Dual-channel CO2 respiration monitoring device and system based on intelligent sensor

Abstract

The invention relates to a dual-channel CO2 respiration monitoring device and system based on an intelligent sensor, and the device comprises a CO2 airway adapter which is used for carrying out gas exchange on CO2 discharged by membrane lungs; the hose is used for being matched with the membrane lung exhaust connector to convey CO2 exhausted by the membrane lung; the membrane lung exhaust connector is used for being matched with a hose to convey CO2 exhausted by the membrane lung; based on the dual-channel CO2 respiration monitoring system, dual-channel CO2 respiration monitoring is carried out through an intelligent sensor arranged in the dual-channel CO2 respiration monitoring system; the membrane lung is connected with the membrane lung exhaust connector and the dual-channel CO2 respiration monitoring system based on the intelligent sensor and is used for simulating the lung to perform gas exchange; and the breathing machine is connected with the dual-channel CO2 breathing monitoring system based on the intelligent sensor and is used for supporting natural breathing of the user. According to the invention, the efficiency and accuracy of monitoring the CO2 emission concentration of the membrane lung and the CO2 emission concentration of the breathing machine in real time according to the change condition of CO2 breathing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a dual-channel CO2 respiratory monitoring device and system based on an intelligent sensor. Background Art

[0002] ECMO refers to an artificial heart-lung machine with extracorporeal membrane oxygenation. The existing respiratory function detection devices are only grafted onto the relevant equipment of the patient's ventilator and cannot be used for ECMO respiratory parameter detection. Most monitoring methods are mainly based on the CO2 concentration curve, which is not in-depth and comprehensive enough. At the same time, the change of CO2 is more sensitive than the change of lactate level, and it cannot be continuously detected compared with lactate.

[0003] Chinese patent publication number CN105536086A discloses a multifunctional ECMO circulation pipeline and a method for extracorporeal membrane oxygenation using the same, including an arterial circulation pipeline, a venous circulation pipeline, a blood purification blood inlet catheter and a blood purification blood transfusion catheter; wherein one end of the blood purification blood inlet catheter is connected to the arterial circulation pipeline, and the other end is used to connect the blood inlet passage of the hemodialysis device; one end of the blood purification blood transfusion catheter is connected to the venous circulation pipeline, and the other end is used to connect the bleeding passage of the hemodialysis device; the arterial circulation pipeline and the venous circulation pipeline are respectively provided with a blood oxygen sensor, and the blood oxygen sensor is connected to a blood gas detection device; the blood purification blood inlet catheter and the blood purification blood transfusion catheter are respectively provided with a one-way valve near one end of the circulation pipeline. This scheme cannot monitor the CO2 emission concentration of the membrane lung and the CO2 emission concentration of the ventilator in real time because it cannot monitor the changes in CO2 breathing according to the changes in CO2 breathing. Summary of the invention

[0004] To this end, the present invention provides a dual-channel CO2 breathing monitoring device and system based on an intelligent sensor to overcome the problem that the prior art cannot monitor the CO2 emission concentration of the membrane lung and the CO2 emission concentration of the ventilator in real time due to the change of CO2 breathing.

[0005] To achieve the above object, the present invention provides a dual-channel CO2 respiratory monitoring device based on an intelligent sensor, comprising:

[0006] A CO2 airway adapter, which is connected to a hose and a membrane lung channel CO2 breathing monitoring unit of a dual-channel CO2 breathing monitoring module in a dual-channel CO2 breathing monitoring system based on an intelligent sensor, and is used for gas exchange of CO2 discharged by the membrane lung;

[0007] A hose, which is connected to the CO2 airway adapter and the membrane lung exhaust interface, and is used to cooperate with the membrane lung exhaust interface to transport the CO2 discharged by the membrane lung;

[0008] The membrane lung exhaust interface is connected to the hose and the membrane lung, and is used to cooperate with the hose to transport the CO2 discharged by the membrane lung;

[0009] A dual-channel CO2 respiratory monitoring system based on an intelligent sensor, which is connected to a CO2 airway adapter, a membrane lung and a ventilator, and is used to perform dual-channel CO2 respiratory monitoring through an intelligent sensor provided in the dual-channel CO2 respiratory monitoring system;

[0010] A membrane lung, which is connected to a membrane lung exhaust interface and a dual-channel CO2 respiratory monitoring system based on an intelligent sensor, and is used to simulate gas exchange in the lungs;

[0011] The ventilator is connected to a dual-channel CO2 respiratory monitoring system based on a smart sensor to support the user to breathe naturally.

[0012] The present invention also provides a dual-channel CO2 respiratory monitoring system based on an intelligent sensor, comprising:

[0013] Intelligent sensor acquisition module, used to collect CO2 breathing test data through intelligent sensors;

[0014] An intelligent sensor intelligent calibration module, used to perform intelligent calibration on the intelligent sensor to obtain an intelligent sensor after intelligent calibration, and use the intelligent sensor after intelligent calibration for the intelligent sensor acquisition module;

[0015] An intelligent processing module of an intelligent sensor, used for preprocessing the CO2 breathing test data through a built-in data preprocessing unit of the intelligent sensor to obtain preprocessed CO2 breathing test data;

[0016] A dual-channel CO2 breathing monitoring module, used to monitor the CO2 breathing of the user channel according to the pre-processed CO2 breathing test data to obtain the user channel CO2 breathing monitoring result, and also used to monitor the CO2 breathing of the membrane lung channel according to the pre-processed CO2 breathing test data to obtain the membrane lung channel CO2 breathing monitoring result;

[0017] The dual-channel CO2 breathing data display module is used to display the CO2 breathing monitoring results of the user channel and the CO2 breathing monitoring results of the membrane lung channel.

[0018] Further, the dual-channel CO2 breathing monitoring module includes a user channel CO2 breathing monitoring unit, and the user channel CO2 breathing monitoring unit tests the user's physique according to a user's physique testing method, and the user's physique testing method includes:

[0019] Step A1, setting parameters for the convolutional neural network model, wherein the convolution kernel size k and the number of convolution kernels of the convolution layer are set according to the type and characteristics of the pre-processed CO2 breathing test data, the convolution kernel size is set to k, and 3≤k≤5 is set, and the convolution layer data output feature map y of the pre-processed CO2 breathing test data is calculated according to the input data x of the pre-processed CO2 breathing test data, the convolution kernel weight w, the convolution kernel size k, the bias term b, the position l of the input data x in the convolution layer, and the channel index n of the input data x in the convolution layer, and the convolution layer data output feature map y of the pre-processed CO2 breathing test data is set.

[0020] A pooling layer is set according to the type and characteristics of the pre-processed CO2 breathing test data, and the pre-processed CO2 breathing test data is processed according to the pooling layer to obtain the pooling layer data features, and the user's physique is classified according to the convolution layer data output feature map y and the pooling layer data features, and the number of output neurons is set according to the number of categories of the user's physique classification, and the Softmax function is used for classification, and the formula is: Among them, y i represents the probability of the i-th category, z i represents the input of the i-th neuron, and C represents the total number of categories;

[0021] Step A2: training the convolutional neural network model, wherein the number of samples N, the number of categories C, and the predicted probability y ij and the true label t ij Calculate the cross entropy loss function L and set

[0022] 80% of the pre-processed CO2 breathing test data is divided into a training set and 20% is divided into a test set, the training set is input into the physical fitness judgment model, and the test set is input into the trained convolutional neural network model, a prediction result is obtained by a forward propagation algorithm, a loss value is calculated according to a cross entropy loss function L, and then the parameters of the physical fitness judgment model are updated by a back propagation algorithm;

[0023] Step A3, evaluate the results of the convolutional neural network model training, wherein the accuracy of the physical fitness judgment model is calculated based on the true positive TP, true negative TN, false positive FP and false negative FN, and set The recall rate Recall is calculated based on the true positive TP and false negative FN. Calculate the precision rate Precision based on the true positive TP and false positive FP, and set Calculate the F1-score based on the recall rate Recall and precision Precision, set The training results are judged based on the F1-score and accuracy, and the results are output based on the judgment results, where:

[0024] When F1>90% and Accuracy>90%, the user channel CO2 breathing monitoring unit determines that the training result meets the standard, and outputs the corresponding convolutional neural network model as a physical fitness judgment model;

[0025] When F1>90% and Accuracy<90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result;

[0026] When F1 is less than 90% and Accuracy is greater than 90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result;

[0027] When F1 is less than 90% and Accuracy is less than 90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result;

[0028] The user channel CO2 breathing monitoring unit inputs the pre-processed CO2 breathing test data into the physical fitness judgment model to test the user's physical fitness, wherein:

[0029] When the physical condition judgment model determines that the user's physical condition is not suitable for using the CO2 respiratory monitor, the user's physical condition is not tested;

[0030] When the physical fitness judgment model determines that the user's physical fitness is suitable for using a CO2 respiratory monitor, the user's physical fitness is tested and lactic acid monitoring, analysis and calculation are performed on the user.

[0031] Furthermore, when the physical condition judgment model determines that the user's physical condition is suitable for using a CO2 respiratory monitor, the user channel CO2 respiratory monitoring unit determines the user as a user with a physical condition suitable for the test, and calculates the mixed venous blood carbon dioxide partial pressure according to the user with a physical condition suitable for the test. and arterial carbon dioxide partial pressure Arteriovenous carbon dioxide pressure difference Calculate and set Arteriovenous carbon dioxide pressure difference Difference between arteriovenous carbon dioxide pressure and preset The comparison is performed, and the lactic acid production of the user with the adapted physique is analyzed according to the comparison result, and the setting in:

[0032] when When the user channel CO2 respiratory monitoring unit analyzes the lactic acid production of the user with the adapted test physique as an abnormal state, and calculates the ventilator adjustment coefficient TZX according to the lactic acid production RS, the respiratory frequency HX and the user's body mass index YT, and sets TZX=c1×RS+c2×HX+c3×YT, c1+c2+c3=1, c1 represents the coefficient for adjusting the lactic acid production RS, c2 represents the coefficient for adjusting the respiratory frequency HX, c3 represents the coefficient for adjusting the user's body mass index YT, and compares the ventilator adjustment coefficient TZX with the preset ventilator adjustment coefficient TZX0, and judges the adjustment result of the ventilator of the user with the adapted test physique according to the comparison result, wherein:

[0033] If RS>RS0, the user channel CO2 breathing monitoring unit determines that the adjustment result of the ventilator of the user with the adapted test physique is in the adjustment state;

[0034] If RS≤RS0, the user channel CO2 breathing monitoring unit determines that the adjustment result of the ventilator of the user with the test fitness is a non-adjustment state;

[0035] when When the user channel CO2 breathing monitoring unit analyzes the lactic acid production of the user with the adapted test physique as a normal state.

[0036] Further, when the user channel CO2 breathing monitoring unit adjusts the ventilator according to the adjustment result of the ventilator, the natural lung ventilation T is calculated according to the tidal volume Q and the number of breaths H of the ventilator monitoring user in the pre-processed CO2 breathing test data, and T=Q×H is set, and the natural lung ventilation T is compared with the preset natural lung ventilation T0, and the adjustment means is judged according to the comparison result, and the ventilator is adjusted according to the adjustment means, and 0L / min<T0<20L / min is set, wherein:

[0037] When T=T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation is normal and does not adjust the ventilator;

[0038] When T>T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation increases, determines that the adjustment means is the first preset adjustment means, and adjusts the ventilator according to the first preset adjustment means;

[0039] When T<T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation is reduced, and determines that the adjustment means is the second preset adjustment means, and adjusts the ventilator according to the second preset adjustment means.

[0040] Furthermore, the dual-channel CO2 breathing monitoring module includes a membrane lung channel CO2 breathing monitoring unit, and the membrane lung channel CO2 breathing monitoring unit is based on the blood flow index Q in the pre-processed CO2 breathing test data. blood , hematocrit HCT, arterial oxygen saturation Sa02, venous oxygen saturation Sv02, arterial oxygen partial pressure Pa02, venous oxygen partial pressure Pv02 and body surface area BSA affect membrane lung oxygen consumption Calculate and set Based on membrane lung ventilation VE, body surface area BSA, nasopharyngeal temperature T and membrane lung oxygen consumption Carbon dioxide production in membrane lung Calculate and set According to membrane lung oxygen consumption and membrane lung carbon dioxide production Calculate the membrane respiratory quotient RQ and set The membrane respiratory quotient RQ is compared with the preset membrane respiratory quotient RQ0, and the lactate anaerobic metabolism is analyzed according to the comparison result, and the tissue hypoxia is judged according to the lactate anaerobic metabolism, and 0.7<RQ0<1.0 is set, wherein:

[0041] When RQ≥RQ0, the membrane lung channel CO2 respiratory monitoring unit determines that the lactate anaerobic metabolism is normal and determines that the tissue hypoxia is normal;

[0042] When RQ<RQ0, the membrane lung channel CO2 respiratory monitoring unit determines that the anaerobic metabolism of lactate is increased, and determines that the tissue hypoxia is severe.

[0043] Furthermore, when the tissue hypoxia condition is severe, the membrane lung channel CO2 breathing monitoring unit compares the oxygen saturation Y1 of the user with the adapted test physique with the preset oxygen saturation Y0, judges the body position type of the user with the adapted test physique according to the comparison result, and performs a body position check on the user with the adapted test physique according to the judgment result, wherein:

[0044] When Y1≥Y0, the membrane lung channel CO2 breathing monitoring unit determines that the body position type of the user with the adapted test physique is a normal body position, and does not perform a body position check on the user with the adapted test physique;

[0045] When Y1<Y0, the membrane lung channel CO2 breathing monitoring unit determines that the body position type of the adaptive test physique user is a special body position, and the membrane lung channel CO2 breathing monitoring unit performs a body position inspection on the adaptive test physique user determined to be in a special body position according to the first body position inspection method.

[0046] Furthermore, when the body position examination result determines that the body position is a special body position, the membrane lung channel CO2 breathing monitoring unit analyzes the vascular compression situation according to the surgical requirements of the user with the adapted test physique and the body position type of the user with the adapted test physique, wherein:

[0047] When the user with the fitness test condition is in a surgical state and in a special position:

[0048] The vascular compression state of the user with the test fitness is identified according to the vascular compression identification model, wherein:

[0049] If the vascular compression recognition model recognizes that the blood vessels of the user with the adaptive test physique are in a compressed state, the membrane lung channel CO2 breathing monitoring unit performs a body position check on the user with the adaptive test physique according to a first body position check method;

[0050] If the vascular compression recognition model identifies the blood vessels of the user with the adapted test physique as being in an uncompressed state, the membrane lung channel CO2 breathing monitoring unit adjusts the user's chest range X and introduces the optimization coefficient α, setting α=0.72+e 0.5×(3-X) , optimize the respiratory quotient RQ according to the optimization coefficient α, and get the optimized respiratory quotient RQ2, set

[0051] When the user with the fitness test condition is in a non-surgical state and in a special position:

[0052] If the vascular compression recognition model recognizes that the blood vessels of the user with the adaptive test physique are in a compressed state, the membrane lung channel CO2 breathing monitoring unit performs a body position check on the user with the adaptive test physique according to a first body position check method;

[0053] If the vascular compression recognition model recognizes that the blood vessels of the user with the adapted test physique are in a non-compressed state, the membrane lung channel CO2 breathing monitoring unit prompts the user to use auxiliary equipment to correct the body position.

[0054] Further, the membrane lung channel CO2 breathing monitoring unit compares the membrane lung use time t in the pre-processed CO2 breathing test data with the preset membrane lung use time t0, and judges the membrane lung cleaning condition according to the comparison result, and cleans the membrane lung according to the judgment result, wherein:

[0055] When t>t0, the membrane lung channel CO2 breathing monitoring unit determines that the membrane lung usage time is abnormal, the detector indicator light flashes yellow and sends a yellow alarm signal, notifying medical staff to check the membrane lung surface;

[0056] When t≤t0, the membrane lung channel CO2 breathing monitoring unit determines that the membrane lung usage time is normal and does not clean the membrane lung.

[0057] Further, the dual-channel CO2 breathing data display module calculates the similarity between the user channel CO2 breathing monitoring result and the preset user channel CO2 breathing monitoring result according to the similarity measurement method to obtain a first actual similarity B1, and compares the first actual similarity B1 with the preset first similarity B0, and displays the user channel CO2 breathing monitoring result according to the comparison result, wherein:

[0058] When B1≥B0, the dual-channel CO2 respiratory data display module displays the user channel CO2 respiratory monitoring results according to the data display scheme;

[0059] When B1<B0, the dual-channel CO2 respiration data display module does not display the CO2 respiration monitoring result of the user channel;

[0060] The dual-channel CO2 breathing data display module calculates the similarity between the membrane lung channel CO2 breathing monitoring result and the preset membrane lung channel CO2 breathing monitoring result according to the similarity measurement method, obtains a second actual similarity K1, and compares the second actual similarity K1 with the preset second similarity K0, and displays the membrane lung channel CO2 breathing monitoring result according to the comparison result, wherein:

[0061] When K1≥K0, the dual-channel CO2 respiratory data display module displays the CO2 respiratory monitoring results of the membrane lung channel according to the data display scheme;

[0062] When K1<K0, the dual-channel CO2 breathing data display module does not display the CO2 breathing monitoring result of the membrane lung channel.

[0063] Compared with the prior art, the beneficial effects of the present invention are that the system can accurately locate and obtain CO2 breathing test data through the intelligent sensor acquisition module, the system can effectively improve the measurement precision and accuracy of the sensor through the intelligent sensor intelligent calibration module, and reduce data deviations caused by errors in the sensor itself, the system can process the CO2 breathing test data through the intelligent sensor intelligent processing module to improve the quality of the data, the system can simultaneously monitor the CO2 breathing conditions of the user channel and the membrane lung channel in real time through the dual-channel CO2 breathing monitoring module, which helps to ensure the user's respiratory health and the normal operation of the membrane lung, and the system can display the CO2 breathing monitoring results of the user channel and the membrane lung channel in real time through the dual-channel CO2 breathing data display module, which helps to understand and analyze the breathing status in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Schematic diagram of the structure of the dual-channel CO2 respiratory monitoring device based on the intelligent sensor of this embodiment;

[0065] Figure 2 Schematic diagram of the structure of the dual-channel CO2 respiratory monitoring system based on the intelligent sensor of this embodiment; Figure 3 Schematic diagram of the structure of the dual-channel CO2 respiratory monitoring module of this embodiment. DETAILED DESCRIPTION

[0066] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0068] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0069] See also Figure 1 As shown, it is a structural schematic diagram of a dual-channel CO2 respiratory monitoring device based on an intelligent sensor in this embodiment, and the device includes:

[0070] A CO2 airway adapter 1, which is connected to a hose 2 and a membrane lung channel CO2 breathing monitoring unit of a dual-channel CO2 breathing monitoring module in a dual-channel CO2 breathing monitoring system 4 based on an intelligent sensor, and is used for gas exchange of CO2 discharged by the membrane lung;

[0071] A hose 2, which is connected to the CO2 airway adapter 1 and the membrane lung exhaust interface 3, and is used to cooperate with the membrane lung exhaust interface 3 to transport the CO2 discharged by the membrane lung 5;

[0072] The membrane lung exhaust interface 3 is connected to the hose 2 and the membrane lung 5 and is used to cooperate with the hose 2 to transport the CO2 discharged by the membrane lung 5;

[0073] A dual-channel CO2 respiratory monitoring system 4 based on an intelligent sensor, which is connected to a CO2 airway adapter 1, a membrane lung 5 and a ventilator 6, and is used for performing dual-channel CO2 respiratory monitoring through an intelligent sensor provided in the dual-channel CO2 respiratory monitoring system;

[0074] A membrane lung 5, which is connected to the membrane lung exhaust interface 3 and a dual-channel CO2 respiratory monitoring system 4 based on an intelligent sensor, and is used to simulate the lungs for gas exchange;

[0075] The ventilator 6 is connected to the dual-channel CO2 breathing monitoring system 4 based on the intelligent sensor, and is used to support the user to breathe naturally.

[0076] Specifically, the device is arranged at a control terminal for real-time monitoring of patients with extracorporeal membrane oxygenation ECMO combined with ventilator-assisted breathing. By using a CO2 airway adapter 1, a hose 2, a membrane lung exhaust interface 3 and a dual-channel CO2 breathing monitoring system 4 based on an intelligent sensor, the CO2 concentration emitted by the membrane lung 5 and the CO2 concentration emitted by the ventilator 6 can be monitored in real time.

[0077] It can be understood that the present embodiment does not limit the location of the smart sensor, and those skilled in the art can freely set it according to actual conditions, as long as the demand for intelligent data collection is met. For example, the smart sensor can be set inside a dual-channel CO2 respiratory monitoring system.

[0078] See also Figure 2 As shown, it is a structural schematic diagram of a dual-channel CO2 respiratory monitoring system based on an intelligent sensor in this embodiment, and the system includes:

[0079] Intelligent sensor acquisition module, used to collect CO2 breathing test data through intelligent sensors;

[0080] An intelligent sensor intelligent calibration module, used to perform intelligent calibration on the intelligent sensor to obtain an intelligent sensor after intelligent calibration, and use the intelligent sensor after intelligent calibration for the intelligent sensor acquisition module, wherein the intelligent sensor intelligent calibration module is connected to the intelligent sensor acquisition module;

[0081] An intelligent processing module of an intelligent sensor, used for preprocessing the CO2 breathing test data through a built-in data preprocessing unit of the intelligent sensor to obtain preprocessed CO2 breathing test data, wherein the intelligent processing module of the intelligent sensor is connected to the intelligent calibration module of the intelligent sensor;

[0082] A dual-channel CO2 breathing monitoring module, used to monitor the CO2 breathing of the user channel according to the pre-processed CO2 breathing test data to obtain the user channel CO2 breathing monitoring result, and also used to monitor the CO2 breathing of the membrane lung channel according to the pre-processed CO2 breathing test data to obtain the membrane lung channel CO2 breathing monitoring result, the dual-channel CO2 breathing monitoring module is connected to the intelligent sensor intelligent processing module;

[0083] The dual-channel CO2 breathing data display module is used to display the CO2 breathing monitoring results of the user channel and the CO2 breathing monitoring results of the membrane lung channel. The dual-channel CO2 breathing data display module is connected to the dual-channel CO2 breathing monitoring module.

[0084] Specifically, the dual-channel CO2 breathing monitoring system based on the intelligent sensor is arranged in a dual-channel CO2 breathing monitoring device based on the intelligent sensor. The system can accurately locate and obtain CO2 breathing test data through the intelligent sensor acquisition module. The system can effectively improve the measurement precision and accuracy of the sensor through the intelligent sensor intelligent calibration module, and reduce data deviations caused by errors in the sensor itself. The system can process the CO2 breathing test data through the intelligent sensor intelligent processing module to improve the quality of the data. The system can simultaneously monitor the CO2 breathing conditions of the user channel and the membrane lung channel in real time through the dual-channel CO2 breathing monitoring module, which helps to ensure the user's respiratory health and the normal operation of the membrane lung. The system can display the CO2 breathing monitoring results of the user channel and the membrane lung channel in real time through the dual-channel CO2 breathing data display module, which helps to understand and analyze the respiratory status in time.

[0085] Specifically, the CO2 breathing test data includes user basic data, user breathing data, extracorporeal circulation basic data, extracorporeal circulation breathing data and other auxiliary data, the user basic data includes user information, operation information, the user's own blood flow, arteriovenous oxygen saturation, red blood cell volume, body surface area and mean arterial pressure, the user breathing data includes the carbon dioxide partial pressure difference of the user's own exhaust, cardiac index, nasopharyngeal temperature, arteriovenous oxygen partial pressure and hemoglobin concentration, the extracorporeal circulation basic data includes membrane lung ventilation, arteriovenous oxygen saturation, red blood cell volume, body surface area and mean arterial pressure, the extracorporeal circulation breathing data includes extracorporeal circulation flow and index, ventilation, nasopharyngeal temperature, carbon dioxide partial pressure and arteriovenous oxygen partial pressure, the other auxiliary data includes respiratory The temperature of the inhaled gas and the time interval for data collection, the intelligent sensor acquisition module collects the user basic data through the user interaction window, the intelligent sensor acquisition module collects the user breathing data through the pressure sensor, the intelligent sensor acquisition module collects the extracorporeal circulation breathing data through the flow sensor, the intelligent sensor acquisition module collects the extracorporeal circulation basic data through the gas concentration sensor, the intelligent sensor acquisition module collects other auxiliary data through the temperature sensor, the intelligent sensor refers to a sensor that can collect data and perform intelligent calibration, and the intelligent sensor in this embodiment includes the pressure sensor, the flow sensor, the gas concentration sensor and the temperature sensor.

[0086] Specifically, the intelligent calibration module of the smart sensor performs real-time fitting on the CO2 breathing test data according to the data fitting model to obtain a real-time fitting result of the sensor data, calculates the calibration coefficient according to the real-time fitting result of the sensor data, performs intelligent calibration on the smart sensor according to the calibration coefficient, obtains an intelligently calibrated smart sensor, and uses the intelligently calibrated smart sensor for the intelligent sensor acquisition module.

[0087] Specifically, the data fitting model refers to a model used to perform real-time fitting of the CO2 breathing test data. This embodiment does not limit the specific implementation scheme of the data fitting model. For example, it can be set to perform real-time fitting of the CO2 breathing test data through a linear regression fitting model. This embodiment does not limit the specific implementation method of the real-time fitting. For example, it can be set to construct a historical linear regression fitting model based on historical CO2 breathing test data, and input the CO2 breathing test data into the historical linear regression fitting model to calculate the actual mean square error, and output the historical linear regression fitting model whose actual mean square error meets the preset mean square error as the actual linear regression fitting model, and perform real-time fitting of the CO2 breathing test data according to the actual linear regression fitting model. The preset mean square error can be set to 5%. The real-time fitting result of the sensor data refers to the result obtained by real-time fitting of the CO2 breathing test data according to the data fitting model. The calibration coefficient refers to the parameter used to adjust the CO2 breathing test data. This embodiment does not limit the specific calculation method of the calibration coefficient. For example, it can be set to the calibration coefficient Where n represents the total number of samples of the CO2 breathing test data, x i represents the CO2 breathing test data of the ith i represents the real-time fitting result of the i-th sensor data, represents the mean of the CO2 breathing test data, It represents the mean value of the real-time fitting result of the sensor data. This embodiment does not limit the specific method of the intelligent calibration. For example, it can be set to adjust the internal parameters of the intelligent sensor by the calibration coefficient.

[0088] Specifically, the smart sensor intelligent calibration module processes CO2 breathing test data in real time according to the data fitting model to improve the accuracy of the data.

[0089] Specifically, the data preprocessing unit calculates the first preset threshold R1 and the second preset threshold R2 according to the mean μ and the standard deviation σ of the CO2 breathing test data, sets R1=μ-3σ, R2=μ+3σ, R1<R2, and compares the CO2 breathing test data R0 with the first preset threshold R1 and the second preset threshold R2, judges the data state type of the CO2 breathing test data according to the comparison result, and performs outlier processing on the CO2 breathing test data according to the judgment result to obtain an outlier processing result, wherein:

[0090] When R1≤RO≤R2, the intelligent sensor intelligent processing module determines that the data state type of the CO2 breathing test data is normal, and the intelligent sensor intelligent processing module does not perform abnormal value processing on the CO2 breathing test data;

[0091] When RO<R1, the intelligent sensor intelligent processing module determines that the data state type of the CO2 breathing test data is abnormal, and removes the CO2 breathing test data, and records the appearance time and location of the CO2 breathing test data;

[0092] When RO>R2, the intelligent sensor intelligent processing module determines that the data state type of the CO2 breathing test data is abnormal, and removes the CO2 breathing test data, and records the appearance time and location of the CO2 breathing test data;

[0093] The data preprocessing unit performs smoothing preprocessing on the abnormal value processing result according to a smoothing processing method to obtain preprocessed CO2 breathing test data.

[0094] Specifically, the first preset threshold R1 refers to the lowest limit value used to determine whether the data is within the normal range, such as 5%, and the second preset threshold R2 refers to the highest limit value used to determine whether the data is within the normal range, such as 35%. The smoothing processing method refers to a method for performing smoothing preprocessing on the abnormal value processing result. This embodiment does not limit the implementation method of the smoothing processing method. For example, the moving average method can be used to perform smoothing preprocessing on the abnormal value processing result.

[0095] Specifically, the CO2 breathing test data is preprocessed by the intelligent sensor intelligent processing module to improve data accuracy and reliability.

[0096] See also Figure 3 As shown, it is a schematic diagram of the structure of the dual-channel CO2 respiratory monitoring module of this embodiment, and the dual-channel CO2 respiratory monitoring module includes:

[0097] A user channel CO2 breathing monitoring unit, which is used to test the user's physique according to the pre-processed CO2 breathing test data, and is also used to calculate the arteriovenous carbon dioxide partial pressure difference of the user with the adapted test physique according to the pre-processed CO2 breathing test data, and analyze the lactic acid production of the user with the adapted test physique according to the arteriovenous carbon dioxide partial pressure difference, and judge the adjustment result of the ventilator of the user with the adapted test physique according to the lactic acid production, and is also used to calculate the natural lung ventilation according to the pre-processed CO2 breathing test data when adjusting the ventilator according to the adjustment result of the ventilator, and judge the adjustment means according to the natural lung ventilation, and adjust the ventilator according to the adjustment means;

[0098] The membrane lung channel CO2 breathing monitoring unit is used to calculate the membrane respiratory quotient according to the pre-treated CO2 breathing test data, analyze the anaerobic metabolism of lactate according to the membrane respiratory quotient, and judge the tissue hypoxia situation according to the anaerobic metabolism of lactate, and also to perform a body position check on the user with the adaptive test constitution according to the oxygen saturation of the user with the adaptive test constitution when the tissue hypoxia situation is severe, and to analyze the vascular compression situation according to the surgical requirements of the user with the adaptive test constitution when the body position check result is determined to be a special body position, and to judge the membrane lung cleaning situation according to the pre-treated CO2 breathing test data, and clean the membrane lung according to the membrane lung cleaning situation. The membrane lung channel CO2 breathing monitoring unit is connected to the user channel CO2 breathing monitoring unit.

[0099] Specifically, the user channel CO2 breathing monitoring unit tests the user's physical fitness according to a user's physical fitness testing method, and the user's physical fitness testing method includes:

[0100] Step A1, setting parameters for the convolutional neural network model, wherein the convolution kernel size k and the number of convolution kernels of the convolution layer are set according to the type and characteristics of the pre-processed CO2 breathing test data, the convolution kernel size is set to k, and 3≤k≤5 is set, and the convolution layer data output feature map y of the pre-processed CO2 breathing test data is calculated according to the input data x of the pre-processed CO2 breathing test data, the convolution kernel weight w, the convolution kernel size k, the bias term b, the position l of the input data x in the convolution layer, and the channel index n of the input data x in the convolution layer, and the convolution layer data output feature map y of the pre-processed CO2 breathing test data is set.

[0101] A pooling layer is set according to the type and characteristics of the pre-processed CO2 breathing test data, and the pre-processed CO2 breathing test data is processed according to the pooling layer to obtain the pooling layer data features, and the user's physique is classified according to the convolution layer data output feature map y and the pooling layer data features, and the number of output neurons is set according to the number of categories of the user's physique classification, and the Softmax function is used for classification, and the formula is: Among them, y i represents the probability of the i-th category, z i represents the input of the i-th neuron, and C represents the total number of categories;

[0102] Step A2: training the convolutional neural network model, wherein the number of samples N, the number of categories C, and the predicted probability y ij and the true label ti j calculates the cross entropy loss function L and sets

[0103] 80% of the pre-processed CO2 breathing test data is divided into a training set and 20% is divided into a test set, the training set is input into the physical fitness judgment model, and the test set is input into the trained convolutional neural network model, a prediction result is obtained by a forward propagation algorithm, a loss value is calculated according to a cross entropy loss function L, and then the parameters of the physical fitness judgment model are updated by a back propagation algorithm;

[0104] Step A3, evaluate the results of the convolutional neural network model training, wherein the accuracy of the physical fitness judgment model is calculated based on the true positive TP, true negative TN, false positive FP and false negative FN, and set The recall rate Recall is calculated based on the true positive TP and false negative FN. Calculate the precision rate Precision based on the true positive TP and false positive FP, and set Calculate the F1-score based on the recall rate Recall and precision Precision, set The training results are judged based on the F1-score and accuracy, and the results are output based on the judgment results, where:

[0105] When F1>90% and Accuracy>90%, the user channel CO2 breathing monitoring unit determines that the training result meets the standard, and outputs the corresponding convolutional neural network model as a physical fitness judgment model;

[0106] When F1>90% and Accuracy<90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result;

[0107] When F1 is less than 90% and Accuracy is greater than 90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result;

[0108] When F1 is less than 90% and Accuracy is less than 90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result;

[0109] The user channel CO2 breathing monitoring unit inputs the pre-processed CO2 breathing test data into the physical fitness judgment model to test the user's physical fitness, wherein:

[0110] When the physical condition judgment model determines that the user's physical condition is not suitable for using the CO2 respiratory monitor, the user's physical condition is not tested;

[0111] When the physical fitness judgment model determines that the user's physical fitness is suitable for using a CO2 respiratory monitor, the user's physical fitness is tested and lactic acid monitoring, analysis and calculation are performed on the user.

[0112] Specifically, the input data x refers to the preprocessed CO2 breathing test data input into the convolutional neural network, the position l of the input data x in the convolutional layer refers to the current position of the sliding window of the input data in the convolutional layer, the channel index n of the input data x in the convolutional layer refers to the channel currently being processed, the convolutional layer data output feature map y refers to the output obtained after the convolutional layer applies a convolution operation to the input data, the pooling layer data feature refers to the feature extracted by downsampling the output of the convolutional layer by the pooling layer, and the physical fitness judgment model refers to a convolutional neural network model that has been trained and the training results meet the standards.

[0113] Specifically, by testing the user's physique according to the user's physique testing method through the user channel CO2 breathing monitoring unit, the accuracy of physique judgment can be improved.

[0114] Specifically, when the physical fitness judgment model determines that the user's physical fitness is suitable for using a CO2 respiratory monitor, the user channel CO2 respiratory monitoring unit determines the user as a user with a physical fitness suitable for the test, and calculates the mixed venous blood carbon dioxide partial pressure according to the physical fitness suitable for the test user. and arterial carbon dioxide partial pressure Arteriovenous carbon dioxide pressure difference Calculate and set Arteriovenous carbon dioxide pressure difference Difference between arteriovenous carbon dioxide pressure and preset The comparison is performed, and the lactic acid production of the user with the adapted physique is analyzed according to the comparison result, and the setting in:

[0115] when When the user channel CO2 respiratory monitoring unit analyzes the lactic acid production of the user with the adapted test physique as an abnormal state, and calculates the ventilator adjustment coefficient TZX according to the lactic acid production RS, the respiratory frequency HX and the user's body mass index YT, and sets TZX=c1×RS+c2×HX+c3×YT, c1+c2+c3=1, c1 represents the coefficient for adjusting the lactic acid production RS, c2 represents the coefficient for adjusting the respiratory frequency HX, c3 represents the coefficient for adjusting the user's body mass index YT, and compares the ventilator adjustment coefficient TZX with the preset ventilator adjustment coefficient TZX0, and judges the adjustment result of the ventilator of the user with the adapted test physique according to the comparison result, wherein:

[0116] If RS>RS0, the user channel CO2 breathing monitoring unit determines that the adjustment result of the ventilator of the user with the adapted test physique is in the adjustment state;

[0117] If RS≤RS0, the user channel CO2 breathing monitoring unit determines that the adjustment result of the ventilator of the user with the test fitness is a non-adjustment state;

[0118] when When the user channel CO2 breathing monitoring unit analyzes the lactic acid production of the user with the adapted test physique as a normal state.

[0119] Specifically, the user with a physique suitable for testing refers to a user whose physical condition is determined to be suitable for testing using a CO2 respiratory monitor after evaluation by a physical fitness judgment model. The arterial carbon dioxide partial pressure refers to the partial pressure of carbon dioxide in arterial blood. The preset arteriovenous carbon dioxide partial pressure difference refers to a preset value compared with the arterial carbon dioxide partial pressure, such as 6 mmHg. The preset ventilator adjustment coefficient TZX0 refers to a reference benchmark for adjusting ventilator parameters, for example, the oxygen concentration provided by the preset ventilator is 55%.

[0120] Specifically, the user channel CO2 respiratory monitoring unit monitors the arteriovenous carbon dioxide partial pressure difference to ensure the accuracy and safety of monitoring.

[0121] Specifically, when the user channel CO2 breathing monitoring unit adjusts the ventilator according to the adjustment result of the ventilator, the natural lung ventilation T is calculated according to the tidal volume Q and the number of breaths H of the ventilator monitoring user in the pre-processed CO2 breathing test data, and T=Q×H is set, and the natural lung ventilation T is compared with the preset natural lung ventilation T0, and the adjustment means is judged according to the comparison result, and the ventilator is adjusted according to the adjustment means, and 0L / min<T0<20L / min is set, wherein:

[0122] When T=T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation is normal and does not adjust the ventilator;

[0123] When T>T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation increases, determines that the adjustment means is the first preset adjustment means, and adjusts the ventilator according to the first preset adjustment means;

[0124] When T<T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation is reduced, and determines that the adjustment means is the second preset adjustment means, and adjusts the ventilator according to the second preset adjustment means.

[0125] Specifically, the preset natural lung ventilation T0 refers to a preset value compared with the natural lung ventilation T, for example 10L / min. The first preset adjustment means refers to the measure taken by the user channel CO2 respiratory monitoring unit to determine that the user's natural lung ventilation has increased when T>T0, such as the ventilator indicator light flashes yellow, a yellow alarm signal is issued, and the ventilator is adjusted to reduce the tidal volume. The second preset adjustment means refers to the measure taken by the user channel CO2 respiratory monitoring unit to determine that the user's natural lung ventilation has decreased when T<T0, such as the ventilator indicator light flashes orange, an orange alarm signal is issued, and the ventilator is adjusted to increase the tidal volume.

[0126] Specifically, the user channel CO2 breathing monitoring unit can accurately determine the user's ventilation status and automatically and accurately adjust the ventilator.

[0127] Specifically, the membrane lung channel CO2 breathing monitoring unit is based on the blood flow index Q in the pre-processed CO2 breathing test data. blood , hematocrit HCT, arterial oxygen saturation Sa02, venous oxygen saturation Sv02, arterial oxygen partial pressure Pa02, venous oxygen partial pressure Pv02 and body surface area BSA affect membrane lung oxygen consumption Calculate and set Based on membrane lung ventilation VE, body surface area BSA, nasopharyngeal temperature T and membrane lung oxygen consumption Carbon dioxide production in membrane lung Calculate and set According to membrane lung oxygen consumption and membrane lung carbon dioxide production Calculate the membrane respiratory quotient RQ and set The membrane respiratory quotient RQ is compared with the preset membrane respiratory quotient RQ0, and the lactate anaerobic metabolism is analyzed according to the comparison result, and the tissue hypoxia is judged according to the lactate anaerobic metabolism, and 0.7<RQ0<1.0 is set, wherein:

[0128] When RQ≥RQ0, the membrane lung channel CO2 respiratory monitoring unit determines that the lactate anaerobic metabolism is normal and determines that the tissue hypoxia is normal;

[0129] When RQ<RQ0, the membrane lung channel CO2 respiratory monitoring unit determines that the anaerobic metabolism of lactate is increased, and determines that the tissue hypoxia is severe.

[0130] Specifically, the blood flow index Q bloodIt refers to the percentage of blood flow through the membrane lung per unit time, the hematocrit HCT refers to the volume percentage of red blood cells in the blood, the arterial oxygen saturation SaO2 refers to the percentage of oxygenated hemoglobin in arterial blood to the total hemoglobin, the venous oxygen saturation SvO2 refers to the percentage of oxygenated hemoglobin in venous blood to the total hemoglobin, the arterial oxygen partial pressure PaO2 refers to the pressure generated by oxygen molecules in arterial blood, the venous oxygen partial pressure PvO2 refers to the pressure generated by oxygen molecules in venous blood, the body surface area BSA refers to the area of ​​the outer surface of the human body, the membrane lung ventilation VE refers to the amount of gas exchanged by the membrane lung per unit time, the nasopharyngeal temperature T refers to the temperature of the nasopharynx, and the preset membrane respiratory quotient RQ0 refers to a preset value compared with the membrane respiratory quotient RQ, such as 0.7.

[0131] Specifically, the membrane lung channel CO2 respiratory monitoring unit can accurately monitor and calculate the membrane lung respiratory quotient, reducing the risk of complications caused by tissue hypoxia.

[0132] Specifically, when the tissue hypoxia condition is severe, the membrane lung channel CO2 breathing monitoring unit compares the oxygen saturation Y1 of the user with the adapted test physique with the preset oxygen saturation Y0, judges the body position type of the user with the adapted test physique according to the comparison result, and performs a body position check on the user with the adapted test physique according to the judgment result, wherein:

[0133] When Y1≥Y0, the membrane lung channel CO2 breathing monitoring unit determines that the body position type of the user with the adapted test physique is a normal body position, and does not perform a body position check on the user with the adapted test physique;

[0134] When Y1<Y0, the membrane lung channel CO2 breathing monitoring unit determines that the body position type of the adaptive test physique user is a special body position, and the membrane lung channel CO2 breathing monitoring unit performs a body position inspection on the adaptive test physique user determined to be in a special body position according to the first body position inspection method.

[0135] Specifically, the oxygen saturation Y1 refers to the percentage of oxygenated hemoglobin in the blood to the total hemoglobin, the preset oxygen saturation Y0 refers to a preset value used for comparison with the oxygen saturation Y1, for example, 95%, and the first body position inspection method refers to the measures taken if the vascular compression recognition model identifies the vascular compression state of the user with the adaptive test physique as a compression state, such as timely checking the specific location and degree of vascular compression.

[0136] Specifically, the membrane lung channel CO2 breathing monitoring unit can promptly detect whether the user is in a state of tissue hypoxia, thereby improving the monitoring accuracy of the user's body position and vascular status.

[0137] Specifically, when the body position examination result determines that the body position is a special body position, the membrane lung channel CO2 breathing monitoring unit analyzes the vascular compression situation according to the surgical requirements of the user with the adapted test physique and the body position type of the user with the adapted test physique, wherein:

[0138] When the user with the fitness test condition is in a surgical state and in a special position:

[0139] The vascular compression state of the user with the test fitness is identified according to the vascular compression identification model, wherein:

[0140] If the vascular compression recognition model recognizes that the blood vessels of the user with the adaptive test physique are in a compressed state, the membrane lung channel CO2 breathing monitoring unit performs a body position check on the user with the adaptive test physique according to a first body position check method;

[0141] If the vascular compression recognition model identifies the blood vessels of the user with the adapted test physique as being in an uncompressed state, the membrane lung channel CO2 breathing monitoring unit adjusts the user's chest range X and introduces the optimization coefficient α, setting α=0.72+e 0.5×(3-X) , optimize the respiratory quotient RQ according to the optimization coefficient α, and get the optimized respiratory quotient RQ2, set

[0142] When the user with the fitness test condition is in a non-surgical state and in a special position:

[0143] If the vascular compression recognition model recognizes that the blood vessels of the user with the adaptive test physique are in a compressed state, the membrane lung channel CO2 breathing monitoring unit performs a body position check on the user with the adaptive test physique according to a first body position check method;

[0144] If the vascular compression recognition model recognizes that the blood vessels of the user with the adapted test physique are in a non-compressed state, the membrane lung channel CO2 breathing monitoring unit prompts the user to use auxiliary equipment to correct the body position.

[0145] Specifically, the vascular compression recognition model refers to a deep learning model that takes the vascular characteristic map of the user with the test physique as input and the vascular compression state recognition result as output. This embodiment does not limit the construction method of the vascular compression recognition model. Those skilled in the art can freely set it according to actual conditions, such as setting the vascular compression recognition model to a convolutional neural network model.

[0146] Specifically, the membrane lung channel CO2 breathing monitoring unit can accurately monitor the vascular compression situation to ensure the physiological safety of the user.

[0147] Specifically, the membrane lung channel CO2 breathing monitoring unit compares the membrane lung use time t in the pre-processed CO2 breathing test data with the preset membrane lung use time t0, and judges the membrane lung cleaning condition according to the comparison result, and cleans the membrane lung according to the judgment result, wherein:

[0148] When t>t0, the membrane lung channel CO2 breathing monitoring unit determines that the membrane lung usage time is abnormal, the detector indicator light flashes yellow and sends a yellow alarm signal, notifying medical staff to check the membrane lung surface;

[0149] When t≤t0, the membrane lung channel CO2 breathing monitoring unit determines that the membrane lung usage time is normal and does not clean the membrane lung.

[0150] Specifically, the membrane lung usage time t refers to the accumulated time of the membrane lung during actual use, and the preset membrane lung usage time t0 refers to a preset value compared with the membrane lung usage time t, for example, 100 hours.

[0151] Specifically, the membrane lung channel CO2 breathing monitoring unit can timely detect the need for membrane lung cleaning to ensure stable membrane lung performance.

[0152] Specifically, the dual-channel CO2 breathing data display module calculates the similarity between the user channel CO2 breathing monitoring result and the preset user channel CO2 breathing monitoring result according to the similarity measurement method, obtains a first actual similarity B1, and compares the first actual similarity B1 with the preset first similarity B0, and displays the user channel CO2 breathing monitoring result according to the comparison result, wherein:

[0153] When B1≥B0, the dual-channel CO2 respiratory data display module displays the user channel CO2 respiratory monitoring results according to the data display scheme;

[0154] When B1<B0, the dual-channel CO2 respiration data display module does not display the CO2 respiration monitoring result of the user channel;

[0155] The dual-channel CO2 breathing data display module calculates the similarity between the membrane lung channel CO2 breathing monitoring result and the preset membrane lung channel CO2 breathing monitoring result according to the similarity measurement method, obtains a second actual similarity K1, and compares the second actual similarity K1 with the preset second similarity K0, and displays the membrane lung channel CO2 breathing monitoring result according to the comparison result, wherein:

[0156] When K1≥K0, the dual-channel CO2 respiratory data display module displays the CO2 respiratory monitoring results of the membrane lung channel according to the data display scheme;

[0157] When K1<K0, the dual-channel CO2 breathing data display module does not display the CO2 breathing monitoring result of the membrane lung channel.

[0158] Specifically, the similarity measurement method refers to a method for quantifying the degree of similarity between two data sets. This embodiment does not limit the specific implementation method of the similarity measurement method. For example, it can be set to be calculated by the cosine similarity method. The preset user channel CO2 breathing monitoring result refers to the expected user CO2 breathing monitoring result, such as 3.0%. The preset first similarity B0 refers to a preset value for comparison with the first actual similarity B1, such as 0.90. The user channel CO2 breathing monitoring result refers to the result obtained by monitoring the user channel CO2 breathing according to the pre-processed CO2 breathing test data. The membrane lung channel CO2 breathing monitoring result refers to the result obtained by monitoring the user channel CO2 breathing according to the pre-processed C The O2 breathing test data is the result of monitoring the CO2 breathing in the membrane lung channel. The data display scheme refers to a measure for displaying the CO2 breathing monitoring results of the user channel and the CO2 breathing monitoring results of the membrane lung channel. This embodiment does not limit the specific implementation method of the data display scheme. For example, it can be set to display the CO2 breathing monitoring results of the user channel and the CO2 breathing monitoring results of the membrane lung channel through a display method based on a JavaScript chart library. The preset membrane lung channel CO2 breathing monitoring result refers to the expected membrane lung CO2 breathing monitoring result, such as 2.8%. The preset second similarity K0 refers to a preset value for comparison with the second actual similarity K1, such as 0.95.

[0159] Specifically, the dual-channel CO2 breathing data display module automatically determines the similarity between the CO2 breathing monitoring results of the user and the membrane lung and the preset results according to the similarity measurement method, thereby improving the accuracy and reliability of the monitoring results.

[0160] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A dual-channel CO2 respiratory monitoring device based on an intelligent sensor, characterized in that: include: A CO2 airway adapter, which is connected to a hose and a membrane lung channel CO2 breathing monitoring unit of a dual-channel CO2 breathing monitoring module in a dual-channel CO2 breathing monitoring system based on an intelligent sensor, and is used for gas exchange of CO2 discharged by the membrane lung; A hose, which is connected to the CO2 airway adapter and the membrane lung exhaust interface, and is used to cooperate with the membrane lung exhaust interface to transport the CO2 discharged by the membrane lung; The membrane lung exhaust interface is connected to the hose and the membrane lung, and is used to cooperate with the hose to transport the CO2 discharged by the membrane lung; A dual-channel CO2 respiratory monitoring system based on an intelligent sensor, which is connected to a CO2 airway adapter, a membrane lung and a ventilator, and is used to perform dual-channel CO2 respiratory monitoring through an intelligent sensor provided in the dual-channel CO2 respiratory monitoring system; A membrane lung, which is connected to a membrane lung exhaust interface and a dual-channel CO2 respiratory monitoring system based on an intelligent sensor, and is used to simulate gas exchange in the lungs; The ventilator is connected to a dual-channel CO2 respiratory monitoring system based on a smart sensor to support the user to breathe naturally.

2. A system applied to the dual-channel CO2 respiratory monitoring device based on intelligent sensor according to claim 1, characterized in that: include: Intelligent sensor acquisition module, used to collect CO2 breathing test data through intelligent sensors; An intelligent sensor intelligent calibration module, used to perform intelligent calibration on the intelligent sensor to obtain an intelligent sensor after intelligent calibration, and use the intelligent sensor after intelligent calibration for the intelligent sensor acquisition module; An intelligent processing module of an intelligent sensor, used for preprocessing the CO2 breathing test data through a built-in data preprocessing unit of the intelligent sensor to obtain preprocessed CO2 breathing test data; A dual-channel CO2 breathing monitoring module, used to monitor the CO2 breathing of the user channel according to the pre-processed CO2 breathing test data to obtain the user channel CO2 breathing monitoring result, and also used to monitor the CO2 breathing of the membrane lung channel according to the pre-processed CO2 breathing test data to obtain the membrane lung channel CO2 breathing monitoring result; The dual-channel CO2 breathing data display module is used to display the CO2 breathing monitoring results of the user channel and the CO2 breathing monitoring results of the membrane lung channel.

3. The system of the dual-channel CO2 respiratory monitoring device based on the intelligent sensor according to claim 2, characterized in that: The dual-channel CO2 breathing monitoring module includes a user channel CO2 breathing monitoring unit, and the user channel CO2 breathing monitoring unit tests the user's physique according to a user physique testing method, and the user physique testing method includes: Step A1, setting parameters for the convolutional neural network model, wherein the convolution kernel size k and the number of convolution kernels of the convolution layer are set according to the type and characteristics of the pre-processed CO2 breathing test data, the convolution kernel size is set to k, and 3≤k≤5 is set, and the convolution layer data output feature map y of the pre-processed CO2 breathing test data is calculated according to the input data x of the pre-processed CO2 breathing test data, the convolution kernel weight w, the convolution kernel size k, the bias term b, the position l of the input data x in the convolution layer, and the channel index n of the input data x in the convolution layer, and the convolution layer data output feature map y of the pre-processed CO2 breathing test data is set. A pooling layer is set according to the type and characteristics of the pre-processed CO2 breathing test data, and the pre-processed CO2 breathing test data is processed according to the pooling layer to obtain the pooling layer data features, and the user's physique is classified according to the convolution layer data output feature map y and the pooling layer data features, and the number of output neurons is set according to the number of categories of the user's physique classification, and the Softmax function is used for classification, and the formula is: Among them, yi represents the probability of the i-th category, zi represents the input of the i-th neuron, and C represents the total number of categories; Step A2, training the convolutional neural network model, wherein the cross entropy loss function L is calculated according to the number of samples N, the number of categories C, the predicted probability yij and the true label tij, and the setting 80% of the pre-processed CO2 breathing test data is divided into a training set and 20% is divided into a test set, the training set is input into the physical fitness judgment model, and the test set is input into the trained convolutional neural network model, a prediction result is obtained by a forward propagation algorithm, a loss value is calculated according to a cross entropy loss function L, and then the parameters of the physical fitness judgment model are updated by a back propagation algorithm; Step A3, evaluate the results of the convolutional neural network model training, wherein the accuracy of the physical fitness judgment model is calculated based on the true positive TP, true negative TN, false positive FP and false negative FN, and set The recall rate Recal l is calculated based on the true positive TP and false negative FN, and the setting Calculate the precision rate Precision based on the true positive TP and false positive FP, and set According to the recall rate Recall and precision Precision F1-score F1 = 2×Precision ×Recall Perform calculations, set Precision+Recall, and judge the training results based on F1-score and accuracy, and output based on the judgment results, where: When F1>90% and Accuracy>90%, the user channel CO2 breathing monitoring unit determines that the training result meets the standard, and outputs the corresponding convolutional neural network model as a physical fitness judgment model; When F1>90% and Accuracy<90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result; When F1 is less than 90% and Accuracy is greater than 90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result; When F1 is less than 90% and Accuracy is less than 90%, the user channel CO2 breathing monitoring unit determines whether the training result meets the standard or not, and does not output the judgment result; The user channel CO2 breathing monitoring unit inputs the pre-processed CO2 breathing test data into the physical fitness judgment model to test the user's physical fitness, wherein: When the physical condition judgment model determines that the user's physical condition is not suitable for using the CO2 respiratory monitor, the user's physical condition is not tested; When the physical fitness judgment model determines that the user's physical fitness is suitable for using a CO2 respiratory monitor, the user's physical fitness is tested and lactic acid monitoring, analysis and calculation are performed on the user.

4. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 3 is characterized in that: When the physique judgment model determines that the user's physique is suitable for using a CO2 respiratory monitor, the user channel CO2 respiratory monitoring unit determines the user as a user with a physique suitable for the test, and calculates the mixed venous blood carbon dioxide partial pressure of the user with a physique suitable for the test. and arterial carbon dioxide partial pressure Arteriovenous carbon dioxide pressure difference Calculate and set Arteriovenous carbon dioxide pressure difference Difference between arteriovenous carbon dioxide pressure and preset The comparison is performed, and the lactic acid production of the user with the adapted physique is analyzed according to the comparison result, and the setting in: when When the user channel CO2 respiratory monitoring unit analyzes the lactic acid production of the user with the adapted test physique as an abnormal state, and calculates the ventilator adjustment coefficient TZX according to the lactic acid production RS, the respiratory frequency HX and the user's body mass index YT, and sets TZX=c1×RS+c2×HX+c3×YT, c1+c2+c3=1, c1 represents the coefficient for adjusting the lactic acid production RS, c2 represents the coefficient for adjusting the respiratory frequency HX, c3 represents the coefficient for adjusting the user's body mass index YT, and compares the ventilator adjustment coefficient TZX with the preset ventilator adjustment coefficient TZX0, and judges the adjustment result of the ventilator of the user with the adapted test physique according to the comparison result, wherein: If RS>RS0, the user channel CO2 breathing monitoring unit determines that the adjustment result of the ventilator of the user with the adapted test physique is in the adjustment state; If RS≤RS0, the user channel CO2 breathing monitoring unit determines that the adjustment result of the ventilator of the user with the test fitness is a non-adjustment state; when When the user channel CO2 breathing monitoring unit analyzes the lactic acid production of the user with the adapted test physique as a normal state.

5. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 4, characterized in that: When the user channel CO2 breathing monitoring unit adjusts the ventilator according to the adjustment result of the ventilator, the natural lung ventilation T is calculated according to the tidal volume Q and the number of breaths H of the ventilator monitoring user in the pre-processed CO2 breathing test data, and T=Q×H is set, and the natural lung ventilation T is compared with the preset natural lung ventilation T0, and the adjustment means is judged according to the comparison result, and the ventilator is adjusted according to the adjustment means, and 0L / min<T0<20L / min is set, wherein: When T=T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation is normal and does not adjust the ventilator; When T>T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation increases, determines that the adjustment means is the first preset adjustment means, and adjusts the ventilator according to the first preset adjustment means; When T<T0, the user channel CO2 breathing monitoring unit determines that the user's natural lung ventilation is reduced, and determines that the adjustment means is the second preset adjustment means, and adjusts the ventilator according to the second preset adjustment means.

6. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 5, characterized in that: The dual-channel CO2 respiratory monitoring module includes a membrane lung channel CO2 respiratory monitoring unit, which monitors the membrane lung oxygen consumption according to the blood flow index Qblood, red blood cell volume HCT, arterial oxygen saturation Sa02, venous oxygen saturation Sv02, arterial oxygen partial pressure Pa02, venous oxygen partial pressure Pv02 and body surface area BSA in the pre-processed CO2 respiratory test data. Calculate and set Based on membrane lung ventilation VE, body surface area BSA, nasopharyngeal temperature T and membrane lung oxygen consumption Carbon dioxide production in membrane lung Calculate and set According to membrane lung oxygen consumption and membrane lung carbon dioxide production Calculate the membrane respiratory quotient RQ and set The membrane respiratory quotient RQ is compared with the preset membrane respiratory quotient RQ0, and the lactate anaerobic metabolism is analyzed according to the comparison result, and the tissue hypoxia is judged according to the lactate anaerobic metabolism, and 0.7<RQ0<1.0 is set, wherein: When RQ≥RQ0, the membrane lung channel CO2 respiratory monitoring unit determines that the lactate anaerobic metabolism is normal and determines that the tissue hypoxia is normal; When RQ<RQ0, the membrane lung channel CO2 respiratory monitoring unit determines that the anaerobic metabolism of lactate is increased, and determines that the tissue hypoxia is severe.

7. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 6, characterized in that: When the tissue hypoxia condition is severe, the membrane lung channel CO2 breathing monitoring unit compares the oxygen saturation Y1 of the user with the adapted test physique with the preset oxygen saturation Y0, judges the body position type of the user with the adapted test physique according to the comparison result, and performs a body position check on the user with the adapted test physique according to the judgment result, wherein: When Y1≥Y0, the membrane lung channel CO2 breathing monitoring unit determines that the body position type of the user with the adapted test physique is a normal body position, and does not perform a body position check on the user with the adapted test physique; When Y1<Y0, the membrane lung channel CO2 breathing monitoring unit determines that the body position type of the adaptive test physique user is a special body position, and the membrane lung channel CO2 breathing monitoring unit performs a body position inspection on the adaptive test physique user determined to be in a special body position according to the first body position inspection method.

8. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 7, characterized in that: When the body position examination result determines that the body position is a special body position, the membrane lung channel CO2 breathing monitoring unit analyzes the vascular compression situation according to the surgical requirements of the user with the adapted physical condition for the test and the body position type of the user with the adapted physical condition for the test, wherein: When the user with the fitness test condition is in a surgical state and in a special position: The vascular compression state of the user with the test fitness is identified according to the vascular compression identification model, wherein: If the vascular compression recognition model recognizes that the blood vessels of the user with the adaptive test physique are in a compressed state, the membrane lung channel CO2 breathing monitoring unit performs a body position check on the user with the adaptive test physique according to a first body position check method; If the vascular compression recognition model identifies the blood vessels of the user with the adapted test physique as being in an uncompressed state, the membrane lung channel CO2 breathing monitoring unit adjusts the user's chest range X and introduces the optimization coefficient α, setting α=0.72+e 0.5 ×(3-X) , optimize the respiratory quotient RQ according to the optimization coefficient α, and get the optimized respiratory quotient RQ2, set When the user with the fitness test condition is in a non-surgical state and in a special position: If the vascular compression recognition model recognizes that the blood vessels of the user with the adaptive test physique are in a compressed state, the membrane lung channel CO2 breathing monitoring unit performs a body position check on the user with the adaptive test physique according to a first body position check method; If the vascular compression recognition model recognizes that the blood vessels of the user with the adapted test physique are in a non-compressed state, the membrane lung channel CO2 breathing monitoring unit prompts the user to use auxiliary equipment to correct the body position.

9. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 8, characterized in that: The membrane lung channel CO2 breathing monitoring unit compares the membrane lung use time t in the pre-processed CO2 breathing test data with the preset membrane lung use time t0, and judges the membrane lung cleaning condition according to the comparison result, and cleans the membrane lung according to the judgment result, wherein: When t>t0, the membrane lung channel CO2 breathing monitoring unit determines that the membrane lung usage time is abnormal, the detector indicator light flashes yellow and sends a yellow alarm signal, notifying medical staff to check the membrane lung surface; When t≤t0, the membrane lung channel CO2 breathing monitoring unit determines that the membrane lung usage time is normal and does not clean the membrane lung.

10. The system of the dual-channel CO2 respiratory monitoring device based on the smart sensor according to claim 2, characterized in that: The dual-channel CO2 breathing data display module calculates the similarity between the user channel CO2 breathing monitoring result and the preset user channel CO2 breathing monitoring result according to the similarity measurement method, obtains a first actual similarity B1, and compares the first actual similarity B1 with the preset first similarity B0, and displays the user channel CO2 breathing monitoring result according to the comparison result, wherein: When B1≥B0, the dual-channel CO2 respiratory data display module displays the user channel CO2 respiratory monitoring results according to the data display scheme; When B1<B0, the dual-channel CO2 respiration data display module does not display the CO2 respiration monitoring result of the user channel; The dual-channel CO2 breathing data display module calculates the similarity between the membrane lung channel CO2 breathing monitoring result and the preset membrane lung channel CO2 breathing monitoring result according to the similarity measurement method, obtains a second actual similarity K1, and compares the second actual similarity K1 with the preset second similarity K0, and displays the membrane lung channel CO2 breathing monitoring result according to the comparison result, wherein: When K1≥K0, the dual-channel CO2 respiratory data display module displays the CO2 respiratory monitoring results of the membrane lung channel according to the data display scheme; When K1<K0, the dual-channel CO2 breathing data display module does not display the CO2 breathing monitoring result of the membrane lung channel.

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