Decentralized control method, device and program for DC controller

Abstract

The invention relates to the technical field of power control, in particular to a decentralized control method, device and program for a direct current controller. The pressure generated by users with different weights on the walking machine is different, and the power required by the direct current controller is influenced. In order to accurately determine the power value at the next moment, historical pressure time sequence data and the current power value of the user are obtained. Under the natural gait of the user, pressure data changes along with foot lifting and foot falling, so that a gait cycle data segment is obtained through periodical division according to the historical pressure data change trend. And then integrating pressure fluctuation and numerical value characteristics of all periodic data segments, determining a load characteristic value, and reflecting gait attributes of the user. Meanwhile, the stride frequency of the user also affects power output, so that the stride frequency coefficient is determined by integrating the length characteristics of the periodic data segment. And finally, adjusting the current power based on the load characteristic value and the stride frequency coefficient to obtain a power value at the next moment. The predictive adjustment enables the power control of the walking machine to be more intelligent, and the use safety and the operation stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power control, in particular to a decentralized control method, device and program for a direct current controller. BACKGROUND

[0002] In the prior art, when the speed of a low-speed treadmill is regulated, the power of the built-in direct current controller is usually controlled, but a fixed power output mode is often used. However, due to individual differences of users, different weights of users generate different pressures on the low-speed treadmill, and even the same user has different movement states at different times, so the resistance that the direct current controller needs to overcome also changes. Therefore, if the power of the direct current controller in the low-speed treadmill uses a fixed power output mode, the speed of the treadmill will eventually not match the movement behavior of the user, it is difficult to maintain the speed of the treadmill stable, and the user may be injured. SUMMARY

[0003] In order to solve the technical problem that users have individual differences, different weights of users generate different pressures on the low-speed treadmill, and even the same user has different movement states at different times, so the resistance that the direct current controller needs to overcome also changes, and if the power of the direct current controller in the low-speed treadmill uses a fixed power output mode, the speed of the treadmill will eventually not match the movement behavior of the user, it is difficult to maintain the speed of the treadmill stable, and the user may be injured, the purpose of the present application is to provide a decentralized control method, device and program for a direct current controller, and the technical solution is as follows: 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 time are obtained; According to the change trend of the 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. The fluctuation and numerical characteristics of the pressure values in all gait cycle data segments are comprehensively determined to determine the load characteristic value of the treadmill. In the historical pressure time series data, the length characteristics of all gait cycle data segments are comprehensively determined to determine the step frequency coefficient of the user on the treadmill. 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 the next time is obtained. The power value of the direct current controller in the treadmill at the current time is adjusted based on the adjustment index, so as to obtain the power value of the direct current controller in the treadmill at the next time.

[0004] Further, the gait cycle data segment acquisition method comprises: fitting the historical pressure time series data based on a least square method to obtain a pressure change curve; In the pressure change curve, a maximum point is obtained, and the pressure change curve is divided based on the maximum point to obtain all curve segments. Each curve segment in the historical pressure time series data is taken as a gait cycle data segment.

[0005] Further, the load characteristic value acquisition method comprises: In each gait cycle data segment, the mean value of all pressure values is taken as a pressure characteristic value. In each gait cycle data segment, based on the fluctuation of the pressure value, the load weight of the treadmill in each gait cycle data segment is determined. 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 is taken as the load factor of the treadmill in each gait cycle data segment. The mean value of the load factors of the treadmill in all gait cycle data segments is taken as the load characteristic value of the treadmill.

[0006] Further, the load weight acquisition method comprises: In each gait cycle data segment, the value obtained by negatively correlating and normalizing the difference between the maximum pressure value and the minimum pressure value is taken as the load weight of the treadmill in each gait cycle data segment.

[0007] Further, the step frequency coefficient acquisition method comprises: The value obtained by negatively correlating the length of each gait cycle data segment is taken as the step frequency factor of the user in each gait cycle data segment. The mean value of the step frequency factors of all gait cycle data segments is taken as the step frequency coefficient of the user on the treadmill.

[0008] Further, the adjustment index acquisition method comprises: The product of the preset parameter and the load characteristic value of the treadmill is taken as a first adjustment factor. The product of the value obtained by negatively correlating the preset parameter and the step frequency coefficient of the user on the treadmill is taken as a second adjustment factor. The value obtained by numerically adjusting the sum of the first adjustment factor and the second adjustment factor is taken as the adjustment index of the power value of the treadmill at the next moment.

[0009] Further, the acquisition method of the power value of the treadmill at the next moment comprises: 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 the power value of the walking machine at the current moment is taken as the power value of the walking machine at the next moment.

[0010] Further, the preset parameter is set to 0.5.

[0011] A control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the distributed control methods for the direct current controller.

[0012] A computer program product includes a computer program executable by a processor to implement the steps of any one of the distributed control methods for the direct current controller.

[0013] The present application has the following beneficial effects: Since different users of different weights generate different pressure on the treadmill when using the treadmill, in order to maintain the normal running speed of the treadmill, the power required by the DC controller in the treadmill to overcome the resistance generated by the load of the treadmill also differs, so when determining the power value of the DC controller in the treadmill at the next moment, the historical pressure time series data of the treadmill when the user uses the treadmill needs to be obtained, and at the same time, the power value of the DC controller in the treadmill at the current moment also needs to be obtained to provide data support. Under the natural gait of the user, each time the foot is lifted and landed, the pressure data will change, and the gait change of the user should be used as the basis for subsequent calculation of the power value of the DC controller at the next moment. Therefore, according to the change trend of the pressure value in the historical pressure time series data, the gait cycle data segments are obtained by periodically dividing the pressure time series data. At this time, each gait cycle data segment represents the pressure change of the treadmill under the action of the user. Since the load borne by the treadmill can be represented by the pressure value, the fluctuation and numerical characteristics of the pressure values in all gait cycle data segments are comprehensively determined to obtain the load characteristic value of the treadmill. The load characteristic value represents the load condition of the treadmill in the historical use process, and to some extent reflects the gait attribute of the user. Further, since the step frequency of the user when moving on the treadmill also affects the power output of the DC controller in the treadmill, and each gait cycle data segment represents the action of the user, the length characteristics of all gait cycle data segments are comprehensively determined to obtain the step frequency coefficient of the user on the treadmill. Finally, 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 treadmill at the next moment is obtained, and the power of the DC controller in the treadmill at the current moment is adjusted according to the adjustment index, so as to obtain 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, so that the speed of the treadmill is more matched with the movement of the user, and the safety of the user and the stability of the treadmill are improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0015] Figure 1 A method flowchart of a distributed control method for a DC controller provided by an embodiment of the present application; Figure 2 A method flowchart of a load characteristic value acquisition method provided by an embodiment of the present application; Figure 3 Fig. 1 is a schematic diagram of a control device according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined object, the following describes in detail the specific implementation, structure, features and effects of a distributed control method, device and program for a DC controller according to the present application, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0017] 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 the present application belongs.

[0018] The following describes in detail the specific scheme of a distributed control method, device and program for a DC controller according to the present application, with reference to the accompanying drawings.

[0019] Referring to Fig. 1, which shows a method flowchart of a distributed control method for a DC controller according to an embodiment of the present application, the method comprises the following steps: Figure 1 Step S1: When a user uses a treadmill, historical pressure time series data of the treadmill and a power value of a DC controller in the treadmill at the current time are obtained.

[0020] A low-speed treadmill is a slow-speed device designed for rehabilitation groups, fitness groups or the elderly, which requires precise and smooth speed control to ensure the safety and comfort of users. Therefore, it often has a built-in DC controller to meet the needs of the application device by controlling the power and other operating parameters of the DC controller. Smooth speed control of the treadmill means that the speed gradually, linearly or according to a set curve changes within a set time, rather than suddenly jumping. However, for different users, due to individual differences, such as heavier users exert more pressure on the treadmill, the DC controller needs more power to overcome more pressure to make the speed of the treadmill tend to be smooth. If the power of the DC controller is insufficient, it will cause the speed of the treadmill to change unstably, resulting in a sense of jerk, which may cause damage to the user in severe cases.

[0021] ​Therefore, the progressive power of the DC controller can be given based on the pressure change of the treadmill, so as to achieve the purpose of smooth transition of the speed of the treadmill. When the user uses the treadmill, the historical pressure time series data of the treadmill and the power value of the DC controller in the treadmill at the current time are obtained. The historical pressure time series data can be obtained based on the pressure sensor installed below the treadmill pedal at a fixed acquisition frequency, and then the acquired data is converted into a digital signal, so as to obtain the historical pressure time series data (the horizontal axis is time and the vertical axis is pressure value); the power value of the DC controller in the treadmill at the current time can be directly measured by a power meter or calculated based on voltage and current using a power calculation formula.

[0022] It should be noted that the acquisition frequency of the pressure sensor is set to 5 times per second, and the specific frequency setting can be adjusted according to the implementation scene, which is not limited here; the length of the historical pressure time series data is set to 3 minutes before the current time, and the specific length can be adjusted according to the implementation scene, which is not limited here.

[0023] Step S2: According to the change trend of the pressure value in the historical pressure time series data, the historical pressure time series data is periodically divided to obtain all gait cycle data segments; the fluctuation and numerical characteristics of the pressure value in all gait cycle data segments are comprehensively determined to determine the load characteristic value of the treadmill; in the historical pressure time series data, the length characteristics of all gait cycle data segments are comprehensively determined to determine the step frequency coefficient of the user on the treadmill.

[0024] Different users have different weights, gait habits and other factors, in order to maintain the smooth speed of the treadmill, the power of the DC controller in the treadmill needs to overcome different resistances, for example, when the user has a larger weight, it will generate a greater pressure on the treadmill, increasing the static friction of the motor during operation. The greater the static friction, the more power the DC controller needs to overcome the resistance in the starting or accelerating stage, so the DC controller needs to give a larger power value in real time at this time; at the same time, the user with a larger weight has a smaller step frequency and a high periodicity in order to match the correct training rhythm. The change of step frequency has a greater impact on the user's experience of smooth transition of the speed of the treadmill, if the step frequency and the speed of the treadmill do not match, the user will feel uncomfortable or unbalanced, therefore, the step frequency of the user on the treadmill also has a certain influence on the real-time regulation of the power of the DC controller during the speed transition process.

[0025] Due to individual differences, different user gait cycles are also different, and the gait cycle mainly represents the whole process of lifting and landing of the user, which causes the stress values in the historical stress time series data to present a certain periodicity in the change trend, so the change trend of the stress values in the historical stress time series data can be analyzed first, so that the historical stress time series data is periodically divided, and all gait cycle data segments are obtained.

[0026] Preferably, in an embodiment of the present application, the method for obtaining the gait cycle data segment comprises: Based on the least square method, the historical stress time series data is fitted, the noise and fluctuations in the historical stress time series data are smoothed, the change trend of the data is clearer, and the stress change curve is obtained.

[0027] Then in the stress change curve, the extreme points usually correspond to some key events in the gait cycle, such as the heel touching the ground or the toe leaving the ground, etc., which are important basis for gait cycle division, so the maximum points in the stress change curve are obtained, and the stress change curve is divided based on the maximum points, and all curve segments are obtained, that is, in time series, the change curve between every two adjacent maximum points is taken as a curve segment.

[0028] Finally, the data segment corresponding to each curve segment in the historical stress time series data is taken as a gait cycle data segment.

[0029] After the historical stress time series data is divided to obtain all gait cycle data segments, the stress data in each gait cycle data segment can represent the lifting and landing movement of the foot in the user's movement process; since the load of the treadmill will have a certain influence on the power output of the DC controller in the treadmill, the fluctuation and numerical characteristics of the stress values in all gait cycle data segments can be comprehensively considered to determine the load characteristic value of the treadmill, so that the power of the DC controller can be more accurately controlled in the subsequent process, and the speed of the treadmill can be more matched with the user's own situation.

[0030] Preferably, in an embodiment of the present application, the method for obtaining the load characteristic value comprises: Please refer to Figure 2 which shows a method flowchart of the method for obtaining the load characteristic value in an embodiment of the present application, and the method comprises the following steps: Step S201: In each gait cycle data segment, based on the numerical characteristics of the stress values, the stress characteristic value is determined.

[0031] Because the pressure value change in each gait cycle data segment can represent an action result, and the pressure value change in different gait cycle data segments will change, measuring the real-time load of the walking machine will make the final result more complex and reduce the accuracy. However, in each gait cycle data segment, the pressure value change will follow a certain mean principle, that is, all pressure values in each gait cycle data segment will fluctuate around the mean value. Therefore, in order to facilitate calculation in the subsequent process, the mean value of all pressure values in each gait cycle data segment is taken as the pressure characteristic value corresponding to each gait cycle data segment, and the greater the pressure characteristic value of a certain gait cycle data segment, the heavier the load received by the walking machine in the gait cycle data segment.

[0032] Step S202: In each gait cycle data segment, based on the fluctuation of the pressure value, the load weight of the walking machine in each gait cycle data segment is determined.

[0033] In step S201, the pressure characteristic value corresponding to each gait cycle data segment is calculated. However, in view of the fact that abnormal load changes may occur during the use of the walking machine by the user, such as unstable pace or change in force application mode, resulting in abnormal increase of the load received by the walking machine, that is, extreme values will be generated, and the extreme values will affect the pressure characteristic value, in the embodiment of the present application, the case of abnormal increase of the load is regarded as noise, and efforts are made to reduce its influence on the subsequent results.

[0034] Therefore, in each gait cycle data segment, the range of pressure value fluctuation is analyzed, that is, the difference between the maximum pressure value and the minimum pressure value is calculated, which reflects the dynamic load change of the walking machine in each gait cycle data segment, and the greater the value, the more significant the load change. Then, the difference between the maximum pressure value and the minimum pressure value is negatively correlated and normalized to correct the logical relationship, and the value obtained at this time is taken as the load weight of the walking machine in each gait cycle data segment; the greater the load weight corresponding to a certain gait cycle data segment, the higher the possibility that the pressure value corresponding to the user's movement process in the gait cycle data segment is in a normal state, and then the reliability of using the pressure value of the gait cycle data segment to calculate the load of the walking machine in the subsequent process is higher.

[0035] It should be noted that the method of negative correlation mapping and normalization in the embodiment of the present application can adopt function, wherein, represents an exponential function with natural constant e as the base, and x represents the independent variable.

[0036] Step S203: In all gait cycle data segments, the pressure characteristic values are weighted based on the load weights, so as to determine the load characteristic value of the walking machine.

[0037] Based on the foregoing steps, the greater the load weight corresponding to a gait cycle data segment, the higher the reliability of calculating the load condition of the walking machine using the pressure value of the gait cycle data segment; the greater the pressure characteristic value corresponding to a gait cycle data segment, the greater the load received by the walking machine in the gait cycle data segment, so the product of the load weight of the walking machine in each gait cycle data segment and the pressure characteristic value of each gait cycle data segment is taken as the load factor of the walking machine in each gait cycle data segment, at this time, the greater the load factor corresponding to a gait cycle data segment, the greater the load received by the walking machine in the gait cycle, and the higher the reliability of the load factor.

[0038] After the foregoing calculation, the walking machine corresponds to a load factor in each gait cycle data segment, and finally the mean value of the load factors of the walking machine in all gait cycle data segments is taken as the load characteristic value of the walking machine, at this time, the greater the load characteristic value, the greater the load received by the walking machine in the historical process.

[0039] When the walking frequency of the user on the walking machine changes, the power provided by the walking machine to support the movement of the user also changes, and then the power output of the DC controller also changes, so in the embodiment of the present application, another factor affecting the power value of the DC controller in the walking machine, i.e., the walking frequency of the user on the walking machine, is continuously analyzed. Since each gait cycle data segment reflects a walking process, and the length value of the gait cycle data segment can be used to directly measure the step frequency of the user on the walking machine, the length characteristics of all gait cycle data segments in the historical pressure time sequence data are integrated to determine the step frequency coefficient of the user on the walking machine.

[0040] Preferably, in an embodiment of the present application, the method for obtaining the step frequency coefficient comprises: Since when the collection frequency of the pressure sensor is fixed, the smaller the length of the gait cycle data segment, the faster the step frequency of the user on the walking machine, so the length of each gait cycle data segment is negatively correlated to map, so as to correct the logical relationship, and then the value after the negative correlation mapping is taken as the step frequency factor of the user in each gait cycle data segment, at this time, the greater the step frequency factor, the faster the step frequency of the user on the walking machine. The negative correlation mapping here can adopt Fraction, wherein x represents the independent variable; or can adopt Function, wherein, Indicates the exponential function with the natural constant e as the base, and x represents the independent variable.

[0041] Each gait cycle data segment in the historical pressure time series data corresponds to a step frequency factor, and finally the average of the step frequency factors of all gait cycle data segments is taken as the step frequency coefficient of the user on the treadmill. The greater the step frequency coefficient, the higher the walking frequency of the user on the treadmill. In order to maintain the speed of the treadmill to match the motion state of the user and reduce the risk of injury of the user, the power value of the corresponding DC controller should be appropriately increased.

[0042] Step S3: 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 DC controller in the treadmill at the next moment is obtained; and 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.

[0043] By comprehensively analyzing the load characteristic value of the treadmill and the step frequency coefficient of the user on the treadmill in the historical process, the adjustment index corresponding to the power value of the DC controller in the treadmill at the next moment can be determined, and 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. The driving force provided by the DC controller for the treadmill can be more matched with the motion of the user, thereby ensuring the stable operation of the treadmill and the motion experience of the user.

[0044] Preferably, in an embodiment of the present application, the adjustment index is obtained by the following method: Based on the analysis in the foregoing steps, there are two factors affecting the power value of the DC controller in the treadmill at the next moment, which are the load characteristic value of the treadmill and the step frequency coefficient of the user on the treadmill. Therefore, the two factors are combined to obtain the adjustment index.

[0045] The product of the preset parameter and the load characteristic value of the treadmill is taken as a first adjustment factor. The greater 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.

[0046] Then, the product of the value of the preset parameter after negative correlation mapping and the step frequency coefficient of the user on the treadmill is taken as a second adjustment factor. Similarly, the greater the second adjustment factor, the greater the step frequency of the user on the treadmill, and the greater the corresponding power value of the DC controller. The realization is shown in the following formula:

[0047] Finally, the sum of the first adjustment factor and the second adjustment factor is numerically adjusted to map it into the expected range, and the numerically adjusted value is used as an adjustment index of the power value of the next time walking machine, and the larger the adjustment index, the greater the degree of adjustment of the power value of the DC controller in the next time walking machine.

[0048] It should be noted that, in the embodiment of the present application, in order to avoid excessive increase of power and achieve the purpose of reducing the power value, the expected range is set to [-0.2, 0.2]; the specific numerical adjustment method can use linear normalization method, and the process is not repeated here; the preset parameter is set to 0.5, and the value range is limited to (0, 1); the larger the preset parameter, the greater the proportion of the load eigenvalue of the walking machine, and vice versa, the greater the proportion of the step frequency coefficient of the user on the walking machine, and the specific value can be adjusted according to the implementation scene, which is not limited here.

[0049] After obtaining the adjustment index of the power value of the next time walking machine, the power value of the current time walking machine can be adjusted by using the adjustment index, so as to obtain the power value of the next time walking machine.

[0050] Preferably, in an embodiment of the present application, the method for obtaining the power value of the next time walking machine comprises: Based on the foregoing analysis, it can be known that the value range of the adjustment index is between [-0.2, 0.2], and the larger the value, the greater the degree of adjustment of the power value of the DC controller in the next time walking machine. Then, the sum of the adjustment index and a preset constant is used as a power adjustment coefficient, in the embodiment of the present application, the preset constant is set to 1, at this time, the value of the power adjustment coefficient is greater than 1, which means that the power value of the next time walking machine needs to be adjusted, otherwise, the value of the power adjustment coefficient is less than 1, which means that the power value of the next time walking machine needs to be adjusted. Finally, the product of the power adjustment coefficient and the power value of the current time walking machine is used as the power value of the next time walking machine, thereby realizing real-time control of the power value output of the DC controller in the walking machine, so that the power output can guarantee the stable operation of the walking machine and be more matched with the movement of the user.

[0051] In summary, since different users of different weights generate different pressures on the treadmill when using the treadmill, in order to maintain the normal running speed of the treadmill, the power required by the DC controller in the treadmill to overcome the resistance generated by the load of the treadmill also differs, so when determining the power value of the DC controller in the treadmill at the next moment, the historical pressure time series data of the treadmill when the user uses the treadmill needs to be obtained, and the power value of the DC controller in the treadmill at the current moment also needs to be obtained to provide data support. Under the natural gait of the user, each lifting and landing of the foot will cause the pressure data to change, and the change of the gait of the user should be taken as the basis for subsequent calculation of the power value of the DC controller at the next moment, so the pressure time series data is periodically divided according to the change trend of the pressure value in the historical pressure time series data to obtain gait cycle data segments, at this time each gait cycle data segment represents the pressure change of the treadmill under one action of the user. Since the load borne by the treadmill can be represented by the pressure value, the fluctuation and numerical characteristics of the pressure values in all gait cycle data segments are comprehensively determined to determine the load characteristic value of the treadmill, which represents the load condition in the historical use process of the treadmill and reflects the gait properties of the user to some extent. Further, since the step frequency of the user when moving on the treadmill also affects the power output of the DC controller in the treadmill, and each gait cycle data segment represents one action of the user, the length characteristics of all gait cycle data segments are comprehensively determined to determine the step frequency coefficient of the user on the treadmill. Finally, 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 treadmill at the next moment is obtained, and the power of the DC controller in the treadmill at the current moment is adjusted according to the adjustment index, so as to obtain 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, so that the speed of the treadmill is more matched with the movement of the user, and the safety of the user and the stability of the operation of the treadmill are improved.

[0052] The embodiment of the present application also provides a control device which is built in the treadmill and comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the distributed control method for the DC controller when executing the computer program.

[0053] Please refer to Figure 3As shown in Fig. 1, which shows a structural schematic diagram of a control device provided by an embodiment of the present application, the control device comprises a processor 300, a memory 301, a bus 302 and a communication interface 303, and the processor 300, the communication interface 303 and the memory 301 are connected through the bus 302; wherein the memory 301 can contain a high-speed random access memory, the bus 302 can be an ISA bus, a PCI bus or an EISA bus, etc., the processor 300 can be an integrated circuit chip with signal processing capability; the memory 301 stores a computer program, which is loaded and executed by the processor to implement the steps in the distributed control method for the DC controller.

[0054] The embodiment of the present application also provides a computer program product comprising a computer program, and the computer program can implement the steps of the control method for the branch controller when executed by a processor.

[0055] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0056] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the difference 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 variation 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 a 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.

2. The decentralized control method for a DC controller according to claim 1, wherein, The method for obtaining the gait cycle data segment comprises: The historical pressure time series data is fitted based on a least square method to obtain a pressure variation curve; In the pressure variation curve, a maximum value point is acquired, and the pressure variation curve is divided based on the maximum value point to obtain all curve segments; A data segment corresponding to each curve segment in the historical pressure time series data is taken as a gait cycle data segment.

3. The decentralized control method for a DC controller according to claim 1, wherein, The method for obtaining the load characteristic value 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 the 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 the load factors of the treadmill in all gait cycle data segments is taken as the load characteristic value of the treadmill.

4. The decentralized control method for a DC controller of claim 1, wherein, The method for obtaining the load weight comprises: In each gait cycle data segment, a value obtained by negatively correlating and normalizing a difference 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.

5. The decentralized control method for a DC controller of claim 1, wherein, The method for obtaining the step frequency coefficient comprises: A value obtained by negatively correlating 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 the step frequency factors of all gait cycle data segments is taken as the step frequency coefficient of the user on the treadmill.

6. The decentralized control method for a DC controller of claim 1, wherein, The method for obtaining the adjustment index 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 negatively correlating the preset parameter and the step frequency coefficient of the user on the treadmill is taken as a second adjustment factor; A value obtained by numerically adjusting a sum of the first adjustment factor and the second adjustment factor is taken as the adjustment index of the power value of the treadmill at the next moment.

7. The decentralized control method for a DC controller of claim 1, wherein, The method for obtaining the power value of the treadmill at the next moment comprises: A sum of the adjustment index and a preset constant is taken as a power adjustment coefficient, and a product of the power adjustment coefficient and the power value of the treadmill at the current moment is taken as the power value of the treadmill at the next moment.

8. The decentralized control method for a DC controller according to claim 6, wherein, The preset parameter is set to 0.

5.

9. 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 direct current controller according to any one of claims 1-8 when executing the computer program.

10. 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 direct current controller according to any one of claims 1-8 when executed by the processor.

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