Anesthesia monitoring advanced life support all-in-one machine and method thereof

Abstract

The invention provides an anesthesia monitoring advanced life support all-in-one machine and a method thereof, and relates to the technical field of advanced life support, the all-in-one machine mainly comprises a respiratory anesthesia part, a monitoring part, an infusion part and a control part; the respiration anesthesia part is used for maintaining respiration of the wounded and executing the respiration anesthesia process and comprises a turbofan, anesthesia target control equipment, respiration loop equipment and a waste gas collector; the breathing loop equipment is used for providing a breathing anesthesia gas path and collecting anesthesia gas data; the infusion part is used for carrying out infusion treatment on the wounded; and the monitoring part is used for collecting and monitoring physiological monitoring data of the wounded. According to the scheme, emergency rescue functions of anesthesia, monitoring, infusion, respiratory support and the like are integrated, the size is small, the weight is light, and the device is particularly suitable for field rapid treatment scenes of batched wounded persons; according to the scheme, multi-source data can be collected and effectively integrated and displayed, the anesthesia state of the wounded can be intelligently evaluated, targeted control can be carried out, and assistance can be provided for treatment decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of advanced life support, and in particular, to an integrated anesthesia monitoring and advanced life support machine and its method. Background Art

[0002] In the event of major disaster accidents, efficient medical rescue equipment is often the key to saving lives. However, although conventional medical equipment has complete functions and remarkable curative effects, it is not applicable to the scenario of treating a large number of critically injured patients at the scene of major disaster accidents. The main problems include: conventional medical equipment is large in volume, heavy in weight, and complex in operation, making it difficult to meet the actual needs of rapid treatment of a large number of wounded in the wild; conventional medical equipment has a single function and cannot integrate emergency rescue functions such as anesthesia, monitoring, and respiratory support; there is a lack of effective communication interfaces between conventional medical equipment, making it impossible to achieve data sharing between rescue equipment at the scene of disaster accidents, and even less able to perform intelligent clinical assistant decision-making. Therefore, there is an urgent need to develop an integrated advanced life support machine suitable for the scene of major disaster accidents to treat a large number of wounded. Summary of the Invention

[0003] The purpose of the present invention is to provide an integrated anesthesia monitoring and advanced life support machine and its method to solve at least one of the above technical problems existing in the prior art.

[0004] To solve the above technical problems, the present invention provides an integrated anesthesia monitoring and advanced life support machine, including a respiratory anesthesia part, a monitoring part, an infusion part, and a control part; The respiratory anesthesia part is used to maintain the respiration of the wounded and perform the respiratory anesthesia process, and includes a turbo fan, an anesthesia target control device, a respiratory circuit device, and an exhaust gas collector; The anesthesia target control device is arranged on the right side of the integrated machine and includes an anesthesia electronic vaporizer and a digital flow control valve; The turbo fan is built into the integrated machine. The first pipeline input end of the turbo fan is connected to a gas source, and the pipeline output end of the turbo fan is connected to the pipeline input end of the anesthesia electronic vaporizer; the pipeline output end of the anesthesia electronic vaporizer is connected to the pipeline input end of the digital flow control valve; the pipeline output end of the digital flow control valve is connected to the anesthesia gas input end of the respiratory circuit device; the respiratory interaction end of the respiratory circuit device is connected to the breathing mask of the wounded; the gas output end of the respiratory circuit device is connected to the second pipeline input end of the turbo fan and the pipeline input end of the exhaust gas collector; The gas source includes an oxygen source and a nitrous oxide source, which is relatively safe and convenient for storage and transportation; The respiratory circuit device is used to provide a respiratory anesthesia gas path and includes a ventilator; the ventilator is arranged at the rear of the integrated machine; The infusion unit is arranged on the left side of the integrated machine and is used for infusing and treating the wounded, including an infusion pipeline and a micro control pump; The monitoring unit is arranged above the infusion unit and is used for collecting and monitoring the physiological monitoring data and anesthesia gas data of the wounded, including an electrocardiogram sensor, a cerebral oxygen saturation sensor, an electroencephalogram sensor, a fingertip oxygen saturation sensor, a blood pressure sensor, a body temperature sensor, a respiration sensor, a tidal volume sensor, an oxygen concentration sensor, a carbon dioxide concentration sensor, and an anesthesia gas concentration sensor; the physiological monitoring data includes electrocardiogram data, cerebral oxygen saturation data, electroencephalogram data, fingertip oxygen saturation data, blood pressure data, body temperature data, and respiration data; the anesthesia gas data includes tidal volume data, oxygen concentration data, carbon dioxide concentration data, and anesthesia gas concentration data; The control unit is arranged at the front of the integrated machine and is electrically connected to the respiratory anesthesia unit, the monitoring unit, and the infusion unit respectively, and is used for program - controlling the coordinated work of each unit; In this way, the functions of respiratory anesthesia, monitoring, and infusion can be integrated into one; in particular, due to the adoption of a micro - type turbine blower, the pressure requirement for the gas source is reduced, and not only the volume and weight of the integrated machine are reduced, but also the portability of the integrated machine is improved, which is beneficial to the rapid treatment of a large number of wounded at the scene of major disaster accidents. In a feasible implementation manner, the waste gas collector includes a soda lime tank for adsorbing waste gases such as carbon dioxide.

[0005] In a feasible implementation manner, the control unit includes a central processing unit, an alarm, a power supply module, a communication module, and a touch screen; The alarm is used for performing audible and visual alarms according to the alarm information; The power supply module is used for stably powering the integrated machine; The communication module is used for information interaction with the outside world; The touch screen is used for displaying anesthesia gas data, physiological monitoring data, infusion parameters, alarm information, etc. and receiving instruction feedback; The central processing unit is electrically connected to the alarm, the power supply module, the communication module, and the touch screen respectively.

[0006] In a feasible implementation manner, the anesthesia control method of the integrated machine includes the following steps: Step a1: After collecting the anesthesia gas data and physiological monitoring data of the on - site wounded and performing data screening, use them as input variables and input them into the trained neural network to output the anesthesia target control value.

[0007] Preferably, the data screening includes performing fuzzy processing on the input variables through a membership function to obtain a fuzzy set so as to express uncertainty and fuzziness.

[0008] Preferably, the membership function is a Gaussian membership function; in this way, the good smoothness and differentiability of the function can be utilized to better express the fuzzy boundary.

[0009] Preferably, the neural network includes a fuzzy rule layer, a normalization layer, an inference layer, and an output layer arranged in sequence: The fuzzy rule layer performs a weighted combination operation on the fuzzy set through preset fuzzy rules to generate the triggering intensity corresponding to different fuzzy rules; each fuzzy rule represents an input-output relationship; the triggering intensity represents the applicability degree of different fuzzy rules under the current conditions; The normalization layer normalizes the triggering intensity of each fuzzy rule to ensure that the triggering intensities of all fuzzy rules are within a unified numerical range; The inference layer generates a fuzzified output value through linear combination calculation of each fuzzy rule based on the triggering intensity; The output layer converts the output value into a specific anesthesia target control value, thereby realizing precise anesthesia target control.

[0010] Step a2: Take the anesthesia target control value as the target value and input it into the PID fuzzy control module of the digital flow control valve to output the PID parameters of the digital flow control valve, thereby realizing real-time and precise adjustment of the anesthesia gas concentration.

[0011] In a feasible implementation manner, the anesthesia evaluation method of the all-in-one machine includes the following steps: Step b1: After collecting and preprocessing the electroencephalogram data (sampling rate is about 256Hz) and cerebral blood oxygen data (sampling rate is about 10Hz) of historical wounded, etc., use them as the first training set to train the first anesthesia depth evaluation model.

[0012] Preferably, the preprocessing is interpolation sampling, which is used to balance the sampling frequency differences between the electroencephalogram data and the cerebral blood oxygen data.

[0013] Preferably, the first anesthesia depth evaluation model: increases the sample quantity of the cerebral blood oxygen data through the SMOTE oversampling method to balance it with the electroencephalogram data; classifies through a cost-sensitive classifier (Cost-Sensitive SVM) and automatically adjusts the weight ratio of the cerebral blood oxygen data; improves the generalization ability by combining the Bagging ensemble learning method.

[0014] Step b2: After collecting the respiratory data, blood pressure data, electrocardiogram data, body temperature data, carbon dioxide concentration data, tidal volume data, etc. of historical wounded, preprocess them and use them as the second training set to train and optimize the first anesthesia depth assessment model to obtain the second anesthesia depth assessment model, thereby improving the assessment accuracy of the model.

[0015] Step b3: Collect the corresponding data of on-site wounded and input it into the second anesthesia depth assessment model to predict the anesthesia state of the wounded; the anesthesia state includes induction anesthesia, light anesthesia, deep anesthesia, anesthesia recovery, etc.

[0016] Preferably, the anesthesia depth assessment model is a support vector machine and / or a fuzzy neural network model.

[0017] Adopting the above technical solution, the present invention has the following beneficial effects: An anesthesia monitoring and advanced life support integrated machine and its method provided by the present invention integrate emergency rescue functions such as anesthesia, monitoring, infusion, and respiratory support. It is small in size and light in weight, and is especially suitable for the rapid treatment scenario of a large number of wounded in the wild; this solution can collect multi-source data, effectively integrate and display it, can intelligently evaluate the anesthesia state of the wounded and perform targeted control, and can provide assistance for treatment decisions. Description of the Drawings

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a three-dimensional structure schematic diagram of an anesthesia monitoring and advanced life support integrated machine provided by an embodiment of the present invention; Figure 2 For Figure 1 Another perspective view; Figure 3 It is a schematic diagram of the transmission lines of gas, liquid and signals in the anesthesia monitoring and advanced life support integrated machine provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the communication principle of the central processing unit provided by an embodiment of the present invention; Figure 5 It is a main pin layout diagram of the central processing unit provided by an embodiment of the present invention.

[0020] Reference Numerals: 1 - Ventilator; 11 - Anesthesia Electronic Vaporizer; 12 - Tracheal Connector; 13 - Soda Lime Canister; 2 - Monitoring Unit; 3 - Infusion Unit; 4 - Touch Screen; 5 - Handle. Detailed Embodiment

[0021] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0022] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0023] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "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 directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0024] The present invention will be further explained below in conjunction with specific embodiments.

[0025] Embodiment 1: As Figure 1-2 shown, an advanced life support integrated machine for anesthesia monitoring provided in this embodiment includes a respiratory anesthesia unit, a monitoring unit 2, an infusion unit 3, and a control unit; The respiratory anesthesia unit is used to maintain the respiration of the wounded and perform the respiratory anesthesia process, and includes a turbo fan, an anesthesia target control device, a respiratory circuit device, and an exhaust gas collector; The anesthesia target control device is arranged on the right side of the integrated machine and includes an anesthesia electronic vaporizer 11 and a digital flow control valve; The turbine blower is built into the integrated machine. The first pipeline input end of the turbine blower is connected to a gas source (external gas tank or external gas pipe), and the pipeline output end of the turbine blower is connected to the pipeline input end of the anesthesia electronic vaporizer 11; the pipeline output end of the anesthesia electronic vaporizer 11 is connected to the pipeline input end of the digital flow control valve; the pipeline output end of the digital flow control valve is connected to the anesthesia gas input end of the breathing circuit device; the breathing interaction end of the breathing circuit device is connected to the breathing mask of the wounded; the gas output end of the breathing circuit device is connected to the second pipeline input end of the turbine blower and the pipeline input end of the waste gas collector; in this way, as Figure 3 shown, the anesthesia gas can start from the gas source, enter the anesthesia electronic vaporizer after being mixed with the external gas (mainly the exhaled gas of the wounded) by the turbine blower, and then enter the breathing circuit device after the concentration is controlled by the digital flow control valve, and then enter the body of the wounded; the exhaled gas of the wounded is also transported to the turbine blower and the waste gas collector through the breathing circuit device; The gas source includes an oxygen source and a nitrous oxide source; The breathing circuit device is used to provide a breathing anesthesia gas path and includes a ventilator 1; the ventilator 1 is arranged at the rear of the integrated machine; the tracheal connector 12 of the breathing circuit device is arranged on the right side of the integrated machine; The infusion unit 3 is arranged on the left side of the integrated machine and is used for infusing and treating the wounded, including an infusion pipeline and a micro control pump; The monitoring unit 2 is arranged above the infusion unit 3 and is used for collecting and monitoring the physiological monitoring data and anesthesia gas data of the wounded, including an electrocardiogram sensor, a cerebral oxygen saturation sensor, an electroencephalogram sensor, a fingertip oxygen saturation sensor, a blood pressure sensor, a body temperature sensor, a respiratory sensor, a tidal volume sensor, an oxygen concentration sensor, a carbon dioxide concentration sensor, and an anesthesia gas concentration sensor; the physiological monitoring data includes electrocardiogram data, cerebral oxygen saturation data, electroencephalogram data, fingertip oxygen saturation data, blood pressure data, body temperature data, and respiratory data; the anesthesia gas data includes tidal volume data, oxygen concentration data, carbon dioxide concentration data, and anesthesia gas concentration data; The control unit is arranged at the front of the integrated machine and is electrically connected to the breathing anesthesia unit, the monitoring unit 2, and the infusion unit 3 respectively, and is used for program - controlling the coordinated work of each unit; In this way, the functions of breathing anesthesia, monitoring, and infusion can be integrated into one; in particular, due to the adoption of a micro turbine blower, the pressure requirement for the gas source is reduced, and not only the volume and weight of the integrated machine are reduced, but also the portability of the integrated machine is improved, which is beneficial to the rapid treatment of a large number of wounded at the scene of major disaster accidents. Further, four handles 5 are evenly distributed on the top of the integrated machine.

[0026] Further, the waste gas collector includes a soda lime tank 13, which is arranged on the left side of the all-in-one machine and is used to adsorb waste gases such as carbon dioxide exhaled by the wounded.

[0027] Further, the control unit includes a central processing unit, an alarm, a power supply module, a communication module, and a touch screen 4; The alarm is used to give an audible and visual alarm according to the alarm information; The power supply module is used to supply power to the all-in-one machine stably; The communication module is used to interact with the outside world for information; The touch screen 4 is arranged on the front surface of the all-in-one machine and is used to display anesthesia gas data, physiological monitoring data, infusion parameters, alarm information, etc. and receive instruction feedback; The central processing unit: inside the control unit, it is electrically connected to the alarm, the power supply module, the communication module, and the touch screen 4 respectively; outside the control unit, it is electrically connected to the monitoring unit, the respiratory anesthesia unit, and the infusion unit respectively, as Figure 4 shown.

[0028] Further, the model of the central processing unit is STM32F479 (STMicroelectronics), as Figure 5 shown, and it specifically includes VDD pin, VSS pin, VBAT pin, PA1 pin, PA5 pin, PA6 pin, PA7 pin, PA11 pin, PA12 pin, PA13 pin, PA14 pin, PA15 pin, PA16 pin, PC4 pin, PC5 pin, GPIO pin, PG11 pin, PG12 pin, and PG13 pin; The VDD pin is connected to the positive power supply (such as 3.3V) and a decoupling capacitor (such as 100nF); The VSS pin is grounded; The VBAT pin is connected to the backup battery; The PA5 pin is connected to the SPI1_SCK clock pin of the communication module; The PA6 pin is connected to the SPI1_MISO master input pin of the communication module; The PA7 pin is connected to the SPI1_MOSI master output pin of the communication module and the CRS_DV composite signal pin; the CRS_DV composite signal pin is used to indicate data validity during reception and carrier sense during idle; The PA11 pin is connected to the CAN1_RX receive data pin of the communication module; The PA12 pin is connected to the CAN1_TX transmit data pin of the communication module; The PA1 pin is connected to the REF_CLK reference clock pin of the communication module; The PC4 pin is connected to the RXD0 receive data pin of the communication module; The PC5 pin is connected to the RXD1 receive data pin of the communication module; The PG11 pin is connected to the TX_EN transmit enable pin of the communication module; The PG12 pin is connected to the TXD1 transmit data pin of the communication module; The PG13 pin is connected to the TXD0 transmit data pin of the communication module; The GPIO pin is connected to the LED display pin of the touch screen; The PA13 pin is connected to the anesthesia control channel of the breathing anesthesia unit; The PA14 pin is connected to the breathing control channel of the breathing anesthesia unit; The PA15 pin is connected to the monitoring control channel of the monitoring unit; The PA16 pin is connected to the infusion control channel of the infusion unit; In this way, through the high-performance central processing unit, programs and instructions can be processed quickly, and various communication protocols (such as SPI, CAN, and Ethernet) can be used for communication, facilitating the functional integration and efficient operation of each part.

[0029] Embodiment 2: Based on Embodiment 1, this embodiment provides an anesthesia control method, which includes the following steps: Step a1: After collecting the anesthesia gas data and physiological monitoring data of the on-site wounded and performing data screening, use them as input variables and input them into the trained neural network to output the anesthesia target control value; Preferably, the data screening includes using a membership function to perform fuzzy processing on the input variables to obtain a fuzzy set, so as to express uncertainty and fuzziness; Preferably, the membership function is a Gaussian membership function; in this way, the good smoothness and differentiability of this function can be utilized to better express the fuzzy boundary; Preferably, the neural network includes a fuzzy rule layer, a normalization layer, an inference layer, and an output layer arranged in sequence: The fuzzy rule layer performs weighted combination operations on the fuzzy set through preset fuzzy rules to generate the trigger intensities corresponding to different fuzzy rules; each fuzzy rule represents an input-output relationship; the trigger intensity represents the applicability of different fuzzy rules under the current conditions; The normalization layer normalizes the trigger intensity of each fuzzy rule to ensure that the trigger intensities of all fuzzy rules are within a unified numerical range; The inference layer generates a fuzzified output value based on the triggering intensity through linear combination calculation of each fuzzy rule. The output layer converts the output value into a specific anesthesia target control value, thereby realizing precise control of anesthesia targeting; the numerical range of the anesthesia target control value is 0 to 10, and the precision is 0.1. Step a2: Take the anesthesia target control value as the target value and input it into the PID fuzzy control module of the anesthesia electronic vaporizer to output the PID parameters of the anesthesia electronic vaporizer, thereby realizing real-time and precise adjustment of the anesthesia gas concentration.

[0030] Furthermore, an anesthesia evaluation method is also provided, which specifically includes the following steps: Step b1: After preprocessing the electroencephalogram data (sampling rate of about 256 Hz) and cerebral blood oxygen data (sampling rate of about 10 Hz) of historical wounded, etc., use them as the first training set to train the first anesthesia depth evaluation model.

[0031] Preferably, the preprocessing is interpolation sampling, which is used to balance the sampling frequency differences between electroencephalogram data and cerebral blood oxygen data.

[0032] Preferably, the first anesthesia depth evaluation model: increases the sample quantity of cerebral blood oxygen data through the SMOTE oversampling method to balance it with electroencephalogram data; classifies through a cost-sensitive classifier (Cost-Sensitive SVM) and automatically adjusts the weight ratio of cerebral blood oxygen data; improves the generalization ability through the combination of the Bagging ensemble learning method.

[0033] Step b2: After preprocessing the respiratory data, blood pressure data, electrocardiogram data, body temperature data, carbon dioxide concentration data, tidal volume data, etc. of historical wounded, use them as the second training set to train and optimize the first anesthesia depth evaluation model to obtain the second anesthesia depth evaluation model, thereby improving the evaluation accuracy of the model.

[0034] Step b3: Collect the corresponding data of the on-site wounded and input it into the second anesthesia depth evaluation model to predict the anesthesia state of the wounded; the anesthesia state includes induction anesthesia, light anesthesia, deep anesthesia, and anesthesia recovery.

[0035] Preferably, the anesthesia depth evaluation model is a fuzzy neural network model.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An advanced life support integrated machine for anesthesia monitoring, characterized in that, It includes a respiratory anesthesia unit, a monitoring unit, an infusion unit and a control unit; The respiratory anesthesia unit is used to maintain the respiration of the wounded and perform the respiratory anesthesia process, and includes a turbo fan, an anesthesia target control device, a respiratory circuit device and an exhaust gas collector; The anesthesia target control device is arranged on the right side of the all-in-one machine and includes an anesthesia electronic vaporizer and a digital flow control valve; The turbo fan is built in the all-in-one machine. The first pipeline input end of the turbo fan is connected to the gas source, and the pipeline output end of the turbo fan is connected to the pipeline input end of the anesthesia electronic vaporizer; the pipeline output end of the anesthesia electronic vaporizer is connected to the pipeline input end of the digital flow control valve; the pipeline output end of the digital flow control valve is connected to the anesthesia gas input end of the respiratory circuit device; the respiratory interaction end of the respiratory circuit device is connected to the breathing mask of the wounded; the gas output end of the respiratory circuit device is connected to the second pipeline input end of the turbo fan and the pipeline input end of the exhaust gas collector; The gas source includes an oxygen source and a nitrous oxide source; The respiratory circuit device is used to provide a respiratory anesthesia gas path and includes a ventilator; the ventilator is arranged at the rear of the all-in-one machine; The infusion unit is arranged on the left side of the all-in-one machine and is used to infuse the wounded, and includes an infusion pipeline and a micro control pump; The monitoring unit is arranged above the infusion unit and is used to collect and monitor the physiological monitoring data and anesthesia gas data of the wounded, and includes an electrocardiogram sensor, a cerebral oxygen saturation sensor, an electroencephalogram sensor, a fingertip oxygen saturation sensor, a blood pressure sensor, a body temperature sensor, a respiratory sensor, a tidal volume sensor, an oxygen concentration sensor, a carbon dioxide concentration sensor, an anesthesia gas concentration sensor; the physiological monitoring data includes electrocardiogram data, cerebral oxygen saturation data, electroencephalogram data, fingertip oxygen saturation data, blood pressure data, body temperature data and respiratory data; the anesthesia gas data includes tidal volume data, oxygen concentration data, carbon dioxide concentration data and anesthesia gas concentration data; The control unit is arranged at the front of the all-in-one machine and is electrically connected to the respiratory anesthesia unit, the monitoring unit and the infusion unit respectively, and is used to program and control the coordinated work of each unit.

2. The all-in-one machine according to claim 1, characterized in that, The exhaust gas collector includes a soda lime tank.

3. The all-in-one machine according to claim 1, characterized in that The control unit includes a central processing unit, an alarm, a power supply module, a communication module and a touch screen; The alarm is used to give an audible and visual alarm according to the alarm information; The power supply module is used to supply power to the all-in-one machine stably; The communication module is used to interact with the outside world; The touch screen is used to display anesthesia gas data, physiological monitoring data, infusion parameters and alarm information and receive instruction feedback; The central processing unit is electrically connected to the alarm, the power supply module, the communication module and the touch screen respectively.

4. The all-in-one machine according to claim 3, characterized in that The model of the central processing unit is STM32F479.

5. The all-in-one machine according to claim 4, characterized in that The central processing unit specifically includes VDD pin, VSS pin, VBAT pin, PA1 pin, PA5 pin, PA6 pin, PA7 pin, PA11 pin, PA12 pin, PA13 pin, PA14 pin, PA15 pin, PA16 pin, PC4 pin, PC5 pin, GPIO pin, PG11 pin, PG12 pin and PG13 pin; The VDD pin is connected to the positive power supply and the decoupling capacitor; The VSS pin is grounded; The VBAT pin is connected to the backup battery; The PA5 pin is connected to the SPI1_SCK clock pin of the communication module; The PA6 pin is connected to the SPI1_MISO master input pin of the communication module; The PA7 pin is connected to the SPI1_MOSI master output pin of the communication module and the CRS_DV composite signal pin; the CRS_DV composite signal pin is used to indicate data validity during reception and carrier sense during idle; The PA11 pin is connected to the CAN1_RX receive data pin of the communication module; The PA12 pin is connected to the CAN1_TX transmit data pin of the communication module; The PA1 pin is connected to the REF_CLK reference clock pin of the communication module; The PC4 pin is connected to the RXD0 receive data pin of the communication module; The PC5 pin is connected to the RXD1 receive data pin of the communication module; The PG11 pin is connected to the TX_EN transmit enable pin of the communication module; The PG12 pin is connected to the TXD1 transmit data pin of the communication module; The PG13 pin is connected to the TXD0 transmit data pin of the communication module; The GPIO pin is connected to the LED display pin of the touch screen; The PA13 pin is connected to the anesthesia control channel of the breathing anesthesia department; The PA14 pin is connected to the breathing control channel of the breathing anesthesia department; The PA15 pin is connected to the monitoring control channel of the monitoring department; The PA16 pin is connected to the infusion control channel of the infusion department.

6. The all-in-one machine according to any one of claims 1-5, characterized in that, The anesthesia control method of this all-in-one machine includes: Step a1, after collecting the anesthesia gas data and physiological monitoring data of the on-site wounded and performing data screening, use them as input variables and input them into the trained neural network to output the anesthesia target control value; Step a2, use the anesthesia target control value as the target value and input it into the PID fuzzy control module of the digital flow control valve to output the PID parameters of the digital flow control valve.

7. The all-in-one machine according to claim 6, characterized in that, The neural network includes a fuzzy rule layer, a normalization layer, an inference layer and an output layer arranged in sequence: The fuzzy rule layer performs a weighted combination operation on the fuzzy sets through preset fuzzy rules to generate the triggering intensities corresponding to different fuzzy rules; Each fuzzy rule represents an input-output relationship; The triggering intensity represents the applicability degree of different fuzzy rules under the current conditions; The normalization layer normalizes the triggering intensity of each fuzzy rule; The inference layer generates a fuzzified output value through a linear combination calculation of each fuzzy rule based on the triggering intensity; The output layer converts the output value into a specific anesthesia target control value.

8. The all-in-one machine according to any one of claims 1-5, characterized in that, The anesthesia evaluation method of this all-in-one machine includes: Step b1: After collecting and preprocessing the electroencephalogram data and cerebral blood oxygen data of historical wounded, use them as the first training set to train the first anesthesia depth evaluation model. Step b2: After collecting and preprocessing the respiratory data, blood pressure data, electrocardiogram data, body temperature data, carbon dioxide concentration data and tidal volume data of historical wounded, use them as the second training set to train and optimize the first anesthesia depth evaluation model to obtain the second anesthesia depth evaluation model. Step b3: Collect the corresponding data of the on-site wounded and input it into the second anesthesia depth evaluation model to predict the anesthesia state of the wounded; the anesthesia state includes induction anesthesia, light anesthesia, deep anesthesia and anesthesia recovery.

9. The all-in-one machine according to claim 8, wherein The preprocessing in step b1 is interpolation sampling, which is used to balance the sampling frequencies of electroencephalogram data and cerebral blood oxygen data.

10. The all-in-one machine according to claim 8, characterized in that, The anesthesia depth evaluation model is a fuzzy neural network model.

Images