Fascia gun operation control method and system based on Internet of Things

Abstract

The invention relates to the technical field of terminal control, in particular to a fascia gun operation control method and system based on the Internet of Things. The method comprises the following steps that a fascia gun is started, pressure sensing data are collected in real time through a main control circuit board, and if the pressure sensing data exceed a preset pressure threshold value, an operation mode is dynamically adjusted; in the operation mode, the main control circuit board electrifies the displacement electromagnet, the electric brush assembly makes contact with the conducting ring, the gear motor is driven to drive the screw to rotate, the screw pushes the sliding block to generate displacement, and the eccentric distance variation is determined. The motion working condition is judged according to the eccentric distance variation; and executing operation mode self-adaptive adjustment according to the motion working condition, and recording a working condition data log. On the basis of a terminal control technology, the fascia gun can automatically switch the eccentric distance and the operation mode in different stress states, so that massage output is matched with the actual use working condition, and the use safety and the control intelligence level are improved.

Description

Technical Field

[0001] This invention relates to the field of terminal control technology, and in particular to a fascia gun operation control method and system based on the Internet of Things. Background Technology

[0002] In existing fascia gun operation control and intelligent massage device technologies, the speed, amplitude, and working time of the fascia gun are typically controlled by preset gears or fixed parameters. For example, different intensity massage modes are switched through mechanical gear switching or simple electronic control logic. This type of control method can basically meet the needs of daily relaxation or simple massage when the use scenario is relatively simple and the user's muscle state does not change significantly. However, in application scenarios with significant individual differences among users, complex massage areas, or continuous changes in external force load during use, traditional control methods are difficult to finely adjust the operating state of the fascia gun. The lack of coordinated analysis of operating conditions and user muscle status has several drawbacks. First, existing control methods typically rely on fixed thresholds or manual mode selection, making it difficult to adjust rotation speed and amplitude in real time based on actual massage pressure changes, resulting in insufficient stability of massage intensity. Second, the adjustment of operating parameters in existing fascia guns is often independently designed from the mechanical structure, with low coupling between stroke adjustment, power output, and control strategies, making it difficult to achieve continuous and controllable intensity changes while ensuring safety. Third, existing fascia gun devices are mostly controlled locally, lacking effective connection with user terminals and remote platforms, making it difficult to continuously record and analyze operating data and usage conditions, thus limiting the further development of fascia guns in personalized massage program development, status tracking, and intelligent applications. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a fascia gun operation control method and system based on the Internet of Things to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an IoT-based fascia gun operation control method is provided. The fascia gun includes a gun body, a main control circuit board, and a variable stroke adjustment assembly. The main control circuit board is installed in the rear middle of the gun body. The variable stroke adjustment assembly is assembled inside the gun body, and the variable stroke adjustment assembly consists of a geared motor, a screw, a slider, a displacement electromagnet, a brush assembly, and a conductive ring. The geared motor and the screw are axially coaxially connected, and the slider is engaged and sleeved on the outside of the screw. The displacement electromagnet is in contact with the brush assembly and away from the conductive ring. The brush assembly is electrically connected to the conductive ring, and the conductive ring is located at the bottom of the geared motor. The IoT-based fascia gun operation control includes the following steps: Step S1: Start the fascia gun and use the main control circuit board to collect pressure sensor data in real time. If the pressure sensor data exceeds the preset pressure threshold, the operating mode will be dynamically adjusted. Step S2: In operation mode, the main control circuit board energizes the displacement electromagnet, causing the brush assembly to contact the conductive ring, driving the reduction motor to rotate the screw, the screw pushes the slider to generate displacement, and determines the amount of eccentricity change. Step S3: Determine the motion condition based on the change in eccentricity; perform adaptive adjustment of the operating mode according to the motion condition and record the working condition data log; upload the working condition data log to the Internet of Things platform, and use the main control circuit board to disconnect the power supply of the displacement electromagnet, so that the brush assembly is separated from the conductive ring, and complete the fascia gun operation control task.

[0005] The beneficial effects of this invention are as follows: (1) The pressure sensor data is collected in real time through the main control circuit board, and the operation mode of the fascia gun is dynamically adjusted in combination with the user's personalized massage plan obtained by the Internet of Things. During use, the speed and torque parameters can be automatically matched according to the changes in external load, avoiding the problem of massage intensity fluctuation under traditional fixed gear control, and improving the stability and consistency of operation control.

[0006] (2) By using a variable stroke adjustment component consisting of a geared motor, screw, slider and displacement electromagnet, the screw is driven to generate controllable displacement according to the operating mode and the change in eccentricity is accurately calculated to achieve continuous adjustable control of amplitude, so that the fascia gun can maintain a smooth transition of output parameters under different massage intensity requirements and improve the controllability of the massage process.

[0007] (3) By quantitatively analyzing the change in eccentricity and classifying motion conditions such as low intensity, medium intensity and high intensity, and combining different working conditions to implement adaptive adjustment of the operation mode, a collaborative control mechanism can be formed between structural adjustment and motor control, avoiding the inadequacy of adaptation caused by single parameter adjustment.

[0008] (4) Upload the working condition data logs during operation to the Internet of Things platform to realize continuous recording and remote management of the fascia gun's operating status, usage conditions and parameter changes, and provide reliable data support for subsequent personalized massage program optimization, equipment status analysis and intelligent application. Attached Figure Description

[0009] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the steps of a fascia gun operation control method based on the Internet of Things according to the present invention; Figure 2 This is a schematic diagram of the core hardware layout of the variable amplitude fascia gun in this invention; Figure 3 This is a schematic diagram of the control and adjustment components for the variable amplitude fascia gun of the present invention; Figure 4 This is a schematic diagram of the core transmission component of the variable amplitude fascia gun of the present invention; Figure 5 This is a schematic plan view of the components of the variable amplitude fascia gun of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 5This invention provides an IoT-based fascia gun operation control method. The fascia gun includes a gun body 10, a main control circuit board 20, and a variable stroke adjustment component 30. The main control circuit board 20 is installed in the rear part of the gun body 10. The variable stroke adjustment component 30 is assembled inside the gun body 10. The variable stroke adjustment component 30 consists of a reduction motor 301, a screw 302, a slider 303, a displacement electromagnet 304, a brush assembly 305, and a conductive ring 306. The reduction motor 301 and the screw 302 are axially coaxially connected. The slider 303 is fitted onto the outside of the screw 302. The displacement electromagnet 304 is in contact with the brush assembly 305 and away from the conductive ring 306. The brush assembly 305 is electrically connected to the conductive ring 306. The conductive ring 306 is located at the bottom of the reduction motor 301. The IoT-based fascia gun operation control method includes the following steps: Step S1: Start the fascia gun and use the main control circuit board to collect pressure sensor data in real time. If the pressure sensor data exceeds the preset pressure threshold, the operating mode will be dynamically adjusted. In one embodiment, after the fascia gun is started, the main control circuit board continuously collects data from the pressure sensor according to a preset sampling frequency, and compares and updates the acquired pressure sensing data in real time. When the current pressure sensing data exceeds the preset pressure threshold, the main control circuit board determines that the fascia gun is in a high-load massage state and triggers the operation mode adjustment process. Based on the pressure change state, it enters the corresponding operation mode, providing control conditions for subsequent stroke adjustment and power control.

[0014] In another embodiment, it is assumed that the main control circuit board collects pressure sensing data at a sampling frequency of 200Hz, and the preset pressure threshold is 30N. When the pressure values ​​in five consecutive sampling cycles are 32N, 35N, 34N, 36N and 33N respectively, the main control circuit board determines that the pressure sensing data continuously exceeds the pressure threshold, and then switches from the initial operation mode to the dynamic adjustment mode to trigger stroke adjustment and eccentricity control operations.

[0015] Step S2: In operation mode, the main control circuit board energizes the displacement electromagnet, causing the brush assembly to contact the conductive ring, driving the reduction motor to rotate the screw, the screw pushes the slider to generate displacement, and determines the amount of eccentricity change. In one embodiment, after determining the operating mode, the main control circuit board energizes the displacement electromagnet, causing the brush assembly to form a conductive connection with the conductive ring; in the conductive state, the main control circuit board controls the geared motor to start, driving the screw to rotate in the set direction, and the screw pushes the slider to generate displacement along the axial direction; the main control circuit board calculates the slider displacement based on the running time of the geared motor and the screw parameters, and determines the eccentricity change based on the initial eccentricity.

[0016] In another embodiment, assuming the driving voltage of the displacement electromagnet is 12V, the speed of the geared motor is 120r / min, and the screw pitch is 2mm / rev; when the geared motor runs continuously for 0.5s, the screw rotation angle is about 60°, corresponding to an axial displacement of about 0.33mm for the slider; with an initial eccentricity of 2.0mm, the main control circuit board calculates the eccentricity change as 0.33mm and uses this value as the input parameter for subsequent working condition determination.

[0017] Step S3: Determine the motion condition based on the change in eccentricity; perform adaptive adjustment of the operating mode according to the motion condition and record the working condition data log; upload the working condition data log to the Internet of Things platform, and use the main control circuit board to disconnect the power supply of the displacement electromagnet, so that the brush assembly is separated from the conductive ring, and complete the fascia gun operation control task.

[0018] In one embodiment, the main control circuit board compares the acquired eccentricity change with a preset eccentricity range to determine the current motion condition of the fascia gun. Based on the determined motion condition, the main control circuit board adaptively adjusts the operating parameters of the fascia gun, such as rotation speed and amplitude, and synchronously records the corresponding working condition data log. Subsequently, the working condition data log is uploaded to the Internet of Things platform, and the power supply of the displacement electromagnet is disconnected, so that the brush assembly is separated from the conductive ring.

[0019] In another embodiment, assuming the eccentricity change is 0.33mm, and a preset range of 0.1mm to 0.5mm corresponds to low-intensity exercise conditions, the main control circuit board determines that the current condition is a low-intensity massage condition and sets the rotation speed to 2100r / min and the amplitude to 7mm. At the same time, it records the condition data log, which includes timestamps, pressure peak of 40N, rotation speed and eccentricity change, and uploads it to the Internet of Things platform via Bluetooth module to complete the fascia gun operation control task.

[0020] It should be noted that you should refer to [link / reference]. Figure 2 This is a core hardware layout diagram of a variable amplitude fascia gun. The overall design features a blue and silver shell. The front area shows a geared motor (which works in conjunction with the screw, slider, and other transmission components to adjust the eccentricity to change the amplitude). The main control circuit board is embedded in the middle (with buttons for switching gears and modes). The handle at the bottom integrates the battery (which powers the entire device). All the core components are arranged in an orderly manner inside the gun body, visually demonstrating the hardware architecture and component distribution of the fascia gun, which consists of "power transmission-control-power supply".

[0021] It should be noted that you should refer to [link / reference]. Figure 3This is a schematic diagram of the control and adjustment components of a variable amplitude fascia gun. The overall shell is blue and silver, and the transmission components are visible at the front. Inside, there is a displacement electromagnet (used to control the on / off state of the brush and the conductive ring). The middle of the gun body integrates the switch button and the working speed adjustment and amplitude adjustment + / - buttons (used to control the gear and amplitude). The components are assembled in an orderly manner, clearly showing the layout of the control, transmission and operation components of the fascia gun.

[0022] It should be noted that you should refer to [link / reference]. Figure 4 This is a partial schematic diagram of the core transmission components of the variable amplitude fascia gun, showing components such as the screw and screw slider. It illustrates that when the geared motor drives the screw to rotate, the slider will change position to change the eccentricity. The eccentricity size can be determined by the position detection of the circuit board and the working time of the geared motor, clearly presenting the core structure and working principle of amplitude adjustment.

[0023] It should be noted that you should refer to [link / reference]. Figure 5 This is a plan view of a variable amplitude fascia gun. The fascia gun includes a gun body 10, a main control circuit board 20, and a variable stroke adjustment assembly 30. The main control circuit board 20 is installed in the middle and rear of the gun body 10. The variable stroke adjustment assembly 30 is assembled inside the gun body 10. The variable stroke adjustment assembly 30 consists of a geared motor 301, a screw 302, a slider 303, a displacement electromagnet 304, a brush assembly 305, and a conductive ring 306. The geared motor 301 and the screw 302 are axially coaxially connected. The slider 303 is fitted on the outside of the screw 302. The displacement electromagnet 304 is in contact with the brush assembly 305 and away from the conductive ring 306. The brush assembly 305 is electrically connected to the conductive ring 306. The conductive ring 306 is located at the bottom of the geared motor 301.

[0024] Preferably, step S1 includes: The fascia gun establishes an IoT connection with the user's mobile terminal via built-in Bluetooth to obtain the user's personalized massage plan; when the fascia gun is turned on, the main control circuit board collects pressure sensor data in real time. In one embodiment, an Internet of Things (IoT) connection is established between the fascia gun and the user's mobile terminal via the built-in Bluetooth communication module. After the connection is established, the user's personalized massage plan is read and downloaded from the user's mobile terminal. The massage plan includes at least the massage intensity level, the duration of a single massage session, and information on the target massage area. Subsequently, the fascia gun is activated, and the main control circuit board collects data in real time from the pressure sensors installed in the massage head assembly, forming a continuously updated pressure sensing data stream. The collected pressure sensing data is used as the basic input parameter for subsequent operation mode adjustment.

[0025] In another embodiment, assuming the fascia gun establishes a connection with a smartphone via Bluetooth 5.0 protocol with a communication cycle of 50ms, and the acquired personalized massage plan is set with a massage intensity level of 3 and a single massage duration of 10min; after the fascia gun is started, the main control circuit board collects data from the pressure sensor at a sampling frequency of 250Hz, continuously acquiring a pressure value sequence, for example, the pressure data obtained during continuous sampling is [22N, 26N, 31N, 34N, 33N], and stores the pressure data sequence in the buffer area for subsequent threshold judgment.

[0026] If the pressure sensor data exceeds the preset pressure threshold, an intensity adjustment command is generated based on the massage intensity in the user's personalized massage plan; the operating mode is dynamically adjusted according to the intensity adjustment command, including a no-power compensation mode and a power compensation mode.

[0027] In one embodiment, the main control circuit board collects pressure sensing data output by the pressure sensor in real time at a preset sampling frequency and has an internal pressure determination logic module. When the pressure sensing data within the current sampling period exceeds a preset pressure threshold, the main control circuit board first performs a continuity check on the pressure sensing data to eliminate instantaneous impacts or noise interference. After confirming that the pressure exceeds the threshold, the main control circuit board combines the massage intensity parameters pre-configured in the user's personalized massage plan to generate a force adjustment command that matches the current pressure state. Subsequently, the main control circuit board parses the force adjustment command and dynamically switches the fascia gun's operating mode between a no-power compensation mode and a power compensation mode based on the parsing results, thereby achieving graded control and adaptive adjustment of the fascia gun's operating state.

[0028] In another embodiment, assuming the preset pressure threshold is set to 30 N, the main control circuit board collects pressure sensing data at a sampling period of 10 ms. When the pressure sensing data collected in three consecutive sampling periods is greater than 30 N, the main control circuit board determines that the current massage contact state is a pressure over-threshold state. At this time, the main control circuit board calls the target massage intensity parameter set in the user's massage plan, assuming this parameter is "enhanced", and generates a corresponding intensity adjustment command accordingly. The main control circuit board parses the intensity adjustment command, determines that the target intensity parameter falls within the preset compensation intensity range, thereby triggering the operation mode switching logic, switching the fascia gun's operation mode from non-powered compensation mode to powered compensation mode. Simultaneously, the main control circuit board writes the pressure over-threshold determination result and the corresponding operation mode switching status into the operation status register for subsequent drive parameter adjustment and operation data recording.

[0029] Preferably, the operating mode that dynamically adjusts according to the force adjustment command includes: The intensity adjustment command is analyzed. When the massage intensity is within the basic intensity range, the fascia gun is set to the non-power compensation mode. The drive motor speed setting value for the basic intensity range is 1800r / min~2600r / min, and the corresponding torque setting value is 0.8N·m~1.5N·m. In one embodiment, the main control circuit board parses the received intensity adjustment command, extracts the target massage intensity parameter from the command data, and performs index matching between the parameter and a pre-established intensity-drive parameter correspondence table; then it reads the speed reference value and torque reference value corresponding to the parameter and writes them into the parameter register area of ​​the motor control module to limit the initial operating state of the drive motor.

[0030] In another embodiment, assuming the target massage intensity parameter carried in the force adjustment command is 2.3 (range 0-5), the main control circuit board maps this value to the medium-low intensity parameter range and reads the corresponding rotation speed range of 1800r / min to 2400r / min and torque range of 0.9N·m to 1.3N·m; the main control circuit board further selects 2200r / min and 1.1N·m as initial operating parameters and completes the loading.

[0031] When the target force parameter falls within the compensation force range, the fascia gun's operating mode is determined to be the power compensation mode. The drive motor speed setting value for the compensation force range is 2600r / min~3400r / min, and the corresponding torque setting value is 1.5N·m~2.5N·m. In one embodiment, after the main control circuit board completes the force parameter analysis, it compares the target massage intensity parameter with the preset compensation trigger range. When the comparison result meets the high load operation conditions, the main control circuit board calls the corresponding high power operation parameters from the compensation parameter table and updates the speed and torque control range in the motor control module.

[0032] In another embodiment, assuming the target massage intensity parameter is 4.6, the main control circuit board determines that the parameter is within the high load range and reads the corresponding drive parameter group from the memory, wherein the speed control range is 2600r / min~3400r / min and the torque control range is 1.6N·m~2.4N·m; then the initial value of the speed is set to 3000r / min and the initial value of the torque is set to 2.0N·m, and the parameter switching is completed.

[0033] When the force adjustment command indicates that the fascia gun is in the no-power compensation mode, the main control circuit board keeps the preset speed setting value and torque setting value unchanged. In one embodiment, the main control circuit board continuously maintains the currently loaded speed and torque parameters without updating during the drive control process, only executes conventional closed-loop control logic to correct motor speed errors, and monitors whether the operating status meets the trigger conditions for re-analyzing the force adjustment command.

[0034] In another embodiment, it is assumed that during the current operating phase, the target speed of the drive motor is 2200 r / min and the target torque is 1.1 N·m; during a continuous 8-second operating cycle, the pressure value detected by the pressure sensor fluctuates between 24 N and 28 N, the main control circuit board does not rewrite the drive parameter register area, and the motor always runs stably according to the predetermined parameters.

[0035] When the force adjustment command indicates that the fascia gun is in power compensation mode, the main control circuit board sets the speed range to 2800r / min~3600r / min and the torque range to 2.0N·m~3.5N·m.

[0036] In one embodiment, the main control circuit board allows the speed and torque of the drive motor to be dynamically adjusted within a preset high-load control range during operation, and updates the target parameters according to real-time load changes to maintain the continuity and stability of the drive output.

[0037] In another embodiment, it is assumed that the main control circuit board sets the speed adjustment range of the drive motor to 2800 r / min to 3600 r / min and the torque adjustment range to 2.0 N·m to 3.5 N·m. When the load is detected to be continuously increasing, the control module gradually increases the speed from 3000 r / min to 3300 r / min and the torque from 2.2 N·m to 2.8 N·m, thus completing one dynamic parameter adjustment process.

[0038] Preferably, the massage intensity is divided into 4 levels, corresponding to lighting 1-4 pressure-sensing LEDs. When there is no pressure, all pressure-sensing LEDs are off. In the power compensation mode, the pressure-sensing LEDs are displayed in orange-red, and in the no-power compensation mode, they are displayed in blue.

[0039] In one embodiment, the main control circuit board divides the massage intensity into four discrete levels and establishes a correspondence between the massage intensity levels and the display status of the pressure-sensing LEDs during system initialization. When no external pressure is applied to the fascia gun by the user, the main control circuit board keeps all pressure-sensing LEDs off. When external pressure is detected and the massage intensity level is determined, the main control circuit board sequentially illuminates the corresponding number of pressure-sensing LEDs according to the level, with 1 LED lit for the first level, 2 LEDs lit for the second level, 3 LEDs lit for the third level, and 4 LEDs lit for the fourth level. Simultaneously, the main control circuit board controls the color of the pressure-sensing LEDs according to the current operating mode. In power compensation mode, the pressure-sensing LEDs are driven to display orange-red, and in no-power compensation mode, they are driven to display blue, thus providing a visual indication of the massage intensity and operating mode.

[0040] In another embodiment, the massage intensity parameter is assumed to range from 0 to 100, where 0 represents no external pressure, 1 to 25 correspond to the first massage intensity level, 26 to 50 correspond to the second massage intensity level, 51 to 75 correspond to the third massage intensity level, and 76 to 100 correspond to the fourth massage intensity level. When the pressure detection value is 0, the main control circuit board shuts off the drive current of the four pressure-sensing LEDs; when the pressure detection value is 18, one pressure-sensing LED is lit; when the pressure detection value is 42, two pressure-sensing LEDs are lit; when the pressure detection value is 63, three pressure-sensing LEDs are lit; and when the pressure detection value is 88, all four pressure-sensing LEDs are lit. Simultaneously, if the current operating mode is determined to be power compensation mode, all four pressure-sensing LEDs display an orange-red light source with a wavelength of approximately 610nm; if the operating mode is determined to be no power compensation mode, the four pressure-sensing LEDs display a blue light source with a wavelength of approximately 470nm, thus completing the synchronous indication of the massage intensity level and the operating mode.

[0041] Preferably, step S2 includes: In operation mode, the main control circuit board energizes the displacement electromagnet, causing the brush assembly to contact the conductive ring, driving the geared motor to rotate, and recording the rotation direction of the geared motor; the direction of rotation of the geared motor is used to determine whether the screw rotates in the forward or reverse direction. In one embodiment, when the fascia gun enters the operating mode, the main control circuit board applies a driving current to the displacement electromagnet, causing the electromagnet to generate an axial attraction force, thereby pushing the brush assembly to move along the guide structure and form a stable electrical contact with the conductive ring. Subsequently, the main control circuit board outputs a start signal to the geared motor, driving the geared motor to start rotating, and collects the motor phase change information in real time through a Hall sensor or encoder to determine the actual rotation direction of the geared motor. Based on the detected rotation direction signal, the main control circuit board maps it to the forward or reverse rotation state of the screw, and records the rotation direction as the direction parameter of the current stroke adjustment for subsequent displacement calculation and stroke control.

[0042] In another embodiment, assuming the displacement electromagnet has a rated operating voltage of 12V and a current of 0.35A, and the brush assembly completes contact with the conductive ring within 20ms after energization; the geared motor is a DC geared motor with a rated speed of 3000 r / min and a reduction ratio of 1:20. The motor encoder detects that phase A is 90° ahead of phase B, determining that the geared motor is rotating clockwise, corresponding to the screw rotating in the forward direction; when phase B is detected to be 90° ahead of phase A, it is determined to be rotating counterclockwise, corresponding to the screw rotating in the reverse direction. The main control circuit board stores this direction information in the control register as the directional basis for subsequent stroke adjustment and eccentricity calculation.

[0043] Calculate the rotation time of the screw in either the forward or reverse direction, and use the rotation time to calculate the screw rotation angle; determine the axial displacement of the slider based on the preset screw pitch and screw rotation angle; calculate the change in eccentricity based on the axial displacement of the slider and the preset initial eccentricity.

[0044] In one embodiment, after determining the screw's rotation direction, the main control circuit board calculates the effective rotation time of the screw within the current control cycle based on the start / stop time and speed parameters of the geared motor, and calculates the actual rotation angle of the screw by combining the motor speed. Subsequently, the main control circuit board converts the screw rotation angle into a linear displacement of the slider along the screw axis based on pre-stored screw pitch parameters. After obtaining the slider's axial displacement, the main control circuit board compares this displacement with the initial eccentricity set during system initialization to obtain the change in the current eccentricity, which serves as the basis for adjusting the subsequent striking stroke control.

[0045] In another embodiment, assuming the screw pitch is 2.0 mm / revolution and the output shaft speed of the geared motor is 150 r / min, and the continuous rotation time during one adjustment is 0.8 s, then the screw rotation angle is 720°, corresponding to 2 revolutions, and the axial displacement of the slider is 4.0 mm. If the system's preset initial eccentricity is 6.0 mm, then the calculated change in eccentricity during this adjustment process is +4.0 mm, making the current eccentricity reach 10.0 mm. When the screw rotates in the opposite direction and the rotation angle is 360°, the corresponding slider retracts by 2.0 mm, and the eccentricity decreases accordingly to 8.0 mm, thereby achieving continuous adjustable control of the striking stroke.

[0046] Preferably, determining the axial displacement of the slider based on the preset screw pitch and screw rotation angle includes: The linear displacement distance of the slider along the screw is calculated based on the preset screw pitch and screw rotation angle. In one embodiment, after acquiring the real-time rotation angle of the screw, the main control circuit board calls the pre-stored screw pitch parameters to convert the angle information into a corresponding linear displacement. Specifically, the main control circuit board calculates the equivalent number of rotations of the screw in the current adjustment cycle based on the proportional relationship between the screw rotation angle and the full rotation angle, and further multiplies this number of rotations by the screw pitch parameters to obtain the theoretical linear displacement distance of the slider along the screw axis. This linear displacement distance, as an intermediate calculation result of the slider displacement, is temporarily stored in the control buffer for subsequent effective stroke judgment and displacement correction processing.

[0047] In another embodiment, assuming the screw pitch is 2.5 mm / revolution, and the main control circuit board detects that the screw rotates at angles of 180°, 360°, and 540° during a single adjustment, the corresponding number of rotations are 0.5, 1, and 1.5 revolutions, respectively. Based on the screw pitch conversion, the corresponding linear displacement distances are 1.25 mm, 2.5 mm, and 3.75 mm, respectively. These linear displacement distances are recorded to describe the axial movement of the slider under different adjustment ranges.

[0048] If the linear displacement distance exceeds the effective stroke range of the screw, the linear displacement distance is limited to the maximum boundary value within the effective stroke range to determine the axial displacement.

[0049] In one embodiment, after obtaining the theoretical linear displacement distance of the slider, the main control circuit board compares this displacement distance with the preset effective stroke range of the screw. If the linear displacement distance exceeds the maximum axial movement range allowed by the screw in the structural design, the main control circuit board performs a limiting process on the linear displacement distance to ensure it does not exceed the boundary value of the effective stroke range, thereby determining the final axial displacement. This method avoids the slider movement from exceeding the allowable range of the mechanical structure, preventing problems such as jamming, impact, or mechanism failure.

[0050] In another embodiment, assuming the effective stroke range of the screw is 0–8.0 mm, when the linear displacement distances calculated in step S1 are 6.5 mm, 8.0 mm, and 9.2 mm, respectively, the main control circuit board determines that the first two displacement results are within the effective stroke range and directly outputs them as the axial displacement. For the calculation result of 9.2 mm, the stroke limit logic is triggered, correcting it to 8.0 mm as the final axial displacement. By constraining the boundary of the over-limit displacement, the safe and controllable axial movement of the slider is achieved.

[0051] Preferably, step S3, determining the motion condition based on the change in eccentricity, includes: The change in eccentricity is compared with the preset eccentricity threshold range. When the change in eccentricity is between 0.5mm and 1.5mm, the current motion condition is determined to be a low-intensity massage condition. In one embodiment, after calculating the motion parameters of the eccentric mechanism, the main control circuit board inputs the obtained change in eccentricity as a key working condition judgment parameter to the working condition identification module. This module pre-stores multiple eccentricity threshold intervals, where the first threshold interval describes a small-amplitude eccentricity adjustment state. The main control circuit board determines whether the current change in eccentricity falls within this interval through interval comparison. When the determination is successful, it generates a corresponding low-intensity massage working condition identification signal to characterize that the current vibration output is in a soft, shallow stimulation state, thereby providing a basis for subsequent operating parameter maintenance, display prompts, and safety control.

[0052] In another embodiment, assuming the first eccentricity threshold range is set to 0.5mm to 1.5mm, when the eccentricity changes continuously collected by the system are 0.7mm, 1.1mm and 1.3mm respectively, the main control circuit board performs range determination on the above values ​​and uniformly identifies them as low-intensity massage conditions; where 0.7mm corresponds to mild soothing mode, 1.1mm corresponds to regular low-intensity mode, and 1.3mm corresponds to low-intensity upper limit mode, which is used to refine the distinction of the operating state within low intensity.

[0053] When the change in eccentricity is between 1.5mm and 3.0mm, the current motion condition is determined to be a medium-intensity massage condition. In one embodiment, when the change in eccentricity does not meet the criteria for a low-intensity massage condition, the main control circuit board automatically switches to a medium-intensity condition determination process and performs a matching analysis between the change in eccentricity and a second eccentricity threshold range. The second threshold range describes the eccentric mechanism in a medium-stroke adjustment state, reflecting the operating characteristics of the fascia gun in daily deep massage scenarios. If the criteria are met, the main control circuit board generates a medium-intensity massage condition identifier and uses this condition as the current primary motion state for subsequent power output management.

[0054] In another embodiment, assuming the second eccentricity threshold range is set to 1.5mm to 3.0mm, when the system detects eccentricity changes of 1.8mm, 2.2mm, and 2.8mm, they correspond to three operating states: medium intensity primary, medium intensity standard, and medium intensity enhanced, respectively. The main control circuit board can make fine adjustments to the vibration frequency or duration without changing the operating condition level to improve the adaptability of the massage process.

[0055] When the change in eccentricity is between 3.0mm and 5.0mm, the current motion condition is determined to be a high-intensity massage condition. In one embodiment, when the change in eccentricity exceeds the upper limit of the medium-intensity threshold range, the main control circuit board inputs it into the high-intensity condition determination logic and compares it with the third eccentricity threshold range. This threshold range characterizes the motion state of the eccentric mechanism producing a large stroke and high impact amplitude. If the change in eccentricity falls within this range, the system determines that the current condition is a high-intensity massage condition and simultaneously triggers a high-intensity operation indicator to remind the user that they are currently in a deep or high-impact massage state, while also serving as an important reference for safety limit control.

[0056] In another embodiment, assuming the third eccentricity threshold range is 3.0mm to 5.0mm, when the detected eccentricity change is 3.4mm, 4.1mm and 4.7mm, the main control circuit board identifies it as one of three states: high-strength foundation, high-strength reinforcement and high-strength limit, respectively; among them, 4.7mm is close to the upper limit value, which can trigger additional protection judgment to prevent long-term overload operation.

[0057] Low-intensity massage, medium-intensity massage, and high-intensity massage are considered as exercise conditions.

[0058] In one embodiment, the main control circuit board integrates the identified low-intensity, medium-intensity, and high-intensity massage conditions into a unified motion condition management system, and uses these motion conditions as standardized descriptive units for the fascia gun's operating status. A one-to-one mapping relationship is established between various motion conditions and their corresponding eccentricity intervals, supporting subsequent parameter scheduling, operation recording, anomaly detection, and human-machine interaction display, thereby achieving structured management of the overall machine's operating status.

[0059] In another embodiment, assuming that during a continuous use, the system sequentially determines three low-intensity working conditions, six medium-intensity working conditions, and two high-intensity working conditions, the main control circuit board stores the above working conditions in chronological order as a motion working condition sequence and associates them with the corresponding eccentricity change, running time, and mode switching information for subsequent use behavior analysis or uploading to the cloud for statistical processing.

[0060] Preferably, in step S3, the operating mode is adaptively adjusted according to the motion conditions, and the recorded operating condition data log includes: The user's muscle fatigue index is determined based on the exercise conditions; when the muscle fatigue index is less than 30, the rotation speed is set to 1800 r / min to 2200 r / min, and the amplitude is 6 mm to 8 mm. In one embodiment, the system first calculates the user's muscle fatigue index based on the currently acquired motion condition data, wherein the motion condition data includes at least the device's operating speed, load change rate, continuous running time per unit time, and vibration output stability parameters. The control module inputs the above-mentioned motion condition data into the fatigue index calculation unit, and generates a muscle fatigue index in the range of 0 to 100 through weighted normalization. When the calculated muscle fatigue index is less than a preset threshold of 30, it is determined that the user's muscles are in a low fatigue state. Based on this, the control module outputs a low-intensity operation control command to the drive unit, limiting the motor speed to the range of 1800 r / min to 2200 r / min, and simultaneously controlling the output amplitude of the vibration mechanism to the range of 6 mm to 8 mm, so as to achieve a soothing massage output.

[0061] In another embodiment, assuming the system continuously collects three sets of motion condition data, the corresponding calculated muscle fatigue indices are 12, 18, and 26. When the fatigue index is 12, the control module sets the rotation speed to 1800 r / min and the amplitude to 6.2 mm; when the fatigue index is 18, the rotation speed is increased to 2000 r / min and the amplitude is adjusted to 7.1 mm; when the fatigue index is 26, the rotation speed is further adjusted to 2200 r / min and the amplitude is adjusted to 7.8 mm. By finely adjusting the speed as the fatigue index increases within the low fatigue range, the output intensity of the device is continuously matched with the user's muscle state, avoiding abrupt stimulation.

[0062] When the muscle fatigue index is between 30 and 60, the rotation speed is set to 2200 r / min to 2800 r / min and the amplitude is 8 mm to 10 mm; when the muscle fatigue index is greater than 60, the rotation speed is set to 2800 r / min to 3400 r / min and the amplitude is 10 mm to 12 mm; and the working condition data log is collected synchronously.

[0063] In one embodiment, when the calculated muscle fatigue index does not meet the low fatigue condition, the system enters the medium-to-high fatigue condition judgment process. If the fatigue index is in the range of 30 to 60, the control module determines that the user's muscles are in a moderate fatigue state and switches the equipment operating parameters to a medium-intensity output mode, keeping the motor speed between 2200 r / min and 2800 r / min, while controlling the amplitude within the range of 8 mm to 10 mm. If the fatigue index is greater than 60, the system determines that the user's muscles are in a high fatigue state, and enters a high-intensity intervention mode, increasing the speed to 2800 r / min to 3400 r / min and expanding the amplitude to 10 mm to 12 mm. Simultaneously, the system records the changes in operating speed, amplitude, duration, and fatigue index in both modes, forming a condition data log.

[0064] In another embodiment, assuming that during a continuous use, the system detects muscle fatigue indices of 35, 48, 59, 65, and 82 respectively; when the fatigue index is 35, the rotation speed is set to 2300 r / min and the amplitude is 8.4 mm; when the fatigue index is 48, the rotation speed is adjusted to 2550 r / min and the amplitude is 9.1 mm; when the fatigue index is 59, the rotation speed is increased to 2750 r / min and the amplitude is 9.8 mm; when the fatigue index is 65, the system enters a high fatigue mode, with the rotation speed set to 3000 r / min and the amplitude to 10.6 mm; when the fatigue index reaches 82, the rotation speed is limited to 3400 r / min and the amplitude is set to 11.8 mm, and this is marked as a high fatigue risk segment in the operating data log for subsequent analysis or safety control.

[0065] Preferably, determining the user's muscle fatigue index based on exercise conditions includes: Electromyography (EMG) electrodes are placed at the massage contact points of the fascia gun; the user's EMG signals are acquired using the EMG electrodes, filtered, and output as standardized EMG signals; the sampling window length for EMG parameters is determined based on the exercise conditions and the standardized EMG signals. In one embodiment, at least two sets of surface electromyography (EMG) electrodes are disposed on the side of the area where the fascia gun massage head contacts the user's skin. The electrodes are made of silver / silver chloride, and the electrode spacing is controlled at 15–25 mm to ensure stable acquisition of the electrical activity of the target muscle group. The EMG electrodes are connected to the signal acquisition module via a front-end analog conditioning circuit. The acquired raw EMG signals are sequentially subjected to bandpass filtering and power frequency notch filtering. The bandpass filtering frequency band is set to 20–450 Hz to suppress low-frequency motion artifacts and high-frequency noise interference; the notch center frequency is set to 50 Hz or 60 Hz. The filtered EMG signals are then normalized to form standardized EMG signals. The control module further combines the current fascia gun's rotation speed, amplitude, and continuous working time, among other motion parameters, to adaptively determine the EMG parameter sampling window length, balancing real-time performance and characteristic stability.

[0066] In another embodiment, it is assumed that three sets of electromyography (EMG) acquisition electrodes are deployed during the massage process, with electrode spacing of 18mm, 20mm, and 22mm, respectively; the peak-to-peak range of the acquired raw EMG signals is 0.3mV to 2.1mV. After bandpass filtering of 20–450Hz and notch filtering of 50Hz, the standardized EMG signal amplitude is mapped to the [0,1] interval. Further assuming that the current rotation speed of the fascia gun is 2000r / min, 2600r / min, and 3200r / min, corresponding to amplitudes of 8mm, 10mm, and 12mm, the system sets the EMG parameter sampling window length to 200ms, 300ms, and 400ms, respectively, to adapt to the rate of change of EMG signals under different intensity conditions.

[0067] Of particular importance is the acquisition of user electromyographic signals using electromyography (EMG) acquisition electrodes, including: The electromyography (EMG) acquisition electrodes are activated and a periodic electrical signal is output; the periodic electrical signal is quantized and a quantized signal is output; the quantized signal is encoded to generate a digital signal. In one embodiment, once the fascia gun is in operation, the control module sends a start command to the electromyography (EMG) electrodes positioned at the massage contact area, causing the electrodes to enter a stable operating mode and continuously output periodic electrical signals corresponding to the electrical activity of human muscles. These periodic electrical signals are amplified and baseline-corrected by a front-end analog signal conditioning circuit before being input to an analog-to-digital converter (ADC) unit. The ADC unit quantizes the periodic electrical signals, converting the continuous analog signals into discrete quantized signals. Subsequently, the processing module encodes the quantized signals according to a preset encoding rule, converting them into digital signals suitable for digital processing and storage, serving as the basis for subsequent amplitude calibration and signal construction.

[0068] In another embodiment, assuming the frequency range of the periodic electrical signal output by the electromyography (EMG) acquisition electrodes is 20Hz to 400Hz, and the original voltage amplitude range is ±2.5mV; and the sampling rate of the analog-to-digital converter is set to 1000Hz and the quantization bit depth is 12 bits, then the resolution of the quantized signal is approximately 1.22μV. Further assuming that encoding processing is performed on 1000 continuously acquired sampling points, a corresponding digital signal sequence with a length of 1000×12 bits is generated using unsigned binary encoding to characterize the EMG activity state within that cycle.

[0069] Amplitude calibration is performed based on the digital signal, and the calibration results are recorded. Time stamps and sampling numbers are added to the calibration results, and the calibration results are formatted using the time stamps and sampling numbers to form the user's electromyographic signal.

[0070] In one embodiment, after receiving the generated digital signal, the processing module performs amplitude calibration processing on the digital signal based on pre-calibrated gain parameters and zero-point offset parameters of the electromyography (EMG) acquisition system to eliminate systematic errors caused by different electrode channels and acquisition environments, and obtains the corresponding calibration results. Subsequently, a time stamp and sampling sequence number are added to each set of calibration results, where the time stamp is used to characterize the acquisition time of the calibration result, and the sampling sequence number is used to characterize its sequential position within the current sampling period. The processing module further encapsulates the calibration results containing the time stamp and sampling sequence number according to a preset data format to form structured user EMG signal data.

[0071] In another embodiment, assuming the digital signal amplitude range obtained in a single sampling process is 0–4095, after amplitude calibration, it is mapped to the actual electromyographic amplitude range of 0–2.0 mV. A time stamp is added to each calibration result, with a time resolution set to 1 ms, and a sampling sequence number incrementing from 1 to 1000 is added. Further assuming the data is encapsulated using a triplet format of “time stamp-sampling sequence number-calibrated amplitude,” the final user electromyographic signal can be represented as a data sequence containing 1000 records, each corresponding to a specific sampling time and calibrated electromyographic amplitude, used for subsequent fatigue analysis or control decisions.

[0072] The standardized electromyographic signal is segmented and sampled within the sampling window length to determine the characteristics of the electromyographic signal; the user's muscle fatigue index is calculated using the characteristics of the electromyographic signal.

[0073] In one embodiment, within a defined sampling window length for electromyography (EMG) parameters, the system segments the standardized EMG signal into multiple continuous EMG signal segments at a fixed step size. For each signal segment, its time-domain and frequency-domain feature parameters are extracted. These feature parameters include at least the root mean square (RMS), mean absolute value (MAV), zero crossover rate (ZCR), and median frequency (MF). The control module performs normalization and fusion processing on the above EMG signal features to construct an EMG feature vector. Combined with historical baseline features, it calculates the user's current muscle fatigue index, enabling the fatigue index to reflect changes in the amplitude and spectral shifts of muscle electrical activity.

[0074] In another embodiment, assuming a sampling window length of 300 ms and a segmentation step size of 50 ms, six electromyographic signal segments can be obtained within a single window. The feature values ​​extracted from one signal segment are: RMS=0.42, MAV=0.36, ZCR=28 Hz, MF=92 Hz; the feature values ​​from another signal segment are: RMS=0.55, MAV=0.48, ZCR=21 Hz, MF=78 Hz. The system weighted and fused these multiple feature segments to obtain a comprehensive electromyographic feature index, and further calculated the corresponding muscle fatigue indices of 32, 47, and 68, respectively, to characterize the evolution of muscle fatigue from mild to moderate and severe fatigue.

[0075] Of particular importance is the segmented sampling of standardized electromyographic signals within the sampling window length to determine the characteristics of the electromyographic signals, including: The electromyography (EMG) parameter sampling window is divided into multiple sampling sub-windows according to a preset time interval; muscle activity signal data of standardized EMG signals are extracted according to the sampling sub-windows respectively; In one embodiment, after obtaining standardized electromyographic (EMG) signals, the processing module divides the EMG parameter sampling window length into multiple equal or unequal-length sub-windows based on the sampling strategy corresponding to the current exercise condition. Each sub-window corresponds to a segment of continuous EMG sampling data. The processing module independently extracts muscle activity signal data within each sub-window and sequentially identifies the data from different sub-windows to maintain the continuity and traceability of the EMG signals in the time dimension. This approach improves the ability to distinguish local muscle activity changes while ensuring the overall sampling integrity.

[0076] In another embodiment, assuming the electromyography parameter sampling window length is set to 2000ms and the preset time interval is 250ms, the sampling window is divided into 8 consecutive sampling sub-windows; each sampling sub-window contains 250 sampling points (sampling rate 1000Hz). Further assuming that the corresponding muscle activity signal data segments are extracted from the 1st to the 8th sampling sub-windows respectively, each with a data length of 250 points, and stored sequentially according to sub-window numbers 1 to 8 for subsequent feature calculation processing.

[0077] Time-domain processing is performed on muscle activity signal data to calculate amplitude-type feature parameters; frequency-domain transformation is performed on muscle activity signal data to extract frequency-type feature parameters; amplitude-type feature parameters and frequency-type feature parameters are used to determine the electromyographic signal characteristics.

[0078] In one embodiment, the processing module performs time-domain analysis on the muscle activity signal data within each sampling sub-window. By statistically analyzing the changes in signal amplitude, amplitude-based feature parameters reflecting muscle contraction intensity are obtained. Simultaneously, frequency-domain transformation is performed on the muscle activity signal data to extract frequency-based feature parameters reflecting muscle activity rhythm and fatigue trends. Subsequently, the processing module combines the amplitude-based and frequency-based feature parameters within the corresponding sampling sub-window to form the electromyographic signal features corresponding to that sub-window, used to characterize the muscle activity state within that time period.

[0079] In another embodiment, it is assumed that 250 muscle activity signal data points within each sampling sub-window are processed. The amplitude-related feature parameters obtained from time-domain processing include: root mean square (RMS), mean absolute value (MAV), and peak-to-peak value (PP), corresponding to value ranges of 0.15–0.85 mV, 0.10–0.70 mV, and 0.30–1.20 mV, respectively. Simultaneously, the signal is analyzed in the frequency domain using Fast Fourier Transform (FFT) to extract the median frequency (MF) (range 60–120 Hz) and the mean frequency (MPF) (range 70–140 Hz) as frequency-related feature parameters. Finally, the three amplitude-related feature parameters and two frequency-related feature parameters corresponding to each sampling sub-window are combined to form a 5-dimensional electromyographic signal feature vector, which is used for subsequent muscle fatigue analysis or operational control decisions.

[0080] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application be incorporated into the invention.

[0081] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for controlling the operation of a fascia gun based on the Internet of Things, characterized in that, The fascia gun includes a gun body, a main control circuit board, and a variable stroke adjustment assembly. The main control circuit board is installed in the rear of the gun body. The variable stroke adjustment assembly is assembled inside the gun body. The variable stroke adjustment assembly consists of a geared motor, a screw, a slider, a displacement electromagnet, a brush assembly, and a conductive ring. The geared motor and the screw are axially coaxially connected, and the slider is fitted on the outside of the screw. The displacement electromagnet is in contact with the brush assembly and away from the conductive ring. The brush assembly is electrically connected to the conductive ring, and the conductive ring is located at the bottom of the geared motor. The method includes the following steps: Step S1: Start the fascia gun and use the main control circuit board to collect pressure sensor data in real time. If the pressure sensor data exceeds the preset pressure threshold, the operating mode will be dynamically adjusted. Step S2: In operation mode, the main control circuit board energizes the displacement electromagnet, causing the brush assembly to contact the conductive ring, driving the reduction motor to rotate the screw, the screw pushes the slider to generate displacement, and determines the amount of eccentricity change. Step S3: Determine the motion condition based on the change in eccentricity; perform adaptive adjustment of the operating mode according to the motion condition and record the working condition data log; upload the working condition data log to the Internet of Things platform, and use the main control circuit board to disconnect the power supply of the displacement electromagnet, so that the brush assembly is separated from the conductive ring, and complete the fascia gun operation control task.

2. The fascia gun operation control method based on the Internet of Things according to claim 1, characterized in that, Step S1 includes: The fascia gun establishes an IoT connection with the user's mobile terminal via built-in Bluetooth to obtain the user's personalized massage plan; when the fascia gun is turned on, the main control circuit board collects pressure sensor data in real time. If the pressure sensor data exceeds the preset pressure threshold, an intensity adjustment command is generated based on the massage intensity in the user's personalized massage plan; the operating mode is dynamically adjusted according to the intensity adjustment command, including a no-power compensation mode and a power compensation mode.

3. The fascia gun operation control method based on the Internet of Things according to claim 2, characterized in that, The operating mode can be dynamically adjusted according to the force adjustment command, including: The intensity adjustment command is analyzed. When the massage intensity is within the basic intensity range, the fascia gun is set to the non-power compensation mode. The drive motor speed setting value for the basic intensity range is 1800r / min~2600r / min, and the corresponding torque setting value is 0.8N·m~1.5N·m. When the target force parameter falls within the compensation force range, the fascia gun's operating mode is determined to be the power compensation mode. The drive motor speed setting value for the compensation force range is 2600r / min~3400r / min, and the corresponding torque setting value is 1.5N·m~2.5N·m. When the force adjustment command indicates that the fascia gun is in the no-power compensation mode, the main control circuit board keeps the preset speed setting value and torque setting value unchanged. When the force adjustment command indicates that the fascia gun is in power compensation mode, the main control circuit board sets the speed range to 2800r / min~3600r / min and the torque range to 2.0N·m~3.5N·m.

4. The fascia gun operation control method based on the Internet of Things according to claim 3, characterized in that, The massage intensity is divided into 4 levels, corresponding to 1-4 pressure-sensing LEDs that light up. When there is no pressure, all pressure-sensing LEDs are off. In power compensation mode, the pressure-sensing LEDs are displayed in orange-red, and in non-power compensation mode, they are displayed in blue.

5. The fascia gun operation control method based on the Internet of Things according to claim 1, characterized in that, Step S2 includes: In operation mode, the main control circuit board energizes the displacement electromagnet, causing the brush assembly to contact the conductive ring, driving the geared motor to rotate, and recording the rotation direction of the geared motor; the direction of rotation of the geared motor is used to determine whether the screw rotates in the forward or reverse direction. Calculate the rotation time of the screw in either the forward or reverse direction, and use the rotation time to calculate the screw rotation angle; determine the axial displacement of the slider based on the preset screw pitch and screw rotation angle; calculate the change in eccentricity based on the axial displacement of the slider and the preset initial eccentricity.

6. The fascia gun operation control method based on the Internet of Things according to claim 5, characterized in that, The axial displacement of the slider is determined based on the preset screw pitch and screw rotation angle, including: The linear displacement distance of the slider along the screw is calculated based on the preset screw pitch and screw rotation angle. If the linear displacement distance exceeds the effective stroke range of the screw, the linear displacement distance is limited to the maximum boundary value within the effective stroke range to determine the axial displacement.

7. The fascia gun operation control method based on the Internet of Things according to claim 1, characterized in that, Step S3, which determines the motion condition based on the change in eccentricity, includes: The change in eccentricity is compared with the preset eccentricity threshold range. When the change in eccentricity is between 0.5mm and 1.5mm, the current motion condition is determined to be a low-intensity massage condition. When the change in eccentricity is between 1.5mm and 3.0mm, the current motion condition is determined to be a medium-intensity massage condition. When the change in eccentricity is between 3.0mm and 5.0mm, the current motion condition is determined to be a high-intensity massage condition. Low-intensity massage, medium-intensity massage, and high-intensity massage are considered as exercise conditions.

8. The fascia gun operation control method based on the Internet of Things according to claim 1, characterized in that, In step S3, the operating mode is adaptively adjusted according to the motion conditions, and the operating condition data log is recorded, including: The user's muscle fatigue index is determined based on the exercise conditions; when the muscle fatigue index is less than 30, the rotation speed is set to 1800 r / min to 2200 r / min, and the amplitude is 6 mm to 8 mm. When the muscle fatigue index is between 30 and 60, the rotation speed is set to 2200 r / min to 2800 r / min and the amplitude is 8 mm to 10 mm; when the muscle fatigue index is greater than 60, the rotation speed is set to 2800 r / min to 3400 r / min and the amplitude is 10 mm to 12 mm; and the working condition data log is collected synchronously.

9. The fascia gun operation control method based on the Internet of Things according to claim 8, characterized in that, Determining a user's muscle fatigue index based on exercise conditions includes: Electromyography (EMG) electrodes are placed at the massage contact points of the fascia gun; the user's EMG signals are acquired using the EMG electrodes, filtered, and output as standardized EMG signals; the sampling window length for EMG parameters is determined based on the exercise conditions and the standardized EMG signals. The standardized electromyographic signal is segmented and sampled within the sampling window length to determine the characteristics of the electromyographic signal; the user's muscle fatigue index is calculated using the characteristics of the electromyographic signal.

10. A fascia gun operation control system based on the Internet of Things, characterized in that, The fascia gun includes a gun body, a main control circuit board, and a variable stroke adjustment assembly. The main control circuit board is installed in the rear of the gun body. The variable stroke adjustment assembly is assembled inside the gun body. The variable stroke adjustment assembly consists of a geared motor, a screw, a slider, a displacement electromagnet, a brush assembly, and a conductive ring. The geared motor and the screw are axially coaxially connected, and the slider is fitted on the outside of the screw. The displacement electromagnet is in contact with the brush assembly and away from the conductive ring. The brush assembly is electrically connected to the conductive ring, and the conductive ring is located at the bottom of the geared motor. This assembly is used to execute the fascia gun operation control method based on the Internet of Things as described in claim 1. The fascia gun operation control system based on the Internet of Things includes: The operation mode adjustment module is used to start the fascia gun. It uses the main control circuit board to collect pressure sensor data in real time. If the pressure sensor data exceeds the preset pressure threshold, the operation mode is dynamically adjusted. The eccentricity change determination module is used in operation mode to energize the displacement electromagnet through the main control circuit board, so that the brush assembly contacts the conductive ring, drives the geared motor to rotate the screw, the screw pushes the slider to generate displacement, and determines the eccentricity change. The motion condition determination module is used to determine the motion condition based on the change in eccentricity; to adaptively adjust the operating mode according to the motion condition and record the working condition data log; to upload the working condition data log to the Internet of Things platform; and to disconnect the power supply of the displacement electromagnet using the main control circuit board, so that the brush assembly is separated from the conductive ring, thus completing the fascia gun operation control task.

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