Oxygen generator remote control method and system based on data analysis

Abstract

The invention relates to the technical field of regression prediction modules, and discloses an oxygen generator remote control method and system based on data analysis. The method comprises the following steps: acquiring historical operation data of the oxygen generator, real-time operation data of the oxygen generator and patient data; according to the patient data, performing oxygen uptake prediction based on a pre-trained linear regression model to obtain predicted oxygen uptake; according to the historical operation data of the oxygenerator and the real-time operation data of the oxygenerator, calculating the oxygen production amount to obtain the oxygen production amount of the oxygenerator; comparing the predicted oxygen uptake amount with the oxygen production amount of the oxygen generator, and performing efficiency calculation to obtain operation efficiency evaluation parameters; and comparing the operation efficiency evaluation parameter with a preset threshold value, and generating an adjustment instruction, so that the oxygen generator performs adjustment according to the adjustment instruction. According to the method, the efficiency of the oxygen generator can be improved under the condition that the requirements of the patient are met.

Description

Technical Field

[0001] The present invention relates to the technical field of oxygen concentrator control, and in particular to a remote control method and system for an oxygen concentrator based on data analysis. Background Art

[0002] An oxygen concentrator is a device that separates a mixture of nitrogen and oxygen in the air through an oxygen production process to obtain oxygen. It is small, lightweight, portable, and easy to use. It is suitable for on-site oxygen production in various occasions and can meet the needs of various diseases for medical gas sources. Especially in field scenes, oxygen concentrators, as important medical equipment, can provide timely treatment and rescue for emergency patients.

[0003] In the process of optimizing the operating efficiency of the oxygen concentrator in the prior art, a traditional control strategy is usually adopted: medical staff manually adjust the operating time and output of the oxygen concentrator according to the patient's medical needs and the operating status of the oxygen concentrator. This method cannot accurately predict the patient's oxygen demand in the future, resulting in a mismatch between the output of the oxygen concentrator and the patient's needs, generating excess oxygen power consumption, and reducing the efficiency of the oxygen concentrator. At the same time, increasing the production capacity of the oxygen concentrator when the patient's demand increases requires manual intervention, resulting in a slow response speed and delaying the patient's medical treatment.

[0004] In summary, the existing oxygen concentrator control technology cannot properly meet the needs of patients, and will also generate excess oxygen power consumption, reducing the efficiency of the oxygen concentrator. Summary of the invention

[0005] The present invention provides a remote control method and system for an oxygen concentrator based on data analysis, which can improve the efficiency of the oxygen concentrator while meeting the needs of patients.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides an oxygen concentrator remote control method based on data analysis, comprising: Obtain oxygen concentrator historical operation data, oxygen concentrator real-time operation data and patient data; According to the patient data, predicting the oxygen uptake based on a pre-trained linear regression model to obtain a predicted oxygen uptake; Calculate the oxygen production according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator; Comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, and performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter; The operating efficiency evaluation parameter is compared with a preset threshold value to generate an adjustment instruction, and the adjustment instruction is sent to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0007] Preferably, the obtaining of historical operating data of the oxygen concentrator, real-time operating data of the oxygen concentrator and patient data includes: The oxygen generator historical operation data includes average oxygen production and average power consumption; The real-time operation data of the oxygen generator includes the oxygen generator oxygen production time, real-time power consumption and oxygen production per unit time; The patient data includes oxygen partial pressure data and respiratory rate data.

[0008] Preferably, the predicting of oxygen uptake based on the patient data and a pre-trained linear regression model to obtain the predicted oxygen uptake includes: Acquire historical patient data; wherein the historical patient data includes historical oxygen partial pressure data and historical respiratory rate data; Based on the historical oxygen partial pressure data and the historical respiratory rate data as input data, a linear regression model is constructed based on the least squares method to optimize the regression coefficient, and the linear regression model is trained according to minimizing the sum of squares of errors between the predicted oxygen uptake and the actual oxygen uptake; When the number of training times is greater than or equal to the preset maximum number of training times, the training is determined to be completed, and a trained linear regression model is obtained.

[0009] Preferably, the oxygen production amount is calculated based on the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production amount of the oxygen concentrator, including: The oxygen production of the first oxygen concentrator is calculated by the following formula: In the formula, is the oxygen production of the first oxygen generator at time t; is the average oxygen production; The real-time power consumption of the oxygen concentrator at time t; is the average power consumption.

[0010] Preferably, the comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain the operation efficiency evaluation parameter, further comprises: comparing the oxygen production of the oxygen concentrator with the predicted oxygen uptake; When the oxygen production of the oxygen concentrator is greater than or equal to the predicted oxygen uptake, based on the real-time operation data of the oxygen concentrator, efficiency calculation is performed to obtain an operation efficiency evaluation parameter; When the oxygen production of the oxygen concentrator is less than the predicted oxygen consumption, the output adjustment value of the first oxygen concentrator is calculated, and the output adjustment value of the first oxygen concentrator is compared with the predicted oxygen consumption; when the output adjustment value of the first oxygen concentrator is greater than the predicted oxygen consumption, efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain operation efficiency evaluation parameters.

[0011] Preferably, the calculating of the output adjustment value of the first oxygen generator comprises: The output adjustment value of the first oxygen generator is calculated by the following formula: In the formula, is the output adjustment value of the first oxygen generator at time t; is the forecast adjustment factor; Predict oxygen uptake at time t; is the oxygen production of the oxygen concentrator at time t; is the oxygen generator production coefficient.

[0012] Preferably, the predicted oxygen uptake is compared with the oxygen production of the oxygen concentrator, and based on the comparison result and the real-time operation data of the oxygen concentrator, an efficiency calculation is performed to obtain an operation efficiency evaluation parameter, including: Subtract the oxygen production of the oxygen concentrator from the predicted oxygen uptake to obtain an oxygen production error; When the oxygen production error is not zero, performing correction calculation based on the PID algorithm according to the oxygen production error to obtain the oxygen production adjustment amount; Adding the oxygen production adjustment amount to the oxygen production amount of the oxygen concentrator to obtain a second oxygen concentrator production adjustment value; The output adjustment value of the second oxygen concentrator is compared with the predicted oxygen uptake. When the output adjustment value of the second oxygen concentrator is equal to the predicted oxygen uptake, an efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter.

[0013] Preferably, the efficiency calculation is performed based on the real-time operating data of the oxygen concentrator to obtain the operating efficiency evaluation parameters, including: The operating performance evaluation parameters are calculated using the following formula: In the formula, is the performance evaluation parameter at time t; The real-time power consumption of the oxygen concentrator at time t; It is the oxygen production per unit time of the oxygen concentrator.

[0014] Preferably, comparing the operating efficiency evaluation parameter with a preset threshold, generating an adjustment instruction, and sending the adjustment instruction to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction, comprises: When the operating performance evaluation parameter is less than the preset threshold, generating a power consumption reduction instruction; When the operating efficiency evaluation parameter is greater than the preset threshold, an instruction to reduce the speed of the oxygen generator is generated.

[0015] In a second aspect, the present invention provides an oxygen concentrator remote control system based on data analysis, comprising: A data acquisition module is used to acquire historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data; A regression prediction module, used to predict the oxygen uptake according to the patient data based on a pre-trained linear regression model to obtain a predicted oxygen uptake; An oxygen production calculation module is used to calculate the oxygen production according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator; an efficiency calculation module, for comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, and performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter; The instruction sending module is used to compare the operating efficiency evaluation parameter with a preset threshold value, generate an adjustment instruction, and send the adjustment instruction to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0016] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the remote control method of an oxygen concentrator based on data analysis as described above is implemented.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned oxygen concentrator remote control methods based on data analysis.

[0018] Compared with the prior art, the present invention has the following beneficial effects: the embodiment of the present invention provides a remote control method and system for an oxygen concentrator based on data analysis. The method includes: obtaining historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data; predicting oxygen uptake based on the patient data and a pre-trained linear regression model to obtain predicted oxygen uptake; calculating oxygen production based on the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain oxygen production of the oxygen concentrator; comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, and performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter; comparing the operation efficiency evaluation parameter with a preset threshold, generating an adjustment instruction, and sending the adjustment instruction to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0019] In the present invention, the method can integrate patient data, historical operating data and real-time operating data of the oxygen concentrator, use a linear regression model to predict the patient's oxygen uptake, and combine the oxygen production calculation and comparison of the oxygen concentrator to ensure that the operating efficiency evaluation is performed on the basis of matching the oxygen production with the oxygen uptake demand. When the operating efficiency evaluation parameter is lower than the preset threshold, the method can generate a corresponding adjustment instruction to adjust to reduce power consumption or optimize the operating mode of the oxygen concentrator, thereby avoiding resource waste and improving equipment energy efficiency. In summary, the method can improve the efficiency of the oxygen concentrator while meeting the needs of the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a method for remotely controlling an oxygen concentrator based on data analysis provided by a first embodiment of the present invention; Figure 2 It is a structural schematic diagram of an oxygen concentrator remote control system based on data analysis provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 The first embodiment of the present invention provides a remote control method for an oxygen concentrator based on data analysis, comprising the following steps: S11, obtaining historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data.

[0023] S12, predicting oxygen uptake based on the patient data and a pre-trained linear regression model to obtain a predicted oxygen uptake.

[0024] S13, calculating the oxygen production according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator.

[0025] S14, comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator, and obtaining an operation efficiency evaluation parameter.

[0026] S15, comparing the operating efficiency evaluation parameter with a preset threshold, generating an adjustment instruction, and sending the adjustment instruction to the oxygen concentrator, so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0027] It is worth mentioning that an oxygen concentrator is a device that separates a mixture of nitrogen and oxygen in the air through an oxygen production process to obtain oxygen. It is small, light, portable, and easy to use. It is suitable for on-site oxygen production in various occasions and can meet the needs of various diseases for medical gas sources. Especially in field scenes, oxygen concentrators, as important medical equipment, can provide timely treatment and rescue for emergency patients.

[0028] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0029] In step S11, historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data are obtained.

[0030] Preferably, the historical operation data of the oxygen concentrator includes average oxygen production and average power consumption; the real-time operation data of the oxygen concentrator includes oxygen production time, real-time power consumption and oxygen production per unit time; and the patient data includes oxygen partial pressure data and respiratory rate data.

[0031] Specifically, obtaining the historical operating data, real-time operating data, and patient data of the oxygen concentrator requires the collaborative work of multiple data acquisition devices and technical means. In one feasible way, the historical operating data, average oxygen production, and average power consumption of the oxygen concentrator can be recorded and stored through the device's own internal storage system. When the oxygen concentrator is running, its sensors and control unit will regularly collect relevant parameters and store these data in the form of time series in the device's internal memory or external database. Users or maintenance personnel can regularly export this data through a dedicated data interface (such as a USB interface or a wireless transmission module), or upload it to a cloud platform for centralized management through the networking function.

[0032] Specifically, real-time operation data needs to be monitored and recorded in real time through the built-in sensors and data acquisition modules of the equipment. In one feasible way, the oxygen generator oxygen production time can be recorded by the switch of the equipment operation status, the real-time power consumption can be obtained through the built-in power metering module, and the oxygen production per unit time can be measured in real time by the flow sensor or quality sensor installed at the oxygen outlet. All these real-time data can be summarized and processed by the microcontroller (MCU) inside the device, and transmitted to the data management platform or directly called by the control system through the wireless communication module (such as Wi-Fi or Bluetooth).

[0033] Specifically, the acquisition of patient data requires the help of external medical equipment and monitoring equipment. Optionally, oxygen partial pressure data can be obtained through a non-invasive oximeter, which measures the oxygen saturation in the patient's blood through infrared and optical sensors, and then calculates the oxygen partial pressure value. Respiratory rate data can be collected by a device that monitors the patient's respiratory status. Optionally, a chest strap sensor placed on the patient's chest monitors the expansion and contraction frequency of breathing, calculates and uploads data in real time. The data from the above medical equipment can be connected to the oxygen concentrator or cloud data platform through wireless transmission technology (such as Bluetooth or ZigBee), thereby realizing real-time data integration.

[0034] In step S12, oxygen uptake is predicted based on the patient data and a pre-trained linear regression model to obtain a predicted oxygen uptake.

[0035] Preferably, the predicting of oxygen uptake based on the patient data and a pre-trained linear regression model to obtain the predicted oxygen uptake includes: Acquire historical patient data; wherein the historical patient data includes historical oxygen partial pressure data and historical respiratory rate data; Based on the historical oxygen partial pressure data and the historical respiratory rate data as input data, a linear regression model is constructed based on the least squares method to optimize the regression coefficient, and the linear regression model is trained according to minimizing the sum of squares of errors between the predicted oxygen uptake and the actual oxygen uptake; When the number of training times is greater than or equal to the preset maximum number of training times, the training is determined to be completed, and a trained linear regression model is obtained.

[0036] Specifically, in the model construction stage, historical oxygen partial pressure data and respiratory rate data are used as input variables, and a linear regression algorithm is used to predict the patient's oxygen demand. Specifically, the linear regression model assumes that there is a linear relationship between oxygen uptake and oxygen partial pressure and respiratory rate, and the goal of the model is to find the optimal regression coefficient to minimize the error between the predicted oxygen uptake and the actual oxygen uptake. The least squares method is used as an optimization method. By repeatedly calculating and adjusting the regression coefficient, the model continuously reduces the sum of squares of the error, thereby gradually optimizing the fitting effect.

[0037] It is worth mentioning that the linear regression model is a prediction method in statistics and machine learning. Its core idea is to predict the value of the target variable (dependent variable) by establishing a linear equation to describe the relationship between variables. Specifically, the linear regression model assumes that there is a linear relationship between the target variable and a set of input variables (independent variables), that is, the target variable can be expressed as a linear combination of the input variables plus an error term. In the process of model construction, the core task of linear regression is to determine the optimal regression coefficient and intercept to minimize the error between the model predicted value and the actual value. The commonly used method is the least squares method, which calculates the sum of squares of the error between the predicted value and the actual value, and continuously optimizes the regression coefficient and intercept to ensure that the sum of squares of the error is minimized. The mathematical basis of this optimization process is calculus. By solving the partial derivative of the sum of squares of the error, the minimum point of the error function is found.

[0038] Specifically, in the present invention, a linear regression model is used to predict oxygen uptake based on the patient's historical oxygen partial pressure data and respiratory rate data. The model assumes that there is a linear relationship between oxygen uptake (target variable) and oxygen partial pressure and respiratory rate (input variables), that is, by constructing a linear equation to express how the patient's oxygen uptake changes with changes in oxygen partial pressure and respiratory rate. The form of the linear regression model can be expressed as ,in, is the predicted oxygen uptake, is the oxygen partial pressure data, The present invention optimizes the regression coefficient and intercept by the least square method to minimize the prediction error of the model for historical patient data. This optimization process is achieved by repeatedly adjusting and The value of is used to minimize the sum of squares of the errors between the oxygen uptake predicted by the model and the actual oxygen uptake. For example, when the model predicted value is significantly lower than the actual value, the regression coefficient will be adjusted to a larger value to increase the prediction result; conversely, when the predicted value is significantly higher than the actual value, the coefficient will be reduced, thereby improving the accuracy of the model.

[0039] After training, the linear regression model can quickly calculate the patient's current oxygen demand through real-time oxygen partial pressure and respiratory rate data. For example, after the patient's oxygen partial pressure and respiratory rate are input into the model, the regression equation will immediately output the corresponding predicted value of oxygen consumption. This predicted value provides a reliable reference for the oxygen concentrator, enabling the device to dynamically adjust the oxygen production to ensure that it can meet the patient's needs while avoiding energy waste.

[0040] In step S13, the oxygen production is calculated according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator.

[0041] Preferably, the oxygen production amount is calculated based on the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production amount of the oxygen concentrator, including: The oxygen production of the oxygen concentrator is calculated by the following formula: In the formula, is the oxygen production of the oxygen concentrator at time t; is the average oxygen production; The real-time power consumption of the oxygen concentrator at time t; is the average power consumption.

[0042] Specifically, The average oxygen production is a constant obtained based on the historical operating data of the equipment. This value is usually obtained by averaging the stable operating data of the equipment over a period of time, reflecting the typical oxygen production capacity of the equipment under normal operating conditions. It provides a benchmark value to measure whether the current oxygen production of the equipment is consistent with its historical operating performance. The real-time power consumption of the oxygen concentrator at time t indicates the power consumption of the oxygen concentrator at time t. This data is collected through the power monitoring module inside the device and can reflect the current load status and operating efficiency of the device in real time. is the average power consumption, another constant calculated based on historical data, used to measure the long-term power consumption level of the oxygen generator. By calculating the ratio of real-time power consumption to average power consumption, we can intuitively describe the degree of deviation of the current energy consumption level of the equipment from the historical average level. The formula adjusts the average oxygen production output by the ratio of real-time power consumption to average power consumption to dynamically calculate the current actual oxygen production. When the real-time power consumption is higher than the average power consumption, the formula amplifies the The value of indicates that the equipment is in a more efficient operating state at this moment, and the oxygen production increases accordingly. On the contrary, when the real-time power consumption is lower than the average power consumption, the formula is reduced. The value of indicates that the equipment is operating at a low efficiency at this time, and the oxygen production is correspondingly reduced. This design of the formula reflects the equipment operating status in real time, ensuring that the calculated oxygen production of the oxygen concentrator at time t can accurately reflect the actual working performance of the oxygen concentrator.

[0043] In step S14, the predicted oxygen uptake is compared with the oxygen production of the oxygen concentrator, and based on the comparison result and the real-time operation data of the oxygen concentrator, an efficiency calculation is performed to obtain an operation efficiency evaluation parameter.

[0044] Preferably, the operating performance evaluation parameter is calculated by the following formula: In the formula, is the performance evaluation parameter at time t; The real-time power consumption of the oxygen concentrator at time t; It is the oxygen production per unit time of the oxygen concentrator.

[0045] Specifically, in the formula, It is the operating efficiency evaluation parameter at time t, reflecting the energy efficiency performance of the oxygen concentrator at time t. It describes the relationship between the power consumed by the equipment per unit time and the amount of oxygen it actually produces, and is a key indicator used to measure the working efficiency of the oxygen concentrator. A value of 1 indicates that the equipment is operating efficiently and can produce more oxygen with less electrical energy; while a higher A low value indicates that the equipment is inefficient and may be wasting energy or in poor working condition. The real-time power consumption of the oxygen concentrator at time t reflects the power consumption of the equipment under the current operating conditions. This parameter is obtained by real-time measurement of the power sensor inside the oxygen concentrator. It changes dynamically and can reflect changes in the operating status of the equipment. is the oxygen production per unit time of the oxygen concentrator, which is the actual oxygen output rate of the oxygen concentrator. This value can be measured in real time by a flow sensor installed at the outlet of the oxygen concentrator, indicating the volume or mass of oxygen that the oxygen concentrator can provide per unit time at time t. By dividing the power consumption by the oxygen production per unit time, the formula can calculate the energy efficiency parameters of the oxygen concentrator at a specific moment. When the power consumption is high and the oxygen production per unit time is low, the value of the efficiency evaluation parameter will be large, indicating that the equipment may be in an inefficient operating state and there is a large room for optimization. Conversely, when the power consumption is low and the oxygen production per unit time is high, the value of the efficiency evaluation parameter will be small, indicating that the equipment is working efficiently and the electrical energy is fully utilized.

[0046] In one implementation, the predicted oxygen uptake is compared with the oxygen production of the oxygen concentrator, and based on the comparison result and the real-time operation data of the oxygen concentrator, an efficiency calculation is performed to obtain an operation efficiency evaluation parameter, including: comparing the oxygen production of the oxygen concentrator with the predicted oxygen uptake; When the oxygen production of the oxygen concentrator is greater than or equal to the predicted oxygen uptake, based on the real-time operation data of the oxygen concentrator, efficiency calculation is performed to obtain an operation efficiency evaluation parameter; When the oxygen production of the oxygen concentrator is less than the predicted oxygen consumption, the output adjustment value of the first oxygen concentrator is calculated, and the output adjustment value of the first oxygen concentrator is compared with the predicted oxygen consumption; when the output adjustment value of the first oxygen concentrator is greater than the predicted oxygen consumption, efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain operation efficiency evaluation parameters.

[0047] Preferably, the output adjustment value of the first oxygen generator is calculated by the following formula: In the formula, is the output adjustment value of the first oxygen generator at time t; is the forecast adjustment factor; Predict oxygen uptake at time t; is the oxygen production of the oxygen concentrator at time t; is the oxygen generator production coefficient.

[0048] Specifically, the system dynamically adjusts the operating status of the oxygen concentrator and performs performance evaluation by comparing the predicted oxygen uptake with the actual oxygen production of the oxygen concentrator to ensure a balance between operating efficiency and patient needs. When the actual oxygen production is greater than the predicted oxygen uptake, it means that the oxygen currently provided by the oxygen concentrator can meet or even exceed the patient's needs. At this time, the performance evaluation parameter can be directly calculated based on the real-time operation data to evaluate the energy efficiency level of the oxygen concentrator in this state. This performance evaluation parameter is calculated by a formula to reflect the ratio of energy consumption to oxygen output per unit time, helping to determine whether the equipment is operating efficiently. However, when the actual oxygen production is less than the predicted oxygen uptake, the system will correct this mismatch through additional adjustment steps. First, the output adjustment value of the first oxygen concentrator is calculated. The calculation of the adjustment value combines the real-time operation data and the predicted oxygen uptake to ensure that the direction and amplitude of the adjustment can accurately reflect the actual needs of the patient. Next, the calculated output adjustment value of the first oxygen concentrator is compared with the predicted oxygen uptake. If the adjustment value is greater than the predicted oxygen uptake, it indicates that the adjusted device can meet or even exceed the demand level. At this time, the system recalculates the performance evaluation parameters based on the real-time operating data of the oxygen concentrator to evaluate the operating efficiency of the adjusted device. If the adjustment value is still less than the predicted oxygen uptake, it means that further adjustments are needed to improve the oxygen production capacity of the device. This design ensures that no matter how the operating status of the oxygen concentrator changes, the system can respond to patient needs in real time and optimize the energy consumption and oxygen production ratio of the device through hierarchical comparison and adjustment.

[0049] Specifically, is the output adjustment value of the first oxygen concentrator at time t, indicating the oxygen output of the oxygen concentrator after adjustment. This is the core goal of the formula, reflecting the matching degree between the current operating status of the equipment and the patient's needs. It is the prediction adjustment coefficient, which is used to adjust the sensitivity of the formula to the prediction error. The size of this coefficient is preset by the system and optimized according to the characteristics of the oxygen concentrator and the specific application scenario. A larger i value will amplify the adjustment range, making the system more sensitive to the prediction error, which is suitable for scenarios that require a quick response; while a smaller i value can smooth the adjustment process and avoid frequent adjustments caused by error fluctuations. The predicted oxygen uptake at time t is the patient's predicted oxygen uptake at time t, calculated based on a linear regression model or other prediction model, reflecting the patient's actual oxygen demand at the current time point. This parameter is one of the inputs to the formula and provides the target value for the system to adjust oxygen production. The oxygen generator production factor is used to normalize and limit the adjustment value. It depends on the physical performance and operating limitations of the equipment, such as maximum oxygen production capacity and adjustment response speed.

[0050] In another implementation, the predicted oxygen uptake is compared with the oxygen production of the oxygen concentrator, and based on the comparison result and the real-time operation data of the oxygen concentrator, an efficiency calculation is performed to obtain an operation efficiency evaluation parameter, including: Subtract the oxygen production of the oxygen concentrator from the predicted oxygen uptake to obtain an oxygen production error; When the oxygen production error is not zero, performing correction calculation based on the PID algorithm according to the oxygen production error to obtain the oxygen production adjustment amount; Adding the oxygen production adjustment amount to the oxygen production amount of the oxygen concentrator to obtain a second oxygen concentrator production adjustment value; The output adjustment value of the second oxygen concentrator is compared with the predicted oxygen uptake. When the output adjustment value of the second oxygen concentrator is equal to the predicted oxygen uptake, an efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter.

[0051] It is worth noting that the PID algorithm is used in this embodiment to achieve regulation. Among them, the PID algorithm (Proportional-Integral-Derivative Control) is a control theory algorithm widely used in industrial automation and dynamic system control. It controls the deviation (i.e., error) of the output from the target value by continuously adjusting the input of the system to achieve stable and efficient operation of the system. The core of the PID algorithm includes three parts: proportion (P), integration (I) and differentiation (D), which adjust the error from different angles. In the present invention, the PID algorithm is used to dynamically adjust the oxygen production of the oxygen concentrator to ensure that it is highly matched with the patient's real-time oxygen demand. When there is a deviation between the predicted oxygen consumption and the actual oxygen production of the oxygen concentrator, the PID algorithm can generate a correction signal in real time according to the deviation value (P), the accumulation of the deviation (I) and the rate of change of the deviation (D). These signals are used to adjust the operating parameters of the oxygen concentrator, such as the speed or operating mode, to accurately control the oxygen output.

[0052] Specifically, proportional control (P) detects the instantaneous difference between the current oxygen demand and oxygen production, and quickly adjusts the operating status of the oxygen concentrator to reduce deviations. This rapid response helps the equipment meet the changing needs of patients in a timely manner. When the oxygen concentrator fails to completely eliminate the deviation in oxygen demand for a long time, integral control gradually increases the adjustment amount until the actual oxygen production fully meets the demand, avoiding long-term deviations that lead to unmet patient needs. By predicting the changing trend of oxygen demand, the operation of the oxygen concentrator can be adjusted in advance. For example, when the oxygen demand increases sharply, differential control can increase oxygen production in advance to prevent the system from reacting lagging.

[0053] Specifically, first, the oxygen production of the oxygen concentrator is compared with the predicted oxygen uptake, and the difference between the two is calculated, which is called the oxygen production error. This error directly reflects whether the current operating state of the oxygen concentrator can meet the patient's oxygen uptake needs. If the error is a positive value, it indicates that the oxygen production is excessive; if it is a negative value, it indicates that the current oxygen production is insufficient. According to this error, the system starts the PID algorithm for correction calculation, comprehensively considering the error value (P), the accumulation of the error (I) and the rate of error change (D), and generates a correction signal, called the oxygen production adjustment. Next, the calculated oxygen production adjustment is added to the current oxygen production of the oxygen concentrator to generate the second oxygen concentrator production adjustment value. This adjustment value is dynamically generated based on the current operating state and patient demand, which can make the oxygen production closer to the actual demand. In further comparison, if the second production adjustment value is equal to the predicted oxygen uptake, it indicates that the system has reached a balanced state. At this time, the efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to re-evaluate the working efficiency of the equipment.

[0054] In step S15, the operating efficiency evaluation parameter is compared with a preset threshold value, an adjustment instruction is generated, and the adjustment instruction is sent to the oxygen concentrator, so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0055] Preferably, comparing the operating efficiency evaluation parameter with a preset threshold, generating an adjustment instruction, and sending the adjustment instruction to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction, comprises: When the operating performance evaluation parameter is less than the preset threshold, generating a power consumption reduction instruction; When the operating efficiency evaluation parameter is greater than the preset threshold, an instruction to reduce the speed of the oxygen generator is generated.

[0056] Specifically, by comparing the operating efficiency evaluation parameters with the preset thresholds, the system can determine whether the current operating status of the oxygen concentrator is efficient, and generate corresponding adjustment instructions based on the evaluation results to ensure that the equipment maintains efficient use of energy while meeting demand. The operating efficiency evaluation parameters are quantitative indicators of the operating efficiency of the oxygen concentrator, reflecting the level of oxygen production per unit of power consumption, while the preset thresholds are efficiency benchmarks set based on equipment performance and application scenarios.

[0057] Specifically, when the operating efficiency evaluation parameter is less than the preset threshold, it indicates that the operating efficiency of the oxygen concentrator is low, and there may be energy waste or excessive operating load. To deal with this situation, the system will generate an instruction to reduce power consumption. This strategy aims to reduce the overall working intensity of the equipment by reducing power consumption, thereby improving the efficiency parameters and making the operation closer to the preset high-efficiency range. On the contrary, when the operating efficiency evaluation parameter is greater than the preset threshold, although the equipment has a high operating efficiency, there may be an unnecessary high speed, which will also lead to unnecessary consumption of resources or accelerated wear of the equipment. In this case, the system will generate an instruction to reduce the speed of the oxygen concentrator to reduce unnecessary oxygen output while reducing the mechanical burden and power consumption of the equipment.

[0058] In summary, the present invention provides an oxygen concentrator remote control method based on data analysis, comprising: acquiring historical operating data of the oxygen concentrator, real-time operating data of the oxygen concentrator and patient data; predicting oxygen uptake based on a pre-trained linear regression model according to the patient data to obtain a predicted oxygen uptake; calculating oxygen production according to the historical operating data of the oxygen concentrator and the real-time operating data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator; comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, performing efficiency calculation based on the real-time operating data of the oxygen concentrator to obtain an operating efficiency evaluation parameter; comparing the operating efficiency evaluation parameter with a preset threshold, generating an adjustment instruction, and sending the adjustment instruction to the oxygen concentrator, so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0059] In the present invention, the method can integrate patient data, historical operating data and real-time operating data of the oxygen concentrator, use a linear regression model to predict the patient's oxygen uptake, and combine the oxygen production calculation and comparison of the oxygen concentrator to ensure that the operating efficiency evaluation is performed on the basis of matching the oxygen production with the oxygen uptake demand. When the operating efficiency evaluation parameter is lower than the preset threshold, the method can generate a corresponding adjustment instruction to adjust to reduce power consumption or optimize the operating mode of the oxygen concentrator, thereby avoiding resource waste and improving equipment energy efficiency. In summary, the method can improve the efficiency of the oxygen concentrator while meeting the needs of the patient.

[0060] Reference Figure 2The second embodiment of the present invention provides an oxygen concentrator remote control system based on data analysis, comprising: A data acquisition module is used to acquire historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data; A regression prediction module, used to predict the oxygen uptake according to the patient data based on a pre-trained linear regression model to obtain a predicted oxygen uptake; An oxygen production calculation module is used to calculate the oxygen production according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator; an efficiency calculation module, for comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, and performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter; The instruction sending module is used to compare the operating efficiency evaluation parameter with a preset threshold value, generate an adjustment instruction, and send the adjustment instruction to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction.

[0061] It should be noted that an oxygen concentrator remote control system based on data analysis provided in an embodiment of the present invention is used to execute all the process steps of an oxygen concentrator remote control method based on data analysis in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0062] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a remote control method program for an oxygen concentrator based on data analysis. When the processor executes the computer program, the steps in the above-mentioned embodiments of the remote control method for an oxygen concentrator based on data analysis are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the regression prediction module.

[0063] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0064] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0065] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0066] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0067] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0068] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0069] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote control method for an oxygen concentrator based on data analysis, characterized in that: include: Obtain oxygen concentrator historical operation data, oxygen concentrator real-time operation data and patient data; According to the patient data, predicting the oxygen uptake based on a pre-trained linear regression model to obtain a predicted oxygen uptake; Calculate the oxygen production according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator; Comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, and performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter; The operating efficiency evaluation parameter is compared with a preset threshold value to generate an adjustment instruction, and the adjustment instruction is sent to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction.

2. The method for remotely controlling an oxygen concentrator based on data analysis according to claim 1, characterized in that: The obtaining of historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data includes: The oxygen generator historical operation data includes average oxygen production and average power consumption; The real-time operation data of the oxygen generator includes the oxygen generator oxygen production time, real-time power consumption and oxygen production per unit time; The patient data includes oxygen partial pressure data and respiratory rate data.

3. The oxygen concentrator remote control method based on data analysis according to claim 1, characterized in that: The step of predicting oxygen uptake based on the patient data and a pre-trained linear regression model to obtain the predicted oxygen uptake includes: Acquire historical patient data; wherein the historical patient data includes historical oxygen partial pressure data and historical respiratory rate data; Based on the historical oxygen partial pressure data and the historical respiratory rate data as input data, a linear regression model is constructed based on the least squares method to optimize the regression coefficient, and the linear regression model is trained according to minimizing the sum of squares of errors between the predicted oxygen uptake and the actual oxygen uptake; When the number of training times is greater than or equal to the preset maximum number of training times, the training is determined to be completed, and a trained linear regression model is obtained.

4. The method for remotely controlling an oxygen concentrator based on data analysis according to claim 1, characterized in that: The oxygen production amount is calculated according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production amount of the oxygen concentrator, including: The oxygen production of the oxygen concentrator is calculated by the following formula: In the formula, is the oxygen production of the oxygen concentrator at time t; is the average oxygen production; The real-time power consumption of the oxygen concentrator at time t; is the average power consumption.

5. The method for remote control of an oxygen concentrator based on data analysis according to claim 1, characterized in that: The predicted oxygen uptake is compared with the oxygen production of the oxygen concentrator, and based on the comparison result and the real-time operation data of the oxygen concentrator, an efficiency calculation is performed to obtain an operation efficiency evaluation parameter, including: comparing the oxygen production of the oxygen concentrator with the predicted oxygen uptake; When the oxygen production of the oxygen concentrator is greater than or equal to the predicted oxygen uptake, based on the real-time operation data of the oxygen concentrator, efficiency calculation is performed to obtain an operation efficiency evaluation parameter; When the oxygen production of the oxygen concentrator is less than the predicted oxygen consumption, the output adjustment value of the first oxygen concentrator is calculated, and the output adjustment value of the first oxygen concentrator is compared with the predicted oxygen consumption; when the output adjustment value of the first oxygen concentrator is greater than the predicted oxygen consumption, efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain operation efficiency evaluation parameters.

6. The method for remotely controlling an oxygen concentrator based on data analysis according to claim 5, characterized in that: The calculating of the output adjustment value of the first oxygen generator comprises: The output adjustment value of the first oxygen generator is calculated by the following formula: In the formula, is the output adjustment value of the first oxygen generator at time t; is the forecast adjustment factor; Predict oxygen uptake at time t; is the oxygen production of the oxygen concentrator at time t; is the oxygen generator production coefficient.

7. The method for remotely controlling an oxygen concentrator based on data analysis according to claim 1, characterized in that: The predicted oxygen uptake is compared with the oxygen production of the oxygen concentrator, and based on the comparison result and the real-time operation data of the oxygen concentrator, an efficiency calculation is performed to obtain an operation efficiency evaluation parameter, including: Subtract the oxygen production of the oxygen concentrator from the predicted oxygen uptake to obtain an oxygen production error; When the oxygen production error is not zero, performing correction calculation based on the PID algorithm according to the oxygen production error to obtain the oxygen production adjustment amount; Adding the oxygen production adjustment amount to the oxygen production amount of the oxygen concentrator to obtain a second oxygen concentrator production adjustment value; The output adjustment value of the second oxygen concentrator is compared with the predicted oxygen uptake. When the output adjustment value of the second oxygen concentrator is equal to the predicted oxygen uptake, an efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter.

8. The oxygen concentrator remote control method based on data analysis according to any one of claims 5 or 7, characterized in that: The efficiency calculation is performed based on the real-time operation data of the oxygen concentrator to obtain the operation efficiency evaluation parameters, including: The operating performance evaluation parameters are calculated using the following formula: In the formula, is the performance evaluation parameter at time t; The real-time power consumption of the oxygen concentrator at time t; It is the oxygen production per unit time of the oxygen concentrator.

9. The method for remotely controlling an oxygen concentrator based on data analysis according to claim 1, characterized in that: The step of comparing the operating performance evaluation parameter with a preset threshold value and generating an adjustment instruction includes: When the operating performance evaluation parameter is less than the preset threshold, generating a power consumption reduction instruction; When the operating efficiency evaluation parameter is greater than the preset threshold, an instruction to reduce the speed of the oxygen generator is generated.

10. A remote control system for an oxygen concentrator based on data analysis, characterized in that: include: A data acquisition module is used to acquire historical operation data of the oxygen concentrator, real-time operation data of the oxygen concentrator and patient data; A regression prediction module, used to predict the oxygen uptake according to the patient data based on a pre-trained linear regression model to obtain a predicted oxygen uptake; An oxygen production calculation module is used to calculate the oxygen production according to the historical operation data of the oxygen concentrator and the real-time operation data of the oxygen concentrator to obtain the oxygen production of the oxygen concentrator; an efficiency calculation module, for comparing the predicted oxygen uptake with the oxygen production of the oxygen concentrator, and performing efficiency calculation based on the comparison result and the real-time operation data of the oxygen concentrator to obtain an operation efficiency evaluation parameter; The instruction sending module is used to compare the operating efficiency evaluation parameter with a preset threshold value, generate an adjustment instruction, and send the adjustment instruction to the oxygen concentrator so that the oxygen concentrator is adjusted according to the adjustment instruction.

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