Anesthesia machine breathing bag with pressure monitoring

Abstract

This invention belongs to the field of breathing bag technology, specifically a breathing bag for an anesthesia machine with pressure monitoring, comprising: a bag body, an inflation tube, a sealing assembly, a sensor group, and a controller; the inflation tube is connected to a gas source, and its exhaust end is fixedly connected to the neck of the bag body through the sealing assembly, with airflow passing through the inflation tube and the sealing assembly to fill the inner cavity of the bag body; the sensor group includes a first temperature sensor, a second temperature sensor, a third temperature sensor, a heat flow sensor, an anemometer, a flow rate sensor, and a pressure sensor, the first temperature sensor being located at the gas source outlet, the second temperature sensor extending into the inner cavity through the sealing assembly, the heat flow sensor being embedded in the contact surface between the sealing assembly and the neck, the third temperature sensor being attached to the outer wall of the inflation tube, the anemometer being located at an external environmental exposure location, and the flow rate sensor and pressure sensor being located in the air path of the inflation tube; this invention eliminates thermal hysteresis distortion and improves the accuracy of anesthesia status monitoring.

Description

Technical Field

[0001] This invention belongs to the field of breathing bag technology, specifically a breathing bag for an anesthesia machine with pressure monitoring. Background Technology

[0002] Anesthesia machine breathing bags with pressure monitoring are the core hub of clinical anesthesia and respiratory support systems. They not only serve as gas storage and buffer devices to meet patients' instantaneous high-flow ventilation needs, but their built-in pressure monitoring module is also crucial for preventing barotrauma and maintaining hemodynamic stability in critically ill patients. The true thermodynamic state of the fluid in the gas path, especially the temperature characteristics, directly determines the accuracy of pressure sensor calibration and volume conversion.

[0003] Existing airbag systems typically only place conventional temperature and pressure probes at the main airway or interface. This basic structure can maintain basic readings in a static gas storage environment. However, during the high-frequency inflation and deflation cycle of the anesthesia ventilation system, the transient compression, expansion, and mass transfer of the gas can cause severe nonlinear physical disturbances. Existing systems only perform simple low-pass filtering on the underlying electrical signal, which can easily lead to severe time delays and distortions in the temperature measurement values ​​under complex dynamic alternating conditions, thereby causing pressure compensation failure.

[0004] At the moment when the gas source first rushes into the deflated gas bladder, the temperature difference between the heat source and the turbulent temperature inside the bladder is limited by the physical heat capacity of the solid-state temperature probe itself, resulting in a severe data lag response. During the airflow filling stage, simple nodal temperature measurement cannot detect and separate the heat stored in the bladder wall material due to its inherent specific heat capacity, as well as the heat leakage caused by the conduction of the sealing joint flange surface. At the same time, it ignores the forced convection heat dissipation disturbance caused by the external laminar flow wind speed on the pipe wall. When the gas expands and does work inside the bladder and the transient mass transport approaches the physical blockage state, the single probe calibration parameters can no longer characterize the dynamic boundary balance between the aerodynamic work potential energy and the multidimensional heat dissipation. Ultimately, this leads to the output digital calibration intervention data deviating significantly from the true objective physical state of the gas path.

[0005] Therefore, the present invention provides an anesthesia machine breathing bag with pressure monitoring. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is as follows: The present invention provides an anesthesia machine breathing bag with pressure monitoring, comprising: a bag body, an inflation tube, a sealing component, a sensor group and a controller; the inflation tube is connected to a gas source, and its exhaust end is fixedly connected to the neck of the bag body through the sealing component, and the airflow passes through the inflation tube and the sealing component to fill the inner cavity of the bag body; The sensor group includes a first temperature sensor, a second temperature sensor, a third temperature sensor, a heat flow sensor, a wind speed sensor, a flow sensor, and a pressure sensor. The first temperature sensor is located at the air source outlet, the second temperature sensor extends into the inner cavity through the sealing assembly, the heat flow sensor is embedded in the contact surface between the sealing assembly and the neck, the third temperature sensor is attached to the outer wall of the inflation tube, the wind speed sensor is located at the external environment exposure position, and the flow sensor and pressure sensor are located in the air passage of the inflation tube. The controller is electrically connected to the sensor group and is configured to receive signals collected by the sensors, extract the absolute temperature difference between the air source and the inner cavity to obtain the initial thermal bias coefficient, combine the material properties of the bladder wall and the heat leakage parameters of the joint to obtain the intrinsic heat sink dissipation, integrate the external wind speed and the internal and external temperature difference to calculate the dynamic convection coupling dissipation, coordinate the work characteristics of instantaneous air pressure and volume area integral and instantaneous mass flow rate to extract the multimodal heat flow coupling fidelity, perform discrete advance calculation on temperature measurement inertia based on comprehensive features, and finally output the target compensation amount for state calibration feedback.

[0008] The beneficial effects of this invention are as follows: 1. This invention, by setting up a capsule, inflation tube, sealing assembly, sensor group and controller, and using the controller to extract the absolute temperature difference between the air source and the inner cavity to obtain the initial thermal bias coefficient, combined with the capsule wall material properties and joint heat leakage parameters to obtain the intrinsic heat sink dissipation, and integrating external wind speed and internal and external temperature difference to calculate dynamic convection coupling dissipation, and coordinating the work characteristics of instantaneous air pressure and volume surface integral and instantaneous mass flow rate to extract multimodal heat flow coupling fidelity, and combining the above features to perform discrete advance calculation on temperature measurement inertia and output target compensation amount, fundamentally eliminates the physical defects of temperature measurement time delay and pressure compensation failure under high frequency inflation and deflation alternating conditions, so that pressure sensing calibration and volume conversion remain extremely accurate under all working conditions.

[0009] 2. By incorporating a second temperature sensor extending into the inner cavity, a heat flow sensor embedded in the contact surface between the sealing component and the neck, and pre-stored bladder wall material properties, the present invention enables the controller to isolate the heat storage effect caused by the specific heat capacity of the bladder wall and the heat conduction leakage interference from the sealing joint flange surface. This allows for precise quantification of the inherent unsteady-state heat loss of the airbag, avoiding measurement deviations caused by material heat capacity and structural heat leakage in simple node temperature measurement, and significantly improving the decoupling accuracy of the true temperature of the inner cavity.

[0010] 3. This invention incorporates wind speed, flow rate, and pressure sensors, and utilizes a controller to weightedly fuse the characteristics of forced convection cooling from the external environment, heat generation from transient gas compression and expansion, and instantaneous mass transport. This constructs a multimodal heat-fluid coupling fidelity, thereby accurately representing the complex thermodynamic state in the dynamic balance between aerodynamic work potential energy and multidimensional heat dissipation. The final output compensation quantity fully reflects the dynamic coupling evolution of the gas path, ensuring that the pressure monitoring data always closely approximates objective physical reality. Attached Figure Description

[0011] The invention will now be further described with reference to the accompanying drawings.

[0012] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a schematic diagram showing the connection between the capsule and the mask in this invention; Figure 3 This is a schematic diagram of the installation location of the flow sensor in this invention; Figure 4 This is a flowchart of the overall solution of the present invention; Figure 5 This is a flowchart of the intrinsic heat sink dissipation calculation of the present invention; Figure 6 This is a flowchart of the dynamic convection coupling dissipation calculation of the present invention; Figure 7 This is a flowchart of the multimodal thermal flux coupling fidelity extraction and advance compensation process of the present invention.

[0013] In the diagram: 1. Bag body; 2. Inflation tube; 3. Sealing assembly; 4. First temperature sensor; 5. Second temperature sensor; 6. Third temperature sensor; 7. Flow sensor; 8. Pressure sensor; 9. Anesthesia machine body; 10. Mask. Detailed Implementation

[0014] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0015] like Figures 1 to 3 As shown, the present invention discloses an anesthesia machine breathing bag with pressure monitoring, comprising a bag body 1, an inflation tube 2, a sealing assembly 3, a sensor group, and a controller. The bag body 1 serves as a gas storage space, and its neck is fixedly connected to the exhaust end of the inflation tube 2 through the sealing assembly 3 to ensure that the airflow can smoothly and leak-free fill the inner cavity of the bag body 1. The bag body 1 is made of medical-grade silicone material, and a mask 10 for the patient is provided at the other end of the bag body 1. The inflation tube 2 can be made of polyvinyl chloride material. The sealing assembly 3 adopts a combination of rubber gaskets and clamps. The controller adopts an embedded microprocessor, such as a single-chip microcomputer based on the ARM architecture, which is electrically connected to the sensor group via a wired connection.

[0016] Sensor configuration: The first temperature sensor 4 is located at the outlet of the gas source (the output nozzle of the built-in gas pump in the anesthesia machine body 1) to monitor the initial gas temperature entering the system. This sensor is a thermistor and is directly installed inside the gas source output interface. The second temperature sensor 5 extends through the sealing assembly 3 into the inner cavity of the bag 1 to measure the real-time temperature of the gas inside the bag 1. This sensor is a platinum resistance thermometer, with its probe extending to the center area of ​​the bag 1. The heat flow sensor is embedded in the contact surface between the sealing assembly 3 and the neck of the bag 1 to quantify heat loss at the joint. This sensor is a thin-film heat flow meter and is directly attached to the inner surface of the connection interface. On the side, the third temperature sensor 6 is attached to the outer wall of the inflation tube 2 to monitor the surface temperature of the tube wall. This sensor is a thermocouple and is fixed to the outer surface of the inflation tube 2 with thermally conductive adhesive. The wind speed sensor is placed in an exposed position to sense the air flow in the surrounding environment. This sensor is a hot-wire anemometer and is installed on the external support of the anesthesia machine's breathing bag. The flow sensor 7 and the pressure sensor 8 are both located in the air path of the inflation tube 2 to measure the instantaneous flow rate and pressure of the gas, respectively. The flow sensor 7 is a differential pressure flow meter, and the pressure sensor 8 can be a piezoresistive pressure sensor. The two are connected in series in the main air path of the inflation tube 2.

[0017] like Figures 4 to 7 As shown, the controller is configured to receive the signals collected by the aforementioned sensors and perform calculations. First, the controller extracts the absolute temperature difference between the air source and the inner cavity to obtain the initial thermal bias coefficient. The controller presets a fixed threshold. When the difference between the air source temperature and the inner cavity temperature exceeds the threshold, the initial thermal bias coefficient is set to a preset fixed value; otherwise, it is set to zero. This method can provide coarse compensation for the initial temperature difference.

[0018] Furthermore, the controller combines the properties of the capsule wall material with the heat leakage parameters of the joint to obtain the intrinsic heat sink dissipation. The controller pre-stores the average specific heat capacity and thickness of the capsule wall material, and estimates the intrinsic heat sink dissipation by linear superposition based on the average heat flux density measured by the heat flux sensor, thereby performing preliminary quantification of the heat storage of the capsule wall and the heat leakage of the joint.

[0019] In addition, the controller integrates external wind speed and internal and external temperature difference to calculate dynamic convection coupling dissipation. Based on the instantaneous wind speed measured by the wind speed sensor, combined with the temperature difference between the outer wall and inner cavity of the pipe measured by the third temperature sensor 6 and the second temperature sensor 5, the controller obtains the dynamic convection coupling dissipation by consulting a preset convection heat transfer coefficient table, thereby making empirical corrections to the external convection heat dissipation.

[0020] Simultaneously, the controller extracts the multimodal thermal-fluid coupling fidelity by combining the work characteristics of instantaneous air pressure and volume area integral with instantaneous mass flow rate. Based on the data from pressure sensor 8 and flow sensor 7, the controller estimates the work characteristics and mass flow rate of the gas through simple product operations and performs a weighted average to obtain the multimodal thermal-fluid coupling fidelity, thus providing a preliminary characterization of pneumatic work and mass transport.

[0021] Finally, the controller performs discrete lead calculation on the temperature inertia based on the above characteristics and outputs a target compensation amount for state calibration feedback. The controller adopts a proportional-integral-derivative (PID) controller, takes the calculated parameters as input, adjusts the PID parameters to compensate for the temperature inertia, and outputs a linear target compensation amount, which can make basic corrections to the temperature lag.

[0022] When the anesthesia machine is started and the gas source begins to inflate the bladder 1: First, the first temperature sensor 4 monitors the temperature at the air source outlet, and the second temperature sensor 5 monitors the initial temperature of the inner cavity of the bladder 1. After receiving these two temperature signals, the controller extracts the absolute temperature difference between the air source and the inner cavity, and obtains the initial thermal bias coefficient according to the preset logic. If the temperature difference is greater than the preset threshold, the controller sets the initial thermal bias coefficient to a fixed value to initially compensate for the thermal shock effect when the airflow is first filled.

[0023] As gas is continuously injected, the capsule 1 gradually expands. During this process, the second temperature sensor 5 continuously monitors the changes in the internal cavity temperature. The heat flow sensor is embedded in the contact surface between the sealing component 3 and the neck of the capsule 1, measuring the heat flow density through this interface in real time. The controller combines pre-stored capsule wall material properties (such as density, specific heat capacity, and thickness) with the joint heat leakage parameters measured by the heat flow sensor to calculate the intrinsic heat sink dissipation. Based on the heat storage characteristics of the capsule wall material and the actual heat loss at the joint, the controller quantifies the impact of these factors on the internal cavity temperature.

[0024] Meanwhile, the third temperature sensor 6 is attached to the outer wall of the inflation pipe 2 to monitor the pipe wall temperature. The wind speed sensor is located at an exposed position to monitor the airflow speed of the surrounding environment. The controller combines the external wind speed measured by the wind speed sensor with the internal and external temperature difference measured by the third temperature sensor 6 and the second temperature sensor 5 to calculate the dynamic convection coupling dissipation. When the external wind speed is high, the controller will recognize that there is strong convective heat transfer between the pipe wall and the environment, and correct the assessment of the internal cavity temperature accordingly.

[0025] During the inflation and deflation cycle of the airbag, the gas undergoes instantaneous compression and expansion within the inner cavity, accompanied by changes in mass flow rate. Flow sensor 7 and pressure sensor 8 are located in the air path of inflation tube 2 to collect instantaneous mass flow rate and instantaneous air pressure in real time. The controller, in conjunction with the work characteristics of the instantaneous air pressure and volume area integral (calculated through pressure and volume changes) and instantaneous mass flow rate, extracts the multimodal thermal-fluid coupling fidelity. When the gas is compressed, the controller will identify the work and heat generated by the gas and, combined with the change in mass flow rate, comprehensively evaluate the degree of realism of thermal-fluid coupling.

[0026] Finally, the controller integrates the initial thermal bias coefficient, intrinsic heat sink dissipation, dynamic convection coupling dissipation, and multimodal heat flow coupling fidelity obtained from the above calculations to perform discrete advance calculations on the temperature inertia. Based on this multi-source information, the controller predicts the lag of the sensor measurement value relative to the actual temperature and performs advance compensation. As a result, the controller outputs the target compensation amount for state calibration feedback. This compensation amount is fed back to the anesthesia machine system to calibrate the reading of pressure sensor 8 or to perform more accurate volume conversion, thereby ensuring the monitoring accuracy of the anesthesia machine breathing bag under complex dynamic conditions.

[0027] This enables comprehensive sensing of the thermodynamic state of the internal and external environment of the anesthesia machine's breathing bag. The coordinated operation of the first temperature sensor 4, the second temperature sensor 5, the third temperature sensor 6, the heat flux sensor, the wind speed sensor, the flow rate sensor 7, and the pressure sensor 8 allows the controller to acquire multi-source data such as gas source temperature, internal cavity temperature, connector heat flux density, tube outer wall temperature, ambient wind speed, mass flow rate, and instantaneous air pressure.

[0028] By extracting the absolute temperature difference between the air source and the inner cavity to obtain the initial thermal bias coefficient, the initial thermal response deviation caused by the temperature difference during the initial airflow can be effectively solved. Traditional systems are unable to perform such fine compensation. In addition, by combining the material properties of the bladder wall and the heat leakage parameters of the joint to obtain the intrinsic heat sink dissipation, this embodiment can eliminate the interference of heat storage in the bladder wall and heat conduction leakage in the joint on the temperature measurement of the inner cavity. This is not possible in traditional systems. By integrating the external wind speed and the internal and external temperature difference to calculate the dynamic convection coupling dissipation, the influence of the external environment on the forced convection heat dissipation of the pipe wall is compensated, further improving the accuracy of temperature measurement.

[0029] Furthermore, the work characteristics of the combined instantaneous pressure and volume area integral, along with the multimodal heat flow coupling fidelity extracted from the instantaneous mass flow rate, characterize the dynamic balance between aerodynamic work and heat dissipation. This enables the system to handle complex situations where the gas expands within the airbag and transient mass transport approaches a physical blockage state. Traditional single-probe calibration parameters are completely incapable of characterizing this dynamic boundary balance. Finally, by comprehensively considering the above characteristics and performing discrete advance calculations on the temperature measurement inertia, the target compensation amount is output, achieving real-time calibration of the temperature measurement time lag and ensuring the accuracy of pressure monitoring and volume conversion.

[0030] The controller is configured to acquire the gas source temperature collected by the first temperature sensor 4 and the zero-state temperature of the inner cavity collected by the second temperature sensor 5, calculate the absolute temperature difference between the gas source and the inner cavity, and perform normalization mapping on the absolute temperature difference using a preset first temperature constant to obtain a dimensionless initial thermal bias coefficient; wherein, the initial thermal bias coefficient is positively correlated with the absolute temperature difference, and its upper limit threshold is limited by the normalization mapping truncation.

[0031] The controller is configured to calculate the initial thermal bias coefficient based on the following formula. :

[0032] In the formula, is the dimensionless initial thermal bias coefficient; The gas source temperature is collected by the first temperature sensor 4, in units of... ; The zero-state temperature of the inner cavity, collected by the second temperature sensor 5 before inflation, is expressed in units of... ; The first preset temperature constant, in units .

[0033] The controller is a dedicated embedded system, such as an ARM Cortex-M series microcontroller, whose main function is to execute the aforementioned computational logic and coordinate sensor data acquisition with system control, including the initial thermal bias coefficient. It is a dimensionless parameter used to quantify the initial thermodynamic imbalance between the gas source and the air bladder cavity. It serves as a benchmark for subsequent heat-fluid coupling calculations and is crucial for calibrating temperature inertia.

[0034] The first temperature sensor 4 is located at the gas source outlet to accurately and stably measure the temperature of the gas entering the airbag. The gas temperature collected by the first temperature sensor 4 represents the main heat input of the system. The second temperature sensor 5 extends through the sealing assembly 3 into the inner cavity to measure the gas temperature inside the airbag, specifically the zero-state temperature of the inner cavity. This refers to the initial temperature of the airbag cavity collected by the second temperature sensor 5 before the inflation operation begins, establishing the initial thermal state of the airbag's internal environment, the first temperature constant. It is a preset constant used as a scaling factor or normalization reference in the formula for calculating the initial thermal bias coefficient. This constant is determined through empirical testing under various operating conditions.

[0035] By introducing an initial thermal bias coefficient, a quantitative characterization of the thermodynamic state of the airbag during the initial inflation stage is achieved. The controller utilizes the air source temperature collected by the first temperature sensor 4. The zero-state temperature of the inner cavity collected by the second temperature sensor 5 before inflation. As input, first, calculate the difference between the two temperatures. This difference is squared to amplify the weight of the temperature difference on the thermal bias, making the system more sensitive to small temperature changes and ensuring that the calculation result is positive, regardless of whether the gas source is hotter or colder than the inner cavity.

[0036] Subsequently, this squared difference is used as the numerator, and it is compared with the squared difference and a preset first temperature constant. The initial thermal bias coefficient is calculated by using the sum of the squares of the terms as the denominator. This normalization process ensures It is a dimensionless value, usually between 0 and 1, which can be used to quantify the initial degree of thermal imbalance in a standardized way.

[0037] Introducing a preset first temperature constant As an adjustment factor in the denominator, it can effectively limit the range of values ​​of the coefficient, ensuring that the coefficient can stably reflect the difference in thermal potential energy between the gas source and the inner cavity under different operating conditions, and preventing the coefficient from becoming unstable when the temperature difference is extremely small or extremely large. Based on the calculation method of the square ratio of the temperature difference, it can effectively smooth the nonlinear response of the sensor at the moment of initial contact, providing an objective initial thermal bias benchmark for subsequent multimodal thermal-fluid coupling fidelity calculation, thereby improving the accuracy of the system's discrete advance calculation of temperature inertia, enabling the controller to more accurately understand and compensate for the thermodynamic transient behavior of the airbag in the initial inflation stage, laying the foundation for the precise operation of the entire anesthesia machine's breathing airbag.

[0038] The controller is configured to extract the discrete rate of change of the second temperature sensor 5 in the time domain, and multiply the discrete rate of change by the pre-stored capsule wall material properties to calculate the capsule wall loss power; wherein, the capsule wall material properties specifically include capsule wall density, capsule wall specific heat capacity, capsule wall thickness and capsule body 1 surface area.

[0039] The controller is configured to acquire the capsule wall temperature rise rate based on discrete difference and calculate the capsule wall power loss based on the following formula. :

[0040]

[0041] In the formula, The rate of temperature rise within the capsule, per unit ; and These are the measured temperatures of the second temperature sensor 5 for the current and previous sampling periods, respectively, in units of... ; The sampling period is expressed in units of... ; For the power loss of the capsule wall, unit ; , , , These are the capsule wall densities (units) stored in the controller. ), Specific heat capacity of the capsule wall (unit) ), Capsule wall thickness (unit) ) and the surface area parameters of the cyst body 1 (unit) ).

[0042] Discrete difference is a mathematical method used to estimate the rate of change of a continuous function at discrete time points. In this embodiment, its function is to quantify the trend of the capsule wall temperature over time by comparing the internal cavity temperature of the capsule 1 collected at different sampling times, thereby reflecting the dynamic process of the capsule wall absorbing or releasing heat. In terms of implementation, the controller can periodically read the temperature of the second temperature sensor 5 at the current time. and the previous moment temperature value and And according to the preset sampling period Through simple subtraction and division operations To calculate the rate of temperature rise inside the capsule .

[0043] Capsule wall power loss The calculation formula aims to quantify the rate of heat absorption or release due to the heat capacity effect of the capsule wall material, where, (capsule wall density) (Specific heat capacity of the capsule wall) (capsule wall thickness) and The (internal surface area of ​​capsule 1) is a key parameter characterizing the physical properties and geometric dimensions of the capsule wall material. These parameters are pre-determined through experimental measurements, material handbook consultation, or design specifications, and are stored in non-volatile memory within the controller, such as flash memory or EEPROM. The controller directly calls these pre-stored parameters during calculations, and the accuracy of these parameters directly affects... The accuracy of the calculations ensures the accurate characterization of the thermal effects of the capsule wall.

[0044] The second temperature sensor 5 extends into the inner cavity through the sealing assembly 3 to measure the gas temperature inside the anesthesia machine's breathing bag in real time. The controller converts the temperature value into a temperature value by measuring its resistance value and looking it up in a table or by fitting a curve.

[0045] By introducing thermodynamic parameters of the airbag wall, a quantitative characterization of unsteady-state heat loss during airbag heat exchange was achieved. The controller first uses the internal cavity temperature data collected by the second temperature sensor 5, and calculates the internal temperature rise rate using the discrete difference method within a continuous sampling period. This allows us to capture the dynamic changes in the internal temperature of the capsule 1 during inflation and deflation caused by gas compression, expansion, and heat exchange with the capsule wall.

[0046] Subsequently, the controller combines pre-stored physical property parameters of the capsule wall material, including capsule wall density. Specific heat capacity of the capsule wall Capsule wall thickness and the surface area of ​​the inner cavity of capsule 1 The calculated rate of temperature rise inside the capsule Substitute into the formula Thus, the power loss of the capsule wall can be calculated. The capsule wall is considered as a heat storage body with thermal inertia, and the heat absorbed or released by the capsule wall is separated from the gas temperature measurement value through physical modeling.

[0047] During the high-frequency inflation and deflation cycle of the anesthesia machine, the capsule wall absorbs or releases heat due to its specific heat capacity. This means that simple measurements of the gas cavity temperature cannot fully reflect the true energy balance. Therefore, calculations are needed to determine the appropriate energy balance. It can correct the temperature measurement lag and deviation caused by the material's heat capacity, thereby more accurately identifying the energy loss caused by material heat exchange inside the capsule 1. The quantification of the capsule wall thermal effect provides a key input for the subsequent calculation of the overall heat flow coupling fidelity, enabling the controller to more comprehensively understand the thermodynamic state inside the capsule 1, and thus providing a more reliable thermodynamic correction basis for high-precision pressure monitoring and state calibration.

[0048] The controller is configured to acquire the absolute value of the wall loss power and extract the absolute value of the joint leakage power corresponding to the joint leakage power parameter by combining the underlying data collected by the heat flow sensor; the absolute value of the wall loss power and the absolute value of the joint leakage power are summed to obtain the comprehensive heat loss; the comprehensive heat loss is compared with the energy reference scale with a preset basic power constant to output the intrinsic heat sink dissipation.

[0049] The controller is configured to calculate the intrinsic heat sink dissipation based on the following formula. :

[0050] In the formula, The intrinsic heat sink dissipation is dimensionless; The heat flux density of the joint collected by the heat flux sensor, in units of ; The thermal conductivity area of ​​the joint contact surface, per unit ; The first power constant is preset, in units of .

[0051] The controller is responsible for receiving signals from the sensor array and performing calculations to calibrate the true thermodynamic state inside the airbag. The controller is specifically configured to calculate the intrinsic heat sink dissipation. It uses a built-in algorithm module to reduce the capsule wall loss power. Joint heat flux density Thermal conductivity area of ​​joint contact surface and the preset first power constant As input parameters, calculations are performed based on a preset mathematical model to quantify the inherent heat loss of the airbag system.

[0052] Among them, intrinsic heat sink dissipation It is a dimensionless physical quantity used to quantify the inherent heat dissipation or absorption effect of the anesthesia machine's breathing bag system during operation due to its own structure and material properties, taking into account the heat capacity effect of the bag wall material (power loss through the bag wall). (manifestation) and heat conduction leakage at the sealed joint (through the heat flux density of the joint) Thermal conductivity area of ​​the joint contact surface The introduction of this parameter aims to characterize and decouple these nonlinear thermal disturbances, thereby providing a basis for subsequent assessment of the true thermodynamic state inside the airbag and avoiding temperature measurement distortion caused by the inherent thermal properties of the structure.

[0053] A heat flux sensor is a device used to measure the rate of heat transfer, that is, the amount of heat passing through a unit area per unit time. The heat flux sensor is embedded in the contact surface between the sealing assembly 3 and the neck, and its function is to collect the conductive heat flux density at the sealing joint in real time. This sensor can be a thermopile-type heat flow sensor, which indirectly calculates heat flow by measuring temperature difference. The measurement results are key data for quantifying joint heat leakage.

[0054] Joint heat flux density It refers to the heat transfer rate per unit area through the contact surface between the sealing component 3 and the neck of the bladder 1. It reflects the heat transferred to or from the external environment through the sealing joint due to factors such as material thermal conductivity, structural design, and internal and external temperature differences. This parameter is collected in real time by a heat flow sensor.

[0055] Thermal conductivity area of ​​joint contact surface This refers to the actual contact area where heat conduction occurs between the sealing component 3 and the neck of the capsule 1. This area is a geometric parameter determined during structural design and is usually pre-stored in the controller. It is related to the heat flux density at the joint. The total amount of heat loss through the sealed joint is determined by the multiplication of these factors, which is the heat leakage power of the joint.

[0056] First power constant It is a preset reference power value, in units of In calculating intrinsic heat sink dissipation In the formula, this constant is used as part of the denominator to normalize the inherent heat loss term in the numerator. This constant can be set according to the typical operating power range of the airbag system, material properties, and empirical values ​​to ensure... It is within a reasonable dimensionless range and provides a stable reference benchmark to avoid calculation instability caused by a denominator that is zero or too small.

[0057] By introducing the intrinsic heat sink dissipation as a quantitative indicator, the precise decoupling and characterization of the inherent heat loss inside the anesthesia machine's breathing cuff system is achieved. The controller receives the connector heat flux density collected from the heat flux sensor. And combined with the preset heat conduction area of ​​the joint contact surface The heat transfer power at the sealed joint is calculated. Simultaneously, the controller also utilizes the wall loss power calculated in the previous scheme. This power characterizes the heat storage or release behavior of the airbag wall material due to its specific heat capacity.

[0058] In calculating intrinsic heat sink dissipation When this happens, the controller will reduce the power loss in the capsule wall. The absolute value of the heat leakage power of the joint ( The sum of the absolute values ​​of the terms represents the total inherent heat loss of the system, while the denominator is determined by a preset first power constant. Combined with the sum of the above total inherent heat losses, the normalization process makes It becomes a dimensionless parameter that can reflect the proportion of the inherent heat sink effect of the system in the total power, thereby solving the nonlinear thermal interference problem caused by the heat capacity effect of the airbag wall material itself and the conduction and leakage of heat at the sealing joint during the high-frequency inflation and deflation cycle of the anesthesia machine.

[0059] The controller is configured to construct a temperature sample sequence and call the residual minimization algorithm to identify and output adaptive weighting coefficients based on the temperature sample sequence; the adaptive weighting coefficients are used to proportionally weight and fuse the gas temperature inside the tube and the surface temperature of the capsule 1 to output a dynamic weighted average air temperature.

[0060] The controller is configured to calculate the dynamically weighted average temperature based on the following formula. :

[0061]

[0062] In the formula, The preset number of sample tests; , , These are the pre-stored test temperatures of the outer wall of the tube, the surface of the capsule, and the gas inside the tube; These are adaptive weighting coefficients; This represents the current gas temperature inside the pipe. The surface temperature of capsule 1.

[0063] Among them, the dynamic weighted average temperature The calculation is performed by the controller by executing a preset mathematical model, which aims to comprehensively consider the gas temperature inside the airbag and the surface temperature of the airbag body 1 to obtain a temperature value that better represents the overall thermodynamic state, using adaptive weighting coefficients. The calculation is based on the least squares principle and is determined by analyzing pre-stored sample data. This coefficient reflects the heat exchange relationship between the outer wall of the tube, the surface of the bladder, and the gas temperature inside the tube. It can dynamically adjust the weight of gas temperature and bladder surface temperature in the calculation of average air temperature.

[0064] It is used for training or determining adaptive weighting coefficients. The number of historical data points can be set according to the actual application scenario and the required accuracy. For example, it can be set to 1,000 data points, or it can be dynamically accumulated based on the system running time.

[0065] Pre-stored test temperatures of the outer wall of the tube, the surface of the capsule, and the gas inside the tube. , , These data are obtained and recorded through experimental testing in a controlled environment during the system design or calibration phase, and stored in the controller's internal non-volatile memory, such as flash memory or EEPROM, for calculating adaptive weighting coefficients. Current gas temperature inside the pipe This refers to the temperature of the capsule 1 surface indirectly derived through the flow sensor 7 or the pressure sensor 8 during real-time operation. This refers to the temperature of the outer surface of the capsule 1 as measured by a temperature sensor (e.g., an infrared temperature sensor or a thermocouple) placed on the outer surface of the capsule 1 during real-time operation.

[0066] The plan introduces a dynamic weighted average temperature The computer mechanism aims to solve the problem of inaccurate thermodynamic state characterization caused by the non-uniformity of gas temperature inside the airbag and surface temperature of the airbag 1, as well as the limitations of a single temperature measurement point, during the dynamic operation of the anesthesia machine's breathing airbag.

[0067] The controller first utilizes the pre-stored temperature of the outer wall of the pipe. Surface temperature of capsule and the gas temperature inside the pipe Adaptive weighting coefficients are calculated using historical sample data and the least squares method. This coefficient can dynamically reflect the heat exchange characteristics between the wall of the air tube 2 and the surface of the bladder 1 under different working conditions, thus providing dynamic weights for subsequent average temperature calculations.

[0068] Specifically, when the system is running in real time, the controller obtains the current gas temperature inside the pipe. and surface temperature of capsule 1 And combined with the pre-calculated adaptive weighting coefficients The dynamic weighted average temperature is calculated using a weighted average method. This weighted average method makes It can more comprehensively and accurately reflect the overall thermodynamic environment of the gas inside the airbag, overcoming the shortcomings of traditional single temperature measurement points in response lag and data distortion under complex dynamic alternating conditions. By providing a temperature reference inside the airbag, it provides more accurate input parameters for subsequent calculation of dynamic convection coupling dissipation, thereby improving the evaluation accuracy of the entire multimodal heat-fluid coupling fidelity, and ultimately making the calculation of the target compensation amount more accurate. This effectively calibrates the temperature inertia and improves the pressure monitoring accuracy of the anesthesia machine breathing airbag under dynamic conditions.

[0069] The controller is configured to acquire the internal and external temperature difference between the pipe wall parameters collected by the third temperature sensor 6 and the dynamically weighted average air temperature, and to obtain the temperature correlation term by performing dimensionless processing on the internal and external temperature difference using the second temperature constant; at the same time, it calculates the convection rate attenuation factor by combining the external wind speed collected by the wind speed sensor; and multiplicatively couples the convection rate attenuation factor with the temperature correlation term to output the dynamic convection coupling dissipation.

[0070] The controller is configured to calculate the dynamic convection coupling dissipation based on the following formula. :

[0071] In the formula, , which is a dimensionless dynamic convection coupling dissipation; The ambient flow velocity collected by the wind speed sensor; This is the preset critical wind speed constant; The temperature of the outer wall of the tube is collected by the third temperature sensor 6; This is a preset second temperature constant.

[0072] Among them, the wind speed sensor adopts a thermistor type, which calculates the wind speed by measuring the rate at which the airflow carries away heat from the thermistor element. These sensors can convert the ambient airflow velocity into an electrical signal and transmit it to the controller for processing. Accurate measurement of ambient airflow velocity is crucial for assessing the intensity of external convective heat dissipation, and the critical wind speed constant... It is a pre-set threshold used to distinguish different convection states, representing the characteristic wind speed at which natural convection transitions to forced convection or where the intensity of forced convection changes significantly. This constant is determined through experimental calibration and numerical simulation. For example, it can be determined through wind tunnel experiments or CFD (Computational Fluid Dynamics) simulations based on the airbag material, geometry, and heat exchange characteristics under typical working conditions. Its function is to normalize the ambient flow velocity when calculating the dynamic convection coupling dissipation, so that the convection intensity factor can reasonably reflect the actual convective heat dissipation effect in different wind speed ranges.

[0073] Pipe outer wall temperature This refers to the temperature of the outer surface of the inflation tube 2. The third temperature sensor 6 is used to measure this temperature in real time. The third temperature sensor 6 uses a high-precision NTC thermistor, which is closely attached to the outer wall of the inflation tube 2. The temperature is calculated by measuring the change in its resistance value. The temperature of the outer wall of the tube is converted into an electrical signal and transmitted to the controller. The temperature of the outer wall of the tube is an important parameter for evaluating the heat exchange between the tube and the external environment. Together with the internal gas temperature and the external environment temperature, it determines the driving force of convective heat dissipation.

[0074] Second temperature constant It is a pre-defined characteristic temperature difference used to normalize the squared temperature difference term. It represents the typical temperature difference range under specific conditions where the system reaches thermal saturation or where heat exchange tends to stabilize. This constant can also be determined through experimental calibration, numerical simulation, or empirical values ​​and stored in the controller's memory. For example, it can be determined by experimentally measuring the typical temperature difference fluctuation range between the outer wall of the tube and the internal gas, based on the thermal balance characteristics of the airbag system under typical operating conditions. Its function is to ensure that the squared temperature difference term has appropriate weight and dimensions in the formula, so that it can accurately reflect the potential for heat exchange and avoid the calculation results being too sensitive or unstable when the temperature difference is small.

[0075] By introducing dynamic convection coupling dissipation The aim is to quantify the dynamic impact of external wind speed on the thermal balance of the anesthesia machine's breathing bag system, thereby improving the accuracy of temperature measurement data. Its working principle involves the controller continuously receiving ambient airflow velocity data collected from a wind speed sensor. and the temperature of the outer wall of the tube collected by the third temperature sensor 6 Meanwhile, the controller uses the dynamically weighted average temperature calculated in the aforementioned steps. and the preset critical wind speed constant Second temperature constant .

[0076] Specifically, the controller first uses the ambient flow rate With critical wind speed constant The ratio of these values ​​is used to construct a factor that reflects the intensity of external convection. This factor can dynamically capture the impact of changes in external wind speed on convective heat dissipation efficiency. At low wind speeds, the factor value is small, reflecting natural convection or weak forced convection; at high wind speeds, the factor value approaches 1, reflecting strong forced convection, enabling the system to adaptively adjust the weight of convective heat dissipation according to the dynamic changes in the external environment.

[0077] Next, the controller calculates the temperature of the outer wall of the tube. With dynamic weighted average temperature The square term of the temperature difference between and compare it with the preset second temperature constant. The sum of squares is normalized to obtain the factor. This term captures the potential energy of heat exchange between the pipe wall and the internal gas, taking into account the real-time fluctuations of the pipe wall temperature and the overall temperature state of the internal gas, thus mathematically eliminating the nonlinear influence of the external environment on the pipeline's thermal balance.

[0078] Finally, the controller multiplies the two factors to obtain the dimensionless dynamic convection coupling dissipation. This dissipation comprehensively reflects the complex heat flow coupling relationship between the external wind speed, the outer wall temperature of the pipe, and the internal gas temperature. It provides key correction parameters for subsequent multimodal heat flow coupling fidelity calculations. In this way, the true thermodynamic state can be extracted from complex external disturbances, effectively solving the problem of temperature measurement distortion caused by fluctuations in external wind speed.

[0079] The controller is configured to synchronously extract instantaneous air pressure data and corresponding airbag volume data collected by pressure sensor 8, calculate the work characteristics of instantaneous air pressure and volume area integral using trapezoidal surface accumulation and summation algorithm, and convert the work characteristics into flow work and heat generation.

[0080] The controller is configured to calculate the heat generated by the flow based on the following numerical integral formula. :

[0081] In the formula, For work done by fluids to generate heat, unit ; It is the mechanical equivalent constant of heat; and These are the gas pressures collected by pressure sensor 8 in the current iteration step and the previous step, respectively, in units of... ; and These are the corresponding internal volumes of the airbags, in units of... ; This represents the total number of iterations.

[0082] The controller is configured to calculate the heat generated by the flow based on a numerical integral formula. Numerical integration is a mathematical method for approximating definite integrals, especially suitable for processing discrete sampled data. In the calculation of dynamic work and heat generation of gas, since pressure and volume data are collected discretely, numerical integration is needed to simulate the continuous work process.

[0083] Flowing work generates heat It characterizes the conversion between mechanical energy and thermal energy caused by volume change during the compression or expansion of a gas in the breathing bag of an anesthesia machine. When the gas is compressed, the external environment does work on the gas, and this part of the energy is converted into the internal energy of the gas, which is manifested as heat generation. When the gas expands, the gas does work on the external environment, and the internal energy decreases, which is manifested as heat absorption.

[0084] Thermo-mechanical equivalent constant It is a physical constant used to express the unit of mechanical work (joule). The unit of heat conversion (joule) This reflects the equivalence between mechanical energy and thermal energy, or conversely, in practical applications. The value of is usually 1 because the unit of work and heat in the International System of Units (SI) is the joule. However, in some engineering fields or under specific unit systems, it may be necessary to introduce a conversion factor to ensure the consistency of units.

[0085] Pressure sensor 8 is used to monitor the gas pressure inside the anesthesia machine's breathing bag in real time. Its function is to convert the physical pressure signal of the gas into an electrical signal that can be processed by the controller. Pressure sensor 8, gas pressure... and These represent two consecutive sampling times (the current iteration step). and the previous step The gas pressure values ​​inside the airbag are collected by pressure sensor 8. These discrete pressure data are the basis for numerical integration calculations and reflect the instantaneous changes in gas pressure inside the airbag.

[0086] airbag internal volume and They represent the current iteration step. and the previous step The actual internal volume of the breathing bag of the anesthesia machine is used to calculate the work done by the gas, together with the corresponding pressure data. The internal volume of the bag can be obtained by pre-calibrating the pressure-volume characteristic curve of the bag and looking up the table based on the real-time pressure value or by calculating through a function relationship.

[0087] Total number of iterations This represents the total number of discrete sampling points for which the controller performs numerical integration throughout the entire gas work and heat generation calculation cycle. The value of directly affects the accuracy and real-time performance of the calculation. Typically, The larger the value, the closer the calculated result is to the true value, but the computational workload also increases accordingly; conversely, A smaller value results in faster calculation speed, but may reduce accuracy. The controller will select an appropriate value based on the system's real-time requirements and computing resources. value.

[0088] The controller continuously monitors the instantaneous gas pressure inside the anesthesia machine's breathing bag and the corresponding internal volume of the bag, and processes this discrete data using a numerical integration formula. Specifically, in each sampling cycle, the controller obtains the current gas pressure from pressure sensor 8. And combined with the current internal volume of the airbag Meanwhile, the controller stores the gas pressure from the previous sampling period. and the internal volume of the airbag This is achieved by averaging the current and previous pressure values ​​and multiplying them by the change in volume. Then, sum the product terms of all iteration steps, and finally multiply by the heat-work equivalent constant. This allows us to calculate the work done by the gas during the entire inflation and deflation cycle and convert it into heat generated by the flow. This allows the system to capture the conversion of mechanical energy into heat energy during gas compression and expansion in real time, overcoming the shortcomings of traditional solutions that only focus on heat conduction and convection while neglecting the important heat source of aerodynamic work. Taking this into account, the controller can obtain more comprehensive energy input parameters when calculating the fidelity of multimodal thermal-fluid coupling, thus making the characterization of the true thermodynamic state inside the airbag more accurate.

[0089] The controller is configured to acquire the first proportional extreme value of the instantaneous mass flow rate collected by the flow sensor 7 relative to the first system limit constant, and to acquire the second proportional extreme value of the heat generated by the flow work relative to the second system limit constant; using the intrinsic heat sink dissipation degree and the dynamic convection coupling dissipation degree as weights respectively, the aforementioned two sets of proportional extreme values ​​are subjected to multivariate homogeneous fusion cross-operation to extract the multimodal heat flow coupling fidelity.

[0090] The controller is configured to calculate the multimodal thermal-fluid coupling fidelity based on the following formula. :

[0091] In the formula, To ensure fidelity of multimodal thermal-fluid coupling; The mass flow rate collected by flow sensor 7; The preset limiting mass flow rate constant (unit) ); The preset strain energy constant (unit) ); This is a preset zero-prevention constant.

[0092] Multimodal thermal-fluid coupling fidelity It is a dimensionless comprehensive index used to quantify the coupling degree between the actual thermodynamic state of the airflow inside the breathing bag of an anesthesia machine under complex dynamic conditions and the external environment and its own physical properties. Its purpose is to provide a high-fidelity physical basis to correct the lag and distortion of traditional temperature measurement methods.

[0093] Mass flow rate collected by flow sensor 7 Mass flow rate refers to the mass of gas flowing through the cross-section of the gas path per unit time. This mass flow rate can be measured in various ways. For example, a thermal mass flow sensor 7 can be used to determine the mass flow rate by measuring the temperature change caused by the heat carried away by the fluid.

[0094] Preset limiting mass flow constant It is a pre-defined reference value used to normalize the mass flow rate. This constant is determined based on the typical operating range, maximum ventilation volume, or system design limits of the anesthesia machine's breathing bag, for example, by experimentally testing the maximum mass flow rate achievable under extreme ventilation conditions, and the heat generated by the flow. This refers to the amount of energy conversion generated by mechanical work during the compression or expansion of airflow inside the airbag. When the airbag is inflated, the external pressure does work on the gas, causing the gas temperature to rise; when the airbag is deflated, the gas expands and does work on the outside, causing the gas temperature to drop. This work-generated heat is an important component of the dynamic changes in the thermodynamic state inside the airbag.

[0095] Preset accommodation strain energy constant It is a pre-set reference value used to normalize the heat generated by the flow work. This constant is determined based on the elastic limit, maximum deformation capacity, or maximum work that may be achieved in extreme ventilation cycles of the airbag material, and is obtained through mechanical testing or simulation calculations of the airbag.

[0096] Intrinsic heat sink dissipation It is a dimensionless parameter used to characterize the heat storage capacity of the airbag wall material and the heat leakage effect at the contact surface between the sealing component 3 and the neck. It reflects the inherent heat loss or storage characteristics of the airbag system and is an important factor affecting the internal temperature field of the airbag. Dynamic convection coupling dissipation. It is a dimensionless parameter used to characterize the forced convection heat dissipation effect caused by the external wind speed on the outer wall of the inflation tube 2 and the surface of the airbag. It reflects the intensity of heat exchange between the airbag system and the external environment and is another important factor affecting the dynamic changes of the internal temperature field of the airbag.

[0097] Preset zero-prevention constant It is a very small positive value, its purpose being to prevent errors in calculating the fidelity of multimodal thermal-fluid coupling. When, the denominator In some extreme cases, the denominator may approach zero, leading to unstable calculation results or mathematical singularities. By introducing this constant, we can ensure that the denominator is always a non-zero value, thereby improving the robustness and stability of the calculation.

[0098] By constructing a multimodal heat-fluid coupling fidelity model, a deep quantification of the thermodynamic state inside the airbag was achieved. The core of this approach lies in the intrinsic heat sink dissipation. Coupling dissipation with dynamic convection As a basic weight, it is respectively related to the percentage of quality traffic. and the proportion of heat generated by work done in motion Perform weighted fusion.

[0099] Specifically, during the dynamic operation of the anesthesia machine's breathing cuff, the mass transport of airflow and the mechanical work done by the cuff occur simultaneously, and are coupled with the inherent heat dissipation characteristics of the cuff and the external convective heat dissipation effect. This is achieved by measuring the mass flow rate collected by the flow sensor 7. With the preset limiting mass flow constant The ratio effectively characterizes the dynamic mass transfer features during airflow transport and correlates it with the intrinsic heat sink dissipation. This combination corrects for the heat distribution deviation caused by airflow, making the assessment of the inherent heat loss inside the airbag more accurate.

[0100] At the same time, heat is generated by doing work through flow. With the preset accommodate strain energy constant The ratio reflects the change in internal energy of the airbag due to mechanical deformation during the inflation and deflation cycle, and is correlated with the dynamic convection coupling dissipation. This combination eliminates the interference of the external environment on the airbag's thermal balance, making the assessment of the external convective heat dissipation effect more accurate, and utilizing the intrinsic heat sink dissipation. Coupling dissipation with dynamic convection The sum is used as the normalized denominator, and a pre-defined zero-prevention constant is introduced. This ensures the stability and numerical robustness of the calculation results under different working conditions.

[0101] The controller is configured to extract the advance compensation reference quantity reflecting the temperature measurement inertia by combining the time-domain difference characteristics of the second temperature sensor 5 with the hardware time constant; then, it constructs a gain adjustment term based on the initial thermal bias coefficient and an attenuation damping term based on the multimodal thermal flux coupling fidelity; and uses the gain adjustment term and the attenuation damping term together to perform feedback perturbation correction on the advance compensation reference quantity, and finally outputs the target compensation quantity.

[0102] The controller is configured to calculate the target compensation amount based on the following formula. :

[0103]

[0104] In the formula, The target compensation amount; Basic thermal response compensation, unit ; Time constant, in units ; and This is the actual measured value from the second temperature sensor 5; This is the first gain adjustment constant; It is the second attenuation adjustment constant and takes a value greater than 0 and less than 1.

[0105] Target compensation amount This is the final feedback value proposed in this scheme for calibrating the temperature inertia of the sensor. Its function is to correct the original measurement value of the second temperature sensor 5 to eliminate the temperature lag and distortion caused by the thermal inertia of the sensor, so that the corrected temperature data is closer to the true instantaneous temperature of the airbag cavity.

[0106] Basic thermal response compensation To capture the instantaneous change trend of the measured value from the second temperature sensor 5, it calculates the temperature difference between the current sampling period and the previous sampling period, and combines this with the time constant. Weighting can provide a preliminary reflection of the sensor's response speed to temperature changes, laying the foundation for subsequent finer compensation.

[0107] time constant It is an adjustment factor used to quantify and adjust the basic thermal response compensation amount. The response strength to temperature change trends can be determined through experimental calibration and empirical values ​​based on the physical characteristics of the second temperature sensor 5, its installation location, and the thermal conductivity of the airbag material.

[0108] and These represent the actual measured values ​​of the internal cavity temperature collected by the second temperature sensor 5 in the current sampling period and the previous sampling period, respectively. The second temperature sensor 5 is usually a type of thermistor, platinum resistance thermometer, or thermocouple with fast response speed and high accuracy. It is configured to pass through the sealing assembly 3 and be fixedly connected to the neck, extending into the internal cavity to directly measure the temperature of the gas inside the airbag. These continuous measurements are the basic data for calculating the temperature change rate and performing thermal response compensation.

[0109] First gain adjustment constant It is a dimensionless weighting factor used to adjust the initial thermal bias coefficient. Target compensation amount The degree of influence is determined through system debugging, experimental optimization, or model prediction based on specific operating conditions, so as to ensure that the basic compensation amount can be appropriately enhanced or suppressed in the early stage of system startup or when there is a large difference between the gas source temperature and the internal cavity temperature.

[0110] Second attenuation adjustment constant It is a dimensionless weighting factor used to adjust the fidelity of multimodal thermal-fluid coupling. Target compensation amount The degree of influence, with a value greater than 0 and less than 1, indicates... It plays a role in attenuating or correcting the compensation amount, and its value can also be determined through system debugging, experimental optimization or model prediction based on specific working conditions, so as to ensure that the basic compensation amount can be finely corrected when the thermal flux coupling state inside the airbag is complex and variable.

[0111] Initial thermal bias coefficient It is a dimensionless parameter that characterizes the initial temperature difference between the gas source temperature and the zero-state temperature of the inner cavity before inflation. This coefficient reflects the initial deviation of the thermodynamic state inside the airbag during the initial stage of system startup or when the gas source temperature changes significantly, and serves as the target compensation amount. The calculation provides an important adjustment factor for the fidelity of multimodal thermal-fluid coupling. It is a comprehensive dimensionless parameter that integrates multiple thermodynamic characteristics such as intrinsic heat sink dissipation, dynamic convection coupling dissipation, instantaneous mass flow rate, and heat generated by flow work. This fidelity can comprehensively reflect the complexity of various heat-fluid coupling effects and the reliability of measurement data during dynamic ventilation inside the airbag, providing a basis for target compensation. The calculation provides a dynamic correction factor.

[0112] The core of this method, which calibrates temperature inertia through discrete advance calculation, lies in the controller receiving the cavity temperature value collected by the second temperature sensor 5. and First, calculate the basic thermal response compensation amount. This compensation amount, by capturing the instantaneous rate of temperature change, initially corrects the hysteresis response of the sensor caused by its own thermal capacity.

[0113] Building upon this, and to more comprehensively address the complex thermodynamic disturbances during anesthesia ventilation, the controller further incorporates an initial thermal bias coefficient. and multimodal heat flux coupling fidelity Initial thermal bias coefficient This reflects the initial temperature difference between the gas source and the inner cavity, and is adjusted by the first gain constant. By incorporating this into the compensation calculation, the system can be specifically enhanced in the initial startup phase or when the gas source temperature changes drastically, so as to quickly adapt to the initial thermal shock.

[0114] Meanwhile, multimodal heat flux coupling fidelity Taking into account various heat flow coupling effects such as heat storage in the airbag wall, heat leakage at the joint, external convection heat dissipation, and work done by gas compression and expansion, its value can dynamically reflect the reliability of the thermodynamic state inside the airbag.

[0115] By adjusting the second attenuation constant By integrating this with the basic compensation amount, the compensation mechanism can make precise adjustments to the compensation amount based on the complex dynamic physical evolution process inside the airbag. For example, when A low value indicates a complex thermal-fluid coupling state and that measurement fidelity may be affected. This will allow the compensation amount to be appropriately attenuated, avoiding overcompensation; conversely, when When the value is higher, the compensation amount can play a more effective role.

[0116] Ultimately, by compensating for the basic thermal response... Initial thermal bias coefficient and multimodal heat flux coupling fidelity By performing collaborative calculations, the controller can output a comprehensive target compensation amount. The target compensation amount Not only does it take into account the physical inertia of the sensor itself, but it also dynamically integrates various thermodynamic influencing factors of the internal and external environment of the airbag, thereby realizing discrete advance calculation of temperature inertia. This ensures that under the dynamic working condition of high-frequency inflation and deflation cycles of the anesthesia ventilation system, the measurement value of the second temperature sensor 5 can more accurately reflect the real instantaneous temperature of the airbag cavity, thus providing a more reliable data basis for pressure monitoring and volume conversion, and effectively solving the problems of temperature measurement lag and distortion in traditional systems.

[0117] Specifically, in order to solve the problem of initial thermal response deviation caused by the agitation of cold and heat sources when the air source first enters the deflated airbag, the controller extracts the absolute temperature difference between the air source temperature collected by the first temperature sensor 4 and the zero-state temperature of the inner cavity collected by the second temperature sensor 5, and calculates the initial thermal bias coefficient, thereby achieving the effect of benchmark quantification and rough compensation for the initial thermodynamic imbalance state. Furthermore, in order to address the problem that simple node temperature measurement during the airflow filling stage cannot detect the heat storage absorbed by the capsule wall material due to its inherent specific heat capacity and the interference of heat leakage conducted by the sealing joint flange surface, the system combines the pre-stored capsule wall material properties (density, specific heat capacity, etc.) with the joint heat leakage parameters collected by the heat flow sensor to calculate and extract the intrinsic heat sink dissipation degree, thereby achieving the effect of accurately removing the interference of nonlinear inherent structural heat loss on the internal cavity temperature measurement. Meanwhile, in order to solve the problem of thermal balance distortion caused by the forced convection heat dissipation disturbance of the pipe wall due to the external laminar flow wind speed, the system integrates the external wind speed collected by the wind speed sensor and the dynamic temperature difference between the inside and outside to calculate the dynamic convection coupling dissipation, thus achieving the effect of adaptively correcting the impact of dynamic changes in the external environment on the heat exchange of the pipeline. In addition, in order to address the problem that a single probe parameter cannot characterize the dynamic boundary balance between the aerodynamic work potential energy and the multidimensional heat dissipation when the gas expands and does work during high-frequency charging and discharging and the transient mass transport approaches the physical blockage state, the system coordinates the flow work heat generated by the pressure sensor 8 and the volume integral to extract the heat generated by the flow, as well as the instantaneous mass flow rate collected by the flow sensor 7, to calculate the multimodal heat flow coupling fidelity, achieving the effect of deep quantification and characterization of the coupling degree of the real thermodynamic state under complex dynamic conditions. Ultimately, to address the fundamental problem of severe time lag in temperature measurement and pressure compensation failure caused by conventional feedback mechanisms being limited to a single hysteresis electrical signal, the controller integrates the aforementioned initial thermal bias coefficient, intrinsic heat sink dissipation, dynamic convection coupling dissipation, and multimodal heat-fluid coupling fidelity to perform discrete advance calculations on the temperature measurement inertia of the second temperature sensor 5, outputting a target compensation amount. This target compensation amount is fed back to the anesthesia machine system, ultimately achieving the technical effect of eliminating the time lag and distortion of temperature monitoring under high-frequency alternating conditions from the underlying physical mechanism, ensuring the ultimate accuracy of pressure sensor calibration and volume conversion in clinical anesthesia ventilation.

[0118] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A breathing bag for an anesthesia machine with pressure monitoring, characterized in that: include: The capsule, inflation tube, sealing assembly, sensor assembly, and controller; The inflation tube is connected to an air source, and its exhaust end is fixedly connected to the neck of the bladder through the sealing assembly. The airflow passes through the inflation tube and the sealing assembly to fill the inner cavity of the bladder. The sensor group includes a first temperature sensor, a second temperature sensor, a third temperature sensor, a heat flow sensor, a wind speed sensor, a flow rate sensor, and a pressure sensor. The first temperature sensor is located at the air source outlet. The second temperature sensor extends into the inner cavity through the sealing assembly. The heat flow sensor is embedded in the contact surface between the sealing assembly and the neck. The third temperature sensor is attached to the outer wall of the inflation tube. The wind speed sensor is located at an external environmental exposure position. The flow rate sensor and the pressure sensor are located in the air passage of the inflation tube. The controller is electrically connected to the sensor group and is configured to receive signals collected by the sensors, extract the absolute temperature difference between the air source and the inner cavity to obtain the initial thermal bias coefficient, combine the material properties of the bladder wall and the heat leakage parameters of the joint to obtain the intrinsic heat sink dissipation, integrate the external wind speed and the internal and external temperature difference to calculate the dynamic convection coupling dissipation, coordinate the work characteristics of instantaneous air pressure and volume area integral and instantaneous mass flow rate to extract the multimodal heat flow coupling fidelity, perform discrete advance calculation on the temperature measurement inertia based on the comprehensive features, and finally output the target compensation amount for state calibration feedback.

2. The anesthesia machine breathing bag with pressure monitoring according to claim 1, characterized in that: The controller is configured to acquire the gas source temperature collected by the first temperature sensor and the zero-state temperature of the inner cavity collected by the second temperature sensor, calculate the absolute temperature difference between the gas source and the inner cavity, and perform normalization mapping processing on the absolute temperature difference using a preset first temperature constant to obtain the dimensionless initial thermal bias coefficient. The initial thermal bias coefficient is positively correlated with the absolute temperature difference, and its upper limit threshold is limited by the normalized mapping truncation.

3. The anesthesia machine breathing bag with pressure monitoring according to claim 2, characterized in that: The controller is configured to extract the discrete rate of change of the second temperature sensor in the time domain, and multiply the discrete rate of change by the pre-stored capsule wall material properties to calculate the capsule wall loss power. The specific material properties of the capsule wall include capsule wall density, capsule wall specific heat capacity, capsule wall thickness, and capsule surface area.

4. The anesthesia machine breathing bag with pressure monitoring according to claim 3, characterized in that: The controller is configured to acquire the absolute value of the wall loss power and extract the absolute value of the joint leakage power corresponding to the joint leakage parameter by combining the underlying data collected by the heat flow sensor. The total heat loss is obtained by summing the absolute value of the power loss in the bladder wall with the absolute value of the heat leakage power at the joint. The intrinsic heat sink dissipation is output by calculating the ratio between the comprehensive heat loss and an energy reference scale that incorporates a preset basic power constant.

5. The anesthesia machine breathing bag with pressure monitoring according to claim 4, characterized in that: The controller is configured to construct a temperature sample sequence and invoke a residual minimization algorithm to identify and output adaptive weighting coefficients based on the temperature sample sequence; The adaptive weighting coefficient is used to proportionally weight and fuse the gas temperature inside the tube and the surface temperature of the capsule to output a dynamically weighted average air temperature.

6. The anesthesia machine breathing bag with pressure monitoring according to claim 5, characterized in that: The controller is configured to acquire the internal and external temperature difference between the pipe wall parameters collected by the third temperature sensor and the dynamic weighted average air temperature, and to use the second temperature constant to perform dimensionless processing on the internal and external temperature difference to obtain a temperature correlation term. Simultaneously, the convection rate attenuation factor is calculated based on the external wind speed collected by the wind speed sensor. The convection rate attenuation factor is multiplicatively coupled with the temperature correlation term to output the dynamic convection coupling dissipation.

7. The anesthesia machine breathing bag with pressure monitoring according to claim 6, characterized in that: The controller is configured to synchronously extract instantaneous air pressure data and corresponding airbag volume data collected by the pressure sensor, calculate the work characteristics of instantaneous air pressure and volume area integral using a trapezoidal surface accumulation and summation algorithm, and convert the work characteristics into flow work and heat generation.

8. The anesthesia machine breathing bag with pressure monitoring according to claim 7, characterized in that: The controller is configured to acquire the first proportional extreme value of the instantaneous mass flow rate collected by the flow sensor relative to the first system limiting constant, and to acquire the second proportional extreme value of the heat generated by the flow work relative to the second system limiting constant. Using the intrinsic heat sink dissipation and dynamic convection coupling dissipation as weights, the aforementioned two sets of extreme values ​​are subjected to multivariate homogeneous fusion and cross-operation to extract the multimodal heat flow coupling fidelity.

9. The anesthesia machine breathing bag with pressure monitoring according to claim 8, characterized in that: The controller is configured to combine the time-domain difference characteristics of the second temperature sensor with the hardware time constant to extract a leading compensation reference quantity that reflects the temperature inertia. Furthermore, a gain adjustment term is constructed based on the initial thermal bias coefficient, and an attenuation damping term is constructed based on the multimodal thermal-fluid coupling fidelity. The gain adjustment term and the attenuation damping term are used together to perform feedback perturbation correction on the lead compensation reference quantity, and finally the target compensation quantity is output.

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