A multi-parameter fusion direct-current brushless motor high-efficiency oxygen compression method and system

Abstract

The application discloses a kind of multi-parameter fusion's direct-current brushless motor high-efficiency oxygen compression method and system, the method includes: obtaining the winding temperature parameter of direct-current brushless motor and the oxygen concentration parameter of oxygen production system output;The winding temperature parameter and oxygen concentration parameter are input into the system efficiency model pre-constructed and are fused to calculate, obtain the target motor speed of making system overall operation efficiency optimal;Control direct-current brushless motor to operate at the target motor speed;The system includes: parameter perception module, decision module and motor drive execution module.The application fuses motor thermal state and output gas quality double parameters, carries out real-time online optimization using embedded physical model, dynamically optimizes the operating point of system overall energy efficiency highest, effectively solves the problem of single optimization dimension of traditional method, significantly improves the operating efficiency and economy of oxygen generator under different working conditions, and has efficiency risk early warning and adaptive mute and other enhancement functions.

Description

Technical Field

[0001] This invention relates to the field of medical oxygen supply equipment technology, and in particular to a high-efficiency oxygen generation and compression method and system using a multi-parameter integrated DC brushless motor. Background Technology

[0002] Molecular sieve oxygen generators based on the pressure swing adsorption (PSA) principle are widely used in medical, industrial, and high-altitude oxygen supply fields due to their compact structure and ease of operation. To improve the energy efficiency and dynamic performance of oxygen generators, using a DC brushless motor as the drive core of the compression system has become an industry trend. Existing advanced control schemes mostly focus on optimizing the system's dynamic response performance, such as rapidly stabilizing gas pressure during sudden load changes through feedforward compensation; however, these methods primarily focus on improving performance during transient processes.

[0003] Currently, the technological optimization of oxygen concentrators mainly focuses on gas path structure design, molecular sieve performance improvement, and oxygen supply safety monitoring. At the control level, existing optimization strategies generally rely on feedback from gas path parameters such as pressure and flow rate, or integrate macroscopic information such as environment and demand for regulation. Although some studies have introduced multi-parameter fusion monitoring to enhance fault diagnosis and system reliability, their control objectives have not penetrated to the level of fine energy efficiency management of the drive motor itself. Existing control systems lack real-time perception of key motor operating states (such as winding temperature and core loss), and have not established a coupling correlation model between motor electrical parameters and gas path load parameters. This leads to a decrease in motor speed and a significant increase in the proportion of core loss in typical scenarios of low oxygen demand and long-term steady-state operation (such as night mode), which becomes the main source of energy consumption. Traditional control loops are powerless to address this, thus creating a "low point" in overall machine energy efficiency under certain operating conditions.

[0004] Therefore, it is urgent to construct a multi-parameter collaborative control mechanism that integrates the internal thermal state of the motor and the external load demand. Under the premise of ensuring oxygen supply quality and air pressure stability, a system loss model with clear physical meaning should be established, and the target motor speed should be dynamically optimized accordingly. This will enable real-time optimization of the operating point with the lowest total loss within the safe operating range, thereby breaking through the bottleneck of existing control strategies in the fine regulation of energy efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a high-efficiency oxygen production and compression method and system for a brushless DC motor with multi-parameter fusion, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A high-efficiency oxygen production and compression method using a brushless DC motor with multi-parameter fusion includes the following steps: Obtain the winding temperature parameters of the DC brushless motor and the oxygen concentration parameters output by the oxygen generation system; Based on the winding temperature parameters and the oxygen concentration parameters, the target motor speed that optimizes the overall system operating efficiency under the current operating conditions is obtained by performing a fusion calculation through a pre-constructed system efficiency model. Control the brushless DC motor to operate at the target motor speed.

[0007] Furthermore, the winding temperature parameter is obtained by a temperature sensor embedded in the stator winding of the DC brushless motor; the oxygen concentration parameter is obtained by an oxygen concentration sensor installed in the outlet flow path of the molecular sieve adsorption tower.

[0008] Furthermore, the calculation process of the target motor speed includes: inputting the real-time collected winding temperature T and oxygen concentration C into the system efficiency model, which is a parameterized model that characterizes the mapping relationship between the total system loss and the motor speed, winding temperature and oxygen concentration. The system efficiency model is solved within a preset safe motor speed range with the goal of minimizing the total system loss, and the target motor speed is output.

[0009] Furthermore, the system efficiency model is expressed as: ;in, For the total system loss, Here, T represents the motor speed, T represents the winding temperature, and C represents the oxygen concentration expressed as a percentage. This is the wind friction loss coefficient. The temperature coefficient of iron loss. Let be a monotonically decreasing function of oxygen concentration C.

[0010] Furthermore, the function Expressed as: ;in, The concentration decay constant is The coefficient is a natural constant. , and constants All were determined by conducting offline calibration experiments on the system under multiple steady-state conditions and using regression fitting algorithms.

[0011] Furthermore, the gradient descent method is used to solve the problem within a preset safe motor speed range. When the winding temperature T is higher than the first temperature threshold and the oxygen concentration C is lower than the first concentration threshold, the optimization solution process is configured to prioritize guiding the target motor speed obtained from the solution to a preset low-loss speed sub-range.

[0012] Furthermore, the method may also include efficiency risk warning and prevention control steps, including: The changing trend of the winding temperature parameter and the continuous level of the oxygen concentration parameter are periodically monitored; When the first condition is met simultaneously—that the oxygen concentration parameter is continuously lower than the required concentration threshold—and the second condition is met—that the winding temperature parameter shows a steady-state upward trend—the preventive control logic is triggered, and the system actively reduces the speed of the target motor.

[0013] A high-efficiency oxygen generation and compression system using a brushless DC motor with multi-parameter fusion, comprising: The parameter sensing module is used to acquire the winding temperature parameters of the brushless DC motor and the oxygen concentration parameters output by the oxygen generation system. The parameter sensing module includes a temperature sensing unit and a concentration sensing unit. The temperature sensing unit includes a temperature sensor embedded in the stator winding of the brushless DC motor for real-time acquisition of winding temperature parameters. The concentration sensing unit includes an oxygen concentration sensor installed on the outlet flow path of the molecular sieve adsorption tower for real-time acquisition of oxygen concentration parameters. The decision module, connected to the parameter sensing module, is used to perform fusion calculations based on the winding temperature parameter and the oxygen concentration parameter through a pre-built system efficiency model to obtain the target motor speed that optimizes the overall operating efficiency of the system under the current operating conditions. The motor drive execution module is connected to the decision module and is used to control the brushless DC motor to run at the target motor speed.

[0014] Furthermore, the decision-making module includes: Storage unit, used to store system efficiency model, the system efficiency model is expressed as: ;in, For the total system loss, Here, T represents the motor speed, T represents the winding temperature, and C represents the oxygen concentration expressed as a percentage. This is the wind friction loss coefficient. The temperature coefficient of iron loss. It is a monotonically decreasing function of oxygen concentration C; The processing unit is configured to perform the optimization solution process of the system efficiency model. It inputs the real-time collected winding temperature T and oxygen concentration C into the system efficiency model, takes minimizing the total system loss as the optimization objective, and uses the gradient descent method to solve the problem within a preset safe motor speed range. It outputs the target motor speed. When the winding temperature T is higher than the first temperature threshold and the oxygen concentration C is lower than the first concentration threshold, it guides the target motor speed to a preset low-loss speed sub-range.

[0015] Furthermore, the system also includes an adaptive mute and integrated control module, which is configured to: By reusing the current sampling circuit and PWM carrier signal in the motor drive execution module, the ambient noise characteristic signal is decoupled and extracted to evaluate the quietness of the working environment of the oxygen concentrator; Learn user routines based on historical operational data to predict periods of quiet operation; When the environment is determined to be quiet and within the predicted quiet demand period, a home quiet optimization strategy is triggered. This strategy modifies the optimization objective function or constraints of the decision module to introduce a quiet preference for motor speed in system efficiency optimization.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: 1. Achieved accurate identification and optimization of inefficient operating conditions: By fusing parameters of winding temperature and oxygen concentration, the system can directly sense and quantify the inefficient state dominated by iron loss under low load, thereby performing targeted optimization and solving the problem that traditional control methods cannot handle this "efficiency blind spot".

[0017] 2. Achieve intelligent thermal management and enhanced reliability: By using the winding temperature, which directly reflects the motor's loss status, as the core control parameter, the system can proactively respond to the motor's thermal state and make preventative adjustments under operating conditions such as high temperature and low concentration that can easily lead to efficiency deterioration. This effectively prevents overheating and improves the long-term reliability of the system.

[0018] 3. Optimized user experience and silent adaptive operation: By reusing hardware resources through software algorithms to indirectly assess environmental noise and learn user habits, the system can automatically adjust control strategies during quiet periods such as nighttime. Under the premise of meeting oxygen supply needs, the system is prioritized to operate in a low-noise range, significantly improving the comfort of home scenarios.

[0019] 4. Possesses forward-looking risk warning and protection capabilities: By continuously monitoring temperature change trends and concentration levels, it can proactively intervene before the system approaches inefficient and high-risk operating conditions, realizing the transformation from passive response to proactive prevention, and enhancing the system's robustness and safety in dealing with abnormal operating conditions. Attached Figure Description

[0020] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic flowchart of a high-efficiency oxygen production and compression method using a brushless DC motor with multi-parameter fusion, provided in Embodiment 1 of the present invention.

[0022] Figure 2 This is a schematic diagram of a high-efficiency oxygen generation and compression system for a brushless DC motor with multi-parameter fusion provided in Embodiment 2 of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0024] Example 1 like Figure 1 As shown, this embodiment discloses a multi-parameter fusion-based high-efficiency oxygen generation and compression method using a brushless DC motor, comprising the following steps: Step S100: Obtain the winding temperature parameters of the DC brushless motor and the oxygen concentration parameters output by the oxygen generation system.

[0025] Step S100 involves simultaneously acquiring two types of key physical quantities through two independent sensing channels. The specific implementation process includes: Step S101: Obtain the winding temperature parameters of the brushless DC motor.

[0026] Specifically, the process of obtaining winding temperature parameters is achieved through a temperature sensor embedded in the stator winding of the brushless DC motor. Considering the cost, reliability, and installation space requirements of home-use oxygen concentrators, miniaturized, insulated encapsulated thermistors (e.g., negative temperature coefficient NTC thermistors) or thin-film platinum resistance thermometers are preferred as sensing elements. After being encapsulated in high-temperature resistant insulating material, the sensor is precisely embedded in the slot gaps or end binding layers of the motor stator winding to directly and accurately sense the winding temperature rise caused by motor copper and iron losses. The temperature measurement range of the temperature sensor is typically -20 degrees Celsius to 150 degrees Celsius, with a measurement accuracy of no less than ±1 degree Celsius. The resistance or voltage signal output by the sensor is standardized by a signal conditioning circuit (including filtering, amplification, and linearization), and then input into the analog-to-digital converter (ADC) channel of the microcontroller to convert it into a digital temperature value T.

[0027] Step S102: Obtain the oxygen concentration parameters output by the oxygen generation system.

[0028] Specifically, the process of obtaining oxygen concentration parameters is achieved through an oxygen concentration sensor installed on the outlet flow path of the molecular sieve adsorption tower of the oxygen concentrator. To adapt to the atmospheric pressure and low flow rate (e.g., 1 to 5 liters per minute) operating conditions of home oxygen concentrators, a long-life, low-power electrochemical oxygen sensor module is preferred. The sensor is installed on the gas pipeline approximately 20 cm from the molecular sieve outlet and is used to monitor the oxygen volume concentration of the separated gas in real time, typically within a range of 21% to 95%, with an accuracy better than ±2%. The sensor outputs an analog or digital signal representing the real-time oxygen concentration C.

[0029] Through step S100 and its detailed steps, the system obtains two core, heterogeneous input parameters required for intelligent efficiency optimization: winding temperature T characterizes the internal loss state of the energy conversion device (motor), and oxygen concentration C characterizes the final output demand intensity of the energy conversion service.

[0030] Step S200: Based on the winding temperature parameter and the oxygen concentration parameter, the target motor speed that optimizes the overall system operating efficiency under the current operating conditions is obtained by performing a fusion calculation through a pre-constructed system efficiency model.

[0031] Step S200 is the core decision-making step for achieving intelligent efficiency optimization in this invention, and its specific implementation process includes: Step S201: Input the real-time collected winding temperature T and oxygen concentration C into the system efficiency model.

[0032] The system efficiency model is a parameterized model that characterizes the mapping relationship between the total system loss and the motor speed, winding temperature and oxygen concentration. This model is pre-stored in the controller's non-volatile memory.

[0033] Specifically, the system efficiency model is expressed as follows: ; in, Where W is the total system loss (W), N is the motor speed (rpm), T is the winding temperature (°C), and C is the oxygen concentration (%). This is the wind friction loss coefficient. Characterized by mechanical wind resistance loss, friction loss, and some stray losses that are proportional to the square of the rotational speed. The temperature coefficient of iron loss. Let C be a monotonically decreasing function of oxygen concentration C. This monotonically decreasing function is used to quantify the impact of oxygen concentration demand on the weights of iron loss-related loss terms. The winding temperature T is correlated with a decay function of oxygen concentration C. Combined, they are used to characterize core loss (iron loss).

[0034] The function The preferred function is an exponential decay function, expressed as follows: ; in, is the concentration decay constant, a fitting parameter greater than 0, used to adjust the degree of influence of concentration changes on the decay rate; e is the natural constant, with a value of approximately 2.71828. For example, when When =10%, f(30)=e -3≈0.05, indicating that under a low oxygen demand of 30%, the weight of the iron loss-related terms is significantly amplified, while when f(90)=e -9 The value ≈0.0001 indicates that the weight of the iron loss-related terms becomes extremely small under a high oxygen concentration requirement of 90%.

[0035] The coefficient , and constants All calibrations were performed offline under multiple steady-state conditions for specific household models, and the results were determined using regression fitting algorithms. The calibration environment should simulate the non-constant temperature and typical heat dissipation conditions of a household to make the model more closely reflect the actual consumption characteristics of a household appliance. The specific calibration method is as follows: In a temperature-controlled experimental environment, the oxygen concentrator is controlled at multiple different steady-state points covering its operating range (combining different rotational speeds N). i And simulated loads, the latter corresponding to different steady-state oxygen concentrations C i After the system reaches thermal equilibrium, record the steady-state winding temperature T. i Simultaneously, the total input power P of the system is measured with high precision. in,i and the compressor's output pneumatic power P out,i Calculate the actual total loss at that point. Collect a sufficient amount of data Then, by using regression fitting algorithms such as nonlinear least squares, the formula of the above system efficiency model is fitted, and the optimal parameter set for this specific type of oxygen generation system can be obtained. , , ).

[0036] As a preferred implementation of the system efficiency model, the total system loss This can be further refined into an extended model that includes copper loss components, expressed as follows: ; in, This is the copper loss coefficient. Let N be the operating current of the motor at the given motor speed N and winding temperature T. The winding resistance at winding temperature T is... It can be obtained through the equivalent circuit model of the motor or a pre-determined INT three-dimensional mapping table, the aforementioned It is calculated based on the physical law that the resistance of a conductor changes with temperature.

[0037] The extended model described above can more accurately characterize the efficiency characteristics of motors in a wide temperature range and under varying loads, and is especially suitable for scenarios with extremely high requirements for efficiency optimization accuracy.

[0038] Step S202: Minimize total system losses To optimize the target, the solution is performed within a preset safe speed range for the motor, and the optimal value obtained is output as the target motor speed N. opt .

[0039] The safe speed range of the motor is preset based on the mechanical strength and heat dissipation conditions of the compressor and motor, for example, the lower limit of motor speed N. min The maximum motor speed is 2000 revolutions per minute (rpm), N. max The speed is 8000 rpm. The optimization solution involves finding the value that satisfies N. min ≤N≤N max Under constraints, the total system loss calculated by the model is... Minimum motor speed N.

[0040] In this embodiment, the gradient descent method is used for numerical solution.

[0041] Specifically, the controller uses the currently acquired (T, C) as a fixed input and calculates the total system loss. Consider it as a univariate function of the independent variable N, in the interval The algorithm performs an iterative search within an initial rotational speed N; start To begin (for example, N from the previous control cycle can be taken). opt ), calculate the gradient (or approximate gradient) of the loss function with respect to the motor speed N, and iteratively update the value of the independent variable N in the opposite direction of the gradient (i.e., the direction of loss decrease) until the value of N makes When the speed reaches its minimum, the value of N is the optimal target motor speed N for this control cycle. opt The solution accuracy is usually controlled within ±50 rpm.

[0042] Furthermore, when the winding temperature T is higher than a first temperature threshold (e.g., T... th =60℃) and the oxygen concentration C is below a first concentration threshold (e.g., C = ...). th When the loss rate is 35%, the optimization process is configured to prioritize guiding the obtained target motor speed to a preset low-loss speed sub-range. This guidance can be achieved by adding a penalty term to the loss function of the gradient descent method. and It takes effect when all conditions are met, penalizing the speed deviation from the known high-efficiency zone (e.g., a speed range determined by historical data or experiments, such as 2500~3500rpm), thereby driving the algorithm to converge to the optimal point within that high-efficiency zone faster and more stably.

[0043] In this embodiment, to improve the robustness of the optimization solution and avoid getting trapped in local optima, the gradient descent method includes the following enhancement strategies: Dynamic initialization: Set the optimal speed N from the previous control cycle. opt Using the inertia of the system operating conditions as the starting point for this search, rapid convergence is achieved. Multi-starting-point search: When a drastic change in operating conditions (T,C) is detected, multiple discrete starting points (such as the lower limit, upper limit, and midpoint of the interval) are selected in parallel or serially within the safe speed range to perform gradient descent. Finally, the total system loss is selected from all results. The minimum rotational speed is N. opt ; Adaptive step size: The iteration step size of gradient descent can be adjusted according to the current total system loss. The gradient magnitude of the rotational speed N is dynamically adjusted. When the gradient is large, the step size is increased to speed up convergence, and when the optimal point is approached, the step size is decreased to improve accuracy.

[0044] In this embodiment, the method further includes an efficiency risk warning and prevention control step: That is, based on steps S1 and S2, a forward-looking control logic is added. The specific process is as follows: The system periodically monitors the changing trend of the winding temperature parameter (e.g., calculating the average slope of the temperature over a recent period) and the sustained level of the oxygen concentration parameter (e.g., determining whether the concentration value has been continuously below a certain threshold for more than 1 minute) in the background (e.g., every 10 seconds). When the first condition is simultaneously met: the oxygen concentration parameter is continuously below the required concentration threshold (e.g., C...), a forward-looking control logic is added. demand =30%), and the second condition: when the winding temperature parameter shows a steady-state upward trend (e.g., the slope is greater than 0.1℃ / minute), the preventive control logic is triggered. At this time, the system will not passively wait for the system efficiency model to calculate a new N. opt Instead, it proactively and preventively adjusts the current target motor speed N. opt By applying a negative offset (e.g., reducing speed by 100-200 rpm) or directly setting the target motor speed to the lower limit of a known high-efficiency range, the system can intervene before it clearly enters a high-loss, low-efficiency operating condition, achieving a smoother and more energy-efficient transition.

[0045] In this embodiment, the method further includes an adaptive mute and integrated control step. This step aims to improve the user experience in a home environment through software algorithms without increasing additional hardware costs, and specifically includes the following sub-steps: Step S211: Indirectly assess the environmental noise level based on motor current harmonic analysis.

[0046] Specifically, step S211 includes: Data Acquisition and Preprocessing: The system reuses the current sampling circuit in the motor drive execution module to synchronously sample the instantaneous value of at least one phase current in the three-phase current at a rate much higher than the PWM carrier frequency (e.g., 50kHz). First of all, Digital filtering is performed to eliminate high-frequency noise at the PWM switching frequency and its harmonics.

[0047] Spectral feature extraction: Perform a short-time Fourier transform or calculate the power spectral density of the preprocessed current signal, focusing on the frequency bands related to the reciprocating motion frequency of the compressor piston and its harmonics. For example, if the compressor is a single-cylinder structure, the fundamental frequency... The load torque fluctuations caused by the mechanical motion of the compressor will modulate the motor current, which is reflected in the current spectrum. 2 Characteristic harmonics are generated at the same frequency.

[0048] Establish a noise correlation model: During the laboratory calibration phase, the oxygen generator was operated at different speeds in an anechoic chamber, and the A-weighted sound pressure level at a distance of 1 meter from the equipment was measured using a standard sound level meter. (As a direct measure of environmental noise), and simultaneously record the motor current. Through data analysis, establish specific harmonics (such as 2) in the current spectrum. amplitude at (location) Compared with the measured sound pressure level The association function or mapping table between them.

[0049] Online indirect evaluation: During online operation, the system calculates the energy of the current signal at the target harmonic frequency based on the current rotational speed N. By querying the aforementioned correlation model, the approximate level of ambient noise generated by the equipment under the current operating conditions can be estimated. .when When the temperature is below a preset quiet threshold (e.g., 35 dBA), the system determines that the current working environment is in a "quiet state".

[0050] Step S212: Predict the user's silent period based on historical operation statistics.

[0051] To avoid complex machine learning models, a lightweight, deterministic statistical method is used to predict users' quiet periods.

[0052] Specifically, step S212 includes: Data logging: The system records, on a daily basis, the periods within a 24-hour period during which the oxygen concentrator operates at a continuous low speed (e.g., less than 4000 r / min) or is turned off. These periods are initially considered to be times when users may need rest or quiet operation.

[0053] Statistical Analysis: The system analyzes data from a historical period (e.g., the past 7 days) every morning. For each time slice of the day (e.g., every 30 minutes), it calculates the percentage of days marked as "low speed / shutdown" in the historical records within that time slice. .

[0054] Time period prediction: Set a proportional threshold (For example, 80%). If a certain time slice corresponding proportion > If the device operates at low speed or is turned off every day from 0:00 to 6:00 for seven consecutive days, this time slot will be robustly predicted as a period of quiet operation. This method is logically simple, requires no training, and can adapt to the different sleep patterns of various users.

[0055] Step S213: Trigger and execute the mute optimization strategy.

[0056] The home noise reduction strategy is triggered when the system meets both of the following conditions: a) Indirect assessment results of environmental noise levels Below the "quiet threshold"; b) The current system time is within the predicted "quiet demand period"; Upon triggering, the system adjusts the optimization objective of the decision-making module using the following approach: Based on the original system efficiency model, a "noise penalty term" positively correlated with rotational speed is added to form a new composite objective function for optimization.

[0057] Specifically, S213 includes: Constructing a composite objective function: The new optimization objective is no longer simply minimizing the total system loss. Instead, it minimizes a comprehensive cost function. : ; in, The original system total loss model; This is the silence weighting coefficient, an adjustable parameter greater than 0. The larger the value, the stronger the system's preference for silence when making decisions; The noise penalty function is designed as a monotonically increasing function of the motor speed N, for example... The physical meaning is that the higher the rotational speed, the greater the aerodynamic noise and mechanical vibration noise generated, so it is "penalized" in the cost function.

[0058] Optimized solution: In step S202, the decision module changes to using a comprehensive cost function. As a new optimization objective, within the safe speed range of the motor, numerical methods such as gradient descent are also used to find the optimal speed. Minimum optimal silent mode motor speed N opt,silent .

[0059] Effect: By introducing The optimization algorithm automatically balances "reducing losses" and "reducing speed to decrease noise." In quiet nighttime conditions, even slightly higher speeds may result in slightly lower noise levels. However, due to the presence of a noise penalty term, the algorithm tends to choose a relatively lower and quieter speed to achieve silent operation. The weighting coefficient γ can be set in stages according to the strength of the silent operation requirement.

[0060] Through the aforementioned adaptive quiet operation and integrated control steps, the system can intelligently sense the level of environmental quietness and user habits without the need for additional hardware sensors, and dynamically adjust and optimize its objectives. This allows it to prioritize a quieter, more suitable operating mode for the home environment while ensuring basic oxygen supply performance, significantly improving the product's intelligence level and user experience.

[0061] Through step S200 and its detailed steps, the system can intelligently determine the most energy-efficient motor operating speed based on real-time, multi-dimensional operating condition information (T,C) and the embedded physical knowledge model, thus realizing a fundamental shift from "constant output" control to "global optimal" control.

[0062] Step S300: Control the brushless DC motor to run at the target motor speed.

[0063] Step S300 is to make decision N opt The execution stage that translates into physical actions.

[0064] Specifically, the controller will calculate N opt As a setpoint, it is sent to the drive unit of the brushless DC motor (typically a variable frequency drive based on field-oriented control). The motor driver compares N. opt The actual motor speed N fed back by the motor encoder actual By using dual closed-loop control of current loop and speed loop, the PWM duty cycle of the inverter bridge is adjusted, thereby controlling the motor torque and increasing the actual motor speed N. actual Track N quickly and smoothly opt During this process, the system limits the rate of change of rotational speed (e.g., acceleration not exceeding 1000 rpm / s) to avoid airflow impact and drastic pressure fluctuations caused by excessively rapid compressor piston movement, thus ensuring the stability of the oxygen production process.

[0065] In summary, the method disclosed in Example 1 effectively identifies and optimizes the iron loss-dominant region of the oxygen generator under low load conditions by coupling the winding temperature and oxygen concentration as dual parameters and constructing a matching efficiency optimization model and algorithm, achieving significant energy-saving effects and possessing intelligent early warning and prevention capabilities.

[0066] Example 2 like Figure 2 As shown, this embodiment discloses a multi-parameter fusion DC brushless motor high-efficiency oxygen generation and compression system, including: Parameter sensing module M10: Used to acquire the winding temperature parameters of the brushless DC motor and the oxygen concentration parameters output by the oxygen generation system.

[0067] The parameter sensing module M10 includes two independent sensing submodules, which are responsible for collecting winding temperature and oxygen concentration signals, respectively, specifically including: Temperature sensing unit M11: includes a temperature sensor embedded in the stator winding of the brushless DC motor, used to collect winding temperature parameters in real time.

[0068] Specifically, the temperature sensing unit consists of a PT100 platinum resistance temperature probe, a signal conditioning circuit, and an analog-to-digital converter (ADC) channel. The PT100 probe is precisely embedded in the winding slots of the motor stator and isolated from the copper wire by a high-temperature resistant insulating material. The signal conditioning circuit converts the resistance change of the PT100 into a standard voltage signal (e.g., 0-3.3V), which is then sampled by the ADC pin of the controller and finally converted into a digital temperature value T. The sensing circuit uses shielded wire, with the shielding layer grounded at a single point to suppress high-frequency PWM noise interference generated by the motor driver.

[0069] Concentration sensing unit M12: includes an oxygen concentration sensor installed on the outlet flow path of the molecular sieve adsorption tower for real-time acquisition of oxygen concentration parameters.

[0070] Specifically, this unit employs a digital output electrochemical oxygen sensor (measurement range 0-100%). The sensor is installed on the outlet pipe of the molecular sieve adsorption tower via a dedicated gas sampling chamber, and its UART or I2C digital interface is directly connected to the corresponding serial communication interface of the controller. The controller reads the internally processed oxygen concentration C output by the sensor at a specific frequency (e.g., 10Hz).

[0071] Decision module M20: Connected to the parameter sensing module M10, it is used to perform fusion calculations based on the winding temperature parameter and the oxygen concentration parameter through a pre-built system efficiency model to obtain the target motor speed that optimizes the overall operating efficiency of the system under the current operating conditions.

[0072] The decision module M20 is physically implemented by a main controller (such as a microcontroller with an ARM Cortex-M4 / M7 core) and its internally running embedded software algorithms. Functionally, this module is specifically subdivided into: Storage unit M21: Used to store the system efficiency model and related parameters.

[0073] Specifically, the storage unit M21 is a specific data area in the internal Flash memory of the main controller or an external EEPROM chip, wherein the stored system efficiency model is the model defined according to Embodiment 1, i.e., formula and its functional form (such as = ) and specific parameters ( , These parameters are determined and programmed before the system leaves the factory using the calibration process mentioned in Example 1.

[0074] Processing unit M22: is configured to perform the optimization solution process of the system efficiency model.

[0075] Specifically, the processing unit M22 is the central processing unit (CPU) of the main controller, which is programmed to execute a periodic task (e.g., once every 50 milliseconds). This task includes the following steps: reading the real-time T and C values ​​provided by the parameter sensing module M10; retrieving the system efficiency model and parameters from the storage unit M21; and minimizing... With the goal, in The gradient descent method is used to solve the problem within the interval. During the solution process, it is determined whether T and C exceed the set thresholds. If so, a guiding strategy is activated to guide the target motor speed to a preset low-loss speed sub-interval. Finally, the calculated optimal target motor speed N is output. opt .

[0076] Motor drive execution module M30: connected to the decision module M20, used to control the brushless DC motor to run at the target motor speed.

[0077] Specifically, the motor drive execution module M30 includes a motor driver M31 and a brushless DC motor M32. The motor driver M31 employs a three-phase full-bridge inverter circuit and an intelligent power module (IPM) integrating a field-oriented control (FOC) algorithm. The motor driver M31 receives speed commands from the decision module M20 (main controller) via a CAN bus or a high-speed PWM / analog interface. The current sampling circuit inside the motor driver M31 detects the three-phase current of the motor in real time. Combined with information from motor position sensors (such as Hall effect sensors or encoders), it runs the FOC algorithm to generate precise SVPWM (space vector pulse width modulation) signals to drive the power switching transistors (such as IGBTs or MOSFETs) of the inverter bridge, thereby controlling the motor to generate the required torque and precisely stabilizing its speed at N. opt The DC brushless motor M32 is rigidly connected to the oil-free air compressor of the oxygen concentrator via a coupling, converting rotational power into compressed air.

[0078] In a system implementation with integrated enhancements, the system further includes an adaptive mute and integrated control module M40. This module physically reuses the processing unit M22 of the decision module M20 and the current sampling resources of the motor drive execution module M30, through additional software algorithms embedded in the firmware of the decision module M20. Its workflow is as follows: Indirect environmental noise level assessment unit M41: When this unit is running, it controls the motor drive unit M31 to acquire the instantaneous signal of one-phase motor current at a high sampling rate (e.g., 50kHz). By performing a Fast Fourier Transform analysis on this current signal, the second harmonic of the compressor's reciprocating motion frequency is extracted. The spectral energy amplitude at () is then used. Subsequently, by querying the "Current Harmonic Amplitude - Noise Sound Pressure Level" calibration lookup table preset in storage unit M21, this energy amplitude is mapped to an estimated ambient noise sound pressure level. .

[0079] User Quiet Period Prediction Unit M42: This unit runs in the background, continuously recording daily periods of low-speed device operation (less than 4000 r / min) or shutdown in a specific circular buffer of storage unit M21. Every 24 hours, it performs a statistical analysis of the records from the past 7 days, calculating the historical probability of low-speed operation or shutdown occurring within each 30-minute time slice. Time slices with a historical probability exceeding 80% are identified as predicted "user quiet period demand periods," and this prediction result is updated in the configuration area of ​​storage unit M21.

[0080] M43, the silent strategy decision and execution unit: This unit periodically (e.g., every 10 seconds) checks the current state. When the "estimated noise level" condition is met... When the system meets both the conditions of "<35dBA" and "current time falls within the predicted quiet demand period", the silent mode is immediately activated. In silent mode, this unit modifies the optimization objective function of the processing unit M22 in the decision module M20, that is, in the original system total loss model... Add a silence weighting factor to the above. =0.5 and noise penalty function The product terms form a new composite objective function. Subsequently, processing unit M22 performs optimization based on this new objective function J, obtaining the target motor speed N. opt,silent The system will automatically balance efficiency and quietness. If either of the above conditions is not met, the system will automatically switch back to the standard optimal efficiency mode (i.e., the objective function reverts to the optimal efficiency mode). ).

[0081] The system disclosed in this embodiment provides a reliable and accurate physical foundation for the method described in Embodiment 1 through modular hardware design and functional division that strictly corresponds to the algorithm. This system not only performs core efficiency optimization calculations, but its special installation method and signal processing design of the sensing module, along with the high-performance control of the drive module, jointly ensure the achievement of the invention's technical effects, enabling the oxygen generator to operate efficiently, stably, and intelligently across a wide range of operating conditions, especially during periods of low demand.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of this invention. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency oxygen production and compression method using a brushless DC motor with multi-parameter fusion, characterized in that, Includes the following steps: Obtain the winding temperature parameters of the DC brushless motor and the oxygen concentration parameters output by the oxygen generation system; Based on the winding temperature parameters and the oxygen concentration parameters, the target motor speed that optimizes the overall system operating efficiency under the current operating conditions is obtained by performing a fusion calculation through a pre-constructed system efficiency model. Control the brushless DC motor to operate at the target motor speed.

2. The method according to claim 1, characterized in that, The winding temperature parameter is obtained by a temperature sensor embedded in the stator winding of the DC brushless motor; the oxygen concentration parameter is obtained by an oxygen concentration sensor installed in the outlet flow path of the molecular sieve adsorption tower.

3. The method according to claim 1, characterized in that, The calculation process of the target motor speed includes: inputting the real-time collected winding temperature T and oxygen concentration C into the system efficiency model. The system efficiency model is a parameterized model that characterizes the mapping relationship between the total system loss and the motor speed, winding temperature and oxygen concentration. The optimization objective is to minimize the total system loss. The solution is performed within the preset safe motor speed range, and the target motor speed is output.

4. The method according to claim 3, characterized in that, The system efficiency model is expressed as follows: ;in, For the total system loss, Here, T represents the motor speed, T represents the winding temperature, and C represents the oxygen concentration expressed as a percentage. This is the wind friction loss coefficient. The temperature coefficient of iron loss. Let be a monotonically decreasing function of oxygen concentration C.

5. The method according to claim 4, characterized in that, The function Expressed as: ;in, Let be the concentration decay constant. The coefficient is a natural constant. , and constants All were determined by conducting offline calibration experiments on the system under multiple steady-state conditions and using regression fitting algorithms.

6. The method according to claim 3, characterized in that, The gradient descent method is used to solve the problem within a preset safe motor speed range. When the winding temperature T is higher than the first temperature threshold and the oxygen concentration C is lower than the first concentration threshold, the optimization solution process is configured to prioritize guiding the target motor speed obtained from the solution to a preset low-loss speed sub-range.

7. The method according to claim 1, 3, or 6, characterized in that, It also includes efficiency risk warning and prevention and control steps: The changing trend of the winding temperature parameter and the continuous level of the oxygen concentration parameter are periodically monitored; When the first condition is met simultaneously—that the oxygen concentration parameter is continuously lower than the required concentration threshold—and the second condition is met—that the winding temperature parameter shows a steady-state upward trend—the preventive control logic is triggered, and the system actively reduces the speed of the target motor.

8. A high-efficiency oxygen generation and compression system using a brushless DC motor with multi-parameter fusion, characterized in that, include: The parameter sensing module is used to obtain the winding temperature parameters of the brushless DC motor and the oxygen concentration parameters output by the oxygen generation system. The parameter sensing module includes a temperature sensing unit and a concentration sensing unit. The temperature sensing unit includes a temperature sensor embedded in the stator winding of the brushless DC motor for real-time acquisition of winding temperature parameters. The concentration sensing unit includes an oxygen concentration sensor installed on the outlet flow path of the molecular sieve adsorption tower for real-time acquisition of oxygen concentration parameters. The decision module, connected to the parameter sensing module, is used to perform fusion calculations based on the winding temperature parameter and the oxygen concentration parameter through a pre-built system efficiency model to obtain the target motor speed that optimizes the overall operating efficiency of the system under the current operating conditions. The motor drive execution module is connected to the decision module and is used to control the brushless DC motor to run at the target motor speed.

9. The system according to claim 8, characterized in that, The decision-making module includes: Storage unit, used to store system efficiency model, the system efficiency model is expressed as: ;in, For the total system loss, Here, T represents the motor speed, T represents the winding temperature, and C represents the oxygen concentration expressed as a percentage. This is the wind friction loss coefficient. The temperature coefficient of iron loss. It is a monotonically decreasing function of oxygen concentration C; The processing unit is configured to perform the optimization solution process of the system efficiency model. It inputs the real-time collected winding temperature T and oxygen concentration C into the system efficiency model, takes minimizing the total system loss as the optimization objective, and uses the gradient descent method to solve the problem within a preset safe motor speed range. It outputs the target motor speed. When the winding temperature T is higher than the first temperature threshold and the oxygen concentration C is lower than the first concentration threshold, it guides the target motor speed to a preset low-loss speed sub-range.

10. The system according to claim 8, characterized in that, It also includes an adaptive mute and integrated control module, which is configured to: By reusing the current sampling circuit and PWM carrier signal in the motor drive execution module, the ambient noise characteristic signal is decoupled and extracted to evaluate the quietness of the working environment of the oxygen concentrator; Learn user routines based on historical operational data to predict periods of quiet operation; When the environment is determined to be quiet and within the predicted quiet demand period, a home quiet optimization strategy is triggered. This strategy modifies the optimization objective function or constraints of the decision module to introduce a quiet preference for motor speed in system efficiency optimization.

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