A method and device for intelligent positioning and safety protection of handheld drilling machines based on load detection

Abstract

This invention discloses a method and device for intelligent positioning and safety protection of a handheld drilling machine based on load detection, relating to the field of electrical automatic control technology. The method includes the following steps: S1, real-time acquisition of equipment operating status data and storage of the data in the equipment operating database with a unified timestamp; S2, data preprocessing and construction of equipment operating status data segments according to a fixed sliding time window; S3, real-time detection of drilling machine vibration during operation and high-frequency energy anomaly analysis to classify disturbance states; S4, acceleration control evaluation and implementation of acceleration control strategies; S5, real-time monitoring of vibration detection, high-frequency energy anomaly analysis, and acceleration control evaluation results, implementation of anomaly protection decisions, and visualization of execution and algorithm parameter optimization. This solves the problem of increased reaction force in the drilling machine due to the difficulty in timely detection of abnormal drill bit vibration, which could lead to injury risks.

Description

Technical Field

[0001] This invention relates to the field of electrical automatic control technology, specifically to a method and device for intelligent positioning and safety protection of handheld drilling machines based on load detection. Background Technology

[0002] Handheld drilling machines are widely used in industrial manufacturing and large-scale infrastructure construction. However, initial positioning with a handheld drilling machine is difficult, and in the event of sudden abnormalities such as drill bit jumping during drilling, the operator can only hold the machine tightly and try again and again. This not only leads to equipment damage and operator safety risks, posing a significant risk of injury, but also damages the cutting head due to high rotation speeds, resulting in wasted resources.

[0003] For example, invention patent CN115167207A discloses a control method and system for a long-stroke drilling equipment, including the following steps: S1: Install graphic codes and cameras; S2: Install various sensors; S3: Connect the control board; S4: Identify and retrieve the parameters corresponding to the graphic codes through the program; S5: Automatically adjust the control parameters of the drill bit. The sensors include ZLS-Px speed sensors, temperature sensors, and displacement sensors. A control system for a long-stroke drilling equipment includes a CPU module. The CPU module contains a data acquisition unit. The CPU module coordinates the entire process, allowing operators to control the long-stroke drilling equipment simply by using the controller. This enables the parameters of each stage of the drilling stroke to be controlled and adjusted when processing different materials, making it convenient for operators without strong programming skills to complete the equipment calibration work.

[0004] For example, invention patent CN119511937B discloses a workpiece drilling path generation method and system based on artificial intelligence. The method includes: obtaining a minimum set of actions based on the hole position information to be processed and the drill bit information on the drill bag; sorting the drilling actions in the minimum set of actions using an ant colony algorithm to obtain a first sorting result; sorting the drilling actions in the minimum set of actions using a genetic algorithm to obtain a second sorting result; comparing the total processing time corresponding to the first sorting result with the total processing time corresponding to the second sorting result, selecting the sorting result with the smaller total processing time as the optimal sorting result, and using the movement path of the machine head corresponding to the optimal sorting result as the workpiece drilling path. Using the workpiece drilling path generation method based on artificial intelligence of this invention can greatly improve the efficiency of workpiece drilling and significantly reduce the energy consumption of drilling equipment.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0006] Existing handheld drilling machines mostly rely on single signals such as current or torque to determine the arrival of load and abnormal conditions. They are unable to accurately distinguish between normal load changes and sudden reaction forces caused by fluctuations, leading to false alarms and difficulty in timely identification of abnormal risks. At the same time, traditional technologies are not sensitive to abnormal responses such as instantaneous impacts and high-frequency vibrations when the drill bit first contacts the workpiece, and are unable to detect fluctuations caused by hard points and surface roughness in a timely manner, resulting in problems of identification lag and omissions.

[0007] Therefore, in order to address the above problems, there is an urgent need for a method and device for intelligent positioning and safety protection of handheld drilling machines based on load detection. Summary of the Invention

[0008] Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides a method and device for intelligent positioning and safety protection of handheld drilling machines based on load detection. This solves the problem that the increased reaction force of the drilling machine due to the difficulty in timely detection of abnormal drill bit movement can lead to injury risks.

[0010] Technical solution

[0011] To achieve the above objectives, the present invention provides the following technical solution: a handheld drilling machine intelligent positioning safety protection method based on load detection, comprising the following steps: S1, real-time acquisition of equipment operating status data, unifying the timestamps of the equipment operating status data, and storing it in the equipment operating database; S2, preprocessing the equipment operating status data, and constructing equipment operating status data segments according to a fixed sliding time window; S3, based on the equipment operating status data segments, real-time detection of the drilling machine's vibration during operation, and high-frequency energy anomaly analysis, and classifying disturbance states based on the vibration detection and high-frequency energy anomaly analysis results; S4, based on the classifying disturbance state classification results, integrating the equipment operating status data, vibration detection, and high-frequency energy anomaly analysis results, performing acceleration control evaluation, and implementing acceleration control strategies based on the acceleration control evaluation results; S5, real-time monitoring of vibration detection, high-frequency energy anomaly analysis, and acceleration control evaluation results, implementing anomaly protection decisions, and achieving visualized execution and algorithm parameter optimization.

[0012] Furthermore, the specific process of real-time acquisition of equipment operating status data, unified timestamps for the equipment operating status data, and storage in the equipment operating database is as follows: A high-precision current sensor is integrated into the main circuit of the drilling machine to acquire the load current in real time and record the sampling frequency in real time. The spindle speed is acquired synchronously through an encoder. A triaxial accelerometer is installed at key positions on the machine body to acquire the vibration acceleration of the machine body. The equipment operating database is constructed, and the load current, spindle speed, and vibration acceleration are recorded as equipment operating status data with unified timestamps and stored in the equipment operating database.

[0013] Furthermore, the specific process of preprocessing the equipment operation status data and constructing equipment operation status data segments according to a fixed sliding time window is as follows: The collected equipment operation status data is synchronized to ensure that all signals are analyzed under the same time reference; signal processing techniques such as moving average and low-pass filtering are used to remove high-frequency noise and instantaneous pulse interference from the equipment operation status data; the equipment operation status data is normalized, and the data is segmented according to a fixed sliding time window to construct equipment operation status data segments that are easy to process in batches; for abnormal equipment operation status data and data with hardware acquisition errors that appear within the fixed time window, median filtering and threshold rejection strategies are used for automatic correction and rejection.

[0014] Furthermore, based on the equipment operating status data segments, the specific process of real-time detection of the drilling machine's fluctuations during operation is as follows: the load current of the drilling machine at the current moment and at historical moments is acquired in real time, and the standard deviation of all load currents collected within the sliding time window is calculated to obtain the standard deviation of the current signal; the load current at the current moment t is subtracted from twice the load current at moment t-1, and the load current at moment t-2 is added, and the absolute value is taken to obtain the second-order difference of the load current; the second-order difference of the load current is divided by the standard deviation of the load current to obtain the instantaneous jump discrimination value.

[0015] Further, the specific process for high-frequency energy anomaly analysis is as follows: The current sampling frequency is obtained, and simultaneously, the load current within a continuous sliding time window is acquired. Using a Fast Fourier Transform (FFT), this set of load currents is transformed from the time domain to the frequency domain, obtaining complex spectra corresponding to different frequencies. The spectral amplitude of each frequency component of the complex spectrum is calculated, i.e., the energy magnitude of each frequency is obtained by taking the square root of the sum of the squares of the real and imaginary parts. The frequency index is converted to the actual physical frequency in conjunction with the sampling frequency, and the spectral amplitudes within the window are extracted to obtain the frequency amplitude at each physical frequency. Half of the sampling frequency is taken as the maximum analysis frequency. For a batch of normally operating load currents sampled within a fixed time window, the frequency amplitude of the load current is acquired to obtain the full-band energy distribution of the load current, and then... The total cumulative energy distribution is statistically obtained. The frequency point corresponding to the proportion of high-frequency distribution that reaches the total cumulative energy distribution across the entire frequency band is identified and set as the boundary point between high and low frequencies. Energy integrals are calculated in two segments: For the low-frequency segment from 0 to the boundary point between high and low frequencies, the frequency amplitude of each corresponding low-frequency segment is obtained. For each low-frequency point, the frequency amplitude is squared and then integrated. A constant of 0.01 is added to the integration result to obtain the low-frequency energy sum. For the high-frequency segment from the boundary point between high and low frequencies to the maximum analysis frequency, the frequency amplitude of each corresponding high-frequency segment is obtained. Similarly, for each high-frequency point, the square of the frequency amplitude is integrated to obtain the high-frequency energy sum. The high-frequency energy sum is divided by the low-frequency energy sum to obtain the high-frequency energy ratio.

[0016] Furthermore, based on the results of fluctuation detection and high-frequency energy anomaly analysis, the specific process for classifying disturbance states is as follows: Real-time comparison of the instantaneous fluctuation discrimination value with the fluctuation threshold, and the high-frequency energy ratio with the anomaly threshold, to identify the drilling machine's state; when the instantaneous fluctuation discrimination value is less than the fluctuation threshold, and the high-frequency energy ratio is less than the anomaly threshold, it indicates that the drilling machine is currently in a normal, stable, and slow drilling state, and is operating well. Initial positioning is performed using no-load and light-load conditions to ensure low current and slow speed. After positioning, the load and current are increased, and the speed is gradually increased to full speed for normal operation, continuously advancing the drill bit into the workpiece without special intervention; when only one of the instantaneous fluctuation discrimination value or the high-frequency energy ratio is greater than or equal to the corresponding threshold, it is considered that the drilling machine is currently experiencing a disturbance. For minor disturbances, only flexible adjustments are made without interrupting operations. A slight rotation is executed to overcome the disturbance, and based on historical equipment operating data, the acceleration increment is reduced and the control step length is shortened. The system then enters the intelligent flexible start-up control module. When the instantaneous jump threshold is greater than or equal to the jump threshold, and the high-frequency energy ratio is greater than or equal to the abnormal threshold, an abnormal disturbance is considered to exist in the drilling machine, triggering protection measures and entering the intelligent flexible start-up control module: the motor acceleration is paused; and upon detecting the abnormality, a slight reverse action is performed, appropriately adjusting the drill bit position and repositioning it; simultaneously, an abnormality warning is issued to the operator; the instantaneous jump threshold and high-frequency energy ratio are written to the equipment operating database in real time, and the equipment operating status data and corresponding working conditions under the current abnormal state are recorded synchronously.

[0017] Furthermore, based on the graded disturbance state discrimination results, and integrating equipment operating status data, fluctuation detection, and high-frequency energy anomaly analysis results, the specific process for acceleration control evaluation is as follows: Real-time reception of drilling machine status identification results, including the instantaneous jump discrimination value, high-frequency energy ratio, and disturbance level of the current window; adaptive acceleration adjustment for drilling machines in slight or abnormal disturbance states; acquisition of historical spindle speeds during normal equipment startup and drilling processes, and calculation of the spindle speed change rate as natural acceleration; acquisition of natural acceleration during each drilling machine slow-start process, and real-time acquisition of abnormal states identified by the instantaneous jump discrimination value and high-frequency energy ratio; identification of startup phases without abnormalities as safe intervals; and statistical analysis and selection of the most stable and stable phases within the safe intervals. The largest natural acceleration is used as the maximum safe acceleration; the normalized instantaneous jump discrimination value and the normalized high-frequency energy ratio are obtained; simultaneously, the difference between the instantaneous jump discrimination value of the current time window and the instantaneous jump discrimination value of the previous time window is calculated to obtain the jump criterion change; the normalized instantaneous jump discrimination value is multiplied by the jump suppression weight factor to obtain the jump suppression term; the normalized high-frequency energy ratio is multiplied by the high-frequency energy suppression weight factor to obtain the high-frequency energy suppression term; the jump criterion change is multiplied by the jump trend weight factor to obtain the jump trend suppression term; the jump suppression term, the high-frequency energy suppression term, and the jump trend suppression term are added together, and a constant is added to obtain the comprehensive anomaly suppression term; the maximum safe acceleration is divided by the comprehensive anomaly suppression term to obtain the anomaly suppression acceleration value.

[0018] Furthermore, based on the acceleration control evaluation results, the specific process of implementing the acceleration control strategy is as follows: The acceleration of the drilling machine under slight and abnormal disturbances is dynamically adjusted according to the abnormal suppression acceleration value to achieve flexible acceleration control. When there is significant fluctuation, i.e., the instantaneous jump judgment value is higher than the fluctuation threshold, the current abnormal suppression acceleration value will automatically decrease, even approaching zero in severe abnormalities, achieving extremely slow start-up and pause. Conversely, when there is no significant fluctuation, the abnormal suppression acceleration value approaches its maximum value, allowing the speed to gradually increase to full speed for normal operation. If the drilling machine pauses due to abnormal fluctuation, it will slowly restart at a low speed when the state recovers, smoothly advancing the drilling process. All acceleration control commands are output to the motor drive system in real time, and linked with protection measures. All abnormal suppression acceleration values ​​and acceleration control measures are written to the equipment operation database in real time.

[0019] Furthermore, the specific process of real-time monitoring of fluctuation detection, high-frequency energy anomaly analysis, and acceleration control evaluation results, implementing anomaly protection decisions, and achieving visualized execution and algorithm parameter optimization is as follows: Throughout the entire operation, instantaneous fluctuation discrimination values, high-frequency energy ratios, anomaly suppression acceleration values, and acceleration control commands are continuously monitored in real time. If the instantaneous fluctuation discrimination value and high-frequency energy ratio both exceed the fluctuation threshold and anomaly threshold three consecutive times, or if multiple jams and strong fluctuations occur, the main circuit is immediately disconnected, switching to protection mode to ensure the safety of the operator and equipment; simultaneously, an audible alarm is activated. The system provides real-time feedback to operators on the current status, causes of anomalies, and specific safety recommendations, and records manual corrections. A real-time safety dashboard visually displays the timeline of abnormal events, disturbance levels, and corresponding indicator trends. Status lights use different colors to distinguish between stable, minor disturbances, and abnormal disturbances. All abnormal events, equipment operating status data, corresponding operating conditions, disturbance levels, acceleration control operations, and manual correction feedback are recorded and archived, building a complete log and anomaly case library. This enables full-process event traceability and optimization of parameters such as instantaneous jump discrimination values, high-frequency energy ratios, and abnormal suppression acceleration values.

[0020] The second aspect of this invention provides an intelligent positioning safety protection device for a handheld drilling machine based on load detection, comprising: a multi-source signal acquisition module for real-time acquisition of equipment operating status data, unifying the timestamps of the equipment operating status data, and storing it in an equipment operating database; a data preprocessing module for preprocessing the equipment operating status data and constructing equipment operating status data segments according to a fixed sliding time window; a dynamic state recognition and discrimination module for real-time detection of the drilling machine's vibration during operation based on the equipment operating status data segments, performing high-frequency energy anomaly analysis, and classifying disturbance states based on the vibration detection and high-frequency energy anomaly analysis results; an intelligent flexible soft-start control module for performing acceleration regulation evaluation based on the graded disturbance state discrimination results, integrating equipment operating status data, vibration detection, and high-frequency energy anomaly analysis results, and implementing acceleration regulation strategies based on the acceleration regulation evaluation results; and a protection decision and visualization execution module for real-time monitoring of vibration detection, high-frequency energy anomaly analysis, and acceleration regulation evaluation results, implementing anomaly protection decisions, and achieving visualization execution and algorithm parameter optimization.

[0021] Beneficial effects

[0022] The present invention has the following beneficial effects:

[0023] (1) This invention integrates instantaneous jump discrimination value and high frequency energy ratio to comprehensively judge drill bit jump and reaction force, effectively distinguishing normal load changes and abnormal disturbances, solving the problem of misjudgment caused by traditional technology relying on a single current signal, and improving the accuracy of abnormal identification.

[0024] (2) The present invention classifies the disturbance state, dynamically adjusts the acceleration of the drilling rig, and implements a flexible start-stop control strategy: when there is a slight disturbance, the rig rotates slightly; when there is a severe disturbance, the rig stops accelerating and the drill bit is fine-tuned, which effectively reduces the risk of drill bit breakage and operator hand injury.

[0025] (3) This invention introduces frequency domain feature analysis, performs fast Fourier transform on the load current signal, identifies abnormal energy distribution in the high-frequency band, and can sense instantaneous impacts caused by conditions such as hard points and rough surfaces, effectively making up for the blind spot of traditional technology in identifying abnormal initial contact states.

[0026] (4) This invention records and archives the entire process of abnormal events, disturbance indicators, acceleration control and manual correction to build an abnormal case library and log. At the same time, it uses a dashboard to visualize the disturbance trend and status warning, supports algorithm parameter optimization, and improves the level of intelligence and long-term adaptability.

[0027] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0028] Figure 1 Here is a flowchart of a method for intelligent positioning and safety protection of handheld drilling machines based on load detection;

[0029] Figure 2 This is a module diagram of an intelligent positioning safety protection device for a handheld drilling machine based on load detection;

[0030] Figure 3 This is a trend chart of flexible acceleration adjustment based on load detection;

[0031] Figure 4 This is a real-time status trend chart of a drilling machine based on load detection. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. As those skilled in the art will understand, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figures 1-4This invention provides a technical solution: a method and device for intelligent positioning and safety protection of a handheld drilling machine based on load detection, comprising the following steps: S1, real-time acquisition of equipment operating status data, unification of the timestamps of the equipment operating status data, and simultaneous storage in the equipment operating database; S2, preprocessing of the equipment operating status data, and constructing equipment operating status data segments according to a fixed sliding time window; S3, real-time detection of the drilling machine's vibration during operation based on the equipment operating status data segments, and high-frequency energy anomaly analysis, and graded disturbance state discrimination based on the vibration detection and high-frequency energy anomaly analysis results; S4, based on the graded disturbance state discrimination results, merging the equipment operating status data, vibration detection, and high-frequency energy anomaly analysis results, performing acceleration control evaluation, and implementing acceleration control strategies based on the acceleration control evaluation results; S5, real-time monitoring of vibration detection, high-frequency energy anomaly analysis, and acceleration control evaluation results, implementing anomaly protection decisions, and achieving visualized execution and algorithm parameter optimization.

[0034] Specifically, the process of real-time acquisition of equipment operating status data, unifying the timestamps of the data, and storing it in the equipment operation database is as follows: A high-precision current sensor is integrated into the main circuit of the drilling machine to collect the load current in real time and record the sampling frequency. The sampling frequency is set to 1kHz or higher to ensure that the sudden changes at the initial contact between the drill bit and the workpiece can be captured, effectively covering dynamic signals such as sudden current disturbances and short-term impacts during the drilling process, thus enhancing the accuracy of abnormal state perception. The spindle speed is synchronously acquired through an encoder for subsequent acceleration-assisted judgment. Changes in spindle speed reflect the motor response state and load changes during drilling, providing important auxiliary basis for determining the drill bit's operating status. Triaxial accelerometers are installed at key locations on the machine body to collect the vibration acceleration of the machine body, helping to capture the axial and radial runout characteristics of the drill bit in real time, providing an important reference dimension for anomaly identification and response decisions. An equipment operation database is constructed, recording the load current, spindle speed, and vibration acceleration as equipment operating status data with a unified timestamp to ensure the time sequence alignment of data from different types of sensors, and storing it in the equipment operation database.

[0035] In this implementation scheme, by integrating a high-precision current sensor, encoder, and triaxial accelerometer into the drilling machine, high-frequency, low-latency, and all-around real-time acquisition of key operational status data such as load current, spindle speed, and vibration acceleration is achieved. A unified timestamp mechanism ensures accurate time-series alignment and synchronous fusion of multi-source data. This not only improves the sensitivity in detecting instantaneous anomalies during the initial contact between the drill bit and the workpiece but also enhances the accuracy and robustness in identifying disturbances during the drilling process. It provides a stable and reliable data foundation for subsequent runout detection, high-frequency energy analysis, acceleration control, and intelligent protection, effectively improving the perception and response capabilities to abnormal states under complex working conditions.

[0036] Specifically, the process of preprocessing equipment operating status data and constructing equipment operating status data segments according to a fixed sliding time window is as follows: The collected equipment operating status data is synchronized to ensure that all signals are analyzed under the same time reference. A unified timestamp mechanism is used to align data collected by different types of sensors on the same time axis, avoiding signal misalignment caused by sampling time differences, thereby improving the timeliness and accuracy of subsequent feature extraction and discrimination analysis. Signal processing techniques using moving average and low-pass filtering are employed to remove high-frequency noise and transient pulse interference from the equipment operating status data, ensuring smooth and continuous signals and effectively suppressing mechanical vibration and electromagnetic interference. Signal distortion caused by non-operational states enhances the stability and robustness of feature extraction. Equipment operating status data is normalized and segmented according to a fixed sliding time window, which is dynamically adjusted based on the specific drilling machine's response speed. This improves the ability to distinguish process perturbations and constructs equipment operating status data segments suitable for batch processing. Abnormal equipment operating status data and data with hardware acquisition errors occurring within the fixed time window are automatically corrected and removed using median filtering and threshold removal strategies. This eliminates sudden strong interference points and sampling spurious values, ensuring input data quality and preventing contamination of the criteria for subsequent state recognition and control.

[0037] In this implementation scheme, a unified timestamp mechanism is used to achieve precise alignment of multi-source sensor data, ensuring signal synchronization and effectively improving the accuracy and real-time performance of feature analysis. Moving average and low-pass filtering are used to eliminate high-frequency noise and transient pulse interference, enhancing signal stability. Normalization and dynamic sliding window segmentation mechanisms improve the standardization of the data structure and its sensitivity to minor perturbations, facilitating subsequent algorithm processing. Simultaneously, median filtering and threshold removal strategies are introduced to automatically clean up abnormal sampling and hardware errors, improving overall data quality and providing a solid foundation for highly reliable state discrimination and control strategies.

[0038] Specifically, based on equipment operating status data segments, the real-time detection process of drill bit jumps during operation is as follows: The load current of the drill bit at the current moment and historical moments is acquired in real time, and a current time series within a sliding time window is constructed to extract the dynamic change characteristics of the current and capture sudden disturbances within a short period. The standard deviation of all load currents collected within the sliding time window is calculated to obtain the current signal standard deviation, which reflects the intensity of current fluctuations in the current period and is used to measure the overall amplitude of current changes. The load current at the current moment t is subtracted from twice the load current at time t-1, and then the load current at time t-2 is added, and the absolute value is taken to obtain the second-order difference of the load current. The second-order difference calculation method can highlight local abrupt changes and enhance the response sensitivity to nonlinear instantaneous disturbances. The second-order difference of the load current is divided by the standard deviation of the load current to obtain the instantaneous jump discrimination value. The dimensionless jump discrimination value can achieve a unified discrimination scale under different load and current reference conditions, possessing good universality and real-time performance, and can serve as a direct criterion for drill bit jumps.

[0039] The specific formula for the instantaneous jump discriminant value is as follows:

[0040] ;

[0041] In the formula, This represents the instantaneous jump discrimination value at the current time t, used to detect sudden changes in the load current signal of the drilling machine in real time, that is, to determine whether the drill bit has experienced abnormal jumping in the early stage of drilling; by performing second-order difference on the actual collected current signal and combining it with window standard deviation normalization, it is determined whether the drill bit has experienced abnormal jumping during startup and positioning, providing a reliable basis for subsequent protection and flexible control. It represents the load current at the current time t, reflects the actual load change of the drilling machine at time t, and is the core physical quantity of the equipment's working status; This represents the load current at time t-1. This represents the load current at time t-2, reflecting the load current at the two most recent sampling points, which facilitates second-order difference operations; It represents the standard deviation of the current signal within the sliding time window, measures the baseline of current fluctuation within the window, and is used to eliminate the scale effects caused by different drilling materials and different operating forces. The second-order difference of the load current is equivalent to the acceleration of the signal and is used to measure the drasticness of the current change. If the second-order difference of the load current is large, it indicates that there is a strong sudden change in the current change, which may indicate that there is an abnormal drill bit jumping problem.

[0042] In this implementation scheme, by constructing a current time series within a sliding time window, instantaneous change features are extracted. Combined with the ratio of standard deviation to second-order difference, sensitive identification of runout behavior during drilling is achieved. It possesses enhanced response capabilities to local abrupt events and maintains consistency of criteria under different load conditions through dimensionless runout discrimination values, improving the real-time performance, universality, and stability of runout identification, and providing a reliable basis for subsequent flexible control and intelligent protection.

[0043] Specifically, the process of high-frequency energy anomaly analysis is as follows: First, acquire the current sampling frequency and simultaneously acquire the load current within a continuous sliding time window to accurately capture current fluctuation characteristics with sufficiently high time-domain resolution, ensuring the frequency accuracy and completeness of the spectrum analysis. Second, use Fast Fourier Transform to transform this set of load currents from the time domain to the frequency domain, obtaining complex spectra corresponding to different frequencies. This step efficiently reveals the frequency domain structure characteristics of the current signal and identifies energy concentration behavior in specific frequency bands. Third, calculate the spectral amplitude for each frequency component of the complex spectrum, i.e., obtain the energy magnitude of each frequency by taking the square root of the sum of the squares of the real and imaginary parts. Fourth, combine the sampling frequency to convert the frequency index to the actual physical frequency, extract the spectral amplitude within the window, and obtain the frequency amplitude at each physical frequency, thus achieving a one-to-one correspondence between frequency domain features and physical frequencies. Fifth, use half of the sampling frequency as the maximum analysis frequency. Sixth, for a batch of load currents sampled within a fixed time window during normal operation, acquire the frequency amplitude of the load current to obtain the full-band energy distribution of the load current, and statistically analyze it to obtain the total cumulative energy distribution, ensuring representativeness while smoothing short-term spectral shifts caused by abnormal interference. Finally, find the full-band energy distribution to achieve the total... The frequency point corresponding to the high-frequency distribution ratio of the cumulative energy distribution is set as the boundary point between high and low frequencies. For example, if the cumulative energy in the frequency range of 0 to 70 reaches 90%, then the boundary point between high and low frequencies is set to 70. This effectively takes into account the energy distribution differences between the low-frequency steady state and the high-frequency abnormal characteristics. The boundary point between high and low frequencies can be periodically updated to adapt to the actual changes of different materials and machine models. The energy integral is calculated in two segments: for the low-frequency segment from 0 to the boundary point between high and low frequencies, the frequency amplitude of each corresponding low-frequency segment is obtained. For each low-frequency point, the frequency amplitude is averaged. The calculation is performed, followed by integration to accurately quantify the energy composition of the stable operating signal in the low-frequency band. A constant of 0.01 is added to the integration result to obtain the low-frequency energy sum. For the high-frequency band from the boundary between high and low frequencies to the maximum analysis frequency, the frequency amplitude of each high-frequency band is obtained. Similarly, for each high-frequency point, the square of the frequency amplitude is integrated to obtain the high-frequency energy sum. The high-frequency energy sum is divided by the low-frequency energy sum to obtain the high-frequency energy ratio, which quantifies the proportion of high-frequency energy in the overall signal energy structure and serves as an important criterion for judging abnormal fluctuations during drilling.

[0044] The specific formula for the high-frequency energy ratio is as follows:

[0045] ;

[0046] In the formula, The high-frequency energy ratio is used to assess the relative proportion of energy distribution in the high-frequency and low-frequency bands of the borehole rig load current signal. It effectively measures the proportion of high-frequency abnormal components in the total energy of the current window and reflects the significance of high-frequency abnormal fluctuations relative to the normal working state within the current window. If the high-frequency energy increases abnormally, the high-frequency energy ratio will be significantly greater than 1, which can be judged as abnormal fluctuations and impacts. This represents the spectral amplitude at high physical frequencies. It represents the spectral amplitude at low physical frequencies and is used to extract the energy distribution of the load current signal at different frequencies. It is the basis of the signal spectrum and is the result of transforming the load current signal into the frequency domain. It can intuitively display the energy distribution at each frequency and identify the physical basis of shock and vibration anomalies. It indicates the boundary between high and low frequencies, used to distinguish the energy range between normal equipment operation and abnormal high-frequency vibrations, control the segmentation of low and high frequency energy, and enable the algorithm to more accurately identify high-frequency anomalies without over-responding to normal low-frequency fluctuations. This indicates the maximum analysis frequency, defining the upper limit of high-frequency integration to ensure it does not exceed the actual sampling bandwidth.

[0047] In this implementation scheme, the load current is accurately mapped from the time domain to the frequency domain using Fast Fourier Transform, systematically extracting the energy characteristics at each physical frequency. This allows for the construction of high-low frequency boundary points and energy integration algorithms, effectively quantifying the energy distribution across different frequency bands. This not only improves the response sensitivity to minute disturbances and fluctuations during the drilling process but also enhances the robustness and universality of abnormal state detection by using the high-frequency energy ratio as a dimensionless indicator. Simultaneously, the dynamically updated high-low frequency boundary mechanism ensures adaptability and cross-material applicability, thus providing solid data support for intelligent diagnostics, flexible control, and safety protection.

[0048] Specifically, based on the results of fluctuation detection and high-frequency energy anomaly analysis, the process for classifying disturbance states is as follows: Real-time comparison of instantaneous fluctuation discrimination values ​​with fluctuation thresholds, and high-frequency energy ratios with anomaly thresholds, to achieve a quantitative assessment of operational stability and disturbance severity during drilling, and to identify the drilling machine's state. When the instantaneous fluctuation discrimination value is less than the fluctuation threshold, and the high-frequency energy ratio is less than the anomaly threshold, it indicates that the drilling machine is currently in a normal, stable, and slow drilling state, and is operating well. Using no-load and light-load conditions to ensure low current and slow speed for initial positioning is beneficial for the drill bit to maintain stable contact with the workpiece surface and reduce initial offset. After positioning, the load and current are increased, and the speed is gradually increased to full speed for normal operation, continuously advancing the drill bit into the workpiece without special intervention. When only one of the instantaneous fluctuation discrimination value or the high-frequency energy ratio is greater than or equal to the corresponding threshold, it is considered that the drilling machine is currently experiencing a slight disturbance. Only flexible adjustments are made without interrupting the operation, and a small-amplitude rotation is performed. This small-amplitude rotation is an angle... The displacement fine-tuning operation avoids the continuous aggravation of abnormalities due to inertia, overcomes minor disturbances, and reduces acceleration increments and shortens control step sizes based on historical equipment operating data, making acceleration adjustment response smoother, reducing the risk of disturbance amplification, and allowing the drill bit to enter the workpiece more smoothly, minimizing disturbances. It then enters the intelligent flexible slow-start control module. When the instantaneous jump judgment value is greater than or equal to the jump threshold, and the high-frequency energy ratio is greater than or equal to the abnormal threshold, it is considered that the drilling machine is currently experiencing abnormal disturbances, triggering protection measures and entering the intelligent flexible slow-start control module: It suspends motor acceleration to prevent further drill bit jumps and the resulting reaction force, reducing the risk of operator injury and equipment damage; after detecting the abnormality, it performs a slight reversal action, appropriately adjusting the drill bit position and repositioning it to reduce the risk of continuous pressure from local hard points; simultaneously, it issues an abnormality warning to the operator; and it writes the instantaneous jump judgment value and high-frequency energy ratio into the equipment operation database in real time, and synchronously records the equipment operating status data and corresponding working conditions under the current abnormal state.

[0049] In this implementation plan, a graded disturbance state discrimination mechanism is constructed by integrating instantaneous jump discrimination values ​​and high-frequency energy ratios, enabling real-time and accurate identification of the dynamic stability of the drilling process. Depending on the disturbance level, differentiated response strategies can be flexibly adopted, ranging from slow, gradual rotation to paused acceleration and self-reversal, ensuring smooth drill bit advance, reducing operational risks, avoiding accidental triggering of protection mechanisms, and improving operational continuity. Simultaneously, key discrimination indicators and operating condition data are archived and stored throughout the entire process, constructing a data closed loop and providing a solid foundation for parameter self-learning and personalized control.

[0050] Specifically, based on the graded disturbance state discrimination results, and integrating equipment operating status data, fluctuation detection, and high-frequency energy anomaly analysis results, the specific process for acceleration control evaluation is as follows: Real-time reception of drilling machine status identification results, including the instantaneous jump discrimination value, high-frequency energy ratio, and disturbance level of the current window, to achieve a closed-loop linkage from status identification to parameter optimization; for drilling machines in slight or abnormal disturbance states, adaptive acceleration adjustment is performed, with dynamic adjustment of the spindle acceleration as the core, to achieve flexible matching between operating rhythm and environmental disturbances; historical data from normal equipment startup and drilling processes are acquired. The spindle speed is measured, and the rate of change of spindle speed is calculated as natural acceleration. Natural acceleration is collected during each slow start-up of the drilling rig. Natural acceleration reflects the equipment's response capability under ideal conditions and can be used to determine whether the current control amplitude is too large or insufficient. Abnormal states identified in real time by instantaneous jump discriminant values ​​and high-frequency energy ratios are acquired, and the start-up phase without abnormalities is identified as a safe range. This safe range is used as the golden sample for minimum disturbance operation of the equipment. The maximum natural acceleration within the safe range is statistically analyzed and selected as the maximum safe acceleration. Normalized instantaneous jump discriminant values ​​and normalized... The high-frequency energy ratio is used to eliminate the influence of different data units on acceleration calculations, ensuring that the calculation standards are unified and comparable. Simultaneously, the difference between the instantaneous jump discriminant value of the current time window and the instantaneous jump discriminant value of the previous time window is calculated to obtain the jump criterion change. This change in the jump criterion is used to measure the speed of the disturbance growth trend and is an important dynamic indicator of abnormal upward trends. The normalized instantaneous jump discriminant value is multiplied by the jump suppression weight factor to obtain the jump suppression term. The normalized high-frequency energy ratio is multiplied by the high-frequency energy suppression weight factor to obtain the high-frequency energy suppression term. The change in the jump criterion is then multiplied by the jump trend... Multiplying the weighting factors yields the jump trend suppression term; the three terms comprehensively characterize the current disturbance state from the perspectives of static amplitude, high-frequency intensity, and trend change, respectively; adding the jump suppression term, high-frequency energy suppression term, and jump trend suppression term, plus a constant 1, yields the comprehensive anomaly suppression term. The addition of the constant 1 ensures that the calculated value is non-zero, facilitating stable operation of subsequent division; dividing the maximum safe acceleration by the comprehensive anomaly suppression term yields the anomaly suppression acceleration value, which serves as the dynamic target acceleration during actual drilling rig operation, guiding the flexible start-up and deceleration control behavior, and effectively preventing anomalies caused by acceleration overshoot and delayed response.

[0051] The specific formula for the abnormal suppression acceleration value is as follows:

[0052] ;

[0053] In the formula, This represents the abnormal acceleration suppression value, used to set the target acceleration of the drilling rig in real time, achieving dynamic and flexible acceleration control. The stronger the abnormal signal, the faster the acceleration decreases. When the abnormal signal is weak, the acceleration is close to the maximum value, achieving efficient propulsion. When the abnormal signal is strong, the acceleration is rapidly suppressed by the terms in the denominator, achieving safety protection. The normalized instantaneous jump discriminant value reflects the intensity of the drill bit being blocked or experiencing abnormal jumping within the current window; It represents the normalized high-frequency energy ratio, which captures the signal characteristics of mechanical shock and abnormal vibration, and serves as an auxiliary indicator of vibration risk. It indicates the change in the jump criterion, which helps determine whether the jump abnormality is worsening and facilitates early suppression of potential risks; It indicates the maximum safe acceleration, reflecting the maximum acceleration that the motor can safely withstand; This represents the jump suppression weight factor, which is used to quantify the frequency and severity of abnormal jump events by statistically analyzing the number of times and duration that the historical instantaneous jump discrimination value exceeds the jump threshold. Based on this, a Bayesian optimization algorithm is used to dynamically adjust the jump suppression weight factor. After each round of adjustment, the merits of the current parameter settings are comprehensively evaluated based on the proportion of anomalies that are effectively suppressed, the false alarm rate, and the duration of anomalies. The optimal jump suppression weight factor is automatically fitted by the Bayesian optimization algorithm, with a value range between 0.1 and 10. This represents the high-frequency energy suppression weight factor. It obtains the high-frequency energy ratio and the peak value of the high-frequency energy ratio during historical operations, as well as each actual jerking anomaly event. Through correlation analysis, it calculates the synchronicity and correlation coefficient between the peak value of the high-frequency energy ratio and the occurrence of the actual jerking event. If the correlation is high, the weight of the high-frequency energy ratio is increased to enhance its influence on acceleration regulation; if the correlation is low, the weight is reduced accordingly, thus obtaining the optimal high-frequency energy suppression weight factor, with a value range between 0.1 and 10. The jump trend weight factor is represented by the change amount of the jump criterion for each window in the historical operation, as well as whether each anomaly was suppressed in a timely and effective manner and the occurrence of false actions. A training dataset is constructed with the goal of maximizing the anomaly suppression rate and minimizing the false action rate. The grid search fitting algorithm is used to evaluate and optimize different jump trend weight factors to obtain the optimal jump trend weight factor, with a value range between 0 and 2.

[0054] The maximum safe acceleration was set to 1.2, the jump suppression weight factor to 1.5, the high-frequency energy suppression weight factor to 1.2, and the jump trend weight factor to 0.8. Under the condition that the instantaneous jump discrimination value, high-frequency energy ratio, and jump criterion changes continuously over time, the abnormal suppression acceleration value at each moment was calculated. The results are shown in Table 1, the abnormal suppression acceleration value data table.

[0055] Table 1. Data on Abnormal Suppression Acceleration Values

[0056]

[0057] like Figure 3 As shown, the load-detection-based flexible acceleration adjustment trend graph provided in this application embodiment illustrates the changing trend of the abnormal suppression acceleration value with time t. As time progresses from 1 to 5, the abnormal suppression acceleration value gradually decreases, and the curve shows a clear decreasing trend, stabilizing at the 5th time. According to Table 1 and... Figure 3 It can be seen that the instantaneous jump discrimination value, high-frequency energy ratio, and jump criterion change all gradually increase over time, while the corresponding abnormal suppression acceleration value shows a downward trend, indicating that an abnormal situation occurred during the drilling machine's operation, and the acceleration was automatically reduced through flexible control. At the first two time points, the decrease was relatively large, indicating that it would be quickly suppressed after detecting a slight abnormality. The decrease slowed down in the later period, indicating that it had entered a protection state and the suppression effect was close to saturation.

[0058] This implementation scheme integrates vibration detection, high-frequency energy anomaly analysis, and equipment operating status data to achieve precise identification and flexible acceleration control of borehole disturbances, constructing a closed-loop control mechanism of state perception, dynamic adjustment, and feedback optimization. It not only adjusts the acceleration target in real time based on the intensity and trend of the disturbance, avoiding equipment damage and operator risks caused by sudden jumps and high-frequency disturbances, but also dynamically sets a safe acceleration upper limit using historical natural acceleration data, improving the rationality and safety of control. It effectively balances operational efficiency and stability, enhancing the borehole rig's adaptability and anomaly prevention capabilities under complex working conditions.

[0059] Specifically, based on the acceleration control evaluation results, the specific process of implementing the acceleration control strategy is as follows: First, the currently calculated abnormal suppression acceleration value is received and analyzed as the target reference value for flexible control in this cycle. Based on the abnormal suppression acceleration value, the acceleration of the drilling machine under slight and abnormal disturbances is dynamically adjusted to achieve flexible acceleration control. The flexible acceleration control strategy allows the acceleration to change in real time with the disturbance state, ensuring the safety and stability of the drilling process. Furthermore, when the jump is significant, i.e., the instantaneous jump judgment value is higher than the jump threshold, the current abnormal suppression acceleration value will automatically decrease, even approaching zero in severe abnormalities, achieving extremely slow start and stop, effectively preventing the drill bit from continuing to advance under severe reaction force and reducing operation. The risk of worker injury and equipment damage is minimized; conversely, when there is no significant fluctuation, the abnormal suppression acceleration value approaches its maximum value, allowing the speed to gradually increase to full speed for normal operation, ensuring maximum drilling efficiency; if the drilling machine is stopped due to abnormal fluctuation, it will be slowly restarted at a low speed when the status is restored, smoothly advancing the drilling process, which helps reduce the risk of mechanical shock and misjudgment during the restart process; all acceleration control commands are output to the motor drive system in real time, and the motor response dynamically adjusts the speed increase curve according to the acceleration control commands to achieve real-time closed-loop control, and links protection measures, including alarms, abnormal prompts and power failure protection logic triggering, to improve the safety level; all abnormal suppression acceleration values ​​and acceleration control measures are written to the equipment operation database in real time.

[0060] In this implementation scheme, the acceleration control strategy integrates abnormal suppression acceleration values ​​to achieve flexible adaptive control of the drilling rig under both minor and abnormal disturbances, effectively improving its dynamic response to drill bit jumps and operational stability. Based on instantaneous jump discrimination values ​​and changes in the high-frequency energy ratio, the spindle acceleration is automatically adjusted, realizing a flexible control mechanism throughout the entire process from slow start-up and pause to recovery, significantly reducing the risk of operator injury and equipment damage. Simultaneously, a real-time linkage protection mechanism ensures accurate response and immediate protection capabilities under abnormal conditions; the entire control data is recorded, guaranteeing the traceability and optimizability of the control strategy, and providing excellent safety, adaptability, and intelligent evolution capabilities.

[0061] Specifically, the process of real-time monitoring of fluctuation detection, high-frequency energy anomaly analysis, and acceleration control evaluation results, implementing anomaly protection decisions, and achieving visualized execution and algorithm parameter optimization is as follows: A complete data monitoring channel is constructed throughout the entire operation. Instantaneous fluctuation discrimination values, high-frequency energy ratios, anomaly suppression acceleration values, and acceleration control commands are continuously monitored in real time throughout the operation, with dynamic status information constantly updated at millisecond sampling cycles to ensure the timeliness and continuity of monitoring. Once the instantaneous fluctuation discrimination value and high-frequency energy ratio are detected to exceed the fluctuation threshold and anomaly threshold three times consecutively, indicating a trend of continuous deterioration of the disturbance, and multiple jams and strong fluctuations occur, the main circuit is immediately disconnected, the energy transmission chain is quickly severed to prevent further mechanical damage, and the system switches to protection mode. In protection mode, the system enters the acceleration freeze, alarm feedback, and manual confirmation process to ensure the safety of the operator and equipment. Simultaneously, an audible alarm is activated. The system includes buzzer prompts and voice broadcasts, providing real-time feedback to operators on the current status, causes of anomalies, and specific safety recommendations, ensuring clarity and high perceptibility of information transmission. It also records manual corrective actions, such as reset attempts, position fine-tuning, and parameter confirmation interventions. A real-time safety dashboard visually displays the timeline of abnormal events, disturbance levels, and corresponding indicator trends, helping operators and administrators quickly understand the evolution of anomalies. Status lights use different colors, such as green, yellow, and red, to distinguish between stable, minor disturbances, and abnormal disturbances. All abnormal events, equipment operating status data, corresponding operating conditions, disturbance levels, acceleration control operations, and feedback from manual corrective actions are recorded and archived, building a complete log and anomaly case library. This enables full-process event traceability and optimization of parameters such as instantaneous jump discrimination values, high-frequency energy ratios, and abnormal suppression acceleration values. Through regular offline analysis and a self-learning mechanism, the adaptability and discrimination accuracy of various parameters are continuously improved.

[0062] like Figure 4 The figure shows a real-time status trend chart of a drilling machine based on load detection provided in this application embodiment. It displays the core visualization images in the real-time safety dashboard, namely the trends of the corresponding instantaneous jump discrimination value, high-frequency energy ratio, and abnormal suppression acceleration value. The horizontal axis in the figure represents time, the left vertical axis represents the abnormal suppression acceleration value, and the right vertical axis represents the instantaneous jump discrimination value and the high-frequency energy ratio. The figure also marks the intervention points in different states with crosses. Green indicates a stable state, yellow indicates a slight disturbance, and red indicates an abnormal disturbance. It not only shows the trend of the indicators but also reflects the correspondence between the instantaneous jump discrimination value, the high-frequency energy ratio, and the abnormal suppression acceleration value, realizing real-time visualization monitoring of the safety status and indicator trends during the drilling process.

[0063] In this implementation plan, by constructing a real-time data monitoring channel throughout the entire process and integrating fluctuation detection, high-frequency energy anomaly analysis, and acceleration control results, accurate identification and graded response to disturbances in the drilling process are achieved. Once significant anomalies are detected continuously, the main circuit is immediately disconnected and the protection mode is switched to ensure operational safety. Abnormal information is fed back to the operator in real time through voice alarms, and in conjunction with status lights and a safety dashboard, intuitive and efficient status visualization is achieved. At the same time, all key parameters, abnormal events, and control operations are archived to form a complete log and case library, supporting subsequent traceability and algorithm parameter optimization. Combined with a self-learning mechanism, the accuracy and adaptability of the judgment are continuously improved, effectively enhancing the intelligent protection capabilities and operational robustness of the drilling equipment.

[0064] Reference Figure 2 As shown, the second aspect of the present invention provides a handheld drilling machine intelligent positioning safety protection device based on load detection, applied to the aforementioned handheld drilling machine intelligent positioning safety protection method based on load detection, comprising: a multi-source signal acquisition module, used to acquire equipment operating status data in real time, unify the timestamp of the equipment operating status data, and store it in the equipment operating database; a data preprocessing module, used to preprocess the equipment operating status data, and construct equipment operating status data segments according to a fixed sliding time window; a dynamic state recognition and discrimination module, used to detect the drilling machine's vibration during operation in real time based on the equipment operating status data segments, and perform high-frequency energy anomaly analysis, and perform graded disturbance state discrimination based on the vibration detection and high-frequency energy anomaly analysis results; an intelligent flexible soft-start control module, used to perform acceleration regulation evaluation based on the graded disturbance state discrimination results, integrate equipment operating status data, vibration detection, and high-frequency energy anomaly analysis results, and implement acceleration regulation strategies based on the acceleration regulation evaluation results; and a protection decision and visualization execution module, used to monitor vibration detection, high-frequency energy anomaly analysis, and acceleration regulation evaluation results in real time, implement anomaly protection decisions, and realize visualization execution and algorithm parameter optimization.

[0065] This implementation scheme achieves efficient perception, anomaly identification, and intelligent control of the drilling rig's operating status through multi-module collaboration. A multi-source signal acquisition and data preprocessing module ensures data quality, a dynamic identification module accurately judges fluctuations and high-frequency anomalies and classifies them into levels, an intelligent flexible start-up module adaptively adjusts acceleration according to the disturbance level, and a protection and visualization module provides automatic anomaly protection and intuitive feedback. Overall, the system offers sensitive response, smooth control, and timely protection, effectively improving the safety and intelligence level of drilling operations.

[0066] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0067] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. As those skilled in the art will understand, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent positioning and safety protection of a handheld drilling machine based on load detection, characterized in that, Includes the following steps: S1 collects equipment operating status data in real time, unifies the timestamp of the equipment operating status data, and stores it in the equipment operating database. S2, perform data preprocessing on the equipment operating status data, and construct equipment operating status data segments according to a fixed sliding time window; S3, based on the equipment operating status data segments, detects the vibration of the drilling machine during operation in real time, performs high-frequency energy anomaly analysis, and performs graded disturbance state discrimination based on the vibration detection and high-frequency energy anomaly analysis results; S4. Based on the results of the graded disturbance state discrimination, the equipment operating status data, the vibration detection and high-frequency energy anomaly analysis results are integrated to conduct acceleration control evaluation, and acceleration control strategy is implemented based on the acceleration control evaluation results. S5 monitors the results of jitter detection, high-frequency energy anomaly analysis and acceleration regulation assessment in real time, implements anomaly protection decisions, and achieves visualized execution and algorithm parameter optimization; The specific process of detecting the drilling machine's vibration during operation in real time based on equipment operating status data segments is as follows: The load current of the drilling machine at the current moment and historical moments is acquired in real time, and the standard deviation of all load currents collected within the sliding time window is calculated to obtain the standard deviation of the current signal. The load current at the current moment t is subtracted from twice the load current at time t-1, and the load current at time t-2 is added, and the absolute value is taken to obtain the second difference of the load current. The second difference of the load current is divided by the standard deviation of the load current to obtain the instantaneous jump discrimination value. The specific process for performing high-frequency energy anomaly analysis is as follows: The current sampling frequency is obtained, and the load current within the continuous sliding time window is also obtained. Using the fast Fourier transform, this set of load currents is transformed from the time domain to the frequency domain to obtain the complex spectrum corresponding to different frequencies. The spectral amplitude of each frequency component of the complex spectrum is calculated, that is, the energy of each frequency is obtained by the sum of the squares of the real and imaginary parts and the square root. Combine the sampling frequency to convert the frequency index into the actual physical frequency, extract the amplitude of each spectrum within the window, and obtain the frequency amplitude at each physical frequency; Half of the sampling frequency is taken as the maximum analysis frequency; for a batch of load currents within a fixed time window of normal operation sampling, the frequency amplitude of the load current is obtained, the full-band energy distribution of the load current is obtained, and the total cumulative energy distribution is obtained by statistics. The frequency point corresponding to the high-frequency distribution ratio of the full-band energy distribution to the total cumulative energy distribution is found and set as the boundary point between high frequency and low frequency. The energy integral is calculated in two segments: For the low-frequency segment from 0 to the boundary between high and low frequencies, the frequency amplitude of each low-frequency segment is obtained. For each low-frequency point, the frequency amplitude is squared and then integrated. The result of the integration is increased by a constant of 0.01 to obtain the low-frequency energy. For the high-frequency range from the boundary between high and low frequencies to the maximum analysis frequency, obtain the frequency amplitude of each high-frequency range. Similarly, for each high-frequency point, integrate the square of the frequency amplitude to obtain the high-frequency energy sum. Divide the high-frequency energy sum by the low-frequency energy sum to obtain the high-frequency energy ratio. The specific process for classifying disturbance states based on the results of jitter detection and high-frequency energy anomaly analysis is as follows: Real-time comparison of instantaneous jump discrimination value with jump threshold, high frequency energy ratio value with abnormal threshold, to identify the status of drilling machine; When the instantaneous jump discrimination value is less than the jump threshold and the high-frequency energy ratio is less than the abnormal threshold, it indicates that the drilling machine is currently in normal and stable slow drilling and is in good operating condition. When only one of the instantaneous jump discrimination value and the high-frequency energy ratio is greater than or equal to the corresponding threshold, it is considered that the drilling machine is currently experiencing a slight disturbance. When the instantaneous jump discrimination value is greater than or equal to the jump threshold and the high-frequency energy ratio is greater than or equal to the abnormal threshold, it is considered that there is an abnormal disturbance in the drilling machine.

2. The intelligent positioning and safety protection method for a handheld drilling machine based on load detection according to claim 1, characterized in that, The specific process of collecting real-time equipment operating status data, unifying the timestamps of the equipment operating status data, and storing it in the equipment operating database is as follows: A high-precision current sensor is integrated into the main circuit of the drilling machine to collect the load current in real time and record the sampling frequency in real time. The spindle speed is collected synchronously through the encoder. A triaxial accelerometer is installed at key positions of the machine body to collect the vibration acceleration of the machine body. A device operation database is constructed, in which load current, spindle speed, and vibration acceleration are recorded as device operation status data, each with a unified timestamp, and stored in the device operation database.

3. The intelligent positioning and safety protection method for a handheld drilling machine based on load detection according to claim 1, characterized in that, The specific process of preprocessing the equipment operating status data and constructing equipment operating status data segments according to a fixed sliding time window is as follows: The collected equipment operation status data is synchronized and aligned to ensure that all signals are analyzed under the same time reference. Signal processing techniques such as moving average and low-pass filtering are used to remove high-frequency noise and transient pulse interference from the equipment operation status data. The equipment operation status data is normalized and segmented according to a fixed sliding time window to construct equipment operation status data segments that are easy to process in batches. For abnormal equipment operation status data and data with hardware acquisition errors that appear within the fixed time window, median filtering and threshold rejection strategies are used for automatic correction and rejection.

4. The intelligent positioning and safety protection method for a handheld drilling machine based on load detection according to claim 1, characterized in that, The specific process for classifying disturbance states based on the results of jitter detection and high-frequency energy anomaly analysis is as follows: When the operation is in good condition, use no-load and light-load conditions to ensure low current and slow speed for initial positioning. After positioning, increase the load and current to gradually increase the speed to full speed for normal operation and continuously advance the drill bit into the workpiece without special intervention. When there is a slight disturbance, only a flexible adjustment is made without interrupting the operation. A small rotation is performed to overcome the slight disturbance. At the same time, based on historical equipment operating status data, the acceleration increment is reduced and the control step length is shortened. Enter the intelligent flexible soft start control module; When an abnormal disturbance occurs, the protection measures are triggered, and the intelligent flexible start-up control module is activated: the acceleration of the motor is suspended. Upon detecting an anomaly, a slight reversal action is performed to adjust the drill bit position and reposition it appropriately; at the same time, an anomaly warning is issued to the operator. The instantaneous jump judgment value and high-frequency energy ratio are written into the equipment operation database in real time, and the equipment operation status data and corresponding working conditions under the current abnormal state are recorded synchronously.

5. The intelligent positioning and safety protection method for a handheld drilling machine based on load detection according to claim 4, characterized in that, The specific process for evaluating acceleration control based on the graded disturbance state discrimination results, integrating equipment operating status data, vibration detection, and high-frequency energy anomaly analysis results, is as follows: The system receives drilling machine status identification results in real time, including the instantaneous jump judgment value of the current window, the high-frequency energy ratio, and the disturbance level; it also performs adaptive acceleration adjustment for drilling machines in slight or abnormal disturbance states. The historical spindle speed during normal equipment startup and drilling process is obtained, and the spindle speed change rate is calculated as the natural acceleration. The natural acceleration during each drilling machine slow start process is collected, and the abnormal state identified by the instantaneous jump discrimination value and high frequency energy ratio is obtained in real time. The startup stage without abnormality is identified as the safe range, and the maximum natural acceleration within the safe range is statistically analyzed and selected as the maximum safe acceleration. Obtain the normalized instantaneous jump discrimination value and the normalized high-frequency energy ratio; at the same time, calculate the difference between the instantaneous jump discrimination value of the current time window and the instantaneous jump discrimination value of the previous time window to obtain the change in jump criterion. Multiply the normalized instantaneous jump discriminant value by the jump suppression weight factor to obtain the jump suppression term; multiply the normalized high-frequency energy ratio by the high-frequency energy suppression weight factor to obtain the high-frequency energy suppression term; multiply the jump criterion change by the jump trend weight factor to obtain the jump trend suppression term. Add the jump suppression term, the high-frequency energy suppression term, and the jump trend suppression term, and then add a constant to obtain the comprehensive anomaly suppression term; Divide the maximum safe acceleration by the comprehensive anomaly suppression term to obtain the anomaly suppression acceleration value.

6. The intelligent positioning and safety protection method for a handheld drilling machine based on load detection according to claim 5, characterized in that, The specific process of implementing the acceleration control strategy based on the acceleration control evaluation results is as follows: Based on the abnormal suppression acceleration value, the acceleration of the drilling machine under slight and abnormal disturbances is dynamically adjusted to achieve flexible acceleration control. When the fluctuation is obvious, that is, when the instantaneous jump judgment value is higher than the fluctuation threshold, the current abnormal suppression acceleration value will automatically decrease, and in the case of severe abnormality, it may even approach zero, to achieve extremely slow start and stop. Conversely, when there is no obvious fluctuation, the abnormal suppression acceleration value is close to the maximum value, so that the speed gradually increases to full speed for normal operation. If the drilling machine stops due to abnormal vibration, it will be restarted slowly at a low speed when the status is restored, and the drilling process will be smoothly advanced. All acceleration control commands are output to the motor drive system in real time, and protection measures are activated in conjunction with them. All abnormal acceleration suppression values ​​and acceleration control measures are written to the equipment operation database in real time.

7. The intelligent positioning and safety protection method for a handheld drilling machine based on load detection according to claim 6, characterized in that, The specific process of real-time monitoring of jitter detection, high-frequency energy anomaly analysis, and acceleration regulation evaluation results, implementing anomaly protection decisions, and achieving visualized execution and algorithm parameter optimization is as follows: Throughout the entire operation, the system continuously monitors instantaneous jump thresholds, high-frequency energy ratios, abnormal suppression acceleration values, and acceleration control commands in real time. If the instantaneous jump threshold and high-frequency energy ratio both exceed the jump threshold and abnormal threshold three times consecutively, or if multiple jams or strong jumps occur, the main circuit is immediately disconnected, and the system switches to protection mode to ensure the safety of the operator and equipment. At the same time, the system provides real-time feedback to the operator via audible alarms, indicating the current status, cause of the abnormality, and specific safety recommendations, and records any manual corrections. A real-time safety dashboard is used to intuitively display the timeline of abnormal events, disturbance levels, and corresponding indicator trends; status lights use different colors to distinguish between stable, slight disturbances, and abnormal disturbances; all abnormal events, equipment operating status data, corresponding operating conditions, disturbance levels, acceleration control operations, and manual correction operation feedback are recorded and archived to build a complete log and abnormal case library, enabling full-process event traceability and optimization of parameters such as instantaneous jump discrimination values, high-frequency energy ratios, and abnormal suppression acceleration values.

8. A handheld drilling machine intelligent positioning safety protection device based on load detection, using the handheld drilling machine intelligent positioning safety protection method based on load detection as described in claim 1, characterized in that, include: The multi-source signal acquisition module is used to collect equipment operating status data in real time, unify the timestamp of the equipment operating status data, and store it in the equipment operating database. The data preprocessing module is used to preprocess the equipment operating status data and construct equipment operating status data segments according to a fixed sliding time window. The dynamic state recognition and discrimination module is used to detect the vibration of the drilling machine in real time based on the equipment operation status data fragments, and to perform high-frequency energy anomaly analysis. Based on the vibration detection and high-frequency energy anomaly analysis results, it performs graded disturbance state discrimination. The intelligent flexible start-up control module is used to evaluate acceleration regulation based on the results of graded disturbance state discrimination, integrate equipment operating status data, vibration detection and high-frequency energy anomaly analysis results, and implement acceleration regulation strategies based on the acceleration regulation evaluation results. The protection decision and visualization execution module is used to monitor the results of fluctuation detection, high-frequency energy anomaly analysis and acceleration regulation evaluation in real time, implement anomaly protection decisions, and realize visualization execution and algorithm parameter optimization.

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