Breathing machine temperature sensor monitoring system

Abstract

The invention relates to the technical field of medical instruments and precision sensing control, in particular to a breathing machine temperature sensor monitoring system. The system comprises a signal acquisition module, a thermal impedance mapping module, a reverse compensation module and a temperature control execution module. The system matches a dynamic time constant by collecting the fluid flow rate and the original temperature; the core of the method is that a reconstitution temperature for eliminating thermal lag is solved by using a reverse compensation algorithm, and the heating power is adjusted through PID (Proportion Integration Differentiation); a flow velocity coupling mechanism is introduced, the response time is remarkably shortened on the premise that the packaging corrosion resistance of the sensor is kept, airway burn caused by heating overshoot is effectively prevented, and the problem of measurement delay of a large-heat-capacity sensor in a dynamic flow field is solved.

Description

Technical Field

[0001] This invention relates to the field of medical devices and precision sensing and control technology, specifically a ventilator temperature sensor monitoring system. Background Technology

[0002] In the current ventilator ventilation treatment environment, temperature monitoring systems need to work for a long time in tubing filled with hot and humid air. In order to balance insulation and corrosion resistance, encapsulated thermistors with physical protective layers are generally used to collect fluid state data. When monitoring these data, existing solutions often directly use the sensor's raw electrical signals for analog-to-digital conversion and feedback control. Although this packaging structure ensures the physical safety of the hardware, the objective existence of thermal capacity and thermal resistance in the packaging layer, and the fact that the sensor's thermal response time constant is not a fixed value but a function that changes non-linearly with airway flow rate, leads to significant phase lag and amplitude attenuation in the measurement results. This thermal lag effect makes it impossible for the temperature control system to keep up with the rapid fluctuations in airflow temperature in time, and it is very easy for the output power of the heating and humidification components to overshoot due to feedback delay, which may cause airway burns to patients. In addition, traditional fixed parameter algorithms are difficult to adapt to a wide dynamic range of respiratory flow rates, often resulting in noise amplification at low flow rates or insufficient compensation at high flow rates, which cannot meet the requirements of high-precision closed-loop control. Therefore, how to solve the thermal hysteresis measurement delay under variable flow rate conditions without sacrificing physical protection, and improve the real-time performance and accuracy of temperature data analysis, has become an urgent technical problem to be solved. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the present invention provides a ventilator temperature sensor monitoring system. Specifically, the technical solution of the present invention includes: The signal acquisition module is used to acquire fluid state data in the ventilator tubing in real time. The fluid state data includes the raw temperature signal acquired by the encapsulated thermistor and the airway flow rate signal measured by the flow rate sensor. The thermal impedance mapping module is used to search and match in a preset thermal impedance-flow velocity mapping library based on the airway flow velocity signal in order to determine the dynamic time constant under the current fluid state. The reverse compensation module is used to calculate the rate of change of the original temperature signal with respect to time, and based on the original temperature signal, the dynamic time constant, and the rate of change, to calculate the reconstructed temperature signal after eliminating the thermal hysteresis effect; The temperature control execution module is used to input the reconstructed temperature signal as a feedback variable into the PID controller and adjust the output power of the heating and humidifying components according to the reconstructed temperature signal.

[0004] Preferably, the reverse compensation module calculates the reconstructed temperature signal after eliminating the thermal hysteresis effect based on the original temperature signal, the dynamic time constant, and the rate of change, including: The original temperature signal, the dynamic time constant, and the rate of change are invoked; The thermal hysteresis compensation amount is obtained by calculating the product of the dynamic time constant and the rate of change. The thermal hysteresis compensation is superimposed on the original temperature signal to generate the reconstructed temperature signal, so as to mathematically restore the airway center fluid temperature before it was smoothed by the encapsulation layer.

[0005] Preferably, the thermal impedance mapping module performs a search and matching in a preset thermal impedance-flow velocity mapping library, including: A heat transfer model is constructed, which includes the convective thermal resistance between the airflow and the sensor surface, and the conductive thermal resistance from the sensor surface to the core. Based on the heat transfer characteristics of the airflow boundary layer, a nonlinear functional relationship between the convective thermal resistance and the flow velocity is established. The nonlinear function relationship is discretized and stored as the thermal impedance-flow velocity mapping library, wherein the higher the flow velocity, the smaller the corresponding dynamic time constant.

[0006] Preferred options also include: The condensation monitoring module is used to construct a temperature-flow rate thermal hysteresis loop in the phase space based on the original temperature signal and the gas flow rate signal. The feature extraction module is used to calculate the geometric area enclosed by the temperature-flow rate thermal hysteresis loop within one respiratory cycle; The status determination module is used to determine whether there is condensation on the surface of the sensor probe based on the comparison result between the geometric area and the preset water accumulation threshold.

[0007] Preferably, the condensation monitoring module constructs a temperature-flow rate thermal hysteresis loop within the phase space, including: Using the airway flow velocity signal as the horizontal axis coordinate and the original temperature signal as the vertical axis coordinate, the flow velocity and temperature data at the same moment are mapped to a two-dimensional phase space. Connect all the mapping points within a respiratory cycle to form a closed temperature-flow rate thermal hysteresis loop trajectory.

[0008] Preferably, the state determination module determines whether condensation accumulates on the surface of the sensor probe based on the comparison result between the geometric area and the preset water accumulation threshold, configured as follows: Call upon the geometric area and the preset water accumulation threshold; If the geometric area is greater than the preset water accumulation threshold, it is determined that there is condensation on the surface of the sensor probe, and a water accumulation alarm signal is generated. If the geometric area is less than or equal to the preset water accumulation threshold, the sensor probe surface is determined to be in normal working condition, and a status flag is generated to maintain the current monitoring mode.

[0009] Preferably, the thermal impedance mapping module further includes a static back-off unit, which is configured as follows: Real-time monitoring of the airway flow rate signal; If the airway flow rate signal is lower than the preset static flow rate threshold, the dynamic time constant is stopped, and the original temperature signal is output as the reconstructed temperature signal. If the airway flow rate signal is greater than or equal to the preset static flow rate threshold, the dynamic time constant is maintained to reconstruct the temperature signal.

[0010] Preferably, the signal acquisition module acquires the raw temperature signal collected by the packaged thermistor, including: The voltage value of the packaged negative temperature coefficient NTC thermistor is read using an analog-to-digital converter. The voltage value is converted into a digital signal, and the digital signal is denoised to obtain the original temperature signal.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This system introduces a reverse compensation mechanism based on flow velocity coupling, which significantly compresses the sensor response time while preserving the corrosion resistance and insulation properties of the sensor encapsulation layer. This hardware and software co-design enables the system to effectively prevent overshoot caused by sensor thermal hysteresis, thereby avoiding the risk of airway burns to patients and solving the safety hazards caused by measurement delays in traditional large heat capacity sensors in dynamic flow fields. 2. This system constructs a virtual double-layer thermal model that includes convective and conductive thermal resistances and establishes a nonlinear mapping relationship between flow rate and dynamic time constant. The system can dynamically adjust compensation parameters according to real-time airway flow rate. This design effectively solves the dilemma of traditional fixed-parameter algorithms that cause noise amplification due to overcompensation at low flow rates or sluggish response due to insufficient compensation at high flow rates. It ensures that the ventilator can always maintain optimal dynamic response characteristics when switching from adult high-flow mode to neonatal low-flow mode. 3. This system utilizes the difference in specific heat capacity between water and air to construct a temperature-flow rate phase space and calculate the geometric area of ​​the hysteresis loop, thus identifying condensation buildup on the probe surface without the need for additional humidity sensors. This mechanism enables real-time monitoring of pipeline status around the clock, and immediately alarms upon detection of water accumulation, effectively preventing sensor reading distortion caused by water accumulation and potential risks of bacterial growth and accidental aspiration. 4. This system integrates a static backoff unit and a signal denoising processing mechanism; when the airway flow rate is lower than the preset threshold, dynamic compensation is automatically stopped to avoid mathematical singularities and noise amplification, and to prevent drastic temperature reading jumps when the ventilator is on standby or the tubing is dislodged; at the same time, in conjunction with hardware filtering and software smoothing algorithms, quantization noise in differential operations is effectively suppressed, ensuring the stable operation of the closed-loop control system. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. Example 1:

[0014] Please see Figure 1 A ventilator temperature sensor monitoring system includes: The signal acquisition module is used to acquire fluid state data in the ventilator tubing in real time. The fluid state data includes the raw temperature signal acquired by the encapsulated thermistor and the airway flow rate signal measured by the flow rate sensor. The thermal impedance mapping module is used to search and match in a preset thermal impedance-flow velocity mapping library based on the airway flow velocity signal in order to determine the dynamic time constant under the current fluid state. The reverse compensation module is used to calculate the rate of change of the original temperature signal with respect to time, and based on the original temperature signal, dynamic time constant, and rate of change, to solve the reconstructed temperature signal after eliminating the thermal hysteresis effect. The temperature control execution module is used to input the reconstructed temperature signal as a feedback variable into the PID controller, and adjust the output power of the heating and humidifying components according to the reconstructed temperature signal.

[0015] This embodiment details the overall architecture and collaborative logic of the aforementioned ventilator temperature sensor monitoring system. This system aims to resolve the inherent contradiction between sensor encapsulation protection and rapid thermal response in traditional ventilator temperature monitoring—the impossible trinity problem. The signal acquisition module, as the sensing front end, acquires the raw temperature signal through an encapsulated thermistor, such as an NTC thermistor with a stainless steel or epoxy resin protective layer. Its source is the thermistor voltage conversion, which physically means the sensor core temperature is delayed due to the thermal resistance of the encapsulation layer, and the airway flow rate signal is simultaneously acquired through the flow rate sensor. Its source is the flow meter reading, and its physical meaning is the real-time velocity of the fluid within the airway; the thermal impedance mapping module, as a parameter optimization engine, is based on the physical fact that the sensor's thermal response speed is not a fixed value but a nonlinear function that varies with the flow velocity, and utilizes the flow velocity signal. Index out the corresponding dynamic time constant Its source is retrieved from the thermal impedance-flow velocity mapping library, and its physical meaning is the time inertia required for the sensor to reach thermal equilibrium at the current flow velocity; the reverse compensation module executes the inverse heat conduction operator to calculate the time change rate of the original temperature signal. Its origin lies in differential operations, and its physical meaning is the instantaneous trend of temperature change, combined with... and dynamic time constant The reconstructed temperature signal was calculated. Its source is the algorithm output, and its physical meaning is the true airflow temperature after removing thermal hysteresis; the temperature control execution module no longer relies on the hysteresis original signal, but instead uses the leading signal. Input to the PID controller to adjust the power of the heating and humidifying components in real time to achieve closed-loop control; This embodiment introduces a reverse compensation mechanism based on flow velocity coupling, which reduces the response time of traditionally packaged sensors from the second level to the hundreds of millisecond level without sacrificing the physical protection performance of the sensors. This hardware and software co-design enables the system to effectively prevent overheating caused by sensor hysteresis, thereby avoiding the risk of airway burns to patients. At the same time, it retains the corrosion resistance and insulation properties of the sensor packaging layer, greatly improving the safety and durability of medical devices.

[0016] Example 2: The reverse compensation module, based on the original temperature signal, dynamic time constant, and rate of change, calculates the reconstructed temperature signal after eliminating thermal hysteresis, including: Call up the original temperature signal, dynamic time constant, and rate of change; The thermal hysteresis compensation is obtained by calculating the product of the dynamic time constant and the rate of change. The thermal hysteresis compensation is superimposed on the original temperature signal to generate a reconstructed temperature signal, so as to mathematically restore the airway center fluid temperature before it was smoothed by the encapsulation layer.

[0017] This embodiment further specifies the solution logic of the reverse compensation module in Embodiment 1. This logic is constructed based on the inverse operation of Newton's law of cooling. The reverse compensation module executes the inverse heat conduction operator to calculate the time rate of change of the original temperature signal. , or marked as Its physical meaning is the instantaneous trend of temperature change. In calculating the rate of change... To prevent single-step differential amplification of sampling noise, this embodiment employs a five-point center difference algorithm for low-noise differential calculation, the formula of which is:

[0018] in, for The rate of temperature change at any given time The original temperature signal, The sampling interval; The core algorithm calculates the dynamic time constant. With this low noise rate of change The product of these two factors yields the thermal hysteresis compensation. Its source is calculated, and its physical meaning is the instantaneous temperature difference between the sensor core and the environment caused by thermal impedance, expressed in degrees Celsius; this compensation amount The physical essence of this is the quantification of the thermal barrier that the sensor must overcome in order to follow changes in ambient temperature; based on this, the system performs a linear superposition operation, which... Compensation back to the original signal Generate reconstructed temperature signal Its core algorithm model follows the formula:

[0019] in, Reconstructing temperature, Original temperature Dynamic time constant : Temperature change rate; This mathematical model is equivalent to deconvolution processing the signal convolved by the sensor's thermal capacity, thereby restoring the high-frequency temperature fluctuation information that was smoothed out by the physical encapsulation layer in the digital domain. This embodiment, by introducing a compensation algorithm with a first derivative term, can keenly capture the initial trend of temperature change; at the moment when the temperature has just begun to rise but the sensor has not yet significantly heated up, due to the rate of change... The signal has already shown a positive value and is being reconstructed. It will generate an immediate jump, thereby enabling predictive measurement; this processing method eliminates the phase lag caused by physical conduction, allowing the control system to sense temperature changes in advance and respond accordingly, thus solving the measurement delay problem of large heat capacity sensors in dynamic flow fields.

[0020] Example 3: The thermal impedance mapping module performs a search and matching within a preset thermal impedance-flow velocity mapping library, including: A heat transfer model is constructed, which includes the convective thermal resistance between the airflow and the sensor surface, as well as the conductive thermal resistance from the sensor surface to the core. Based on the heat transfer characteristics of the airflow boundary layer, a nonlinear functional relationship between convective thermal resistance and flow velocity is established. The nonlinear functional relationship is discretized and stored as a thermal impedance-flow velocity mapping library, where the higher the flow velocity, the smaller the corresponding dynamic time constant.

[0021] This embodiment further specifies the construction principle of the thermal impedance mapping module in Embodiment 1, aiming to solve the problem that a single time constant cannot adapt to variable flow rate conditions; the heat transfer model in this embodiment is embodied as a virtual two-layer thermal model; this embodiment abstracts the sensor system into a virtual two-layer thermal model, clearly defining two thermal resistance components: convective thermal resistance. Its origin is from the model definition, and its physical meaning is the heat exchange resistance between the airflow and the outer surface of the sensor, which is affected by the thickness of the fluid boundary layer and the conductive thermal resistance. Its origin lies in material properties, and its physical meaning is the heat transfer resistance from the sensor surface to the internal temperature-sensing core, which is usually considered a constant; the total thermal impedance of this model Expressed as a series structure This leads to the introduction of sensor heat capacity. Conductive thermal resistance and convection thermal resistance Using physical quantities, the dynamic time constant is derived. Physical model:

[0022]

[0023] To establish the relationship between the physical model and the flow velocity, based on the relationship between the Nusselt number and the Reynolds number in fluid mechanics, i.e., based on King's Law, the convective thermal resistance is considered. With flow rate It exhibits a negative exponential relationship; and considering that the fluid is at rest... The finite thermal resistance caused by natural convection necessitates the introduction of a natural convection compensation term. To eliminate mathematical singularities; based on this, inherent limit is defined. Natural convection compensation item and fitting coefficients Considering that the ventilator's working cycle includes inhalation and exhalation, and that the fluid cooling effect is independent of the flow direction and only related to the absolute magnitude of the flow rate, this embodiment uses a corrected fitting formula that includes absolute value calculation:

[0024] in, Dynamic time constant Inherent limit, The absolute value of airway flow rate. Natural convection compensation term, used to eliminate The mathematical singularity of time and characterizes the fundamental convection effect under static fluid conditions. : Fitting coefficients; introduced by introducing the absolute value operator This ensures that the flow rate When it is negative, the base It is always a positive real number, avoiding the undefined operation error caused by taking the non-integer power of a negative number in the real number field, thus realizing continuous calculation throughout the entire breathing cycle; Regarding the dimensional consistency of the above formula, the left side of the equation... Dimensions are Flow velocity term on the right Dimensions are ,That The exponentiation results in a dimensionless quantity. Therefore, the coefficient Defined as a parameter containing a specific dimension conversion factor, whose dimensions are... This ensures balance with the time dimension on the left; the above formula effectively establishes the physical parameters. With measurement variables The mathematical mapping between them, where, Corresponding to item, Corresponding to The system was pre-tested in a laboratory environment to measure the step response at different flow velocities; for example, in a standard test wind tunnel, the ambient temperature was constant at 25 degrees Celsius, and the airflow temperature abruptly changed to 37 degrees Celsius. The measured data are as follows: When At L / min seconds; when At L / min seconds; when At L / min seconds; based on the above experimental data, a nonlinear least squares fitting method is used to obtain the parameters. Second, a coefficient with dimensional conversion properties , , L / min; This introduction The proposed model successfully eliminated the infinite singularity problem in the original model at zero flow velocity, ensuring... The calculated time is approximately 5.2 seconds, which is consistent with physical facts; By performing multi-point calibration or frequency sweep testing across the entire measurement range, the above formula is fitted using the least squares method, and the calculated continuous curve is discretized and stored as a thermal impedance-flow velocity mapping library, physically implemented as a lookup table (LUT). In this thermal impedance-flow velocity mapping library, the higher the flow velocity, the higher the dynamic time constant indexed. The smaller; This embodiment achieves adaptive and precise compensation across the entire flow rate range by establishing a mapping between flow rate and dynamic time constant. This design effectively solves the dilemma of traditional algorithms using a fixed time constant, which results in overcompensation at low flow rates (i.e., noise amplification) or undercompensation at high flow rates (i.e., sluggish response). It ensures that the temperature monitoring system maintains optimal dynamic response characteristics when the ventilator switches from adult high-flow mode to neonatal low-flow mode, thus enhancing the algorithm's robustness under operating conditions.

[0025] Example 4: Also includes: The condensation monitoring module is used to construct a temperature-flow rate thermal hysteresis loop in phase space based on the original temperature signal and the gas flow rate signal. The feature extraction module is used to calculate the geometric area enclosed by the temperature-flow rate thermal hysteresis loop within a respiratory cycle. The status determination module is used to determine whether there is condensation on the surface of the sensor probe based on the comparison result between the geometric area and the preset water accumulation threshold.

[0026] This embodiment introduces a non-invasive condensate detection function, aiming to uncover potential physical anomalies using existing temperature monitoring data; the condensate monitoring module constructs a phase space analysis model, utilizing synchronously acquired raw temperature signals. and airway flow rate signal As an orthogonal basis, the feature extraction module performs integral calculations on the hysteresis loop trajectory over a complete respiratory cycle, including both inspiratory and expiratory phases, to calculate the geometric area enclosed by the temperature-flow rate thermal hysteresis loop. Its origin is integral calculation, and its physical meaning is to characterize the heat energy intake and loss characteristics of the system in the inspiratory and expiratory cycle, with arbitrary units; the state determination module acts as a comparator, and calculates the geometric area. With preset water accumulation threshold A comparison is performed to determine whether condensation has accumulated on the surface of the sensor probe; This embodiment cleverly utilizes the physical fact that water's specific heat capacity is much greater than that of air to achieve water accumulation diagnosis without additional hardware. When condensation adheres to the sensor surface, its equivalent heat capacity increases dramatically, causing a significant hysteresis in temperature change under the same flow rate variation. This results in a thick hysteresis loop in phase space, i.e., an area... The solution significantly increases the safety of clinical care. It eliminates the need for additional humidity sensors and achieves water accumulation alarms solely through algorithm analysis, reducing hardware costs and avoiding the risk of aspiration caused by condensate flowing back into the patient's airway.

[0027] Example 5: The condensation monitoring module constructs a temperature-flow rate thermal hysteresis loop within the phase space, including: Using the airway flow velocity signal as the horizontal axis and the original temperature signal as the vertical axis, the flow velocity and temperature data at the same moment are mapped to a two-dimensional phase space. Connect all the mapping points within a respiratory cycle to form a closed temperature-flow thermal hysteresis loop trajectory.

[0028] This embodiment is a further specification of the phase space trajectory construction method in Embodiment 4; the system defines a two-dimensional Cartesian coordinate system, explicitly using the airway flow velocity signal. The horizontal axis is the X-axis, based on the original temperature signal. The vertical axis is the Y-axis; at each sampling time... The collected data will be processed. The system is mapped to a point within this space. Since the ventilator operates in a periodic state of inhalation (i.e., flow rate is positive or high) and exhalation (i.e., flow rate is zero, low, or negative), and the airway temperature fluctuates periodically with the airflow, the system connects all the mapped points within a respiratory cycle, naturally forming a closed temperature-flow rate thermal hysteresis loop trajectory. Under normal dry conditions, this trajectory appears as a narrow spindle shape, while under waterlogged conditions, the trajectory expands into a wide ellipse or an irregular ring. Specifically, the feature extraction module uses Discrete Green's Theorem to calculate the geometric area enclosed by the closed trajectory. Assuming the system collects data within one respiratory cycle... Group of discrete data point sequences ,in, For the first The flow rate of time, For the first The original temperature at that moment, and let Given a closed path, the geometric area is... The calculation formula is configured as follows:

[0029] in, and Representing the first The flow rate and original temperature values ​​at each sampling point are compared, and boundary conditions are set. To ensure path closure; this formula transforms continuous integration in two-dimensional phase space into a computer-executable vector product summation operation, which can accurately quantify the topological characteristics of hysteresis loops and avoid the sign cancellation problem of direct integration methods when dealing with non-monotonic closed curves. This embodiment achieves dimensionality reduction and feature decoupling by converting waveform data in the time domain to the flow velocity-temperature phase space. This processing method makes it possible to diagnose the physical state, i.e., water accumulation, using topological geometric features, i.e., area, thus overcoming the limitations of simply relying on threshold judgment. The morphological features of the phase space trajectory have high recognizability and can effectively distinguish whether the sensor is in a normal thermal fluctuation state or an abnormal condensation water encapsulation state, significantly improving the anti-interference ability of the detection algorithm.

[0030] Example 6: The status determination module determines whether condensation has accumulated on the surface of the sensor probe based on a comparison between the geometric area and a preset water accumulation threshold. The configuration is as follows: Call up the geometric area and preset water accumulation threshold; If the geometric area is greater than the preset water accumulation threshold, it is determined that there is condensation on the surface of the sensor probe and a water accumulation alarm signal is generated. If the geometric area is less than or equal to the preset water accumulation threshold, the sensor probe surface is determined to be in normal working condition, and a status flag is generated to maintain the current monitoring mode.

[0031] This embodiment further specifies the state determination logic in Embodiment 4, establishing a quantified water accumulation detection standard; the module retrieves the geometric area of ​​the current period calculated by the feature extraction module from memory. and the preset water accumulation threshold The threshold The determination was based on extensive clinical simulation experiments: in terms of tube diameter In a standard breathing circuit of mm, the humidifier was set to a relative humidity of 100%, and the gas flow rate exhibited sinusoidal fluctuations with a peak value of 60 L / min. Experiments were conducted to determine the calculated hysteresis loop geometric area when the sensor surface was dry and clean. Stable within the range When 0.5 ml of simulated condensate is added to the sensor surface using a micro-syringe, the thermal hysteresis is exacerbated due to the specific heat capacity of water, resulting in an increase in the geometric area. Surge to range Based on this significant difference, this embodiment uses the maximum interval classification principle to determine the threshold, that is, defines the threshold as the maximum boundary value of the normal state interval. Minimum boundary value of the water accumulation state interval Arithmetic mean:

[0032] Considering system noise tolerance and the convenience of integer arithmetic, the preset water accumulation threshold is ultimately set by rounding up. ; System execution logic judgment: response to geometric area Greater than the preset water accumulation threshold For example, a real-time calculated value of 20.5 indicates an abnormally significant thermal hysteresis. The system determines that condensation has accumulated on the sensor probe surface and immediately generates a condensation alarm signal. This signal can trigger an audible and visual alarm on the ventilator's UI, prompting medical staff to clean the tubing. Conversely, the response to the geometric area... Less than or equal to the preset water accumulation threshold For example, if the real-time calculated value is 3.2, it indicates that the thermal hysteresis is within the normal range. The system determines that the sensor probe surface is in normal working condition and generates a status flag to maintain the current monitoring mode without intervention. The judgment logic provided in this embodiment is based on rigorous experimental data. The physical definition of the threshold is clarified through statistical boundary calculation, providing an objective means of detecting water accumulation. Compared with the traditional method of relying on manual visual inspection of pipeline leaks, this automated judgment mechanism can monitor the pipeline status in real time around the clock. Once water accumulation is detected, an alarm is immediately triggered, effectively preventing sensor reading distortion caused by water accumulation and the potential risk of bacterial growth, ensuring the hygiene and accuracy of the respiratory treatment process.

[0033] Example 7: The thermal impedance mapping module also includes a static back-off unit, which is configured as follows: Real-time monitoring of airway flow rate signals; If the airway flow rate signal is lower than the preset static flow rate threshold, the dynamic time constant will be stopped and the original temperature signal will be output as the reconstructed temperature signal. If the airway flow rate signal is greater than or equal to the preset static flow rate threshold, the dynamic time constant is maintained to reconstruct the temperature signal.

[0034] This embodiment enhances the security of the thermal impedance mapping module in Embodiment 1 by adding a static backoff unit as a system security firewall; this unit continuously monitors the airway flow rate signal. The numerical value; system execution condition judgment and mode switching: in response to airway flow rate signal Below the preset static flow rate threshold Its source is preset parameters, such as 2 L / min, which physically represent the critical point defining a static or extremely low-velocity fluid state. The system identifies that it is currently in a static or extremely low-velocity environment; under this environment, the model... The term may tend to infinity or be extremely unstable, and the calculation at this time... Electromagnetic noise is easily amplified, therefore the unit is forced to stop calling the dynamic time constant. The raw temperature signal after low-pass filtering is directly used. As a reconstructed temperature signal Output, equivalent to letting Conversely, if the flow rate signal is greater than or equal to the threshold, the system confirms that it is in a normal ventilation state and maintains the flow rate-related dynamic time constant. To reconstruct the temperature signal; This embodiment effectively solves the singularity problem of the algorithm near the zero flow rate by introducing a static backoff mechanism. This design prevents drastic temperature readings caused by noise amplification when the ventilator is on standby or the tubing is dislodged, ensuring the stability of the system output. This smooth switching strategy from dynamic compensation to static pass-through reflects the fail-safe principle in control system design and ensures the reliability of temperature monitoring data under extreme conditions.

[0035] Example 8: The signal acquisition module acquires the raw temperature signal collected by the packaged thermistor, including: The voltage value of the packaged negative temperature coefficient NTC thermistor is read using an analog-to-digital converter. The voltage value is converted into a digital signal, and the digital signal is then denoised to obtain the original temperature signal.

[0036] This embodiment further specifies the hardware implementation path of the signal acquisition module in Embodiment 1. The system uses a high-precision analog-to-digital converter (ADC), sourced from hardware circuitry such as a 12-bit resolution chip, connected to the voltage divider circuit of a packaged NTC thermistor to read the voltage value across its terminals. The NTC thermistor is chosen as the core temperature sensor due to its high sensitivity and low cost. The system utilizes the resistance-temperature characteristic curve of the NTC, i.e., the Steinhart-Hart equation, to convert the voltage value into a linear digital temperature signal. Considering the need for subsequent differential calculations... Noise is amplified by differential operations. In this embodiment, the digital signal is denoised using a weighted exponential moving average filter. This filter removes high-frequency noise while preserving the signal's dominant frequency characteristics, ultimately yielding a smooth original temperature signal. Its core discrete iterative formula is:

[0037] in, This is the filtered output value at the current moment, i.e., the original temperature signal in the aforementioned embodiment. This represents the unprocessed input value acquired by the ADC at the current moment. This is the filtered output value from the previous time step. It is a smoothing factor; The algorithm only requires saving one historical variable in its code implementation, which significantly reduces the RAM overhead of the MCU compared to higher-order filters, while effectively suppressing the quantization noise amplification phenomenon that may occur in subsequent differentiation operations. This embodiment emphasizes the importance of high-quality raw signal acquisition for the inverse compensation algorithm. By combining hardware filtering and software denoising, the system provides high signal-to-noise ratio input data for the subsequent inverse model. This front-end processing strategy ensures the stability of the differential operation and prevents reconstructed signal oscillations caused by quantization noise or electromagnetic interference, thereby guaranteeing the measurement accuracy and control stability of the entire temperature monitoring system.

[0038] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A ventilator temperature sensor monitoring system, characterized in that, include: The signal acquisition module is used to acquire fluid state data in the ventilator tubing in real time. The fluid state data includes the raw temperature signal acquired by the encapsulated thermistor and the airway flow rate signal measured by the flow rate sensor. The thermal impedance mapping module is used to search and match in a preset thermal impedance-flow velocity mapping library based on the airway flow velocity signal in order to determine the dynamic time constant under the current fluid state. The reverse compensation module is used to calculate the rate of change of the original temperature signal with respect to time, and based on the original temperature signal, the dynamic time constant, and the rate of change, to solve the reconstructed temperature signal after eliminating the thermal hysteresis effect; The temperature control execution module is used to input the reconstructed temperature signal as a feedback variable into the PID controller and adjust the output power of the heating and humidifying components according to the reconstructed temperature signal.

2. The ventilator temperature sensor monitoring system according to claim 1, characterized in that, The reverse compensation module calculates the reconstructed temperature signal after eliminating the thermal hysteresis effect based on the original temperature signal, the dynamic time constant, and the rate of change, including: The original temperature signal, the dynamic time constant, and the rate of change are invoked; The thermal hysteresis compensation amount is obtained by calculating the product of the dynamic time constant and the rate of change. The thermal hysteresis compensation is superimposed on the original temperature signal to generate the reconstructed temperature signal, so as to mathematically restore the airway center fluid temperature before it was smoothed by the encapsulation layer.

3. The ventilator temperature sensor monitoring system according to claim 1, characterized in that, The thermal impedance mapping module performs a search and matching in a preset thermal impedance-flow velocity mapping library, including: A heat transfer model is constructed, which includes the convective thermal resistance between the airflow and the sensor surface, and the conductive thermal resistance from the sensor surface to the core. Based on the heat transfer characteristics of the airflow boundary layer, a nonlinear functional relationship between the convective thermal resistance and the flow velocity is established. The nonlinear function relationship is discretized and stored as the thermal impedance-flow velocity mapping library, wherein the higher the flow velocity, the smaller the corresponding dynamic time constant.

4. The ventilator temperature sensor monitoring system according to claim 1, characterized in that, Also includes: The condensation monitoring module is used to construct a temperature-flow rate thermal hysteresis loop in the phase space based on the original temperature signal and the gas flow rate signal. The feature extraction module is used to calculate the geometric area enclosed by the temperature-flow rate thermal hysteresis loop within one respiratory cycle; The status determination module is used to determine whether there is condensation on the surface of the sensor probe based on the comparison result between the geometric area and the preset water accumulation threshold.

5. The ventilator temperature sensor monitoring system according to claim 4, characterized in that, The condensation monitoring module constructs a temperature-flow rate thermal hysteresis loop within the phase space, including: Using the airway flow velocity signal as the horizontal axis coordinate and the original temperature signal as the vertical axis coordinate, the flow velocity and temperature data at the same moment are mapped to a two-dimensional phase space. Connect all the mapping points within a respiratory cycle to form a closed temperature-flow rate thermal hysteresis loop trajectory.

6. The ventilator temperature sensor monitoring system according to claim 4, characterized in that, The state determination module determines whether condensation accumulates on the surface of the sensor probe based on the comparison between the geometric area and a preset water accumulation threshold. The configuration is as follows: Call the geometric area and the preset water accumulation threshold; If the geometric area is greater than the preset water accumulation threshold, it is determined that there is condensation on the surface of the sensor probe, and a water accumulation alarm signal is generated. If the geometric area is less than or equal to the preset water accumulation threshold, the sensor probe surface is determined to be in normal working condition, and a status flag is generated to maintain the current monitoring mode.

7. The ventilator temperature sensor monitoring system according to claim 1, characterized in that, The thermal impedance mapping module further includes a static back-off unit, which is configured as follows: Real-time monitoring of the airway flow rate signal; If the airway flow rate signal is lower than the preset static flow rate threshold, the dynamic time constant is stopped, and the original temperature signal is output as the reconstructed temperature signal. If the airway flow rate signal is greater than or equal to the preset static flow rate threshold, the dynamic time constant is maintained to reconstruct the temperature signal.

8. The ventilator temperature sensor monitoring system according to claim 1, characterized in that, The signal acquisition module acquires the raw temperature signal collected by the packaged thermistor, including: The voltage value of the packaged negative temperature coefficient NTC thermistor is read using an analog-to-digital converter. The voltage value is converted into a digital signal, and the digital signal is denoised to obtain the original temperature signal.

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