Self-adaptive grooving machine and method for pre-burying and laying of electric power construction pipeline

Abstract

The invention discloses an electric power construction pipeline pre-buried laying self-adaptive grooving machine and method. The electric power construction pipeline pre-buried laying self-adaptive grooving machine comprises an equipment body, and a grooving mechanism for grooving on the ground and a dust recycling mechanism for collecting dust generated in the grooving process are arranged at the front end of the equipment body; when the equipment body moves forwards, under the action of the grooving mechanism, grooving is conducted on the ground, efficient grooving is achieved, meanwhile, the dust recycling mechanism recycles dust generated in the construction process, the dust generated in the grooving process is effectively controlled, and the health of workers is protected.

Description

Technical Field

[0001] The invention relates to the field of electric power construction, in particular to an adaptive grooving machine and method for pre-buried laying of electric power construction pipelines. Background Art

[0002] Pre-buried pipe laying in power construction is an essential component of building and infrastructure development. Its core task is to create grooves in walls or the ground to bury cables, pipes, and other facilities, ensuring the safe and stable operation of the power system. The importance of this field is self-evident. Efficient and accurate groove laying not only affects construction progress but also directly impacts project quality and subsequent maintenance costs. With the acceleration of urbanization and the diversification of building structures, the technical requirements for groove laying equipment are increasing. Traditional groove laying methods can no longer meet the comprehensive requirements of efficiency, precision, and environmental protection in modern construction.

[0003] The grooving equipment widely used on the market mainly relies on manual operation or semi-automatic machinery. These devices expose significant limitations when faced with complex construction scenarios. Traditional equipment usually requires operators to manually adjust the tool speed and feed speed based on experience. This method is often difficult to achieve precise adaptation when faced with ground of different hardness. If the equipment parameters are not adjusted properly, it may cause excessive wear of the tool or low grooving efficiency. In addition, the dust generated by existing equipment during the grooving process is not well controlled. The dust at the construction site not only affects the health of workers, but may also interfere with subsequent construction processes due to deposition. These limitations make the existing methods incapable of meeting diverse construction needs. Summary of the Invention

[0004] The purpose of the present invention is to solve the above problems and provide an adaptive grooving machine and method for pre-buried laying of power construction pipelines, which realizes dynamic adaptive adjustment of the tool operation speed and feed speed, and integrates an efficient dust control system to ensure efficient, accurate and environmentally friendly grooving operations.

[0005] The technical solution adopted by the present invention to solve the technical problem is: An adaptive grooving machine and method for pre-buried laying of power construction pipelines, comprising a device body, a grooving mechanism for grooving on the ground and a dust recovery mechanism for collecting dust generated during the grooving process provided at the front end of the device body; The grooving mechanism includes a conveyor belt provided at the front end of the equipment body and having a lower end that can swing up and down, the conveyor belt is provided with a plurality of cutters evenly distributed along the running direction, and an output mechanism for outputting the excavated soil is provided at the front end of the equipment body; The dust recovery mechanism includes a dust recovery port symmetrically arranged at the front end of the equipment body, a negative pressure fan and a bag dust collector arranged on the equipment body, and the dust recovery port is connected to the negative pressure fan and the bag dust collector in sequence through a hose.

[0006] Furthermore, an opening is provided at a position at the front end of the equipment body corresponding to the conveyor belt, and the conveyor belt is arranged in the opening and swings up and down in the opening.

[0007] Furthermore, it also includes a frame, the conveyor belt is arranged on the frame and runs on the frame, hydraulic cylinders are provided on both sides of the conveyor belt in the opening, the cylinder body ends of the hydraulic cylinders are rotatably connected to the inner wall of the opening, and the piston rod ends of the hydraulic cylinders are rotatably connected to the frame.

[0008] Furthermore, the output mechanism includes a shell provided on the device body and an auger provided in the shell and rotating in the shell.

[0009] Furthermore, a material drop chute is provided on the side of the device body at a position corresponding to the output end of the shell.

[0010] Furthermore, a method for self-adaptive grooving for pre-buried laying of electric power construction pipelines, using the device, comprises the following steps: S101 obtains soil resistance data through a sensor array to obtain an original signal data set; S102 uses signal processing technology to process the original signal data set to obtain a first processed data set; S103 analyzes the first processed data set through a neural network to obtain a soil hardness value; S104 adjusts the belt running speed and feed speed based on the comparison of the soil hardness value with a preset hardness threshold to obtain an adjusted processing parameter combination; S105 uses a controller to apply a control signal to the belt drive motor to obtain a stable cutting state; S106 obtains dust concentration data in the cutting area through a sensor to determine the dust generation rate; S107 adjusts the dust collection module operating parameters according to the dust generation rate to obtain clean cutting environment data.

[0011] Furthermore, step S103 includes: The first processed data set is subjected to feature fusion and pattern recognition through a convolutional neural network to obtain a feature vector set; if the strength value of the feature vector set is greater than a preset threshold, the soil hardness value is determined through regression analysis to obtain a hardness estimate; classification processing is performed based on the hardness estimate to obtain hardness grade data; the hardness grade data is calibrated through a pre-established hardness mapping model to obtain a final hardness value; and the final hardness value is verified using a data verification algorithm to obtain a calibrated hardness value.

[0012] Furthermore, step S104 includes: Obtain the soil hardness value and compare it with a preset hardness threshold to generate a hardness comparison result; calculate the belt running speed adjustment range based on the hardness comparison result to obtain the adjusted belt running speed parameter; calculate the feed speed adjustment range based on the hardness comparison result to obtain the adjusted feed speed parameter; use the adjusted belt running speed parameter and the adjusted feed speed parameter to determine the processing parameter combination.

[0013] Furthermore, step S105 includes: The current speed and feed speed of the belt-driven motor are collected by sensors to obtain initial operating parameters; if the initial operating parameters deviate from the preset stable cutting threshold, the proportional-integral-differential controller is used to calculate the deviation to obtain a control signal adjustment amount; the speed and feed speed of the belt-driven motor are dynamically adjusted according to the control signal adjustment amount to obtain updated operating parameters; the cutting state stability is analyzed by a state monitoring system to obtain a stable cutting state.

[0014] Furthermore, step S107 includes: Real-time dust particle concentration data is obtained through the dust monitoring sensor to determine the dust generation rate; if the dust generation rate exceeds the preset dust threshold, the support vector machine algorithm is used to classify the dust particle concentration data to determine the triggering of the synchronous dust collection module; the dust collection module operating parameters are adjusted according to the matching coefficient between the cutting speed and the dust collection wind speed to obtain the dust collection wind speed synchronized with the cutting speed; the clean cutting environment data is obtained through the environmental data acquisition device.

[0015] The beneficial effects of the present invention are: 1. The present invention includes an equipment main body, the front end of which is provided with a grooving mechanism for grooving on the ground and a dust recovery mechanism for collecting dust generated during the grooving process; when the equipment main body moves forward, the grooving mechanism acts to grooving on the ground, thereby achieving efficient grooving, and at the same time, the dust recovery mechanism recovers the dust generated during the construction process, effectively controlling the dust generated during the grooving process and protecting the health of workers.

[0016] 2. This invention uses a sensor array to collect real-time wall vibration signals and cutting resistance data. It then uses a convolutional neural network to process the resulting hardness value, compares it with preset thresholds, and automatically adjusts the tool rotation speed and feed rate to optimize cutting parameters. Furthermore, a proportional-integral-differential controller stabilizes tool operation. Infrared sensors monitor dust concentration and dynamically adjust the suction air speed to ensure a clean cutting environment. This achieves efficient, stable, and low-dust adaptive grooving operations, significantly improving the efficiency and quality of pre-buried pipeline installation in power construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the structure of the present invention; Figure 2 A top view of the present invention; Figure 3 Flowchart of the present invention; Figure 4 This is a flow chart of the processing parameter adjustment of the present invention.

[0018] In the figure: equipment body 1, conveyor belt 2, cutter 3, dust recovery port 4, negative pressure fan 5, bag dust collector 6, opening 7, frame 8, hydraulic cylinder 9, shell 10, auger 11, and blanking chute 12. DETAILED DESCRIPTION

[0019] An adaptive grooving machine and method for pre-buried laying of power construction pipelines include an equipment main body 1. A grooving mechanism for grooving on the ground and a dust recovery mechanism for collecting dust generated during the grooving process are provided at the front end of the equipment main body 1. When the equipment main body 1 moves forward, the grooving mechanism acts to grooving on the ground, thereby achieving efficient grooving. At the same time, the dust recovery mechanism recovers dust generated during the construction process, effectively controlling the dust generated during the grooving process and protecting the health of workers.

[0020] like Figure 1 As shown, the grooving mechanism includes a conveyor belt 2 arranged at the front end of the equipment body 1 and the lower end of which can swing up and down. The conveyor belt 2 is evenly provided with a number of cutters 3 along the running direction, and the front end of the equipment body 1 is provided with an output mechanism for outputting the excavated soil; when grooving, the conveyor belt 2 is turned downward, and the conveyor belt 2 runs, and grooves are cut on the ground under the action of the cutter 3, and the soil lifted by the conveyor belt 2 enters the output mechanism and is output through the output mechanism.

[0021] like Figure 2 As shown, the dust recovery mechanism includes a dust recovery port 4 symmetrically arranged at the front end of the equipment body 1, a negative pressure fan 5 and a bag dust collector 6 arranged on the equipment body 1, and the dust recovery port 4 is connected to the negative pressure fan 5 and the bag dust collector 6 in sequence through a hose. The negative pressure fan 5 generates a negative pressure at the dust recovery port 4 through the hose, recovers the dust raised by the grooving mechanism, and removes the dust through the bag dust collector 6, thereby solving the dust problem during the construction process.

[0022] like Figure 1 As shown, an opening 7 is provided at the front end of the device body 1 at a position corresponding to the conveyor belt 2 , and the conveyor belt 2 is arranged in the opening 7 and swings up and down in the opening 7 .

[0023] like Figure 1 and Figure 2As shown, the device further includes a frame 8. A bracket is provided on the device body 1, and the upper end of the frame 8 is rotatably connected to the bracket. The conveyor belt 2 is arranged on the frame 8 and operates on the frame 8. Hydraulic cylinders 9 are provided on both sides of the conveyor belt 2 within the opening 7. The cylinder ends of the hydraulic cylinders 9 are rotatably connected to the inner wall of the opening 7, and the piston rod ends of the hydraulic cylinders 9 are rotatably connected to the frame 8. The frame 8 is driven to swing up and down by the extension and retraction of the piston rod of the hydraulic cylinder 9. When the device body 1 moves, the piston rod of the hydraulic cylinder 9 extends, and the frame 8 swings upward without contacting the ground, facilitating the movement of the device body 1. When grooving, the piston rod of the hydraulic cylinder 9 retracts, and the frame 8 swings downward. As the conveyor belt 2 operates, the cutter 3 cuts grooves on the ground.

[0024] like Figure 1 As shown, the output mechanism includes a housing 10 mounted on the device body 1 and an auger 11 disposed within the housing 10 and rotating therein. The upper end of the housing 10 is provided with an opening, which corresponds to the upper end of the conveyor belt 2. Soil lifted by the cutter on the conveyor belt 2 enters the housing 10 through the opening. The auger 11 within the housing 10 then discharges the soil laterally, where it falls through the opening on the side of the housing. By providing the output mechanism, soil lifted by the slots is discharged without flowing back into the slots, thereby improving efficiency.

[0025] like Figure 1 As shown, a material drop chute 12 is provided on the side of the device body 1 at a position corresponding to the output end of the shell 10.

[0026] like Figure 3 As shown, a method for self-adaptive grooving for pre-buried pipelines in power construction, using the device, comprises the following steps: S101 obtains soil resistance data through a sensor array to obtain an original signal data set; S102 uses signal processing technology to process the original signal data set to obtain a first processed data set; S103 analyzes the first processed data set through a neural network to obtain a soil hardness value; S104 adjusts the belt running speed and feed speed based on the comparison of the soil hardness value with a preset hardness threshold to obtain an adjusted processing parameter combination; S105 uses a controller to apply a control signal to the belt drive motor to obtain a stable cutting state; S106 obtains dust concentration data in the cutting area through a sensor to determine the dust generation rate; S107 adjusts the dust collection module operating parameters according to the dust generation rate to obtain clean cutting environment data.

[0027] In step S101, when collecting the resistance data of the soil in the cutter trench in real time through the sensor array, a high-precision force-sensitive sensor array can be used and arranged on the cutter surface to directly sense the change in soil resistance.

[0028] Step S103 includes: performing feature fusion and pattern recognition on the first processed data set through a convolutional neural network to obtain a feature vector set; if the strength value of the feature vector set is greater than a preset threshold, determining the soil hardness value through regression analysis to obtain a hardness estimate; performing classification processing based on the hardness estimate to obtain hardness grade data; calibrating the hardness grade data through a pre-established hardness mapping model to obtain a final hardness value; and verifying the final hardness value using a data verification algorithm to obtain a calibrated hardness value.

[0029] Convolutional neural networks are used for feature fusion and pattern recognition. The first processed dataset is fed into the convolutional neural network, which, through multiple layers of convolution and pooling, fuses features such as resistance peaks and frequencies to generate a set of feature vectors. For example, the network can distinguish between the resistance characteristics of clay and sand: clay may exhibit a high resistance peak, while sand exhibits more gradual fluctuations.

[0030] If the strength value of the feature vector set exceeds a preset threshold (e.g., 0.8), indicating significant soil resistance, regression analysis can be used to determine the wall hardness. Regression analysis can use a linear regression model to predict the hardness value based on the feature vector set, such as an estimated hardness value of 50 MPa. This method provides a reliable hardness assessment by quantifying the relationship between features and hardness.

[0031] like Figure 4 Said step S104 includes: obtaining the soil hardness value and comparing it with a preset hardness threshold to generate a hardness comparison result; calculating the belt running speed adjustment range according to the hardness comparison result to obtain an adjusted belt running speed parameter; calculating the feed speed adjustment range according to the hardness comparison result to obtain an adjusted feed speed parameter; and using the adjusted belt running speed parameter and the adjusted feed speed parameter to determine a processing parameter combination.

[0032] When calculating belt speed adjustments based on hardness comparison results, a linear mapping can be performed based on the percentage of hardness exceeding the standard. For example, if the normal belt speed is 2 m / s and the hardness exceeds the standard by 20%, the speed needs to be reduced by 10%, to 1.8 m / s. This adjustment can be achieved using a pre-set mapping table, for example, where for every 10% increase in hardness, the speed is reduced by 5%. These adjusted belt speed parameters can effectively reduce equipment load and improve operational stability.

[0033] Feed speed adjustments are also based on the hardness comparison results. The normal feed speed is 0.5 m / min. If the hardness exceeds the standard by 20%, the feed speed can be reduced to 0.4 m / min to reduce the impact load on the cutter. The adjustment range can be determined through historical data analysis. For example, by analyzing machine performance at different hardness levels, it can be determined that for every 10% increase in hardness, the feed speed should be reduced by 0.05 m / min. This approach ensures a smooth machining process.

[0034] Using the adjusted belt speed and feed speed parameters, a parameter optimization algorithm can be used to determine the processing parameter combination. A belt speed of 1.8 m / s and a feed speed of 0.4 m / min can be combined with factors such as machine power and cutter material to form a complete processing parameter set. This parameter combination adapts to the processing requirements of soils of varying hardness, ensuring a balance between high efficiency and durability.

[0035] Step S105 includes: collecting the current speed and feed speed of the belt drive motor through a sensor to obtain initial operating parameters; if the initial operating parameters deviate from a preset stable cutting threshold, using a proportional integral differential controller to calculate the deviation to obtain a control signal adjustment amount; dynamically adjusting the speed and feed speed of the belt drive motor according to the control signal adjustment amount to obtain updated operating parameters; analyzing the cutting state stability through a state monitoring system to obtain a stable cutting state.

[0036] After dynamic adjustment, the belt-driven motor speed is adjusted to 1300 rpm, and the feed rate is reduced to 0.55 m / s. The condition monitoring system collects updated operating parameters in real time and analyzes the stability of the cutting state using vibration and force sensors. Suppose the monitored vibration frequency is slightly elevated, with a state deviation of 0.1, exceeding the preset stability range of 0.05. Based on this deviation, the system iteratively optimizes the PID controller parameters, for example, by increasing the proportional gain from 1.5 to 1.8 and reducing the integral time constant. This optimization, through repeated corrections, ensures that the control signal is more adapted to the current operating conditions.

[0037] The optimized control signals further fine-tuned the rotational speed to 1280 rpm and the feed rate to 0.52 m / s to ensure stable cutting conditions. A continuous monitoring system verified stable cutting conditions by recording changes in vibration and cutting forces.

[0038] Step S105 includes: acquiring data from the cutting state sensor, collecting the dust particle density in the cutting area using an infrared sensor, and obtaining real-time dust concentration data. The real-time dust concentration data is processed using time series analysis to extract the temporal variation characteristics of the dust concentration and determine the dust variation trend. If the dust variation trend exceeds a preset threshold, the real-time dust concentration data is segmented using a sliding window method to obtain a dust concentration fluctuation range. Based on the dust concentration fluctuation range, the K-means clustering algorithm is used to classify the dust particle density during the cutting process to obtain a particle distribution characteristic. The dust generation rate is calculated by combining the particle distribution characteristic with the operating parameters of the cutting process to obtain a dust generation rate sequence. The dust generation rate sequence is smoothed using a moving average method to extract the stable trend of the dust generation rate and determine the dust characteristics of the cutting environment. If the dust characteristics of the cutting environment exceed the preset threshold, the sensor data acquisition frequency is adjusted to obtain a more accurate dust generation rate.

[0039] Step S107 includes: obtaining real-time dust particle concentration data through a dust monitoring sensor to determine the dust generation rate; if the dust generation rate exceeds a preset dust threshold, using a support vector machine algorithm to classify the dust particle concentration data, and judging whether to trigger the synchronous dust collection module to adjust the dust collection module operating parameters according to the matching coefficient between the cutting speed and the dust collection wind speed to obtain the dust collection wind speed synchronized with the cutting speed; obtaining clean cutting environment data through an environmental data acquisition device.

[0040] The dust monitoring sensor uses infrared light scattering to collect real-time data on dust particle concentration in the cutting area. This data is analyzed to calculate the dust concentration increment per unit time and determine the dust generation rate. If the preset threshold is 15 mg / m³·s and the current rate exceeds 20 mg / m³·s, a support vector machine (SVM) algorithm is triggered for classification. The SVM, trained using historical concentration data, distinguishes between normal and abnormal dust conditions and determines whether to activate the dust collection module.

[0041] After the dust collection module is activated, the system obtains the current cutting speed (e.g., 1000 rpm) from the cutting equipment. Using the dust collection module's operating data, such as a wind speed of 5 m / s, the system calculates a matching coefficient. This matching coefficient is determined by the ratio of cutting speed to wind speed. For example, 1000 / 5 = 200, indicating that the wind speed needs to be adjusted to match the cutting intensity. After adjustment, the wind speed is increased to 6 m / s, ensuring that dust collection efficiency is synchronized with the cutting process. The environmental data acquisition device then monitors the adjusted dust concentration, for example, a decrease from 12 mg / m³ to 8 mg / m³, indicating a dust reduction of approximately 33%.

[0042] Based on the degree of dust concentration reduction, a linear regression algorithm analyzes the relationship between operating parameters such as cutting speed and feed rate and dust concentration, predicting the optimal value. Assuming the regression model shows that the dust concentration can be further reduced to 6 mg / m³ when the cutting speed is reduced to 900 rpm, the system updates the cutting equipment configuration accordingly, adjusts the speed to 900 rpm, collects new environmental data, and confirms that the dust concentration remains stable below 6 mg / m³, indicating that a clean cutting environment has been achieved.

Claims

1. An adaptive slotting machine for pre-buried laying of electric power construction pipelines, comprising a device body (1), characterized in that: The front end of the equipment body (1) is provided with a slotting mechanism for slotting on the ground and a dust recovery mechanism for collecting dust generated during the slotting process; The grooving mechanism comprises a conveyor belt (2) arranged at the front end of the equipment body (1) and having a lower end capable of swinging up and down, the conveyor belt (2) being provided with a plurality of cutters (3) evenly spaced along the running direction, and an output mechanism for outputting excavated soil being provided at the front end of the equipment body (1); The dust recovery mechanism comprises a dust recovery port (4) symmetrically arranged at the front end of the device body (1), a negative pressure fan (5) and a bag dust collector (6) arranged on the device body (1), and the dust recovery port (4) is connected to the negative pressure fan (5) and the bag dust collector (6) in sequence through a hose.

2. The self-adaptive slotting machine for pre-buried laying of electric power construction pipelines according to claim 1, characterized in that: An opening (7) is provided at a position on the front end of the equipment body (1) corresponding to the conveyor belt (2); the conveyor belt (2) is arranged in the opening (7) and swings up and down in the opening (7).

3. The self-adaptive slotting machine for pre-buried laying of electric power construction pipelines according to claim 2, characterized in that: It also includes a frame (8), the conveyor belt (2) is arranged on the frame (8) and operates on the frame (8), and hydraulic cylinders (9) are provided on both sides of the conveyor belt (2) in the opening (7), the cylinder end of the hydraulic cylinder (9) is rotatably connected to the inner wall of the opening (7), and the piston rod end of the hydraulic cylinder (9) is rotatably connected to the frame (8).

4. The self-adaptive slotting machine for pre-buried laying of electric power construction pipelines according to claim 1, characterized in that: The output mechanism comprises a housing (10) arranged on the device body (1) and an auger (11) arranged in the housing (10) and rotating in the housing (10).

5. The self-adaptive slotting machine for pre-buried laying of electric power construction pipelines according to claim 4, characterized in that: A material drop chute (12) is provided on the side of the device body (1) at a position corresponding to the output end of the housing (10).

6. An adaptive grooving method for pre-buried laying of power construction pipelines, using the device according to any one of claims 1 to 5, characterized in that: The following steps are involved: S101 obtains soil resistance data through a sensor array to obtain an original signal data set; S102 uses signal processing technology to process the original signal data set to obtain a first processed data set; S103 analyzes the first processed data set through a neural network to obtain a soil hardness value; S104 adjusts the belt running speed and feed speed based on the comparison of the soil hardness value with a preset hardness threshold to obtain an adjusted processing parameter combination; S105 uses a controller to apply a control signal to the belt drive motor to obtain a stable cutting state; S106 obtains dust concentration data in the cutting area through a sensor to determine the dust generation rate; S107 adjusts the dust collection module operating parameters according to the dust generation rate to obtain clean cutting environment data.

7. The adaptive grooving method for pre-buried laying of electric power construction pipelines according to claim 6, characterized in that: Step S103 includes: The first processed data set is subjected to feature fusion and pattern recognition through a convolutional neural network to obtain a feature vector set; if the strength value of the feature vector set is greater than a preset threshold, the soil hardness value is determined through regression analysis to obtain a hardness estimate; classification processing is performed based on the hardness estimate to obtain hardness grade data; the hardness grade data is calibrated through a pre-established hardness mapping model to obtain a final hardness value; and the final hardness value is verified using a data verification algorithm to obtain a calibrated hardness value.

8. The adaptive grooving method for pre-buried laying of electric power construction pipelines according to claim 6, characterized in that: Step S104 includes: Obtain the soil hardness value and compare it with a preset hardness threshold to generate a hardness comparison result; calculate the belt running speed adjustment range based on the hardness comparison result to obtain the adjusted belt running speed parameter; calculate the feed speed adjustment range based on the hardness comparison result to obtain the adjusted feed speed parameter; use the adjusted belt running speed parameter and the adjusted feed speed parameter to determine the processing parameter combination.

9. The adaptive grooving method for pre-buried laying of electric power construction pipelines according to claim 6, characterized in that: Step S105 includes: The current speed and feed speed of the belt-driven motor are collected by sensors to obtain initial operating parameters; if the initial operating parameters deviate from the preset stable cutting threshold, the proportional-integral-differential controller is used to calculate the deviation to obtain a control signal adjustment amount; the speed and feed speed of the belt-driven motor are dynamically adjusted according to the control signal adjustment amount to obtain updated operating parameters; the cutting state stability is analyzed by a state monitoring system to obtain a stable cutting state.

10. The adaptive grooving method for pre-buried laying of electric power construction pipelines according to claim 6, characterized in that: Step S107 includes: Real-time dust particle concentration data is obtained through the dust monitoring sensor to determine the dust generation rate; if the dust generation rate exceeds the preset dust threshold, the support vector machine algorithm is used to classify the dust particle concentration data to determine the triggering of the synchronous dust collection module; the dust collection module operating parameters are adjusted according to the matching coefficient between the cutting speed and the dust collection wind speed to obtain the dust collection wind speed synchronized with the cutting speed; the clean cutting environment data is obtained through the environmental data acquisition device.

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