Gyroscope-based intelligent correction system and method for preventing deviation of percussion drill

Abstract

The invention discloses a percussion drill anti-deviation intelligent correction system and method based on gyroscopes, and particularly relates to the technical field of intelligent correction.The method includes the steps that motion data of a percussion drill are collected through the multiple gyroscopes, the motion data are preprocessed and compared with a preset path, and the collection error of each gyroscope is calculated; then, the percussion drill is partitioned, and the temperature and the vibration state of each region are monitored; according to the temperature and vibration conditions, each area is endowed with a weight, and a comprehensive error is obtained through a weighted average method. Then, actual errors (such as position, angle and speed errors) are converted into fuzzy values, and a fuzzy set is defined to represent the errors and the change rate of the errors; through fuzzification processing, input errors are mapped to corresponding fuzzy sets. And deducing control output by using a fuzzy inference engine. And finally, a reasoning result is converted into a clear control signal, and the system performs control or correction according to the signal to ensure accurate adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent correction, and more specifically, to a gyroscope-based impact drill anti-deviation intelligent correction system and method. Background Art

[0002] Impact drill is a tool commonly used for drilling operations, and is widely used in construction, mining, oil exploration, geological survey and other fields. The working principle of impact drill is usually to break the rock or soil through the rapid rotation of the drill bit and the impact force synchronized with it, so as to achieve drilling.

[0003] However, in actual operations, when the impact drill is drilling deep holes or working in complex environments, it may deviate due to various factors, causing the drill bit to be unable to drill along the predetermined path. This deviation not only reduces the operating efficiency, but may also cause damage to the equipment and the working environment.

[0004] A gyroscope is an inertial sensor that can accurately measure angle changes and is commonly used in navigation, attitude control, and precise positioning. During impact drilling operations, the gyroscope can detect whether an offset has occurred by monitoring the angle changes of the drill bit in real time. Specifically, the gyroscope measures the angular velocity of the device in all directions and, combined with integral operations, can accurately estimate the current attitude of the impact drill.

[0005] The gyroscope-based impact drill anti-drift intelligent correction system combines the gyroscope with the control system of the impact drill to monitor and correct the movement trajectory of the drill bit in real time, thereby effectively preventing deviation. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a gyroscope-based impact drill anti-deviation intelligent correction system and method to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: The gyroscope-based intelligent correction method for impact drill anti-drift includes the following steps: Step S1: collecting motion data of the impact drill, preprocessing the collected motion data; and comparing the processed data with the planned path to obtain the acquisition errors of each gyroscope; Step S2: partition the entire impact drill, monitor the temperature and vibration frequency of each area respectively, assign weights to each area according to the temperature and vibration frequency of each area using hierarchical analysis, and obtain the error of the overall impact drill by weighted average of each area; Step S3: convert the error and error change rate into fuzzy values, define a series of fuzzy sets to express these errors and error change rates, and map them to corresponding fuzzy sets; use the fuzzy inference engine to derive the control output control signal fuzzy set; Step S4: Defuzzify the control signal fuzzy set and convert it into a clear control signal, and the system performs control or correction according to the clear control signal.

[0008] In a preferred embodiment, in step S1, multiple gyroscopes collect motion data of the impact drill, while ensuring that the timestamps of all gyroscope data are consistent; then a low-pass filter and a Kalman filter are used to remove noise; The angular velocity is converted into angular displacement by integration: ; where θ(t) represents the rotation angle and ω(t) is the angular velocity.

[0009] In a preferred embodiment, in step S1, path planning refers to planning the impact drill to move along a predetermined path, which may be a path input manually or a path generated by a more complex algorithm; the path may be represented by a set of three-dimensional coordinates or rotation angles; Compare the data collected by the gyroscope with the predetermined path, and obtain the error by calculating the deviation between the current posture of the device and the target path; The position information provided by the gyroscope is compared with the position points in the predetermined path; for displacement, the Euclidean distance is calculated to represent the distance error; the angle data is compared with the predetermined angle path; and the error between the two angles is calculated.

[0010] In a preferred embodiment, in step S2, the entire impact drill is partitioned, the temperature and vibration state of each area are monitored separately, a weight is assigned to each area according to the temperature and vibration state of each area, and the error of the entire impact drill is obtained by weighted average of each area.

[0011] In a preferred embodiment, in step S2, the error weight coefficient of each region is determined according to the temperature coefficient and the vibration frequency coefficient, the error weight coefficients of each region are sorted from small to large, and the weights are configured using the hierarchical analysis method to determine the error weight of each region.

[0012] In a preferred embodiment, in step S3, the error value is converted into a fuzzy value, and a series of fuzzy sets are defined to express the errors and error change rates; the error change rate is equal to the error change difference per unit time divided by the unit time; The error and error change rate are fuzzified, that is, according to the input error and error change rate, they are mapped to the corresponding fuzzy set; the fuzzy inference engine is used to derive the fuzzy set of the control output control signal.

[0013] In a preferred embodiment, in step S4, the acquired control signal fuzzy set is defuzzified; the defuzzification method used is as follows: The specific control signal is obtained by calculating the centroid of the fuzzy control result; the fuzzy value with the largest membership is selected as the output result; the output values ​​of each rule are weighted averaged to obtain the final control signal.

[0014] In a preferred embodiment, the following modules are included: a data acquisition and preprocessing module, an error calculation and comparison module, a region division and weighted average module, a fuzzification and reasoning module, and a control signal generation and execution module; The data acquisition and preprocessing module is used to collect the motion data of the impact drill, and perform preprocessing such as filtering and denoising on the collected motion data; the processed data is transmitted to the error calculation and comparison module; The error calculation and comparison module compares the processed data with the planned path, calculates the errors collected by each gyroscope, and transmits the errors collected by each gyroscope to the area division and weighted average module; The area division and weighted average module divides the entire impact drill into multiple monitoring areas, and monitors the temperature and vibration frequency of each area respectively; according to the temperature and vibration frequency of each area, a weight is assigned to each area, and the errors of each area are weighted averaged according to these weights to obtain the overall error; the overall error is transmitted to the fuzzification and reasoning module; The fuzzification and reasoning module converts the actual error and error change rate into fuzzy values, defines fuzzy sets to represent the different degrees of error and error change rate; uses the fuzzy reasoning engine to reason about the fuzzified error and error change rate to obtain the control output; and transmits the control output to the control signal generation and execution module; The control signal generation and execution module converts the fuzzy reasoning results into clear control signals; according to the defuzzified control signals, the control system performs corresponding correction actions to adjust the motion trajectory or other parameters of the impact drill.

[0015] Technical effects and advantages of the present invention: The present invention collects the motion data of the impact drill by setting multiple gyroscopes, first pre-processes these data, then compares the processed data with the preset path, and then calculates the acquisition error of each gyroscope. Next, the impact drill is partitioned, and the temperature and vibration state of each area are monitored respectively. According to the temperature and vibration conditions of each area, corresponding weights are assigned, and the comprehensive error of the entire impact drill is obtained by the weighted average method. Subsequently, the actual error (such as position error, angle error, speed error, etc.) is converted into a fuzzy value, and a series of fuzzy sets are defined to express these errors and their change rates. The error and its change rate are processed by fuzzification, and the actual value of the input will be mapped to the corresponding fuzzy set. The fuzzification result is derived using a fuzzy inference engine (such as a Mamdani inference engine or a Sugeno inference engine), and the control output is finally obtained. Finally, the inference result is converted into a clear control signal, and the system is controlled or corrected accordingly, thereby achieving precise adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 It is a flow chart of the gyroscope-based intelligent correction method for impact drill anti-drift of the present invention; Figure 2 The present invention is a schematic diagram of the structure of the gyroscope-based impact drill anti-deviation intelligent correction system. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The present invention collects the motion data of the impact drill by setting multiple gyroscopes, first pre-processes these data, then compares the processed data with the preset path, and then calculates the acquisition error of each gyroscope. Next, the impact drill is partitioned, and the temperature and vibration state of each area are monitored respectively. According to the temperature and vibration conditions of each area, corresponding weights are assigned, and the comprehensive error of the entire impact drill is obtained by the weighted average method. Subsequently, the actual error (such as position error, angle error, speed error, etc.) is converted into a fuzzy value, and a series of fuzzy sets are defined to express these errors and their change rates. The error and its change rate are processed by fuzzification, and the actual value of the input will be mapped to the corresponding fuzzy set. The fuzzification result is derived using a fuzzy inference engine (such as a Mamdani inference engine or a Sugeno inference engine), and the control output is finally obtained. Finally, the inference result is converted into a clear control signal, and the system is controlled or corrected accordingly, thereby achieving precise adjustment.

[0019] Embodiment, the present invention discloses a gyroscope-based impact drill anti-drift intelligent correction method, such as Figure 1 As shown, the following steps are included: Step S1: collecting motion data of the impact drill, preprocessing the collected motion data; and comparing the processed data with the planned path to obtain the acquisition errors of each gyroscope; Step S2: partition the entire impact drill, monitor the temperature and vibration frequency of each area respectively, assign weights to each area according to the temperature and vibration frequency of each area using hierarchical analysis, and obtain the error of the overall impact drill by weighted average of each area; Step S3: convert the error and error change rate into fuzzy values, define a series of fuzzy sets to express these errors and error change rates, and map them to corresponding fuzzy sets; use the fuzzy inference engine to derive the control output; Step S4: Convert the results of fuzzy reasoning into clear control signals so that the system can be controlled or corrected according to these signals.

[0020] In step S1, multiple gyroscopes collect motion data of the impact drill. A gyroscope is a sensor that can measure angular velocity and is commonly used to detect rotational motion. For devices such as impact drills, the gyroscope can provide rotation data of the device in space, usually angular velocity data.

[0021] Install multiple gyroscopes on the impact drill to more accurately capture rotation information at different angles and positions. For example, gyroscopes can be installed at different positions of the drill bit (such as the front end of the drill bit, the middle of the drill bit, the top of the drill bit, etc.) to ensure that comprehensive motion data is obtained. Furthermore, it is necessary to ensure that the timestamps of all gyroscope data are consistent so that data collected at different positions can be accurately compared and analyzed.

[0022] Gyroscope data may be affected by environmental noise and hardware noise, resulting in unstable data. The following methods can be used to remove noise: Low-pass filter: used to filter out high-frequency noise and retain low-frequency signals (such as the main components of motion).

[0023] Kalman filter: A more advanced filtering technique that combines multi-sensor information to estimate the true motion trajectory, thereby reducing the impact of noise on the data.

[0024] The data collected by the gyroscope is usually angular velocity data (such as the rotation speed about three axes). If you need to get the rotation angle of the device, you may need to convert the angular velocity into angular displacement by integration: ; where θ(t) represents the rotation angle and ω(t) is the angular velocity.

[0025] Path planning refers to planning the impact drill to move along a predetermined path, which may be a path entered manually or generated by a more complex algorithm (such as a trajectory planning algorithm). The path can be represented in different forms, the most common being a collection of 3D coordinates or rotation angles.

[0026] Predetermined path: For example, the expected trajectory of the device in the operating area is obtained through GPS systems, laser scanning or other positioning methods. These path data can be expressed as time-series data points, such as P(t)=(x(t), y(t), z(t)) or the expected rotation angle of the device.

[0027] Expected motion trajectory: The core goal of path planning is to set the target position and angle of the device to ensure that the device rotates and moves in a specific way.

[0028] The data collected by the gyroscope is compared with the predetermined path, usually by calculating the deviation between the current posture of the device and the target path to obtain the error.

[0029] The error can be calculated using: Position error: Compare the position information provided by the gyroscope with the position points in the predetermined path. For displacement, the Euclidean distance can be calculated: ;W represents the position error, is the actual measured value, is the value on the predetermined path.

[0030] Angular Error: Compares the angular data (rotation information) to a predetermined angular path. The error between two angles can be calculated, and rotations are usually represented and compared using an angular difference or a quaternion.

[0031] It is necessary to ensure that the gyroscope data is time-aligned with the planned path data, which means processing data at different points in time so that they can be compared correctly.

[0032] In step S2, the entire impact drill is divided into zones, the temperature and vibration state of each zone are monitored respectively, weights are assigned to each zone according to the temperature and vibration state of each zone, and the error of the entire impact drill is obtained by weighted average of each zone; Gyroscopes are provided at different positions on the impact drill; the temperature conditions at the corresponding positions are obtained through temperature sensors; multiple temperature sensors can be provided in the area to detect the temperature conditions in real time and take the average value; this can make the obtained temperature more accurate.

[0033] By installing an accelerometer, the acceleration change can be directly measured. It can capture the vibration generated by the impact drill and convert it into an electrical signal to obtain the vibration frequency.

[0034] The temperature and vibration frequency are normalized, and the normalized expression can be .in, is the temperature value or vibration frequency, is the minimum temperature or the minimum vibration frequency, is the maximum temperature or the maximum vibration frequency, The normalized value of the temperature or vibration frequency at the corresponding position. The normalized value of the temperature at each position of the impact drill is used as the temperature coefficient of each position of the impact drill and marked ; The normalized value of the vibration frequency of each position of the impact drill is used as the vibration frequency coefficient of each position of the impact drill and marked as .

[0035] Determine the error weight coefficient of each region based on the temperature coefficient and vibration frequency coefficient. The formula is as follows: in is the error weight coefficient, and i is the sequence number of each position.

[0036] Sort the error weight coefficients of each region from small to large, use the hierarchical analysis method to configure the weights, and determine the error weights of each region. The specific steps are as follows: A judgment matrix was constructed to compare the relative importance of each position. As shown in Table 1, a 3-point scale from 1 to 3 was used, where 1 represents the same importance, 2 represents a moderate difference in importance, and 3 represents a very significant difference.

[0037] Table 1

[0038]

[0039] Calculate the eigenvector: Calculate the eigenvector of each factor. The eigenvector is the weighted average of each column of the judgment matrix. First, normalize each column of the judgment matrix so that the sum of each column is equal to 1. Then for each column, calculate the weighted average as the element value of the eigenvector. For the above judgment matrix, the normalized matrix is ​​shown in Table 2 below: Table 2

[0040] Then, calculate the weighted average for each factor: A position importance = (0.167 + 0.144 + 0.182) / 3 = 0.164; B position importance = (0.333 + 0.286 + 0.273) / 3 = 0.297; C position importance = (0.500 + 0.571 + 0.545) / 3 = 0.539; The A, B, and C areas correspond to the three positions of the impact drill in this example. After the error weight coefficients of each position of the impact drill are sorted according to size, they correspond to the A, B, and C areas in ascending order.

[0041] It should be noted that this embodiment takes division into three areas as an example, and several areas can be divided as needed.

[0042] In step S3, the error values ​​(position error, angle error, speed error, etc.) are converted into fuzzy values, and a series of fuzzy sets are defined to express these errors and error change rates; the error change rate is equal to the error change difference per unit time divided by the unit time; the unit time is set by the staff according to the actual situation.

[0043] The error and error change rate are fuzzified, that is, they are mapped to corresponding fuzzy sets according to the input error and error change rate; the control output is derived using a fuzzy inference engine (such as a Mamdani inference engine or a Sugeno inference engine); Fuzzification is the process of converting error values ​​(e.g., position error, angle error, speed error, etc.) into fuzzy values. The core idea of ​​fuzzy control is to express these errors and error change rates by defining a series of fuzzy sets. For example, we can define fuzzy sets of errors and error change rates: Error: negative big (NB), negative small (NS), zero (Z), positive small (PS), positive big (PB); Error change rate (Change in Error): negative large change (NVB), negative small change (NSV), zero change (ZV), positive small change (PSV), positive large change (PVB); The definitions of these fuzzy sets reflect different degrees of error and error change. In order to obtain fuzzy values, errors and error change rates need to be fuzzified, that is, according to the actual input errors and error change rates, they are mapped to the corresponding fuzzy sets.

[0044] Fuzzy reasoning is the core of fuzzy control methods, which mainly uses fuzzy reasoning engines (such as Mamdani reasoning engines or Sugeno reasoning engines) to derive control outputs based on fuzzy rule bases. Fuzzy rule bases are generally composed of rules in the format of "if...then...", which describe the impact of errors and error change rates on control signals. For example: Rule 1: If the error is negative (NB) and the error rate of change is negative (NVB), then the control signal is positive (PB).

[0045] Rule 2: If the error is zero (Z), and the rate of change of the error is zero change (ZV), then the control signal is zero (Z).

[0046] The goal of fuzzy reasoning is to obtain the fuzzy result of the control signal through the reasoning process. The key to this process is the "weighted average" or "weighted sum" of the reasoning process, which depends on the weight of the rule and the membership function of the fuzzy set.

[0047] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment takes five fuzzy sets as an example, the error, error change rate and control signal can actually be divided into more than five sets to facilitate better precise adjustment according to different temperatures.

[0048] In step S4, the obtained control signal fuzzy set is defuzzified; defuzzification is the process of converting the result of fuzzy reasoning into a clear control signal. The defuzzification methods used are: Centroid Method: Obtain specific control signals by calculating the "centroid" of the fuzzy control results.

[0049] Max Membership Method: Select the fuzzy value with the largest membership as the output result.

[0050] Weighted Average Method: The output values ​​of each rule are weighted averaged to obtain the final control signal.

[0051] The purpose of defuzzification is to convert fuzzy results into practical control signals so that the system can be controlled or corrected according to these signals.

[0052] The present invention discloses a gyroscope-based impact drill anti-drift intelligent correction system, such as Figure 2 As shown, it includes the following modules: data acquisition and preprocessing module, error calculation and comparison module, area division and weighted average module, fuzzification and reasoning module, control signal generation and execution module, and storage module; The data acquisition and preprocessing module is used to collect the motion data of the impact drill, and perform preprocessing such as filtering and denoising on the collected motion data to ensure the accuracy and reliability of the data; the processed data is transmitted to the error calculation and comparison module; The error calculation and comparison module compares the processed data with the planned path, calculates the errors collected by each gyroscope, and transmits the errors collected by each gyroscope to the area division and weighted average module; The area division and weighted average module divides the entire impact drill into multiple monitoring areas, and monitors the temperature and vibration frequency of each area respectively; according to the temperature and vibration frequency of each area, a weight is assigned to each area, and the errors of each area are weighted averaged according to these weights to obtain the overall error; the overall error is transmitted to the fuzzification and reasoning module; The fuzzification and reasoning module converts the actual error (position, angle, speed, etc.) and error change rate into fuzzy values, defines fuzzy sets to represent the different degrees of error and error change rate; uses a fuzzy inference engine (such as Mamdani inference engine or Sugeno inference engine) to infer the fuzzified error and error change rate to obtain the control output; and transmits the control output to the control signal generation and execution module; The control signal generation and execution module converts the fuzzy reasoning results into clear control signals (such as the speed and angle adjustment of the motor). According to the defuzzified control signals, the control system performs corresponding correction actions to adjust the motion trajectory or other parameters of the impact drill. The storage module is used to store data during system operation.

[0053] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0054] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0055] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0056] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0057] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A gyroscope-based intelligent correction method for impact drill anti-drift, characterized in that: The following steps are involved: Step S1: collecting motion data of the impact drill, preprocessing the collected motion data; and comparing the processed data with the planned path to obtain the acquisition errors of each gyroscope; Step S2: partition the entire impact drill, monitor the temperature and vibration frequency of each area respectively, assign weights to each area according to the temperature and vibration frequency of each area using hierarchical analysis, and obtain the error of the entire impact drill by weighted average of each area; Step S3: convert the error and error change rate into fuzzy values, define a series of fuzzy sets to express these errors and error change rates, and map them to corresponding fuzzy sets; use the fuzzy inference engine to derive the control output control signal fuzzy set; Step S4: Defuzzify the control signal fuzzy set and convert it into a clear control signal, and the system performs control or correction according to the clear control signal.

2. The gyroscope-based intelligent correction method for anti-drifting of impact drill according to claim 1, characterized in that: In step S1, multiple gyroscopes collect motion data of the impact drill, while ensuring that the timestamps of all gyroscope data are consistent; then a low-pass filter and a Kalman filter are used to remove noise; The angular velocity is converted into angular displacement by integration: ; where θ(t) represents the rotation angle and ω(t) is the angular velocity.

3. The gyroscope-based intelligent correction method for impact drill anti-drift according to claim 1, characterized in that: In step S1, path planning refers to planning the impact drill to move along a predetermined path, which may be a path input manually or a path generated by a more complex algorithm; the path is represented by a set of three-dimensional coordinates or rotation angles; Compare the data collected by the gyroscope with the predetermined path, and obtain the error by calculating the deviation between the current posture of the device and the target path; Comparing the position information provided by the gyroscope with the position points in the predetermined path; For displacement, the Euclidean distance is calculated to represent the distance error; the angle data is compared with the predetermined angle path; and the error between the two angles is calculated.

4. The gyroscope-based intelligent correction method for impact drill anti-deviation according to claim 1, characterized in that: In step S2, the entire impact drill is divided into zones, and the temperature and vibration state of each zone are monitored respectively. A weight is assigned to each zone according to the temperature and vibration state of each zone, and the error of the entire impact drill is obtained by weighted average of each zone.

5. The gyroscope-based intelligent correction method for impact drill anti-drift according to claim 4, characterized in that: In step S2, the error weight coefficient of each region is determined according to the temperature coefficient and the vibration frequency coefficient, the error weight coefficients of each region are sorted from small to large, and the weight is configured using the hierarchical analysis method to determine the error weight of each region.

6. The gyroscope-based intelligent correction method for impact drill anti-deviation according to claim 1, characterized in that: In step S3, the error values ​​are converted into fuzzy values, and a series of fuzzy sets are defined to express these errors and error change rates; The error change rate is equal to the error change difference per unit time divided by the unit time; The error and error change rate are fuzzified, that is, according to the input error and error change rate, they are mapped to the corresponding fuzzy set; the fuzzy inference engine is used to derive the fuzzy set of the control output control signal.

7. The gyroscope-based intelligent correction method for impact drill anti-deviation according to claim 1, characterized in that: In step S4, the acquired control signal fuzzy set is defuzzified; the defuzzification method used is as follows: The specific control signal is obtained by calculating the centroid of the fuzzy control result; the fuzzy value with the largest membership is selected as the output result; the output values ​​of each rule are weighted averaged to obtain the final control signal.

8. A gyroscope-based impact drill anti-deviation intelligent correction system, used to implement the gyroscope-based impact drill anti-deviation intelligent correction method according to any one of claims 1 to 7, characterized in that: It includes the following modules: data acquisition and preprocessing module, error calculation and comparison module, area division and weighted average module, fuzzification and reasoning module, control signal generation and execution module; The data acquisition and preprocessing module is used to collect the motion data of the impact drill, filter and denoise the collected motion data, and transmit the processed data to the error calculation and comparison module; The error calculation and comparison module compares the processed data with the planned path, calculates the errors collected by each gyroscope, and transmits the errors collected by each gyroscope to the area division and weighted average module; The area division and weighted average module divides the entire impact drill into multiple monitoring areas, and monitors the temperature and vibration frequency of each area respectively; according to the temperature and vibration frequency of each area, a weight is assigned to each area, and the errors of each area are weighted averaged according to these weights to obtain the overall error; the overall error is transmitted to the fuzzification and reasoning module; The fuzzification and reasoning module converts the actual error and error change rate into fuzzy values, defines fuzzy sets to represent the different degrees of error and error change rate; uses the fuzzy reasoning engine to reason about the fuzzified error and error change rate to obtain the control output; and transmits the control output to the control signal generation and execution module; The control signal generation and execution module converts the fuzzy reasoning results into clear control signals; according to the defuzzified control signals, the control system performs corresponding correction actions to adjust the motion trajectory or other parameters of the impact drill.

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