A device and method for sampling bedrock and rock cuttings samples

Abstract

The present invention is applicable to the technical field of bedrock sampling, and relates to a device and method for sampling bedrock and rock cuttings samples, including a sampling base, a gantry guide frame, a power slide driven by a servo motor and a ball screw, a drilling motor and a hollow coring drill bit, and an integrated sensor and cognitive assembly: an axial force sensor measures the axial pressure of the drill bit, a high-frequency vibration sensor captures the drilling vibration signal, a macro camera captures the rock cutting morphology in the chip removal area, and mechanical sensor data representing the rock crushing response and visual sensor data representing the rock cutting morphology are simultaneously obtained during drilling; the visual data is verified for validity and a visual data contamination status is generated; if there is no contamination, the mechanical and visual data are fused and analyzed to determine the physical properties of the rock in the current layer; if there is contamination, the mechanical data is used as the main judgment characteristic and an obstacle clearance instruction is triggered; based on the judged rock characteristics, the servo motor feed speed and the drilling motor speed are adaptively optimized to achieve adaptive control of the drilling process.

Description

Technical Field

[0001] The present invention belongs to the technical field of bedrock sampling, and in particular relates to a device and method for sampling bedrock and rock cuttings. Background Art

[0002] Existing rock sampling operations typically rely on operator experience and utilize pre-set, relatively fixed drilling parameters (such as drilling speed and feed rate) for control. Throughout the drilling process, operators may manually fine-tune these parameters based on the physical changes in the sound or vibration emitted by the equipment. In terms of data recording, traditional field logs typically only contain basic information such as sample number, sampling depth, and operation time. Although some advanced equipment may be equipped with basic sensors such as torque or pressure, they generally lack a comprehensive sensing and decision-making system that can perceive rock-tool interactions online, in real time, and in multiple dimensions.

[0003] Existing sampling methods have the following major shortcomings: Because the physical and mechanical properties of rock constantly change with vertical depth, fixed drilling parameters are difficult to adapt to the requirements of different lithologies. When encountering high-hardness and high-brittle rock, inappropriate parameters may cause core breakage, affecting sample integrity. In highly ductile rock, drilling efficiency is low. During operation, the equipment lacks effective real-time perception and avoidance capabilities for unknown geological conditions ahead (such as unexpected holes or large fractures), which can easily cause the drill tool to run idle and impact, causing damage to the equipment and the hole wall. Furthermore, in water-bearing or highly viscous formations, mud easily contaminates observation sensors (such as cameras). Traditional equipment cannot automatically identify and clear this "sensor contamination", resulting in the failure of visual monitoring functions. Finally, the physical core sample obtained is disconnected from the mechanical response data during its formation. Subsequent analysts cannot obtain detailed information about the process the sample underwent during sampling, which limits the possibility of comprehensive and accurate judgment of formation characteristics. Summary of the Invention

[0004] The present invention provides a device and method for sampling bedrock and rock cuttings samples, aiming to solve the problem of improper parameter adjustment.

[0005] The present invention is achieved by providing a sampling device for bedrock and rock cuttings samples, comprising:

[0006] A sampling base for supporting the device; a gantry guide frame vertically fixed on the sampling base; a power slide slidably mounted on the gantry guide frame; a power feed assembly for driving the power slide to move vertically along the gantry guide frame, the power feed assembly comprising a servo motor fixed to the gantry guide frame, and a ball screw transmission-connected to the servo motor and meshing with the power slide; a drilling motor mounted on the power slide; a hollow coring drill bit connected to the main shaft of the drilling motor; a sensor recognition assembly comprising: an axial force sensor arranged between the drilling motor and the power slide for measuring the axial pressure exerted on the coring drill bit; a high-frequency vibration sensor fixed on the housing of the drilling motor for capturing vibration signals during drilling; a macro camera fixed on the bottom of the power slide, with its lens aimed at the area where cuttings are removed by the coring drill bit, for capturing the shape of rock cuttings.

[0007] Preferably, the gantry guide rail frame includes two parallel vertical linear guide rails, and the power slide is installed on the two linear guide rails through sliders.

[0008] Preferably, the sensing and recognition assembly further includes: an acoustic probe, which is encapsulated in a protective cover and installed at the bottom of the power slide, and is used to collect the sound generated when the rock is broken.

[0009] Preferably, the outer ring of the lens of the macro camera is provided with a ring-shaped LED light for providing lighting for capturing the rock debris morphology.

[0010] A method for sampling bedrock and rock cuttings comprises the following steps:

[0011] During the drilling process, the sensor-recognition assembly synchronously acquires a mechanical sensing data set representing the physical response of rock crushing and a visual sensing data set representing the physical morphology of rock cuttings;

[0012] Performing validity verification on the visual sensing data set to generate a visual data contamination state;

[0013] When the visual data pollution state is unpolluted, the mechanical sensing data set and the visual sensing data set are fused and analyzed to determine the rock physical properties of the current layer;

[0014] When the visual data pollution state is polluted, the mechanical sensor data set is used as the main basis for determining the physical properties of the rock, and an obstacle clearance instruction is triggered;

[0015] Based on the identified rock physical properties, the feed speed controlled by the servo motor and the rotation speed controlled by the drilling motor are adaptively optimized; or, in response to the obstacle clearance instruction, a preset obstacle clearance operation is performed.

[0016] Preferably, the step of performing validity verification on the visual sensing data set includes:

[0017] Continuously calculate the clarity gradient index that represents the trend of image quality changes, and the cuttings refresh rate index that represents the speed of replacement of new and old cuttings in the field of view;

[0018] When the negative gradient of the clarity gradient index exceeds a first threshold and the rock fragment refresh rate index is lower than a second threshold, the visual data pollution state is set to polluted.

[0019] Preferably, the steps of performing adaptive optimization based on rock physical properties include:

[0020] When the rock physical properties are determined to be high hardness and high brittleness, the rotation speed and the feed speed are set to lower values;

[0021] When the rock physical properties are determined to be high hardness and high toughness, the rotational speed is set to a higher value, and the feed pressure is closed-loop controlled based on the reading of the axial force sensor to increase the feed speed.

[0022] Preferably, the obstacle removal operation includes:

[0023] The servo motor drives the power slide to stop feeding and withdraw slightly upward;

[0024] The drilling motor performs a pulsed high-speed motion to shake off the adhered matter.

[0025] Preferably, after performing the obstacle clearance operation, the process returns to the step of performing validity verification on the visual sensing data set; if the visual data contamination state is still contaminated after performing the obstacle clearance operation for a preset number of consecutive times, the process switches to a conservative mode, which drills at a low rotation speed and a low feed speed, and actively performs the obstacle clearance operation at preset depth intervals.

[0026] Preferably, the method further comprises the following steps:

[0027] A digital archive is generated using the drilling depth controlled by the servo motor as an index, the archive including at least the mechanical sensing dataset accurately corresponding to the depth, rock cuttings characteristics extracted based on the visual sensing dataset, the identified rock physical properties, the feed speed and rotation speed used, and a log record of the visual data contamination status and obstacle clearance operation.

[0028] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0029] Through the sensor recognition assembly, a mechanical sensor data set representing the physical response of rock crushing and a visual sensor data set representing the physical morphology of rock cuttings are simultaneously acquired, and the two are fused and analyzed to determine the rock physical properties of the current layer in real time and online. This solves the fundamental problem of traditional sampling methods being unable to perceive lithology during operation, resulting in low operation efficiency and sample quality. A step for validating the effectiveness of the visual sensor data set is introduced. By calculating indicators such as clarity gradient and rock cutting refresh rate, the system can intelligently identify visual data contamination caused by mud gelatinization and other reasons. When contamination occurs, the system can automatically switch the decision logic, using the mechanical sensor data set as the main basis, and trigger obstacle clearance instructions, greatly enhancing the working reliability and autonomy of the device in complex and changeable field environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the three-dimensional structure of the present invention;

[0031] Figure 2 This is a schematic diagram of the local three-dimensional structure of the present invention Figure 1 ;

[0032] Figure 3 This is a schematic diagram of the local three-dimensional structure of the present invention Figure 2 ;

[0033] Figure 4 This is a schematic diagram of the local three-dimensional structure of the present invention Figure 3 ;

[0034] Figure 5 It is a rock property dynamic identification curve diagram of the present invention;

[0035] In the figure: 100, sampling base; 200, gantry guide frame; 210, linear guide; 300, power slide; 310, servo motor; 320, ball screw; 400, drilling motor; 410, coring drill bit; 510, axial force sensor; 520, high-frequency vibration sensor; 530, acoustic probe; 540, macro camera; 541, ring LED light. DETAILED DESCRIPTION

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0037] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] See Figures 1 to 5 The present application discloses a device for sampling bedrock and rock cuttings samples, comprising: a sampling base 100 for supporting the device; a gantry guide frame 200 vertically fixed on the sampling base 100; a power slide 300 slidably mounted on the gantry guide frame 200; a power feed assembly for driving the power slide 300 to move vertically along the gantry guide frame 200, the power feed assembly comprising a servo motor 310 fixed to the gantry guide frame 200, and a ball screw 320 transmission-connected to the servo motor 310 and meshing with the power slide 300; a drilling motor The core drill bit 410 is connected to the main shaft of the drilling motor 400; the core drill bit 410 is connected to the main shaft of the drilling motor 400; a set of sensing and cognitive assemblies includes: an axial force sensor 510, which is arranged between the drilling motor 400 and the power slide 300, and is used to measure the axial pressure exerted on the core drill bit 410; a high-frequency vibration sensor 520, which is fixed to the housing of the drilling motor 400, and is used to capture vibration signals during drilling; a macro camera 540, which is fixed to the bottom of the power slide 300, with its lens aimed at the area where the cuttings are discharged by the core drill bit 410, and is used to capture the shape of the cuttings.

[0039] The sampling base 100 of the device provides a stable ground support for the entire system, on which a gantry guide frame 200 is vertically fixed, and the power slide 300 can slide on the guide frame. Specifically, the servo motor 310 fixed on the guide frame drives the ball screw 320 to rotate through a coupling, and the ball screw 320 engages with the nut seat on the power slide 300, thereby converting the rotational motion of the motor into a stable and controllable linear vertical motion of the slide, providing precise feed speed control for drilling, and the drilling motor 400 installed on the power slide 300 drives the hollow coring drill bit 410 to perform rotary drilling and coring, wherein the axial force sensor 510 is sandwiched between the drilling motor 400 and the power slide 300 , which is used to measure the axial pressure on the drill bit in real time. This pressure is a direct basis for judging the hardness of the rock; the high-frequency vibration sensor 520 is fastened to the casing of the drilling motor 400 and is used to capture the vibration signal generated when the rock is broken. This signal is an important feature for identifying the brittleness of the rock; and the lens of the macro camera 540 is precisely aimed at the chip discharge area of ​​the coring drill bit 410 to directly observe and capture the physical form of the newly discharged rock cuttings. By combining a high-precision mechanical execution system with a multimodal sensing and cognitive system, this device constructs an intelligent platform that can perceive and respond to the physical characteristics of the drilling object in real time, solving the technical problem that traditional sampling equipment cannot perceive the rock properties online during operation and therefore it is difficult to optimize the sampling parameters.

[0040] Furthermore, the gantry guide rail frame 200 includes two parallel vertical linear guide rails 210, and the power slide 300 is installed on the two linear guide rails 210 through sliders.

[0041] The gantry guide rail frame 200 in this embodiment has a specific structure using two parallel vertical linear guide rails 210, and the power slide 300 is installed on these two linear guide rails 210 through precise sliders, thereby ensuring that the signals collected by the axial force sensor 510 and the high-frequency vibration sensor 520 can truly reflect the crushing response of the rock itself.

[0042] Furthermore, the sensing and recognition assembly also includes: an acoustic probe 530, which is encapsulated in a protective cover and installed at the bottom of the power slide 300, and is used to collect the sound generated when the rock is broken.

[0043] The sensor recognition assembly in this embodiment further adds an acoustic probe 530 on the original basis. The probe is enclosed in a soundproof and dustproof protective cover and installed at the bottom of the power slide 300 near the drill hole to clearly collect the acoustic signals emitted when the rock is broken. The purpose of adding the acoustic probe 530 is to achieve cross-validation and high-confidence identification of rock brittle fracture events. When hard and brittle rocks (such as quartzite) fracture, not only high-frequency vibrations and violent fluctuations in axial force are generated, but also crisp high-frequency popping sounds are accompanied. By synchronously correlating and analyzing the sensor data of three different modes of force, vibration, and sound, when the system detects synchronized peaks in the three signals, it can be highly confirmed that brittle fracture has occurred. This multimodal data fusion strategy effectively overcomes the multi-solution problem that may exist in a single sensor signal (for example, high vibrations may also be caused by drill resonance) and significantly improves the accuracy and reliability of online identification of rock physical properties.

[0044] Furthermore, the outer ring of the lens of the macro camera 540 is provided with a ring-shaped LED light 541 for providing lighting for capturing the rock debris morphology.

[0045] The macro camera 540 in this embodiment has a ring-shaped LED light 541 mounted on the outer ring of its lens. This design is intended to provide stable, uniform, and shadow-free lighting conditions for visually capturing the morphology of rock fragments.

[0046] Furthermore, the present application also provides a method for sampling bedrock and rock cuttings. During the drilling process, a mechanical sensing data set representing the physical response of rock crushing and a visual sensing data set representing the physical morphology of rock cuttings are synchronously acquired through the sensing and cognitive assembly; the validity of the visual sensing data set is verified to generate a visual data contamination status; when the visual data contamination status is uncontaminated, the mechanical sensing data set and the visual sensing data set are fused and analyzed to determine the rock physical properties of the current layer; when the visual data contamination status is contaminated, the mechanical sensing data set is used as the main basis for determining the rock physical properties, and an obstacle clearance instruction is triggered; based on the determined rock physical properties, the feed speed controlled by the servo motor 310 and the speed controlled by the drilling motor 400 are adaptively optimized; or, in response to the obstacle clearance instruction, a preset obstacle clearance operation is performed.

[0047] During the drilling process, the method of this embodiment first uses the sensor recognition assembly to synchronously acquire two types of data: one is a mechanical sensor data set composed of axial force, vibration, and sound sensors, which indirectly reflects the rock crushing response; the other is a visual sensor data set composed of a macro camera 540, which directly shows the physical form of the rock cuttings. It does not directly use the visual data, but first verifies its validity to determine whether there is a "visual data contamination state" caused by mud gelatinization and other reasons. Subsequently, the control logic enters a diversion: when the visual data is reliable (i.e., uncontaminated), the system will fuse and analyze the visual and mechanical data, and verify or correct the inference of the mechanical data (such as high brittleness) through the directly observed rock cuttings morphology (such as large particles and sharp edges). Conversely, if the visual data is judged to be contaminated, the system switches to a fault-tolerant mode, using the more reliable mechanical sensor data set as the primary basis for judgment and simultaneously triggering an obstacle clearance command to prepare to resolve the visual contamination problem. Finally, based on the obtained lithology judgment or the triggered obstacle clearance command, the system performs the corresponding action: either adaptively optimize the drilling parameters (i.e., the feed speed controlled by the servo motor 310 and the rotation speed controlled by the drilling motor 400) to match the current lithology; or execute a preset obstacle clearance operation to restore the visual sensing capability. By introducing a visual validity self-verification mechanism, this method can proactively detect and handle sensor-level faults, greatly enhancing the reliability of the device in complex and changing geological environments.

[0048] Furthermore, the step of verifying the validity of the visual sensor data set includes: continuously calculating a clarity gradient index representing the trend of image quality changes, and a rock debris refresh rate index representing the speed of replacement of new and old rock debris in the field of view; when the negative gradient of the clarity gradient index exceeds a first threshold and the rock debris refresh rate index is lower than a second threshold, the visual data pollution state is set to polluted.

[0049] The validation step for the visual sensor dataset in this embodiment relies on two algorithmic metrics based on image sequence analysis. The first metric is the sharpness gradient. This algorithm doesn't determine the sharpness of a single frame. Instead, it continuously calculates the "trend" of image quality changes by tracking parameters such as image contrast and edge sharpness. The underlying logic is that if the lens is contaminated by slowly deposited mud, image quality will inevitably show a continuous, trending decline, manifested as a negative gradient in the sharpness index. The second indicator is the rock cuttings refresh rate. This algorithm quantifies the replacement rate of new and old rock cuttings in the field of view by comparing the texture or feature points of images between consecutive frames. Its internal logic is: during normal drilling, newly generated rock cuttings will be continuously discharged, refreshing the field of view; if the servo motor 310 shows that it is still feeding, but the camera image has not changed significantly for a long time, it means that the area in front of the lens is covered by stationary mud. Ultimately, the system determines the visual data contamination status as "contaminated" when the negative gradient of the clarity gradient indicator (i.e., the quality degradation rate) exceeds a preset first threshold and the rock cuttings refresh rate indicator is also lower than a preset second threshold. This dual-condition judgment mechanism, by combining the two dimensions of "whether the image is becoming blurred" and "whether the image content is updating," can very accurately identify the condition of lens contamination, avoiding misjudgment caused by jitter or temporary occlusion in a single field, and providing a reliable trigger basis for subsequent decision-making switching.

[0050] Furthermore, the step of adaptive optimization based on rock physical properties includes: when the rock physical properties are judged to be high hardness and high brittleness, setting the rotational speed and the feed speed to lower values; when the rock physical properties are judged to be high hardness and high toughness, setting the rotational speed to a higher value, and performing closed-loop control of the feed pressure based on the reading of the axial force sensor 510 to increase the feed speed.

[0051] The steps of adaptive optimization based on rock physical properties in this embodiment reflect a refined sampling strategy for different rock crushing mechanisms. When the system identifies the rock as high hardness and high brittleness (such as granite and quartzite) through fusion analysis, the controller aims to use its brittleness to perform efficient volume crushing. To this end, it strategically sets the speed of the drilling motor 400 and the feed speed of the servo motor 310 to lower values. The purpose of this is to avoid unnecessary grinding caused by excessive speed, achieve effective rock fracture through lower energy input, and thus obtain a more complete core sample. On the contrary, when the rock is identified as high hardness and high toughness ( For example, for dense basalt, grinding is the primary crushing method. In this case, the controller will set the speed of the drilling motor 400 to a higher value to improve grinding efficiency. More importantly, to prevent inefficient "slipping" grinding caused by insufficient pressure, the system will use the real-time readings of the axial force sensor 510 and the drive instructions of the servo motor 310 to form a closed-loop control system. By dynamically adjusting the output torque of the servo motor 310, the feed speed is increased to maintain a constant and efficient feed pressure. This targeted parameter optimization strategy adopts the optimal crushing method for different rock types, thereby achieving a balance between ensuring sample quality and improving sampling efficiency.

[0052] Furthermore, the obstacle removal operation includes: the servo motor 310 drives the power slide 300 to stop feeding and slightly withdraw upward; and the drilling motor 400 performs a pulsed high-speed action to shake off the adhesion.

[0053] In the obstacle removal operation of this embodiment, the servo motor 310 precisely controls the power slide 300 to immediately stop the downward feed and perform a short upward slight retraction action. The purpose of this step is to instantly relieve the extrusion stress at the front end of the drill bit to destroy the structural stability of the mud cake that may have formed and adhered to the lens or drill bit. Then, while the drill tool remains in the retracted position, the controller instructs the drilling motor 400 to perform a "pulse high speed" action. This action is specifically manifested in rapidly increasing the speed to a peak value and then lowering it in a very short time, and can be repeated several times. The purpose of this move is to use the strong centrifugal force generated by high-speed rotation and the accompanying mechanical vibration to forcefully shake off mud or rock chips and other adherent materials that adhere to the drill bit or chip removal channel and cause visual obstruction. This set of combined actions constitutes a non-contact obstacle removal solution that does not require external intervention. It can quickly self-repair after discovering the problem and restore the normal function of the sensing system.

[0054] Furthermore, after performing the obstacle removal operation, the process returns to the step of performing validity verification on the visual sensor data set; if the visual data contamination state is still contaminated after performing the obstacle removal operation for a preset number of consecutive times, the process switches to a conservative mode, which drills at a low rotation speed and a low feed speed, and actively performs the obstacle removal operation at preset depth intervals.

[0055] After performing the obstacle clearance operation, this embodiment designs a set of cyclic verification and strategy upgrade logic. After completing an obstacle clearance action, the system does not blindly resume normal drilling. Instead, it first returns to perform the validity verification step of the visual sensor data set to evaluate the obstacle clearance effect. If the visual contamination status is lifted, the system resumes normal adaptive optimization drilling. However, if the verification result shows that the visual data contamination status is still contaminated, the system will repeat the obstacle clearance operation. If the problem persists after a preset number of clearances (for example, three times), the system will make a more advanced judgment: the current situation is not sporadic contamination, but persistent strong mud or high-viscosity formations. At this time, the system will switch to a preset conservative mode. In this mode, the system will use a combination of low rotation speed and low feed rate to drill prudently in order to achieve stability and avoid blockage; more importantly, it will actively perform an obstacle clearance operation at a preset depth interval (for example, every 5 mm of drilling). The design concept of this mode changes from "passive response" to "active prevention". By performing periodic cleaning before the problem worsens, it ensures that operations can still be maintained under extremely harsh formation conditions. Although the efficiency is lower, it guarantees the continuity of the mission and the ultimate success rate.

[0056] Furthermore, a digital file is generated with the drilling depth controlled by the servo motor 310 as an index, and the file contains at least the mechanical sensing data set accurately corresponding to the depth, the rock cuttings characteristics extracted based on the visual sensing data set, the identified rock physical properties, the feed speed and rotation speed adopted, and the log records of the visual data contamination status and obstacle clearance operations.

[0057] The method of this embodiment also includes a step of generating a digital archive, which constitutes a complete digital reproduction of the sampling process. This method uses the drilling depth controlled and recorded by the high-precision servo motor 310 as a unique index, ensuring that any point on the physical core can be accurately matched with the digital record. The digital archive generated after the operation is completed is far richer in content than traditional records. It contains at least the following information accurately tied to the depth: 1) a complete set of original mechanical sensor data sets (force, vibration, sound); 2) rock cuttings characteristics (such as size and sharpness) extracted based on the visual sensor data set; 3) system fusion analysis 4) the feed rate and rotational speed parameters actually used by the controller at the corresponding depth. Crucially, this archive also includes a detailed system status and decision log, specifically a log of visual data contamination status and obstacle removal operations. It is no longer a passive record of results, but an active explanation of the reasons for the decisions and how the challenges were addressed. When analyzing cores, geologists can simultaneously consult this "digital twin" archive to understand not only that at a certain depth, the equipment not only identified the lithology as a certain type, but also that it encountered visual contamination and successfully overcame it through specific obstacle removal operations. This incorporation of process information provides rich data for subsequent scientific analysis, making the assessment of formation characteristics more accurate and comprehensive.

[0058] It should be noted that for the aforementioned embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0059] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative, such as the division of the above-mentioned units. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be in the form of telecommunications or other forms.

[0060] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the scope of protection of the invention. Obviously, the embodiments described are only some embodiments of the present invention, rather than all embodiments. Based on these embodiments, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field can still combine, add, delete or make other adjustments to the features in the various embodiments of the present invention according to the circumstances without conflict, without making creative work, so as to obtain different other technical solutions that do not deviate from the concept of the present invention in essence, and these technical solutions also fall within the scope of protection of the present invention.

Claims

1. A sampling device for bedrock and rock cuttings, characterized in that: include: a sampling base for supporting the device; A gantry guide frame is vertically fixed on the sampling base; a power slide is slidably mounted on the gantry guide frame; a power feed assembly is used to drive the power slide to move vertically along the gantry guide frame, the power feed assembly includes a servo motor fixed to the gantry guide frame, and a ball screw connected to the servo motor and engaged with the power slide; a drilling motor is mounted on the power slide; a hollow coring drill bit connected to the main shaft of the drilling motor; A sensor recognition assembly includes: an axial force sensor disposed between the drilling motor and the power slide for measuring the axial pressure exerted on the coring drill bit; a high-frequency vibration sensor fixed to the housing of the drilling motor for capturing vibration signals during drilling; an acoustic probe enclosed in a protective sleeve and mounted on the bottom of the power slide for collecting the sound emitted by rock breaking; and a macro camera fixed to the bottom of the power slide with its lens aimed at the area where the coring drill bit removes cuttings for capturing the shape of the cuttings. The sensor-cognitive assembly simultaneously acquires two types of data: a mechanical sensor dataset consisting of an axial force sensor, a high-frequency vibration sensor, and an acoustic probe, which indirectly reflects the rock's crushing response; and a visual sensor dataset composed of a macro camera, which directly displays the physical form of the rock fragments. This dataset does not directly use visual data, but first verifies its validity. Through the sensor recognition assembly, a mechanical sensing data set representing the physical response of rock crushing and a visual sensing data set representing the physical morphology of rock cuttings are synchronously acquired, and the two are fused and analyzed to determine the rock physical properties of the current layer in real time and online.

2. The bedrock and rock cuttings sampling device according to claim 1, characterized in that: The gantry guide rail frame includes two parallel vertical linear guide rails, and the power slide is installed on the two linear guide rails through sliders.

3. The bedrock and rock cuttings sampling device according to claim 1, characterized in that: The outer ring of the lens of the macro camera is provided with a ring-shaped LED light for providing lighting for capturing the rock fragments morphology.

4. A method for sampling bedrock and rock cuttings, characterized in that: The device for collecting bedrock and rock cuttings samples according to claim 1 comprises the following steps: During the drilling process, the sensor-recognition assembly synchronously acquires a mechanical sensing data set representing the physical response of rock crushing and a visual sensing data set representing the physical morphology of rock cuttings; Performing validity verification on the visual sensing data set to generate a visual data contamination state; When the visual data pollution state is unpolluted, the mechanical sensing data set and the visual sensing data set are fused and analyzed to determine the rock physical properties of the current layer; When the visual data pollution state is polluted, the mechanical sensor data set is used as the main basis for determining the physical properties of the rock, and an obstacle clearance instruction is triggered; Based on the identified rock physical properties, the feed speed controlled by the servo motor and the rotation speed controlled by the drilling motor are adaptively optimized; or, in response to the obstacle clearance instruction, a preset obstacle clearance operation is performed.

5. The sampling method according to claim 4, characterized in that: The steps for validating the visual sensing dataset include: Continuously calculate the clarity gradient index that represents the trend of image quality changes, and the cuttings refresh rate index that represents the speed of replacement of new and old cuttings in the field of view; When the negative gradient of the clarity gradient index exceeds a first threshold and the rock fragment refresh rate index is lower than a second threshold, the visual data pollution state is set to polluted.

6. The sampling method according to claim 4, characterized in that The steps of adaptive optimization based on rock physical properties include: When the rock physical properties are determined to be high hardness and high brittleness, the rotation speed and the feed speed are set to lower values; When the rock physical properties are determined to be high hardness and high toughness, the rotational speed is set to a higher value, and the feed pressure is closed-loop controlled based on the reading of the axial force sensor to increase the feed speed.

7. The sampling method according to claim 4, characterized in that: The obstacle removal operation includes: The servo motor drives the power slide to stop feeding and withdraw slightly upward; The drilling motor performs a pulsed high-speed motion to shake off the adhered matter.

8. The sampling method according to claim 7, characterized in that: After performing the obstacle removal operation, returning to the step of performing validity verification on the visual sensor data set; if the visual data contamination state is still contaminated after performing the obstacle removal operation for a preset number of consecutive times, switching to a conservative mode, which drills at a low rotation speed and a low feed rate and actively performs the obstacle removal operation at preset depth intervals.

9. The sampling method according to claim 4, characterized in that: The following steps are also included: A digital archive is generated using the drilling depth controlled by the servo motor as an index, the archive including at least the mechanical sensing dataset accurately corresponding to the depth, rock cuttings characteristics extracted based on the visual sensing dataset, the identified rock physical properties, the feed speed and rotation speed used, and a log record of the visual data contamination status and obstacle clearance operation.

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