Distributed control method, device and program for direct current controller

Abstract

The present application relates to the technical field of power control, in particular to a dispersion control method, device and program for a direct current controller. Different body weight users generate different pressure on the treadmill, which affects the power required by the direct current controller. In order to accurately determine the power value at the next moment, the historical pressure time series data and the current power value of the user are obtained. Under the natural gait of the user, the pressure data changes with the lifting and landing of the foot, so according to the change trend of the historical pressure data, the gait cycle data segment is periodically divided. Then, the load characteristic value is determined by comprehensively considering the pressure fluctuation and numerical characteristics of all cycle data segments, which reflects the gait attribute of the user. At the same time, the step frequency also affects the power output, so the step frequency coefficient is determined by comprehensively considering the length characteristics of the cycle data segment. Finally, the current power is adjusted based on the load characteristic value and the step frequency coefficient to obtain the power value at the next moment. This predictive adjustment makes the power control of the treadmill more intelligent, improves the use safety and running stability.

Description

Technical Field

[0001] This invention relates to the field of power control technology, and more specifically to a distributed control method, apparatus, and program for a DC controller. Background Technology

[0002] Existing technologies for regulating the speed of low-speed treadmills typically control the power of their built-in DC controller, often employing a fixed power output mode. However, due to individual differences among users, users of different weights exert varying pressures on the low-speed treadmill, and even the same user's movement patterns differ at different times. Consequently, the resistance that the DC controller needs to overcome also varies. Therefore, if the DC controller in a low-speed treadmill uses a fixed power output mode, it will ultimately lead to a mismatch between the treadmill's speed and the user's movement behavior, making it difficult to maintain stable treadmill speed and potentially causing injury to the user. Summary of the Invention

[0003] To address the issue of individual user differences, where users of different weights experience varying pressures on low-speed treadmills, and even the same user's movement patterns differ at different times, thus affecting the resistance the DC controller needs to overcome, a fixed power output mode for the DC controller in a low-speed treadmill would ultimately lead to a mismatch between the treadmill's speed and the user's movement behavior, making it difficult to maintain stable speed and potentially causing injury to the user. The present invention aims to provide a distributed control method, device, and program for a DC controller, the specific technical solution of which is as follows:

[0004] When a user is using the treadmill, the historical pressure timing data of the treadmill and the power value of the DC controller in the treadmill at the current moment are obtained.

[0005] Based on the changing trend of pressure values ​​in the historical pressure time series data, the historical pressure time series data is periodically divided to obtain all gait cycle data segments; by combining the fluctuation and numerical characteristics of pressure values ​​in all gait cycle data segments, the load characteristic value of the walking machine is determined; by combining the length characteristics of all gait cycle data segments in the historical pressure time series data, the step frequency coefficient of the user on the walking machine is determined.

[0006] Based on the load characteristic value of the treadmill and the step frequency coefficient of the user on the treadmill, the adjustment index corresponding to the power value of the DC controller in the treadmill at the next moment is obtained; the power value of the DC controller in the treadmill at the current moment is adjusted based on the adjustment index, so as to obtain the power value of the DC controller in the treadmill at the next moment.

[0007] Furthermore, the method for acquiring the gait cycle data segment includes:

[0008] The historical pressure time series data were fitted using the least squares method to obtain the pressure change curve;

[0009] In the pressure change curve, the maximum point is obtained, and the pressure change curve is divided based on the maximum point to obtain all curve segments;

[0010] Each curve segment corresponds to a data segment in the historical pressure time series data as a gait cycle data segment.

[0011] Furthermore, the method for obtaining the load characteristic values ​​includes:

[0012] In each gait cycle data segment, the mean of all pressure values ​​is used as the pressure characteristic value;

[0013] In each gait cycle data segment, the load weight of the walking machine in each gait cycle data segment is determined based on the fluctuation of the pressure value;

[0014] The load factor of the treadmill in each gait cycle data segment is the product of the load weight of the treadmill in each gait cycle data segment and the pressure characteristic value of each gait cycle data segment.

[0015] The average load factor of the treadmill across all gait cycle data segments is used as the load characteristic value of the treadmill.

[0016] Furthermore, the method for obtaining the load weight includes:

[0017] In each gait cycle data segment, the difference between the maximum and minimum pressure values ​​is negatively correlated and normalized, and this value is used as the load weight of the treadmill in each gait cycle data segment.

[0018] Furthermore, the method for obtaining the step frequency coefficient includes:

[0019] The value obtained by negatively correlated mapping of the length of each gait cycle data segment is used as the step frequency factor for the user in each gait cycle data segment;

[0020] The mean of the gait frequency factors of all gait cycle data segments is used as the user's gait frequency coefficient on the treadmill.

[0021] Furthermore, the method for obtaining the adjustment indicators includes:

[0022] The product of the preset parameters and the load characteristic value of the treadmill is used as the first adjustment factor;

[0023] The product of the preset parameters after negative correlation mapping and the user's step frequency coefficient on the treadmill is used as the second adjustment factor;

[0024] The sum of the first adjustment factor and the second adjustment factor is numerically adjusted and used as the adjustment index for the power value of the treadmill at the next moment.

[0025] Furthermore, the method for obtaining the power value of the treadmill at the next moment includes:

[0026] The sum of the adjustment index and the preset constant is used as the power adjustment coefficient, and the product of the power adjustment coefficient and the power value of the treadmill at the current moment is used as the power value of the treadmill at the next moment.

[0027] Furthermore, the preset parameter is set to 0.5.

[0028] A control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of any of the distributed control methods for a DC controller described above.

[0029] A computer program product includes a computer program that, when executed by a processor, can be used to implement the steps of any one of the distributed control methods for a DC controller.

[0030] The present invention has the following beneficial effects:

[0031] Because users of different weights exert varying pressures on the treadmill, the power required by the DC controller to overcome the resistance generated by the treadmill load in order to maintain normal operating speed will also differ. Therefore, determining the power value of the DC controller in the treadmill at the next moment requires obtaining historical pressure time-series data of the treadmill during user use, as well as the power value of the DC controller at the current moment, to provide data support. During a user's natural gait, each foot lift and landing causes changes in pressure data. These gait variations should serve as the basis for calculating the DC controller power value at the next moment. Therefore, based on the pressure value change trend in the historical pressure time-series data, the pressure time-series data is periodically divided into gait cycle data segments. Each gait cycle data segment represents the pressure change of the treadmill during a single user movement. Since the load on the treadmill can be characterized by pressure values, the load characteristic value of the treadmill is determined by comprehensively considering the fluctuations and numerical characteristics of pressure values ​​in all gait cycle data segments. This load characteristic value represents the load situation of the treadmill during its historical use and, to some extent, reflects the user's gait attributes. Furthermore, since the user's step frequency on the treadmill also affects the power output of the DC controller, and each gait cycle data segment represents one of the user's movements, the step frequency coefficient of the user on the treadmill is determined by comprehensively considering the length characteristics of all gait cycle data segments. Finally, based on the load characteristic value of the treadmill and the user's step frequency coefficient, an adjustment index corresponding to the power value of the treadmill at the next moment is obtained. The power of the DC controller in the treadmill at the current moment is adjusted according to this adjustment index, thus obtaining the power value of the DC controller in the treadmill at the next moment. This predictive adjustment capability makes the power control of the DC controller in the treadmill more intelligent, thereby making the speed of the treadmill better match the user's movement, improving user safety and the smoothness of the treadmill's operation. Attached Figure Description

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating a distributed control method for a DC controller, provided as an embodiment of the present invention;

[0034] Figure 2This is a flowchart illustrating a method for obtaining load characteristic values ​​according to an embodiment of the present invention.

[0035] Figure 3 This is a schematic diagram of a control device provided in one embodiment of the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a distributed control method, apparatus, and program for a DC controller proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details a specific solution for a distributed control method, apparatus, and program for a DC controller provided by the present invention.

[0039] Please see Figure 1 The diagram illustrates a method flowchart for a distributed control method for a DC controller according to an embodiment of the present invention, the method comprising the following steps:

[0040] Step S1: When the user is using the treadmill, obtain the historical pressure timing data of the treadmill and the power value of the DC controller in the treadmill at the current moment.

[0041] Low-speed treadmills are slow-speed devices designed specifically for rehabilitation, fitness, or the elderly. They require precise and smooth speed control to ensure user safety and comfort. Therefore, they often have a built-in DC controller, whose power and other operating parameters are controlled to meet the device's needs. Smooth speed control means that the speed changes gradually, linearly, or along a set curve within a set time, rather than abruptly jumping. However, due to individual differences, such as heavier users putting more pressure on the treadmill, the DC controller needs more power to overcome this pressure and maintain a smooth speed. Insufficient power in the DC controller can lead to unstable speed changes, causing a jerky feeling and potentially causing injury to the user in severe cases.

[0042] Therefore, based on the pressure changes experienced by the treadmill, the DC controller can be gradually supplied with power, thereby achieving a smooth transition in the treadmill's speed. Thus, when the user is using the treadmill, historical pressure time-series data and the current power value of the DC controller in the treadmill are obtained. Historical pressure time-series data can be acquired by pressure sensors installed under the treadmill pedals at a fixed sampling frequency, and then the acquired data is converted into digital signals to obtain historical pressure time-series data (horizontal axis: time, vertical axis: pressure value). The current power value of the DC controller in the treadmill can be directly measured using a power meter or other testing tools, or calculated using power calculation formulas based on voltage and current.

[0043] It should be noted that the pressure sensor's acquisition frequency is set to 5 times / second. The specific frequency setting can be adjusted according to the implementation scenario and is not limited here. The length of the historical pressure time series data is set to 3 minutes prior to the current moment. The specific length can be adjusted according to the implementation scenario and is not limited here.

[0044] Step S2: Based on the changing trend of pressure values ​​in the historical pressure time series data, the historical pressure time series data is periodically divided to obtain all gait cycle data segments; by combining the fluctuation and numerical characteristics of pressure values ​​in all gait cycle data segments, the load characteristic value of the walking machine is determined; in the historical pressure time series data, by combining the length characteristics of all gait cycle data segments, the step frequency coefficient of the user on the walking machine is determined.

[0045] Different users have varying weights, gait habits, and other factors, requiring different levels of resistance from the DC controller in the treadmill to maintain a stable speed. For example, heavier users exert greater pressure on the treadmill, increasing static friction on the motor. Greater static friction necessitates more power from the DC controller to overcome resistance during startup or acceleration, requiring a higher real-time power output. Furthermore, heavier users typically have lower stride frequencies and higher periodicity to match the correct training rhythm. Changes in stride frequency significantly impact the user's experience of smooth speed transitions on the treadmill. A mismatch between stride frequency and treadmill speed can cause discomfort or imbalance. Therefore, the user's stride frequency also influences the real-time power adjustment of the DC controller during speed transitions.

[0046] Due to individual differences, different users will have different gait cycles. The gait cycle is mainly manifested in the entire process of lifting and lowering the foot. This will cause the pressure values ​​in the historical pressure time series data to show a certain periodicity in the trend of change. Therefore, we can first analyze the trend of pressure values ​​in the historical pressure time series data, and then divide the historical pressure time series data into periods to obtain all gait cycle data segments.

[0047] Preferably, in one embodiment of the present invention, the method for obtaining gait cycle data segments includes:

[0048] By fitting historical pressure time series data using the least squares method, noise and fluctuations in the historical pressure time series data can be smoothed out, making the trend of data change clearer, thereby obtaining the pressure change curve.

[0049] Then, in the pressure change curve, the extreme points usually correspond to certain key events in the gait cycle, such as heel strike or toe lift. These events are important bases for dividing the gait cycle. Therefore, the maximum points in the pressure change curve are obtained, and the pressure change curve is divided based on the maximum points to obtain all the curve segments. That is, in terms of time, the change curve between each two adjacent maximum points is taken as a curve segment.

[0050] Finally, the data segment corresponding to each curve segment in the historical pressure time series data is taken as a gait cycle data segment.

[0051] After dividing the historical pressure time series data into all gait cycle data segments, the pressure data in each gait cycle data segment can represent a single foot lift and landing movement during the user's exercise. Since the load of the treadmill will have a certain impact on the power output of the DC controller in the treadmill, the load characteristic value of the treadmill can be determined by comprehensively considering the fluctuation and numerical characteristics of the pressure values ​​in all gait cycle data segments. This allows for more precise control of the DC controller's power in subsequent processes, making the treadmill speed more matched to the user's own situation.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining load characteristic values ​​includes:

[0053] Please see Figure 2 The diagram illustrates a method flowchart for obtaining load feature values ​​according to an embodiment of the present invention. The method includes the following steps:

[0054] Step S201: In each gait cycle data segment, determine the pressure characteristic value based on the numerical characteristics of the pressure value.

[0055] Because the pressure changes in each gait cycle data segment can characterize a movement outcome, and the pressure changes vary across different gait cycle data segments, measuring the real-time load on the treadmill makes the final result complex and reduces accuracy. However, the pressure changes in each gait cycle data segment follow a certain mean principle; that is, all pressure values ​​in each gait cycle data segment fluctuate around the mean. Therefore, for ease of calculation in subsequent processes, the mean of all pressure values ​​in each gait cycle data segment is used as the pressure characteristic value corresponding to each gait cycle data segment. The larger the pressure characteristic value of a certain gait cycle data segment, the heavier the load on the treadmill in that gait cycle data segment.

[0056] Step S202: In each gait cycle data segment, based on the fluctuation of the pressure value, determine the load weight of the treadmill in each gait cycle data segment.

[0057] In step S201, the pressure characteristic value corresponding to each gait cycle data segment is calculated. However, given that abnormal load changes may occur during the use of the treadmill, such as unstable steps or changes in the force application method, which may cause the load on the treadmill to increase abnormally, that is, extreme values ​​will be generated. Extreme values ​​will affect the pressure characteristic value. Therefore, in this embodiment of the invention, the abnormal increase in load is regarded as noise, and efforts are made to reduce its impact on subsequent results.

[0058] Therefore, within each gait cycle data segment, the range of pressure value fluctuations is analyzed, which involves calculating the difference between the maximum and minimum pressure values. This difference reflects the dynamic load change experienced by the treadmill in each gait cycle data segment, and the larger the value, the more significant the load change. Then, the difference between the maximum and minimum pressure values ​​is negatively correlated and normalized to correct the logical relationship. The resulting value is used as the load weight of the treadmill in each gait cycle data segment. The larger the load weight corresponding to a certain gait cycle data segment, the higher the probability that the user's movement process is in a normal state corresponding to the pressure value in that gait cycle data segment. Therefore, the reliability of using the pressure value of that gait cycle data segment for subsequent calculations of the treadmill's load is higher.

[0059] It should be noted that the method for negative correlation mapping and normalization in the embodiments of the present invention can be adopted. function, where, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0060] Step S203: In all gait cycle data segments, the pressure characteristic values ​​are weighted based on the load weight to determine the load characteristic values ​​of the treadmill.

[0061] Based on the aforementioned steps, the larger the load weight corresponding to a certain gait cycle data segment, the higher the reliability of calculating the treadmill's load using the pressure value of that gait cycle data segment. The larger the pressure characteristic value corresponding to a certain gait cycle data segment, the greater the load the treadmill experiences in that gait cycle data segment. Therefore, the product of the treadmill's load weight in each gait cycle data segment and the pressure characteristic value of each gait cycle data segment is used as the treadmill's load factor in each gait cycle data segment. At this time, the larger the load factor corresponding to a certain gait cycle data segment, the greater the load the treadmill experiences in that gait cycle, and the higher the reliability of this load factor.

[0062] Based on the aforementioned calculations, the treadmill corresponds to a load factor in each gait cycle data segment. Finally, the average load factor of the treadmill in all gait cycle data segments is taken as the load characteristic value of the treadmill. At this time, the larger the load characteristic value, the greater the load on the treadmill in the historical process.

[0063] When a user's walking frequency on the treadmill changes, the power required by the treadmill to support the user's movement also changes, thus altering the power output of the DC controller. Therefore, in this embodiment of the invention, another factor affecting the power value of the DC controller in the treadmill—the user's walking frequency—is analyzed. Given that each gait cycle data segment reflects one walking process, and the length of the gait cycle data segment can be used to directly measure the user's step frequency on the treadmill, the step frequency coefficient of the user on the treadmill is determined by comprehensively considering the length characteristics of all gait cycle data segments in the historical pressure time series data.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining the step frequency coefficient includes:

[0065] Since a fixed sampling frequency of the pressure sensor indicates a shorter gait cycle data segment, a faster cadence of the user on the treadmill, a negative correlation mapping is applied to the length of each gait cycle data segment to correct the logical relationship. The mapped value is then used as the user's cadence factor in each gait cycle data segment; a larger cadence factor indicates a faster cadence of the user on the treadmill. This negative correlation mapping can be achieved using... Fractions, where x represents the independent variable; alternatively, the following can be used: function, where, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0066] Each gait cycle data segment in the historical stress time series data corresponds to a step frequency factor. Finally, the average of the step frequency factors of all gait cycle data segments is used as the step frequency coefficient of the user on the treadmill. The larger the step frequency coefficient, the higher the walking frequency of the user on the treadmill. Therefore, in order to maintain the speed of the treadmill and match the user's movement state and reduce the risk of user injury, the power value of the corresponding DC controller should be appropriately increased.

[0067] Step S3: Based on the load characteristic value of the treadmill and the step frequency coefficient of the user on the treadmill, obtain the adjustment index corresponding to the power value of the DC controller in the treadmill at the next moment; adjust the power value of the DC controller in the treadmill at the current moment based on the adjustment index, so as to obtain the power value of the DC controller in the treadmill at the next moment.

[0068] By comprehensively analyzing the load characteristics of the treadmill and the user's step frequency coefficient during historical data processing, the adjustment index corresponding to the power value of the DC controller in the treadmill at the next moment can be determined. Based on the adjustment index, the power value of the DC controller in the treadmill at the current moment is adjusted to obtain the power value of the DC controller in the treadmill at the next moment. This allows the driving force provided by the DC controller to the treadmill to be more matched with the user's exercise, thereby ensuring the stable operation of the treadmill and the user's exercise experience.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the adjustment index includes:

[0070] Based on the analysis in the preceding steps, it can be seen that there are two factors affecting the power value of the DC controller in the treadmill at the next moment: the load characteristic value of the treadmill and the step frequency coefficient of the user on the treadmill. Therefore, the two are combined here to obtain the adjustment index.

[0071] The product of the preset parameters and the load characteristic value of the treadmill is used as the first adjustment factor. The larger the first adjustment factor, the greater the load on the treadmill, and the greater the power value required by the DC controller in the treadmill to ensure the smooth speed of the treadmill.

[0072] Then, the product of the preset parameters after negative correlation mapping and the user's step frequency coefficient on the treadmill is used as the second adjustment factor. Similarly, the larger the second adjustment factor, the higher the user's step frequency on the treadmill, indicating a higher walking frequency. Therefore, the corresponding power value of the DC controller should be increased. This negative correlation mapping is achieved through… Implementation, where x represents the independent variable.

[0073] Finally, the sum of the first adjustment factor and the second adjustment factor is numerically adjusted to map it into the expected range. The adjusted value is used as the adjustment index for the power value of the treadmill at the next moment. The larger the adjustment index, the greater the power value of the DC controller in the treadmill needs to be increased at the next moment.

[0074] It should be noted that, in this embodiment of the invention, in order to avoid excessive power increase and to reduce the power value, the expected range is set to [-0.2, 0.2]. The specific method for adjusting the value can be linear normalization or other methods, and the process will not be described in detail here. The preset parameter is set to 0.5, and the value range is limited to (0, 1). The larger the preset parameter, the larger the proportion of the load characteristic value of the walking machine. Conversely, the smaller the parameter, the larger the proportion of the user's step frequency coefficient on the walking machine. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0075] After obtaining the adjustment index for the power value of the treadmill at the next moment, it can be used to adjust the power value of the treadmill at the current moment, thereby obtaining the power value of the treadmill at the next moment.

[0076] Preferably, in one embodiment of the present invention, the method for obtaining the power value of the treadmill at the next moment includes:

[0077] Based on the foregoing analysis, the adjustment index ranges from -0.2 to 0.2, and the larger the value, the greater the need to increase the power output of the DC controller in the treadmill at the next moment. The sum of the adjustment index and a preset constant is then used as the power adjustment coefficient. In this embodiment, the preset constant is set to 1. A power adjustment coefficient greater than 1 indicates that the power output of the treadmill needs to be increased at the next moment; conversely, a power adjustment coefficient less than 1 indicates that the power output needs to be decreased. Finally, the product of the power adjustment coefficient and the current power output of the treadmill is used as the power output of the treadmill at the next moment. This achieves real-time control of the power output of the DC controller in the treadmill, ensuring stable operation of the treadmill and better matching the user's exercise patterns.

[0078] In summary, because users of different weights exert varying pressures on the treadmill, the power required by the DC controller in the treadmill to overcome the resistance generated by the load will also differ in order to maintain the treadmill's normal operating speed. Therefore, determining the power value of the DC controller in the treadmill at the next moment requires obtaining historical pressure time-series data of the treadmill during user use, as well as the power value of the DC controller at the current moment, to provide data support. Under the user's natural gait, each foot lift and landing causes changes in pressure data, and these gait variations should serve as the basis for calculating the DC controller power value at the next moment. Therefore, based on the pressure value change trend in the historical pressure time-series data, the pressure time-series data is periodically divided into gait cycle data segments. Each gait cycle data segment represents the pressure change of the treadmill during a single user movement. Since the load on the treadmill can be characterized by pressure values, the load characteristic value of the treadmill is determined by comprehensively considering the fluctuations and numerical characteristics of pressure values ​​in all gait cycle data segments. This load characteristic value represents the load situation of the treadmill during its historical use and, to some extent, reflects the user's gait attributes. Furthermore, since the user's step frequency on the treadmill also affects the power output of the DC controller, and each gait cycle data segment represents one of the user's movements, the step frequency coefficient of the user on the treadmill is determined by comprehensively considering the length characteristics of all gait cycle data segments. Finally, based on the load characteristic value of the treadmill and the user's step frequency coefficient, an adjustment index corresponding to the power value of the treadmill at the next moment is obtained. The power of the DC controller in the treadmill at the current moment is adjusted according to this adjustment index, thus obtaining the power value of the DC controller in the treadmill at the next moment. This predictive adjustment capability makes the power control of the DC controller in the treadmill more intelligent, thereby making the speed of the treadmill better match the user's movement, improving user safety and the smoothness of the treadmill's operation.

[0079] This invention also proposes a control device built into a treadmill, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a distributed control method for a DC controller.

[0080] Please see Figure 3The diagram illustrates the structure of a control device provided in an embodiment of the present invention, including a processor 300, a memory 301, a bus 302, and a communication interface 303. The processor 300, the communication interface 303, and the memory 301 are connected via the bus 302. The memory 301 may contain a high-speed random access memory, and the bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The processor 300 may be an integrated circuit chip with signal processing capabilities. The memory 301 stores a computer program, which, when loaded and executed by the processor, implements the steps in a distributed control method for a DC controller.

[0081] This invention also provides a computer program product, including a computer program, which, when executed by a processor, can implement the steps of a control method for a tributary controller.

[0082] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A decentralized control method for a direct current controller, characterized by, The method comprises: When a user uses a treadmill, historical pressure time series data of the treadmill and a power value of a direct current controller in the treadmill at a current moment are acquired; According to a change trend of pressure values in the historical pressure time series data, the historical pressure time series data is periodically divided to obtain all gait cycle data segments; by comprehensively considering fluctuation and numerical characteristics of pressure values in all gait cycle data segments, a load characteristic value of the treadmill is determined; and by comprehensively considering length characteristics of all gait cycle data segments in the historical pressure time series data, a step frequency coefficient of the user on the treadmill is determined; Based on the load characteristic value of the treadmill and the step frequency coefficient of the user on the treadmill, an adjustment index corresponding to the power value of the direct current controller in the treadmill at a next moment is obtained; and based on the adjustment index, the power value of the direct current controller in the treadmill at the current moment is adjusted to obtain the power value of the direct current controller in the treadmill at the next moment; the gait cycle data segment acquisition method comprises: The historical pressure time series data is fitted based on a least square method to obtain a pressure change curve; In the pressure change curve, a maximum value point is acquired, and the pressure change curve is divided based on the maximum value point to obtain all curve segments; Each data segment corresponding to each curve segment in the historical pressure time series data is taken as a gait cycle data segment; the load characteristic value acquisition method comprises: In each gait cycle data segment, a mean value of all pressure values is taken as a pressure characteristic value; In each gait cycle data segment, based on fluctuation of pressure values, a load weight of the treadmill in each gait cycle data segment is determined; A product of the load weight of the treadmill in each gait cycle data segment and the pressure characteristic value of each gait cycle data segment is taken as a load factor of the treadmill in each gait cycle data segment; A mean value of load factors of the treadmill in all gait cycle data segments is taken as the load characteristic value of the treadmill; the load weight acquisition method comprises: In each gait cycle data segment, a value obtained by performing negative correlation mapping and normalization on a difference value between a maximum pressure value and a minimum pressure value is taken as the load weight of the treadmill in each gait cycle data segment; the step frequency coefficient acquisition method comprises: A value obtained by performing negative correlation mapping on a length of each gait cycle data segment is taken as a step frequency factor of the user in each gait cycle data segment; A mean value of step frequency factors of all gait cycle data segments is taken as the step frequency coefficient of the user on the treadmill; and the adjustment index acquisition method comprises: A product of a preset parameter and the load characteristic value of the treadmill is taken as a first adjustment factor; A product of a value obtained by performing negative correlation mapping on the preset parameter and the step frequency coefficient of the user on the treadmill is taken as a second adjustment factor; and A value obtained by performing numerical adjustment on a sum value of the first adjustment factor and the second adjustment factor is taken as the adjustment index of the power value of the direct current controller in the treadmill at the next moment.

2. The decentralized control method for a DC controller according to claim 1, wherein, The power value of the direct current controller in the treadmill at the next moment is acquired by: The sum of the adjustment index and a preset constant is taken as a power adjustment coefficient, and the product of the power adjustment coefficient and a power value of the walking machine at a current moment is taken as a power value of a DC controller in the walking machine at a next moment.

3. A decentralized control method for a DC controller as recited in claim 2, wherein, The preset parameter is set to 0.

5.

4. A control device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the steps of the distributed control method for the DC controller when executing the computer program.

5. A computer program product comprising a computer program, characterized in that, The computer program can be used to implement the steps of the distributed control method for the DC controller when executed by the processor.

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