Compression pump control system and method for vehicle-mounted oxygen concentrator

Abstract

The present invention relates to the field of medical equipment technology, and in particular to a compression pump control system and method for a vehicle-mounted oxygen concentrator. Environmental data is collected in real time by an environmental sensor, an intelligent control unit analyzes the data and selects the best working mode, and a separation device dynamically adjusts working parameters according to pattern recognition; a real-time monitoring module of the system continuously tracks the oxygen output state, and a feedback regulation module automatically optimizes the airflow path and separation parameters to ensure that the oxygen concentration is not less than 90%; ultimately, through the comprehensive application of the above technical means, the present invention significantly improves the efficiency and stability of oxygen production and provides reliable oxygen support for patients.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a compression pump control system and method for a vehicle-mounted oxygen concentrator. Background Art

[0002] The compression pump for vehicle-mounted oxygen concentrator is a portable medical device designed to meet the oxygen needs of patients in various environments. It mainly compresses air and separates oxygen to supply patients who need oxygen assistance, especially in ambulances, mobile medical equipment and other occasions. The performance of the vehicle-mounted oxygen concentrator directly affects the concentration, flow and purity of oxygen, which in turn affects the patient's treatment effect.

[0003] The existing technology (Chinese invention patent, publication number: CN103185001B, name: Compression pump control system and control method for oxygen generator) mainly adopts the traditional compression pump control system, which is usually equipped with only one compression pump, which can meet basic needs in a plain environment. However, when the equipment is used in a plateau or low-pressure environment, a single compression pump often cannot provide sufficient oxygen concentration and flow, resulting in a significant decrease in oxygen production and oxygen purity. In addition, in order to adapt to the plateau environment, it is sometimes necessary to increase to two compression pumps working in parallel, but this will cause a waste of resources, and it is difficult to switch flexibly in a plain environment, which can easily cause equipment failures, such as startup speed mismatch and other problems. Therefore, the existing technology has obvious deficiencies in dealing with complex environmental changes. Summary of the invention

[0004] In view of the many problems existing in the above-mentioned prior art, the present invention provides a compression pump control system and method for a vehicle-mounted oxygen concentrator. The present invention comprehensively uses environmental sensors, intelligent control units and feedback adjustment modules to monitor and analyze environmental data in real time, and flexibly adjust the working mode of the oxygen concentrator and the operating parameters of the compression pump. This mechanism ensures that under different climate and altitude conditions, the oxygen output is always maintained within a safe and effective range, thereby improving the safety of oxygen use for patients.

[0005] A compression pump control system for a vehicle-mounted oxygen concentrator, comprising:

[0006] Environmental sensors are used to collect environmental pressure, ambient temperature and ambient humidity in real time to generate environmental data;

[0007] An intelligent control unit, for receiving environmental data and processing and extracting features of the environmental data through an integrated learning algorithm to identify the best working mode and output pattern recognition data;

[0008] A separation device configured to select a membrane separation technology or a pressure swing adsorption technology according to the pattern recognition data, and set working parameters matching the membrane separation technology or the pressure swing adsorption technology to generate separation working parameter data;

[0009] A real-time monitoring module is used to monitor the state parameters of oxygen output, including oxygen concentration, flow rate and pressure, and generate operating status data;

[0010] A feedback regulation module, used for providing real-time feedback based on the operating status data, wherein the feedback regulation module automatically adjusts the airflow path and separation parameters to ensure that the oxygen output meets the predetermined standards;

[0011] The optimization module is used to collect operating status data and feedback data, and apply machine learning algorithms to optimize system performance and generate optimization strategy data to improve the overall operating efficiency of the system.

[0012] Preferably, the environmental sensor includes a pressure sensor, a temperature sensor and a humidity sensor, and the environmental sensor is configured to collect environmental pressure, ambient temperature and ambient humidity in real time to generate environmental data, and after the data is generated, provide it to the intelligent control unit for subsequent analysis and processing.

[0013] Preferably, the integrated learning algorithm of the intelligent control unit includes a support vector machine and a random forest algorithm. The integrated learning algorithm performs feature extraction by analyzing the collected environmental data, historical operation data and operating status data to establish a model and identify the optimal working mode, thereby outputting pattern recognition data of the optimal working mode.

[0014] Preferably, when the separation device selects membrane separation technology or pressure swing adsorption technology according to pattern recognition data, the working parameters set by the separation device include intake flow rate and membrane working pressure, and the setting of the working parameters is based on real-time analysis of current environmental data to generate optimized separation working parameter data.

[0015] Preferably, the operating status data generated by the real-time monitoring module is used to evaluate the current separation effect, and trigger a corresponding adjustment strategy after analyzing the evaluation results to ensure that the oxygen output meets the predetermined standard, and the predetermined standard is that the oxygen concentration is not less than 90%.

[0016] Preferably, the feedback regulation module performs real-time feedback according to the operating status data, and the feedback regulation module includes an airflow path regulating valve and a separation parameter control unit. The feedback regulation module automatically adjusts the airflow path and separation parameters by analyzing the monitored oxygen concentration and flow rate to ensure that the oxygen output reaches the required predetermined standard, and re-evaluates the status after the airflow path is adjusted.

[0017] Preferably, the machine learning algorithm is a reinforcement learning algorithm, which dynamically adjusts the control strategy by learning historical data and real-time feedback to improve the overall operating efficiency and adaptability of the system.

[0018] Preferably, the optimization strategy data is generated based on the following calculation expression:

[0019]

[0020] in, is the percentage change in efficiency, is the real-time oxygen concentration, To set the target oxygen concentration, the calculation expression is used to evaluate the performance of the system and guide further optimization.

[0021] Preferably, the system further comprises a data storage module, and the data storage module is used to store environmental data, operating status data and optimization strategy data to facilitate subsequent analysis and optimization.

[0022] A method for controlling a compression pump for a vehicle-mounted oxygen concentrator comprises the following steps:

[0023] Environmental sensors are used to collect environmental pressure, temperature and humidity in real time to generate environmental data;

[0024] Input the environmental data into the intelligent control unit, use the integrated learning algorithm to process and extract features of the environmental data, so as to identify the best working mode and output the pattern recognition data;

[0025] Select membrane separation technology or pressure swing adsorption technology according to pattern recognition data, set matching working parameters, and generate separation working parameter data;

[0026] Starting the separation device and monitoring the state parameters of oxygen output in real time, the state parameters including oxygen concentration, flow rate and pressure, and generating operation state data;

[0027] Provide real-time feedback based on the operating status data, and automatically adjust the airflow path and separation parameters through the feedback regulation module to ensure that the oxygen output meets the predetermined standards;

[0028] Collect operating status data and feedback data, apply machine learning algorithms to optimize the performance of the compression pump control system for vehicle-mounted oxygen concentrators, and generate optimization strategy data to improve the overall operating efficiency of the system.

[0029] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0030] By introducing an intelligent control unit and a real-time monitoring module, the present invention realizes real-time analysis and feedback adjustment of environmental data in the compression pump control system for a vehicle-mounted oxygen concentrator. This technical means can automatically select a suitable working mode according to environmental changes to ensure the stability and reliability of oxygen output. Compared with the prior art, the present invention can dynamically adjust the operating parameters of the compression pump according to real-time data, greatly improving the adaptability and operating efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a system structure block diagram of the present invention;

[0032] Figure 2 This is a schematic diagram of environmental data collection in the present invention;

[0033] Figure 3 This is a working principle diagram of the optimization algorithm in the present invention;

[0034] Figure 4 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0035] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0036] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0037] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0038] like Figure 1 As shown, a compression pump control system for a vehicle-mounted oxygen concentrator includes:

[0039] Environmental sensors are used to collect environmental pressure, ambient temperature and ambient humidity in real time to generate environmental data;

[0040] Preferably, Figure 2As shown, the environmental sensor includes a pressure sensor, a temperature sensor and a humidity sensor. The environmental sensor is configured to collect environmental pressure, ambient temperature and ambient humidity in real time to generate environmental data, and after the data is generated, provide it to the intelligent control unit for subsequent analysis and processing.

[0041] In the present invention, the application of environmental sensors is crucial, and their main function is to collect environmental pressure, ambient temperature and ambient humidity in real time to generate environmental data. These environmental data provide basic information for the intelligent control of the system, ensuring that the system can be dynamically adjusted according to external conditions. Specifically, the environmental sensors include pressure sensors, temperature sensors and humidity sensors, and each sensor uses mature technology to ensure high accuracy and high response speed.

[0042] The pressure sensor can accurately obtain the current air pressure status of the environment by measuring the change in static pressure of the gas. This information is crucial for the normal operation of the oxygen concentrator, because changes in air pressure under different altitudes and weather conditions will directly affect the concentration and production efficiency of oxygen. The temperature sensor is responsible for monitoring the temperature of the surrounding environment. Too high or too low temperature may affect the working performance of the oxygen concentrator. The humidity sensor is used to detect the moisture content in the air. Excessive humidity may cause condensation inside the equipment, thus affecting the normal operation of the equipment.

[0043] After the data is generated, the environmental sensor transmits the real-time collected data to the intelligent control unit. The intelligent control unit uses an integrated learning algorithm to process and extract features from these environmental data, and then identify the best working mode. For example, when the system detects that the ambient temperature is too high, the separation parameters can be adjusted to optimize the oxygen output. In addition, if the ambient pressure is too low, the system can automatically increase the working force of the compression pump to ensure that the oxygen concentration meets the predetermined standard.

[0044] Through such a design, environmental sensors not only enhance the intelligence level of the vehicle-mounted oxygen concentrator, but also improve the adaptability and operating efficiency of the equipment. The accurate collection and real-time processing of environmental data enable the oxygen concentrator to work stably under various environmental conditions, ensuring that patients can obtain sufficient oxygen support, greatly improving the practicality and safety of the equipment.

[0045] An intelligent control unit, for receiving environmental data and processing and extracting features of the environmental data through an integrated learning algorithm to identify the best working mode and output pattern recognition data;

[0046] Preferably, the integrated learning algorithm of the intelligent control unit includes a support vector machine and a random forest algorithm. The integrated learning algorithm performs feature extraction by analyzing the collected environmental data, historical operation data and operating status data to establish a model and identify the optimal working mode, thereby outputting pattern recognition data of the optimal working mode.

[0047] In the present invention, the intelligent control unit plays a core role. Its main function is to receive data from environmental sensors and process and extract features of these environmental data through integrated learning algorithms, thereby identifying the best working mode and outputting pattern recognition data. The intelligent control unit adopts mature integrated learning algorithms, including support vector machines and random forest algorithms, which can effectively process high-dimensional data and capture complex nonlinear relationships.

[0048] Specifically, the intelligent control unit first receives environmental data, such as ambient pressure, temperature, and humidity, and also obtains historical operation data and operating status data. After preprocessing, these data enter the feature extraction stage. In this stage, the support vector machine algorithm can be used to identify key features in the data, such as the trend of oxygen concentration changes under specific environmental conditions. The random forest algorithm evaluates the importance of different features by integrating multiple decision trees, thereby selecting the features that have the greatest impact on identifying the best working mode.

[0049] Once the feature extraction is completed, the intelligent control unit builds a model based on these features and trains it through algorithms to identify the best working mode under different environmental conditions. For example, when the system detects that the ambient temperature is high and the pressure is low, the model may recognize that the compression pump needs to increase the working speed to ensure the stability of the oxygen concentration, and then output the corresponding pattern recognition data to guide the subsequent separation device and feedback regulation module to make adjustments.

[0050] Through this implementation, the intelligent control unit not only improves the system's response speed to environmental changes, but also greatly enhances the adaptability and operating efficiency of the vehicle-mounted oxygen concentrator. Ultimately, the system can provide stable oxygen output under a variety of environmental conditions, ensuring that patients receive adequate oxygen support, improving the reliability of the equipment and user satisfaction.

[0051] A separation device configured to select a membrane separation technology or a pressure swing adsorption technology according to the pattern recognition data, and set working parameters matching the membrane separation technology or the pressure swing adsorption technology to generate separation working parameter data;

[0052] Preferably, when the separation device selects membrane separation technology or pressure swing adsorption technology according to pattern recognition data, the working parameters set by the separation device include intake flow rate and membrane working pressure, and the setting of the working parameters is based on real-time analysis of current environmental data to generate optimized separation working parameter data.

[0053] In the present invention, the separation device is one of the core components, which is responsible for selecting the appropriate separation technology according to the pattern recognition data output by the intelligent control unit to achieve efficient oxygen extraction. The separation device can select membrane separation technology or pressure swing adsorption technology according to the specific application. These two technologies have their own advantages and disadvantages in the oxygen extraction process. Membrane separation technology usually has lower energy consumption and faster reaction speed, while pressure swing adsorption technology is superior in the production of high-purity oxygen.

[0054] After selecting the appropriate separation technology based on the pattern recognition data, the separation device will set the matching working parameters, which mainly include the intake flow rate and membrane working pressure. The intake flow rate determines the amount of air entering the separation device, while the membrane working pressure affects the separation efficiency and oxygen concentration of the membrane. In order to ensure the optimization of these two parameters, the separation device will generate optimized separation working parameter data based on real-time analysis of current environmental data, taking into account factors such as temperature, humidity and pressure.

[0055] For example, in a high-altitude, low-pressure environment, the system may choose to increase the membrane operating pressure and air flow rate to overcome the problem of insufficient oxygen concentration caused by low external air pressure. In this case, the separation device will dynamically adjust its parameters and continuously optimize through real-time monitoring and feedback mechanisms to ensure that the output oxygen concentration meets or exceeds the set standard. This intelligent parameter adjustment not only improves the oxygen extraction efficiency, but also reduces energy consumption and improves the overall stability and reliability of the system.

[0056] Through such a design, the separation device plays a key role in the vehicle-mounted oxygen concentrator, enabling the system to flexibly adjust operating parameters under different environmental conditions, thereby achieving efficient and stable oxygen production, ensuring that users can obtain sufficient oxygen support in various situations.

[0057] A real-time monitoring module is used to monitor the state parameters of oxygen output, including oxygen concentration, flow rate and pressure, and generate operating status data;

[0058] Preferably, the operating status data generated by the real-time monitoring module is used to evaluate the current separation effect, and trigger a corresponding adjustment strategy after analyzing the evaluation results to ensure that the oxygen output meets the predetermined standard, and the predetermined standard is that the oxygen concentration is not less than 90%.

[0059] In the present invention, the real-time monitoring module is a key component responsible for monitoring the state parameters of oxygen output. These state parameters include oxygen concentration, flow rate and pressure. The real-time monitoring module obtains these data through high-precision sensors and generates operating status data. The design of this module is to ensure that the system can continuously evaluate its performance and make timely adjustments to maintain the best working state.

[0060] Specifically, the oxygen concentration sensor can measure the oxygen concentration output by the oxygen concentrator in real time to ensure that it meets the predetermined standard. The flow sensor monitors the flow rate of oxygen to evaluate the oxygen supply capacity of the system, while the pressure sensor measures the output pressure to ensure that the system operates within a safe range. The data collected by these sensors will be summarized and transmitted to the intelligent control unit for further analysis and processing.

[0061] Based on the generated operating status data, the real-time monitoring module evaluates the current separation effect. If the monitored oxygen concentration is lower than 90% of the set standard, the system will trigger the corresponding adjustment strategy. The adjustment strategy may include increasing the workload of the compression pump, increasing the membrane working pressure, or adjusting the airflow path to optimize the oxygen separation efficiency. For example, when the oxygen concentration is detected to be reduced, the system can automatically increase the intake flow rate to increase the amount of air entering the separation device, thereby increasing the oxygen concentration.

[0062] Through this feedback mechanism, the real-time monitoring module can ensure that the oxygen output always meets the predetermined standards, greatly improving the stability and reliability of the vehicle-mounted oxygen concentrator. The design of this system not only achieves precise control of oxygen output, but also effectively improves the safety of oxygen use for patients in different environments, ensuring that reliable oxygen support can still be provided under changing conditions.

[0063] A feedback regulation module, used for providing real-time feedback based on the operating status data, wherein the feedback regulation module automatically adjusts the airflow path and separation parameters to ensure that the oxygen output meets the predetermined standards;

[0064] Preferably, the feedback regulation module performs real-time feedback according to the operating status data, and the feedback regulation module includes an airflow path regulating valve and a separation parameter control unit. The feedback regulation module automatically adjusts the airflow path and separation parameters by analyzing the monitored oxygen concentration and flow rate to ensure that the oxygen output reaches the required predetermined standard, and re-evaluates the status after the airflow path is adjusted.

[0065] In the present invention, the feedback regulation module is a key component to ensure the stable operation of the system. The main function of this module is to provide feedback based on real-time operating status data to automatically adjust the airflow path and separation parameters to ensure that the oxygen output meets the predetermined standards, which are usually set to an oxygen concentration of not less than 90%. The feedback regulation module is designed to respond to changes in oxygen output in real time to ensure that the patient always has adequate oxygen support.

[0066] Specifically, the feedback regulation module consists of an airflow path regulating valve and a separation parameter control unit. The airflow path regulating valve is responsible for controlling the direction and speed of the airflow to ensure the optimal flow of air in the separation device. The separation parameter control unit is used to adjust parameters related to the separation process, such as membrane working pressure and intake air flow. When the oxygen concentration and flow data collected by the real-time monitoring module show that the output does not meet the standard, the feedback regulation module will be activated immediately.

[0067] For example, if the oxygen concentration is detected to be lower than the set value, the feedback control module will analyze the current oxygen concentration and flow data to determine whether it is necessary to increase the intake flow or increase the membrane working pressure. At this time, the airflow path regulating valve will be adjusted to increase the flow of air entering the separation device, and the separation parameter control unit will automatically increase the membrane working pressure to speed up the oxygen separation efficiency. After the adjustment is completed, the system will re-evaluate the changes in the airflow path and separation parameters to ensure that the oxygen output meets the predetermined standards.

[0068] Through this automated feedback mechanism, the feedback regulation module can achieve real-time adjustment and optimization, improve the operating efficiency and reliability of the vehicle-mounted oxygen concentrator, and ensure stable oxygen support under various environmental conditions. This not only enhances the intelligence level of the equipment, but also improves the safety and comfort of oxygen use for patients, ensuring the best results under different operating conditions.

[0069] The optimization module is used to collect operating status data and feedback data, and apply machine learning algorithms to optimize system performance and generate optimization strategy data to improve the overall operating efficiency of the system.

[0070] Preferably, Figure 3 As shown, the machine learning algorithm is a reinforcement learning algorithm. The reinforcement learning algorithm dynamically adjusts the control strategy by learning historical data and real-time feedback to improve the overall operating efficiency and adaptability of the system.

[0071] In the present invention, the optimization module is a key component to achieve system performance improvement. Its main function is to collect operating status data and feedback data, and apply machine learning algorithms to optimize system performance, thereby generating optimization strategy data to improve the overall operating efficiency of the system. This module can dynamically adjust the control strategy according to real-time data to ensure that the system maintains efficient and reliable operation under various environmental conditions.

[0072] Specifically, the optimization module integrates data from the real-time monitoring module and the feedback adjustment module to form a comprehensive operating status database. This database contains historical operation data, real-time feedback data, and environmental change data. Machine learning algorithms, especially reinforcement learning algorithms, use this data for learning and model training. During the training process, the reinforcement learning algorithm evaluates the impact of different control strategies on system performance to identify the best adjustment plan.

[0073] For example, if the system detects that the oxygen concentration is lower than the set value under certain environmental conditions, the reinforcement learning algorithm will analyze the measures taken under similar conditions in the historical data and their effects, calculate the corresponding reward value, and feed it back to the learning model. Through continuous learning and adjustment, the system can gradually optimize the control strategy, such as dynamically adjusting the operating speed of the compression pump, the working parameters of the separation device, and even the changes in the airflow path to achieve the best oxygen output.

[0074] Through this optimization process, the optimization module not only improves the overall operating efficiency of the system, but also enhances the system's ability to adapt to environmental changes. This means that the vehicle-mounted oxygen concentrator can quickly adapt and adjust its operating status under a variety of environmental conditions to ensure that patients always have access to a stable and sufficient supply of oxygen. Ultimately, the design of the optimization module enables the oxygen concentrator to meet medical needs while also improving the intelligence level of the equipment and user experience, ensuring its reliability and effectiveness in different scenarios.

[0075] Preferably, the optimization strategy data is generated based on the following calculation expression:

[0076]

[0077] in, is the percentage change in efficiency, is the real-time oxygen concentration, To set the target oxygen concentration, the calculation expression is used to evaluate the performance of the system and guide further optimization.

[0078] Through this expression, the system can quantify the actual efficiency of oxygen output and compare it with the target efficiency to determine whether the current system performance is good or bad.

[0079] Specifically, when the real-time monitoring module detects the real-time oxygen concentration, the generated real-time oxygen concentration data will be input into the optimization module. Based on this, the optimization module will calculate the current efficiency change percentage. If the calculation result shows that the efficiency change is negative, it means that the real-time oxygen concentration is lower than the target concentration. The system will trigger the corresponding adjustment strategy, such as increasing the working speed of the compression pump, increasing the membrane working pressure of the separation device, or adjusting the airflow path to optimize the oxygen extraction process.

[0080] For example, assuming that the target oxygen concentration is set to 92%, and the real-time monitoring data shows that the actual oxygen concentration is 85%, after substituting it into the calculation expression, we get:

[0081]

[0082] This negative value indicates that the system's current performance is not up to standard, so the system will automatically take measures to adjust relevant parameters to improve the oxygen concentration output. Through this feedback mechanism, the optimization module can effectively improve the operating efficiency of the vehicle-mounted oxygen concentrator and ensure that users can obtain sufficient oxygen support under different environmental conditions.

[0083] In summary, the optimization strategy data not only provides a real-time evaluation of system performance, but also provides a clear basis for subsequent adjustments and optimizations. This mechanism enables the vehicle-mounted oxygen concentrator to flexibly adapt to changing environments, ensuring the stability and reliability of the system, thereby greatly improving the practicality of the equipment and the safety of oxygen use for patients.

[0084] Preferably, the system further comprises a data storage module, and the data storage module is used to store environmental data, operating status data and optimization strategy data to facilitate subsequent analysis and optimization.

[0085] In the present invention, the data storage module is a crucial component, and its main function is to store environmental data, operating status data and optimization strategy data. Through effective data storage, the system can achieve the accumulation of historical data, providing an important basis for subsequent analysis and optimization.

[0086] The data storage module first receives real-time environmental data from environmental sensors, including current environmental pressure, temperature, and humidity. The operating status data provided by the real-time monitoring module, such as oxygen concentration, flow rate, and pressure, are also recorded. In addition, the optimization strategy data generated by the optimization module will also be stored for subsequent performance evaluation and optimization strategy adjustment. The data storage uses high-capacity, fast-read and write storage media to ensure that the system can respond in a timely manner during operation.

[0087] In principle, the design of the data storage module is based on a data-driven decision-making mechanism. By analyzing historical data, the intelligent control unit can identify trends and patterns in operating status. This trend analysis not only helps the system monitor current performance in real time, but also provides data support for long-term optimization. For example, by analyzing oxygen output data over the past few weeks, the system may find that the output efficiency is low under certain environmental conditions, so that when similar conditions occur again, the operating parameters can be adjusted in advance to prevent performance degradation.

[0088] For example, if the system is operating in a high temperature and high humidity environment, historical data may show that the oxygen concentration is usually lower than the standard under this condition. After the data storage module records these key data, the intelligent control unit will refer to the historical data when encountering a similar environment again, and automatically adjust the membrane working pressure and intake flow rate to optimize the oxygen extraction process. This adaptive capability significantly improves the operating efficiency and reliability of the vehicle-mounted oxygen concentrator.

[0089] In summary, the data storage module plays a core role in the present invention. It not only ensures the effective storage of real-time data, but also provides a basis for analysis and optimization of the system, enhances the responsiveness and adaptability of the equipment to environmental changes, and ultimately improves the user's oxygen experience and safety.

[0090] like Figure 4 As shown, a method for controlling a compression pump for a vehicle-mounted oxygen concentrator comprises the following steps:

[0091] Environmental sensors are used to collect environmental pressure, temperature and humidity in real time to generate environmental data;

[0092] Input the environmental data into the intelligent control unit, use the integrated learning algorithm to process and extract features of the environmental data, so as to identify the best working mode and output the pattern recognition data;

[0093] Select membrane separation technology or pressure swing adsorption technology according to pattern recognition data, set matching working parameters, and generate separation working parameter data;

[0094] Starting the separation device and monitoring the state parameters of oxygen output in real time, the state parameters including oxygen concentration, flow rate and pressure, and generating operation state data;

[0095] Provide real-time feedback based on the operating status data, and automatically adjust the airflow path and separation parameters through the feedback regulation module to ensure that the oxygen output meets the predetermined standards;

[0096] Collect operating status data and feedback data, apply machine learning algorithms to optimize the performance of the compression pump control system for vehicle-mounted oxygen concentrators, and generate optimization strategy data to improve the overall operating efficiency of the system.

[0097] In the present invention, real-time data collection of environmental sensors is the basis for ensuring efficient operation of the system. By monitoring the ambient pressure, ambient temperature and ambient humidity, the environmental data generated by the environmental sensors provide key information for the intelligent control unit. These data will be used as input and processed and feature extracted by an integrated learning algorithm to identify the optimal working mode. This process relies on machine learning technology, which can analyze complex environmental changes and dynamically adapt to different conditions to improve the efficiency of oxygen production.

[0098] After identifying the best working mode, the system will select the appropriate separation technology, such as membrane separation technology or pressure swing adsorption technology, and set the matching working parameters, such as inlet flow rate and membrane working pressure. This selection is based on real-time analysis of pattern recognition data to ensure the best separation efficiency under current environmental conditions. Next, the separation device is started to monitor the state parameters of oxygen output in real time, including oxygen concentration, flow rate and pressure, to generate operating status data.

[0099] After the system collects the operating status data, it provides real-time feedback through the feedback adjustment module. The core function of this module is to automatically adjust the airflow path and separation parameters to ensure that the oxygen output reaches the predetermined standard, which is usually set to an oxygen concentration of not less than 90%. For example, when the oxygen concentration is detected to be lower than the standard value, the system can automatically increase the working speed of the compression pump or adjust the separation parameters to optimize the output effect.

[0100] Finally, the system collects the operating status data and feedback data and applies machine learning algorithms to optimize performance and generate optimization strategy data. This process dynamically adjusts the control strategy by learning from historical data and real-time feedback to further improve the overall operating efficiency. For example, if historical data shows that the oxygen output is not good at a specific temperature, the system will adjust the working parameters in advance under similar conditions to avoid efficiency loss. Through this series of automated and intelligent steps, the compression pump control system for the vehicle-mounted oxygen concentrator can provide stable and reliable oxygen output under a variety of environmental conditions to ensure that the patient's oxygen needs are met.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0102] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A compression pump control system for a vehicle-mounted oxygen concentrator, characterized in that: include: Environmental sensors are used to collect environmental pressure, ambient temperature and ambient humidity in real time to generate environmental data; An intelligent control unit, used to receive environmental data and process and extract features of the environmental data through an integrated learning algorithm to identify the optimal working mode and output pattern recognition data; the integrated learning algorithm of the intelligent control unit includes a support vector machine and a random forest algorithm, and the integrated learning algorithm extracts features by analyzing the collected environmental data, historical operation data, and operating status data to establish a model and identify the optimal working mode, thereby outputting the pattern recognition data of the optimal working mode; A separation device configured to select a membrane separation technology or a pressure swing adsorption technology according to the pattern recognition data, and set working parameters matching the membrane separation technology or the pressure swing adsorption technology to generate separation working parameter data; When the separation device selects membrane separation technology or pressure swing adsorption technology according to the pattern recognition data, the operating parameters set by the separation device include intake flow rate and membrane working pressure, and the setting of the operating parameters is based on real-time analysis of current environmental data to generate optimized separation operating parameter data; A real-time monitoring module is used to monitor the state parameters of oxygen output, including oxygen concentration, flow rate and pressure, and generate operating status data; A feedback regulation module, used for providing real-time feedback according to the operation status data, the feedback regulation module automatically adjusts the airflow path and separation parameters to ensure that the oxygen output reaches the predetermined standard; the feedback regulation module provides real-time feedback according to the operation status data, the feedback regulation module includes an airflow path regulation valve and a separation parameter control unit, the feedback regulation module automatically adjusts the airflow path and separation parameters by analyzing the monitored oxygen concentration and flow rate to ensure that the oxygen output reaches the required predetermined standard, and re-evaluates the state after the airflow path is adjusted; The optimization module is used to collect operating status data and feedback data, and apply machine learning algorithms to optimize system performance and generate optimization strategy data to improve the overall operating efficiency of the system.

2. The compression pump control system for a vehicle-mounted oxygen concentrator according to claim 1, characterized in that: The environmental sensor includes a pressure sensor, a temperature sensor and a humidity sensor. The environmental sensor is configured to collect environmental pressure, environmental temperature and environmental humidity in real time to generate environmental data, and after the data is generated, provide it to the intelligent control unit for subsequent analysis and processing.

3. The compression pump control system for a vehicle-mounted oxygen concentrator according to claim 1, characterized in that: The operating status data generated by the real-time monitoring module is used to evaluate the current separation effect, and trigger the corresponding adjustment strategy after analyzing the evaluation results to ensure that the oxygen output meets the predetermined standard, and the predetermined standard is that the oxygen concentration is not less than 90%.

4. The compression pump control system for a vehicle-mounted oxygen concentrator according to claim 1, characterized in that: The machine learning algorithm is a reinforcement learning algorithm, which dynamically adjusts the control strategy by learning historical data and real-time feedback to improve the overall operating efficiency and adaptability of the system.

5. The compression pump control system for a vehicle-mounted oxygen concentrator according to claim 1, characterized in that: The optimization strategy data is generated based on the following calculation expression: in, is the percentage change in efficiency, is the real-time oxygen concentration, To set the target oxygen concentration, the calculation expression is used to evaluate the performance of the system and guide further optimization.

6. The compression pump control system for a vehicle-mounted oxygen concentrator according to claim 1, characterized in that: The system further includes a data storage module, which is used to store environmental data, operating status data and optimization strategy data to facilitate subsequent analysis and optimization.

7. A method for executing the compression pump control system for a vehicle-mounted oxygen concentrator according to any one of claims 1 to 6, characterized in that: The following steps are involved: Environmental sensors are used to collect environmental pressure, temperature and humidity in real time to generate environmental data; Input the environmental data into the intelligent control unit, use the integrated learning algorithm to process and extract features of the environmental data, so as to identify the best working mode and output the pattern recognition data; Select membrane separation technology or pressure swing adsorption technology according to pattern recognition data, set matching working parameters, and generate separation working parameter data; Starting the separation device and monitoring the state parameters of oxygen output in real time, the state parameters including oxygen concentration, flow rate and pressure, and generating operation state data; Provide real-time feedback based on the operating status data, and automatically adjust the airflow path and separation parameters through the feedback regulation module to ensure that the oxygen output meets the predetermined standards; Collect operating status data and feedback data, apply machine learning algorithms to optimize the performance of the compression pump control system for vehicle-mounted oxygen concentrators, and generate optimization strategy data to improve the overall operating efficiency of the system.

Images