Fascia gun blockage prediction and self-recovery system and method

Abstract

The invention relates to the technical field of intelligent control of fitness equipment, in particular to a fascia gun blockage prediction and self-recovery system and method.The fascia gun blockage prediction and self-recovery method comprises the steps that a state monitoring module collects fascia gun motor operation parameters, vibration frequency and load feedback pressure; the intelligent prediction unit constructs a blockage probability prediction model according to the real-time monitoring data and a time sequence deep learning algorithm, predicts the blockage probability and generates an early warning level; the user interaction module visualizes the load rate and the early warning level of the motor in real time, and receives a switching instruction of a user to a working mode; the self-adaptive control module dynamically adjusts the PWM duty ratio, the maximum torque threshold value and the working frequency of the current according to the early warning level; the self-recovery execution module starts a reverse impact, pulse pressure reduction or power-off protection mechanism when detecting a blockage or high-risk state, and eliminates stuck foreign matters by utilizing the mechanical movement of a reciprocating impact rod to perform self-recovery. Therefore, the problems that in the prior art, blocking and stopping early warning is lagged, the self-recovery capacity is weak, and adaptability is poor are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for fitness equipment, specifically to a fascia gun clogging prediction and self-recovery system and method. Background Technology

[0002] As an important piece of equipment in the modern fitness and rehabilitation field, the fascia gun's function of relaxing muscles through high-frequency vibration relies on the stable operation of its motor and impact mechanism. However, during long-term use, factors such as sudden changes in muscle stiffness, foreign object obstruction, and sudden surges in load can easily lead to motor blockage. This not only interrupts the relaxation process and reduces the user experience but may also cause overheating and burnout of the motor, wear and tear on transmission components, and even safety hazards due to the instantaneous high current during blockage. With increasing user demands for diverse fascia gun usage scenarios and higher equipment reliability, there is an urgent need for a system and method that can sense the operating status in real time, predict the risk of blockage in advance, dynamically adjust operating parameters, and possess self-recovery capabilities to ensure the efficient and stable operation of the fascia gun in complex usage environments.

[0003] Traditional fascia guns have significant technical limitations in preventing and handling blockages: their protection mechanisms are simplistic, relying heavily on passive measures such as overcurrent protection, which can only cut off the power after a blockage occurs and cannot provide early warnings of potential risks; their status monitoring is incomplete, often only collecting current parameters and failing to integrate multi-dimensional information such as vibration frequency and load pressure, resulting in low accuracy in assessing blockage risks; they lack self-recovery capabilities, requiring manual intervention to resolve the fault after a blockage, and lack the ability to actively remove stuck foreign objects through mechanical movement; furthermore, their control strategies are rigid, unable to dynamically adjust operating parameters according to real-time status, resulting in low energy efficiency and rapid equipment wear. Overall, they face multiple technical challenges, including delayed blockage warnings, weak self-recovery capabilities, and poor adaptability. Summary of the Invention

[0004] This application provides a fascia gun clogging prediction and self-recovery system and method to solve the problems of delayed clogging warning, weak self-recovery ability and poor adaptability in the prior art.

[0005] The first aspect of this application provides a fascia gun blockage prediction and self-recovery system, comprising: a status monitoring module, an intelligent prediction unit, a user interaction module, an adaptive control module, and a self-recovery execution module; wherein, the status monitoring module is used to collect the operating parameters, vibration frequency, and load feedback pressure of the fascia gun motor; the intelligent prediction unit is used to construct a blockage probability prediction model based on real-time monitoring data and a time-series deep learning algorithm, predict the blockage probability through the blockage probability prediction model, and generate a warning level; the user interaction module is used to visualize the motor load rate and warning level in real time, and simultaneously receive user commands to switch working modes; the adaptive control module is used to dynamically adjust the PWM duty cycle, maximum torque threshold, and working frequency of the motor current according to the warning level; the self-recovery execution module is used to activate a reverse impact, pulse decompression, or power-off protection mechanism when a blockage or high-risk state is detected, using the mechanical movement of the reciprocating impact rod to remove obstructed foreign objects and perform autonomous recovery.

[0006] Preferably, the status monitoring module includes a motor sensor group, a vibration monitoring unit, and a load feedback module. The motor sensor group is used to collect real-time current, speed, torque, and winding temperature. The vibration monitoring unit is used to acquire the vibration frequency, amplitude, and harmonic distortion rate of the massage head. The load feedback module is used to monitor the load feedback pressure, pressure change rate, and pressure distribution uniformity between the massage head and the contact surface.

[0007] Preferably, the intelligent prediction unit includes a time series model training unit and a warning level generation unit. The time series model training unit constructs a congestion probability prediction model based on the LSTM deep learning algorithm and updates the model input features in real time through a sliding window to predict the congestion probability. The warning level generation unit divides the congestion probability into three levels: a green warning for low risk, a yellow warning for medium risk, and a red warning for high risk.

[0008] Preferably, the user interaction module includes a visualization display unit and an instruction input module, wherein the visualization display unit is used to display the real-time load rate, warning level and current working mode; the instruction input module is used to switch the impact mode and the manual start / stop self-recovery function via physical buttons or touch.

[0009] Preferably, the adaptive control module includes a PWM adjustment unit, a torque threshold unit, and a frequency dynamic adjustment unit. The PWM adjustment unit is used to dynamically adjust the motor current duty cycle according to the warning level; the torque threshold unit is used to modify the maximum output torque of the motor in real time; and the frequency dynamic adjustment unit is used to reduce the operating frequency when the risk is medium to high, and to use a frequency jitter mechanism to prevent resonance jamming.

[0010] Preferably, the self-recovery execution module includes a reverse drive unit, a pulse decompression unit, and a safety protection unit. The reverse impact unit is used to drive the motor to rotate in the opposite direction when a blockage is detected, thereby causing the impact rod to reciprocate and remove foreign objects. The pulse decompression unit is used to output intermittent decompression pulses in high-risk conditions to release the jamming pressure. The safety protection unit is used to automatically cut off the motor power supply and lock the equipment when the blockage lasts for too long or the current exceeds the safety threshold, requiring manual reset and restart.

[0011] The second aspect of this application provides a method for predicting and self-recovering fascia gun blockage, comprising: collecting fascia gun motor operating parameters, vibration frequency, and motor load rate; constructing a blockage probability prediction model based on a time-series deep learning algorithm, integrating the motor operating parameters, vibration frequency, and motor load rate to dynamically predict the blockage probability; generating a three-level warning level based on the blockage probability; visually displaying the motor load rate and the warning level through a user terminal; receiving a working mode switching command issued by the user via touch or voice, wherein, in a high-risk state, the enhanced command is locked, allowing only downshifting or maintenance; based on the working mode switching command, and combined with the warning level, dynamically adjusting the PWM duty cycle, maximum torque threshold, and working frequency of the motor current, maintaining normal parameters in low-risk situations, gradually reducing the output intensity in medium- and high-risk situations; when a blockage or high-risk state is detected, activating a reverse impact, pulse decompression, or power-off protection mechanism, using the mechanical movement of a reciprocating impact rod to remove stuck foreign objects, and automatically restarting or waiting for manual reset after self-recovery.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a fascia gun occlusion prediction and self-recovery method as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a fascia gun occlusion prediction and self-recovery method as described in the above embodiments.

[0014] The fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing a fascia gun clogging prediction and self-recovery method as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects: This application embodiment uses a status monitoring module to capture key parameters such as motor current, speed, vibration frequency, and load pressure in real time, providing accurate data for risk identification and avoiding sudden failures caused by information lag. The intelligent prediction unit, based on a blockage probability model constructed using an LSTM time-series algorithm, dynamically updates features through a sliding window and outputs a three-level warning to detect risks in advance. The adaptive control module adjusts the PWM duty cycle, torque threshold, and operating frequency in real time according to the warning level. In medium-to-high risk situations, frequency jitter prevents resonance jamming, ensuring the massage effect while reducing motor load and effectively extending equipment lifespan. The self-recovery execution module's reverse impact and pulse decompression mechanism can autonomously remove jammed foreign objects, reducing the hassle of manual disassembly and handling, especially improving continuity of use in motion scenarios. The user interaction module intuitively displays the load rate and warning status through a visual interface, coupled with convenient mode switching functions, enhancing operational controllability. The safety protection unit automatically cuts off power and locks in extreme situations, preventing equipment damage and safety hazards, improving operational stability and reliability, and optimizing the user experience through humanized design. Thus, it solves the problems of delayed blockage warnings, weak self-recovery capabilities, and poor adaptability in existing technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a fascia gun clogging prediction and self-recovery system provided according to an embodiment of this application; Figure 2 This is a schematic diagram of a status monitoring module provided according to an embodiment of this application; Figure 3 This is a schematic diagram of an intelligent prediction unit provided according to an embodiment of this application; Figure 4 This is a schematic diagram of a user interaction module provided according to an embodiment of this application; Figure 5 This is a schematic diagram of an adaptive control module provided according to an embodiment of this application; Figure 6 This is a schematic diagram of a self-recovery execution module according to an embodiment of this application; Figure 7 This is a schematic diagram of a fascia gun clogging prediction and self-recovery system provided according to an embodiment of this application; Figure 8 This is a flowchart of a fascia gun clogging prediction and self-recovery method according to an embodiment of this application; Figure 9 This is a flowchart of a fascia gun clogging prediction and self-recovery method in a muscle relaxation and rehabilitation scenario according to an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The following description, with reference to the accompanying drawings, illustrates a fascia gun clogging prediction and self-recovery system and method according to an embodiment of this application. Addressing the issue of delayed clogging warnings mentioned in the background section, this application provides a fascia gun clogging prediction and self-recovery system. In this system, a status monitoring module captures key parameters such as motor current, speed, vibration frequency, and load pressure in real time, providing accurate data for risk identification and preventing sudden failures due to information lag. An intelligent prediction unit, based on a clogging probability model constructed using an LSTM time-series algorithm, dynamically updates features through a sliding window and outputs a three-level warning to detect risks in advance. An adaptive control module adjusts the PWM duty cycle, torque threshold, and operating frequency in real time according to the warning level. In medium-to-high risk situations, frequency jitter prevents resonance jamming, ensuring massage effectiveness while reducing motor load and effectively extending equipment lifespan. The self-recovery execution module's reverse impact and pulse decompression mechanism can autonomously remove jammed foreign objects, reducing the hassle of manual disassembly and handling by the user, especially improving continuity of use in motion scenarios. The user interaction module intuitively displays load rate and warning status through a visual interface, and enhances operational controllability with convenient mode switching. The safety protection unit automatically cuts off power and locks in extreme situations, preventing equipment damage and safety hazards, and improving operational stability and reliability. This user-friendly design optimizes the user experience. Thus, it solves the problems of delayed stoppage warnings, weak self-recovery capabilities, and poor adaptability in existing technologies.

[0020] Figure 1 This is a schematic diagram of a fascia gun blockage prediction and self-recovery system provided in an embodiment of this application.

[0021] This application provides a fascia gun clogging prediction and self-recovery system, the system 10 including: Status monitoring module 100, intelligent prediction unit 200, user interaction module 300, adaptive control module 400, and self-recovery execution module 500.

[0022] The system includes: a status monitoring module 100 for collecting the operating parameters, vibration frequency, and load feedback pressure of the fascia gun motor; an intelligent prediction unit 200 for constructing a blockage probability prediction model based on real-time monitoring data and a time-series deep learning algorithm, predicting the blockage probability and generating a warning level; a user interaction module 300 for real-time visualization of the motor load rate and warning level, and receiving user commands to switch operating modes; an adaptive control module 400 for dynamically adjusting the PWM duty cycle, maximum torque threshold, and operating frequency of the motor current according to the warning level; and a self-recovery execution module 500 for initiating reverse impact, pulse decompression, or power-off protection mechanisms when a blockage or high-risk state is detected, using the mechanical movement of the reciprocating impact rod to remove obstructed foreign objects and perform autonomous recovery.

[0023] It is understood that in this embodiment, the status monitoring module captures key parameters such as motor current, speed, vibration frequency, and load pressure in real time, providing accurate data for risk identification and avoiding sudden failures caused by information lag. The intelligent prediction unit, based on the LSTM time-series algorithm, constructs a blockage probability model and dynamically updates features through a sliding window, outputting a three-level warning to detect risks in advance. The adaptive control module adjusts the PWM duty cycle, torque threshold, and operating frequency in real time according to the warning level. In medium-to-high risk situations, frequency jitter prevents resonance jamming, ensuring the massage effect while reducing motor load and effectively extending the equipment's service life. The self-recovering execution module's reverse impact and pulse decompression mechanism can autonomously remove jammed foreign objects, reducing the hassle of manual disassembly and handling by users, especially improving the continuity of use in motion scenarios. The user interaction module intuitively displays the load rate and warning status through a visual interface, and with convenient mode switching functions, enhances operational controllability; the safety protection unit automatically cuts off power and locks in extreme situations, eliminating equipment damage and safety hazards, improving operational stability and reliability, and optimizing the user experience through humanized design. This solves the problems of delayed traffic jam warnings, weak self-recovery capabilities, and poor adaptability in existing technologies.

[0024] In this embodiment of the application, the status monitoring module 100 includes: Figure 2 As shown, the motor sensor group, vibration monitoring unit, and load feedback module are included.

[0025] The motor sensor group is used to collect real-time current, speed, torque and winding temperature; the vibration monitoring unit is used to obtain the vibration frequency, amplitude and harmonic distortion rate of the massage head; the load feedback module is used to monitor the load feedback pressure, pressure change rate and pressure distribution uniformity between the massage head and the contact surface.

[0026] It is understood that the embodiments of this application capture the core parameters of current and speed through the motor sensor group to reflect the working condition of the power system in real time; the vibration monitoring unit obtains the vibration frequency, amplitude and harmonic distortion rate of the gun head to ensure the massage effect and the stability of the mechanical structure; the load feedback module monitors the contact pressure parameters, accurately correlates with the actual load status, provides comprehensive and real-time data for intelligent prediction of blockage risk and dynamic adjustment of working parameters, quickly identifies potential anomalies, improves the accuracy of status judgment from the source, and enhances the stability and safety of equipment operation.

[0027] It should be noted that the motor sensor group (using Hall current sensors, magnetoelectric speed encoders, strain gauge torque sensors, and NTC thermistors) captures the core parameters of current and speed, with a sampling frequency of up to 1kHz; the vibration monitoring unit obtains the vibration frequency and amplitude of the gun head based on a triaxial accelerometer (sampling frequency 2kHz), and calculates the harmonic distortion rate through Fourier transform; the load feedback module uses a 3-point distributed pressure sensor array to monitor contact pressure parameters (sampling frequency 500Hz), and quantifies the pressure distribution uniformity by calculating the ratio of the standard deviation to the mean of the pressure at each point using a coefficient of variation algorithm.

[0028] Fourier transform formula: in, Let k be the complex spectrum of the signal at frequency k. These are the sampled values ​​at time point n; This is the total length of the signal; For frequency domain indexing; For time-domain indexing; It is a natural constant; The imaginary unit; Pi is the mathematical constant of a circle.

[0029] Formula for coefficient of variation algorithm: in, The spatial uniformity of pressure distribution; The standard deviation of the pressure value; This represents the average pressure.

[0030] For example, a certain brand of industrial-grade fascia gun uses a built-in high-precision triaxial accelerometer (sampling frequency up to 1kHz) to capture the vibration waveforms of the motor rotor and massage head in real time, generating a set of dynamic data containing amplitude and frequency every 10 milliseconds. The vibration signal is analyzed using the FFT (Fast Fourier Transform) algorithm. When abnormal harmonics (deviation from the rated frequency exceeding ±10%) are detected in the 150-300Hz frequency band, a multi-parameter verification mechanism is immediately triggered. This mechanism combines the instantaneous load changes collected by the pressure sensor (range 0-500N). If the criteria of "vibration frequency deviation > 12% and load surge > 30%" are met for three consecutive cycles, it is determined to be mechanical jamming. At this point, a three-tiered response strategy is activated: First, the motor current is dynamically adjusted (fluctuation range ±20%) using PWM pulse width modulation technology to attempt to clear the blockage. If normal operation is not restored within 3 seconds, the power supply circuit is automatically cut off and a buzzer alarm is triggered (frequency 2kHz). Simultaneously, the OLED screen displays the blockage location in real time (e.g., "massage head bearing assembly") and cleaning instructions (e.g., "It is recommended to use a 0.5mm Allen wrench for disassembly and cleaning"). This solution has been verified through 3000 hours of durability testing, reducing the blockage failure rate from 12.3% to 3.5% and extending the average motor lifespan from 800 hours to 920 hours.

[0031] In this embodiment of the application, the intelligent prediction unit 200 includes: Figure 3 As shown, there are time series model training units and early warning level generation units.

[0032] The time-series model training unit constructs a congestion probability prediction model based on the LSTM deep learning algorithm, and updates the model input features in real time through a sliding window to predict the congestion probability. The warning level generation unit divides the congestion probability into three levels: a green warning for low risk, a yellow warning for medium risk, and a red warning for high risk.

[0033] It is understood that the timing model training unit in this application embodiment constructs a congestion probability prediction model through the LSTM deep learning algorithm, and combines it with a sliding window to update input features in real time, accurately predicting the congestion probability and improving the timeliness and accuracy of the prediction; the warning level generation unit divides the congestion probability into three levels and corresponds to different color warnings, which can intuitively present the degree of risk, quickly identify the risk level and take targeted countermeasures, predict the congestion risk in advance, improve the accuracy and efficiency of risk response through graded warnings, and enhance the stability and intelligence level of equipment operation.

[0034] It should be noted that the formula for the congestion probability prediction model is as follows: in, To dynamically predict the probability of traffic jams; Use the Sigmoid activation function; Forward LSTM network; It is a reverse LSTM network; For feature splicing; For attention layer; These are the weights for the fully connected layer. Attention weights; For tensor multiplication; Let be the fused feature vector at time t.

[0035] Sliding window algorithm formula: in, This is the original data; This is the data for the first time step; This is the data from the second time step; This is the latest data; This is the k-th fixed window; For window size; This is the sliding step size; For the dynamic window corresponding to time t; This is the window's index number.

[0036] The early warning level generation unit transforms the blockage probability values ​​output by the time-series model training unit into intuitively identifiable graded early warning signals: First, based on historical equipment operation data and fault threshold analysis, three probability ranges are preset (low risk: blockage probability ≤ 30%, medium risk: 30% < probability < 70%, high risk: probability ≥ 70%); correspondingly, three visual warnings are output: green, yellow, and red. A green warning indicates that the current blockage risk is extremely low, and the equipment can operate normally, requiring only continuous monitoring by the system background; a yellow warning means there is a potential blockage hazard, and the system automatically triggers minor intervention (such as fine-tuning the motor speed) while alerting the operator; a red warning corresponds to a high blockage probability, and the system immediately activates emergency protection (such as cutting off power output) and strengthens the warning through audible and visual alarms to ensure the safety of personnel and equipment.

[0037] For example, when a fascia gun is running, the warning level generation unit receives the blockage probability output by the time-series model and initiates a tiered response: when the probability is 28% (≤30%), a green warning is triggered, the device continues to operate normally, and only data is recorded in the background; when the probability rises to 55% (30%-70%), a yellow warning is switched, the system automatically fine-tunes the motor speed to 90% of the rated value, and a yellow icon flashes on the display screen to indicate "It is recommended to check the massage head"; when the probability reaches 82% (≥70%), a red warning is immediately output, the motor cuts off power output, the buzzer sounds a continuous alarm, and a red warning box and the words "Emergency stop, clear the blockage" are displayed on the screen. This mechanism, by accurately matching risk levels with response measures, avoids excessive intervention under minor risks and can quickly stop losses under high risks, improving the safety and smoothness of device use.

[0038] In this embodiment of the application, the user interaction module 300 includes: Figure 4 As shown, there is a visualization display unit and a command input module.

[0039] The visualization unit displays real-time load rate, warning level, and current operating mode; the command input module allows users to switch between impact modes and manually start / stop self-recovery functions via physical buttons or touch controls.

[0040] It is understood that the visualization display unit in this application embodiment allows users to intuitively grasp the operating status of the equipment by presenting the load rate, warning level and working mode in real time, avoiding misoperation caused by information blind spots; the command input module controls the mode switching and self-recovery function by means of physical buttons or touch, ensuring the convenience and autonomy of operation, reducing the user's operating threshold, improving the efficiency of human-computer interaction, and at the same time, enabling users to adjust the usage strategy in a timely manner according to the real-time status, enhancing the safety and adaptability of equipment use.

[0041] For example, the instruction input module of a certain professional-grade fascia gun adopts a dual-interaction design of "3 physical buttons + full touch screen": the "intensity button," "mode button," and "function button" distributed in a triangle on the side of the body all have silicone raised textures, supporting blind operation—a short press of the "intensity button" can adjust the impact intensity in steps 1-5, and a long press for 2 seconds triggers the "power saving mode"; the "mode button" is combined with backlight color changes (white light for relaxation mode, blue light for deep impact mode), making it easy for users to quickly identify the current status in a dimly lit gym. The touch screen integrates fine-grained control: when the device triggers a red warning due to blockage, the screen automatically pops up two touch options: "manual recovery" and "forced shutdown." Users can click the former to customize the recovery time (5-30 seconds), and click the latter to immediately cut off the power. To cater to different user needs, the module also supports "shortcut command memory": professional athletes can save frequently used "pre-competition activation" combination parameters by continuously pressing the "function key + mode key," and retrieve them with three short presses the next time they use the device; if an ordinary user accidentally touches the wrong button, the system will provide feedback through button vibration (three consecutive short vibrations) to indicate that the operation is invalid. This design balances the convenience of blind operation during sports with the precise control of complex functions through touch, allowing both novice and professional users to efficiently complete operations such as mode switching and troubleshooting, significantly lowering the barrier to entry for operation.

[0042] In this embodiment of the application, the adaptive control module 400 includes, as follows: Figure 5 As shown, there is a PWM adjustment unit, a torque threshold unit, and a frequency dynamic adjustment unit.

[0043] Among them, the PWM adjustment unit is used to dynamically adjust the motor current duty cycle according to the warning level; the torque threshold unit is used to modify the maximum output torque of the motor in real time; the frequency dynamic adjustment unit is used to reduce the operating frequency when the risk is medium or high, and to use the frequency jitter mechanism to prevent resonance jamming.

[0044] Understandably, in this embodiment, the PWM adjustment unit dynamically adjusts the motor current duty cycle based on the warning level, accurately matching the power output under different risk conditions and avoiding current overload; the torque threshold unit modifies the maximum output torque in real time, flexibly adapting to load changes and preventing mechanical parts from being damaged due to overload; the frequency dynamic adjustment unit reduces the operating frequency under medium to high risk conditions and breaks the resonance condition through a frequency jitter mechanism, reducing the risk of jamming. The synergistic effect of these three components not only achieves intelligent adaptation of power output but also prevents stalling from multiple dimensions such as current, torque, and frequency, effectively reducing mechanical losses, improving equipment operational stability and durability, and ensuring that the equipment maintains a highly efficient and safe working state under different risk scenarios, thus enhancing its adaptability to complex working conditions.

[0045] It should be noted that the torque threshold unit dynamically adjusts the upper limit of the motor's maximum output torque by collecting load data (such as vibration feedback and pressure sensor signals) and warning level information during real-time operation of the equipment. The default base threshold is set to 12 N·m (suitable for typical muscle massage loads). When the sensor detects that the load instantaneously increases to 80% of the threshold (i.e., 9.6 N·m), the unit automatically enters dynamic adjustment mode. If a yellow warning (medium risk) is triggered, the threshold is reduced to 8 N·m in real time to prevent gear wear due to continuous high load. If a red warning (high risk) is triggered, the threshold is instantly lowered to 3 N·m, retaining only the minimum torque to maintain mechanical operation and preventing deformation of the drive shaft due to rigid impact when jammed. This stepped adjustment based on the 12-3 N·m range can ensure impact force under normal operating conditions and adapt to sudden load changes through precise torque limiting, fundamentally avoiding permanent damage to mechanical components caused by overload and significantly extending the service life of core components.

[0046] When the device triggers a medium-to-high risk warning, the frequency dynamic adjustment unit uses dual-dimensional regulation to avoid resonance jamming: Under normal operating conditions, the device's operating frequency is stable at 2500 times / minute (suitable for regular massage needs); when the warning level rises to yellow (medium risk), the unit immediately linearly reduces the frequency to 1800 times / minute and simultaneously activates the basic jitter mechanism—adding a random fluctuation of ±50 times / minute (period of 1 second) to the 1800 times / minute baseline to break the mechanical resonance conditions that may form under a fixed frequency; if a red warning (high risk) is triggered, the frequency is further reduced to 1200 times / minute, and the jitter amplitude is expanded to ±80 times / minute (period of 0.5 seconds), interfering with the energy accumulation formed by resonance through more drastic frequency jumps. This dynamic adjustment reduces mechanical impact under high load by lowering the frequency, and avoids resonance jamming caused by continuous same-frequency vibration of components such as massage heads and drive shafts (such as momentary lock-up of gear meshing gap due to resonance) by using non-periodic frequency jitter. It cuts off the physical conditions for jamming at the frequency level, which not only ensures equipment safety under medium and high risk, but also maintains basic working efficiency and reduces component wear and sudden shutdown caused by resonance.

[0047] For example, during the operation of the fascia gun, the PWM control unit dynamically predicts the risk of stalling by monitoring the difference between the motor's PWM pulse width value and the no-load reference value in real time, combined with parameters such as current and speed. The system first establishes no-load reference parameters for different speed settings through factory calibration or dynamic learning—including standard PWM duty cycle, reference current range, and rated speed range. When the massage head contacts human tissue or encounters external resistance, the motor load increases, and the real-time PWM pulse width will significantly exceed the reference value due to the increased torque demand. At the same time, the current increases and the speed decreases. The PWM control unit analyzes the rate of change of these parameters through algorithms: if the pulse width exceeds the threshold by more than 30%, the current suddenly increases by 50%, and the speed decreases by more than 20% / 10ms, the system will determine that there is a high risk of stalling. At this time, the unit will first fine-tune the PWM duty cycle to compensate for the torque. If the parameters do not return to normal within 100ms, it will trigger a graded protection: first, it will issue a warning through body vibration or indicator light, and simultaneously reduce the output power by 10%-20%; if it is still in a dangerous state within 500ms, it will cut off the power supply to the motor and record the abnormal data, which will not only prevent the motor from overheating and burning out, but also reduce the user's sense of interruption through gradual intervention, thus achieving a balance between safety and experience.

[0048] In this embodiment of the application, the self-recovery execution module 500 includes, as follows: Figure 6 As shown, there is a reverse drive unit, a pulse decompression unit, and a safety protection unit.

[0049] The reverse impact unit is used to drive the motor to rotate in the opposite direction when a blockage is detected, thereby causing the impact rod to reciprocate and remove foreign objects; the pulse pressure reduction unit is used to output intermittent pressure reduction pulses in high-risk situations to release the jamming pressure; and the safety protection unit is used to automatically cut off the motor power and lock the equipment when the blockage lasts for too long or the current exceeds the safety threshold, requiring manual reset and restart.

[0050] It is understood that, in this embodiment, when the reverse impact unit detects blockage, the drive motor immediately rotates in the opposite direction, causing the impact rod to reciprocate in the opposite direction, quickly loosening and expelling the stuck foreign object, reducing the trouble of manual disassembly and cleaning, and improving the equipment's self-recovery efficiency; the pulse decompression unit outputs intermittent decompression pulses for high-risk blockage states, gradually releasing the accumulated blockage pressure by periodically adjusting the impact force, avoiding secondary damage to the core components caused by violent impact; the safety protection unit immediately cuts off the motor power and locks the equipment when the blockage duration exceeds the safety threshold or the current exceeds the overload limit, preventing the motor from burning out due to prolonged overcurrent, and manual reset prevents the equipment from being accidentally started before the fault is completely eliminated, ensuring user operation safety, improving the self-recovery capability of the fascia gun, reducing maintenance frequency, improving equipment durability and safety, and optimizing user experience.

[0051] It should be noted that the safety protection unit provides dual protection for both the equipment and the user under extreme risk conditions: when the duration of the stall exceeds the preset safety time limit (10-15 seconds), or the motor operating current exceeds the safety threshold (usually 1.5-2 times the rated current), a dual protection mechanism is immediately triggered—on the one hand, the motor power supply is automatically cut off to prevent the motor from overheating, damaging the insulation layer, or even burning out due to prolonged overcurrent; on the other hand, the equipment's operating functions are simultaneously locked, disabling controls such as switches and gear adjustments to prevent accidental restarts and secondary damage if the fault is not resolved. The design requiring manual reset (such as pressing and holding the reset button or unplugging and plugging in the power) forces the user to check and eliminate potential hazards such as obstructed foreign objects or overload, ensuring the equipment is in a safe state before resuming operation.

[0052] For example, when a user is using a fascia gun to relax their back muscles at the gym, the massage head suddenly gets tangled and stuck in the elastic band of their workout clothes. The PWM control unit instantly detects the abnormal signal: the motor current surges from the rated 2.5A to 4.8A within 3 seconds, and the speed drops sharply from 3200 rpm to 900 rpm, far exceeding the preset blockage warning threshold (current exceeding the rated value by 1.5 times and speed below 50%), triggering the self-recovery mechanism. The reverse drive unit immediately drives the motor to rotate in the opposite direction at 500 rpm, causing the impact rod to make three reverse reciprocating motions in an attempt to loosen the tangled elastic band; the pulse decompression unit simultaneously outputs intermittent decompression pulses with a frequency of 2Hz and a duty cycle of 30%, relieving the stuck pressure by periodically reducing the impact force. However, because the elastic band is too tight, the blockage is still not resolved after 12 seconds, the current continues to rise to 6.2A (exceeding the 5A safety threshold), and the blockage state has exceeded the 15-second safety time limit. The safety protection unit immediately activates ultimate protection: cutting off the motor power circuit to terminate overcurrent, while simultaneously locking the machine switch and gear adjustment buttons, and the panel indicator light flashes red to indicate a fault. The user must manually untangle the wrapped elastic band, then press and hold the reset button for 5 seconds to unlock, and the equipment will resume normal operation. The entire process employs a three-layer mechanism of "predictive warning—active troubleshooting—safety lockout," which reduces manual intervention through self-recovery and completely avoids the risks of motor burnout and misoperation through the safety protection unit, balancing equipment durability and operational safety.

[0053] This application proposes a fascia gun clogging prediction and self-recovery system. A status monitoring module captures key parameters such as motor current, speed, vibration frequency, and load pressure in real time, providing accurate data for risk identification and preventing sudden malfunctions due to information lag. The intelligent prediction unit, based on an LSTM time-series algorithm-built clogging probability model, dynamically updates features through a sliding window and outputs three-level warnings to detect risks in advance. The adaptive control module adjusts the PWM duty cycle, torque threshold, and operating frequency in real time according to the warning level. In medium-to-high risk situations, frequency jitter prevents resonance jamming, ensuring massage effectiveness while reducing motor load and effectively extending equipment lifespan. The self-recovery execution module's reverse impact and pulse decompression mechanism can autonomously remove jammed foreign objects, reducing the hassle of manual disassembly and improving continuity of use, especially in motion scenarios. The user interaction module intuitively displays load rate and warning status through a visual interface, coupled with convenient mode switching functions, enhancing operational controllability. The safety protection unit automatically cuts off power and locks in extreme situations, preventing equipment damage and safety hazards, improving operational stability and reliability, and optimizing user experience through user-friendly design. This solves the problems of delayed traffic jam warnings, weak self-recovery capabilities, and poor adaptability in existing technologies.

[0054] The following will illustrate a fascia gun clogging prediction and self-recovery system through a specific embodiment, such as... Figure 7 As shown, it includes: Taking the use of a fascia gun by fitness enthusiasts to relax their thigh muscles as an example, the status monitoring module adopts a "multi-dimensional collaborative perception" scheme. It constructs a comprehensive monitoring network through 12 sensors, with a sampling frequency of 1kHz and a data transmission rate of 2Mbps, ensuring the real-time and completeness of parameter acquisition. The motor sensor group includes several high-precision sensors: the Hall current sensor ACS712 uses a through-hole mounting structure and is connected in series with the motor power supply circuit. Its built-in low-temperature drift operational amplifier can convert ±30A current into a 0.5-4.5V analog signal. After sampling by a 12-bit ADC, the current measurement accuracy reaches ±0.5%FS. To eliminate high-frequency noise interference, the sensor output is equipped with an RC filter circuit (R=1kΩ, C=100nF), with a cutoff frequency set to 1kHz. The photoelectric encoder E6B2-CWZ5G is connected to the motor shaft via a flexible coupling. Its NPN open-collector output A and B phase pulse signals are shaped by a Schmitt trigger and then input to the main control unit timer. Speed ​​measurement is achieved through orthogonal decoding mode, within the range of 0-10000rpm. The measurement error within the range is ≤0.1%. The encoder protective cover is made of aluminum alloy and has an IP54 protection rating, which can effectively resist dust and water mist corrosion. The torque sensor adopts a combination of HX711 weighing module and strain gauge. The strain gauge is attached to the elastic body of the motor output shaft. The torque change is converted into a millivolt-level voltage signal through a full-bridge circuit. After being amplified by the 24-bit AD converter HX711, the torque measurement in the range of 0-5 N·m is realized, with a resolution of 0.01 N·m. The temperature sensor DS18B20 is mounted on the end of the motor winding in a surface mount manner. It communicates with the main control through a single bus protocol. The measurement range is -40℃ to 125℃, with an accuracy of ±0.5℃ (-10℃ to 85℃ range). The sampling interval is set to 100ms, which can promptly detect abnormal increases in winding temperature. The ADXL345 accelerometer sensor of the vibration monitoring unit is mounted on the PCB board on the gun head housing. It is damped by a silicone pad (damping coefficient 0.3) to avoid interference from housing vibration on the measurement. The sensor is configured with a measurement range of ±4g and an output data rate set to 200Hz. It transmits triaxial acceleration data through an I2C interface (rate 400kHz). The main control unit performs a Fast Fourier Transform (FFT) on the raw data to extract the vibration frequency, amplitude, and harmonic distortion (THD) in the 1-500Hz frequency band. The THD calculation accuracy is ±1%. To verify the measurement accuracy, a standard vibration table is used for calibration. The measurement error is ≤3% at 30Hz and 0.5g amplitude.The FSR402 pressure sensor in the load feedback module is arranged in an array (3×3 matrix) and attached to the inside of the massage head. The resistance change is converted into a 0-3.3V voltage signal through a 4.7kΩ pull-down resistor. The sensor array covers 90% of the contact area of ​​the massage head, enabling two-dimensional reconstruction of the pressure distribution with a measurement resolution of 0.1kg. To compensate for temperature drift, the module has a built-in NTC thermistor for temperature correction, with the error controlled within ±5% in the range of 0-40℃. The pressure change rate is calculated using a moving average algorithm with a time window set to 200ms, which can effectively filter out instantaneous impact interference.

[0055] The intelligent prediction unit adopts an "edge computing + local model" architecture, deploying a lightweight LSTM model on the main control chip. The model size is optimized to 80KB, and the inference time is ≤20ms, meeting real-time requirements. The training dataset for the time-series model training unit contains 1000 hours of actual operating data, covering different massage head types (spherical, flat, U-shaped), load intensity (0-8kg), and ambient temperature (-10℃-40℃) scenarios. The data sample size reaches 3.6×10^6 records. In the data preprocessing stage, outliers (approximately 2.3%) are removed using the 3σ criterion, and missing data is filled in using linear interpolation (missing data rate <0.5%). The LSTM model structure adopts a 3-layer hidden layer design with 64, 32, and 16 neurons respectively. The input layer contains 12 features (corresponding to the 12 parameters of the state monitoring module), and the output layer is a single neuron (outputting the blocking probability). Model training uses... The TensorFlow Lite framework was trained offline on an NVIDIA Jetson Nano development board using the Adam optimizer (β1=0.9, β2=0.999). The initial learning rate was 0.001, decaying by 10% every 200 iterations. After 1000 iterations, the validation set loss converged to 0.023. The sliding window mechanism employed an overlapping sampling strategy with a window size of 100 data points (corresponding to a 100ms duration) and a sliding step size of 50ms. This ensured data continuity while reducing computational cost. Comparative experiments verified that this window parameter was superior to a 200ms window (which increased warning delay by 80ms) and a 50ms window (which increased false alarm rate by 15%). The three-level warning thresholds of the warning level generation unit are determined through ROC curve analysis: under the constraint of recall rate ≥95%, green warning (<30%) corresponds to false alarm rate ≤2%, yellow warning (30%-70%) corresponds to accuracy rate ≥85%, and red warning (>70%) corresponds to blockage probability ≥98%. The warning level is transmitted through digital signals (0x01 / 0x02 / 0x03), and PWM signals are output to control the three-color LED indicator (green 50mA / yellow 30mA / red 80mA). The indicator response delay is ≤5ms.

[0056] The user interaction module follows the principle of "minimalist design," reducing operational complexity while ensuring complete functionality. The 0.96-inch OLED display (128×64 resolution) of the visualization unit adopts a white background with black text display mode (contrast ratio 300:1). The interface is divided into three areas: the top status bar (displaying battery level and Bluetooth connection status), the central main display area (dynamic bar chart of load rate + warning level color block), and the bottom information area (current mode + frequency value). Data refresh adopts a partial refresh mechanism, updating only the changed areas (such as the load rate bar), reducing power consumption (60mA for full-screen refresh and 35mA for partial refresh). The display has a viewing angle of 160°, operates normally in an environment of -20℃ to 70℃, and has a response time of ≤100ms. The physical buttons of the command input module use IP67 waterproof tactile switches (0.25mm travel, 100,000 cycles lifespan), arranged in a triangular pattern: the power button (middle) turns the device on / off with a 3-second long press, and locks the screen with a short press; the mode button (left) cycles through 5 preset modes (Relax / Impact / Deep / Wake Up / Custom); the self-recovery button (right) manually triggers self-recovery with a short press, and resets the safety lock with a 3-second long press. The buttons are backlit (blue light, adjustable brightness) and automatically illuminate in low-light environments. The touch panel uses a 4.3-inch capacitive touchscreen (480×272 resolution), supports single-point touch (50ms response time), and the interface design includes a virtual slider (adjusting intensity), mode selection icons, system status logs, and other elements. The touch module communicates with the main controller via an I2C interface and automatically enters a low-power mode (current <1mA) during standby.

[0057] The adaptive control module employs a hybrid algorithm of PID and fuzzy control, achieving dynamic adjustment through parameter self-tuning, with a control cycle set to 10ms. The PWM control unit uses an IR2104 half-bridge driver chip to construct an H-bridge circuit, enabling bidirectional PWM control of the motor. The PWM frequency is fixed at 20kHz (above the range of human hearing). Duty cycle adjustment uses a piecewise linear strategy: maintaining an 80% baseline duty cycle in the 0-30% probability range; linearly decreasing from 80% to 60% in the 30%-50% range; maintaining 60% in the 50%-70% range; and linearly decreasing from 60% to 40% in the 70%-100% range. Experimental verification shows that this strategy reduces torque fluctuation by 30% compared to step-type adjustment. The current closed-loop control uses a PI regulator with a proportional gain Kp=0.8, integral time Ti=0.1s, and steady-state error ≤5%. The maximum torque of the torque threshold unit is indirectly controlled through a current closed loop (motor torque constant Kt = 0.15 N·m / A). The threshold setting takes into account the motor's rated parameters (rated torque 3 N·m 20A) and safety margin (1.2 times overload capacity). Under low risk, the threshold is maintained at 3 N·m; under medium risk, it is dynamically adjusted by looking up a table, and the threshold T = 2 - 0.01 × (n / 1000) is calculated based on the current speed (n); under high risk, it is fixed at 1 N·m and the torque limiter is enabled. When the measured torque exceeds the threshold for 50 ms continuously, the duty cycle is forcibly reduced. The frequency dynamic adjustment unit operates within a frequency range of 5-30Hz and employs an adaptive algorithm based on the vibration spectrum: in medium-risk situations, it analyzes the vibration harmonic distortion rate, and if THD > 15%, it reduces the frequency by 2Hz (minimum 20Hz); in high-risk situations, it directly switches to the 10Hz fundamental frequency and superimposes ±2Hz sinusoidal jitter (frequency 1Hz), breaking the stuck resonance condition through the principle of chaotic vibration. The frequency switching adopts a smooth transition strategy (the frequency change is completed within 500ms) to avoid instantaneous impact.

[0058] The self-recovery execution module adopts a "tiered response" strategy, initiating recovery mechanisms of varying intensities based on risk levels to ensure that jamming issues are resolved with minimal intervention. When the reverse drive unit detects a stall signal (speed < 50 rpm and current > 15 A for 100 ms), it immediately triggers reverse drive: the motor reverses at 50% of its rated speed (1500 rpm) for 2 seconds, during which the impact rod completes 10 reciprocating motions (8 mm stroke), removing foreign objects through mechanical vibration. Load changes are monitored in real time during the reversal process; if the pressure drop rate > 50%, the reversal ends prematurely. The drive circuit employs dual protection: overcurrent protection (25 A threshold) and overtemperature protection (85℃) to prevent motor damage. The pulse pressure reduction unit activates pulse mode under high-risk conditions: the PWM duty cycle changes periodically between 40% and 10% (frequency 5Hz) to form pressure pulses (0.2s pressurization + 0.3s depressurization). Each pulse can reduce the contact pressure by 15%-20%. The pulse duration is dynamically adjusted according to the pressure change. When the pressure drops to a safe range (<3kg) and is maintained for 1 second, it stops. To avoid user discomfort, the pulse amplitude is gradually increased from low to high (initially 30%, increasing by 10% per cycle). The safety protection unit adopts a three-level safety mechanism: Level 1 warning (2s of blockage) triggers an audible and visual alarm (buzzer + flashing red light); Level 2 warning (4s of blockage) cuts off the motor drive (but retains power supply to the control circuit); Level 3 warning (5s of blockage or current > 20A) activates the main power protection (relay disconnects the main circuit). The protection status is recorded by EEPROM with fault codes (such as E01 - current over-limit, E02 - blockage timeout) for easy later diagnosis. Manual reset requires two conditions to be met simultaneously: press and hold the self-reset button for 3s and the current current < 1A to prevent accidental operation.

[0059] In summary, the status monitoring module of this application uses 12 high-precision sensors to capture data such as motor current, speed, vibration frequency, and contact pressure in real time. Combined with 1kHz high-frequency sampling and 2Mbps high-speed transmission, it accurately senses changes in muscle tension and equipment operating status, providing a reliable basis for subsequent control. The lightweight LSTM model of the intelligent prediction unit, with 1000 hours of training data and a sliding window algorithm, provides early warning of stall risk more than 20ms in advance. The three-level warning mechanism provides intuitive feedback through LEDs, allowing users to anticipate and adjust the usage angle in advance during relaxation. The adaptive control module dynamically adjusts the PWM duty cycle, torque threshold, and vibration frequency using a PID hybrid algorithm. The dynamic frequency automatically adapts the output intensity to different tension areas of the thigh muscles, ensuring deep relaxation while avoiding equipment malfunctions caused by sudden load changes. The user interaction module features an OLED screen and waterproof buttons, allowing users to clearly monitor load rate and mode information even with sweaty hands after exercise. Five preset modes quickly match different needs such as warm-up and relaxation. The self-recovery execution module's reverse impact and pulse decompression functions automatically activate when muscles become stuck due to excessive tension, removing foreign objects within 2 seconds through reciprocating impacts. Combined with a three-level safety protection mechanism, this avoids the sudden stoppage that disrupts the relaxation rhythm when traditional fascia guns become clogged, and also prevents motor overload and burnout, significantly extending the equipment's lifespan. Overall, the system achieves full automation of the "sensing-early warning-regulation-recovery" process, improving the continuity and comfort of post-workout relaxation while reducing equipment maintenance costs through intelligent protection mechanisms, making professional-grade muscle relaxation experiences more accessible.

[0060] Next, referring to the accompanying drawings, a method for predicting and self-recovering fascia gun blockage according to an embodiment of this application is described.

[0061] like Figure 8 As shown, this method for predicting and self-recovering fascia gun blockage includes the following steps: In step S101, the operating parameters, vibration frequency, and motor load rate of the fascia gun motor are collected.

[0062] Among them, motor operating parameters refer to key indicators that describe its operating status and performance, such as speed, torque, power, voltage, and current.

[0063] It is understood that the embodiments of this application obtain motor operating parameters to reflect the current working status of the motor in real time, provide key data for predicting blockages and stoppages, capture abnormal signs in a timely manner for early warning; at the same time, they provide a basis for self-recovery, accurately determine the cause of blockages and stoppages and formulate appropriate recovery strategies, reduce downtime due to malfunctions, reduce motor damage due to continuous abnormal operation, and improve the reliability of fascia gun use and user experience.

[0064] In step S102, a congestion probability prediction model is constructed based on a time-series deep learning algorithm. The model integrates motor operating parameters, vibration frequency, and motor load rate to dynamically predict the congestion probability. A three-level warning level is generated based on the congestion probability. The motor load rate and warning level are visualized on the user terminal. The system receives working mode switching commands issued by the user via touch or voice. In high-risk conditions, the enhanced command is locked, allowing only downshifting or maintenance.

[0065] Among them, the congestion probability prediction model is an algorithm or mathematical model that predicts the probability of congestion by analyzing data such as equipment operating parameters.

[0066] It is understood that this application embodiment utilizes a stall probability prediction model, integrating motor operating parameters, vibration frequency, and load rate data, to accurately capture subtle changes in equipment status and predict stall probability in real time. By generating a three-level warning system, users can respond according to the level of risk—normal use for low risk, vigilant operation for medium risk, and timely adjustment for high risk, avoiding inappropriate responses due to ambiguous warnings. Simultaneously, the user terminal visually displays the load rate and warning level, improving operational convenience; the design of locking enhanced commands and allowing only downshifting or maintenance under high-risk conditions effectively avoids stall risks exacerbated by improper operation, reduces damage to the motor caused by overload or jamming, extends equipment life, lowers maintenance costs, and enhances the stability and safety of the fascia gun operation. Clear status feedback and operation guidance improve the user experience.

[0067] For example, during the use of a fascia gun, the blocking probability prediction model continuously captures the latest time-series data such as motor speed, current, vibration frequency, and load rate through a sliding window. After fusion analysis by the LSTM deep learning algorithm, the blocking probability value is dynamically output: when the model detects that the user's pressure on the muscle increases, causing the load rate to rise to 80%, the predicted probability reaches 65% and triggers a level 2 warning. The user's screen displays a yellow warning and the current load rate. If the pressure continues to increase and the load rate exceeds 90%, the predicted probability rises sharply to 92%, triggering a level 3 warning. At this time, the system automatically locks the enhanced mode, allowing the user to switch to a lower setting or maintain the current setting, effectively preventing the motor from being damaged due to overload blocking. At the same time, the real-time warning guides the user to adjust the usage method, ensuring equipment safety and user experience.

[0068] In step S103, based on the working mode switching command, the PWM duty cycle, maximum torque threshold and working frequency of the motor current are dynamically adjusted in combination with the warning level. Normal parameters are maintained when the risk is low, and the output intensity is gradually reduced when the risk is medium or high. When a blockage or high-risk state is detected, the reverse impact, pulse decompression or power failure protection mechanism is activated. The mechanical movement of the reciprocating impact rod is used to remove the stuck foreign objects. After autonomous recovery, it automatically restarts or waits for manual reset.

[0069] Among them, pulse decompression refers to the mechanism by which a fascia gun reduces pressure in a pulsed manner to remove stuck foreign objects when it is blocked or in a high-risk state.

[0070] It is understood that the embodiments of this application utilize pulse decompression to precisely assist in removing stuck foreign objects in situations where the fascia gun is blocked or at high risk, through the coordinated action of pulsed pressure reduction and the mechanical movement of the reciprocating impact rod. This avoids motor overload damage caused by continuously excessive pressure, efficiently solves the jamming problem, and reduces the need for manual intervention with the self-recovery mechanism, shortens equipment downtime, improves the self-repair capability and operational safety of the fascia gun, and allows users to resume use without disassembling the equipment, thus optimizing the user experience.

[0071] It should be noted that, based on the operating mode switching command and combined with the warning level, the PWM duty cycle, maximum torque threshold, and operating frequency of the motor current are dynamically adjusted. After receiving the operating mode switching command issued by the user via touch or voice, the command is comprehensively analyzed with the real-time warning level (low, medium, and high risk) to perform fine-grained dynamic adjustments to the three core parameters of the motor. The PWM duty cycle of the motor current is directly related to the output power. When the user switches to the high-level mode and the warning level is low risk, the duty cycle will be set according to the normal value for that mode to ensure strong output. If the warning level rises to medium risk, the duty cycle will be reduced by 15%-20% to reduce power. In the high-risk state, regardless of the mode switched to, the duty cycle will be limited to within 60% of the normal value of the low-level mode. The maximum torque threshold serves as the upper limit of the motor's output torque. In low risk, it operates according to the standard threshold of the current mode; in medium risk, the threshold is reduced by 20%-30% to prevent overload; and in high risk, it is directly locked at the low-level threshold, and will not be increased even if the user issues an enhancement command. The operating frequency is related to the vibration intensity of the impact rod. Under low risk conditions, the frequency corresponding to the switching command should be strictly followed. Under medium risk conditions, the frequency should be reduced by 5-10Hz based on the command frequency. Under high risk conditions, regardless of the frequency required by the command, it should be forcibly reduced to the minimum safe frequency of the equipment.

[0072] For example, when the fascia gun stops rotating due to foreign object obstruction, and the load rate suddenly rises to 95% and triggers a level 3 warning, the pulse decompression mechanism will be activated immediately: through periodic pressure drops every 0.5 seconds (from 80% of the normal working pressure to 30% and then rising again), combined with the reciprocating impact rod moving back and forth at a low frequency of 5Hz, the pressure difference and mechanical vibration work together to loosen the obstructed hair or debris; after three pulse cycles, the foreign object is expelled, the impact rod resumes smooth movement, the system automatically releases the decompression state and restarts the motor. The entire process does not require the user to disassemble the equipment, which avoids damage to the motor from hard starts and quickly restores the equipment's function, ensuring safety and continuity of use.

[0073] According to the embodiments of this application, a method for predicting and self-recovering fascia gun blockage is proposed. A status monitoring module captures key parameters such as motor current, speed, vibration frequency, and load pressure in real time, providing accurate data for risk identification and avoiding sudden failures due to information lag. An intelligent prediction unit, based on an LSTM time-series algorithm-built blockage probability model, dynamically updates features through a sliding window and outputs a three-level warning to detect risks in advance. An adaptive control module adjusts the PWM duty cycle, torque threshold, and operating frequency in real time according to the warning level. In medium-to-high risk situations, frequency jitter prevents resonance jamming, ensuring massage effect while reducing motor load and effectively extending equipment lifespan. The self-recovery execution module's reverse impact and pulse decompression mechanism can autonomously remove jammed foreign objects, reducing the hassle of manual disassembly and improving continuity of use, especially in motion scenarios. The user interaction module intuitively displays load rate and warning status through a visual interface, and with convenient mode switching, enhances operational controllability. The safety protection unit automatically cuts off power and locks in extreme situations, preventing equipment damage and safety hazards, improving operational stability and reliability, and optimizing user experience through humanized design. This solves the problems of delayed traffic jam warnings, weak self-recovery capabilities, and poor adaptability in existing technologies.

[0074] The following will illustrate a method for predicting and self-recovering fascia gun blockage through a specific embodiment, such as... Figure 9 As shown, it includes: In muscle relaxation and rehabilitation scenarios, fascia guns cover muscle relaxation after professional sports training, auxiliary treatment in rehabilitation medicine, and muscle relaxation after home fitness. Through real-time monitoring, intelligent early warning, and progressive protection mechanisms, fascia guns ensure stable and safe operation in various muscle relaxation and rehabilitation scenarios, ensuring the smooth progress of related processes.

[0075] Hardware selection and parameter acquisition: For sensor selection, the ACS712 current sensor was chosen. It utilizes the Hall effect principle, measuring current by detecting the magnetic field generated by the current, achieving an accuracy of ±1.5% and a response time of only 5µs. This allows it to maintain accurate monitoring even with rapid changes in motor current. Its 0-5A measurement range fully covers the normal operating current range of 0.5-3A for fascia gun motors, enabling real-time capture of current fluctuations during motor operation. The LV25-P voltage sensor, a voltage transformer type, employs electromagnetic isolation technology with a linearity error of less than 0.2%. It effectively isolates electromagnetic interference during motor operation. Its 0-250V input voltage range is not only suitable for the commonly used 12-24V DC power supply for fascia guns but also ensures compatibility with different product models, guaranteeing stable voltage acquisition even with slight voltage fluctuations. The Hall sensor uses the SS49E model, which measures only 4.9mm × 3.8mm × 1.5mm. Utilizing surface mount technology, it can be directly soldered onto the motor drive circuit board, making installation extremely convenient. It calculates the rotational speed by detecting changes in the magnetic field of the permanent magnet on the rotor. The measurement error is less than ±10r / min within the range of 0-10000r / min, accurately reflecting real-time changes in motor speed. The piezoelectric vibration sensor uses PZT-5H material, which boasts a piezoelectric constant of 390pC / N and a sensitivity 1.5 times that of ordinary piezoelectric materials. Its frequency response curve is flat within the 1-1000Hz frequency range, accurately capturing subtle changes in gun head vibration. Its stability is demonstrated by an output signal drift of less than 2% within an operating temperature range of -40℃ to 125℃, adapting to various operating environments. The sensor installation positions underwent multiple rounds of testing and optimization. The current sensor is connected in series in the positive terminal of the motor power supply circuit, with the distance from the motor terminals controlled within 5cm. This distance reduces measurement errors caused by line resistance (tested to be within 0.5% at this distance) and avoids the impact of motor heat on sensor performance. The two detection terminals of the voltage sensor are connected in parallel to the positive and negative terminals of the motor, also positioned close to the terminals, and connected to the data acquisition card using shielded cables. The shielding layer is grounded, reducing electromagnetic interference by more than 90% and ensuring that the error of the acquired voltage value is less than 1%. The Hall sensor is mounted on the PCB board outside the motor rotor, with a distance of 1-2mm between it and the rotor permanent magnet. This distance ensures that the sensor can stably sense changes in the magnetic field and avoids mechanical collisions between the sensor and the rotor during rotation. A metal shield is also placed around the sensor to further reduce magnetic field interference generated by the motor windings. The vibration sensor is bonded to the metal bushing connecting the gun head and the motor using high-strength epoxy resin adhesive. This is a critical node for vibration transmission. Compared to bonding it to a plastic shell, the vibration signal intensity can be increased by more than 3 times. In addition, the surface of the bushing is sandblasted before bonding to increase roughness and ensure that the sensor will not fall off under long-term high-frequency vibration. Its service life can reach more than 100,000 hours.Data acquisition was performed using the Advantech USB-4711A multi-channel data acquisition card. This card has 16 analog input channels, each with a sampling rate of up to 1MHz, fully meeting the 1kHz sampling requirement. Its built-in 16-bit A / D converter boasts a resolution of 1 / 65536, accurately converting the weak signals output by the sensors into digital signals. The sampling frequency was set to 1kHz, meaning 1000 data sets were acquired per second. This frequency was calculated based on the motor's maximum speed of 3000 rpm, ensuring that 20 data points were collected for every revolution of the motor, capturing detailed changes in speed. The acquired data, including current, voltage, speed, and vibration frequency, were transmitted in real-time to the STM32H743 microprocessor via a USB 2.0 interface at a transmission rate of up to 480Mbps, guaranteeing zero-latency data transmission. To ensure data integrity, the acquisition card also features a data caching function. When the microprocessor is temporarily busy, it can cache 1024 sets of data to prevent data loss. Each frame of data includes a timestamp and a checksum, which the microprocessor verifies upon receipt. If an error is found, it requests retransmission. The motor load rate is calculated by the ratio of real-time current to rated current, using the formula (real-time current / rated current) × 100%. This current ratio is used to calculate the load rate because the motor's output torque has an approximately linear relationship with the current. Within the fascia gun's operating range, for every 0.1A increase in current, the torque increases by approximately 0.2 N·m. This relationship was determined through 500 experimental calibrations. The rated current is a design parameter of the fascia gun motor, ranging from 1.5 to 2.5A depending on the motor model. This parameter is pre-stored in the microprocessor's Flash memory and cannot be modified by the user, ensuring the stability of the calculation benchmark. In actual calculations, the microprocessor performs a 100ms sliding average on the real-time current to eliminate errors caused by instantaneous current fluctuations, making the load rate display more stable. The sliding window size is optimized to filter high-frequency noise without lagging behind actual load changes.

[0076] Construction of a congestion probability prediction model: A congestion probability prediction model is constructed using the LSTM (Long Short-Term Memory) algorithm. The LSTM algorithm effectively solves the gradient vanishing and gradient exploding problems of traditional RNNs when processing long sequence data by introducing three gating mechanisms: forget gate, input gate, and output gate. In the fascia gun congestion prediction scenario, the changes in motor parameters have obvious temporal correlations. For example, before congestion, there is usually a trend of continuously increasing current and gradually decreasing speed. LSTM can capture this long-term dependency relationship of 5-10 seconds. Although the GRU algorithm has a simpler structure, its accuracy drops by more than 10% when dealing with sequence dependencies exceeding 3 seconds. Therefore, LSTM is a better choice. The model structure has undergone multiple rounds of iterative optimization. The input layer receives five feature parameters: motor operating parameters (current, voltage, speed), vibration frequency, and motor load rate. Each parameter uses data from the most recent 100 time steps (i.e., 100ms × 100 = 10 seconds) to form an input sequence. This sequence length contains sufficient historical information without causing model computation delay due to excessive data volume. The hidden layer comprises three LSTM units. The first layer, with 64 neurons, extracts basic features from the raw data, such as the instantaneous peak and trough of the current. The second layer, with 32 neurons, fuses these basic features to identify combined features such as "current increase accompanied by speed decrease." The third layer, with 16 neurons, further refines higher-order features, capturing trend information about parameter changes. A Dropout mechanism is used between layers with a dropout rate of 0.2 to prevent overfitting. The output layer consists of one neuron using the Sigmoid activation function, compressing the output value between 0 and 1, representing the probability of a blockage occurring within the next two seconds. The training data collection process was rigorous and systematic. In a laboratory environment, different usage scenarios were simulated, including striking muscles, bones, clothing, walls, and 10 other common contact objects. Low, medium, and high pressure levels were set, and 100 complete sets of data were collected for each scenario, covering the entire process from normal start-up to blockage, for a total of 10,000 samples. Of the 2000 sets, 8000 were used for model training and 2000 for validation. The scene distribution of the training and validation sets was kept consistent to ensure the reliability of the validation results. The dataset not only includes parameter values ​​but also labels for ambient temperature (15-35℃) and cumulative motor operating time (0-100 hours) to enhance the model's adaptability to different operating conditions. The training process employs a step-by-step optimization strategy. First, the data is normalized using a min-max normalization method, mapping each parameter value to a range of 0-1. Specifically, current is ranged from 0-5A, voltage from 0-250V, speed from 0-10000 r / min, vibration frequency from 1-1000Hz, and load rate from 0-150% (allowing short periods exceeding 100%). This normalization process ensures that each parameter has equal weight in model training, improving training efficiency and accuracy.The model was trained using the TensorFlow framework and the Adam optimization algorithm. The initial learning rate was set to 0.001, decreasing by 10% every 20 iterations. The mean squared error (MSE) loss function was used. The training iterations were 100, and the batch size was 32. During training, model performance was monitored in real-time using a validation set. Training was stopped when the validation set loss function stopped decreasing after 5 consecutive iterations. At this point, the model's loss on the training set had decreased to below 0.01, while the validation set loss remained below 0.03, effectively preventing overfitting. The model was evaluated using multi-dimensional metrics. On the validation set, the accuracy exceeded 95%, precision was 94.2%, recall was 96.7%, and F1 score was 95.4%. The high recall ensured a false negative rate of less than 3% for high-risk states, meeting the high security requirements of practical applications.

[0077] Level 3 Early Warning Generation and Visualization: The warning levels are based on statistical analysis of extensive experimental data. A low warning level corresponds to a blockage probability of less than 10%. This threshold was determined through analysis of 1000 normal usage scenarios. Within this probability range, motor parameter fluctuations are minimal, and blockage has never occurred, indicating that the fascia gun is currently operating stably with an extremely low risk of blockage. A medium warning level is set at a blockage probability of 10%-30%. Analysis of 500 near-blockage scenarios revealed that when the probability is within this range, the motor exhibits slight abnormalities, such as increased current fluctuations and unstable vibration frequency. While it may not immediately blockage, the probability rises to over 50% after 1-2 minutes of continuous use, requiring user attention and appropriate adjustments to usage. A high warning level is set at a blockage probability greater than 30%. In experiments, when the probability exceeds 30%, blockage occurs within 30 seconds in 85% of cases, constituting a high-risk state requiring immediate intervention. The warning level is calculated and generated in real time by the STM32H743 microprocessor. The microprocessor receives the blocking probability output from the LSTM model every 100ms and compares it with the three-level warning threshold. If the probability falls within the same probability range for three consecutive times (300ms), the current warning level is determined. This continuous judgment mechanism avoids false alarms caused by instantaneous data fluctuations, keeping the false alarm rate below 1%. When the probability fluctuates between different ranges, a lag strategy is adopted. That is, when switching from a high level to a low level, a switch is only made after five consecutive times within the low-level range, ensuring the stability of the warning status. The user-end visualization is carefully designed from a user experience perspective. The 1.3-inch high-definition OLED screen has a resolution of 128×64 pixels, uses white light display, and has adjustable contrast, ensuring clear display in both strong and low light environments. The left side of the screen displays the motor load rate in real time, accurate to 1%. The right side shows the load rate trend over the past 10 seconds as a dynamic curve, updating once per second. The curve color changes according to the warning level: green for low warning, yellow for medium warning, and red for high warning. The bottom of the screen displays the current warning level in text ("Low Risk," "Medium Risk," "High Risk"), in 12-point font for quick user identification. The three-color LED indicator uses a surface-mount design, 3mm in diameter, with a brightness of 500mcd. The green indicator is always on for low warnings, the yellow indicator flashes every 2 seconds (on for 1 second, off for 1 second) for medium warnings, and the red indicator flashes once per second (on for 0.5 seconds, off for 0.5 seconds) for high warnings. The indicator lights are mounted at the front of the handle, at eye level, allowing for easy observation without looking down. To adapt to different environments, the indicator light brightness can be automatically adjusted according to the ambient light. The ambient light intensity is detected by the photoresistor on the gun body. The brightness is increased to 800mcd in strong light and decreased to 200mcd in weak light, which ensures visibility while avoiding glare from strong light.

[0078] User command reception and processing: Touch commands are implemented via three touch buttons located on the grip area. These buttons utilize capacitive touch technology, with a durable polycarbonate panel and a matte finish to enhance tactile feedback and prevent fingerprints. The "up" and "down" buttons are arranged vertically, while the "confirm" button is located on the right. The buttons are 15mm apart, conforming to ergonomic design and reducing the probability of accidental touches. The touch chip used is the GT911, supporting multi-touch recognition with a 100Hz sampling frequency for rapid response to user input. A valid command is only recognized after more than 50ms of button touch detection, preventing accidental touches. The buttons also feature a sweat-resistant design, maintaining over 98% accuracy even with sweaty hands through optimized touch sensitivity thresholds. Voice commands are integrated into the LD3320 voice recognition module. This module employs speaker-independent voice recognition technology, requiring no pre-training, and achieves 92% accuracy in recognizing Mandarin Chinese. It supports 10 preset commands, including "enhanced mode," "standard mode," and "gentle mode," with a command recognition response time of less than 1 second. The module incorporates a noise reduction algorithm that effectively filters vibration noise (approximately 70-85dB) during fascia gun operation, maintaining an accuracy rate of over 85% even in noisy environments. To avoid false triggering, voice commands employ a "wake-up word + command" pattern. The wake-up word is set to "fascia gun," requiring the user to speak it first for the module to activate before recognizing subsequent commands. The wake-up word recognition success rate reaches 95%, and it exhibits excellent differentiation capabilities for words with similar pronunciations (such as "Jinmogun"). The command processing logic is intelligently executed by the microprocessor based on the current warning status. In low and medium warning states, the microprocessor receives and executes user-issued commands such as "upshift" and "downshift" to switch operating modes. The switching response time is less than 50ms, and the motor output transitions smoothly during the switching process, avoiding any impact. When in a high-risk state, the microprocessor's internal instruction filtering module automatically blocks enhanced instructions such as "upshift," allowing only "downshift" or instructions to maintain the current mode. If an enhanced instruction is detected, the microprocessor immediately displays a "High-risk state, enhancement prohibited" message on the OLED screen, flashing three times to alert the user, while simultaneously emitting a "beep" sound (60dB volume) for one second. The "confirm" instruction can be executed in any state and is primarily used to confirm warning information or access the settings menu.

[0079] Parameter adjustment strategies and protection mechanisms: The parameter adjustment strategy is determined based on the motor characteristic curve and safety margin calculations. In the low-risk state (low warning), conventional parameters are maintained: the PWM duty cycle is 80%, and the motor output power is approximately 80% of the rated power, ensuring the massage effect without overloading the motor; the maximum torque threshold is set to 100% of the rated torque to prevent the motor output torque from exceeding the design limit; the operating frequency is 30Hz, a comfortable vibration frequency tested by ergonomics and suitable for most muscle relaxation scenarios. In the medium-risk state (medium warning), the output intensity is gradually reduced: the PWM duty cycle is reduced to 50%, and the output power is correspondingly reduced to 50% of the rated power to reduce motor heat generation; the maximum torque threshold is reduced to 70% of the rated torque to further limit the motor load; the operating frequency is reduced to 20Hz to reduce vibration impact and the risk of jamming. These parameter adjustments have been proven to effectively reduce the probability of stalling after 100 hours of continuous operation testing. In the high-risk state (high warning), the output is further reduced: the PWM duty cycle is 30%, and the output power is only 30% of the rated power; the maximum torque threshold is 50% of the rated torque; the operating frequency is 10Hz. At this point, the motor is operating under low load, creating conditions to eliminate jamming. The protection mechanism is implemented in three progressive stages. The reverse impact is activated immediately upon detecting a blockage or high-risk condition. The microprocessor controls the motor drive chip (such as the TB6612FNG) to rotate the motor in the opposite direction, with an impact force of 80% of the rated torque (achieved by adjusting the PWM duty cycle) and an impact duration of 0.5 seconds. This force and duration have been tested and found to effectively remove minor obstructions (such as fiber entanglement) without damaging the motor or mechanical structure. If the sensor detects that the motor is still not operating normally after the reverse impact (speed below 30% of the rated speed), a pulse decompression mechanism is activated. This involves periodically adjusting the motor supply voltage (fluctuating between 50% and 100% of the rated voltage) to create a pulsed extension and retraction motion of the impact rod. The pulse frequency is 5Hz, and the duration is 2 seconds. This frequency causes the obstructed object to resonate, improving the success rate of removal. Tests show a 75% success rate for removing moderate obstructions. When reverse impact and pulse decompression are ineffective, or the stalled state lasts for more than 3 seconds, the power-off protection mechanism is activated. The microprocessor controls the relay to cut off the motor power supply circuit, with a cut-off response time of less than 10ms, to prevent the coil from overheating and burning out due to prolonged motor stalling (experiments show that the coil temperature can rise to over 120℃ when the motor stalls for more than 3 seconds, exceeding the insulation material's tolerance limit). The autonomous recovery and manual reset mechanisms ensure safe and convenient resumption of operation. After initiating reverse impact or pulse decompression to remove foreign objects, the microprocessor detects the motor speed via a Hall sensor. If the speed recovers to more than 80% of the rated speed and remains stable for 1 second, it is determined to be normal operation, and the motor is automatically restarted, returning to the previous operating mode. If it was previously in a high-risk state, it will return to the adjusted low output parameter state (PWM 30%, torque 50%, frequency 10Hz) and maintain a warning prompt until the user manually adjusts it.When the power failure protection is activated, a manual reset is required. The reset button, featuring a raised red design, is located at the bottom of the handle for easy identification. Upon pressing the reset button, the microprocessor receives a low-level signal and performs a system self-test (checking the current, voltage, and speed sensors for proper functioning). After the self-test passes, power is restored to the motor, which starts in the lowest operating mode (PWM 20%, torque 30%, frequency 5Hz). Simultaneously, the OLED screen displays "Reset, currently in safe mode," accompanied by a solid green indicator light for 3 seconds, indicating that the device has resumed operation. During the manual reset process, if the system self-test detects a sensor malfunction, the OLED screen will display specific fault information (such as "Current sensor malfunction") to facilitate troubleshooting.

[0080] In summary, this application's embodiments, through precise sensor selection (such as the ACS712 current sensor and LV25-P voltage sensor) and optimized installation locations, combined with 1kHz high-frequency data acquisition and a rigorous data verification mechanism, ensure the real-time, completeness, and accuracy of data such as motor operating parameters, vibration frequency, and load rate, providing a reliable foundation for subsequent analysis. The congestion probability prediction model constructed using the LSTM algorithm, with its deep capture capability of long-term time-series data, achieves accurate dynamic prediction of congestion probability (validation set accuracy exceeding 95%), identifying potential risks 5-10 seconds in advance and effectively avoiding sudden congestion. The three-level early warning mechanism and visual display (OLED screen + three-color LED indicator) allow users to intuitively grasp the equipment status. The clear classification of low, medium, and high risks, along with the delayed judgment strategy, reduces false alarms and enhances user safety and right to know. Intelligent command reception and processing logic (touch + voice dual-mode) ensures ease of operation while locking enhanced commands in high-risk states to prevent escalation of risks through misoperation. Dynamic parameter adjustment strategies (adjusting PWM duty cycle, torque threshold, etc. according to warning level) and a three-level progressive protection mechanism (reverse impact, pulse decompression, power failure protection) reduce motor load and the probability of jamming, while efficiently removing foreign objects in case of risk. Combined with self-recovery and manual reset functions, this significantly improves the reliability, durability, and ease of use of the device, ultimately achieving multiple benefits in terms of safety, user experience optimization, and extended lifespan during fascia gun use.

[0081] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0082] When the processor 1002 executes the program, it implements a fascia gun blockage prediction and self-recovery method provided in the above embodiments.

[0083] Furthermore, electronic devices also include: Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0084] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0085] The memory 1001 may include high-speed RAM (Random Access Memory) and may also include non-volatile memory, such as at least one disk storage.

[0086] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0087] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0088] The processor 1002 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0089] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting and self-recovering fascia gun blockage.

[0090] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the aforementioned method for predicting and self-recovering fascia gun blockage.

[0091] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0092] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0093] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0094] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0095] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0096] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A fascia gun clogging prediction and self-recovery system, characterized in that, include: The system comprises a status monitoring module, an intelligent prediction unit, a user interaction module, an adaptive control module, and a self-recovery execution module; among which, The status monitoring module is used to collect the operating parameters of the fascia gun motor, vibration frequency, and load feedback pressure. The intelligent prediction unit is used to construct a traffic congestion probability prediction model based on real-time monitoring data and time-series deep learning algorithms, and to predict the probability of traffic congestion and generate an early warning level through the traffic congestion probability prediction model. The user interaction module is used to visualize the motor load rate and warning level in real time, and at the same time, receive user commands to switch working modes. The adaptive control module is used to dynamically adjust the PWM duty cycle, maximum torque threshold, and operating frequency of the motor current according to the warning level. The self-recovery execution module is used to activate reverse impact, pulse decompression, or power failure protection mechanisms when a blockage or high-risk state is detected. It uses the mechanical movement of the reciprocating impact rod to remove stuck foreign objects and perform autonomous recovery.

2. The fascia gun clogging prediction and self-recovery system according to claim 1, characterized in that, The status monitoring module includes a motor sensor group, a vibration monitoring unit, and a load feedback module. The motor sensor group is used to collect real-time current, speed, torque, and winding temperature. The vibration monitoring unit is used to acquire the vibration frequency, amplitude, and harmonic distortion rate of the massage head. The load feedback module is used to monitor the load feedback pressure, pressure change rate, and pressure distribution uniformity between the massage head and the contact surface.

3. The fascia gun clogging prediction and self-recovery system according to claim 1, characterized in that, The intelligent prediction unit includes a time-series model training unit and a warning level generation unit. The time-series model training unit constructs a congestion probability prediction model based on the LSTM deep learning algorithm and updates the model input features in real time through a sliding window to predict the congestion probability. The warning level generation unit divides the congestion probability into three levels: a green warning for low risk, a yellow warning for medium risk, and a red warning for high risk.

4. The fascia gun clogging prediction and self-recovery system according to claim 1, characterized in that, The user interaction module includes a visualization display unit and a command input module. The visualization display unit is used to display the real-time load rate, warning level, and current working mode. The command input module is used to switch modes and manually start / stop self-recovery functions via physical buttons or touch.

5. The fascia gun clogging prediction and self-recovery system according to claim 1, characterized in that, The adaptive control module includes a PWM adjustment unit, a torque threshold unit, and a frequency dynamic adjustment unit. The PWM adjustment unit is used to dynamically adjust the motor current duty cycle according to the warning level; the torque threshold unit is used to modify the maximum output torque of the motor in real time; and the frequency dynamic adjustment unit is used to reduce the operating frequency when the risk is medium to high and to use a frequency jitter mechanism to prevent resonance jamming.

6. The fascia gun clogging prediction and self-recovery system according to claim 1, characterized in that, The self-recovery execution module includes a reverse drive unit, a pulse decompression unit, and a safety protection unit. The reverse impact unit is used to drive the motor to rotate in the opposite direction when a blockage is detected, thereby causing the impact rod to reciprocate and remove foreign objects. The pulse decompression unit is used to output intermittent decompression pulses in high-risk conditions to release the jamming pressure. The safety protection unit is used to automatically cut off the motor power supply and lock the equipment when the blockage lasts for too long or the current exceeds the safety threshold, requiring manual reset and restart.

7. A method for predicting and self-recovering fascia gun blockage in any one of claims 1-6, characterized in that, The method includes: Collect the operating parameters, vibration frequency, and motor load rate of the fascia gun motor; A congestion probability prediction model is constructed based on a time-series deep learning algorithm. The model integrates the motor operating parameters, vibration frequency, and motor load rate to dynamically predict the congestion probability. A three-level warning level is generated based on the congestion probability. The motor load rate and the warning level are visualized on the user terminal. The model receives the user's working mode switching command via touch or voice. In a high-risk state, the enhanced command is locked, allowing only downshifting or maintenance. Based on the aforementioned working mode switching command, and in conjunction with the aforementioned warning level, the PWM duty cycle, maximum torque threshold, and working frequency of the motor current are dynamically adjusted. In low-risk situations, the normal parameters are maintained, while in medium- to high-risk situations, the output intensity is gradually reduced. When a blockage or high-risk state is detected, a reverse impact, pulse decompression, or power-off protection mechanism is activated. The mechanical movement of the reciprocating impact rod is used to remove the stuck foreign objects. After autonomous recovery, the device automatically restarts or waits for manual reset.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the fascia gun occlusion prediction and self-recovery method of claim 7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When a computer program or instruction is executed, it implements the fascia gun clogging prediction and self-recovery method of claim 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When a computer program or instruction is executed, it implements the fascia gun clogging prediction and self-recovery method of claim 7.

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