Handheld drilling machine intelligent positioning safety protection method and device based on load detection

Abstract

The invention discloses a handheld drilling machine intelligent positioning safety protection method and device based on load detection, and relates to the technical field of electrical automatic control, and the method comprises the following steps: S1, collecting equipment operation state data in real time, unifying timestamps, and storing the data in an equipment operation database; s2, carrying out data preprocessing, and constructing equipment operation state data fragments according to a fixed sliding time window; s3, jumping of the drilling machine in the operation process is detected in real time, high-frequency energy anomaly analysis is carried out, and therefore grading disturbance state judgment is carried out; s4, performing acceleration regulation and control evaluation, and implementing an acceleration regulation and control strategy; s5, monitoring run-out detection, high-frequency energy anomaly analysis and acceleration regulation and control evaluation results in real time, implementing an anomaly protection decision, and realizing visual execution and algorithm parameter tuning; the problems that the counter-acting force of a drilling machine is increased and the risk of injury is caused due to the fact that abnormal jumping of a drill bit is difficult to detect in time are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical automatic control, and in particular to a load detection-based intelligent positioning safety protection method and device for a handheld drilling machine. Background Art

[0002] Handheld drills are widely used in industrial manufacturing and large-scale infrastructure construction. Initial positioning is difficult, and if the drill bit suddenly jumps during drilling, the operator has no choice but to hold the drill tightly and try again. This not only damages the equipment and risks operator safety, but also poses a significant risk of injury. The high rotational speed can damage the cutter head, resulting in wasted resources.

[0003] For example, the invention patent with publication number CN115167207A discloses a control method and system for long-stroke drilling equipment, including the following steps: S1: installing graphic codes and cameras; S2: installing various sensors; S3: connecting the control board; S4: identifying and retrieving parameters corresponding to the graphic codes through the program; S5: realizing automatic adjustment of the control parameters of the drill bit, the sensors include ZLS-Px speed sensors, temperature sensors, and displacement sensors, a control system for long-stroke drilling equipment, including a CPU module, the interior of the CPU module includes an acquisition unit, and the entire process is coordinated by the CPU module, so that the operator only needs to control the long-stroke drilling equipment through the controller, so that when drilling on different materials, the parameters of each stage of the stroke are controlled and adjusted, so that operators who do not have strong programming skills can also complete the equipment adjustment work.

[0004] For example, the invention patent with announcement number CN119511937B discloses a method and system for generating a workpiece drilling path based on artificial intelligence. The method includes: obtaining a minimum action set based on the hole position information to be processed and the drill bit information on the drill kit; sorting the drilling actions in the minimum action set using an ant colony algorithm to obtain a first sorting result; sorting the drilling actions in the minimum action set 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 shorter 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. The use of the workpiece drilling path generation method based on artificial intelligence of the present invention can greatly improve the efficiency of workpiece drilling and greatly reduce the energy consumption of drilling equipment.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: Existing handheld drills often rely on single signals, such as current or torque, to determine load arrival and abnormal conditions. This makes it difficult to accurately distinguish normal load changes from the sudden reaction force of a runout, leading to false alarms and difficulty identifying abnormal risks. Furthermore, traditional technology is insensitive to abnormalities such as the instantaneous impact and high-frequency vibrations caused by the drill bit's initial contact with the workpiece, making it difficult to promptly detect runout caused by hard spots and surface roughness, resulting in delayed recognition and missed detections.

[0006] Therefore, in response to the above problems, there is an urgent need for a handheld drilling machine intelligent positioning safety protection method and device based on load detection. Summary of the Invention

[0007] Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a handheld drilling machine intelligent positioning safety protection method and device based on load detection, which solves the problem that it is difficult to detect abnormal drill bit vibration in time, resulting in increased reaction force of the drilling machine and causing injury risks.

[0008] Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a handheld drilling machine intelligent positioning safety protection method based on load detection, comprising the following steps: S1, real-time collection of equipment operation status data, and unification of the timestamps of the equipment operation status data, and storage in the equipment operation database at the same time; S2, data preprocessing of the equipment operation status data, and construction of equipment operation status data segments according to fixed sliding time windows; S3, based on the equipment operation status data segments, real-time detection of the drilling machine's vibration during operation, and high-frequency energy anomaly analysis, and hierarchical disturbance state judgment based on the vibration detection and high-frequency energy anomaly analysis results; S4, based on the hierarchical disturbance state judgment results, the equipment operation status data, vibration detection and high-frequency energy anomaly analysis results are integrated to perform acceleration control evaluation, and according to the acceleration control evaluation results, the acceleration control strategy is implemented; S5, real-time monitoring of the vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation results, implementation of abnormal protection decisions, and realization of visual execution and algorithm parameter tuning.

[0009] Furthermore, the specific process of collecting equipment operation status data in real time, unifying the timestamp of the equipment operation status data, and storing it in the equipment operation database is as follows: integrating a high-precision current sensor in the main circuit of the drilling machine, collecting the load current in real time, and recording the sampling frequency in real time, synchronously collecting the spindle speed through the encoder, installing a three-axis accelerometer at the key position of the machine body, and collecting the vibration acceleration of the machine body; building an equipment operation database, recording the load current, spindle speed, and vibration acceleration as equipment operation status data, and with a unified timestamp, storing them in the equipment operation database.

[0010] Furthermore, the equipment operation status data is preprocessed, and the specific process of constructing equipment operation 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 each signal is analyzed under the same time reference; the signal processing technology of sliding average and low-pass filtering is used to remove high-frequency noise and instantaneous pulse interference in 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 convenient for batch processing; for abnormal equipment operation status data and data with hardware collection errors that appear within the fixed time window, median filtering and threshold elimination strategies are used to automatically correct and eliminate them.

[0011] Furthermore, based on the equipment operation status data fragments, the specific process of real-time detection of the drilling machine's jump during operation is as follows: real-time acquisition of the drilling machine's load current at the current moment and the historical moment, and calculation of the standard deviation of all load currents collected within the sliding time window to obtain the current signal standard deviation; subtract twice the load current at the moment t from the load current at the current moment t, and add the load current at the moment t-2 and take the absolute value to obtain the second-order difference of the load current; divide the second-order difference of the load current by the load current standard deviation to obtain the instantaneous jump judgment value.

[0012] Furthermore, the specific process of high-frequency energy anomaly analysis is as follows: obtain the current sampling frequency, and at the same time obtain the load current in the continuous sliding time window, use the fast Fourier transform to convert this group of load currents from the time domain to the frequency domain, and obtain the complex spectrum corresponding to different frequencies, and calculate the spectrum amplitude for the frequency component of each complex spectrum, that is, obtain the energy of each frequency by the square sum of the real part and the imaginary part and the square root method; convert the frequency index into the actual physical frequency in combination with the sampling frequency, extract the spectrum amplitude in the window, and obtain the frequency amplitude at each physical frequency; take half of the sampling frequency as the maximum analysis frequency; for a batch of load currents in a fixed time window of normal working sampling, obtain the frequency amplitude of the load current, obtain the full-band energy distribution of the load current, and perform The cumulative distribution of total energy is obtained by statistics, and the frequency point corresponding to the high-frequency distribution ratio of the energy distribution of the entire frequency band reaching the cumulative distribution of total energy is found, and set as the dividing point between high frequency and low frequency; the energy integral is calculated in two sections: for the low-frequency section from 0 to the dividing point between high frequency and low frequency, the frequency amplitude of each corresponding low-frequency section is obtained, and for each low-frequency point, the frequency amplitude is squared and then integrated, and the integral result is added with a constant 0.01 to obtain the low-frequency energy sum; for the high-frequency section from the dividing point between high frequency and low frequency to the maximum analysis frequency, the frequency amplitude of each corresponding high-frequency section is obtained, and 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.

[0013] Furthermore, based on the results of the beat detection and high-frequency energy anomaly analysis, the specific process of graded disturbance state discrimination is as follows: real-time comparison of the instantaneous jump discrimination value and the jump threshold, the high-frequency energy ratio and the abnormal threshold to identify the state of the 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 means that the drilling machine is currently in a normal, stable and slow drilling state, and is in good operating condition. No-load and light load are used to ensure low current and slow speed for initial positioning. After positioning, the load is increased while also increasing the current, and the speed is gradually increased to full speed for normal operation, continuously pushing the drill bit into the workpiece without special intervention; when only one indicator among 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 currently has For slight disturbances, only flexible adjustments are made without interrupting the operation, and slight rotations are performed to overcome slight disturbances. At the same time, based on the historical equipment operation status data, the acceleration increment is reduced and the control step length is shortened; the intelligent flexible slow-start control module is entered; 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 currently has an abnormal disturbance, triggering the protection measures and entering the intelligent flexible slow-start control module: pausing the acceleration of the motor; and after detecting the abnormality, performing a slight reversal action, appropriately adjusting the drill position and repositioning; at the same time, issuing an abnormality warning to the operator; writing the instantaneous jump judgment value and the high-frequency energy ratio into the equipment operation database in real time, and synchronously recording the equipment operation status data and the corresponding working conditions under the current abnormal state.

[0014] Furthermore, according to the results of graded disturbance state discrimination, the specific process of acceleration control evaluation is carried out by integrating the equipment operation state data, jump detection and high-frequency energy anomaly analysis results as follows: receiving the drilling machine state recognition results in real time, including the instantaneous jump discrimination value, high-frequency energy ratio and disturbance level of the current window; performing adaptive acceleration adjustment on the drilling machine in the state of slight disturbance and abnormal disturbance; obtaining the historical spindle speed during the normal startup and drilling process of the equipment, and calculating the spindle speed change rate as the natural acceleration, collecting the natural acceleration during each slow start of the drilling machine, and obtaining the abnormal state identified by the instantaneous jump discrimination value and high-frequency energy ratio in real time, identifying the startup stage without abnormality as the safe interval, and counting and screening the most abnormal states in the safe interval. The largest natural acceleration is used as the maximum safe acceleration; the normalized instantaneous jump judgment value and the normalized high-frequency energy ratio are obtained; at the same time, the difference between the instantaneous jump judgment value of the current time window and the instantaneous jump judgment value of the previous time window is calculated to obtain the jump judgment criterion change; the normalized instantaneous jump judgment 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 judgment 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, and then a constant of one is added to obtain the comprehensive abnormal suppression term; the maximum safe acceleration is divided by the comprehensive abnormal suppression term to obtain the abnormal suppression acceleration value.

[0015] Furthermore, according to the acceleration control evaluation results, the specific process of implementing the acceleration control strategy is as follows: according to the abnormal suppression acceleration value, the acceleration of the drilling machine in the state of slight disturbance and abnormal disturbance is dynamically adjusted to realize flexible acceleration control; and when the jump is obvious, that is, the instantaneous jump judgment value is higher than the jump threshold, the current abnormal suppression acceleration value will automatically decrease, and even close to zero in severe abnormalities, realizing extremely slow start and pause; on the contrary, when there is no obvious jump, the abnormal suppression acceleration value is close to the maximum value, realizing the speed gradually increasing to full speed for normal operation; if the drilling machine is paused due to abnormal jump, the equipment will be slowly restarted at a low speed when the state is restored, and the drilling process will be smoothly promoted; all acceleration control instructions are output to the motor drive system in real time, and the protection measures are linked; all abnormal suppression acceleration values ​​and acceleration control measures are written into the equipment operation database in real time.

[0016] Furthermore, the specific process of real-time monitoring of the results of vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation, implementation of abnormal protection decisions, and realization of visual execution and algorithm parameter tuning is as follows: continuous real-time monitoring of the instantaneous jump judgment value, high-frequency energy ratio, abnormal suppression acceleration value and acceleration control instruction throughout the operation; once it is detected that the instantaneous jump judgment value and the high-frequency energy ratio exceed the jump threshold and the abnormal threshold for three consecutive times, and when multiple jams and strong jumps occur, the main circuit is immediately disconnected and switched to protection mode to ensure the safety of the operator and equipment; at the same time, a sound alarm is sounded. The system provides real-time feedback on the current status, causes of abnormalities, and specific safety recommendations to the operator, and records manual corrective actions. 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, slightly disturbed, and abnormal disturbances. All abnormal events, equipment operating status data, corresponding working conditions, disturbance levels, acceleration control operations, and manual corrective action feedback are recorded and archived to build a complete log and abnormal case library, enabling full-process event tracing and optimization of parameters such as instantaneous jump discrimination value, high-frequency energy ratio, and abnormal suppression acceleration value.

[0017] The second aspect of the present invention provides a handheld drilling machine intelligent positioning safety protection device based on load detection, including: a multi-source signal acquisition module, which is used to collect equipment operation status data in real time, unify the timestamp of the equipment operation status data, and store it in the equipment operation database at the same time; a data preprocessing module, which is used to perform data preprocessing on the equipment operation status data, and construct equipment operation status data segments according to a fixed sliding time window; a dynamic state recognition and judgment module, which is used to detect the vibration of the drilling machine during operation in real time according to the equipment operation status data segments, and perform high-frequency energy anomaly analysis, and perform graded disturbance state judgment based on the results of the vibration detection and high-frequency energy anomaly analysis; an intelligent flexible soft-start control module, which is used to perform acceleration control evaluation based on the graded disturbance state judgment results, integrate the equipment operation status data, vibration detection and high-frequency energy anomaly analysis results, and implement acceleration control strategies based on the acceleration control evaluation results; a protection decision and visualization execution module, which is used to monitor the results of vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation in real time, implement abnormal protection decisions, and realize visualization execution and algorithm parameter tuning.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) The present invention integrates the instantaneous jump discrimination value and the high-frequency energy ratio to comprehensively judge the drill bit jump and reaction force, which can effectively distinguish normal load changes from abnormal disturbances, solve the misjudgment problem caused by traditional technology relying on a single current signal, and improve the accuracy of abnormal identification.

[0019] (2) The present invention classifies the disturbance state, dynamically adjusts the acceleration of the drill rig, and implements a flexible start-stop control strategy: slight rotation in the case of slight disturbance, and suspension of acceleration and fine-tuning of the drill bit in the case of severe disturbance, effectively reducing the risk of drill bit breakage and operator hand injury.

[0020] (3) The present invention introduces frequency domain feature analysis to perform fast Fourier transform on the load current signal, thereby identifying abnormal energy distribution in the high-frequency band. It can sense instantaneous impacts caused by conditions such as hard points and rough surfaces, and effectively make up for the blind spot of traditional technology in identifying abnormal initial contact states.

[0021] (4) The present invention records and archives the entire process of abnormal events, disturbance indicators, acceleration control and manual correction, builds an abnormal case library and log, and uses a dashboard to visualize disturbance trends and status warnings, supports algorithm parameter optimization, and improves the level of intelligence and long-term adaptability.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a handheld drilling machine intelligent positioning safety protection method based on load detection; Figure 2 This is a module diagram of the intelligent positioning safety protection device for a handheld drilling machine based on load detection; Figure 3 This is a trend diagram of flexible acceleration adjustment based on load detection; Figure 4 This is a real-time status trend diagram of the drilling machine based on load detection. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. As those skilled in the art will understand, the described embodiments are only part of the embodiments of the present invention, not 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0025] See also Figures 1-4, an embodiment of the present invention provides a technical solution: a handheld drilling machine intelligent positioning safety protection method and device based on load detection, comprising the following steps: S1, real-time collection of equipment operation status data, and unification of the timestamps of the equipment operation status data, and storage in the equipment operation database at the same time; S2, data preprocessing of the equipment operation status data, and construction of equipment operation status data segments according to fixed sliding time windows; S3, real-time detection of the drilling machine's vibration during operation based on the equipment operation status data segments, and high-frequency energy anomaly analysis, and hierarchical disturbance state discrimination based on the vibration detection and high-frequency energy anomaly analysis results; S4, based on the hierarchical disturbance state discrimination results, the equipment operation status data, vibration detection and high-frequency energy anomaly analysis results are integrated to perform acceleration control evaluation, and according to the acceleration control evaluation results, an acceleration control strategy is implemented; S5, real-time monitoring of the vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation results, implementation of abnormal protection decisions, and realization of visual execution and algorithm parameter tuning.

[0026] Specifically, the process of collecting equipment operating status data in real time, unifying the timestamp of the equipment operating status 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 in real time. The sampling frequency is set to 1kHz or above to ensure that the sudden changes in details when the drill bit initially contacts the workpiece can be captured, effectively covering dynamic signals such as sudden current disturbances and short-term impacts during the drilling process, and enhancing the accuracy of abnormal state perception; the spindle speed is synchronously collected through an encoder for subsequent acceleration-assisted judgment. At the same time, the spindle speed change can reflect the motor response state and the load change during the drilling process, providing an important auxiliary basis for determining the drill bit's operating status; a three-axis accelerometer is installed at a key position on the machine body to collect the vibration acceleration of the machine body, which helps to capture the axial and radial runout characteristics of the drill bit in real time, providing an important reference dimension for abnormality identification and response decision-making; a device operation database is constructed, and the load current, spindle speed, and vibration acceleration are recorded as equipment operating status data with a unified timestamp to ensure the time sequence alignment of data from different types of sensors, and then stored in the equipment operation database.

[0027] In this implementation, by integrating high-precision current sensors, encoders, and triaxial accelerometers into the drilling machine, high-frequency, low-latency, and all-encompassing real-time acquisition of key operating status data such as load current, spindle speed, and vibration acceleration is achieved. A unified timestamp mechanism ensures precise timing alignment and synchronous fusion of multi-source data. This not only enhances the ability to sensitively capture transient anomalies during initial contact between the drill bit and the workpiece, but also enhances the accuracy and robustness of identifying disturbances during the drilling process. This provides a stable and reliable data foundation for subsequent runout detection, high-frequency energy analysis, acceleration control, and intelligent protection, effectively improving the ability to perceive and respond to abnormal conditions under complex working conditions.

[0028] Specifically, the equipment operation status data is preprocessed, and the specific process of constructing the equipment operation status data fragment according to the fixed sliding time window is as follows: the collected equipment operation status data is synchronously aligned to ensure that each signal is analyzed under the same time reference, and the data collected by different types of sensors are aligned on the same time axis through a unified timestamp mechanism to avoid signal misalignment caused by sampling time difference, thereby improving the timeliness and accuracy of subsequent feature extraction and discriminant analysis; the signal processing technology of sliding average and low-pass filtering is used to remove high-frequency noise and instantaneous pulse interference in the equipment operation status data, ensure the smoothness and continuity of the signal, and effectively suppress mechanical vibration and electromagnetic interference. Signal distortion caused by non-working state is eliminated to enhance the stability and robustness of feature extraction; the equipment operation status data is normalized and segmented according to a fixed sliding time window. The fixed sliding time window is dynamically adjusted according to the specific response speed of the drilling machine to improve the resolution of process perturbations and 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 a fixed time window, median filtering and threshold elimination strategies are used to automatically correct and eliminate them, thereby eliminating sudden strong interference points and sampling pseudo-values, ensuring the quality of input data, and avoiding contamination of the judgment basis of subsequent state identification and regulation links.

[0029] In this implementation, 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. Sliding average and low-pass filtering are used to eliminate high-frequency noise and instantaneous pulse interference, thereby enhancing signal smoothness. Normalization processing and dynamic sliding window segmentation mechanisms improve the standardization of data structures and their sensitivity to perturbation changes, facilitating subsequent algorithm processing. At the same time, median filtering and threshold rejection strategies are introduced to automatically clean abnormal sampling and hardware errors, thereby improving overall data quality and providing a solid foundation for high-reliability state identification and control strategies.

[0030] Specifically, based on the equipment operation status data fragments, the specific process of real-time detection of drill bit jump during operation is as follows: the load current of the drill bit at the current moment and the historical moment is obtained in real time, and the current time series within the sliding time window is constructed to extract the dynamic change characteristics of the current and capture the sudden disturbance behavior in a short time; the standard deviation of all load currents collected in the sliding time window is calculated to obtain the current signal standard deviation. The current signal standard deviation reflects the intensity of current fluctuation in the current period and is used to measure the overall amplitude level of current change; the load current at the current moment t is subtracted from twice the load current at the moment t-1, and the absolute value is added to the load current at the moment t-2 to obtain the second-order difference of the load current. The second-order difference calculation method can highlight local mutation behavior and enhance the response sensitivity to nonlinear transient disturbances; the instantaneous jump discrimination value is obtained by dividing the second-order difference of the load current by the load current standard deviation. The dimensionless jump discrimination value can achieve a unified discrimination scale under different load and current reference conditions, has good universality and real-time performance, and can be used as a direct criterion for drill bit jump.

[0031] Among them, the specific formula of the instantaneous jump judgment value is: ; Where, The instantaneous jump discrimination value at the current moment t is used to detect sudden changes in the drilling machine's load current signal in real time, that is, to determine whether the drill bit has experienced abnormal jumps in the early stages of drilling. By taking the second-order difference of the actual collected current signal and combining it with window standard deviation normalization, it can be determined whether the drill bit has experienced abnormal jumps during startup and positioning, providing a reliable basis for subsequent protection and flexible control. Indicates the load current at the current moment t, reflecting the actual load change of the drilling machine at the moment t, and is the core physical quantity of the equipment's working status; represents the load current at time t-1, Indicates the load current at time t-2, reflecting the load current at the two most recent sampling points, which is convenient for second-order difference calculation; Indicates 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; It represents the second-order differential of the load current, which is equivalent to the acceleration of the signal and is used to measure the severity of the current change. If the second-order differential of the load current is large, it means that the current change has a strong mutation, that is, there may be an abnormal drill bit vibration problem.

[0032] This implementation achieves sensitive identification of runout during drilling by constructing a current time series within a sliding time window, extracting transient variation characteristics, and calculating the ratio of standard deviation to second-order difference. This approach offers enhanced responsiveness to localized sudden changes, and through dimensionless jump discriminant values, maintains consistency across different load conditions. This improves the real-time, universal, and stable nature of runout identification, providing a reliable basis for subsequent flexible control and intelligent protection.

[0033] Specifically, the specific process of high-frequency energy anomaly analysis is as follows: obtain the current sampling frequency and the load current in the continuous sliding time window at the same time, accurately capture the current fluctuation characteristics with a sufficiently high time domain resolution, and ensure the frequency accuracy and integrity of the spectrum analysis; use the fast Fourier transform to convert this group of load currents from the time domain to the frequency domain to obtain the complex spectrum corresponding to different frequencies. This step can effectively reveal the frequency domain structural characteristics of the current signal and identify the energy concentration behavior of a specific frequency band; calculate the spectrum amplitude for each frequency component of the complex spectrum, that is, obtain the energy of each frequency by the square sum of the real part and the imaginary part and the square root method; convert the frequency index into the actual physical frequency in combination with the sampling frequency, extract the spectrum amplitude of each spectrum in the window, and obtain the frequency amplitude at each physical frequency, thereby realizing a one-to-one correspondence between the frequency domain characteristics and the physical frequency; take half of the sampling frequency as the maximum analysis frequency; for a batch of load currents in a fixed time window sampled for normal operation, obtain the frequency amplitude of the load current, obtain the full-band energy distribution of the load current, and perform statistics to obtain the total energy cumulative distribution, while ensuring representativeness, smoothing the short-term spectrum offset caused by abnormal interference; find the full-band energy distribution that reaches the total The frequency point corresponding to the high-frequency distribution ratio of the cumulative energy distribution is set as the dividing point between high frequency and low frequency. For example, if the cumulative energy in the frequency range of 0 to 70 reaches 90%, the dividing point between high frequency and low frequency is set to 70, which effectively takes into account the energy distribution difference between the low-frequency stable state and the high-frequency abnormal characteristics. The dividing point between high frequency and low frequency can be updated regularly to adapt to the actual changes of different materials and different models. The energy integral is calculated in two sections: for the low-frequency section from 0 to the dividing point between high frequency and low frequency, the frequency amplitude of each low-frequency section is obtained, and for each low-frequency point, the frequency amplitude is averaged. The square calculation is performed, and then the integration operation is performed to accurately quantify the energy composition of the stable operation signal in the low-frequency band. The constant 0.01 is added to the integration operation result to obtain the low-frequency energy sum; for the high-frequency segment from the dividing 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, which quantifies the proportion of high-frequency energy in the overall signal energy structure and serves as an important criterion for the occurrence of abnormal vibration during the drilling process.

[0034] Among them, the specific formula of high-frequency energy ratio is: ; Where, Represents the high-frequency energy ratio, which is used to evaluate the relative proportion of the energy distribution of the drilling machine load current signal in the high-frequency band and the low-frequency band. It effectively measures the proportion of high-frequency abnormal components in the total energy of the current window and reflects the significance of the high-frequency abnormal fluctuation in the current window relative to the normal working state. If the high-frequency energy increases abnormally, the high-frequency energy ratio will be significantly greater than 1, which can be judged as abnormal beating and impact. represents the spectrum amplitude at high physical frequency, Indicates the spectrum amplitude at low physical frequencies, 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 of each frequency and identify the physical basis of shock and beat anomalies. Indicates the dividing point between high and low frequencies, used to distinguish the energy range of normal equipment operation from abnormal high-frequency vibration, and control the division of low and high-frequency energy, so that the algorithm can more accurately identify high-frequency anomalies without over-responding to normal low-frequency fluctuations; Indicates the maximum analysis frequency and defines the upper limit of high-frequency integration to ensure that it does not exceed the actual sampling bandwidth.

[0035] In this implementation, a fast Fourier transform (FFT) is used to precisely map the load current from the time domain to the frequency domain. The energy characteristics at each physical frequency are systematically extracted, and a high- and low-frequency demarcation point and energy integration algorithm are constructed to effectively quantify the energy distribution in different frequency bands. This not only improves the sensitivity to small perturbations and jitters during the drilling process, but also enhances the robustness and universality of abnormal state identification by using the high-frequency energy ratio as a dimensionless indicator. Furthermore, the dynamically updated high- and low-frequency demarcation mechanism ensures adaptability and cross-material applicability, providing solid data support for intelligent diagnosis, flexible control, and safety protection.

[0036] Specifically, according to the results of the vibration detection and high-frequency energy anomaly analysis, the specific process of graded disturbance state judgment is as follows: real-time comparison of the instantaneous jump judgment value and the jump threshold, the high-frequency energy ratio and the abnormal threshold, so as to achieve a quantitative assessment of the operation stability and the severity of the disturbance during the drilling process, and perform drilling machine state identification; when the instantaneous jump judgment value is less than the jump threshold, and the high-frequency energy ratio is less than the abnormal threshold, it means that the drilling machine is currently in a normal, stable and slow drilling state, and the operation state is good. The initial positioning is carried out by using no-load and light load to ensure low current and slow speed, which is conducive to the steady-state fit of the drill bit to the workpiece surface and reduce the initial offset. After positioning, the load is increased while the current is also increased, and the speed is gradually increased to full speed for normal operation, and the drill bit is continuously pushed into the workpiece without special intervention; when only one indicator among the instantaneous jump judgment value and the high-frequency energy ratio is greater than or equal to the corresponding threshold, it is considered that the drilling machine currently has a slight disturbance, and only flexible adjustments are made without interrupting the operation, and a slight rotation is performed. The slight rotation is angular Displacement fine-tuning operation can avoid inertia from continuously aggravating abnormalities and overcome slight disturbances. At the same time, based on historical equipment operation status data, the acceleration increment is reduced and the control step length is shortened to make the acceleration adjustment response smoother, reduce the risk of disturbance amplification, and allow the drill bit to enter the workpiece more smoothly, minimizing disturbances; enter 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 currently has an abnormal disturbance, triggering protection measures and entering the intelligent flexible slow-start control module: suspending the acceleration of the motor to prevent the drill bit from further jumping and causing reaction force, reducing the risk of operator injury and equipment damage; and after detecting the abnormality, performing a slight reversal action, appropriately adjusting the drill bit position and repositioning, reducing the risk of continuous pressure on local hard points; at the same time, issuing an abnormal warning to the operator; writing the instantaneous jump judgment value and high-frequency energy ratio into the equipment operation database in real time, and synchronously recording the equipment operation status data and corresponding working conditions under the current abnormal state.

[0037] This implementation integrates the instantaneous jump discrimination value with the high-frequency energy ratio to establish a hierarchical disturbance state discrimination mechanism, enabling real-time and accurate identification of the dynamic stability of the drilling process. Based on the disturbance level, differentiated response strategies can be flexibly adopted, ranging from slow release with slight rotation to pause acceleration and reverse self-release. This ensures smooth drill bit advancement, reduces operational risks, avoids false triggering of protection, and improves operational continuity. Furthermore, key discrimination indicators and operating condition data are archived and stored throughout the entire process, creating a closed data loop and providing a solid foundation for parameter self-learning and personalized control.

[0038] Specifically, according to the results of graded disturbance state identification, the specific process of acceleration control evaluation is carried out by integrating the equipment operation state data, jump detection and high-frequency energy anomaly analysis results: receiving the drilling machine state identification results in real time, including the instantaneous jump judgment value of the current window, the high-frequency energy ratio and the disturbance level, to realize the linkage closed loop from state identification to parameter tuning; for the drilling machine in the state of slight disturbance and abnormal disturbance, adaptive acceleration adjustment is carried out, with dynamic adjustment of the spindle acceleration as the core, to realize the flexible matching of the operation rhythm and the environmental disturbance; obtaining the historical main acceleration data during the normal startup of the equipment and the drilling process The spindle speed is calculated and the spindle speed change rate is used as the natural acceleration. The natural acceleration of each drilling machine slow start process is collected. The natural acceleration reflects the response ability of the equipment under ideal conditions and can be used to judge whether the current control amplitude is too large or insufficient. The abnormal state identified by the instantaneous jump judgment value and the high-frequency energy ratio is obtained in real time. The startup phase without abnormality is identified as the safe interval. The safe interval is used as the golden sample for the minimum disturbance operation of the equipment. The maximum natural acceleration in the safe interval is counted and selected as the maximum safe acceleration. The normalized instantaneous jump judgment value and the normalized The high-frequency energy ratio is used to eliminate the influence of different data dimensions on the acceleration calculation and ensure that the calculation standards are unified and comparable. At the same time, the difference between the instantaneous jump judgment value of the current time window and the instantaneous jump judgment value of the previous time window is calculated to obtain the jump judgment change. The jump judgment change is used to measure the speed of the disturbance growth trend and is an important dynamic indicator of the abnormal rising trend. The normalized instantaneous jump judgment 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 judgment change is multiplied by the jump trend. The weight factors are multiplied to obtain 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; the jump suppression term, high-frequency energy suppression term and jump trend suppression term are added together, and then a constant of one is added to obtain the comprehensive abnormality suppression term. The addition of constant one ensures that the calculated value is non-zero, which facilitates stable operation of subsequent division; the maximum safe acceleration is divided by the comprehensive abnormality suppression term to obtain the abnormality suppression acceleration value. This value is used as the dynamic target acceleration in the actual operation of the drilling machine to guide the flexible slow start and deceleration control behavior, effectively preventing abnormalities caused by acceleration overshoot and delayed response.

[0039] The specific formula for abnormal suppression acceleration value is: ; Where, Indicates the abnormal suppression acceleration value, which is used to set the target acceleration of the drilling machine in real time to achieve dynamic and flexible acceleration control. The stronger the abnormal signal, the faster the acceleration decreases. When the abnormal signal is weak, the acceleration approaches the maximum value, achieving efficient propulsion. When the abnormal signal is strong, the acceleration is quickly suppressed by the terms in the denominator to achieve safety protection. It represents the normalized instantaneous jump discrimination value, reflecting the intensity of the drill bit being blocked or jumping abnormally in the current window; It represents the normalized high-frequency energy ratio, captures the signal characteristics of mechanical shock and abnormal vibration, and serves as an auxiliary indicator of vibration risk; Indicates the change in the jump criterion, judging whether the jump abnormality is getting worse, so as to suppress potential risks in advance; Indicates the maximum safe acceleration, reflecting the maximum acceleration that the motor can safely withstand; The jump suppression weight factor is a statistically analyzed number of times and durations when the historical instantaneous jump discrimination value exceeds the jump threshold. This is used to quantify the frequency and severity of abnormal jump events. Based on this, the Bayesian optimization algorithm is used to dynamically adjust the jump suppression weight factor. After each round of adjustment, the current parameter settings are comprehensively evaluated based on the proportion of anomalies effectively suppressed, the false alarm rate, and the duration of the anomaly. The Bayesian optimization algorithm is then used to automatically fit the optimal jump suppression weight factor, which ranges from 0.1 to 10. Represents the high-frequency energy suppression weight factor. The high-frequency energy ratio and the peak value of the high-frequency energy ratio in the historical operation process, as well as each actual abnormal beating event, are obtained. Through correlation analysis, the synchronization and correlation coefficient of the high-frequency energy ratio peak and the actual beating event are calculated. If the correlation between the two is high, the high-frequency energy ratio weight is increased to enhance its influence on acceleration regulation; if the correlation is low, the weight is reduced accordingly, thereby obtaining the optimal high-frequency energy suppression weight factor, which ranges from 0.1 to 10. It represents the jump trend weight factor. The change in jump criteria for each window in historical operations, as well as whether each anomaly is effectively suppressed in a timely manner and whether malfunctions occur, are collected. A training data set is constructed. With the goal of maximizing the anomaly suppression rate and minimizing the malfunction rate, a grid search fitting algorithm is used to evaluate and optimize different jump trend weight factors to obtain the optimal jump trend weight factor, which ranges from 0 to 2.

[0040] The maximum safe acceleration is set to 1.2, the jump suppression weight factor is set to 1.5, the high-frequency energy suppression weight factor is set to 1.2, and the jump trend weight factor is set to 0.8. As the instantaneous jump discrimination value, high-frequency energy ratio, and jump criterion change continuously change over time, the abnormal suppression acceleration value at each moment is calculated. Table 1 shows the abnormal suppression acceleration value data table.

[0041] Table 1 Abnormal suppression acceleration value data table like Figure 3 As shown in FIG, the flexible acceleration regulation trend diagram based on load detection provided by the embodiment of the present application shows the changing trend of the abnormal suppression acceleration value with time t. As time goes from 1 to 5, the abnormal suppression acceleration value gradually decreases, and the curve shows an obvious decreasing trend and tends to be stable at the 5th moment; 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, and the corresponding abnormal suppression acceleration value shows a downward trend, indicating that an abnormal situation occurred during the operation of the drilling machine, and the acceleration was flexibly controlled to automatically reduce. At the first two time points, the decline was large, indicating that it was quickly suppressed after a slight abnormality was detected; the decline slowed down in the later period, indicating that it has entered a protection state and the suppression effect is close to saturation.

[0042] This implementation integrates vibration detection, high-frequency energy anomaly analysis, and equipment operating status data to achieve precise identification of drilling disturbance states and flexible acceleration control, establishing a closed-loop control mechanism combining state perception, dynamic adjustment, and feedback optimization. This not only adjusts acceleration targets in real time based on disturbance intensity and trends, preventing equipment damage and operator risks caused by sudden jumps and high-frequency disturbances, but also dynamically sets a safe acceleration limit using historical natural acceleration data, improving both control rationality and safety. This effectively balances operational efficiency and stability, enhancing the drilling machine's adaptability and anomaly prevention capabilities under complex working conditions.

[0043] Specifically, according to the acceleration control evaluation results, the specific process of implementing the acceleration control strategy is: first, receive and parse the currently calculated abnormal suppression acceleration value as the target reference value of the flexible control of this cycle, and dynamically adjust the acceleration of the drilling machine in the state of slight disturbance and abnormal disturbance according to the abnormal suppression acceleration value to realize flexible acceleration control. The flexible acceleration control strategy allows the acceleration to change in real time with the disturbance state to ensure the safety and stability of the drilling process; and when the jump is obvious, that is, the instantaneous jump judgment value is higher than the jump threshold, the current abnormal suppression acceleration value will automatically decrease, and even close to zero in severe abnormalities, to achieve extremely slow start and pause, effectively avoiding the drill bit from continuing to advance under severe reaction force, reducing operation The risk of injury to personnel and damage to equipment is reduced; on the contrary, when there is no obvious vibration, the abnormal suppression acceleration value is close to the maximum value, and the speed is gradually increased to full speed for normal operation, ensuring maximum drilling efficiency; if the drilling machine is paused due to abnormal vibration, the equipment will be slowly restarted at a low speed when the state is restored, and the drilling process will be smoothly promoted, which helps to reduce the mechanical shock and misjudgment risk during the restart process; all acceleration control instructions 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 instructions to achieve real-time closed-loop control and link protection measures, including alarms, abnormal prompts and power-off 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.

[0044] In this implementation, the acceleration control strategy achieves flexible adaptive control of the drilling machine under both slight and abnormal disturbance conditions by integrating the abnormal suppression acceleration value, effectively improving the dynamic response capability to drill bit bounce and operational stability. Based on the changes in the instantaneous jump discrimination value and the high-frequency energy ratio, the spindle acceleration is automatically adjusted, achieving a flexible control mechanism for the entire process from slow start, pause, to recovery, significantly reducing the risk of operator injury and equipment damage. At the same time, a real-time linkage protection mechanism ensures that the equipment has the ability to respond accurately and provide immediate protection in abnormal conditions; the control data is recorded throughout the process to ensure the traceability and optimizability of the control strategy, and it has excellent safety, adaptability, and intelligent evolution capabilities.

[0045] Specifically, the specific process of real-time monitoring of jitter detection, high-frequency energy anomaly analysis and acceleration control evaluation results, implementation of abnormal protection decisions, and realization of visual execution and algorithm parameter tuning is as follows: building a complete data monitoring channel during the entire operation process, continuously monitoring the instantaneous jump judgment value, high-frequency energy ratio, abnormal suppression acceleration value and acceleration control instruction in real time throughout the operation, and continuously updating dynamic status information with a millisecond sampling cycle to ensure the timeliness and continuity of monitoring; once it is detected that the instantaneous jump judgment value and the high-frequency energy ratio exceed the jump threshold and the abnormal threshold for three consecutive times, indicating a trend of continued deterioration of the disturbance, and when multiple jams and strong jitters occur, the main circuit is immediately disconnected, the energy transmission chain is quickly cut off to prevent further expansion of mechanical damage, and the protection mode is switched to the protection mode. In the protection mode, the acceleration freezing, alarm feedback and manual confirmation process are entered to ensure the safety of the operator and equipment; at the same time, the sound alarm is used The system uses a buzzer prompt and voice broadcast to provide real-time feedback to the operator on the current status, cause of abnormality and specific safety suggestions, ensuring the clarity and high perceptibility of information transmission, and records manual correction operations, such as reset attempts, position fine-tuning and parameter confirmation intervention behaviors; a real-time safety dashboard is used to intuitively display the timeline of abnormal events, disturbance levels and corresponding indicator trends, helping operators and administrators to quickly understand the abnormal evolution process; status lights use different colors, such as green, yellow and red, to distinguish between stable, slight disturbances and abnormal disturbances; all abnormal events, equipment operating status data, corresponding working conditions, disturbance levels, acceleration control operations and manual correction operation feedback are recorded and archived to build a complete log and abnormal case library, realizing full-process event tracing and optimization of instantaneous jump judgment values, high-frequency energy ratios and abnormal suppression acceleration value parameters, and continuously improving the adaptability and judgment accuracy of various parameters through regular offline analysis and self-learning mechanisms.

[0046] like Figure 4 As shown, a real-time status trend diagram of a drilling machine based on load detection provided in an embodiment of the present application is displayed, which shows the core visualization images in the real-time safety dashboard, namely the corresponding instantaneous jump judgment value, high-frequency energy ratio and abnormal suppression acceleration value indicator trend. 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 judgment value and the high-frequency energy ratio. The figure also uses crosses to mark intervention points under different states, green indicates a stable state, yellow indicates a slight disturbance, and red indicates an abnormal disturbance; it not only displays the indicator trend, but also reflects the correspondence between the instantaneous jump judgment value, the high-frequency energy ratio indicator and the abnormal suppression acceleration value, thereby realizing real-time visualization monitoring of the safety status and indicator trend during the drilling process.

[0047] In this implementation plan, by building a full-process real-time data monitoring channel and integrating the results of vibration detection, high-frequency energy anomaly analysis and acceleration control, accurate identification and graded response to the disturbance state of the drilling process are achieved; once significant anomalies are detected continuously, the main circuit disconnection and protection mode switching are immediately triggered to ensure operational safety; abnormal information is fed back to the operator in real time through voice alarms, and intuitive and efficient status visualization is achieved in conjunction with status lights and safety dashboards; at the same time, all key parameters, abnormal events and control operations are archived to form a complete log and case library, which supports subsequent tracing and algorithm parameter optimization, and combines with the self-learning mechanism to continuously improve the judgment accuracy and adaptability, effectively enhancing the intelligent protection capability and operational robustness of the drilling equipment.

[0048] 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, which is applied to the above-mentioned handheld drilling machine intelligent positioning safety protection method based on load detection, including: a multi-source signal acquisition module, which is used to collect equipment operation status data in real time, unify the timestamps of the equipment operation status data, and store them in the equipment operation database; a data preprocessing module, which is used to preprocess the equipment operation status data and construct equipment operation status data segments according to a fixed sliding time window; a dynamic state recognition and discrimination module, which is used to detect the vibration of the drilling machine during operation in real time based on the equipment operation status data segments, and perform high-frequency energy anomaly analysis, and perform hierarchical disturbance state discrimination based on the vibration detection and high-frequency energy anomaly analysis results; an intelligent flexible soft start control module, which is used to perform acceleration control evaluation based on the hierarchical disturbance state discrimination results, integrate the equipment operation status data, vibration detection and high-frequency energy anomaly analysis results, and implement acceleration control strategies based on the acceleration control evaluation results; a protection decision and visual execution module, which is used to monitor the vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation results in real time, implement abnormal protection decisions, and realize visual execution and algorithm parameter tuning.

[0049] In this implementation, multiple modules collaborate to achieve efficient sensing of the drilling machine's operating status, anomaly identification, and intelligent control. The multi-source signal acquisition and data preprocessing module ensures data quality. The dynamic recognition module accurately identifies and categorizes jitter and high-frequency anomalies. The intelligent flexible soft-start module adaptively controls acceleration based on the disturbance level. The protection and visualization module provides automatic protection and intuitive feedback. The overall system delivers sensitive response, gentle control, and timely protection, effectively enhancing the safety and intelligence of drilling operations.

[0050] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. The preferred embodiments do not describe all details in detail, nor do they limit the invention to the specific embodiments described. As those skilled in the art will appreciate, numerous modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A handheld drilling machine intelligent positioning safety protection method based on load detection, characterized in that: The following steps are involved: S1, collects equipment operation status data in real time, unifies the timestamp of the equipment operation status data, and stores it in the equipment operation database; S2, preprocessing the equipment operation status data and constructing equipment operation status data segments according to a fixed sliding time window; S3, based on the equipment operation status data fragment, real-time detection of drilling machine vibration during operation, and high-frequency energy anomaly analysis, and based on the vibration detection and high-frequency energy anomaly analysis results, graded disturbance state discrimination; S4, based on the results of the graded disturbance state identification, integrates the equipment operation state data, the vibration detection and the high-frequency energy anomaly analysis results, conducts an acceleration control evaluation, and implements the acceleration control strategy based on the acceleration control evaluation results; S5 monitors the results of vibration detection, high-frequency energy anomaly analysis, and acceleration control assessment in real time, implements abnormal protection decisions, and realizes visual execution and algorithm parameter tuning.

2. The handheld drilling machine intelligent positioning safety protection method based on load detection according to claim 1 is characterized in that: The specific process of collecting the device operation status data in real time, unifying the timestamp of the device operation status data, and storing it in the device 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 in real time. The spindle speed is synchronously collected through an encoder, and three-axis accelerometers are installed at key locations on the machine body to collect the vibration acceleration of the machine body. Build an equipment operation database, record the load current, spindle speed, and vibration acceleration as equipment operation status data, and store them in the equipment operation database with a unified timestamp.

3. The handheld drilling machine intelligent positioning safety protection method based on load detection according to claim 1 is characterized in that: The specific process of performing data preprocessing on 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 and aligned to ensure that each signal is analyzed under the same time reference; the signal processing technology of sliding average and low-pass filtering is used to remove high-frequency noise and instantaneous pulse interference in 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 fragments that are convenient for batch processing; for abnormal equipment operation status data and data with hardware collection errors that appear within a fixed time window, median filtering and threshold elimination strategies are used to automatically correct and eliminate them.

4. The intelligent positioning safety protection method for a handheld drilling machine based on load detection according to claim 1 is characterized in that: The specific process of detecting the vibration of the drilling machine in real time during operation based on the equipment operation status data fragment is as follows: The load current of the drilling machine at the current moment and historical moments is obtained 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 second-order difference of the load current is obtained by subtracting twice the load current at time t-1 from the load current at the current moment t, adding the load current at time t-2 and taking the absolute value. 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.

5. The intelligent positioning safety protection method for a handheld drilling machine based on load detection according to claim 1 is characterized in that: The specific process of performing high-frequency energy anomaly analysis is as follows: Obtain the current sampling frequency and the load current within the continuous sliding time window. Use the fast Fourier transform to convert this set of load currents from the time domain to the frequency domain to obtain the complex spectrum corresponding to different frequencies. Calculate the spectrum amplitude for each frequency component of the complex spectrum, that is, obtain the energy of each frequency by taking the square root of the sum of the real and imaginary parts. The frequency index is converted into the actual physical frequency in combination with the sampling frequency, and the amplitude of each spectrum in the window is extracted to obtain the frequency amplitude at each physical frequency; Take half of the sampling frequency as the maximum analysis frequency; for a batch of load currents within a fixed time window sampled during normal operation, obtain the frequency amplitude of the load current, obtain the full-band energy distribution of the load current, and perform statistics to obtain the total energy cumulative distribution. Find the frequency point corresponding to the high-frequency distribution ratio of the full-band energy distribution to the total energy cumulative distribution, and set it as the dividing point between high frequency and low frequency; The energy integral is calculated in two stages: for the low-frequency segment from 0 to the dividing point between high frequency and low frequency, the frequency amplitude of each low-frequency segment is obtained. For each low-frequency point, the frequency amplitude is squared and then integrated. The constant 0.01 is added to the integral result to obtain the low-frequency energy sum. For the high-frequency segment from the dividing point between high frequency and low frequency to the maximum analysis frequency, obtain the corresponding frequency amplitude in each high-frequency segment. 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.

6. The handheld drilling machine intelligent positioning safety protection method based on load detection according to claim 1 is characterized in that: The specific process of performing graded disturbance state discrimination based on the results of beat detection and high-frequency energy anomaly analysis is as follows: Real-time comparison of instantaneous jump discrimination value and jump threshold, high-frequency energy ratio and abnormal threshold to identify drilling machine status; 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 a normal, stable and slow drilling state, and is in good operating condition. Use no-load and light load to ensure low current and slow speed for initial positioning. After positioning, increase the load and current at the same time, gradually increase the speed to full speed for normal operation, and continue to push the drill into the workpiece without special intervention; When only one of the indicators, the instantaneous jump judgment 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. Only flexible adjustments are made without interrupting the operation, and slight rotations are 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; the machine 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: pausing the acceleration of the motor; After detecting an abnormality, it will perform a slight reversal, adjust the drill bit position appropriately, and reposition it; at the same time, it will issue an abnormality warning 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 synchronously recorded.

7. The handheld drilling machine intelligent positioning safety protection method based on load detection according to claim 1 is characterized in that: The specific process of performing acceleration control evaluation based on the hierarchical disturbance state identification results, integrating the equipment operation state data, jitter detection and high-frequency energy anomaly analysis results is as follows: Receive real-time drilling machine status recognition results, including the instantaneous jump judgment value, high-frequency energy ratio, and disturbance level of the current window; and perform adaptive acceleration adjustments for drilling machines in slight disturbance and abnormal disturbance states; The historical spindle speeds during normal startup and drilling are obtained, and the spindle speed change rate is calculated as the natural acceleration. The natural acceleration during each slow start of the drilling machine is collected, and abnormal states identified by the instantaneous jump discrimination value and the high-frequency energy ratio are obtained in real time. The startup phase without abnormalities is identified as the safe interval, and the maximum natural acceleration within the safe interval is counted and selected as the maximum safe acceleration. Obtain the normalized instantaneous jump judgment value and the normalized high-frequency energy ratio; at the same time, calculate the difference between the instantaneous jump judgment value of the current time window and the instantaneous jump judgment value of the previous time window to obtain the jump judgment 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; Add the jump suppression term, high-frequency energy suppression term, jump trend suppression term, and add a constant of one to obtain the comprehensive abnormality suppression term; Divide the maximum safe acceleration by the comprehensive abnormal suppression term to obtain the abnormal suppression acceleration value.

8. The handheld drilling machine intelligent positioning safety protection method based on load detection according to claim 1 is characterized in that: The specific process of implementing the acceleration control strategy according to the acceleration control evaluation result is as follows: According to the abnormal suppression acceleration value, the acceleration of the drilling machine in the state of slight disturbance and abnormal disturbance is dynamically adjusted to achieve flexible acceleration control. When the jump is obvious, that is, the instantaneous jump judgment value is higher than the jump threshold, the current abnormal suppression acceleration value will automatically decrease. In severe abnormalities, it will even be close to zero, achieving extremely slow start and pause. Conversely, when there is no obvious jump, the abnormal suppression acceleration value is close to the maximum value, and the speed is gradually increased to full speed for normal operation. If the drilling machine pauses due to abnormal vibration, the equipment will slowly restart at a low speed when the state is restored to smoothly advance the drilling process; All acceleration control instructions are output to the motor drive system in real time and protection measures are linked; All abnormal suppression acceleration values ​​and acceleration control measures are written into the equipment operation database in real time.

9. The intelligent positioning safety protection method for a handheld drilling machine based on load detection according to claim 1 is characterized in that: The specific process of real-time monitoring of vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation results, implementation of abnormal protection decision-making, and realization of visual execution and algorithm parameter tuning is as follows: Continuously monitor the instantaneous jump discrimination value, high-frequency energy ratio, abnormal suppression acceleration value, and acceleration control instructions in real time throughout the operation. Once it is detected that the instantaneous jump discrimination value and high-frequency energy ratio have exceeded the jump threshold and abnormal threshold three times in a row, or if multiple jams and strong jumps occur, the main circuit will be immediately disconnected and the system will switch to protection mode to ensure the safety of the operator and equipment. At the same time, the current status, abnormal cause, and specific safety suggestions will be fed back to the operator in real time through an audible alarm, and manual correction operations will be recorded. 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, slightly disturbed, and abnormal disturbances; all abnormal events, equipment operating status data, corresponding working conditions, disturbance levels, acceleration control operations, and manual correction operation feedback are recorded and archived, building a complete log and abnormal case library to achieve full-process event tracing and optimization of instantaneous jump judgment values, high-frequency energy ratios, and abnormal suppression acceleration value parameters.

10. The intelligent positioning safety protection device for handheld drilling machine based on load detection is characterized in that: include: Multi-source signal acquisition module, used to collect equipment operation status data in real time, unify the timestamp of equipment operation status data, and store it in the equipment operation database; The data preprocessing module is used to preprocess the equipment operation status data and construct equipment operation status data segments according to a fixed sliding time window; The dynamic state recognition and discrimination module is used to detect the drilling machine's vibration in real time during operation based on the equipment operation status data fragments, and perform high-frequency energy anomaly analysis. Based on the vibration detection and high-frequency energy anomaly analysis results, a graded disturbance state discrimination is performed; The intelligent flexible soft-start control module is used to evaluate acceleration control based on the results of graded disturbance state identification, integrating equipment operating status data, vibration detection, and high-frequency energy anomaly analysis results, and implement acceleration control strategies based on the acceleration control evaluation results; The protection decision-making and visual execution module is used to monitor the results of vibration detection, high-frequency energy anomaly analysis and acceleration control evaluation in real time, implement abnormal protection decisions, and realize visual execution and algorithm parameter tuning.

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