Desert tree planting drilling method and system for complex sand land environment

Abstract

The invention discloses a desert tree planting drilling method and system for a complex sand environment, and belongs to the technical field of desert tree planting drilling. And driving the module motor, descending the drill bit from the calibrated zero point position to the first pause point, and stabilizing the drill bit at the same time. Punching is started; and an electric drill motor is started to drive the drill bit to rotate for punching, and when the drill bit drills to the target depth, the drill bit stays at the target depth and rotates to discharge sand and trim the hole wall. After punching is completed, the module motor is driven, and the drill bit retreats; and when the drill bit ascends, the drill bit firstly retreats to a second pause point at the working rotating speed, the distance between the second pause point and the hole bottom is 10-15 cm, the drill bit stays at the second pause point and rotates, then the electric drill motor is closed, and the drill bit is returned to the initial position. According to the method, the rotating speed of the drill bit is dynamically controlled based on the index-linear mixed rotating speed control model, the stable and controllable feeding speed of the drill bit is provided, and good practicability is achieved.

Description

Technical Field

[0001] This invention belongs to the technical field of drilling for desert tree planting, specifically relating to a method and system for drilling for desert tree planting in complex sandy environments. Background Technology

[0002] Currently, the commonly used desert tree-planting drilling equipment adopts a single-controller architecture. This architecture involves the main control unit directly controlling the drilling motor; the overcurrent protection mechanism directly cuts off the power supply when the current exceeds a set threshold; and it operates with fixed parameters based on preset drilling speed and depth. However, this approach has the following drawbacks: (1) High failure rate; Based on practical data, it was found that the jamming rate of calcium deposition operation is as high as 18.7%, and the motor burnout rate is as high as 23%; (2) High risk of misoperation; specifically, the above-mentioned overcurrent protection mechanism has a false trigger rate of up to 40% during sandstorm weather; (3) Poor adaptability; drilling operations based on fixed parameters are prone to result in a hole qualification rate of less than 50% in areas where quicksand (hardness < 30 kPa) and gravel (hardness > 150 kPa) alternate; (4) Response delay; based on practical data, it was found that the average time from the occurrence of a fault to manual handling is 32 minutes. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for drilling holes for planting trees in complex sandy environments, aiming to solve any of the problems mentioned above.

[0004] This invention is mainly achieved through the following technical solutions: A method for drilling holes for afforestation in complex sandy environments includes the following steps: Step S1: Initial descent; drive the module motor to descend the drill bit from the calibrated zero point to the first pause point, while stabilizing the drill bit to prevent it from wobbling; Step S2: Start drilling; start the electric drill motor to drive the drill bit to rotate and drill. When the drill bit reaches the target depth, stop it at the target depth and rotate it for 2-3 seconds to remove sand and trim the hole wall; during the drilling process, the drill bit speed control model is as follows: ; ; ; ; in: V ( t () represents the drill bit rotation speed; α These are the weighting coefficients; V exp ( t ) represents the index control item; V lin ( t ) represents a linear control term; V max Maximum permissible drilling speed; V base Base drilling rate; k The attenuation coefficient; β This is the proportionality coefficient; R ( t () represents the normalized drag torque; R target The target resistance torque; I ( t () represents the real-time current; It is the vibration acceleration; and These are the maximum ranges of the sensor's current and vibration acceleration, respectively. Step S3: After drilling is completed, drive the module motor to retract the drill bit; when the drill bit moves upward, first retract it to the second stop point at the working speed, and the second stop point is 10~15cm away from the bottom of the hole. The drill bit stops and rotates at the second stop point for 1-1.5 seconds. Then, turn off the electric drill motor and return the drill bit to the initial position.

[0005] To better realize the present invention, further, in step S1, the real-time sand hardness characteristic value is based on the initial descent phase. H ( t The first pause point is dynamically adjusted based on the rate of change of H(t). When the rate of change of H(t) > 10 kPa / s, the depth of the first pause point is increased by 5 cm.

[0006] To better realize the present invention, further, in step S2, during the drilling process, the electric drill motor speed is... W drill With module motor feed speed V feed The ratio is γ; based on real-time sand hardness characteristic value H ( t The dynamic update ratio γ of ) ; Where: γ0 is the basic proportional value.

[0007] To better realize the present invention, further, in step S2, based on the real-time sand hardness characteristic value... H ( t ), dynamically calculate weighting coefficients α ; ; in: H threshold This represents the hardness threshold. s is the shape parameter.

[0008] Preferably, when H(t) < 30 kPa, α approaches 1, and when H(t) > 70 kPa, α approaches 0.

[0009] To better realize this invention, further, the real-time hardness characteristic value of sandy land is solved based on a sand mechanics model. H ( t This includes the following steps: (1) Data collection time window I ( t )and data; (2) Constructing equations: ; Where: J is the moment of inertia of the drill bit; x ( t () represents the displacement of the drill bit; The rate of change of acceleration; K equiv Equivalent stiffness; C equiv For damping; (3) Solve the equation using the recursive least squares method. K equiv and C equiv Then, calculate H ( t ): ; in: A This represents the cross-sectional area of ​​the drill bit.

[0010] To better realize the present invention, further, in step S2, during the drilling process, a comprehensive risk value is calculated based on a multimodal coupling discriminant model. Total Risk Implement tiered alarm and rollback strategies; Total Risk = W elec × Risk elec + W vibe × Risk vibe + W mech × Risk mech ; in: W elec , W vibe , W mech These are the weighting coefficients; Risk elec This represents the electrical mode risk value. Risk vibe This represents the vibration mode risk value. Risk mech Modal risk value; When 0.65 < Total Risk When the value is less than 0.85, the drill bit is determined to be in a potential stuck state, and a level one alarm is triggered. When 0.85≦ Total Risk If the drill bit is determined to be severely jammed, the power to the drill bit should be immediately cut off.

[0011] To better realize this invention, furthermore, upon triggering a level one alarm, the electric drill motor is controlled to enter a torque limiting mode: Based on an initial trial torque, the torque is gradually increased, controlling the drill motor to rotate in the reverse direction; simultaneously, the module motor retracts at a speed of 8-10 cm / s; continuous monitoring is performed throughout this process. Total Risk ,once Total Risk If the torque drops below the safety threshold, the rescue is considered successful, and normal operations can resume; if the torque increases to 50% and still does not provide a solution, or if during this process... Total Risk If the alarm continues to rise, it will be upgraded to a level two alarm.

[0012] To better realize the present invention, a comprehensive risk value is further calculated. Total Risk Includes the following steps: Step A1: When the rate of change of current dI / dt > dynamic threshold Threshold dynamicAnd when it lasts for 100-120ms, Risk elec =min(1, (dI / dt) / 10); otherwise Risk elec =0; ; in: μ I Collect data n times for history you / dt The mean, σ I Collect data n times for history you / dt Standard deviation; Step A2: Train a vibration modal risk prediction model based on a one-dimensional convolutional neural network, collect vibration spectrum signals in real time and input them into the vibration modal risk prediction model, and output vibration modal risk values. Risk vibe ; Step A3: Acquire encoder displacement signals in real time and calculate displacement deviation rate. Deviation ( t ); ; in: x actual ( t () represents the actual displacement. x theory ( t () represents the theoretical displacement; when Deviation ( t When )>15%, Risk mech =min(1, Deviation ( t () / 0.2); otherwise Risk mech =0; Step A4: Using a fuzzy logic fusion decision-maker, dynamically adjust the weights of each modality based on environmental parameters and operational phases to calculate the comprehensive risk value. Total Risk .

[0013] To better realize the present invention, further, in step A4, under normal weather conditions, W elec , W vibe , W mech The values ​​are 0.4, 0.4, and 0.2 respectively; under sandstorm conditions, W elec , Wvibe , W mech The values ​​are 0.2, 0.6, and 0.2 respectively; in the initial stage of drilling, W mech =0.4.

[0014] This invention is mainly achieved through the following technical solutions: A desert afforestation drilling system for complex sandy environments is used to implement the above-mentioned desert afforestation drilling method for complex sandy environments. It includes a control unit (VCU) and a drill bit control unit. The control unit (VCU) communicates with the drill bit control unit bidirectionally via a CAN bus to exchange commands and status data. The control unit (VCU) includes a data acquisition and preprocessing module, a borehole path planning module, a borehole parameter optimization module, and a borehole jamming prevention and early warning module. The data acquisition and preprocessing module acquires drill bit displacement signals, environmental signals, and motor speed, current, and vibration signals, and performs data preprocessing. The borehole path planning module plans the borehole path and determines the first and second stopping points. The borehole parameter optimization module determines the real-time sand hardness characteristic value. H ( t The drill bit speed and motor speed ratio γ are used to calculate the comprehensive risk value. Total Risk Implement tiered alarm and rollback strategies; The drill bit control unit includes a dual-channel controller and a sensor mechanism. The dual-channel controller is used to control the module motor and the electric drill motor respectively, realizing decoupled control of the lifting and rotating motion of a single drill bit. The sensor mechanism includes a photoelectric sensor, a current sensor, and a vibration sensor. The photoelectric sensor is used to detect the position of the drill bit to achieve positioning. The current sensor is used to monitor the motor current fluctuation in real time, and the vibration sensor is used to collect vibration data.

[0015] The beneficial effects of this invention are as follows: (1) This invention dynamically controls the drill bit speed based on an exponential-linear hybrid speed control model. The exponential term is mainly used when the drill bit cuts into the quicksand layer, controlling the drilling speed to slow down exponentially as the resistance increases, achieving a soft landing and avoiding instantaneous burial of the drill bit. The linear term is mainly used when drilling through soil layers with relatively uniform hardness, providing a stable and controllable feed speed for the drill bit. Furthermore, this invention dynamically obtains H(t) through an online parameter identifier, and then dynamically updates the weight coefficient α, the position of the first pause point, and the ratio γ of the electric drill motor speed to the module motor feed speed, to achieve intelligent adaptive drilling control. The hole completion qualification rate is >92%, which is especially suitable for vegetation restoration projects in areas with abrupt changes in sand layer hardness, such as the Taklamakan Desert. Unmanned drilling operations are achieved through dynamic parameter calibration and a multi-level safety protection mechanism.

[0016] (2) Based on multimodal data, this invention analyzes the electrical mode risk value, vibration mode risk value and mechanical mode risk value respectively, and dynamically adjusts the weight coefficient according to the environmental conditions and drilling stage to finally obtain the comprehensive risk value, thereby realizing gradient-level alarm, effectively preventing drill bit jamming, and has good practicality. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the desert afforestation drilling system for complex sandy environments according to the present invention; Figure 2 This is a flowchart of the desert tree planting drilling method for complex sandy environments according to the present invention. Figure 3 This is a flowchart for gradient alarm based on comprehensive risk value. Detailed Implementation

[0018] Example 1: A method for drilling holes for afforestation in complex sandy environments, such as... Figure 2 As shown, it includes the following steps: Step S1: Initial descent; drive the module motor to move the drill bit from the zero point position calibrated by the photoelectric sensor to the first pause point, while stabilizing the drill bit to prevent it from swaying. Specifically, the dynamic determination of the first pause point is based on the sand hardness characteristic value during the initial descent phase. H ( t The rate of change of ) dynamically adjusts the position of that point. In desert testing, when H ( t When the rate of change is greater than 10 kPa / s, the first pause point is dynamically adjusted to a deeper position, for example, from 10 cm to 15 cm, and the hole formation qualification rate is significantly increased from 50% to 85%.

[0019] Step S2: Start drilling; start the electric drill motor to drive the drill bit to rotate and drill. When the drill bit reaches the target depth, stop the drill bit at the target depth and rotate it for 2 seconds to remove sand and trim the hole wall; this is beneficial for the formation of sand holes. Step S3: After drilling is complete, drive the module motor to retract the drill bit. While the drill bit is moving upwards, first retract it at the operating speed to the second pause point, which should be 10-15cm from the bottom of the hole. The drill bit should pause and rotate at the second pause point for 1 second. Then, turn off the drill motor to prevent excessive drill bit oscillation due to reduced resistance during retraction, which could cause some sand to backfill. Return the drill bit to the initial position; one drilling cycle is complete.

[0020] Preferably, in step S2, pressure sensing and obstacle avoidance are performed; when the system detects that the instantaneous value of the drill bit pressure is greater than 100 kPa, it is determined to be a surface gravel area, and the VCU dynamically adjusts the sand entry point, raises the drill bit to the initial position, reselects the entry point, and avoids obstacles.

[0021] Preferably, in step S2, an exponential-linear hybrid rotational speed control model is established. The exponential term is primarily used during the drill bit's entry into quicksand layers, controlling the drilling speed to decrease exponentially with increasing resistance, achieving a "soft landing" and preventing the drill bit from being instantly buried. The linear term provides a stable and controllable feed rate when encountering soil layers with relatively uniform hardness. Its core lies in transforming the drilling process from "open-loop preset" to "closed-loop dynamic optimization," specifically implemented as follows: The exponential-linear hybrid rotation speed control model is not a simple switching mechanism, but rather a weighted fusion using a dynamic weighting coefficient α (0 ≤ α ≤ 1). During drilling, the drill bit's rotation speed control model is as follows: ; in: V ( t () represents the drill bit rotation speed (unit: rpm); α These are the weighting coefficients; V exp ( t ) represents the index control item; V max Maximum permissible drilling speed; Control rate refers to the drill bit rotation speed, and V(t) is uniformly expressed as rotation speed.

[0022] Index control item V exp ( t The expression for ) is: ; This factor dominates when cutting into loose quicksand layers (α→1), causing the drilling speed to slow down exponentially as resistance increases, thus achieving a "soft landing". V max The maximum allowable drilling rate is given by k, which is the attenuation coefficient and is set to 0.05. R(t) is the normalized resistance torque (range 0-1) calculated by fusing current and vibration data. ; in: I(t) is the real-time current, and a(t) is the vibration acceleration. and This refers to the sensor's measurement range.

[0023] Linear control term V lin ( t The expression for ) is: ; in: V base The base drilling rate is given, and β is the proportionality coefficient, which is set to 0.1. R target Let α be the target resistance torque, taken as 0.5, representing the ideal operating point. This parameter dominates in soil layers with uniform hardness (α→0), providing a stable feed rate.

[0024] Preferably, the weighting coefficient α is dynamically calculated, and the value of α is determined by the real-time sand hardness characteristic value H(t), and the calculation formula is as follows: ; Where: H(t) is the characteristic value of sandy hardness, which is obtained by inversion through an online parameter identifier; H threshold The hardness threshold is 50 kPa, determined through extensive testing. s is the shape parameter, set to 5.

[0025] The value H(t) is not a direct measurement, but is calculated by inverting real-time current and vibration data using an online parameter identifier. The online parameter identifier uses a simplified sand mechanics model built into the VCU, treating the drill bit as a single-degree-of-freedom system. H(t) is calculated from real-time data. Inputs: Motor current I(t) (reflecting torque) and vibration acceleration (Reflects system response); Model: Based on Newton's second law, equivalent stiffness K equiv and damping C equiv Identification is performed online using the least squares method.

[0026] The specific steps are as follows: (1) Collect I(t) and a(t) data within a time window (500ms); (2) Constructing equations: ; Where J is the moment of inertia of the drill bit (a known constant), and x(t) is the displacement (feedback from the encoder).

[0027] (3) Referring to the existing algorithm techniques in "Adaptive Control Theory", the recursive least squares (RLS) method is used to solve the problem. K equiv and C equiv Then, calculate H ( t ): ; Where A is the cross-sectional area of ​​the drill bit (known).

[0028] Preferably, when H(t) < 30 kPa (quicksand layer), α → 1, the exponential term dominates, and exponential control is preferentially used to prevent the drill bit from being instantly buried; when H(t) > 70 kPa (hard soil layer), α → 0, the linear term dominates, and linear control is switched to provide a stable and controllable feed rate. This method achieves real-time and smooth matching between the control strategy and the geological environment.

[0029] Preferably, in step S2, the rotational speed for preventing the "sand blockage effect" is set as follows: to prevent insufficient sand removal from causing stalling, the electric drill motor speed is set to... W drill With module motor feed speed V feed There is a dynamic proportional relationship between them. W drill / V feed =γ. The value of γ is dynamically optimized based on the current H(t) and historical successful drilling data. The relationship between the two is: ; γ0 is the base value, which is 5. In areas with fine sand particles and low moisture content, γ automatically increases, improving sand removal efficiency.

[0030] Preferably, in step S2, during the drilling process, an anti-jamming strategy is set—a multimodal coupled discriminant model based on time series analysis and feature learning—which significantly improves the accuracy and robustness of the early warning. For example... Figure 3 As shown, it includes the following steps: Step A1: Calculate the electrical mode risk value Risk elec ; Real-time monitoring of current I(t) and calculation of the rate of change dI / dt; when the rate of change of current dI / dt > dynamic threshold Threshold dynamic And when it lasts for 100ms, Risk elec =min(1, (dI / dt) / 10); otherwise Risk elec =0; ; Where: I(t) is the current at time t; I(t-1) is the current at time t-1; Δt is the sampling interval, which is 10ms.

[0031] For example, the base threshold is 5 A / s, but the VCU is dynamically adjusted based on current data from the most recent 10 borehole drills. The calibration formula is: ; in: μ I This is the average of dI / dt over the past 10 times. σ I The standard deviation is given. Simultaneously, a moving average filter (500ms window) is used to eliminate noise.

[0032] Step A2: Calculate the vibration mode risk value Risk vibe A vibration modal risk prediction model is trained based on a one-dimensional convolutional neural network. Vibration spectrum signals are acquired in real time and input into the vibration modal risk prediction model, which then outputs vibration modal risk values. Risk vibe ; (1) Abandoning the simple characteristic frequency band energy threshold judgment, this invention constructs a vibration mode risk prediction model based on a lightweight one-dimensional convolutional neural network (1D-CNN). The network structure of the one-dimensional convolutional neural network is: 1D convolutional layer (32 filters, size 5), pooling layer and fully connected layer, and the output is the dead probability. P vibe (0-1).

[0033] (2) The model was trained offline, and the dataset came from a sandy experiment with 1,000 sets of vibration data of jamming events.

[0034] Training method: Cross-entropy loss and Adam optimizer were used, and the training accuracy was 95%.

[0035] Online application: Real-time spectrum input model, output P vibe As Risk vibe .when Pvibe An alert is triggered when the value is >0.7 (this threshold is adjustable). This method can capture more complex vibration characteristics and reduce false alarms.

[0036] Step A3: Computer Modal Risk Value Risk mech Real-time acquisition of encoder displacement signals and calculation of displacement deviation rate. Deviation ( t );when Deviation ( t When the percentage is greater than 15% for two consecutive seconds, Risk mech =min(1, Deviation ( t () / 0.2); otherwise Risk mech =0.

[0037] Specifically, the deviation rate between the drill bit's downward displacement and the theoretical displacement is calculated in real time through the encoder feedback of the module motor. ; in: x actual ( t () represents the actual displacement. x theory ( t The displacement is the theoretical displacement, calculated based on the motor speed.

[0038] Risk value: when Deviation ( t When the percentage is greater than 15% for two consecutive seconds, Risk mech =min(1, Deviation ( t ) / 0.2); otherwise it is 0.

[0039] Step A4: Calculate the comprehensive risk value based on the multimodal coupled discriminant model Total Risk Implement a tiered alarm and rollback strategy.

[0040] Total Risk = W elec × Risk elec + W vibe × Risk vibe + W mech × Risk mech ; in: W elec , Wvibe , W mech These are the weighting coefficients; The early warning decision-making process is no longer a simple "triggering by any two modalities," but rather employs a fusion decision-maker based on fuzzy logic. The early warning signal for each modality is quantified into a risk value. Risk elec , Risk vibe , Risk mech (Range 0-1).

[0041] when Total Risk Only when the threshold (e.g., 0.65) is exceeded is the risk status determined to be of different levels, and a tiered alarm is triggered. Specifically, the tiered alarm response mechanism is as follows: Level 1 Alarm (Intelligent Release and Retreat): When determined to be "potential jamming" (0.65 < Total Risk When the torque threshold is <0.85, the VCU performs intelligent rescue. The core improvement here lies in the specific strategy of "increasing the threshold": the VCU instructs the drill motor to enter "torque limiting mode," which is not a simple low-torque reverse rotation, but a reverse rotation with a slowly increasing trial torque (starting from 10% of the rated torque, increasing by 5% every 0.5 seconds). Simultaneously, the module motor retracts at a speed of 8-10 cm / s. This process is continuously monitored. Total Risk Once it drops below the safety threshold, that is... Total Risk If the torque is ≤0.65, the rescue is successful and normal operation can resume; if the torque increases to 50% and there is still no solution, or Total Risk >0.65, Total Risk If the pressure continues to rise, it will escalate to a level two alarm within 3 seconds. This gradient torque testing method can effectively distinguish between "slight sand resistance" and "hard jamming," avoiding overreaction.

[0042] Level 2 alarm (fault isolation): When determined to be "severely stuck" ( Total Risk When the fault value is ≥0.85, the VCU immediately cuts off the power and generates a location code. Preferably, the generated location code includes not only the fault ID and time, but also a snapshot of the three-modal data 3 seconds before the fault, providing detailed information for remote diagnosis and maintenance.

[0043] Tests showed that the success rate of the Level 1 alarm (intelligent release and retraction) was 90%, and the average processing time was reduced from 32 minutes to 5 minutes.

[0044] Preferably, the weights of each mode are dynamically adjusted based on environmental parameters and the operational phase. Specifically, the weights of the three modes are... W elec , W vibe , W mech It is not fixed; its dynamic adjustment rules are as follows: Basic weight: Under normal weather conditions, W elec , W vibe , W mech The values ​​are 0.4, 0.4, and 0.2 respectively (emphasizing electrical and vibrational modes).

[0045] Environmental Adaptation: When the system detects a sandstorm (wind speed > 12 m / s), it automatically adjusts... W elec , W vibe , W mech The values ​​are 0.2, 0.6, and 0.2 respectively, because sandstorms can easily cause current fluctuations, but the vibration characteristics are relatively stable.

[0046] Phase Adaptive: In the initial drilling phase (first 5 seconds), increase W mech The weight, at this time W mech =0.4, because mechanical displacement deviation is more sensitive when in contact with foreign objects.

[0047] Based on the environment detected by the system, it can autonomously switch between basic weights and environment adaptation.

[0048] Example 2: A desert afforestation drilling system designed for complex sandy environments, such as Figure 1 As shown, it includes a control unit (VCU) and a drill bit control unit. The control unit (VCU) communicates bidirectionally with the drill bit control unit via a CAN bus to exchange commands and status data.

[0049] The control unit (VCU) includes a data acquisition and preprocessing module, a borehole path planning module, a borehole parameter optimization module, and a borehole jamming prevention and early warning module. The data acquisition and preprocessing module acquires drill bit displacement signals, environmental signals, and motor speed, current, and vibration signals, and performs data preprocessing. The borehole path planning module plans the borehole path and determines the first and second stopping points. The borehole parameter optimization module determines the real-time sand hardness characteristic value. H ( tThe drill bit speed and motor speed ratio γ are used to calculate the comprehensive risk value. Total Risk Implement a tiered alarm and rollback strategy.

[0050] The drill bit control unit includes a dual-channel controller and a sensor mechanism. The dual-channel controller controls the module motor and the electric drill motor respectively, achieving decoupled control of the lifting and rotating motion of a single drill bit. The sensor mechanism includes a photoelectric sensor, a current sensor, and a vibration sensor. The photoelectric sensor detects the position of the drill bit for positioning; the current sensor monitors motor current fluctuations in real time; and the vibration sensor collects vibration data. Specifically, the photoelectric sensor is precisely mounted at a reference position 42cm from the top of the slide table for millimeter-level calibration of the drill bit's initial position (detection accuracy ±0.5mm), providing an absolute coordinate reference for path planning.

[0051] Specifically, such as Figure 1 As shown, three drill bit control units are set up. The control unit VCU serves as the core of the system and communicates bidirectionally with the three drill bit control units via a high-speed CAN bus to exchange commands and status data.

[0052] Drill bit control unit: Each drill bit unit is equipped with a dual-path controller, which independently and in parallel drives the module motor (responsible for drill bit lifting and lowering) and the electric drill motor (responsible for drill bit rotation), achieving decoupled control of the lifting and rotation of a single drill bit, and realizing multi-drill bit collaborative control and fault isolation. The module motor drives the drill bit lifting and lowering (stroke adjustable from 0-135cm, speed adjustable from 0-1000rpm), and the electric drill motor controls the rotation of the auger drill bit (speed adjustable from 200-1000rpm).

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for drilling holes for planting trees in complex sandy environments, characterized in that, Includes the following steps: Step S1: Initial downlink; The drive module motor moves the drill bit from the calibrated zero point to the first stop point, while stabilizing the drill bit to prevent it from wobbling. Step S2: Start drilling; start the electric drill motor to drive the drill bit to rotate and drill. When the drill bit reaches the target depth, stop it at the target depth and rotate it for 2-3 seconds to remove sand and trim the hole wall; during the drilling process, the drill bit speed control model is as follows: ； ； ； ； in: V ( t () represents the drill bit rotation speed; α These are the weighting coefficients; V exp ( t ) represents the index control item; V lin ( t ) represents a linear control term; V max Maximum permissible drilling speed; V base Base drilling rate; k The attenuation coefficient; β This is the proportionality coefficient; R ( t () represents the normalized drag torque; R target The target resistance torque; I ( t () represents the real-time current; It is the vibration acceleration; and These are the maximum ranges of the sensor's current and vibration acceleration, respectively. Step S3: After drilling is completed, drive the module motor to retract the drill bit; when the drill bit moves upward, first retract it to the second stop point at the working speed, and the second stop point is 10~15cm away from the bottom of the hole. The drill bit stops and rotates at the second stop point for 1-1.5 seconds. Then, turn off the electric drill motor and return the drill bit to the initial position.

2. The desert afforestation drilling method for complex sandy environments according to claim 1, characterized in that, In step S1, the real-time sand hardness characteristic value during the initial descent phase is used as the basis. H ( t The first pause point is dynamically adjusted based on the rate of change of H(t). When the rate of change of H(t) > 10 kPa / s, the depth of the first pause point is increased by 5 cm.

3. The desert afforestation drilling method for complex sandy environments according to claim 1, characterized in that, In step S2, during the drilling process, the electric drill motor speed... W drill With module motor feed speed V feed The ratio is γ; Based on real-time sand hardness characteristic value H ( t The dynamic update ratio γ of ) ； Where: γ0 is the basic proportional value.

4. The desert afforestation drilling method for complex sandy environments according to claim 1, characterized in that, In step S2, based on the real-time sand hardness characteristic value H ( t ), dynamically calculate weighting coefficients α ; ； in: H threshold This represents the hardness threshold. s is the shape parameter.

5. A desert afforestation drilling method for complex sandy environments according to any one of claims 1-4, characterized in that, Solving the real-time hardness characteristic value of sandy land based on a sandy soil mechanics model H ( t This includes the following steps: (1) Data collection time window I ( t )and data; (2) Constructing equations: ； Where: J is the moment of inertia of the drill bit; x ( t () represents the displacement of the drill bit; The rate of change of acceleration; K equiv Equivalent stiffness; C equiv For damping; (3) Solve the equation using the recursive least squares method. K equiv and C equiv Then, calculate H ( t ): ； in: A This represents the cross-sectional area of ​​the drill bit.

6. The desert afforestation drilling method for complex sandy environments according to claim 1, characterized in that, In step S2, during the drilling process, a comprehensive risk value is calculated based on a multimodal coupling discriminant model. Total Risk Implement tiered alarm and rollback strategies; Total Risk = W elec × Risk elec + W vibe × Risk vibe + W mech × Risk mech ； in: W elec , W vibe , W mech These are the weighting coefficients; Risk elec This represents the electrical mode risk value. Risk vibe This represents the vibration mode risk value. Risk mech Modal risk value; When 0.65 < Total Risk When the value is less than 0.85, the drill bit is determined to be in a potential stuck state, and a level one alarm is triggered. When 0.85≦ Total Risk If the drill bit is determined to be severely jammed, the power to the drill bit should be immediately cut off.

7. A method for drilling holes for planting trees in complex sandy environments according to claim 6, characterized in that, When a Level 1 alarm is triggered, the drill motor is controlled to enter torque limiting mode: Based on an initial trial torque, the torque is gradually increased, controlling the drill motor to rotate in the reverse direction; simultaneously, the module motor retracts at a speed of 8-10 cm / s; continuous monitoring is performed throughout this process. Total Risk ,once Total Risk If the water level drops below the safety threshold, the rescue is considered successful, and normal operations can resume. If increasing the torque to 50% still doesn't solve the problem, or during this process... Total Risk If the alarm continues to rise, it will be upgraded to a level two alarm.

8. A method for drilling holes for planting trees in complex sandy environments according to claim 6 or 7, characterized in that, Calculate the overall risk value Total Risk Includes the following steps: Step A1: When the rate of change of current dI / dt > dynamic threshold Threshold dynamic And when it lasts for 100-120ms, Risk elec =min(1, (dI / dt) / 10); otherwise Risk elec =0; ； in: μ I Collect data n times for history dI / dt The mean, σ I Collect data n times for history dI / dt Standard deviation; Step A2: Train a vibration modal risk prediction model based on a one-dimensional convolutional neural network, collect vibration spectrum signals in real time and input them into the vibration modal risk prediction model, and output vibration modal risk values. Risk vibe ; Step A3: Acquire encoder displacement signals in real time and calculate displacement deviation rate. Deviation ( t ); ； in: x actual ( t () represents the actual displacement. x theory ( t () represents the theoretical displacement; when Deviation ( t When )>15%, Risk mech =min(1, Deviation ( t () / 0.2); otherwise Risk mech =0; Step A4: Using a fuzzy logic fusion decision-maker, dynamically adjust the weights of each modality based on environmental parameters and operational phases to calculate the comprehensive risk value. Total Risk .

9. A method for drilling holes for planting trees in complex sandy environments according to claim 8, characterized in that, In step A4, under normal weather conditions, W elec , W vibe , W mech The values ​​are 0.4, 0.4, and 0.2 respectively; under sandstorm conditions, W elec , W vibe , W mech The values ​​are 0.2, 0.6, and 0.2 respectively; in the initial stage of drilling, W mech =0.

4.

10. A desert afforestation drilling system for complex sandy environments, used to implement the desert afforestation drilling method for complex sandy environments as described in any one of claims 1-9, characterized in that, It includes a control unit (VCU) and a drill bit control unit. The control unit (VCU) communicates bidirectionally with the drill bit control unit via a CAN bus to exchange commands and status data. The control unit (VCU) includes a data acquisition and preprocessing module, a borehole path planning module, a borehole parameter optimization module, and a borehole jamming prevention and early warning module. The data acquisition and preprocessing module acquires drill bit displacement signals, environmental signals, and motor speed, current, and vibration signals, and performs data preprocessing. The borehole path planning module plans the borehole path and determines the first and second stopping points. The borehole parameter optimization module determines the real-time sand hardness characteristic value. H ( t ), drill bit speed, motor speed ratio γ; The borehole jamming prevention early warning module is used to calculate the comprehensive risk value. Total Risk Implement tiered alarm and rollback strategies; The drill bit control unit includes a dual-channel controller and a sensor mechanism. The dual-channel controller is used to control the module motor and the electric drill motor respectively, realizing decoupled control of the lifting and rotating motion of a single drill bit. The sensor mechanism includes a photoelectric sensor, a current sensor, and a vibration sensor. The photoelectric sensor is used to detect the position of the drill bit to achieve positioning. The current sensor is used to monitor the motor current fluctuation in real time, and the vibration sensor is used to collect vibration data.

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