CN118137895B

CN118137895B - Brushless motor sampling circuit, lifting support leg and lifting table adopting Hall element

Info

Publication number: CN118137895B
Application number: CN202311701410.4A
Authority: CN
Inventors: 黄春荣; 赵振普
Original assignee: Nantong Jiuzheng Ergonomic Co ltd
Current assignee: Nantong Jiuzheng Ergonomic Co ltd
Priority date: 2023-12-12
Filing date: 2023-12-12
Publication date: 2024-08-09
Anticipated expiration: 2043-12-12
Also published as: CN118137895A

Abstract

The invention relates to the technical field of brushless motors, in particular to a brushless motor sampling circuit, a lifting supporting leg and a lifting table which adopt Hall elements, wherein the Hall elements comprise a push-pull output structure, the push-pull output structure is provided with an output end, and the sampling circuit further comprises: the signal input end, the microcontroller interface, the signal output end and the power supply circuit, the microcontroller interface is connected with the microcontroller, and the digital signal is connected to the microcontroller, so that the microcontroller obtains information collected by the Hall element and converts the information into an output signal. The invention improves the control precision of the operation of the brushless motor, obtains real-time information and solves the problem possibly caused by lack of real-time feedback information, and because the push-pull output structure of the Hall element is adopted, the system can sense the state change of the brushless motor in real time, the microcontroller generates pulse signals through the digital output port, and then the pulse signals are transmitted to the controller of the brushless motor through the push-pull output structure, so that the accurate control of the electric lifting table can be realized.

Description

Brushless motor sampling circuit, lifting support leg and lifting table adopting Hall element

Technical Field

The invention relates to the technical field of brushless motors, in particular to a brushless motor sampling circuit, lifting support legs and a lifting table which adopt Hall elements.

Background

At present, the electric lifting table has started to use a brushless motor as power for lifting the telescopic supporting legs, and the brushless motor has the following advantages in the using process:

Because the brushless motor has no structure of the electric brush, electric sparks generated during the operation of the brush motor are avoided, and thus the interference of the electric sparks on remote control radio equipment is greatly reduced; the friction force is greatly reduced when the brushless motor runs, the running is smooth, and the noise is greatly reduced; the abrasion of the brushless motor is mainly on the bearing, and the brushless motor is almost a maintenance-free motor from the mechanical point of view, and only basic dust removal maintenance is needed when necessary.

In the working process, the brushless motor usually needs accurate position control, especially in applications such as electric lifting tables and the like which need accurate positioning, but when no real-time feedback information exists, the accuracy of the position can have certain control difficulty; also, the lack of real-time feedback information may also result in a delay in the response time of the system to external changes, for example, if the motorized lift table needs to be adjusted from one height to another in a short period of time, the lack of real-time feedback may cause the response speed of the control system to be limited.

Disclosure of Invention

The invention provides a brushless motor sampling circuit, lifting support legs and a lifting table adopting a Hall element, thereby effectively meeting the problems pointed out in the background art.

In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:

adopt hall element's brushless motor sampling circuit, hall element includes push-pull output structure, push-pull output structure has the output, and sampling circuit is connected with microcontroller, sampling circuit still includes:

the signal input end is connected with the output end of the push-pull output structure and receives a digital signal generated by the push-pull output structure;

The microcontroller interface is connected with the microcontroller and is used for connecting the digital signal to the microcontroller so that the microcontroller can acquire the information acquired by the Hall element and convert the information into an output signal;

The signal output end is connected with a controller of the brushless motor and transmits the output signal to the controller;

and the power supply circuit is used for providing the power supply voltage required by the sampling circuit.

Further, the microcontroller generates a pulse signal as the output signal through a digital output port.

Further, the push-pull output structure comprises a bipolar transistor NPN1, a bipolar transistor NPN2, a bipolar transistor PNP1, a bipolar transistor PNP2 and a reference resistor;

The collector of the bipolar transistor NPN1 is connected to a power supply, the emitter is connected to the base of the bipolar transistor NPN2, and the base is connected to a digital output port of the microcontroller through the reference resistor;

the collector of the bipolar transistor NPN2 is connected to the output end, the emitter is connected to the ground, and the base is connected to the base of the bipolar transistor NPN 1;

The collector of the bipolar transistor PNP1 is connected to a power supply, the emitter is connected to the base of the bipolar transistor PNP2, and the base is connected to a digital output port of the microcontroller through the reference resistor;

the collector of the bipolar transistor PNP2 is connected to ground, the emitter is connected to the output terminal, and the base is connected to the base of the bipolar transistor PNP 1.

Further, the microcontroller comprises:

an input/output port for communicating with other devices;

A counter connected to the hall element for counting the number of pulses;

a program memory storing a specific control program;

And the central processing unit executes the control program, wherein the control program at least completes one of the following tasks: tracking the number of pulses to obtain a rotor position of the brushless motor, or deducing a rotational speed of the brushless motor by counting a speed at which the pulses are generated by the hall element, or implementing closed-loop control using the number of pulses;

and the interrupt controller is connected with the counter and the brushless motor controller, processes interrupt requests, and responds and manages the interrupt.

Further, a deep reinforcement learning model is stored in the program memory, and the control program is updated.

Further, the deep reinforcement learning model includes:

An input layer for receiving input of the model, wherein each neuron corresponds to a specific input characteristic;

The full-connection layer is used for the model to learn the relation between input data and comprises a plurality of neurons, and each neuron is connected with all neurons of the input layer;

Activating a function layer, and introducing nonlinear properties after the fully connected layer;

and an output layer for outputting the decision of the model.

Further, the neuron number of the full connection layer is updated by a network architecture search algorithm, including:

defining the range of the number of the neurons of the full connection layer as a search space;

Determining a genetic algorithm as a search algorithm;

Defining an objective function for evaluating the performance of the deep reinforcement learning model;

searching in the defined search space by using the selected search algorithm, and gradually updating the neuron number of the full-connection layer according to feedback of an objective function.

A lifting leg adopts a brushless motor as high lifting power, and samples the brushless motor through a brushless motor sampling circuit adopting a Hall element.

A lifting table supports a table top through a plurality of lifting supporting legs, wherein the brushless motor is fixed through a structure positioned on the lifting supporting legs and/or the structure connected with different lifting supporting legs.

A table top is supported by a plurality of lifting support legs, each lifting motor provides lifting power for one lifting support leg or provides lifting power for at least two lifting support legs through a transmission device.

By the technical scheme of the invention, the following technical effects can be realized:

The technical scheme of the invention has the advantages of improving the control precision of the operation of the brushless motor, obtaining the real-time information and solving the problem possibly caused by the lack of the real-time feedback information, and the system can sense the state change of the brushless motor in real time due to the push-pull output structure of the Hall element, and the digital signal generated by the push-pull output structure can reflect the magnetic field change sensed by the Hall element in real time, so that the real-time state information of the brushless motor is provided.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.

Fig. 1 is a frame diagram of a brushless motor sampling circuit employing hall elements;

FIG. 2 is a flowchart of the updating of the neuron number of the full connection layer by the network architecture search algorithm;

FIG. 3 is a schematic view of the lifting leg and protective housing installation;

FIG. 4 is a schematic illustration of the lifting leg portion rod body and the protective housing portion shell omitted after the lifting leg and protective housing are installed;

FIG. 5 is a schematic view of a brushless motor mounted inside a lifting leg;

FIG. 6 is a schematic illustration of two lifting legs connected by a connecting structure;

FIG. 7 is an enlarged view of a portion of FIG. 6 at A;

FIG. 8 is a schematic view of three lifting legs connected by a connecting structure;

FIG. 9 is a partial enlarged view at B in FIG. 8;

FIG. 10 is a schematic illustration of providing lifting power to two lifting legs via a transmission;

FIG. 11 is an enlarged view of a portion of FIG. 10 at C;

reference numerals: 1. a brushless motor; 2. a protective housing; 3. lifting the supporting leg; 4. a connection structure; 5. a transmission device.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.

Unless defined otherwise, 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 belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

Embodiment one:

as shown in fig. 1, a brushless motor sampling circuit employing a hall element, the hall element includes a push-pull output structure having an output end, the sampling circuit is connected with a microcontroller, the sampling circuit further includes: the signal input end is connected with the output end of the push-pull output structure and receives a digital signal generated by the push-pull output structure; the microcontroller interface is connected with the microcontroller and is used for connecting the digital signal to the microcontroller so that the microcontroller can acquire information acquired by the Hall element and convert the information into an output signal; the signal output end is connected with the controller of the brushless motor and transmits an output signal to the controller; and the power supply circuit is used for providing the power supply voltage required by the sampling circuit.

By the method and the device, the real-time information about the state of the brushless motor can be obtained, so that the control accuracy of the operation of the brushless motor is improved, and the problem possibly caused by lack of real-time feedback information is solved. The hall element is a sensor capable of sensing a magnetic field change, and in the above embodiment, the design of the hall element includes a push-pull output structure, which is a circuit configuration, so that when the hall element senses the magnetic field change, the push-pull output structure can generate a corresponding digital signal, and an output end of the push-pull output structure refers to an output interface of the structure, that is, an output point of the generated digital signal.

In the implementation process, by acquiring real-time brushless motor state information, the system can realize more accurate motor control, and the microcontroller can adjust the running state of the motor in real time according to the information, so that the control precision is improved, and the requirements under different working conditions are met; the problem that the traditional brushless motor control system is low in control precision and low in response speed due to lack of real-time feedback information is effectively solved by using the Hall element and the push-pull output structure, so that the system senses the state of the motor more timely and accurately; the push-pull output structure of the Hall element can reduce electromagnetic interference when the motor operates, and the output signal of the push-pull output structure is relatively clean because the push-pull output structure is a digital circuit, thereby reducing the electromagnetic interference to surrounding equipment.

As a preference to the above-described embodiments, the microcontroller generates a pulse signal via a digital output port as an output signal, which gives the system a highly accurate control capability. Parameters such as frequency, duty ratio and the like of the pulse signals can be adjusted through program control of the microcontroller, and accurate height control of an electric lifting table can be realized; in addition, the design of generating the pulse signals by using the microcontroller increases the flexibility of the system, and the characteristics of the pulse signals can be easily changed by adjusting the program of the microcontroller so as to adapt to different use scenes or user requirements.

The push-pull output structure comprises a bipolar transistor NPN1, a bipolar transistor NPN2, a bipolar transistor PNP1, a bipolar transistor PNP2 and a reference resistor as a specific mode; the collector of the bipolar transistor NPN1 is connected to a power supply, the emitter is connected to the base of the bipolar transistor NPN2, and the base is connected to a digital output port of the microcontroller through a reference resistor; the collector of the bipolar transistor NPN2 is connected to the output end, the emitter is connected to the ground, and the base is connected to the base of the bipolar transistor NPN 1; the collector of the bipolar transistor PNP1 is connected to a power supply, the emitter is connected to the base of the bipolar transistor PNP2, and the base is connected to a digital output port of the microcontroller through a reference resistor; the collector of bipolar transistor PNP2 is connected to ground, the emitter is connected to the output, and the base is connected to the base of bipolar transistor PNP 1.

Through the push-pull output structure, the system design that pulse signals are generated through the microcontroller and transmitted through the push-pull output structure is realized, and the system has the following advantages: the digital output port is adopted to generate a pulse signal, the pulse signal is transmitted to the push-pull output structure, the transmission of the digital signal is realized, the digital signal has stable level logic, the system error can be reduced, the anti-interference performance can be improved, and the stability of the system is improved; the push-pull output structure can effectively transmit pulse signals generated by the microcontroller to the signal output end and then to the controller of the brushless motor, so that the brushless motor is effectively driven, and the efficiency and performance of the system are improved.

As a preference of the above embodiment, the microcontroller comprises: input/output ports for communicating with other devices, which may be digital input/output ports, or may include analog input/output ports, serial ports, parallel ports, etc.; the counter is connected with the Hall element and used for counting the pulse number, and when the brushless motor is applied to the electric lifting table, the counted pulse number is closely related to multiple parameters of the lifting table; a program memory storing a specific control program, typically of the Flash memory type; and the central processing unit executes a control program, wherein the control program at least completes one of the following tasks: tracking the number of pulses to obtain the rotor position of the brushless motor, or deducing the rotating speed of the brushless motor by counting the speed of the pulse generated by the Hall element, or using the number of pulses to realize closed-loop control; central processing units typically include registers for fast access to data, arithmetic Logic Units (ALUs) that perform arithmetic and logical operations, and the like; and the interrupt controller is connected with the counter and the brushless motor controller, processes interrupt requests, and responds and manages the interrupt.

In practice, interrupts are a mechanism that allows a microcontroller to respond in time to a particular event while performing a current task. In the present invention, the above-mentioned brushless motor sampling circuit is applied to an electric lifting table using a brushless motor, and is a specific embodiment:

Other devices with input/output ports capable of communication connection, besides the hall element or other type of sensor in the above embodiments, may be used to monitor the status, height or other relevant parameters of the lifting table in real time, and may further include: the user control panel is provided with the electric lifting table, and a user can manually control the lifting operation of the table through the panel to select different heights; a display or indicator light for displaying the information of the current state, the height and the like of the desk, or prompting the user about the information of the operation state of the desk through the indicator light; if necessary, the data associated with the lift table may be stored or retrieved and communicated to an external storage device via an input/output port.

In the application scenario, the interrupt request processed by the interrupt controller in the embodiment includes:

Limit detection interrupts: when the electric lifting table reaches the set upper limit or lower limit height, an interrupt can be triggered to stop the motor, so as to prevent excessive lifting or lowering.

Overload detection interrupts: if the motor is subjected to an abnormal load, which may indicate that an object is obstructing the movement of the table, by detecting the current or torque, an interrupt may be triggered to stop the movement and avoid damaging the motor.

Hall element signal interruption: when the hall element detects a change in the magnetic field, indicating a change in the position of the motor rotor, an interrupt may be triggered to update the motor position information.

Emergency stop button interrupt: in an emergency, a user can press an emergency stop button to trigger an interrupt to immediately stop the motor motion, so that the safety of the user is ensured.

Communication interruption: if communication with the external equipment is wrong or interrupted, the interruption can be triggered to deal with the communication problem, so that the normal operation of the control system is ensured.

Preferably, the program memory stores a deep reinforcement learning model for updating the control program. The deep reinforcement learning model is a machine learning paradigm, and combines deep learning and reinforcement learning methods to enable a system to learn an optimal behavior strategy through interactions with an environment. Taking the use situation of the electric lifting table as an example, the use habit of a user for the brushless motor can be learned through the deep reinforcement learning model, the interaction data of the user and the electric lifting table, such as lifting frequency, lifting amplitude, use time and the like, can be collected, and the deep neural network can be trained by utilizing the data, so that the network can output an optimal control strategy to meet the personalized requirements of the user.

In an implementation, the deep reinforcement learning model includes: an input layer for receiving input of the model, wherein each neuron corresponds to a specific input characteristic; such as status information of the lift table, number of pulses, etc.; the full-connection layer is used for the model to learn the relation between input data and comprises a plurality of neurons, and each neuron is connected with all neurons of the input layer; activating a function layer, and introducing nonlinear properties after the function layer is fully connected; and an output layer for outputting the decision of the model. In particular, the output layer may correspond to the control actions or decisions of the brushless motor, and in deep reinforcement learning, the output layer is generally matched to the specific requirements of the task, and may be one or more neurons, representing different actions or categories.

In the above model form, the input of the model may include: the lifting table state information comprises the current height, speed, movement direction and the like of the lifting table; the pulse number, which is used as a counter for the height of the lifting table, can be used for representing the movement track of the lifting table; user interaction information, if any, such as key operation, frequency of use of the lifting table, etc.

Based on the data obtained above, further processing is required, for example, table status information and pulse number are encoded for better understanding of the neural network, and in particular, normalization processing can be considered to ensure that the input data is within a similar range of values; if more advanced status information exists, such as movement patterns of the lift table, user preferences, etc., this information may be extracted by appropriate feature engineering.

In practice, a ReLU activation function may be employed that introduces non-linear properties by thresholding the input signal, with a mathematical expression of f (x) =max (0, x). Briefly, for input x, if x is greater than zero, output f (x) =x, and if x is equal to or less than zero, output f (x) =0. This thresholding results in a nonlinear output because the output is linear with the input when x is greater than zero; and when x is less than or equal to zero, the output is always zero, forming a piecewise linear function. The introduction of non-linear properties is very important for deep learning models, since the combination of multiple linear layers is still linear, by using non-linear activation functions, the model can learn more complex features and relationships, improving its representation capabilities, and the ReLU activation functions have simplicity and computational efficiency.

In the above-mentioned optimization scheme, taking an electric lifting table as an example, in a deep reinforcement learning model of the lifting table, the decision of the output layer generally includes controlling the specific action of the lifting table to meet the user requirement. In particular, the output layer may have a plurality of neurons, each corresponding to an action or policy, and outputtable action decisions include, but are not limited to:

Lifting/lowering action: one neuron represents the action of raising the lift table and the other neuron represents the action of lowering the lift table, with a corresponding decision being made by the model by activating one of the neurons.

Lifting speed: if the lifting speed of the lifting table needs to be finely tuned, a plurality of neurons can be used to represent different speed selections, and the model can adjust the lifting speed of the lifting table by activating the corresponding neurons.

Stopping: a neuron may indicate a stop of the elevating table, and activating the neuron may stop the elevating table.

Other specific actions: depending on the particular lift table function and user requirements, the output layer may also include other specific actions, such as positioning to a specific height, performing user preference actions in memory, and so forth.

In deep reinforcement learning, the model optimizes output actions by learning interactions with the environment, making the control of the lift table more intelligent and adaptable to the needs of the user, the choice of which will depend on the requirements and goals of the particular problem.

As a preference of the above embodiment, the neuron number of the full connection layer is updated by a network architecture search algorithm, as shown in fig. 2, including:

s1: defining the range of the number of the neurons of the full connection layer as a search space;

S2: determining a genetic algorithm as a search algorithm;

for example, it is desirable that the number of neurons of the fully connected layer be searched between 50 and 200, the search space may be defined as 50,51,52,..200, and then the search algorithm may search through this range to determine the optimal number of neurons by evaluating the objective function.

S3: defining an objective function for evaluating the performance of the deep reinforcement learning model, wherein the objective function is an object optimized by a search algorithm and is used for measuring the influence of the number of neurons on the performance;

S4: searching in the defined search space by using a selected searching algorithm, and gradually updating the neuron number of the full-connection layer according to the feedback of the objective function, namely, by continuously evaluating the objective function, the algorithm can gradually adjust the neuron number to find the configuration with optimal performance.

Through the optimization scheme, the number of neurons of the full-connection layer is automatically adjusted through a network architecture search algorithm, so that the deep reinforcement learning model can be better adapted to specific use scenes, and the self-adaption can improve the performance and the adaptability of the system. Taking a brushless motor used for an electric lifting table as an example, better control can be realized under different users and use situations; through the deep reinforcement learning model, the system can learn the use habit of a user, including lifting frequency, lifting amplitude, use time and the like, so that the control of the electric lifting table is more personalized, the personalized requirements of the user are met, and more comfortable and convenient lifting experience is provided; the use scene of the electric lifting table is different according to the factors of user habit, physical characteristics, working environment and the like, the system can automatically adjust the quantity of neurons through a network architecture search algorithm so as to adapt to the personalized requirements of different users, for example, some users may prefer quick lifting speed, while other users may prefer slow and stable adjustment, and the personalized adaptation can improve the satisfaction degree and the use experience of the users; the electric lifting table may be faced with different environmental changes in use, such as articles on a table top, weight changes of a user, etc., and the system can dynamically adjust the number of neurons during operation through a network architecture search algorithm so as to adapt to different working scenes and environmental changes.

In the above preferred scheme, the number of neurons is effectively utilized, and the efficiency of the deep reinforcement learning model is improved, so that the use of resources is reduced and the operation efficiency of the system is improved while the performance is maintained; due to the adoption of deep reinforcement learning, the system can realize online learning through real-time interaction with the environment, so that the electric lifting table can continuously optimize control strategies in different use situations, and the sensitivity to the demands of users is kept.

Embodiment two:

A lifting leg employs a brushless motor 1 as a height lifting power, and samples the brushless motor by a brushless motor sampling circuit employing a Hall element as in the first embodiment. As shown in fig. 3 or 4, in the implementation, the brushless motor 1 may be directly mounted to the outside of the lifting leg 3, and in this way, the brushless motor 1 may be protected by the protection housing 2, and the sampling circuit is mounted on the structure of the brushless motor 1. As another embodiment, as shown in fig. 5, the brushless motor 1 may be built in the lifting leg 3, that is, the protection of the housing 2 is achieved by a rod body of a section of the lifting leg 3.

Example III

A lifting table supports a table top through lifting support legs 3 of a second embodiment, and a brushless motor 1 is fixed through structures positioned on the lifting support legs 3 inside or outside, and/or fixed through connecting structures 4 connecting different lifting support legs 3.

In the present embodiment, the brushless motor 1 is fixed by the structure located inside or outside the lifting leg 3, as described in the second embodiment, wherein the inside or outside structure is used for fixing the brushless motor 1, and may be any structure such as a plate body or a bracket. However, because there may be a force applied to the table body by the user in a specific use scenario of the lifting table, so that the brushless motor 1 may have vibration or the like, in order to avoid instability in installation of the brushless motor 1 in such a situation, the brushless motor 1 may be fixed by the connection structure 4 connected with different lifting legs 3, as shown in fig. 6, since the connection structure 4 may be more flexibly arranged, stable installation of the brushless motor 1 may be more easily obtained, and structural strength for fixing the motor may be effectively ensured.

Of course, in the above embodiment, the fixed connection between the brushless motor 1 and the lifting support leg 3 is a convenient form, so that the preassembly of the lifting support leg 3 can be realized; however, when the above-mentioned factors are not considered, it is also within the scope of the present invention to install the brushless motor 1 after the connection of the lifting leg 3 other than the brushless motor 1 to the connection structure 4 is completed; or after the brushless motor 1 is installed for the lifting support leg 3, and the lifting support leg 3 is installed for the connecting structure 4, the brushless motor 1 and the connecting structure 4 can be fixedly connected, as shown in fig. 7, so that the preassembling of the lifting support leg 3 can be realized, and the brushless motor 1 can be stably installed after the preassembling is finished, and of course, the brushless motor 1 needs to be located outside the lifting support leg 3 in the mode.

In the present embodiment, the number of lifting legs 3 is not particularly limited, and any lifting table that can be assembled to meet the use requirement is within the scope of the present invention, which is often related to the distribution form of the table top, the structural strength of the lifting legs 3, and the like. In fig. 6, the two lifting legs 3 are connected by the connecting structure 4, in which the corresponding table top is generally rectangular, while in fig. 8, the three lifting legs 3 are connected by the connecting structure 4, in which the corresponding table top is generally L-shaped.

Example IV

A lifting table supports a table top through a plurality of lifting support legs 3 according to the second embodiment, wherein each lifting motor provides lifting power for one lifting support leg 3, in a manner as shown in fig. 3-9; in this embodiment, when the power requirement is met, the lifting power can be provided to at least two lifting legs 3 through the transmission device 5, as in fig. 10 and 11, the manner of providing power to two lifting legs 3 through one brushless motor 1 is shown, wherein the lifting structure of the interior of the lifting legs 3 and the specific structural form of the transmission device 5 are all available in the prior art, and will not be repeated herein.

The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims

1. The brushless motor sampling circuit adopting the Hall element is characterized in that the Hall element comprises a push-pull output structure, the push-pull output structure is provided with an output end, and the sampling circuit is connected with the microcontroller;

The sampling circuit further includes:

a power supply circuit for providing a power supply voltage required by the sampling circuit;

the microcontroller generates a pulse signal through a digital output port and takes the pulse signal as the output signal;

The push-pull output structure comprises a bipolar transistor NPN1, a bipolar transistor NPN2, a bipolar transistor PNP1, a bipolar transistor PNP2 and a reference resistor;

2. The brushless motor sampling circuit employing a hall element according to claim 1, wherein the microcontroller comprises:

an input/output port for communicating with other devices;

A counter connected to the hall element for counting the number of pulses;

a program memory storing a control program;

3. The brushless motor sampling circuit using a hall element according to claim 2, wherein the program memory stores a deep reinforcement learning model, and the control program is updated.

4. The brushless motor sampling circuit using hall elements according to claim 3, wherein the deep reinforcement learning model comprises:

an input layer for receiving input of the model, wherein each neuron corresponds to an input feature;

and an output layer for outputting the decision of the model.

5. The brushless motor sampling circuit using hall elements according to claim 4, wherein the neuron number of the full connection layer is updated by a network architecture search algorithm, comprising:

Determining a genetic algorithm as a search algorithm;

6. A lifting support leg, characterized in that a brushless motor is used as a height lifting power, and the brushless motor is sampled by the brushless motor sampling circuit using a hall element according to any one of claims 1 to 5.

7. A lifting table, characterized in that the table top is supported by a number of lifting legs according to claim 6, the brushless motor being fixed by means of structures located inside or outside the lifting legs and/or by means of connecting structures connecting different lifting legs.

8. A lifting table, wherein the table top is supported by a plurality of lifting legs according to claim 6, each lifting motor providing lifting power to one of the lifting legs or to at least two lifting legs via a transmission.