Self-adaptive noise reduction control method and system for synchronous belt roller of motorized treadmill

Abstract

The invention relates to the technical field of treadmill noise reduction control, and particularly provides a self-adaptive noise reduction control method and system for a synchronous belt roller of an electric treadmill, and the method comprises the steps: constructing a linkage estimation model through a multi-source signal; identifying a meshing main order component and a resonance order component on line; generating a motor current feed-forward compensation signal and a micro-phase synchronization instruction based on the meshing phase and the torque harmonic component which are identified on line; according to the real-time speed, the load and the abrasion state, self-adaptive switching and smooth transition are carried out among multiple sets of preset normal operation control parameters; and triggering the degradation operation mode when detecting that the abnormal state is about to enter or already enters through combined monitoring of the criterion and the parameter. The system comprises a component identification module, a parameter switching module and a mode degradation module. The self-adaptive noise and vibration suppression, stable control and abnormal safety degradation of the running machine in a full-range running state are realized, and the use comfort, reliability and safety are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of treadmill noise reduction control technology, and in particular to an adaptive noise reduction control method and system for synchronous belt rollers in an electric treadmill. Background Technology

[0002] With the rapid growth in demand for home fitness, electric treadmills have become the mainstream equipment for home exercise. Traditional treadmills mainly use a fixed drive belt and a single roller structure, with the drive motor directly driving the running belt. The overall noise mainly comes from the following aspects: friction noise between the drive chain and belt - when the motor speed changes, the unstable belt tension causes impact noise. Impact noise from the relative movement of the roller and the running belt - mismatch between the roller surface material and the speed will generate high-frequency noise. Chassis resonance - insufficient rigidity of the machine structure, vibration is transmitted to the ground through the chassis, forming low-frequency noise. The above noises not only affect the user's exercise experience, but also cause noise pollution problems between residential floors, becoming a key technical bottleneck restricting the further penetration of the electric treadmill market.

[0003] Prior art 1, Chinese patent application number 202410085723.X, discloses a novel noise-reducing treadmill, including side frames symmetrically arranged on both sides and rollers installed between the two sets of side frames. One end of each side frame is provided with an adjusting bolt for adjusting the front-to-back position of the rollers. A running board assembly is provided between the two side frames. The running board assembly includes a base plate and a running board body disposed on top of the base plate. The running board body is symmetrically arranged on both sides of the base plate and forms a vertical sliding connection with the base plate. A soft layer is provided on the top of the running board body. A cavity is provided inside the running board, and a sound-absorbing hole communicating with the cavity is provided on the top. Sound-absorbing cotton is provided on the inner wall of the cavity. A sealing component for sealing the sound-absorbing hole is also provided inside the running board body. Although the noise generated by the exerciser running on the treadmill can be reduced by the running board with a sound-absorbing structure, the noise reduction achieved by passive sound-absorbing structures such as sound-absorbing cotton and sound-absorbing holes inside the running board does not fundamentally suppress the mechanical excitations that generate noise (gear meshing, roller vibration, drive current pulsation).

[0004] Prior art 2, Chinese patent application number 202211434568.5 discloses an active noise reduction method and device for treadmills. The treadmill noise reduction module of the active noise reduction device includes a noise reduction microphone for collecting noise sources from the treadmill, a noise reduction speaker for playing a reverse sound wave opposite to the noise sound wave emitted by the noise reduction microphone, and a noise reduction processing module for controlling the noise reduction speaker. The active noise reduction method for treadmills involves the noise generated by the treadmill motor and integrated control circuit being collected by the noise reduction microphone, and then the noise reduction processing module controlling the noise reduction speaker to play a reverse waveform sound source opposite to the noise waveform to suppress the noise source. Although noise suppression is achieved by controlling the noise reduction module to play reverse sound waves through the noise reduction speaker, thus realizing active noise reduction of the treadmill and improving the comfort of runners during the running process, making the running experience more pleasant; however, active sound wave cancellation only plays reverse sound waves for the sound waves generated by the motor and control circuit, and cannot eliminate noise caused by structural vibrations such as gear meshing and roller resonance, and also lacks real-time identification and compensation of noise sources.

[0005] Prior art three, Chinese patent application number 202310036575.8, discloses a treadmill including a running belt, a base frame assembly, and a running board. The treadmill also includes a cushioning device disposed between the running board and the base frame assembly, and the cushioning device has multiple through holes to present a honeycomb structure. The honeycomb structure of the cushioning device can better buffer forces and achieve excellent noise reduction. Although the cushioning device also has grooves and support surfaces, making its installation and disassembly simple, relying on the honeycomb structure for dynamic cushioning and sound absorption does not actively control the torque pulsation and engagement phase error of the drive system.

[0006] Current technologies 1, 2, and 3 suffer from the inability to accurately capture the principal and resonant harmonic components during meshing, lack adaptive switching of control strategy parameters under different operating conditions, and struggle to ensure system stability and safety under abnormal circumstances. Therefore, this invention provides an adaptive noise reduction control method and system for the synchronous belt rollers of an electric treadmill. Summary of the Invention

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In one aspect, the present invention provides an adaptive noise reduction control method for synchronous belt rollers in an electric treadmill, comprising the following steps: Based on the online identification of meshing phase and torque harmonic components, a motor current feedforward compensation signal and a micro-phase synchronization command are generated to suppress excitation during the toothed engagement and disengagement processes. At the same time, the roller vibration acceleration signal is used as feedback to adjust the speed loop control bandwidth and the current loop harmonic suppression coefficient online. According to the real-time speed, load and wear status, adaptive switching and smooth transition are performed between multiple preset sets of normal operation control parameters. The system sets up abnormal jump criteria, temperature rise criteria, and slippage criteria for the running platform; it introduces online adjustable speed loop control bandwidth and current loop harmonic suppression coefficient as auxiliary monitoring parameters; by combining the monitoring criteria and parameters, it triggers a degraded operation mode when an abnormal state is detected; and it limits the motor output power for multiple preset normal operation control parameters that are already in operation.

[0008] In one optional implementation, the process of online adjustment of the speed loop control bandwidth and the current loop harmonic suppression coefficient includes the following steps: The amplitude and phase information of the torque harmonic components are converted into corresponding current modulation amounts. The current modulation amounts are injected into the motor drive system through the current controller to counteract the excitation caused by torque pulsation. Based on the magnitude and direction of the meshing phase error, the fine-tuning phase offset of the motor rotor is calculated. The fine-tuning phase offset is fed back to the motor position control loop through the encoder phase signal to adjust the rotation phase of the motor so that the toothed engagement and disengagement processes are synchronized with the motor motion. The amplitude and frequency characteristics of the drum vibration acceleration signal are analyzed to extract the vibration energy associated with the identified resonant order component. When the vibration energy exceeds a preset threshold, the speed loop control bandwidth is reduced to enhance system stability. When the vibration energy is below the threshold, the speed loop control bandwidth is increased to improve dynamic response. Based on the frequency components in the drum vibration acceleration signal corresponding to the identified meshing principal order component and resonant order component, the suppression strength of harmonics in the current loop is dynamically adjusted. The encoder phase signal is used to obtain the real-time speed, the current ripple signal and torque pulsation estimation are used to obtain the load, and the long-term trend analysis of the meshing phase error is used to obtain the wear state. The current real-time state is matched with the conditions of the preset parameter group to select the optimal parameter group. When switching parameters, a gradual algorithm is used to adjust the control parameter values ​​step by step.

[0009] In one optional implementation, the process of triggering a degraded operating mode includes the following steps: The binary judgment results of the abnormal jump criteria, temperature rise criteria, and slippage criteria of the running track are converted into numerical levels; the real-time value of the speed loop control bandwidth is compared with the reference value under normal conditions, and the relative change is defined as the dynamic stability deviation; similarly, the real-time value of the current loop harmonic suppression coefficient is compared with its reference value, and the relative change is defined as the active suppression deviation; the numerical levels of each criterion, the dynamic stability deviation, and the active suppression deviation are weighted and fused to generate a comprehensive anomaly degree. Multiple consecutive anomaly thresholds are preset, each corresponding to a degradation level. When the comprehensive anomaly index exceeds the first threshold, a warning degradation level is triggered. When the comprehensive anomaly index further exceeds a higher anomaly threshold, a higher-level mild degradation level and a deep degradation level are triggered in sequence. Each degradation level specifies the degree of limitation on motor output power and the adjustment range of the control strategy. For the currently effective set of control parameters, the maximum allowable output power limit is obtained according to the power limit ratio specified by the degradation level. At the same time, the internal variables related to dynamic response speed in the control parameters are scaled proportionally according to the degradation level until the comprehensive anomaly index falls back below the safety threshold, thus completing the triggering and execution of the degradation operation mode.

[0010] In one optional implementation, each anomaly threshold corresponds to a downgrade level process, including the following steps: Based on the overall anomaly degree and wear status, a baseline anomaly degree profile is established; the baseline anomaly degree profile is defined as the upper limit of the overall anomaly degree allowed when the system is at the edge of the normal state under typical wear stage, and is set as the baseline early warning anomaly degree threshold. The baseline warning anomaly threshold is corrected using the real-time acquired wear status; the baseline warning anomaly threshold is added to the status compensation amount to generate an adaptive warning anomaly threshold applicable to the current wear status; the adaptive warning anomaly threshold is the condition for triggering the lowest level of degradation. Starting with the adaptive warning anomaly threshold, higher-level warning thresholds are calculated sequentially according to preset proportional coefficients. The adaptive warning threshold is multiplied by the first proportional coefficient to obtain the mild degradation anomaly threshold. The mild degradation anomaly threshold is then multiplied by the second proportional coefficient to obtain the deep degradation anomaly threshold. The adaptive warning anomaly threshold, the mild degradation anomaly threshold, and the deep degradation anomaly threshold together constitute a multi-level continuous anomaly threshold sequence that is associated with the equipment wear status. Each anomaly threshold in the anomaly threshold sequence is bound to a degradation level.

[0011] In one optional implementation, the process of setting the preset scaling factor includes the following steps: Analyze the amplitude fluctuation characteristics of the resonance order components in the historical operation of the system; based on the maximum and typical values ​​of the amplitude fluctuation characteristics, determine the resonance risk factor used to set the primary anomaly threshold; The wear status value is combined with the resonance risk factor to generate a first dynamic proportional coefficient. Extract the average energy of the drum vibration acceleration signal used for online adjustment of the speed loop control bandwidth, which is defined as the background vibration intensity. Based on the statistical distribution of the background vibration intensity, determine the vibration stability factor used to set the second-level anomaly threshold. The first dynamic proportional coefficient is coupled with the vibration stability factor of the second-level anomaly threshold to generate the second dynamic proportional coefficient.

[0012] In one optional implementation, the process of determining the vibration stability factor used to set the second-order anomaly threshold includes the following steps: By continuously collecting and using the drum vibration acceleration signal for online adjustment of the speed loop control bandwidth, a data sequence defining the change of background vibration intensity over time is obtained; the data sequence is then aggregated to form a statistical set describing the probability distribution characteristics of the background vibration intensity. Based on the established statistical set, a high quantile is located; the value of the high quantile is set as a high-intensity reference value, representing the upper limit of the background vibration intensity that can be tolerated under non-abnormal conditions. The real-time acquired background vibration intensity value is compared with the determined high-intensity reference value; based on the ratio of the real-time background vibration intensity to the high-intensity reference value, the vibration stability factor is output.

[0013] In one optional implementation, the process of comparing the real-time acquired background vibration intensity value with a determined high-intensity reference value includes the following steps: The real-time acquired background vibration intensity value is used as the dividend, and the determined high-intensity reference value is used as the divisor; a dimensionless instantaneous intensity ratio is generated. The instantaneous intensity ratio is generated and input to the function mapping unit. The function mapping unit stores a response curve. The response curve maintains a high output when the instantaneous intensity ratio is below the first critical point, the output value decreases linearly when the instantaneous intensity ratio is between the first and second critical points, and the output value remains at a low saturation value when the instantaneous intensity ratio exceeds the second critical point. The output of the function mapping unit is defined as the baseline stability coefficient. A negative scaling factor is generated based on the magnitude of the resonance risk factor. The benchmark stability factor is then reduced using the negative scaling factor, and the reduced result is output as the vibration stability factor.

[0014] In one optional implementation, the process of generating a negative scaling factor based on the magnitude of the resonance risk factor includes the following steps: The determined resonance risk factor is input into a preset mapping relationship, which includes multiple discrete resonance risk intervals and their corresponding intensity benchmark values. When the resonance risk factor falls into a certain resonance risk interval, the intensity benchmark value corresponding to the resonance risk interval is selected as the output. The output value is the benchmark scaled intensity. Based on the acquired resonance order component time series data, the resonance order component amplitude difference between the current sampling time and the previous sampling time is calculated; when the resonance order component amplitude difference is less than the set maximum allowable value, it is directly output; when it exceeds the set maximum allowable value, it is output; the output value is the trend enhancement factor. The obtained baseline scaling intensity and the obtained trend enhancement factor are linearly combined according to preset weight coefficients; the linear combination result is input into a limiter, which has preset lower and upper limits; when the linear combination result is lower than the lower limit, the lower limit is output; when it is higher than the upper limit, the upper limit is output; and when it is in between, it is output directly; the output value of the limiter is a negative scaling coefficient.

[0015] In one optional implementation, a multi-source signal is acquired, including motor current ripple signal, encoder phase signal, drum vibration acceleration signal, and microphone sound pressure signal inside the hood; a linkage estimation model for meshing phase error, equivalent meshing stiffness, and torque pulsation is constructed using the multi-source signal; and the main meshing order component and resonance order component are identified online by combining order tracking with recursive least squares method.

[0016] In another aspect, the present invention provides an adaptive noise reduction control system for a synchronized belt roller of an electric treadmill, used to implement the aforementioned adaptive noise reduction control method for a synchronized belt roller of an electric treadmill, the adaptive noise reduction control system for a synchronized belt roller of an electric treadmill comprising: The component identification module is used to collect multi-source signals, including motor current ripple signal, encoder phase signal, drum vibration acceleration signal, and microphone sound pressure signal inside the machine hood; it uses the multi-source signals to construct a linkage estimation model for meshing phase error, equivalent meshing stiffness, and torque pulsation; and it identifies the main meshing component and resonance component online by combining order tracking with recursive least squares method. The parameter switching module is used to generate a motor current feedforward compensation signal and a micro-phase synchronization command based on the online identified meshing phase and torque harmonic components, in order to suppress the excitation during the toothed engagement and disengagement process; at the same time, it uses the drum vibration acceleration signal as feedback to adjust the speed loop control bandwidth and the current loop harmonic suppression coefficient online; and it adaptively switches and smoothly transitions between multiple preset sets of normal operation control parameters according to real-time speed, load and wear status. The mode degradation module is used to set the abnormal jump criteria, temperature rise criteria, and slippage criteria for the running platform; it introduces online adjustable speed loop control bandwidth and current loop harmonic suppression coefficient as auxiliary monitoring parameters; by combining the monitoring criteria and parameters, it triggers the degradation operation mode when it detects that an abnormal state is about to occur or has already occurred; it limits the motor output power for multiple preset normal operation control parameters that are already in operation.

[0017] This invention constructs a linkage estimation model for meshing phase error, equivalent meshing stiffness, and torque pulsation by synchronously acquiring and fusing multi-source signals such as motor current ripple, encoder phase, drum vibration acceleration, and hood sound pressure. It utilizes order tracking and recursive least squares methods to achieve online identification of the main meshing order and resonant order components, enabling real-time capture and separation of key harmonic information from noise and vibration. Based on the identified meshing phase and torque harmonics, the generated motor current feedforward compensation signal and micro-phase synchronization command pre-cancel the excitation during gear engagement and disengagement. Using drum vibration acceleration as feedback, the speed loop bandwidth and current loop harmonic suppression coefficient are dynamically adjusted to achieve adaptive control parameter switching and smooth transition under different speeds, loads, and wear states, thus maintaining optimal noise and vibration suppression under various operating conditions. The set treadmill jump, temperature rise, and slippage criteria, combined with the online adjustable control bandwidth and suppression coefficient, serve as auxiliary monitoring quantities. In case of abnormality or impending abnormality, a degraded operation mode is triggered, limiting the motor output power and ensuring stable and safe operation of the system under abnormal conditions. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the adaptive noise reduction control method for synchronous rollers of an electric treadmill provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the adaptive noise reduction control method for synchronous rollers in an electric treadmill provided in Embodiment 1 of the present invention. Figure 3 This is a process diagram of online identification of the main meshing order component and the resonance order component by combining order tracking with recursive least squares method provided in Embodiment 2 of the present invention; Figure 4 This is a process diagram of online adjustment of speed loop control bandwidth and current loop harmonic suppression coefficient provided in Embodiment 6 of the present invention; Figure 5 This is a process diagram of triggering the degradation operation mode provided in Embodiment 8 of the present invention; Figure 6 This is a block diagram of the adaptive noise reduction control system for the synchronous belt rollers of the electric treadmill provided in Embodiment 14 of the present invention; Figure 7 A block diagram of the electronic device provided by the present invention; Figure 8 A block diagram of a computer-readable storage medium provided for this invention.

[0019] Reference numerals: 1. Component identification module; 2. Parameter switching module; 3. Mode degradation module; 4. Central processing unit / microprocessor / main control chip; 5. Storage medium; 6. Data bus; 7. Input / output bus / external bus / device bus; 8. Display; 9. Input / output device; 10. Computer-readable instructions; 11. Non-transitory computer-readable storage medium. Detailed Implementation

[0020] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0022] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0023] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0024] Example 1: As Figure 1 As shown, this embodiment of the invention provides an adaptive noise reduction control method for synchronous belt rollers in an electric treadmill, comprising the following steps: Step S100: Collect multi-source signals including motor current ripple signal, encoder phase signal, drum vibration acceleration signal, and microphone sound pressure signal inside the machine hood; construct a linkage estimation model for meshing phase error, equivalent meshing stiffness, and torque pulsation using multi-source signals; identify the main meshing order component and resonance order component online by combining order tracking with recursive least squares method. Step S200: Based on the online identification of meshing phase and torque harmonic components, generate motor current feedforward compensation signal and micro-phase synchronization command to suppress excitation during tooth engagement and disengagement; at the same time, use the drum vibration acceleration signal as feedback quantity to adjust the speed loop control bandwidth and current loop harmonic suppression coefficient online; according to real-time speed, load and wear status, adaptively switch and smoothly transition between multiple preset normal operation control parameters. Step S300: Set the abnormal jump criteria, temperature rise criteria, and slippage criteria for the running platform; introduce the online adjustable speed loop control bandwidth and current loop harmonic suppression coefficient as auxiliary monitoring parameters; trigger the degraded operation mode when an abnormal state is detected to be about to or has already been entered by combining the monitoring criteria and parameters; limit the motor output power for multiple preset normal operation control parameters that are already in operation.

[0025] For details on the principles described in the above embodiments, please refer to the appendix. Figure 2This embodiment synchronously collects and fuses multi-source signals such as motor current ripple, encoder phase, drum vibration acceleration, and hood sound pressure to construct a linkage estimation model for meshing phase error, equivalent meshing stiffness, and torque pulsation. It utilizes order tracking and recursive least squares methods to achieve online identification of the main meshing order and resonant order components, enabling real-time capture and separation of key harmonic information from noise and vibration. Based on the identified meshing phase and torque harmonics, the generated motor current feedforward compensation signal and micro-phase synchronization command pre-cancel the excitation during gear engagement and disengagement. Using drum vibration acceleration as feedback, the speed loop bandwidth and current loop harmonic suppression coefficient are dynamically adjusted to achieve adaptive control parameter switching and smooth transition under different speeds, loads, and wear states, thus maintaining optimal noise and vibration suppression under various operating conditions. The set treadmill jump, temperature rise, and slippage criteria, combined with the online-adjusted control bandwidth and suppression coefficient, serve as auxiliary monitoring quantities. In case of abnormality or impending abnormality, a degraded operation mode is triggered to limit the motor output power, ensuring stable and safe operation of the system under abnormal conditions. Step S200 is responsible for generating feedforward compensation using real-time identified meshing phase and torque harmonics under normal operating conditions, and dynamically adjusting the control bandwidth of the speed loop and the harmonic suppression coefficient of the current loop using the drum vibration acceleration as feedback. This achieves adaptive parameter switching and smooth transition based on real-time speed, load, and wear. Step S300, upon detecting abnormal jumps, temperature rises, or slippage, uses the bandwidth and suppression coefficient adjusted in Step S200 as auxiliary monitoring quantities, triggers a degraded operation mode in conjunction with the criteria, and limits the power of the selected normal control parameter group to ensure the system's safety and stability under abnormal conditions. Therefore, the online adjustment parameters provided by Step S200 are both the core of adaptive control and the monitoring basis for abnormal detection and degraded decision-making in Step S300. The two are interconnected in the same control loop: Step S200 achieves optimal adjustment under normal operating conditions, and Step S300 takes over and limits the output when an abnormality occurs, forming a complete adaptive control system. Downgrade closed loop.

[0026] In summary, this embodiment achieves adaptive noise and vibration suppression, smooth control, and abnormal safety degradation of the treadmill in all operating conditions, significantly improving user comfort, reliability, and safety.

[0027] Example 2: As Figure 2 As shown, based on Example 1, the process of identifying the main meshing order component and the resonance order component online by combining order tracking with recursive least squares method in step S100 of this embodiment of the invention specifically includes the following steps: Step S101: Using the encoder phase signal as an angle reference, perform order tracking analysis on the drum vibration acceleration signal; obtain the vibration order component corresponding to the meshing frequency from the order tracking analysis; extract the meshing phase error value from the vibration order component; Step S102: Combine the extracted meshing phase error value with the sound pressure signal from the microphone inside the hood, and estimate the equivalent meshing stiffness parameter online using the recursive least squares method; correlate the estimated equivalent meshing stiffness parameter with the drum vibration acceleration signal to form an equivalent meshing stiffness variation curve; Step S103: Using the formed equivalent meshing stiffness change curve and motor current ripple signal, the meshing principal order component and resonant order component in torque pulsation are identified online by recursive least squares method; based on the identified meshing principal order and resonant order components, meshing phase error value and equivalent meshing stiffness change curve, a linkage estimation model of meshing phase error-equivalent meshing stiffness-torque pulsation is constructed.

[0028] In the above embodiments, this embodiment accurately extracts the vibration component corresponding to the meshing frequency and obtains the phase error by order tracking based on the encoder phase. Then, the phase error is combined with the sound pressure signal, and the equivalent meshing stiffness is estimated in real time using the recursive least squares method to form its variation curve. This stiffness curve is then combined with the current ripple signal to perform recursive least squares, separating the principal and resonant order components in the torque pulsation online. This embodiment achieves synchronous, real-time, and accurate linkage estimation of meshing phase, equivalent stiffness, and torque pulsation, providing a reliable dynamic model basis for subsequent feedforward compensation, control bandwidth adjustment, and anomaly degradation, thereby significantly improving the response speed and suppression accuracy of noise and vibration suppression.

[0029] Example 3: Based on Example 2, the process of estimating the equivalent meshing stiffness parameters online using the recursive least squares method in step S102 of this embodiment of the invention specifically includes the following steps: Step S1021: The extracted meshing phase error value is combined with the amplitude of the sound pressure signal from the microphone inside the hood. The equivalent meshing stiffness observation sequence is generated by multiplying the sound pressure signal amplitude and the phase error value. Step S1022: Input the generated equivalent meshing stiffness observation sequence into the recursive least squares calculation process, update the covariance matrix using the covariance matrix of the previous moment and the current observation, and calculate the new equivalent meshing stiffness parameter value. Step S1023: Arrange the estimated equivalent meshing stiffness parameter values ​​and the drum vibration acceleration signal according to the time index provided by the encoder phase signal to form an equivalent meshing stiffness change curve, so that the stiffness change curve and the vibration acceleration signal maintain a corresponding relationship in the time dimension.

[0030] In the above embodiments, this embodiment inputs the observation sequence obtained by multiplying the meshing phase error by the sound pressure amplitude into a recursive least squares algorithm to achieve real-time estimation of the equivalent meshing stiffness and update the covariance matrix. Subsequently, the estimated stiffness parameters are mapped to the drum acceleration signal according to the time index provided by the encoder phase, forming a stiffness curve that varies with time. This embodiment achieves the fusion estimation of acoustic and mechanical signals, enabling the meshing stiffness to be continuously and accurately captured during operation, providing reliable dynamic stiffness information for subsequent noise suppression and control strategies.

[0031] Example 4: Based on Example 2, the process of identifying the meshing principal component and resonance component in torque pulsation online by using the recursive least squares method in step S103 of this embodiment of the invention specifically includes the following steps: Step S1031: Using the formed equivalent meshing stiffness change curve and motor current ripple signal, the value of the equivalent meshing stiffness change curve and the value of the motor current ripple signal are multiplied to generate a sequence of observed torque pulsations. Step S1032: Input the generated torque pulsation observation sequence into the recursive least squares calculation process, update the order amplitude parameters through recursion, and identify the meshing principal order component and resonance order component in the torque pulsation online; Step S1033: Based on the identified meshing principal components and resonance components, the extracted meshing phase error value, and the formed equivalent meshing stiffness variation curve, construct a linkage estimation model of meshing phase error-equivalent meshing stiffness-torque pulsation.

[0032] In the above embodiments, this embodiment obtains the torque pulsation observation sequence by multiplying the equivalent meshing stiffness curve with the current ripple, and updates the order amplitude in real time within the recursive least squares framework to achieve instantaneous separation of the principal order and resonant order components in the torque pulsation; then, the identified order components, phase errors, and stiffness change curves are uniformly constructed into a linkage estimation model to form a synchronous and dynamic correlation description of meshing phase, equivalent stiffness, and torque pulsation, providing accurate real-time parameter basis for feedforward compensation and adaptive control, and significantly improving the response speed and suppression accuracy of noise and vibration suppression.

[0033] Example 5: Based on Example 4, the process of constructing the linkage estimation model of meshing phase error-equivalent meshing stiffness-torque pulsation in step S1033 of this embodiment of the invention specifically includes the following steps: Step S10331: Using the extracted meshing phase error value and the formed equivalent meshing stiffness change curve, the phase error value and stiffness value are arranged according to the timestamp of the encoder phase signal to generate a phase-stiffness correlation data sequence; using the formed equivalent meshing stiffness change curve and the identified meshing principal order component and resonance order component, the stiffness value and torque order amplitude value are arranged according to the time series to generate a stiffness-torque order correlation data sequence; Step S10332: Match the phase-stiffness correlation data sequence with the stiffness-torque order correlation data sequence based on the equivalent meshing stiffness value, and establish the correspondence between the phase error and the torque order amplitude based on the stiffness value. Step S10333: Combine the matched phase error value, equivalent meshing stiffness value, and torque order amplitude value into three-dimensional data points in chronological order to form a three-dimensional data sequence of phase-stiffness-torque order; based on the three-dimensional data sequence, establish a dual mapping relationship between meshing phase error and equivalent meshing stiffness, and between equivalent meshing stiffness and torque pulsation order amplitude, and complete the construction of the linkage estimation model of meshing phase error-equivalent meshing stiffness-torque pulsation.

[0034] In the above embodiments, this embodiment forms a phase through the pairing and matching of time-synchronized phase error, equivalent stiffness, and torque order amplitude data. stiffness A three-dimensional torque correlation sequence is generated, and a bidirectional mapping from phase error to stiffness and from stiffness to torque order is established on this sequence to complete the meshing phase. equivalent stiffness A linkage estimation model for torque pulsation was developed. This model achieves synchronous and dynamic correlation description of three key variables, providing accurate and real-time parameter data for subsequent feedforward compensation, control bandwidth adjustment, and anomaly degradation. This significantly improves the system's noise suppression, vibration control, and safety reliability under different operating conditions.

[0035] Example 6: As Figure 4 As shown, based on Example 1, the process of online adjustment of the speed loop control bandwidth and the current loop harmonic suppression coefficient in step S200 of this embodiment of the invention specifically includes the following steps: Step S201: Convert the amplitude and phase information of the torque harmonic components into corresponding current modulation amounts. The current modulation amounts are injected into the motor drive system through the current controller to counteract the excitation caused by torque pulsation. Based on the magnitude and direction of the meshing phase error, calculate the fine-tuning phase offset of the motor rotor. The fine-tuning phase offset is fed back to the motor position control loop through the encoder phase signal to adjust the rotation phase of the motor so that the toothed engagement and disengagement processes are synchronized with the motor movement. The real-time extracted meshing phase error is combined with its positive and negative directions and mapped to a tiny phase offset based on the error amplitude. The phase offset is sent to the motor position control circuit through the phase feedback provided by the encoder, so that the motor rotor adds a phase offset to the original command phase each time it rotates, thereby making the gear engagement and disengagement moments more precisely synchronized with the motor rotation. Step S202: Analyze the amplitude and frequency characteristics of the drum vibration acceleration signal and extract the vibration energy related to the identified resonant order component; when the vibration energy exceeds a preset threshold, reduce the speed loop control bandwidth to enhance system stability; when the vibration energy is below the threshold, increase the speed loop control bandwidth to improve dynamic response; dynamically adjust the suppression strength of specific harmonics in the current loop according to the frequency components in the drum vibration acceleration signal corresponding to the identified meshing principal order component and resonant order component. Step S203: The encoder phase signal is used to obtain the real-time speed, the current ripple signal and torque pulsation estimation are used to obtain the load, and the long-term trend analysis of the meshing phase error is used to obtain the wear state; the current real-time state is matched with the conditions of the preset parameter group to select the optimal parameter group; when switching parameters, the control parameter values ​​are gradually adjusted using a gradual algorithm.

[0036] In the above embodiments, this embodiment directly compensates for torque pulsations by converting torque harmonics into current modulation and injecting it into the drive; it finely adjusts the motor rotor phase in real time based on the meshing phase error to synchronize the gear meshing process with the motor motion; it dynamically adjusts the speed loop bandwidth based on the energy characteristics of the drum vibration acceleration, narrowing the bandwidth to improve stability when the resonant energy increases and widening the bandwidth to improve response when the energy decreases; simultaneously, it adjusts the corresponding harmonic suppression intensity in the current loop for the identified principal and resonant frequency components; and it achieves adaptive and smooth transition of control parameters by matching preset parameter sets with real-time speed, load, and wear conditions and using a gradual algorithm for smooth switching. This embodiment can achieve real-time compensation for torque pulsations, phase synchronization, and dynamic optimization of bandwidth and harmonic suppression under different operating conditions, while maintaining stability during parameter switching, significantly improving noise and vibration suppression effects and overall control stability.

[0037] Example 7: Based on Example 6, the process of gradually adjusting the control parameter values ​​using a gradual algorithm in step S203 of this embodiment of the invention specifically includes the following steps: Step S2031: Multiply the real-time speed and load values, and define the product as an instantaneous kinetic energy index that reflects the current dynamic energy level of the system; preset two proportional coefficients for the instantaneous kinetic energy index and the wear state quantification value; multiply the instantaneous kinetic energy index by the corresponding proportional coefficient to obtain a kinetic energy time compensation amount; multiply the wear state quantification value by its corresponding proportional coefficient to obtain a wear time compensation amount; add the basic transition time, the kinetic energy time compensation amount, and the wear time compensation amount together, and the sum is the transition time reference. Step S2032: Divide the transition time base by the system's operation cycle to obtain a total number of steps; initialize a smooth transition weight starting from zero; at the beginning of each adjacent control operation cycle, the smooth transition weight value is increased by the reciprocal of the total number of steps; the smooth transition weight then starts from zero and increases linearly in each operation cycle until it reaches one after the complete transition time base. Step S2033: Subtract the current actual value of the target parameter from a parameter value in the selected target parameter group; multiply the generated smooth transition weight by the calculated parameter difference to obtain the parameter increment; add the parameter increment to the current actual value of the target parameter to generate the instantaneous command value for the current control cycle; run the transition to the target parameter value according to the instantaneous command value.

[0038] In the above embodiments, this embodiment maps real-time speed, load, and wear status to kinetic energy and wear time compensation amounts. The calculated transition time reference is transformed into a linearly increasing smooth weight within the control cycle. This weight is multiplied by the difference between the target parameter and the current value to form a gradual increment, allowing the control parameters to smoothly and continuously approach the target set of parameters throughout the transition period. This embodiment achieves adaptive, abrupt parameter switching based on the system's dynamic energy and wear degree, ensuring stable control response, noise suppression, and system stability under different operating conditions.

[0039] Example 8: As Figure 5 As shown, based on Example 1, the process of triggering the degraded operation mode in step S300 provided in this embodiment of the invention specifically includes the following steps: Step S301: Convert the binary judgment results of the abnormal jump criterion, temperature rise criterion, and slippage criterion of the running platform into numerical levels; compare the real-time value of the speed loop control bandwidth with the reference value under normal conditions, and define the relative change as the dynamic stability deviation; similarly, compare the real-time value of the current loop harmonic suppression coefficient with its reference value, and define the relative change as the active suppression deviation; weight and fuse the numerical levels of each criterion, the dynamic stability deviation, and the active suppression deviation to generate a comprehensive anomaly degree. Step S302: Preset multiple consecutive anomaly thresholds, each anomaly threshold corresponding to a degradation level; when the comprehensive anomaly index exceeds the first threshold, trigger the warning degradation level; when the comprehensive anomaly index further exceeds a higher anomaly threshold, trigger the higher-level mild degradation level and deep degradation level in sequence; each degradation level specifies the degree of limitation on motor output power and the adjustment range of the control strategy. Step S303: For the currently effective set of control parameters, obtain the maximum allowable output power limit according to the power limit ratio specified by the degradation level; at the same time, scale the internal variables related to dynamic response speed in the control parameters according to the degradation level until the comprehensive anomaly index falls back below the safety threshold, thus completing the triggering and execution of the degradation operation mode.

[0040] In the above embodiments, this embodiment converts binary criteria such as abnormal jumps, temperature rises, and slippage into numerical levels, and compares the real-time deviations of the speed loop bandwidth and current loop suppression coefficient with the benchmark values ​​to obtain the dynamic stability deviation and active suppression deviation. After weighted fusion to generate a comprehensive anomaly degree, it implements hierarchical degradation based on preset multi-level thresholds, from early warning to mild degradation to deep degradation, with each level corresponding to different power limits and control strategy scaling. When the degradation level takes effect, the control parameters are adjusted according to the corresponding power upper limit and response variable scaling, so that the system can quickly reduce the output power and tighten the dynamic response in abnormal conditions until the anomaly degree falls back to a safe range, ensuring the stability and safety of operation.

[0041] Example 9: Based on Example 8, the process of each anomaly threshold corresponding to a downgrade level in step S302 of this embodiment of the invention specifically includes the following steps: Step S3021: Based on the comprehensive anomaly degree and wear status, establish a baseline anomaly degree profile; the baseline anomaly degree profile is defined as the upper limit of the comprehensive anomaly degree allowed when the system is at the edge of the normal state under typical wear stage, and is set as the baseline early warning anomaly degree threshold. Step S3022: Correct the baseline warning anomaly threshold using the real-time acquired wear status; add the baseline warning anomaly threshold to the status compensation amount to generate the adaptive warning anomaly threshold applicable to the current wear status; the adaptive warning anomaly threshold is the condition for triggering the lowest level downgrade. Step S3023: Starting from the adaptive early warning anomaly threshold, calculate the higher-level early warning thresholds sequentially according to the preset proportional coefficient; multiply the adaptive early warning threshold by the first proportional coefficient to obtain the mild degradation anomaly threshold; then multiply the mild degradation anomaly threshold by the second proportional coefficient to obtain the deep degradation anomaly threshold; the adaptive early warning anomaly threshold, the mild degradation anomaly threshold, and the deep degradation anomaly threshold together constitute a multi-level continuous anomaly threshold sequence associated with the equipment wear status; each anomaly threshold in the anomaly threshold sequence is bound to a degradation level one-to-one.

[0042] In the above embodiments, this embodiment establishes a baseline anomaly profile at a typical wear stage and sets a warning threshold accordingly. Then, the threshold is adaptively corrected based on the real-time wear status to form a warning anomaly threshold for the current wear level. The threshold is then amplified layer by layer according to a preset ratio to generate mild and deep degradation thresholds, forming a multi-level threshold sequence coupled with the wear status. The multi-level threshold sequence realizes dynamic adjustment of the anomaly with the wear level and corresponds to different degradation levels, enabling the system to achieve layered and timely power limiting and control strategy tightening at different wear levels, ensuring safe and stable operation.

[0043] Example 10: Based on Example 9, the process of setting the preset scaling factor provided in this embodiment of the invention specifically includes the following steps: Step S30231: Analyze the amplitude fluctuation characteristics of the resonance order component in the system's historical operation; based on the maximum and typical values ​​of the amplitude fluctuation characteristics, determine the resonance risk factor used to set the primary anomaly threshold; Step S30232: Combine the wear state value with the resonance risk factor to generate a first dynamic proportional coefficient; the logic of the combination operation is that when the wear state intensifies or the resonance risk factor increases, the first dynamic proportional coefficient decreases accordingly, making the mild degradation threshold calculated by the adaptive early warning anomaly threshold more sensitive, that is, more likely to trigger the primary degradation. Step S30233: Extract the average energy of the drum vibration acceleration signal used for online adjustment of the speed loop control bandwidth within the frequency band. The average energy is defined as the background vibration intensity. Based on the statistical distribution of the background vibration intensity, determine the vibration stability factor used to set the secondary anomaly threshold. Step S30234: Couple the first dynamic proportional coefficient with the vibration stability factor of the secondary anomaly threshold to generate the second dynamic proportional coefficient; the logic of the coupling operation is that when the background vibration intensity increases, the second dynamic proportional coefficient decreases significantly, so that the deep degradation threshold calculated from the mild degradation threshold is set in advance, and the deep degradation mode is activated earlier to ensure safety when the system vibration background deteriorates.

[0044] In the above embodiments, this embodiment determines the resonance risk factor by analyzing historical resonance amplitude fluctuations and couples it with real-time wear status to obtain a first dynamic proportional coefficient, making the mild degradation threshold more sensitive when wear intensifies or resonance risk increases. Subsequently, a vibration stability factor is determined based on the background energy statistics of drum vibration acceleration and coupled with the first dynamic proportional coefficient to generate a second dynamic proportional coefficient, causing the deep degradation threshold to be triggered earlier when background vibration intensifies. This embodiment realizes dynamic adjustment of the proportional coefficient according to resonance risk, wear degree, and vibration background, ensuring that the anomaly threshold advances earlier as operating conditions worsen, improving system safety and reliability.

[0045] Example 11: Based on Example 10, the process of determining the vibration stability factor for setting the second-level anomaly threshold in step S30233 of this embodiment of the invention specifically includes the following steps: Step S302331: By continuously collecting and using the drum vibration acceleration signal for online adjustment of the speed loop control bandwidth, a data sequence defining the change of background vibration intensity over time is obtained; the data sequence is aggregated to form a statistical set describing the probability distribution characteristics of the background vibration intensity; Step S302332: Based on the established statistical set, locate a high quantile; the value of the high quantile is set as a high intensity reference value, representing the upper limit of the background vibration intensity that can be tolerated under non-abnormal conditions; Step S302333: Compare the real-time acquired background vibration intensity value with the determined high-intensity reference value; output the vibration stability factor based on the ratio of the real-time background vibration intensity to the high-intensity reference value.

[0046] In the above embodiments, this embodiment continuously collects the roller acceleration and constructs the probability distribution of background vibration intensity to locate high quantiles and obtain the tolerable intensity upper limit; the ratio of real-time intensity to this upper limit generates a vibration stability factor, realizing a quantitative assessment of the background vibration level, providing a basis for the dynamic setting of the secondary anomaly threshold, and improving the adaptive safety of the system under different vibration environments.

[0047] Example 12: Based on Example 11, the process of comparing the real-time acquired background vibration intensity value with the determined high-intensity reference value in step S302333 of this embodiment of the invention specifically includes the following steps: Step S3023331: Use the real-time acquired background vibration intensity value as the dividend and the determined high-intensity reference value as the divisor; generate a dimensionless instantaneous intensity ratio. Step S3023332: Generate an instantaneous intensity ratio input function mapping unit; the function mapping unit stores a response curve. The response curve maintains a high output when the instantaneous intensity ratio is below the first critical point, the output value decreases linearly when the instantaneous intensity ratio is between the first and second critical points, and the output value remains at a low saturation value when the instantaneous intensity ratio exceeds the second critical point; the output of the function mapping unit is defined as the reference stability coefficient. Step S3023333: Generate a negative scaling factor based on the magnitude of the resonance risk factor, and use the negative scaling factor to reduce the benchmark stability factor. The reduced result is output as the vibration stability factor.

[0048] In the above embodiments, this embodiment forms a dimensionless intensity ratio by the ratio of real-time background vibration intensity to a high-intensity reference value, and achieves a three-stage response of high-level, linearly decreasing, and low-level based on two preset critical points in the function mapping unit to obtain the reference stability coefficient. Subsequently, the reference coefficient is reduced based on the negative scaling factor generated by the resonance risk factor. The output vibration stability factor can be rapidly reduced when the background vibration increases or the resonance risk intensifies, providing a sensitive and quantitative attenuation factor for the adaptive adjustment of the anomaly threshold, and realizing the system's timely response and safety guarantee to the deterioration of the vibration environment.

[0049] Example 13: Based on Example 12, the process of generating a negative scaling coefficient according to the magnitude of the resonance risk factor in step S3023333 of this embodiment of the invention specifically includes the following steps: Step S30233331: Input the determined resonance risk factor into the preset mapping relationship. The mapping relationship includes multiple discrete resonance risk intervals and their corresponding intensity benchmark values. When the resonance risk factor falls into a certain resonance risk interval, the intensity benchmark value corresponding to the resonance risk interval is selected as the output. The output value is the benchmark scaled intensity. Step S30233332: Based on the acquired resonance order component time series data, calculate the resonance order component amplitude difference between the current sampling time and the previous sampling time; output the value directly when the resonance order component amplitude difference is less than the set maximum allowable value, and output the set maximum allowable value when it exceeds the maximum allowable value; the output value is the trend enhancement factor. Step S30233333: The obtained baseline scaling intensity and the obtained trend enhancement factor are linearly combined according to the preset weight coefficients; the linear combination result is input into the limiter, which has preset lower and upper limits; when the linear combination result is lower than the lower limit, the lower limit is output; when it is higher than the upper limit, the upper limit is output; when it is in between, it is directly output; the output value of the limiter is the negative scaling coefficient.

[0050] In the above embodiments, this embodiment maps the resonance risk factor to the corresponding benchmark scaling intensity, combines it with the trend enhancement factor obtained from the change in resonance amplitude, and performs linear fusion according to preset weights and then amplitude limiting processing. The resulting negative scaling coefficient can reduce its value when the resonance risk increases or the resonance amplitude grows rapidly. In subsequent calculations, this coefficient reduces the vibration stability factor, so that the abnormal threshold tightens in advance as the resonance risk and trend worsen, thereby achieving sensitive suppression of potential resonance hazards and enhancing system safety.

[0051] Example 14: As Figure 6 As shown, based on Embodiments 1-13, the adaptive noise reduction control system for synchronous belt rollers of the electric treadmill provided in this embodiment of the invention includes: The component identification module 1 is used to collect multi-source signals, including motor current ripple signal, encoder phase signal, drum vibration acceleration signal, and microphone sound pressure signal inside the machine hood; it uses the multi-source signals to construct a linkage estimation model for meshing phase error, equivalent meshing stiffness, and torque pulsation; and it identifies the main meshing order component and resonance order component online by combining order tracking with recursive least squares method. The parameter switching module 2 is used to generate a motor current feedforward compensation signal and a micro-phase synchronization command based on the online identified meshing phase and torque harmonic components, in order to suppress the excitation during the meshing and disengagement of the belt; at the same time, it uses the drum vibration acceleration signal as feedback to adjust the speed loop control bandwidth and the current loop harmonic suppression coefficient online; and it performs adaptive switching and smooth transition between multiple preset sets of normal operation control parameters according to real-time speed, load and wear status. The mode degradation module 3 is used to set the abnormal jump criteria, temperature rise criteria, and slippage criteria of the running platform; it introduces the online adjustable speed loop control bandwidth and current loop harmonic suppression coefficient as auxiliary monitoring parameters; by combining the monitoring criteria and parameters, it triggers the degradation operation mode when it detects that an abnormal state is about to occur or has already occurred; it limits the motor output power of multiple preset normal operation control parameters that are already in operation.

[0052] In the above embodiments, this embodiment uses a component identification module to achieve synchronous acquisition and linkage estimation of multi-source signals, and to separate and identify the main harmonic and resonance components in the meshing process in real time. The parameter switching module generates feedforward compensation and micro-phase synchronization commands based on the identification results, and dynamically adjusts the speed loop bandwidth and harmonic suppression coefficient of the current loop using vibration feedback to achieve smooth switching of adaptive control parameters for different speeds, loads, and wear states. When triggered by criteria such as abnormal jumps, temperature rises, or slippage, the mode degradation module uses the adjusted bandwidth and suppression coefficient as monitoring quantities to automatically enter degraded operation and limit motor power, ensuring stable and safe operation of the system under abnormal conditions. In summary, the system of this embodiment can achieve active and precise noise and vibration suppression under all operating conditions and has abnormal self-protection capabilities, significantly improving the comfort, reliability, and safety of the treadmill.

[0053] Figure 7 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0054] The electronic device may include a central processing unit / microprocessor / main control chip 4; and a storage medium 5 coupled to the central processing unit / microprocessor / main control chip 4 and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by the processor.

[0055] The central processing unit / microprocessor / main control chip 4 may include, but is not limited to, one or more processors or microprocessors.

[0056] Storage medium 5 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0057] In addition, the electronic device may include (but is not limited to) a data bus 6, an input / output bus / external bus / device bus 7, a display 8, and input / output devices 9 (e.g., keyboard, mouse, speaker, etc.).

[0058] The central processing unit / microprocessor / main control chip 4 can communicate with external devices (8, 9, etc.) via wired or wireless networks (not shown) through the input / output bus / external bus / device bus 7.

[0059] The storage medium 5 may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip 4 is running.

[0060] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0061] Figure 8 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0062] like Figure 8 As shown, the non-transitory computer-readable storage medium 11 stores instructions, such as computer-readable instructions 10. When the computer-readable instructions 10 are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 11 can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions 10 stored on the non-transitory computer-readable storage medium 11, the various methods described above can be performed.

[0063] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electric treadmill synchronous belt roller adaptive noise reduction control method, characterized in that, The method comprises the following steps: Based on the meshing phase and torque harmonic components identified online, a motor current feedforward compensation signal and a micro-phase synchronization instruction are generated to suppress the excitation during the tooth meshing-in and meshing-out process; at the same time, the roller vibration acceleration signal is used as a feedback quantity to online adjust the speed loop control bandwidth and the current loop harmonic suppression coefficient; according to the real-time speed, load and wear state, adaptive switching and smooth transition are performed among the preset multiple groups of normal operation control parameters; The treadmill abnormal jump criterion, temperature rise criterion and slip criterion are set; the speed loop control bandwidth and the current loop harmonic suppression coefficient adjusted online are introduced as auxiliary monitoring parameters; through joint monitoring of the criterion and the parameters, the degradation operation mode is triggered when an abnormal state is detected; the motor output power is limited to the preset multiple groups of normal operation control parameters.

2. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 1, wherein, The process of online adjusting the speed loop control bandwidth and the current loop harmonic suppression coefficient comprises the following steps: The amplitude and phase information of the torque harmonic components are converted into corresponding current modulation quantities, which are injected into the motor drive system through the current controller to offset the excitation caused by torque pulsation; according to the size and direction of the meshing phase error, the fine-tuning phase offset of the motor rotor is calculated, which is fed back to the motor position control loop through the encoder phase signal to adjust the rotation phase of the motor, so that the tooth meshing-in and meshing-out process is synchronized with the motor movement; The amplitude frequency characteristics of the roller vibration acceleration signal are analyzed, and the vibration energy related to the identified resonance order components is extracted; when the vibration energy exceeds the preset threshold, the speed loop control bandwidth is reduced to enhance the system stability; when the vibration energy is lower than the threshold, the speed loop control bandwidth is increased to improve the dynamic response; according to the frequency components corresponding to the identified meshing main order components and resonance order components in the roller vibration acceleration signal, the suppression strength of the current loop to the harmonic is dynamically adjusted; The encoder phase signal obtains the real-time speed, the current ripple signal and the torque pulsation estimation obtain the load, and the long-term trend analysis of the meshing phase error obtains the wear state; the current real-time state is matched with the conditions of the preset parameter groups to select the optimal parameter group; when the parameters are switched, a gradual change algorithm is used to gradually adjust the control parameter value.

3. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 1, wherein, The process of triggering the degradation operation mode comprises the following steps: The binary determination results of the treadmill abnormal jump criterion, the temperature rise criterion and the slip criterion are converted into numerical levels; the real-time numerical value of the speed loop control bandwidth is compared with the reference value in the normal state, and the relative change quantity is defined as the dynamic stability deviation; similarly, the real-time numerical value of the current loop harmonic suppression coefficient is compared with the reference value, and the relative change quantity is defined as the active suppression deviation; the numerical levels of the criteria, the dynamic stability deviation and the active suppression deviation are weighted and fused to generate a comprehensive abnormality degree; A plurality of continuous abnormality thresholds are preset, each of which corresponds to a degradation level; when the comprehensive abnormality index exceeds the first threshold, a warning degradation level is triggered; when the comprehensive abnormality index further exceeds a higher abnormality threshold, a high-level mild degradation level and a deep degradation level are triggered in turn; each degradation level specifies the degree of restriction on the motor output power and the adjustment range of the control strategy; For the currently effective set of control parameters, the allowed maximum output power upper limit is obtained according to the power restriction ratio specified by the degradation level; at the same time, the internal variables related to the dynamic response speed in the control parameters are proportionally scaled according to the degradation level until the comprehensive abnormality index falls below the safety threshold, completing the triggering and execution of the degradation operation mode.

4. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 3, wherein, The process of each abnormality threshold corresponding to a degradation level includes the following steps: Based on the comprehensive abnormality and the wear state, a reference abnormality profile is established; the reference abnormality profile defines the upper limit of the comprehensive abnormality allowed when the system is on the edge of the normal state under the typical wear stage, which is set as the reference warning abnormality threshold; The reference warning abnormality threshold is corrected using the real-time acquired wear state; the reference warning abnormality threshold is added to the state compensation amount to generate an adaptive warning abnormality threshold applicable under the current wear state; the adaptive warning abnormality threshold is the condition for triggering the lowest level of degradation; Starting from the adaptive warning abnormality threshold, higher-level warning thresholds are calculated in turn according to the preset proportion coefficients; the adaptive warning threshold is multiplied by the first proportion coefficient to obtain the mild degradation abnormality threshold; then the mild degradation abnormality threshold is multiplied by the second proportion coefficient to obtain the deep degradation abnormality threshold; the adaptive warning abnormality threshold, the mild degradation abnormality threshold and the deep degradation abnormality threshold together constitute a sequence of abnormality thresholds associated with the equipment wear state and multi-level continuous; each abnormality threshold in the abnormality threshold sequence is one-to-one bound with a degradation level.

5. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 4, wherein, The setting process of the preset proportion coefficient includes the following steps: Analyze the amplitude fluctuation characteristics of the resonance order components in the system historical operation; based on the maximum and typical value of the amplitude fluctuation characteristics, determine the resonance risk factor for setting the primary abnormality threshold; Perform a synthetic operation on the value of the wear state and the resonance risk factor to generate a first dynamic proportion coefficient; Extract the roller vibration acceleration signal used for online adjusting the speed loop control bandwidth, the energy mean value in the frequency band, which is defined as the background vibration intensity; based on the statistical distribution of the background vibration intensity, determine the vibration stability factor for setting the secondary abnormality threshold; Perform a coupling operation on the first dynamic proportion coefficient and the vibration stability factor of the secondary abnormality threshold to generate a second dynamic proportion coefficient.

6. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 5, wherein, The process of determining the vibration stability factor for setting the secondary abnormality threshold includes the following steps: A data sequence defining the background vibration intensity over time is obtained by continuously collecting and using the drum vibration acceleration signal for online adjustment of the speed loop control bandwidth; the data sequence is collected to form a statistical set describing the probability distribution characteristics of the background vibration intensity; Based on the established statistical set, a high quantile point is located; the value of the high quantile point is set as a high-intensity reference value, representing the upper limit of the background vibration intensity that can be tolerated under non-abnormal conditions; The real-time obtained background vibration intensity value is compared with the determined high-intensity reference value; according to the proportional relationship between the real-time background vibration intensity and the high-intensity reference value, a vibration stability factor is output.

7. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 6, wherein, The process of comparing the real-time obtained background vibration intensity value with the determined high-intensity reference value includes the following steps: The real-time obtained background vibration intensity value is used as the dividend and the determined high-intensity reference value is used as the divisor; a dimensionless instant intensity ratio is generated; The generated instant intensity ratio is input into a function mapping unit; the function mapping unit internally stores a response curve, which maintains a high output when the instant intensity ratio is below a first critical point, linearly decreases the output value when the instant intensity ratio is between the first critical point and a second critical point, and maintains the output value at a low saturation value when the instant intensity ratio exceeds the second critical point; the output of the function mapping unit is defined as a reference stability coefficient; A negative scaling coefficient is generated according to the size of the resonance risk factor, and the reference stability coefficient is reduced using the negative scaling coefficient, and the result after reduction is output as the vibration stability factor.

8. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 7, wherein, The process of generating a negative scaling coefficient according to the size of the resonance risk factor includes the following steps: The determined resonance risk factor is input into a preset mapping relationship, which contains multiple discrete resonance risk intervals and their corresponding intensity reference values; when the resonance risk factor falls into a certain resonance risk interval, the intensity reference value corresponding to the resonance risk interval is selected as the output; the output value is the reference scaling intensity; Based on the obtained resonance order component time series data, the amplitude difference of the resonance order component between the current sampling time and the previous sampling time is calculated; when the amplitude difference of the resonance order component is less than the set maximum allowed value, the set maximum allowed value is directly output; the output value is the trend enhancement factor; The obtained reference scaling intensity and the obtained trend enhancement factor are linearly combined according to the preset weight coefficient; the linear combination result is input into a limiter, which is provided with a preset lower limit value and an upper limit value; when the linear combination result is lower than the lower limit value, the lower limit value is output, when it is higher than the upper limit value, the upper limit value is output, and between them, it is directly output; the output value of the limiter is the negative scaling coefficient.

9. The electric treadmill synchronous belt roller adaptive noise reduction control method of claim 1, wherein, The motor current ripple signal, the encoder phase signal, the drum vibration acceleration signal, and the microphone sound pressure signal inside the machine cover are collected; a linkage estimation model of meshing phase error, equivalent meshing stiffness, and torque pulsation is constructed using the multi-source signals; the meshing main order component and the resonance order component are identified online by order tracking combined with recursive least squares.

10. An electric treadmill synchronous belt roller adaptive noise reduction control system for implementing the electric treadmill synchronous belt roller adaptive noise reduction control method according to any one of claims 1 to 9, characterized in that, The electric treadmill synchronous belt drum adaptive noise reduction control system comprises: The component identification module is used for collecting multi-source signals of motor current ripple signals, encoder phase signals, drum vibration acceleration signals and sound pressure signals of the microphone in the machine cover; a linkage estimation model of meshing phase error, equivalent meshing stiffness and torque pulsation is constructed by using the multi-source signals; the meshing main order component and the resonance order component are identified online by using order tracking combined with recursive least squares; The parameter switching module is used for generating motor current feedforward compensation signals and micro-phase synchronous instructions based on the online identified meshing phase and torque harmonic components, so as to suppress the excitation in the process of tooth meshing in and out; at the same time, the drum vibration acceleration signal is used as a feedback quantity to online adjust the speed loop control bandwidth and the current loop harmonic suppression coefficient; according to the real-time speed, load and wear state, adaptive switching and smooth transition are carried out among the preset multiple groups of normal operation control parameters; The mode degradation module is used for setting the running track abnormal jump criterion, the temperature rise criterion and the slip criterion; the online adjusted speed loop control bandwidth and the current loop harmonic suppression coefficient are introduced as auxiliary monitoring parameters; by jointly monitoring the criterion and the parameters, when it is detected that the abnormal state is about to enter or has entered, the degradation operation mode is triggered; the motor output power is limited to the preset multiple groups of normal operation control parameters which have been operated.

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