sampling, according to an exemplary embodiment.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the application is susceptible of embodiment in the drawings, it is to be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the application. It should be understood that the drawings and embodiments of the application are for illustration purposes only and are not intended to limit the scope of the present application.
It should be understood that the various steps recited in the method embodiments of the present application may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the application is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the devices in the embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of such messages or information.
In order to solve the problems, the application provides a vehicle-mounted reverse circulation drilling machine and a method suitable for sampling a loose stratum in a shallow coverage area, and the vehicle-mounted reverse circulation drilling machine comprises a power system, an electric control system, a drilling machine, a vacuum pump and a drilling tower, and is characterized in that the drilling tower is suitable for being mounted on an automobile chassis and used for adjusting the height of the drilling machine; the vacuum pump is used for maintaining negative pressure in a drilling hole to prevent mud from entering the drilling hole, and the electric control system is used for automatically adjusting parameters of the drilling machine; the power system is used for providing power for the drilling machine. Thus, the vehicle-mounted reverse circulation drilling machine suitable for sampling the loose stratum in the shallow coverage area combines the high mobility of the vehicle-mounted platform and the high efficiency of the reverse circulation drilling technology, and can realize the high-efficiency sampling in the loose stratum in the shallow coverage area.
The following describes specific embodiments of the present application in detail with reference to the drawings.
FIG. 1 is a schematic diagram illustrating an on-board reverse circulation rig suitable for shallow coverage area unconsolidated formation sampling, according to an exemplary embodiment. As shown in fig. 1, the vehicle-mounted reverse circulation drilling machine includes: the drilling machine is characterized in that the drilling tower 5 is suitable for being mounted on an automobile chassis and used for adjusting the height of the drilling machine 3; the vacuum pump 4 is used for maintaining negative pressure in a drilling hole to prevent mud from entering the drilling hole, and the electric control system 2 is used for automatically adjusting parameters of the drilling machine 3; the power system 1 is used for providing power to the drilling machine 3. The vehicle-mounted reverse circulation drilling machine suitable for sampling the loose stratum in the shallow coverage area combines the high mobility of the vehicle-mounted platform and the high efficiency of the reverse circulation drilling technology, and can realize the high-efficiency sampling in the loose stratum in the shallow coverage area.
In particular, in the vehicle-mounted reverse circulation drilling machine, the electronic control system can utilize an intelligent data processing technology to monitor and analyze key parameters in the drilling process, such as the bit load and the pressure in a drill rod, in real time in the drilling process, so as to realize the self-adaptive adjustment of the working state of the drilling machine, so as to adapt to complex and changeable geological environments and working conditions.
Specifically, the technical concept of the application is that in the working process of the vehicle-mounted reverse circulation drilling machine, the drill bit load data and the pressure data in the drill rod are monitored and collected in real time through the sensor, and the time sequence coordination and the associated analysis of the drill bit load data and the pressure data in the drill rod are carried out by introducing an artificial intelligence-based data processing and analyzing algorithm at the rear end, so that the real-time self-adaptive adjustment of the drilling machine propelling speed is carried out. Therefore, the advancing speed of the drilling machine can be automatically adjusted according to the cooperative change of the bit load and the time sequence of the pressure in the drill rod, so as to adapt to stratum with different hardness, thereby meeting the requirements of different geological environments and working conditions and being beneficial to high-efficiency and real sampling in loose stratum in a shallow coverage area.
Fig. 2 is a block diagram illustrating the electrical control system of an on-board reverse circulation drilling rig suitable for shallow coverage area unconsolidated formation sampling, according to an exemplary embodiment. As shown in fig. 2, the electronic control system 2 includes: the data acquisition module 101 is used for acquiring a time sequence of drill bit load data acquired by the sensor and a time sequence of pressure in the drill rod; the drilling machine data time sequence autocorrelation module 102 is used for respectively constructing a spatial covariance matrix of the time sequence of the drill bit load data and the time sequence of the pressure in the drill rod so as to obtain a drill bit load time sequence correlation matrix and a drill rod pressure time sequence correlation matrix; the drilling machine data time sequence mode feature extraction module 103 is used for respectively carrying out feature extraction on the drill bit load time sequence incidence matrix and the drill rod internal pressure time sequence incidence matrix through a time sequence mode feature extractor based on a deep neural network model so as to obtain a drill bit load time sequence incidence feature vector and a drill rod internal pressure time sequence incidence feature vector; the drill load-drill rod internal pressure time sequence characteristic interaction module 104 is used for inputting the drill load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector into a nonlinear coupling interaction module to obtain a drill load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; the drill propulsion speed control module 105 is configured to determine that the propulsion speed at the current point in time should be increased, decreased or remain unchanged based on the drill bit load-intra-drill rod pressure time sequence nonlinear cross-coupling representation vector.
More specifically, in the electronic control system, a time series of bit load data and a time series of pressure in the drill pipe acquired by the sensor are first acquired. It will be appreciated that since bit loading can reflect the hardness of the rock, higher bit loading generally means that the drilling encounters harder rock, while lower loading may indicate that the rock is softer or has been drilled through. The change in bit loading may provide real-time information of formation changes. The pressure in the drill rod can reflect the circulation efficiency of drilling fluid and the pore pressure condition of stratum. The hardness and the characteristics of the stratum can change along with the change of depth and position, and the pushing speed can be adjusted in real time by monitoring the load of the drill bit so as to adapt to the stratum with different hardness, and the damage of the drill bit or the reduction of drilling efficiency are avoided. The change in pressure in the drill pipe can indicate the circulation condition of drilling fluid, if the pressure is abnormal, the circulation of the drilling fluid is blocked or the formation pressure is changed, and the drill pipe is prevented from being damaged or the well wall is prevented from collapsing by adjusting the advancing speed.
In one embodiment of the application, the rig data timing autocorrelation module comprises: the data normalization unit is used for performing data normalization on the time sequence of the drill bit load data and the time sequence of the pressure in the drill rod according to the time dimension to obtain a drill bit load time sequence input vector and a drill rod pressure time sequence input vector; the space covariance calculation unit is used for calculating the space covariance matrix of the bit load time sequence input vector and the drill rod internal pressure time sequence input vector to obtain the bit load time sequence incidence matrix and the drill rod internal pressure time sequence incidence matrix.
Then, considering that when the data monitoring and analysis of the bit load and the pressure in the drill rod in the drilling process are actually performed, the data of the bit load and the pressure in the drill rod continuously change along with the time, so that in order to better capture the time sequence mode and the change characteristic of the bit load and the pressure in the drill rod in the drilling process of the vehicle-mounted reverse circulation drilling machine, in the technical scheme of the application, the time sequence of the bit load data and the space covariance matrix of the time sequence of the pressure in the drill rod are needed to be respectively constructed to obtain a bit load time sequence correlation matrix and a pressure in the drill rod time sequence correlation matrix. By constructing the spatial covariance matrix of the time sequence of the bit load data and the time sequence of the pressure in the drill rod, the time sequence information of the bit load data and the pressure data in the drill rod can be subjected to autocorrelation modeling respectively, so that a time sequence autocorrelation mode of the time sequence data of the respective parameters is established, and the time sequence characteristics of the bit load and the pressure in the drill rod and hidden interaction information can be better captured later.
Calculating a spatial covariance matrix of the bit load time sequence input vector according to the following spatial covariance formula to obtain a bit load time sequence incidence matrix; wherein, the spatial covariance formula is: ; wherein, Representing the bit load timing input vector,A transpose of the bit loading timing input vector,Representing the sum of the individual elements on the diagonal of the computation matrix,And loading the time sequence incidence matrix for the drill bit.
Then, in order to further capture the time sequence autocorrelation mode and the implicit characteristic information of the drill bit load and the pressure in the drill rod in the time dimension, in the technical scheme of the application, the drill bit load time sequence correlation matrix and the pressure in the drill rod are further respectively passed through a time sequence mode characteristic extractor based on a cavity convolutional neural network model so as to obtain a drill bit load time sequence correlation characteristic vector and a pressure in the drill rod time sequence correlation characteristic vector. It should be understood that the hole convolutional neural network is an effective deep learning model, and can introduce hole rate into the convolutional kernel to expand the receptive field while capturing local characteristic information, so as to better capture the time sequence long-range dependency relationship in each parameter data. Compared with the traditional convolutional neural network, the parameters of the cavity convolutional network are fewer when the characteristics are extracted, so that the overfitting risk of the model is reduced, and the generalization capability of the model is improved. In particular, the hole convolution network is able to adaptively learn from the data the features most relevant to the current drilling conditions, also considering that the formation conditions during drilling are variable, thereby providing more accurate control instructions. Therefore, by applying the time sequence pattern feature extractor based on the cavity convolutional neural network model, time sequence self-correlation feature information of the drill bit load and the pressure in the drill rod can be extracted respectively, and a data basis is provided for the subsequent establishment of the association pattern of the drill bit load time sequence feature and the pressure in the drill rod and the capture of interaction implicit information.
In one embodiment of the application, the drilling machine data time sequence pattern feature extraction module is used for respectively enabling the drill bit load time sequence correlation matrix and the intra-drill rod pressure time sequence correlation matrix to pass through a time sequence pattern feature extractor based on a cavity convolutional neural network model so as to obtain the drill bit load time sequence correlation feature vector and the intra-drill rod pressure time sequence correlation feature vector.
Further, because the bit load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector respectively contain time sequence autocorrelation characteristic and mode information related to bit load and drill rod internal pressure in a time dimension, different aspects in the drilling process are respectively reflected, and the time sequence autocorrelation characteristic and the mode information are very important for judging stratum states and controlling drilling advancing speed. In addition, because the bit load time sequence autocorrelation characteristic and the pressure time sequence autocorrelation characteristic in the drill rod do not exist independently, a hidden nonlinear association relationship and a complex interaction mode are arranged between the bit load time sequence autocorrelation characteristic and the pressure time sequence autocorrelation characteristic. Based on the above, in the technical scheme of the application, the bit load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector are further input into a nonlinear coupling interaction module to obtain a bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector. By processing of the nonlinear coupling interaction module, a time sequence potential nonlinear correlation mode and complex interaction between the drill bit load and the pressure in the drill rod can be automatically learned and captured, so that the time sequence correlation characteristic vector of the drill bit load and the time sequence correlation characteristic vector of the pressure in the drill rod are combined to form a more representative and comprehensive characteristic representation, and the characteristic representation can more comprehensively consider the complex time sequence correlation mode and nonlinear relation between data, so that understanding and control capability of a model on a drilling process are improved. In addition, the nonlinear coupling interaction module can also provide adaptability to different drilling conditions through learning, so that the control system can adjust the drilling parameters according to the comprehensive feature vector to adapt to the continuously-changing geological conditions.
In one embodiment of the present application, the bit load-intra-drill pipe pressure timing feature interaction module comprises: the drill bit load-drill rod internal pressure time sequence response unit is used for calculating position-by-position response between the drill rod internal pressure time sequence correlation characteristic vector and the drill bit load time sequence correlation characteristic vector so as to obtain a drill bit load-drill rod internal pressure time sequence interaction characteristic vector; and the drill bit load-drill rod internal pressure time sequence interactive coupling representation unit is used for carrying out nonlinear response optimization processing on the drill bit load-drill rod internal pressure time sequence interactive characteristic vector so as to obtain the drill bit load-drill rod internal pressure time sequence nonlinear interactive coupling representation vector.
Further, in one embodiment of the present application, the bit load-intra-drill-rod pressure time sequence interactive coupling representation unit is configured to calculate a product between a square value of each position feature value in the bit load-intra-drill-rod pressure time sequence interactive feature vector and a first predetermined adjustment super-parameter to obtain a first bit load-intra-drill-rod pressure time sequence interactive nonlinear factor quadratic term adjustment parameter; calculating the product of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a second preset adjustment super parameter to obtain a first drill bit load-drill rod internal pressure time sequence interaction nonlinear factor primary item adjustment parameter; calculating the sum of the first bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter and the first bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter to obtain a first bit load-drill rod internal pressure time sequence interaction nonlinear factor; calculating the product of the square value of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a third preset adjustment super-parameter to obtain a second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter; calculating the product of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a fourth preset adjustment super parameter to obtain a second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor primary term adjustment parameter; calculating the sum of the second bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter and the second bit load-drill rod internal pressure time sequence interaction nonlinear factor once term adjustment parameter, and then adding a constant term and modulating to obtain a second bit load-drill rod internal pressure time sequence interaction nonlinear factor; calculating a division between the first drill bit load-drill rod internal pressure time sequence interaction nonlinear factor and the second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor to obtain drill bit load-drill rod internal pressure time sequence interaction nonlinear response values, and performing vectorization normalization on the plurality of drill bit load-drill rod internal pressure time sequence interaction nonlinear response values according to a time dimension to obtain the drill bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector.
Specifically, inputting the bit load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector into the nonlinear coupling interaction module to process according to the following nonlinear coupling interaction formula so as to obtain the bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; the nonlinear coupling interaction formula is as follows: ; wherein, Correlating the bit load time sequence with the first bit load time sequence in the characteristic vectorThe characteristic value of the individual position is used,Is the first in the time sequence associated characteristic vector of the pressure in the drill rodThe characteristic value of the individual position is used,Is the bit load-the first in the time sequence interaction characteristic vector in the drill rodThe characteristic value of the individual position is used,、、AndIn order to adjust the super-parameters of the device,Non-linear cross-coupling representation vectors for the bit load-intra-drill-pipe pressure timingCharacteristic values of the individual positions.
Further, the bit load-intra-drill rod pressure time sequence nonlinear cross coupling representation vector is input into a classifier-based propulsion speed controller to obtain a control command, wherein the control command is used for representing that the propulsion speed at the current time point should be increased, decreased or kept unchanged. That is, the real-time adaptive adjustment of the drill advancing speed is performed by classifying the drill load time sequence autocorrelation characteristic and the nonlinear cross coupling characterization information between the drill pressure time sequence autocorrelation characteristic and the drill pressure time sequence autocorrelation characteristic. Therefore, the advancing speed of the drilling machine can be automatically adjusted according to the cooperative change of the bit load and the time sequence of the pressure in the drill rod, so as to adapt to stratum with different hardness, thereby meeting the requirements of different geological environments and working conditions and being beneficial to high-efficiency and real sampling in loose stratum in a shallow coverage area.
In one embodiment of the application, the drill rig propulsion speed control module is configured to input the drill bit load-intra-drill-rod pressure time sequence nonlinear cross-coupling representation vector into a classifier-based propulsion speed controller to obtain a control command, wherein the control command is used for representing that the propulsion speed at the current time point should be increased, decreased or kept unchanged.
Further, in an embodiment of the present application, the vehicle-mounted reverse circulation drilling machine suitable for sampling a loose stratum in a shallow coverage area further includes a time sequence pattern feature extractor based on the cavity convolutional neural network model, the nonlinear coupling interaction module, and a training module for training the propulsion speed controller based on the classifier.
The training module comprises: the training data acquisition unit is used for acquiring a time sequence of training bit load data acquired by the sensor and a time sequence of pressure in the training drill rod; the training drill data time sequence autocorrelation unit is used for respectively constructing a time sequence of the training drill bit load data and a spatial covariance matrix of the time sequence of the pressure in the training drill rod so as to obtain a training drill bit load time sequence correlation matrix and a training drill rod pressure time sequence correlation matrix; the training drill data time sequence pattern feature extraction unit is used for extracting features of the training drill bit load time sequence correlation matrix and the training drill rod internal pressure time sequence correlation matrix through a time sequence pattern feature extractor based on the deep neural network model so as to obtain a training drill bit load time sequence correlation feature vector and a training drill rod internal pressure time sequence correlation feature vector; the training bit load-internal pressure time sequence characteristic interaction unit is used for inputting the training bit load time sequence related characteristic vector and the training internal pressure time sequence related characteristic vector into the nonlinear coupling interaction module to obtain a training bit load-internal pressure time sequence nonlinear interaction coupling representation vector; the training classification unit is used for inputting the training bit load-internal pressure time sequence nonlinear interactive coupling representation vector in the drill rod into the classifier-based propulsion speed controller to obtain a training control instruction; a loss function calculation unit for calculating a cross entropy loss function value between the training control instruction and the real instruction to obtain a classification loss function value; and the training unit is used for training the time sequence mode feature extractor based on the cavity convolutional neural network model, the nonlinear coupling interaction module and the propulsion speed controller based on the classifier by using the classification loss function value.
In a preferred embodiment, inputting the training bit load-intra-drill-rod pressure timing nonlinear cross-coupled representation vector into a classifier-based propulsion speed controller to derive control instructions includes: determining an increased probability value, a decreased probability value and a constant probability value which are respectively corresponding to the propulsion speed representing the current time point and are obtained by a propulsion speed controller based on a classifier according to the training bit load-internal pressure time sequence nonlinear interactive coupling representation vector; calculating a second norm of a probability distribution vector formed by the increasing probability value, the decreasing probability value and the invariable probability value, and dividing the second norm by a square root of three to obtain a coupling probability value; subtracting the joint probability value from one to obtain an inverse coupling probability value; respectively calculating power functions of each characteristic value of the training bit load-drill rod internal pressure time sequence nonlinear interactive coupling expression vector by taking the coupling class probability value as an exponent so as to obtain bit load-drill rod internal pressure time sequence nonlinear interactive coupling class vectors; respectively calculating power functions of each characteristic value of the training bit load-drill rod internal pressure time sequence nonlinear interactive coupling expression vector by taking the inverse coupling class probability value as an exponent so as to obtain a bit load-drill rod internal pressure time sequence nonlinear interactive coupling inverse class vector; performing point multiplication on the coupling probability value and the bit load-drill rod internal pressure time sequence nonlinear interactive coupling inverse vector to obtain a first bit load-drill rod internal pressure time sequence nonlinear interactive coupling intermediate vector, and performing point multiplication on the inverse coupling probability value and the bit load-drill rod internal pressure time sequence nonlinear interactive coupling intermediate vector to obtain a second bit load-drill rod internal pressure time sequence nonlinear interactive coupling intermediate vector; after the first bit load-drill rod internal pressure time sequence nonlinear interactive coupling intermediate class vector and the second bit load-drill rod internal pressure time sequence nonlinear interactive coupling intermediate class vector are subjected to dot multiplication, dot addition is further carried out on the coupling class probability value and dot multiplication results of the bit load-drill rod internal pressure time sequence nonlinear interactive coupling class vector, so that an optimized bit load-drill rod internal pressure time sequence nonlinear interactive coupling representation vector is obtained; and inputting the optimized bit load-intra-drill rod pressure time sequence nonlinear cross-coupling representation vector into a classifier-based propulsion speed controller to obtain a control instruction.
Here, considering the non-correspondence of the time sequence distribution of the local time sequence correlation characteristic of the full time domain space covariance distribution caused by the misalignment of the source time sequence distribution of the drill bit load data and the pressure in the drill rod, the time sequence nonlinear interaction coupling expression vector of the drill bit load-pressure in the drill rod further obtained through nonlinear coupling interaction also has the knowledge offset relative to the time sequence distribution of the source time sequence distribution, thereby causing the uncertainty of the time sequence distribution relative to the class probability understanding of the classifier, and reducing the speed of classification training and the accuracy of classification results.
Based on the above, when the applicant inputs the training bit load-intra-drill rod pressure time sequence nonlinear interactive coupling representation vector to the classifier-based propulsion speed controller for classification, in order to realize the unsupervised domain adaptation from the characteristic distribution domain of the training bit load-intra-drill rod pressure time sequence nonlinear interactive coupling representation vector to the probability distribution domain of the classification probability, the class probability coupling representation of each control class obtained by the training bit load-intra-drill rod pressure time sequence nonlinear interactive coupling representation vector through the classifier-based propulsion speed controller is used as a domain proxy, the movement averaging of the probability distribution of the training bit load-intra-drill rod pressure time sequence nonlinear interactive coupling representation vector is carried out through power function class distribution interaction serving as an index, and the knowledge migration from the feature domain probability distribution of the training bit load-intra-drill rod pressure time sequence nonlinear interactive coupling representation vector to the probability distribution of label is realized through superposition, so that the classification operation of the training bit load-intra-drill rod pressure time sequence nonlinear interactive coupling representation vector to the probability distribution of label is promoted, namely the classification operation of the classifier-based propulsion speed controller and the accuracy of classification training result of the classifier-based propulsion speed controller are promoted. Therefore, the advancing speed of the drilling machine can be adjusted more accurately according to the cooperative time sequence change of the load of the drill bit and the pressure in the drill rod so as to adapt to stratum with different hardness.
In summary, by adopting the scheme, the drill bit load data and the pressure data in the drill rod are monitored and collected in real time through the sensor, and the time sequence cooperation and the associated analysis of the drill bit load data and the pressure data in the drill rod are carried out by introducing an artificial intelligence-based data processing and analyzing algorithm at the rear end, so that the real-time self-adaptive adjustment of the advancing speed of the drilling machine is carried out. Therefore, the advancing speed of the drilling machine can be automatically adjusted according to the cooperative change of the bit load and the time sequence of the pressure in the drill rod, so as to adapt to stratum with different hardness, thereby meeting the requirements of different geological environments and working conditions and being beneficial to high-efficiency and real sampling in loose stratum in a shallow coverage area.
In one embodiment of the application, a vehicle-mounted RC drilling machine suitable for rapid sampling of loose strata in a shallow coverage area is provided, and main equipment such as a truck chassis, a crawler-type RC drilling machine, an air compressor and the like suitable for the research and development are selected; adjusting the appearance, performance, power and the like of the equipment according to the research and development requirements; designing a vehicle-mounted RC drilling machine according to the determined main equipment to finish the manufacture of a design drawing; manufacturing a vehicle-mounted RC drilling machine according to the design drawing, and simultaneously completing research and development and manufacturing of a power output conversion device (power takeoff) and special parts; performing test and debugging of the vehicle-mounted RC drilling machine; the product is put into use.
The problem that the shallow coverage area chemical detection sampling has no quick, accurate and efficient sampling equipment is solved by researching and developing the vehicle-mounted RC drilling machine, so that the efficiency of the shallow coverage area chemical detection sampling work is greatly improved, and the shallow coverage area mining evaluation work is accelerated. The vehicle-mounted RC drilling machine (air reverse circulation drilling machine) is researched and developed aiming at shallow coverage area chemical exploration sampling, equipment and materials required by construction are collected in one vehicle in a mode of combining the vehicle with the RC drilling machine, and meanwhile, a set of power output conversion device is researched and developed, so that the vehicle power can meet the use of the drilling machine and an air compressor, the mobility and the flexibility of the drilling machine in the shallow coverage area are ensured, and the sampling work is completed efficiently.
FIG. 3 is a flow chart illustrating a method for sampling a loose formation in a shallow coverage area, according to an exemplary embodiment, as shown in FIG. 3, the method comprising: adjusting the height of a drilling machine by using a drilling tower, wherein the drilling tower is mounted on an automobile chassis; maintaining negative pressure in a drilling hole by using a vacuum pump to prevent mud from entering the drilling hole, and automatically adjusting parameters of the drilling machine by using an electric control system; providing power to the drilling machine using a power system; wherein, the electrical control system includes: step S201, acquiring a time sequence of drill bit load data acquired by a sensor and a time sequence of pressure in a drill rod; step S202, respectively constructing a space covariance matrix of the time sequence of the bit load data and the time sequence of the pressure in the drill rod to obtain a bit load time sequence correlation matrix and a pressure in the drill rod time sequence correlation matrix; step S203, respectively extracting features of the drill bit load time sequence correlation matrix and the drill rod internal pressure time sequence correlation matrix through a time sequence mode feature extractor based on a deep neural network model to obtain a drill bit load time sequence correlation feature vector and a drill rod internal pressure time sequence correlation feature vector; s204, inputting the bit load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector into a nonlinear coupling interaction module to obtain a bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; step S205, based on the bit load-internal pressure time sequence nonlinear interaction coupling expression vector, determining that the propulsion speed at the current time point should be increased, decreased or kept unchanged.
Referring now to fig. 4, a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application is shown. The terminal device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the application.
As shown in fig. 4, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM602, and the RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the method of the embodiment of the present application are performed when the computer program is executed by the processing means 601.
The computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText TransferProtocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. The name of the module is not limited to the module itself in some cases, and for example, the test parameter obtaining module may also be described as "a module for obtaining the device test parameter corresponding to the target device".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
FIG. 5 is a diagram illustrating an application scenario of a vehicle-mounted reverse circulation drilling rig suitable for shallow coverage area unconsolidated formation sampling, according to an exemplary embodiment. As shown in fig. 5, in this application scenario, first, a time series of drill bit load data acquired by a sensor (e.g., C1 as illustrated in fig. 5) and a time series of pressure in the drill pipe (e.g., C2 as illustrated in fig. 5) are acquired; the time series of bit loading data and the time series of pressure in the drill pipe are then input to a server (e.g., S as illustrated in fig. 5) deployed with a vehicle-mounted reverse circulation drilling machine algorithm suitable for shallow coverage area unconsolidated formation sampling, wherein the server is capable of processing the time series of bit loading data and the time series of pressure in the drill pipe based on the vehicle-mounted reverse circulation drilling machine algorithm suitable for shallow coverage area unconsolidated formation sampling to determine that the rate of advance at the current point in time should be increased, should be decreased, or should remain unchanged.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (9)
1. The vehicle-mounted reverse circulation drilling machine suitable for sampling the loose stratum in the shallow coverage area comprises a power system, an electric control system, a drilling machine, a vacuum pump and a drilling tower, and is characterized in that the drilling tower is suitable for being installed on an automobile chassis and used for adjusting the height of the drilling machine; the vacuum pump is used for maintaining negative pressure in a drilling hole to prevent mud from entering the drilling hole, and the electric control system is used for automatically adjusting parameters of the drilling machine; the power system is used for providing power for the drilling machine; wherein, the electrical control system includes: the data acquisition module is used for acquiring the time sequence of the bit load data acquired by the sensor and the time sequence of the pressure in the drill rod; the drilling machine data time sequence autocorrelation module is used for respectively constructing a time sequence of the drill bit load data and a spatial covariance matrix of the time sequence of the pressure in the drill rod so as to obtain a drill bit load time sequence correlation matrix and a pressure in the drill rod time sequence correlation matrix; the drilling machine data time sequence mode feature extraction module is used for respectively carrying out feature extraction on the drill bit load time sequence incidence matrix and the drill rod internal pressure time sequence incidence matrix through a time sequence mode feature extractor based on a deep neural network model so as to obtain a drill bit load time sequence incidence feature vector and a drill rod internal pressure time sequence incidence feature vector; the drill bit load-drill rod internal pressure time sequence characteristic interaction module is used for inputting the drill bit load time sequence association characteristic vector and the drill rod internal pressure time sequence association characteristic vector into the nonlinear coupling interaction module to obtain a drill bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; and the drill propulsion speed control module is used for determining that the propulsion speed at the current time point should be increased, decreased or kept unchanged based on the drill bit load-internal pressure time sequence nonlinear interactive coupling representation vector.
2. The on-board reverse circulation drilling rig for shallow coverage area unconsolidated formation sampling of claim 1, wherein the rig data timing autocorrelation module comprises: the data normalization unit is used for performing data normalization on the time sequence of the drill bit load data and the time sequence of the pressure in the drill rod according to the time dimension to obtain a drill bit load time sequence input vector and a drill rod pressure time sequence input vector; the space covariance calculation unit is used for calculating the space covariance matrix of the bit load time sequence input vector and the drill rod internal pressure time sequence input vector to obtain the bit load time sequence incidence matrix and the drill rod internal pressure time sequence incidence matrix.
3. The vehicle-mounted reverse circulation drilling machine suitable for shallow coverage area unconsolidated formation sampling according to claim 2, wherein the drilling machine data time sequence pattern feature extraction module is configured to pass the drill bit load time sequence correlation matrix and the intra-drill-rod pressure time sequence correlation matrix through a time sequence pattern feature extractor based on a cavity convolutional neural network model to obtain the drill bit load time sequence correlation feature vector and the intra-drill-rod pressure time sequence correlation feature vector, respectively.
4. The on-board reverse circulation drilling machine for shallow coverage area unconsolidated formation sampling of claim 3, wherein the bit load-on-drill pipe pressure timing feature interaction module comprises: the drill bit load-drill rod internal pressure time sequence response unit is used for calculating position-by-position response between the drill rod internal pressure time sequence correlation characteristic vector and the drill bit load time sequence correlation characteristic vector so as to obtain a drill bit load-drill rod internal pressure time sequence interaction characteristic vector; and the drill bit load-drill rod internal pressure time sequence interactive coupling representation unit is used for carrying out nonlinear response optimization processing on the drill bit load-drill rod internal pressure time sequence interactive characteristic vector so as to obtain the drill bit load-drill rod internal pressure time sequence nonlinear interactive coupling representation vector.
5. The vehicle-mounted reverse circulation drilling machine suitable for shallow coverage area unconsolidated formation sampling according to claim 4, wherein the bit load-in-drill pipe pressure time sequence interactive coupling representation unit is configured to calculate a product between a square value of each position characteristic value in the bit load-in-drill pipe pressure time sequence interactive characteristic vector and a first predetermined adjustment super-parameter to obtain a first bit load-in-drill pipe pressure time sequence interactive nonlinear factor quadratic term adjustment parameter; calculating the product of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a second preset adjustment super parameter to obtain a first drill bit load-drill rod internal pressure time sequence interaction nonlinear factor primary item adjustment parameter; calculating the sum of the first bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter and the first bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter to obtain a first bit load-drill rod internal pressure time sequence interaction nonlinear factor; calculating the product of the square value of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a third preset adjustment super-parameter to obtain a second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter; calculating the product of each position characteristic value in the drill bit load-drill rod internal pressure time sequence interaction characteristic vector and a fourth preset adjustment super parameter to obtain a second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor primary term adjustment parameter; calculating the sum of the second bit load-drill rod internal pressure time sequence interaction nonlinear factor quadratic term adjustment parameter and the second bit load-drill rod internal pressure time sequence interaction nonlinear factor once term adjustment parameter, and then adding a constant term and modulating to obtain a second bit load-drill rod internal pressure time sequence interaction nonlinear factor; calculating a division between the first drill bit load-drill rod internal pressure time sequence interaction nonlinear factor and the second drill bit load-drill rod internal pressure time sequence interaction nonlinear factor to obtain drill bit load-drill rod internal pressure time sequence interaction nonlinear response values, and performing vectorization normalization on the plurality of drill bit load-drill rod internal pressure time sequence interaction nonlinear response values according to a time dimension to obtain the drill bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector.
6. The on-board reverse circulation drilling machine for shallow coverage area unconsolidated formation sampling of claim 5, wherein the drilling machine propulsion speed control module is configured to input the bit load-intra-drill-rod pressure time-series nonlinear cross-coupled representation vector into a classifier-based propulsion speed controller for a control command indicating that the propulsion speed at the current point in time should be increased, decreased, or should be maintained.
7. The on-board reverse circulation drilling rig for shallow coverage area unconsolidated formation sampling of claim 6, further comprising a training module for training the void convolutional neural network model-based timing pattern feature extractor, the nonlinear coupling interaction module, and the classifier-based propulsion speed controller.
8. The on-board reverse circulation drilling machine adapted for shallow coverage area unconsolidated formation sampling of claim 7, wherein the training module comprises: the training data acquisition unit is used for acquiring a time sequence of training bit load data acquired by the sensor and a time sequence of pressure in the training drill rod; the training drill data time sequence autocorrelation unit is used for respectively constructing a time sequence of the training drill bit load data and a spatial covariance matrix of the time sequence of the pressure in the training drill rod so as to obtain a training drill bit load time sequence correlation matrix and a training drill rod pressure time sequence correlation matrix; the training drill data time sequence pattern feature extraction unit is used for extracting features of the training drill bit load time sequence correlation matrix and the training drill rod internal pressure time sequence correlation matrix through a time sequence pattern feature extractor based on the deep neural network model so as to obtain a training drill bit load time sequence correlation feature vector and a training drill rod internal pressure time sequence correlation feature vector; the training bit load-internal pressure time sequence characteristic interaction unit is used for inputting the training bit load time sequence related characteristic vector and the training internal pressure time sequence related characteristic vector into the nonlinear coupling interaction module to obtain a training bit load-internal pressure time sequence nonlinear interaction coupling representation vector; the training classification unit is used for inputting the training bit load-internal pressure time sequence nonlinear interactive coupling representation vector in the drill rod into the classifier-based propulsion speed controller to obtain a training control instruction; a loss function calculation unit for calculating a cross entropy loss function value between the training control instruction and the real instruction to obtain a classification loss function value; and the training unit is used for training the time sequence mode feature extractor based on the cavity convolutional neural network model, the nonlinear coupling interaction module and the propulsion speed controller based on the classifier by using the classification loss function value.
9. A method for sampling a loose strata in a shallow coverage area, comprising: adjusting the height of a drilling machine by using a drilling tower, wherein the drilling tower is mounted on an automobile chassis; maintaining negative pressure in a drilling hole by using a vacuum pump to prevent mud from entering the drilling hole, and automatically adjusting parameters of the drilling machine by using an electric control system; providing power to the drilling machine using a power system; wherein, the electrical control system includes: acquiring a time sequence of drill bit load data acquired by a sensor and a time sequence of pressure in a drill rod; respectively constructing a space covariance matrix of the time sequence of the bit load data and the time sequence of the pressure in the drill rod to obtain a bit load time sequence correlation matrix and a drill rod pressure time sequence correlation matrix; the time sequence pattern feature extractor based on the deep neural network model is used for extracting features of the drill bit load time sequence correlation matrix and the drill rod internal pressure time sequence correlation matrix respectively so as to obtain a drill bit load time sequence correlation feature vector and a drill rod internal pressure time sequence correlation feature vector; inputting the bit load time sequence related characteristic vector and the drill rod internal pressure time sequence related characteristic vector into a nonlinear coupling interaction module to obtain a bit load-drill rod internal pressure time sequence nonlinear interaction coupling representation vector; based on the bit load-intra-drill pipe pressure time sequence nonlinear cross coupling representation vector, the propulsion speed at the current time point is determined to be increased, decreased or kept unchanged.
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