Optimization control method for oxygen generator

Abstract

The invention discloses an oxygen generator tuning control method, which comprises the following steps of: collecting breathing state data of a user, and judging a current oxygen supply demand of the user according to the collected data; generating first input data of a PID algorithm based on the current oxygen supply demand in combination with the breathing stage of the user; calculating the current aging degree of the molecular sieve to generate second input data of the PID algorithm; and the first input data and the second data are superposed to serve as input of a PID algorithm, and a control signal for adjusting the rotating speed and the working time of the compressor is generated. The method has the beneficial effects that when the working state of the compressor is controlled through the PID algorithm, the input of the PID algorithm is dynamically adjusted in combination with the breathing state of the user, and the dynamic response capability of the oxygenerator can be effectively improved. And meanwhile, the aging degree of the molecular sieve is detected, so that the control precision of the PID algorithm on the compressor is further optimized.

Description

Technical Field

[0001] The present application relates to the technical field of oxygen concentrators, and in particular to an oxygen concentrator tuning and control method. Background Art

[0002] An oxygen concentrator is a machine that produces oxygen. It utilizes air separation technology. First, air is compressed to a high density. The different condensation points of the air components are then used to separate the gas and liquid at a specific temperature. This is then separated into oxygen and nitrogen through distillation. Depending on the specific application, oxygen concentrators can be categorized as medical, healthcare, or portable.

[0003] Portable oxygen concentrators (POCs) experience fluctuations in oxygen concentration output due to factors such as substandard hardware process consistency. Furthermore, as the POCs are used for extended periods, the molecular sieve adsorption efficiency deteriorates, leading to a continuous decrease in oxygen concentration. Traditionally, manual adjustments to valve opening times have been used to improve oxygen concentration, a complex operation for end users and a lack of optimization capabilities. Summary of the Invention

[0004] One of the objectives of the present application is to provide an oxygen concentrator tuning and control method that can solve at least one of the defects in the above-mentioned background technology.

[0005] To achieve at least one of the above purposes, the technical solution adopted in this application is: an oxygen concentrator tuning control method, comprising the following steps: S100: Collecting the user's respiratory status data, and determining the user's current oxygen demand based on the collected data; generating first input data for a PID algorithm based on the current oxygen demand and the user's respiratory stage; S200: Calculating the current aging degree of the molecular sieve to generate second input data for the PID algorithm; S300: Superimposing the first input data and the second data quantity as inputs of a PID algorithm to generate a control signal for adjusting the speed and working time of the compressor.

[0006] Preferably, in step S100, the oxygen concentrator is started according to the collected user breathing, and the oxygen concentration output by the oxygen concentrator is detected; if the oxygen concentration output by the oxygen concentrator is lower than the set threshold, the first input data is generated according to the difference between the current oxygen concentration output by the oxygen concentrator and the set threshold, and then the compressor is controlled to increase the speed through the PID algorithm.

[0007] Preferably, when the oxygen concentrator is operating normally, the data collected in step S100 includes the user's respiratory rate and / or respiratory pressure; the user's current oxygen supply scenario is judged based on the obtained data, including an oxygen concentration increase scenario, a current oxygen concentration maintenance scenario, and an oxygen concentration reduction scenario; corresponding first input data is generated based on the current oxygen supply scenario obtained by judgment; wherein, for the oxygen concentration increase scenario, a given single oxygen concentration increase is used as the first input data, or the total oxygen concentration increase for the corresponding respiratory rate and / or respiratory pressure scenario in the historical data is used as the first input data; for the current oxygen concentration maintenance scenario, the first input data at the current moment is equal to the first input data at the previous moment; for the oxygen concentration reduction scenario, a given single oxygen concentration decrease is used as the first input data, or the total oxygen concentration decrease for the corresponding respiratory rate and / or respiratory pressure scenario in the historical data is used as the first input data.

[0008] Preferably, in step S100, the collected user breathing state data is recorded, and a prediction model is constructed based on the recorded historical data; the oxygen supply scene at the next moment is predicted by the prediction model and corresponding input prediction data is generated; if the actual oxygen supply scene at the next moment is consistent with the prediction result, the input prediction data is used as the first input data; otherwise, the corresponding first input data is generated according to the actual oxygen supply scene.

[0009] Preferably, the prediction model includes a long-term prediction module and a short-term correction module; the long-term prediction module predicts the oxygen supply scenario at the next moment based on the user's respiratory status data within a set time or a set number of respiratory status data; the short-term correction module compares the respiratory status data at the current moment with that at the previous moment, and corrects the predicted oxygen supply scenario based on the comparison result.

[0010] Preferably, the user's breathing phase includes an inhalation phase and an exhalation phase; during the inhalation phase, first input data is generated to reduce the integral coefficient of the PID algorithm so that the compressor operates in a rapid oxygen supply mode with increased speed; during the exhalation phase, first input data is generated to increase the integral coefficient of the PID algorithm so that the compressor operates in an energy-saving mode with reduced speed.

[0011] Preferably, the oxygen supply scenario corresponds to a rapid oxygen supply mode; there is a breathing pause stage between adjacent breathing stages; the operation time of the PID algorithm is Δt1, and the response time of the compressor to the PID algorithm is Δt2; for the rapid oxygen supply mode, it is suitable to send the corresponding first input data to the PID algorithm at Δt1+Δt2 time before the end of the breathing pause stage; for the energy-saving mode, it is suitable to send the corresponding first input data to the PID algorithm at Δt1 time before the end of the inhalation stage; wherein, the duration of the inhalation stage and the breathing pause stage is suitable to be calculated based on historical average values.

[0012] Preferably, step S200 includes the following processes: obtaining an adsorption efficiency curve of the molecular sieve with respect to the working time; obtaining the adsorption efficiency corresponding to the molecular sieve from the adsorption efficiency curve according to the current moment; using the obtained molecular sieve adsorption efficiency as the aging degree to generate the second input data; or, detecting the oxygen concentration output by the oxygen concentrator at each set time interval; calculating the sliding average of the oxygen concentration detected per unit time as the aging degree; and generating the second input data according to the difference between the sliding average and the set threshold.

[0013] Preferably, the degree of aging of the molecular sieve includes mild, moderate and severe; for mild aging, the calculated sliding average is 85%~90%, and the compressor speed is increased by 10% by controlling the PID algorithm; for moderate aging, the calculated sliding average is 80%~85%, and the compressor speed is increased by 20% by controlling the PID algorithm; for severe aging, the calculated sliding average is less than 80%, and an alarm is triggered to prompt the replacement of the molecular sieve, and the compressor is controlled to operate at the maximum speed by the PID algorithm.

[0014] Preferably, a relationship curve between oxygen concentration in the air and altitude is obtained; when the oxygen concentrator is working, the current operating altitude of the oxygen concentrator is detected; corresponding oxygen concentration data is obtained from the relationship curve according to the current operating altitude of the oxygen concentrator as third input data of the PID algorithm; and then in step S300, the third input data is superimposed with the first input data and the second input data to serve as input of the PID algorithm.

[0015] Compared with the prior art, the present invention has the following advantages: Compared to traditional methods, this application dynamically adjusts the PID algorithm input based on the user's breathing status when controlling the compressor's operating state through the PID algorithm, thereby achieving dynamic adjustment of the compressor's operating state, effectively improving the oxygen concentrator's dynamic response capability. At the same time, the aging degree of the molecular sieve is tested to further optimize the PID algorithm's control accuracy over the compressor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the structure of one example of the oxygen concentrator of this application.

[0017] Figure 2 This is a schematic diagram of another example of the oxygen concentrator of the present application.

[0018] Figure 3 This is a schematic diagram of the overall workflow of this application.

[0019] In the figure: compressor 101, first solenoid valve 1021, second solenoid valve 1022, first molecular sieve 1031, second molecular sieve 1032, oxygen bridge 104, back-blowing valve 105, first one-way valve 1061, second one-way valve 1062, gas storage tank 107, injection valve 108, oxygen outlet nozzle 109, and balancing valve 110. DETAILED DESCRIPTION

[0020] Below, the present application is further described in conjunction with specific implementation methods. It should be noted that, in the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like are intended to mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0021] In the description of this application, it should be noted that for directional words, such as the terms "center", "horizontal", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and so on, indicating the orientation and position relationship are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and cannot be understood as limiting the specific scope of protection of this application.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0023] In this application, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they may refer to connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0024] In this application, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Moreover, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0025] The terms "comprises" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to such process, method, product or apparatus.

[0026] In order to facilitate understanding of the technical solution of this application, one aspect of this application provides an oxygen concentrator that adopts VPSA technology. Figure 1 As shown, one preferred embodiment includes a compressor 101, two solenoid valves, two molecular sieves, a gas storage tank 107, and an injection valve 108. The two solenoid valves can be defined as a first solenoid valve 1021 and a second solenoid valve 1022, respectively; the two molecular sieves can be defined as a first molecular sieve 1031 and a second molecular sieve 1032, respectively. The output of the compressor 101 can be connected to both the inlet of the first solenoid valve 1021 and the inlet of the second solenoid valve 1022. The outlet of the first solenoid valve 1021 is connected to the inlet of the first molecular sieve 1031, while the outlet of the second solenoid valve 1022 is connected to the inlet of the second molecular sieve 1032. The outlet of the first molecular sieve 1031 is connected to the inlet of the gas storage tank 107 via a first one-way valve 1061, while the outlet of the second molecular sieve 1031 is connected to the inlet of the gas storage tank 107 via a second one-way valve 1062. The outlet of the gas storage tank 107 is connected to the oxygen outlet nozzle 109 via an injection valve 108.

[0027] It is understood that the above-mentioned oxygen concentrator adopts a dual molecular sieve structure, that is, when one of the solenoid valves is opened, such as the first solenoid valve 1021, the compressor 101 can supply gas to the second molecular sieve 1032 through the second solenoid valve 1022. At this time, the first molecular sieve 1031 discharges the adsorbed nitrogen through the opened first solenoid valve 1021 by reducing the pressure. Similarly, when the second solenoid valve 1022 is opened, the compressor 101 can supply gas to the first molecular sieve 1031 through the first solenoid valve 1021. At this time, the second molecular sieve 1032 discharges nitrogen through the opened second solenoid valve 1022.

[0028] Since the molecular sieve is in a low-pressure state when discharging nitrogen, in order to accelerate the nitrogen discharging process of the molecular sieve, a back-blowing valve 105 can be connected between the gas outlets of the first molecular sieve 1031 and the second molecular sieve 1032 through an oxygen bridge 104. That is, when one of the molecular sieves is discharging nitrogen, the other molecular sieve passes part of the high-pressure oxygen through the back-blowing valve 105 and the oxygen bridge 104 to the molecular sieve that is discharging nitrogen, thereby accelerating the nitrogen discharging process of the molecular sieve. At the same time, after the molecular sieve has completed nitrogen discharging, nitrogen adsorption needs to be performed again. At this time, the environment inside the molecular sieve needs to be raised from low pressure to high pressure; through the setting of the back-blowing valve 105, the pressure of the molecular sieve that is discharging nitrogen can be gradually increased during the nitrogen discharging process, and then the pressure can be quickly increased when the molecular sieve is re-adsorbed with nitrogen, so as to improve the oxygen production efficiency of the entire oxygen generator. In order to further accelerate the air pressure balance between the two molecular sieves, such as Figure 2 As shown, a balancing valve 110 can be connected between the outlets of the first molecular sieve 1031 and the second molecular sieve 1032. That is, when one molecular sieve is discharging nitrogen, the other molecular sieve, which is supplied by the compressor 101, can pass some of the produced oxygen through the backflush valve 105 and the balancing valve 110 into the molecular sieve discharging nitrogen, thereby facilitating rapid nitrogen discharge and subsequent pressure buildup. The specific structures and operating principles of the backflush valve 105, the balancing valve 110, the solenoid valve, the one-way valve, and the air injection valve 108 are well known to those skilled in the art and will not be elaborated on here.

[0029] It should be noted that based on the above-mentioned oxygen concentrator architecture, when the oxygen concentrator is operating, the traditional control method uses a PID algorithm to control the opening time of the injection valve 108 or the speed of the compressor 101. Specifically, by detecting the oxygen concentration output by the oxygen concentrator's oxygen outlet 109, if the detected oxygen concentration is lower than or higher than a threshold set by a high pressure, the specific threshold can be selected according to the actual needs of those skilled in the art. For example, the threshold is an oxygen concentration of 90%. If the oxygen concentration output by the outlet 109 is lower than 90%, the PID algorithm will appropriately extend the opening time of the injection valve 108 and / or appropriately increase the speed of the compressor 101, thereby increasing the oxygen concentration at the outlet 109 to above 90%. Although the above-mentioned traditional control method can increase the output oxygen concentration to a certain extent, it does not take into account the actual oxygen supply needs of the user. In other words, the traditional control method often uses a single threshold as the control condition. Even if the oxygen concentration reaches 90%, it may still not meet the needs of some users, resulting in a poor user experience. In addition, the control process of the traditional PID algorithm does not take into account the aging degree of the molecular sieve, which leads to poor control accuracy of the PID algorithm.

[0030] Based on the defects of traditional PID algorithm control, another aspect of the present application provides an oxygen concentrator tuning control method, which can be applied to the above-mentioned double molecular sieve structure oxygen concentrator and can also be applied to other forms of oxygen concentrators; Figure 3 As shown, one of the preferred embodiments includes the following steps: S100: Collecting the user's respiratory status data, and judging the user's current oxygen supply demand based on the collected data; generating first input data for the PID algorithm based on the current oxygen supply demand and the user's respiratory stage.

[0031] S200: Calculate the current aging degree of the molecular sieve to generate second input data for the PID algorithm.

[0032] S300: The first input data and the second data quantity are superimposed as inputs of a PID algorithm to generate a control signal for adjusting the speed and working time of the compressor 101 .

[0033] It is understood that, compared to conventional methods, the present application dynamically adjusts the input of the PID algorithm based on the user's breathing state when controlling the operating state of the compressor 101 using the PID algorithm, thereby achieving dynamic adjustment of the operating state of the compressor 101, thereby effectively improving the dynamic response capability of the oxygen concentrator. At the same time, the aging degree of the molecular sieve is detected to further optimize the control accuracy of the PID algorithm on the compressor 101.

[0034] It should be noted that in step S100, the oxygen concentrator can be activated based on the user's breathing. That is, when the flow sensor or pressure sensor connected to the oxygen outlet 109 detects airflow or pressure, it indicates that the user has completed the wearing of the oxygen outlet 109, and the oxygen concentrator can automatically start. When the oxygen concentrator is started, the stable operating state of the oxygen concentrator requires an oxygen concentration of at least 90%. Therefore, after the oxygen concentrator is started, the oxygen concentration output by the oxygen concentrator needs to be detected by the oxygen concentration sensor connected to the oxygen outlet 109. If the oxygen concentration output by the oxygen concentrator is lower than a set threshold value, i.e., 90%, the first input data is generated based on the difference between the current oxygen concentration output by the oxygen concentrator and the set threshold value. Then, the PID algorithm is used to control the compressor 101 to increase its speed so that the oxygen concentration output by the oxygen concentrator reaches at least 90%.

[0035] In this embodiment, when the oxygen concentrator is working normally, that is, when the oxygen concentration output by the oxygen concentrator reaches at least 90%, due to the different physiques or physical conditions of different users, their requirements for oxygen concentration are different; at this time, the oxygen concentration output by the oxygen concentrator needs to be adjusted according to the actual condition of the user.

[0036] Specifically, the data collected in step S100 includes the user's respiratory rate and / or respiratory pressure; the user's current oxygen supply scenario is judged based on the obtained data, including an oxygen concentration increase scenario, a current oxygen concentration maintenance scenario, and an oxygen concentration reduction scenario; corresponding first input data is generated based on the current oxygen supply scenario obtained by judgment; wherein, for the oxygen concentration increase scenario, a given single oxygen concentration increase is used as the first input data, or the total oxygen concentration increase for the corresponding respiratory rate and / or respiratory pressure scenario in the historical data is used as the first input data; for the current oxygen concentration maintenance scenario, the first input data at the current moment is equal to the first input data at the previous moment; for the oxygen concentration reduction scenario, a given single oxygen concentration decrease is used as the first input data, or the total oxygen concentration decrease for the corresponding respiratory rate and / or respiratory pressure scenario in the historical data is used as the first input data.

[0037] It is understandable that when the oxygen concentration normally output by the oxygen concentrator is insufficient, the user will subconsciously increase their breathing rate and breathing depth. The breathing depth can generally be reflected by the breathing pressure. At this time, it can be determined that the oxygen concentrator is insufficiently supplying oxygen, and the user's current oxygen supply scenario is the insufficient oxygen supply and increase oxygen concentration scenario. If the user's breathing rate and / or breathing pressure remain basically unchanged or vary within a small range over a certain period of time, it can be determined that the oxygen concentrator is supplying appropriate oxygen, and the user's current oxygen supply scenario is the maintain current oxygen concentration scenario. When the oxygen concentration output by the oxygen concentrator exceeds the user's normal needs, the user's breathing rate and / or breathing pressure will appropriately decrease over a certain period of time. At this time, it can be determined that the oxygen concentration supplied by the oxygen concentrator is too high, and the user's current oxygen supply scenario is the reduce oxygen concentration scenario. It should be noted that long-term exposure to high oxygen concentrations may lead to oxygen poisoning, so scenarios where the oxygen concentration output by the oxygen concentrator is too high also need to be suppressed.

[0038] It should be known that when the user is in the oxygen concentration increasing scenario and the oxygen concentration decreasing scenario, the specific values ​​of the single oxygen concentration increase and the single oxygen concentration decrease can be selected according to the actual needs of technical personnel in this field; for example, the single oxygen concentration increase and the single oxygen concentration decrease can be both 0.5% or 1%.

[0039] It can also be understood that for the scenarios of increasing oxygen concentration and decreasing oxygen concentration, it may be necessary to perform multiple single oxygen concentration increase and single oxygen concentration decrease controls. Taking the scenario of increasing oxygen concentration as an example, assuming that the current oxygen concentration output by the oxygen concentrator is 90%, and the oxygen concentration actually required by the user is 93%; if the single oxygen concentration increase is 1%, then the PID algorithm needs to execute three oxygen concentration increase controls to increase the oxygen concentration output by the oxygen concentrator from 90% to 93%. In the early stage of the oxygen concentrator operation, due to the lack of historical data, the PID algorithm can only meet the needs of the increased oxygen concentration scenario by increasing the oxygen concentration once.

[0040] However, when the oxygen concentrator is working, it can record the user's respiratory rate, respiratory pressure, and PID algorithm control data as historical data. Then, after the oxygen concentrator has been working for a period of time, if the user's respiratory rate and / or respiratory pressure changes, the oxygen supply scene will be switched; for example, when it is necessary to switch from the current concentration maintenance scene to the oxygen concentration increase scene, assuming that the oxygen concentration output by the oxygen concentrator needs to be increased from 91% to 93%, the oxygen concentrator can search the historical data based on the user's current respiratory rate and / or respiratory pressure, and obtain the historical data of the user's required oxygen concentration corresponding to the current respiratory rate and / or respiratory pressure as 93%. At this time, the difference between the final required oxygen concentration obtained from the historical data and the current oxygen concentration output by the oxygen concentrator, that is, the 2% total oxygen concentration increase, can be directly substituted into the PID algorithm as the first input data to adjust the oxygen concentration output by the oxygen concentrator. Compared with the adjustment method based on the single oxygen concentration change, the method of using the PID algorithm to control the total oxygen concentration demand obtained from historical data can effectively shorten the switching time of the oxygen supply scene, that is, quickly meet the user's needs; however, there may be a certain deviation between the historical data and the actual situation. Therefore, those skilled in the art can choose the specific switching method of the oxygen supply scene according to actual needs.

[0041] In this embodiment, the speed of the compressor 101 can be divided into multiple gears, each corresponding to a different output oxygen concentration of the oxygen concentrator. For example, if the output oxygen concentration of the oxygen concentrator ranges from 90% to 95%, the gears of the oxygen concentrator can be divided into six gears, specifically including an energy-saving gear and gears 1 to 5. Among them, the energy-saving gear corresponds to an oxygen concentration of 90%, and gears 1 to 5 correspond to oxygen concentrations of 91% to 95%. Different models or types of compressors have different speed ranges. For ease of understanding, taking the maximum compressor speed of 2350 rpm as an example, the energy-saving gear corresponds to a compressor speed of 600 rpm, the first gear corresponds to a compressor speed of 800 rpm, the second gear corresponds to a compressor speed of 1150 rpm, the third gear corresponds to a compressor speed of 1500 rpm, the fourth gear corresponds to a compressor speed of 1900 rpm, and the fifth gear corresponds to a compressor speed of 2350 rpm.

[0042] In this embodiment, during step S100, the collected user respiratory status data is recorded, and a prediction model is constructed based on the recorded historical data. The prediction model is used to predict the oxygen supply scenario at the next moment and generate corresponding input prediction data. If the actual oxygen supply scenario at the next moment matches the prediction result, the input prediction data is used as the first input data; otherwise, the corresponding first input data is generated based on the actual oxygen supply scenario.

[0043] It should be noted that data collection of the user's respiratory rate and / or respiratory pressure is continuous, with each breath corresponding to a detection process. Therefore, as can be seen from the foregoing, before the PID algorithm is executed, a pre-calculation of the PID algorithm is performed based on the data collected at each detection moment to obtain the corresponding first input data. Because this pre-calculation includes data collection and calculation, the PID algorithm requires a certain amount of time before it can execute the calculation, which increases the overall response time of the PID algorithm.

[0044] Therefore, in this embodiment, a prediction model can be constructed based on the user's historical respiratory state data. Based on the prediction model, the first input data for the PID algorithm pre-calculation at the next moment can be predicted at the current moment. Therefore, at the next moment, the PID algorithm can generate the corresponding control signal without going through the pre-calculation moment. However, considering the possibility of unexpected situations causing the user's respiratory rate and / or respiratory pressure to change significantly at the next moment, the user's actual respiratory rate and / or respiratory pressure collected at the next moment can be compared with the predicted data of the prediction model. If the two match, the PID algorithm directly adopts the predicted result; if the two do not match, the PID algorithm performs the corresponding calculation process based on the actual collected data.

[0045] Specifically, the prediction model includes a long-term prediction module and a short-term correction module; the long-term prediction module predicts the oxygen supply scenario at the next moment based on the user's respiratory status data within a set time or a set number of times; the short-term correction module compares the respiratory status data at the current moment with the previous moment, and corrects the predicted oxygen supply scenario based on the comparison results.

[0046] It is understood that there are various ways to construct a prediction model. For example, by using the Autoregressive Integrated Moving Average (ARIMA) algorithm, which decomposes the time series into trend, seasonal, and random fluctuation components, the autocorrelation of the user's historical respiratory state data can be used to predict the user's future respiratory state. Machine learning algorithms can also be used to establish a linear relationship between input features (historical respiratory state data) and output (the respiratory state at the next moment) for prediction. Recurrent Neural Networks (RNNs) can also be used. By introducing a loop structure, they can model the temporal dependencies in time series data. The output at each time step depends not only on the current input but also on the previous hidden state. The specific principles and working processes of the above algorithms are well known to those skilled in the art and will not be elaborated on here. The specific algorithm for constructing the prediction model can be selected according to the actual needs of those skilled in the art.

[0047] Those skilled in the art should know that the user's breathing phase includes an inhalation phase and an exhalation phase; in the actual breathing process, a high flow rate of oxygen, such as 3-5 L / min, needs to be immediately obtained during the inhalation phase, otherwise a feeling of suffocation will occur due to delayed supply; high flow rate oxygen is not required during the exhalation phase, but the molecular sieve adsorption pressure needs to be maintained stable to avoid excessive fluctuations in oxygen concentration. Therefore, in this embodiment, a first input data for reducing the integral coefficient of the PID algorithm is generated during the inhalation phase, so that the compressor 101 operates in a fast oxygen supply mode with increased speed; a first input data for increasing the integral coefficient of the PID algorithm is generated during the exhalation phase, so that the compressor 101 operates in an energy-saving mode with reduced speed, i.e., the above-mentioned energy-saving gear.

[0048] It should be noted that the specific architecture of the PID algorithm is as follows: .

[0049] Where u(t) represents the control quantity, through which the speed and working time of the compressor can be controlled; e(t) represents the deviation, that is, the deviation between the target value and the actual value; K p Represents the proportional coefficient. Proportional control directly adjusts the control amount according to the size of the deviation. The larger the deviation, the greater the change in the control amount. However, it cannot completely eliminate the steady-state error. i Integral coefficient. Integral control adjusts the control quantity by accumulating the deviation over time, and is mainly used to eliminate steady-state errors. d Represents the differential coefficient. Differential control adjusts the control quantity according to the rate of change of the deviation. It is mainly used to suppress the rapid change of the deviation, thereby improving the stability of the system. Differential control can predict the changing trend of the deviation and adjust the control quantity in advance, thereby reducing the overshoot and oscillation of the system, but it is more sensitive to noise.

[0050] Based on the PID algorithm architecture, during the inhalation phase, the demand for oxygen flow increases, so compressor 101 needs to increase its speed to rapidly deliver oxygen. This emphasizes the proportional control component of the PID algorithm. Therefore, in this embodiment, the integral coefficient is reduced to minimize its impact on the proportional control component. During the exhalation phase, the user has no requirements for changes in oxygen concentration. In this case, the integral coefficient can be increased to emphasize the integral control component, strengthening steady-state control of oxygen concentration while simultaneously reducing the compressor speed to save energy.

[0051] It should be noted that the changes in the oxygen concentration output by the oxygen concentrator in the aforementioned oxygen supply scenarios all occur during the inhalation phase, when the compressor operates in rapid oxygen supply mode.

[0052] It should be known that the duration of a single breath for an adult is generally 3 to 5 seconds, of which the duration of the inhalation phase is about 0.8 to 1.2 seconds, the duration of the exhalation phase is about 1.2 to 2 seconds, and there is a 1 to 2 second breathing pause phase between adjacent breathing phases; wherein, the compressor 101 can also operate in energy-saving mode during the breathing pause phase. The operation time of the PID algorithm is generally 200ms; the time required for the compressor 101 to change the speed from receiving the adjustment signal to controlling the speed to stabilization, that is, the response time of the compressor 101 to the PID algorithm is generally within 100ms. Therefore, when switching the oxygen supply scene, if the PID algorithm operation is started directly at the initial stage of the inhalation phase, it may result in the inability to adjust the oxygen concentration in the first 0.3 seconds of the inhalation phase, thereby affecting the actual user experience.

[0053] Therefore, in this embodiment, the operation time of the PID algorithm can be set to Δt1, and the response time of the compressor 101 to the PID algorithm can be set to Δt2. Then, when executing the rapid oxygen supply mode, the corresponding first input data can be sent to the PID algorithm at a time Δt1+Δt2 before the end of the apnea phase between the previous breathing phase and the current breathing phase. When executing the energy-saving mode, the corresponding first input data can be sent to the PID algorithm at a time Δt1 before the end of the inhalation phase. The duration of the inhalation phase and the apnea phase can be calculated based on historical average values.

[0054] It is understandable that by advancing the calculation process of the PID algorithm by Δt1+Δt2 before the start of the inhalation phase, it can be ensured that the compressor 101 is already working in the rapid oxygen supply mode and maintains a steady state when the inhalation phase begins, thereby ensuring that the user's oxygen supply is unobstructed during the inhalation phase. At the same time, by advancing the calculation process of the PID algorithm by Δt1 before the end of the inhalation phase, it can be ensured that the compressor 101 starts to execute the energy-saving mode at the beginning of the exhalation phase, thereby ensuring that the compressor always works in the rapid oxygen supply mode during the duration of the inhalation phase. Based on the above-mentioned setting method, the working efficiency of the oxygen concentrator can be guaranteed while effectively reducing the energy consumption of the oxygen concentrator.

[0055] It should be known that when the adsorption efficiency of the molecular sieve is 100%, or when the molecular sieve has not aged, the second input data is zero; that is, at this time, the PID algorithm only needs to calculate through the first input data. However, in actual use, the adsorption efficiency of the molecular sieve will gradually decay with the actual situation, that is, the molecular sieve has aged, which in turn leads to a decrease in the oxygen production capacity of the molecular sieve. The decrease in the oxygen production capacity of the molecular sieve will cause the oxygen concentration output by the oxygen outlet nozzle 109 to still fail to meet the standard after the compressor 101 executes the adjustment of the PID algorithm. Therefore, in this embodiment, when the compressor 101 is adjusted by the PID algorithm to control the output oxygen concentration of the oxygen generator, the aging degree of the molecular sieve needs to be considered. There are many ways to obtain the aging degree of the molecular sieve. For the sake of ease of understanding, two specific examples will be used to illustrate.

[0056] Example 1: Based on the molecular sieve model, the adsorption efficiency curve for the molecular sieve over operating time is obtained by consulting the technical manual. The molecular sieve's operating time is then recorded during the oxygen concentrator's operation. The adsorption efficiency corresponding to the current operating time can be obtained from the adsorption efficiency curve. Finally, the obtained molecular sieve adsorption efficiency is used as the aging degree to generate the second input data, which is then superimposed on the first input data as the input data for the PID algorithm.

[0057] Example 2: The oxygen concentration output by the oxygen concentrator is detected at set intervals; a sliding average of the oxygen concentration detected per unit time is calculated as the aging degree; and second input data is generated based on the difference between the sliding average and a set threshold.

[0058] It is understandable that for the above-mentioned example one, since the adsorption efficiency is directly obtained based on the adsorption efficiency curve, the corresponding second input data can be quickly generated, and the second input data is a theoretical value. However, there may be a certain degree of mismatch between the adsorption efficiency curve of the molecular sieve during actual use and the adsorption efficiency curve obtained by the query, which may cause the actual value of the second input data to deviate from the theoretical value, thereby affecting the control accuracy of the PID algorithm. Corresponding to the above-mentioned example two, it is a real-time detection of the adsorption efficiency of the molecular sieve, that is, the degree of aging, which can ensure the accuracy of the second input data obtained, but the real-time detection will generate a certain length of data acquisition time, which will lead to an increase in the overall response time of the PID algorithm. Both of the above examples can meet the actual needs of this application, and those skilled in the art can make their own choices based on actual needs.

[0059] It should be noted that the control method for compressor 101 is ultimately different depending on the degree of aging of the molecular sieve. That is, when the molecular sieve is relatively lightly aged, the oxygen loss caused by the aging of the molecular sieve can be compensated by increasing the speed of compressor 101. However, when the molecular sieve is severely aged, the compressor 101 will not be able to compensate by speed, and the molecular sieve will need to be replaced. For ease of understanding, the specific compensation process for different degrees of molecular sieve aging will be described in detail below using Example 2 as an example.

[0060] Specifically, the degree of aging of the molecular sieve includes mild, moderate and severe. For mild aging, the calculated sliding average is 85%~90%. At this time, the PID algorithm can be used to control the speed of the compressor 101 to increase by 10%. For moderate aging, the calculated sliding average is 80%~85%. At this time, the PID algorithm can be used to control the speed of the compressor 101 to increase by 20%. For severe aging, the calculated sliding average is less than 80%. At this time, an alarm prompts to replace the molecular sieve, and the PID algorithm is used to control the compressor 101 to operate at the maximum speed to maximize the oxygen supply capacity of the oxygen concentrator and reduce the impact on the user experience.

[0061] Those skilled in the art should know that as the altitude increases, the oxygen concentration in the air will gradually decrease; therefore, when performing PID algorithm control of the oxygen concentrator, the current altitude of the oxygen concentrator needs to be considered, that is, the input of the PID algorithm needs to be corrected by superimposing the altitude data.

[0062] In this embodiment, the method for correcting the operating results of the PID algorithm based on altitude is as follows: a curve is obtained showing the relationship between oxygen concentration in air and altitude. While the oxygen concentrator is operating, the current operating altitude of the oxygen concentrator is detected. Based on the current operating altitude of the oxygen concentrator, corresponding oxygen concentration data is obtained from the curve as third input data for the PID algorithm. Then, in step S300, the third input data is superimposed with the first and second input data to serve as input for the PID algorithm.

[0063] It should be noted that the relationship curve between the oxygen concentration in the air and the altitude is a well-known technology for those skilled in the art, so it will not be elaborated in detail here; in order to further facilitate the calculation of the PID algorithm, the oxygen concentration in the air and the altitude can be defaulted to a linear relationship.

[0064] The above describes the basic principles, main features, and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-described embodiments. The above-described embodiments and the specification merely illustrate the principles of the present application. Various changes and improvements may be made to the present application without departing from the spirit and scope of the present application. These changes and improvements fall within the scope of the present application for which protection is sought. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. An oxygen concentrator tuning control method, characterized in that: The steps include: S100: Collecting the user's respiratory status data, and determining the user's current oxygen demand based on the collected data; generating first input data for a PID algorithm based on the current oxygen demand and the user's respiratory stage; S200: Calculating the current aging degree of the molecular sieve to generate second input data for the PID algorithm; S300: Superimposing the first input data and the second data quantity as inputs of a PID algorithm to generate a control signal for adjusting the speed and working time of the compressor.

2. The oxygen concentrator tuning control method according to claim 1, characterized in that: In step S100, an oxygen concentrator is started according to the collected user's breathing, and the oxygen concentration output by the oxygen concentrator is detected; If the oxygen concentration output by the oxygen concentrator is lower than the set threshold, the first input data is generated according to the difference between the current oxygen concentration output by the oxygen concentrator and the set threshold, and the compressor is controlled to increase the speed through the PID algorithm.

3. The oxygen concentrator tuning control method according to claim 1, wherein: When the oxygen concentrator is operating normally, the data collected in step S100 includes the user's respiratory rate and / or respiratory pressure; Determine the user's current oxygen supply scenario based on the acquired data, including increasing oxygen concentration, maintaining the current oxygen concentration, and decreasing oxygen concentration; Generate corresponding first input data according to the determined current oxygen supply scenario; For the oxygen concentration increase scenario, a given single oxygen concentration increase is used as the first input data, or a total oxygen concentration increase for the corresponding respiratory rate and / or respiratory pressure scenario in the historical data is used as the first input data; For the scenario of maintaining the current oxygen concentration, the first input data at the current moment is equal to the first input data at the previous moment; For the oxygen concentration reduction scenario, a given single oxygen concentration reduction amount is used as the first input data, or a total oxygen concentration reduction amount for a corresponding respiratory rate and / or respiratory pressure scenario in historical data is used as the first input data.

4. The oxygen concentrator tuning control method according to claim 1, characterized in that: In step S100, the collected user breathing state data is recorded, and a prediction model is constructed based on the recorded historical data; Use the prediction model to predict the oxygen supply scenario at the next moment and generate corresponding input prediction data; If the actual oxygen supply scenario at the next moment meets the prediction result, the prediction data is input as the first input data; Otherwise, corresponding first input data is generated according to the actual oxygen supply scenario.

5. The oxygen concentrator tuning control method according to claim 4, characterized in that: The prediction model includes a long-term prediction module and a short-term correction module; The long-term prediction module predicts the oxygen supply scenario at the next moment based on the user's respiratory status data within a set time or a set number of respiratory status data; The short-term correction module compares the respiratory status data at the current moment with the previous moment, and corrects the predicted oxygen supply scenario based on the comparison results.

6. The oxygen concentrator tuning control method according to claim 3, characterized in that: The user's breathing phase includes an inhalation phase and an exhalation phase; generating first input data for reducing an integral coefficient of a PID algorithm during an inhalation phase, so as to cause the compressor to operate in a rapid oxygen supply mode with an increased speed; During the exhalation phase, first input data for increasing the integral coefficient of the PID algorithm is generated, so that the compressor operates in an energy-saving mode with a reduced speed.

7. The oxygen concentrator tuning control method according to claim 6, characterized in that: The oxygen supply scenario corresponds to the rapid oxygen supply mode; there is a pause in breathing between adjacent breathing phases; the operation time of the PID algorithm is Δt1, and the response time of the compressor to the PID algorithm is Δt2; For the rapid oxygen supply mode, it is suitable to send the corresponding first input data to the PID algorithm at a time Δt1+Δt2 before the end of the apnea phase; For the energy-saving mode, it is suitable to send the corresponding first input data to the PID algorithm at a time Δt1 before the end of the inhalation phase; The durations of the inhalation phase and the apnea phase are suitably calculated based on historical average values.

8. The oxygen concentrator tuning control method according to any one of claims 1 to 7, characterized in that: Step S200 includes the following process: Obtaining an adsorption efficiency curve of the molecular sieve with respect to working time; obtaining the adsorption efficiency corresponding to the molecular sieve from the adsorption efficiency curve according to the current moment; and using the obtained adsorption efficiency of the molecular sieve as the aging degree to generate second input data; Alternatively, the oxygen concentration output by the oxygen concentrator is tested at set intervals; A sliding average value of the oxygen concentration detected per unit time is calculated as the aging degree; and second input data is generated according to a difference between the sliding average value and a set threshold value.

9. The oxygen concentrator tuning control method according to claim 8, characterized in that: The degree of aging of molecular sieves includes mild, moderate and severe; For mild aging, the calculated sliding average is 85% to 90%. At this time, the PID algorithm is used to control the compressor speed to increase by 10%. For moderate aging, the calculated sliding average is 80% to 85%. At this time, the PID algorithm is used to control the compressor speed to increase by 20%. For severe aging, the calculated sliding average is less than 80%. At this time, an alarm prompts to replace the molecular sieve, and the compressor is controlled to operate at the maximum speed through the PID algorithm.

10. The oxygen concentrator tuning control method according to claim 1, characterized in that: Obtain a relationship curve between oxygen concentration in the air and altitude; while the oxygen concentrator is operating, detect the current operating altitude of the oxygen concentrator; obtain corresponding oxygen concentration data from the relationship curve based on the current operating altitude of the oxygen concentrator as third input data of the PID algorithm; and then in step S300, superimpose the third input data with the first input data and the second input data to serve as input of the PID algorithm.

Images