Gyroscope-based Intelligent Correction System and Method for Impact Drill Anti-offset

Abstract

The present invention discloses an intelligent correction system and method for preventing deviation of an impact drill based on a gyroscope, specifically relating to the technical field of intelligent correction. The present invention collects the motion data of the impact drill through multiple gyroscopes, first performs preprocessing, compares it with a preset path, and calculates the acquisition errors of each gyroscope. Then, the impact drill is partitioned, and the temperature and vibration states of each area are monitored. According to the temperature and vibration conditions, weights are assigned to each area, and the comprehensive error is obtained through the weighted average method. Next, the actual errors (such as position, angle, and speed errors) are converted into fuzzy values, and fuzzy sets are defined to represent the errors and their change rates. Through fuzzy processing, the input errors are mapped to the corresponding fuzzy sets. The fuzzy inference engine is used to deduce the control output. Finally, the inference result is converted into a definite control signal, and the system controls or corrects according to this signal to ensure precise adjustment.

Description

Technical Field

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

[0002] An impact drill is a tool commonly used for drilling operations and is widely applied in fields such as construction, mining, oil exploration, and geological survey. The working principle of an impact drill is usually to break rock formations or soil through a rapidly rotating drill bit and a synchronized impact force, thereby achieving drilling.

[0003] However, in actual operations, when an impact drill is performing deep-hole drilling or working in a complex environment, it may deviate due to various factors, resulting in the drill bit being unable to drill along the predetermined path. This deviation not only reduces the operation efficiency but may also cause damage to the equipment and the operation environment.

[0004] A gyroscope is an inertial sensor that can accurately measure angular changes and is commonly used in fields such as navigation, attitude control, and precise positioning. In the operation of an impact drill, a gyroscope can detect whether there is a deviation by real-time monitoring of the angular changes of the drill bit. Specifically, the gyroscope can accurately estimate the current attitude of the impact drill by measuring the rotational angular velocity of the device in various directions and combining integral operations.

[0005] The intelligent correction system for preventing deviation of an impact drill based on a gyroscope combines the gyroscope with the control system of the impact drill to real-time monitor and correct the movement trajectory of the drill bit, thereby effectively preventing deviation. Summary of the Invention

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

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent correction method for preventing deviation of an impact drill based on a gyroscope, comprising the following steps:

[0009] Step S1: Collect the motion data of the impact drill, preprocess the collected motion data; and compare the processed data with the planned path to obtain the acquisition errors of each gyroscope.

[0010] Step S2: Divide the entire impact drill into regions, respectively monitor the temperature and vibration frequency of each region, assign weights to each region according to the temperature and vibration frequency of each region using the analytic hierarchy process, and obtain the overall error of the impact drill by weighted averaging of each region.

[0011] Step S3: Convert the error and the error change rate into fuzzy values. Define a series of fuzzy sets to represent these errors and error change rates, and map them into the corresponding fuzzy sets; Use a fuzzy inference engine to derive the fuzzy set of the control output control signal;

[0012] Step S4: Defuzzify the fuzzy set of the control signal to convert it into a definite control signal, and the system controls or corrects according to the definite control signal.

[0013] In a preferred embodiment, in step S1, multiple gyroscopes collect the motion data of the impact drill while ensuring that the timestamps of all gyroscope data are consistent; Subsequently, a low-pass filter and a Kalman filter are used to remove noise;

[0014] Convert the angular velocity into angular displacement by integration: ; where, θ(t) represents the rotation angle and ω(t) is the angular velocity.

[0015] In a preferred embodiment, in step S1, path planning means planning that the impact drill moves along a predetermined path, which may be a path input manually or a path generated by a more complex algorithm; The path can be represented by a set of three-dimensional coordinates or rotation angles;

[0016] Compare the data collected by the gyroscope with the predetermined path, and obtain the error by calculating the deviation between the current attitude of the device and the target path;

[0017] Compare the position information provided by the gyroscope with the position points in the predetermined path; For displacement, calculate the Euclidean distance to represent the distance error; Compare the angle data with the predetermined angle path; Calculate the error between the two angles.

[0018] In a preferred embodiment, in step S2, partition the entire impact drill, monitor the temperature and vibration states of each area respectively, assign weights to each area according to the temperature and vibration states of each area, and obtain the error of the overall impact drill by weighted averaging of each area.

[0019] In a preferred embodiment, in step S2, determine the error weight coefficient of each area according to the temperature coefficient and the vibration frequency coefficient, sort the error weight coefficients of each area from small to large, and use the analytic hierarchy process for weight allocation to determine the error weight of each area.

[0020] In a preferred embodiment, in step S3, convert the error value into a fuzzy value, and define a series of fuzzy sets to represent these errors and error change rates; The error change rate is equal to the difference in error change within a unit time divided by the unit time;

[0021] The error and the error change rate are subjected to fuzzy processing, that is, according to the input error and error change rate, they are mapped into the corresponding fuzzy sets; a fuzzy inference engine is used to derive the fuzzy set of the control output control signal.

[0022] In a preferred embodiment, in step S4, the obtained fuzzy set of the control signal is defuzzified; the defuzzification method used is as follows:

[0023] The specific control signal is obtained by calculating the centroid of the fuzzy control result; the fuzzy value with the largest membership degree is selected as the output result; the output values of each rule are weighted and averaged to obtain the final control signal.

[0024] 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 inference module, and a control signal generation and execution module;

[0025] The data acquisition and preprocessing module is used to collect the motion data of the impact drill, perform preprocessing such as filtering and denoising on the collected motion data, and transmit the processed data to the error calculation and comparison module;

[0026] 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 region division and weighted average module;

[0027] The region division and weighted average module divides the entire impact drill into multiple monitoring regions, monitors the temperature and vibration frequency of each region respectively, assigns a weight to each region according to the temperature and vibration frequency of each region, and performs weighted averaging on the errors of each region according to these weights to obtain the overall error, and transmits the overall error to the fuzzification and inference module;

[0028] The fuzzification and inference module converts the actual error and error change rate into fuzzy values, defines fuzzy sets to represent different degrees of the error and error change rate, uses a fuzzy inference engine to infer the fuzzified error and error change rate, obtains the control output, and transmits the control output to the control signal generation and execution module;

[0029] The control signal generation and execution module converts the fuzzy inference result into a definite control signal, and according to the defuzzified control signal, the control system performs corresponding correction actions to adjust the motion trajectory or other parameters of the impact drill.

[0030] The technical effects and advantages of the present invention:

[0031] The present invention collects the motion data of a percussion drill by setting multiple gyroscopes. First, preprocess these data, then compare the processed data with a preset path, and further calculate the acquisition errors of each gyroscope. Next, divide the percussion drill into zones, and respectively monitor the temperature and vibration states of each zone. According to the temperature and vibration conditions of each zone, assign corresponding weights, and obtain the comprehensive error of the entire percussion drill through the weighted average method. Subsequently, convert the actual errors (such as position error, angle error, speed error, etc.) into fuzzy values, and define a series of fuzzy sets to represent these errors and their rates of change. Through fuzzy processing of the errors and their rates of change, the input actual values will be mapped into the corresponding fuzzy sets. Use a fuzzy inference engine (such as Mamdani inference engine or Sugeno inference engine) to deduce the fuzzy results, and finally obtain the control output. Finally, convert the inference result into a definite control signal, and the system controls or corrects accordingly, so as to achieve precise adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0033] Figure 1 It is a schematic flow chart of the intelligent correction method for preventing the deviation of a percussion drill based on a gyroscope according to the present invention;

[0034] Figure 2 It is a schematic structural diagram of the intelligent correction system for preventing the deviation of a percussion drill based on a gyroscope according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] In the present invention, a plurality of gyroscopes are provided to collect the motion data of the impact drill. First, these data are preprocessed, and then the processed data are compared with a preset path to calculate the acquisition errors of each gyroscope. Next, the impact drill is partitioned, and the temperature and vibration states 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 errors (such as position error, angle error, speed error, etc.) are converted into fuzzy values, and a series of fuzzy sets are defined to represent these errors and their change rates. The errors and their change rates are processed by fuzzyfication, and the input actual values will be mapped into the corresponding fuzzy sets. Using a fuzzy inference engine (such as Mamdani inference engine or Sugeno inference engine), the fuzzyfication results are deduced, and finally the control output is obtained. Finally, the inference results are converted into explicit control signals, and the system controls or corrects accordingly to achieve precise adjustment.

[0037] Embodiment, the present invention discloses an intelligent correction method for preventing deviation of an impact drill based on a gyroscope, as Figure 1 shown, which includes the following steps:

[0038] Step S1: Collect the motion data of the impact drill, preprocess the collected motion data; and compare the processed data with the planned path to obtain the acquisition errors of each gyroscope;

[0039] 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 by using the analytic hierarchy process, and obtain the error of the whole impact drill by weighted averaging of each area;

[0040] Step S3: Convert the error and the error change rate into fuzzy values, define a series of fuzzy sets to represent these errors and error change rates, and map them into the corresponding fuzzy sets; use a fuzzy inference engine to deduce the control output;

[0041] Step S4: Convert the results of fuzzy inference into explicit control signals, so that the system can control or correct according to these signals.

[0042] In step S1, a plurality of gyroscopes collect the 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 the rotational data of the device in space, usually angular velocity data.

[0043] Install multiple gyroscopes on the impact drill to more accurately capture rotational 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 comprehensive motion data is obtained. Further, it is necessary to ensure that the timestamps of all gyroscope data are consistent so that the data collected at different positions can be accurately compared and analyzed.

[0044] Gyroscope data may be affected by environmental noise and hardware noise, resulting in unstable data. The following methods can be used to remove noise:

[0045] Low-pass filter: Used to filter out high-frequency noise and retain low-frequency signals (such as the main components of motion).

[0046] Kalman filter: A more advanced filtering technique that can combine multi-sensor information to estimate the true motion trajectory, thereby reducing the impact of noise on the data.

[0047] The data collected by the gyroscope is usually angular velocity data (such as the rotational speed about three axes). If the rotation angle of the device is required, it may be necessary to convert the angular velocity to angular displacement by integration: ; where θ(t) represents the rotation angle and ω(t) is the angular velocity.

[0048] Path planning refers to planning the impact drill to move along a predetermined path, which may be a path manually input or generated by a more complex algorithm (such as a trajectory planning algorithm). The path can be represented in different forms, and the most common is a set of three-dimensional coordinates or rotation angles.

[0049] Predetermined path: For example, through a GPS system, laser scanning, or other positioning methods, the desired trajectory of the device in the operating area is obtained. These path data can be represented as time-series data points, such as P(t)=(x(t),y(t),z(t)) or the desired rotation angle of the device.

[0050] Desired 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 manner.

[0051] Compare the data collected by the gyroscope with the predetermined path, usually by calculating the deviation between the current pose of the device and the target path to obtain the error.

[0052] The following methods can be used to calculate the error:

[0053] 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: ; where W represents the position error, is the actual measured value, is the value on the predetermined path.

[0054] Angle error: Compare the angle data (rotation information) with the predetermined angle path. The error between two angles can be calculated, and usually the angle difference or quaternion is used to represent and compare rotations.

[0055] It is necessary to ensure the time alignment of the gyroscope data and the planned path data, which means dealing with data at different time points for correct comparison.

[0056] In step S2, the entire electric drill is partitioned, the temperature and vibration states of each area are monitored respectively, weights are assigned to each area according to the temperature and vibration states of each area, and the overall error of the electric drill is obtained by weighted averaging of each area;

[0057] Gyroscopes are set at different positions on the electric drill; the temperature conditions at the corresponding positions are obtained through temperature sensors; multiple temperature sensors can be set in this area to detect the temperature situation in real time and take the average value; this can make the obtained temperature more accurate.

[0058] By installing an accelerometer, the acceleration change can be directly measured. It can capture the vibration generated by the electric drill and convert it into an electrical signal to obtain the vibration frequency.

[0059] Normalize the temperature and vibration frequency. The normalization formula can be . Among them, is the temperature value or vibration frequency, is the minimum temperature or minimum vibration frequency, is the maximum temperature or maximum vibration frequency, is the normalized value of the temperature value or vibration frequency at the corresponding position. The normalized values of the temperatures at each position of the electric drill are used as the temperature coefficients at each position of the electric drill and marked as ; the normalized values of the vibration frequencies at each position of the electric drill are used as the vibration frequency coefficients at each position of the electric drill and marked as .

[0060] Determine the error weight coefficients of each area according to the temperature coefficients and vibration frequency coefficients. The formula is as follows: Among them is the error weight coefficient, and i is the serial number of each position.

[0061] Sort the error weight coefficients of each area from small to large, use the analytic hierarchy process for weight allocation, and determine the error weights of each area. The specific steps are as follows:

[0062] Construct a judgment matrix to compare the relative importance between positions. As shown in Table 1, a 3-point scale from 1 to 3 is used, where 1 represents equal importance, 2 represents moderate importance difference, and 3 represents very important difference.

[0063] Table 1

[0064]

[0065] 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:

[0066] Table 2

[0067]

[0068] Then, calculate the weighted average of each factor:

[0069] Importance of position A = (0.167 + 0.144 + 0.182) / 3 = 0.164;

[0070] Importance of position B = (0.333 + 0.286 + 0.273) / 3 = 0.297;

[0071] Importance of position C = (0.500 + 0.571 + 0.545) / 3 = 0.539;

[0072] Among them, areas A, B, and C correspond one-to-one to the three positions of the electric drill in this example. After sorting the error weight coefficients of each position of the electric drill according to the size, they correspond to areas A, B, and C in ascending order.

[0073] It should be noted that in this embodiment, taking the division of three areas as an example, several areas can also be divided according to needs.

[0074] In step S3, convert the error values (position error, angle error, speed error, etc.) into fuzzy values, and define a series of fuzzy sets to express these errors and error change rates; the error change rate is equal to the difference in error change within a unit time divided by the unit time; the unit time is set by the staff according to the actual situation.

[0075] The errors and error change rates are subjected to fuzzy processing, that is, according to the input errors and error change rates, map them into the corresponding fuzzy sets; use a fuzzy inference engine (such as a Mamdani inference engine or a Sugeno inference engine) to derive the control output;

[0076] Fuzzification is the process of converting the error value (e.g., position error, angle error, speed error, etc.) into a fuzzy value. The core idea of fuzzy control is to express these errors and the rate of change of errors by defining a series of fuzzy sets. For example, we can define the fuzzy sets of error and the rate of change of error:

[0077] Error: Negative Big (NB), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Big (PB);

[0078] Change in Error: Negative Big Variation (NVB), Negative Small Variation (NSV), Zero Variation (ZV), Positive Small Variation (PSV), Positive Big Variation (PVB);

[0079] The definitions of these fuzzy sets reflect different degrees of error and the situation of the rate of change of error. To obtain the fuzzy value, the error and the rate of change of error need to undergo fuzzification processing, that is, according to the actual error and the rate of change of error input, map them into the corresponding fuzzy sets.

[0080] Fuzzy inference is the core of the fuzzy control method. It mainly derives the control output according to the fuzzy rule base by using a fuzzy inference engine (such as Mamdani inference engine or Sugeno inference engine). The fuzzy rule base generally consists of rules in the format of "if... then...", which describes the influence of the error and the rate of change of error on the control signal. For example:

[0081] Rule 1: If the error is Negative Big (NB) and the rate of change of error is Negative Big Variation (NVB), then the control signal is Positive Big (PB).

[0082] Rule 2: If the error is Zero (Z) and the rate of change of error is Zero Variation (ZV), then the control signal is Zero (Z).

[0083] The goal of fuzzy inference is to obtain the fuzzy result of the control signal through the inference process. The key to this process is the "weighted average" or "weighted summation" of the inference process, and these processes rely on the weights of the rules and the membership functions of the fuzzy sets.

[0084] It should be noted that the division of the fuzzy sets can be adjusted according to the actual situation. For example, although five fuzzy sets are taken as an example in this embodiment, in fact, the error, the rate of change of error, and the control signal can be divided into more than five sets to facilitate more precise adjustment according to different temperatures.

[0085] In step S4, defuzzification is performed on the obtained fuzzy set of the control signal; defuzzification is the process of converting the result of fuzzy inference into a definite control signal. The defuzzification methods used are:

[0086] Centroid Method: Obtain specific control signals by calculating the "centroid" of the fuzzy control results.

[0087] Max Membership Method: Select the fuzzy value with the largest membership degree as the output result.

[0088] Weighted Average Method: Perform weighted averaging on the output values of each rule to obtain the final control signal.

[0089] The purpose of defuzzification is to convert the fuzzy results into practically usable control signals, enabling the system to control or correct based on these signals.

[0090] The present invention discloses an intelligent correction system for preventing the deviation of a percussion drill based on a gyroscope, as Figure 2 shown, including the following modules: a data acquisition and preprocessing module, an error calculation and comparison module, a region division and weighted average module, a fuzzification and inference module, a control signal generation and execution module, and a storage module;

[0091] The data acquisition and preprocessing module is used to collect the motion data of the percussion drill, perform preprocessing such as filtering and denoising on the collected motion data to ensure the accuracy and reliability of the data; and transmit the processed data to the error calculation and comparison module;

[0092] The error calculation and comparison module compares the processed data with the planned path and calculates the errors collected by each gyroscope; and transmits the errors collected by each gyroscope to the region division and weighted average module;

[0093] The region division and weighted average module divides the entire percussion drill into multiple monitoring regions, monitors the temperature and vibration frequency of each region respectively; assigns a weight to each region according to the temperature and vibration frequency of each region, and performs weighted averaging on the errors of each region according to these weights to obtain the overall error; and transmits the overall error to the fuzzification and inference module;

[0094] The fuzzification and inference module converts the actual errors (such as position, angle, speed, etc.) and the error change rate into fuzzy values, defines fuzzy sets to represent different degrees of errors and error change rates; uses a fuzzy inference engine (such as a Mamdani inference engine or a Sugeno inference engine) to infer the fuzzified errors and error change rate to obtain a control output; and transmits the control output to the control signal generation and execution module;

[0095] The control signal generation and execution module converts the fuzzy inference result into a definite control signal (such as the speed and angle adjustment of a motor, etc.). According to the defuzzified control signal, the control system performs corresponding correction actions to adjust the movement trajectory or other parameters of the impact drill;

[0096] The storage module is used to store the data during the operation of the system.

[0097] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0098] In several embodiments provided in this 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0099] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0101] As mentioned above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited to this. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to 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 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 controls or corrects according to the clear control signal; 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; 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.

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-deviation 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 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.

5. 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.

6. 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 5, 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.

Images