A smart water level monitoring and control system and method based on a breathing humidifier

Abstract

This invention discloses an intelligent water level monitoring and control system and method based on a respiratory humidifier. A data acquisition module monitors the real-time temperature and humidity inside the humidifier tank, the external ambient temperature and humidity, and the air supply flow rate, and transmits the collected data to a processing and identification module. The processing and identification module receives the temperature, humidity, and flow rate data from the data acquisition module, calculates key differences and rate of change indicators, and trains a function on the monitored data by constructing a water level monitoring data model. The main control module executes the corresponding humidifier water delivery command to the flow control module, controlling the flow regulating valve to adjust the humidifier water delivery volume. When the processing and identification module detects an anomaly, the alarm feedback module receives an alarm command from the main control module and issues an alarm sound. This invention can monitor data inside and outside the respiratory humidifier in real time and intelligently control the water level in the humidifier tank through a data model to achieve the target temperature and humidity range for the patient, thereby providing the patient with the most appropriate airway humidification solution.

Description

Technical Field

[0001] This invention relates to a system and method for water level monitoring and control, and particularly to an intelligent water level monitoring and control system and method based on a breathing humidifier. Background Technology

[0002] A respiratory humidifier is a medical device that warms and humidifies respiratory gases to help maintain the normal physiological function of a patient's airway system. Its main purpose is to prevent airway dryness and inflammation, and to improve the mucus clearance function of the airway surface. It is usually used in conjunction with a ventilator to provide patients with warmed and humidified respiratory gases to maintain the normal clearance function of the mucociliary system on the airway surface and the normal contraction, relaxation, and diffusion characteristics of the alveolar epithelium, helping patients maintain normal airway function and avoid airway dryness and inflammatory symptoms.

[0003] A typical respiratory humidifier includes a heating plate, a humidification tank, a temperature and humidity monitoring device, and a control display panel. During operation, sterile water for injection enters the humidification tank through a dedicated pipeline. The control display panel adjusts the heating plate power according to preset parameters, heating the humidified water in the tank to generate water vapor, which is then carried to the patient through the breathing tubing.

[0004] In this process, the water level in the humidification tank is crucial to the humidification effect. However, in practical applications, busy medical staff may overlook or mishandle the water level in the humidification tank, leading to abnormalities. With a fixed heating plate power, a water level that is too low may cause the humidification tank to burn dry or result in insufficient water evaporation, which can damage the equipment and reduce the humidification level at the patient's end, failing to meet treatment needs. Conversely, a water level that is too high may cause water to overflow from the humidification tank or result in excessive humidification, affecting the normal operation of the equipment and the humidification effect, posing unnecessary risks to the patient.

[0005] Existing technologies typically determine the water level in the humidification tank by setting up a liquid level monitor at a fixed location, or by acquiring data such as the temperature of the humidification tank's outlet and the temperature of the heating plate, and then estimating the water level based on the heating plate's power. However, this method is inaccurate and cannot report water level changes in real time, leading to an inability to accurately identify the water level and increasing potential risks to patients during treatment. Furthermore, existing technologies rely on adjusting the heating rate of the heating plate to quickly bring the gas to the patient's desired temperature and humidity. However, this approach not only has safety concerns but also easily leads to excessively rapid consumption of humidification water, making timely replenishment difficult.

[0006] Therefore, existing technologies have significant shortcomings in monitoring and controlling the water level of humidification tanks. There is an urgent need for a more accurate, real-time, and intelligent water level monitoring and control system to ensure that patients achieve the ideal airway humidification effect, while improving the safety and reliability of the equipment. Summary of the Invention

[0007] The purpose of this invention is to overcome the defects of the prior art and provide a water level intelligent monitoring and control system and method based on a respiratory humidifier, which monitors water level changes in real time and intelligently adjusts the infusion volume of humidified water according to the temperature and humidity feedback from the patient; based on historical data and real-time data, the constructed water level monitoring data model is used to predict the water level change trend in the next 24 hours.

[0008] The objective of this invention is achieved in one aspect as follows: a water level intelligent monitoring and control system based on a breathing humidifier, comprising a data acquisition module, a processing and identification module, a main control module, a flow control module, an alarm feedback module, a wireless module, and a user terminal display module;

[0009] The data acquisition module is used to monitor the temperature and humidity data inside the humidification tank, the ambient temperature and humidity data, the gas source flow rate data, the humidification water injection flow rate data, and the user terminal temperature and humidity data in real time; and transmits the collected data to the processing and identification module.

[0010] The processing and identification module is used to calculate the collected data to obtain key difference and change rate indicators. Based on historical data and real-time data, it uses the constructed water level monitoring data model to predict the water level change trend in the next 24 hours and sends real-time signals to the main control module according to the water level status in the tank.

[0011] The main control module receives signals from the processing module, executes corresponding humidification water flow instructions to the flow control module, intelligently controls the flow regulating valve, and automatically adds humidification water to the humidification water tank to a safe water level range. At the same time, the main control module also coordinates with the real-time temperature and humidity feedback from the patient to ensure sufficient humidification water volume, which can meet the water volume requirements of the patient to achieve the desired target temperature and humidity after heating. When an abnormal signal is received, an alarm instruction is sent to the alarm feedback module.

[0012] The alarm feedback module is used to receive instructions from the main control module and issue an alarm sound when the data is abnormal, indicating that the water level is abnormal or that backup humidifying water needs to be added in time.

[0013] The wireless module is used to upload data from the main control module and the alarm feedback module to the user terminal display module;

[0014] The flow control module is connected to the flow regulating valve installed at the inlet of the humidification tank, and is used to receive signals from the main control module, adjust the amount of humidification water supplied, and keep the water level in the humidification tank within a safe range.

[0015] The user terminal display module is used to display the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning and audible and visual alarm information to prompt medical staff to take action.

[0016] The data acquisition module, processing and identification module, flow control module, alarm feedback module, wireless module and main control module are electrically connected;

[0017] The data acquisition module is electrically connected to the internal temperature and humidity sensor, the ambient temperature and humidity sensor, the gas source flow sensor, the humidification water infusion flow sensor, and the patient-side temperature and humidity sensor.

[0018] As a further limitation of the present invention, the air source flow sensor is disposed on the inner wall of the air inlet of the humidification tank, the internal temperature and humidity sensor is disposed inside the humidification tank; the ambient humidity sensor is disposed on the base of the humidification tank; the humidification water infusion flow sensor is disposed on the inner wall of the liquid inlet of the humidification tank; and the patient end temperature and humidity sensor is located near the patient end of the ventilator tubing.

[0019] Another aspect of the objective of this invention is achieved as follows: a method for intelligent monitoring and control of water level based on a breathing humidifier, comprising the following steps:

[0020] 1) The data acquisition module monitors the real-time temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)) inside the humidification tank, the external ambient temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)), the air source flow rate (units: liters per minute (L / min)), the humidification water infusion flow rate (units: milliliters per minute (ml / min)), and the patient's temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)) in real time through internal temperature and humidity sensors, ambient temperature and humidity sensors, air source flow rate (units: liters per minute (L / min)), and patient's temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)), and transmits the collected data to the processing and identification module;

[0021] 2) The processing and identification module receives data from the data acquisition module, calculates key differences and change rate indicators, and trains functions on the monitored and calculated data through the constructed water level monitoring data model; it generates the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve and abnormal mode early warning signal, and transmits the corresponding signals to the main control module.

[0022] 3) The main control module receives signals from the processing module, executes the corresponding humidification water flow command to the flow control module, intelligently controls the flow regulating valve, and automatically adds humidification water to the humidification water tank to the safe water level range; at the same time, the main control module also combines the real-time temperature and humidity feedback from the patient's end to perform coordinated adjustment to ensure that the humidification water volume is sufficient to meet the water volume requirements of the patient's end to achieve the desired target temperature and humidity after heating; when an abnormal signal is received, an alarm command is sent to the alarm feedback module.

[0023] 4) When the processing module detects abnormal data, the alarm feedback module receives instructions from the main control module and issues an alarm sound to indicate that the water level is abnormal or that backup humidification water needs to be added in time. The main control module uploads the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning signal, and audible and visual alarm information to the user terminal display module through the wireless module to prompt medical staff to take action.

[0024] As a further limitation of the present invention, the processing and identification module receiving data from the data acquisition module and calculating the key difference and rate of change indicators in step 2) specifically includes:

[0025] Temperature difference ΔT between the humidification tank and the ambient temperature: ΔT = T 罐 -T 环 ΔT reflects the difference between the temperature inside the humidification tank and the ambient temperature;

[0026] Humidity difference between the humidification tank and the ambient environment ΔRH: ΔRH = RH 罐 -RH 环 The ΔRH difference represents the difference between the humidity inside the humidification tank and the ambient humidity.

[0027] 10s Temperature Difference Change Rate ΔT' / Δt: First, record the temperature difference values ​​ΔT1 and ΔT2 at two adjacent 10-second time points, corresponding to the temperature difference between the humidification tank and the environment at times t1 and t2, respectively. Then, calculate the change between these two temperature differences, ΔT' = ΔT2 - ΔT1. The 10s temperature difference change rate is calculated using the formula: ΔT' / Δt = ΔT' / 10s. The ΔT' / Δt change rate reflects the rate of change of the temperature difference between the humidification tank and the environment within 10 seconds.

[0028] 10s Humidity Difference Change Rate ΔRH' / Δt: Record the humidity difference ΔRH1 and ΔRH2 at two adjacent 10-second time points, then calculate the change between these two humidity differences ΔRH' = ΔRH2 - ΔRH1, and finally obtain the 10s humidity difference change rate using the formula: ΔRH' / Δt = ΔRH' / 10s. The ΔRH' / Δt change rate indicates how quickly the humidity difference between the humidification tank and the environment changes within 10 seconds.

[0029] 10s air source flow rate change rate ΔQ / Δt: Record the air source flow rates Q1 and Q2 at two adjacent 10-second time points, and calculate the change between these two flow rates ΔQ = Q2 - Q1; calculate the 10s air source flow rate change rate using the formula: ΔQ / Δt = ΔQ / 10s. The ΔQ / Δt change rate reveals the change in air source flow rate within 10 seconds.

[0030] As a further limitation of the present invention, the function training of the data by constructing the water level monitoring data model in step 2) specifically includes: using 1D-CNN to extract the temperature and humidity inside the humidification tank, the temperature and humidity of the external environment, the temperature and humidity difference between the inside and outside of the humidification tank, the air source flow rate and its rate of change, and then using LSTM network to learn the time series information to complete the prediction of water level, water consumption rate, remaining available time, etc.

[0031] As a further limitation of this invention, preprocessing is required before using 1D-CNN to extract the temperature and humidity inside the humidification tank, the temperature and humidity of the external environment, the temperature and humidity difference between the inside and outside of the humidification tank, the air source flow rate and its rate of change. Specifically, this includes:

[0032] Input data includes the temperature data T inside the humidification tank. 罐 Humidity data (RH) inside the humidification tank 罐 Ambient temperature data T 环 Ambient temperature data RH 环 The data for each time step t are represented as a one-dimensional vector containing multiple features: air source flow rate Q, temperature difference between the humidification tank and the environment ΔT, humidity difference between the humidification tank and the environment ΔRH, 10s temperature difference change rate ΔT' / Δt, 10s humidity difference change rate ΔRH' / Δt, and 10s air source flow rate change rate ΔQ / Δt.

[0033] Before being input into the 1D-CNN-LSTM model, the data needs to be weighted, with different data items assigned different weight coefficients according to their importance:

[0034] The primary weight w1 (0.7–0.9) is suitable for raw collected data (T). 罐 (t), RH 罐 (t), T 环 (t), RH 环 (t), Q(t))

[0035] The intermediate weight w2 (0.9~1.1) is suitable for difference data (ΔT(t), ΔRH(t));

[0036] Advanced weights w3 (1.1–1.3) are suitable for rate of change data.

[0037] The features at each time step are weighted to form a weighted data matrix, represented as follows:

[0038]

[0039] Weighted data matrix X weightedIts shape is (n, m), where n is the number of time steps and m is the number of features at each time step; this data matrix will be used as input to subsequent 1D-CNN and LSTM layers for further analysis and prediction.

[0040] As a further limitation of the present invention, the generation of the 24-hour water level change prediction curve in step 2) specifically includes:

[0041] The 1D-CNN-LSTM model is used to predict the water level change trend of the humidification tank in the next 24 hours and output the water level change curve to help medical staff analyze the water level dynamics in advance.

[0042] The 1D-CNN-LSTM model outputs the predicted water level for the next 24 hours:

[0043]

[0044] in, Let N be the predicted water level at time t+1, N be the number of time steps in the next 24 hours, and t be the time step in minutes (set to 5 minutes).

[0045] Predicted water level value The system generates a water level change curve, outputs a water level change trend chart for the next 24 hours, uploads it to the user terminal display module for real-time updates, and embeds it into the interactive interface of medical staff.

[0046] As a further limitation of the present invention, the abnormal mode warning signal in step 2) specifically includes:

[0047] Real-time detection of water level changes in the humidification tank and abnormal patterns of humidification water delivery can trigger an early warning so that medical staff can promptly handle equipment malfunctions or replace the humidification water with a backup.

[0048] The formula for calculating the rate of water level change is:

[0049]

[0050] Where Δt represents the time interval. The water level value at the current time t is output by the 1D-CNN-LSTM model. The water level value at the previous time t-1 is output by the 1D-CNN-LSTM model;

[0051] The criteria for determining abnormal modes are set as follows:

[0052] Abnormal rate of water level drop Abnormal rate of water level rise Abnormal humidification water delivery Q add =0;

[0053] Where, r 正常上限 r represents the normal upper limit of the rate of water level drop. 正常下限 Q is the normal lower limit of the rate of water level rise. add Add flow rate to the humidification water, unit: ml / s;

[0054] When an abnormal pattern is detected, an alarm signal is generated and transmitted to the main control module.

[0055] As a further limitation of the present invention, the main control module in step 3) performs coordinated adjustment in conjunction with the real-time temperature and humidity feedback from the patient, specifically including:

[0056] Water level conversion in humidification tank:

[0057]

[0058] Among them: W level This represents the relative position of the current water level in the humidification tank within the safe water level range, which is from 0 to 1. H represents the current water level in the humidification tank (unit: mm). max The maximum water level in the humidification tank (unit: mm, upper limit of the humidification tank);

[0059] W level =0 indicates that the water level in the humidification tank has dropped to the minimum safe threshold; W level =1 indicates that the water level in the humidification tank has reached the maximum safe water level limit; A value between 0 and 1 indicates that the water level is within a safe range, and the ratio reflects the relative relationship between the current water level and the safe water level.

[0060] Patient-side temperature difference ΔT: Represents the difference between the patient-side temperature and the target temperature. ΔT = T p -T target When ΔT > 0, the temperature is too high; when ΔT = 0, the temperature is suitable; when ΔT < 0, the temperature is too low.

[0061] Patient-side humidity difference ΔH: Represents the difference between the patient-side humidity and the target humidity. ΔH = H p -H target When ΔH > 0, the humidity is too high; when ΔH = 0, the humidity is suitable; when ΔH < 0, the humidity is too low.

[0062] Add humidification water flow calculation formula

[0063] Q add =k×(ΔT·w T +ΔH·w H )×(1-W level )

[0064] Among them, Q add Add the flow rate of humidifying water (unit: ml / s); ΔT = T p -T target The temperature difference; ΔH = H p -H target For humidity difference; w T (0.6~0.7) and w H (0.4~0.5) are the weighting coefficients for temperature and humidity, respectively, used to adjust the relative influence of temperature and humidity on the humidification water flow rate; W level The current water level ratio of the humidification tank (between 0 and 1); k (0.8 to 1.2) is the adjustment coefficient.

[0065] During operation, this invention collects temperature and humidity data from the humidification tank, ambient temperature and humidity data, air source flow rate data, humidification water infusion flow rate, and patient-side temperature and humidity data via a data acquisition module. A processing and identification module calculates key differences and rate of change indicators within the humidification tank and determines the water level based on a preset algorithm. It also predicts the humidification water consumption rate, remaining usable time, a 24-hour water level change prediction curve, and abnormal mode warning signals. Upon receiving signals from the processing module, the main control module executes corresponding humidification water flow commands to the flow control module, intelligently controlling the flow regulating valve to automatically add humidification water to the tank to a safe level. Simultaneously, the main control module coordinates with real-time temperature and humidity feedback from the patient to ensure sufficient humidification water volume to meet the patient's desired temperature and humidity after heating. When an abnormal signal is received, an alarm command is sent to the alarm feedback module. Finally, the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning and audible and visual alarm information are displayed in real time on the computer screens of medical staff through the wireless module.

[0066] Compared with existing technologies, the beneficial effects of this invention, which employs the above technical solutions, are as follows: This invention utilizes a 1D-CNN-LSTM structure, which can effectively extract features such as the temperature and humidity difference inside and outside the humidification tank, air source flow rate, and its rate of change. Simultaneously, it significantly reduces the number of network parameters and computational complexity, providing assurance for real-time water level monitoring. Subsequently, the LSTM network learns from the time-series information to predict water level, water consumption rate, and remaining available time. An alarm is issued when an anomaly occurs, providing medical staff with accurate and safe operating time, thus improving the safety and reliability of the respiratory humidification equipment. Finally, combined with real-time temperature and humidity feedback from the patient, the flow regulating valve is intelligently controlled to automatically add humidification water to the humidification tank to a safe water level range. This also ensures that the water volume meets the patient's target temperature and humidity requirements after heating, allowing the patient's temperature and humidity to reach the target range, thereby providing the patient with the most appropriate airway humidification solution. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the system of the present invention.

[0068] Figure 2 This is a flowchart of the method of the present invention.

[0069] Figure 3 This is a schematic diagram of the input data for the water level monitoring data model of the present invention (taking the temperature data inside the humidification tank as an example).

[0070] Figure 4 This is a schematic diagram of the 1DD-CNN-LSTM model structure of the present invention.

[0071] Figure 5 This is a schematic diagram of the 24-hour water level change prediction curve output by the present invention. Detailed Implementation

[0072] like Figure 1 The water level intelligent monitoring and control system based on a breathing humidifier shown includes a data acquisition module, a processing and identification module, a main control module, a flow control module, an alarm feedback module, a wireless module, and a user terminal display module;

[0073] The data acquisition module is used to monitor the temperature and humidity data inside the humidification tank, the ambient temperature and humidity data, the air source flow rate data, the humidification water infusion flow rate data, and the temperature and humidity data at the patient end in real time; and transmits the collected data to the processing and recognition module.

[0074] The processing and identification module receives temperature, humidity, and flow data from the data acquisition module, calculates key differences and change rate indicators, and trains functions on the monitored temperature and humidity data using the constructed water level monitoring data model; it generates the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, and abnormal mode early warning signal, and transmits the corresponding signals to the main control module.

[0075] The main control module receives signals from the processing and identification module and executes the corresponding humidification water flow command to the flow control module; when an alarm command is received, it is sent to the alarm feedback module.

[0076] When the processing module detects abnormal data, the alarm feedback module receives instructions from the main control module and issues an alarm sound to indicate that the water level is abnormal or that backup humidifying water needs to be added in time.

[0077] The wireless module is used to upload data from the main control module and the alarm feedback module to the user terminal display module;

[0078] The flow control module is connected to the flow regulating valve installed at the inlet of the humidification water tank. It is used to receive signals from the main control module, adjust the humidification water delivery rate, and keep the humidification water level within a safe range.

[0079] The user-end display module is used to display the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning and audible and visual alarm information to prompt medical staff to take action.

[0080] The data acquisition module, processing and identification module, flow control module, alarm feedback module, wireless module, and main control module are electrically connected. The data acquisition module is electrically connected to the internal temperature and humidity sensor, the ambient temperature and humidity sensor, the air source flow sensor, the humidification water infusion flow sensor, and the patient-end temperature and humidity sensor. The internal temperature and humidity sensor is located inside the humidification water tank. The ambient humidity sensor is located on the base of the humidification water tank. The air source flow sensor is located on the inner wall of the air inlet of the humidification water tank. The humidification water infusion flow sensor is located on the inner wall of the liquid inlet of the humidification water tank. The patient-end temperature and humidity sensor is located near the patient end of the ventilator tubing.

[0081] like Figure 2 As shown, a method for intelligent monitoring and control of water level based on a breathing humidifier includes the following steps:

[0082] 1) The data acquisition module monitors the real-time temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)) inside the humidification tank, the external ambient temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)), the air source flow rate (units: liters per minute (L / min)), the humidification water infusion flow rate (units: milliliters per minute (ml / min)), and the patient's temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)) in real time through internal temperature and humidity sensors, ambient temperature and humidity sensors, air source flow rate (units: liters per minute (L / min)), and patient's temperature and humidity (units: degrees Celsius (°C) and relative humidity (%RH)), and transmits the collected data (such as...) Figure 3 As shown, (two decimal places are retained) the data is transmitted to the processing and recognition module;

[0083] 2) The processing and identification module receives temperature, humidity and flow data from the data acquisition module, calculates key differences and change rate indicators, and trains functions on the monitored temperature and humidity data through the constructed water level monitoring data model; generates the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, and abnormal mode early warning signal, and transmits the corresponding signals to the main control module.

[0084] The processing and identification module receives temperature, humidity, and flow data from the data acquisition module and calculates key difference and rate of change indicators, including:

[0085] Temperature difference ΔT between the humidification tank and the ambient temperature: ΔT = T 罐 -T 环 ΔT reflects the difference between the temperature inside the humidification tank and the ambient temperature;

[0086] Humidity difference between the humidification tank and the ambient environment ΔRH: ΔRH = RH 罐 -RH 环 The ΔRH difference represents the difference between the humidity inside the humidification tank and the ambient humidity.

[0087] 10s Temperature Difference Change Rate ΔT' / Δt: First, record the temperature difference ΔT1 and ΔT2 at two adjacent 10-second time points (t1 and t2, where t2 = t1 + 10s), corresponding to the temperature difference between the humidification tank and the environment at times t1 and t2, respectively. Then, calculate the change between these two temperature differences ΔT' = ΔT2 - ΔT1. The 10s temperature difference change rate is calculated using the formula: ΔT' / Δt = ΔT' / 10s. The ΔT' / Δt change rate reflects the rate of change of the temperature difference between the humidification tank and the environment within 10 seconds.

[0088] 10s Humidity Difference Change Rate ΔRH' / Δt: Record the humidity difference ΔRH1 and ΔRH2 at two adjacent 10-second time points, then calculate the change between these two humidity differences ΔRH' = ΔRH2 - ΔRH1, and finally obtain the 10s humidity difference change rate using the formula: ΔRH' / Δt = ΔRH' / 10s. The ΔRH' / Δt change rate indicates how quickly the humidity difference between the humidification tank and the environment changes within 10 seconds.

[0089] 10s air source flow rate change rate ΔQ / Δt: Record the air source flow rates Q1 and Q2 at two adjacent 10-second time points, and calculate the change between these two flow rates ΔQ = Q2 - Q1; calculate the 10s air source flow rate change rate using the formula: ΔQ / Δt = ΔQ / 10s. The ΔQ / Δt change rate reveals the change in air source flow rate within 10 seconds.

[0090] To further optimize the computational efficiency of the network while ensuring high accuracy of water level monitoring, 1D-CNN was used to extract the temperature and humidity inside the humidification tank, the temperature and humidity of the external environment, the temperature and humidity difference between the inside and outside of the humidification tank, the air source flow rate and its rate of change. Then, the time series information was learned through an LSTM network to complete the prediction of water level, humidification water consumption rate, and remaining available time.

[0091] Input data includes the temperature data T inside the humidification tank. 罐 Humidity data (RH) inside the humidification tank 罐 Ambient temperature data T 环 Ambient temperature data RH 环The data include: air source flow rate Q, temperature difference between the humidification tank and the environment ΔT, humidity difference between the humidification tank and the environment ΔRH, 10-second temperature difference change rate ΔT' / Δt, 10-second humidity difference change rate ΔRH' / Δt, and 10-second air source flow rate change rate ΔQ / Δt. The data at each time step t (set as every 5 minutes) can be represented as a one-dimensional vector containing multiple features.

[0092] Before being input into the 1D-CNN-LSTM model, the data needs to be weighted, with different data items assigned different weight coefficients according to their importance:

[0093] The primary weight w1 (0.7–0.9) is suitable for raw collected data (T). 罐 (t), RH 罐 (t), T 环 (t), RH 环 (t), Q(t))

[0094] The intermediate weight w2 (0.9~1.1) is suitable for difference data (ΔT(t), ΔRH(t));

[0095] Advanced weights w3 (1.1–1.3) are suitable for rate of change data.

[0096] The features at each time step are weighted to form a weighted data matrix, represented as follows:

[0097]

[0098] Weighted data matrix X weighted Its shape is (n, m), where n is the number of time steps and m is the number of features at each time step.

[0099] First, the weighted data matrix is ​​input into a 1D-CNN layer to extract local features from the multidimensional features at each time step. The convolutional kernel slides across the time series to extract local features. Assuming the kernel size is k, meaning each convolution operation covers k time steps of data, the output feature map size is n-k+1. The output of the convolutional layer is a multidimensional feature map with the shape (n-k+1, filters), where filters is the number of convolutional kernels.

[0100] Convolutional layer formula:

[0101] Z1 = Conv1D(X) weighted ,filters=64,kernel_size=3,activation=′relu′)

[0102] Here, filters=64 indicates that there are 64 convolutional kernels, kernel_size=3 indicates that each convolutional kernel covers data for 3 time steps, and activation=relu indicates that the activation function uses ReLU.

[0103] The feature map Z1 extracted by the 1D-CNN layer, with a shape of (n-k+1, filters), is passed to the LSTM layer. The LSTM layer captures the temporal dependencies in the sequence through cyclic processing of the time series data. The LSTM layer contains "cell states" and "hidden states," and its update process is regulated by forget gates, input gates, and output gates.

[0104] Forget Gate:

[0105] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0106] Input Gate:

[0107] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0108]

[0109] Cell State Update:

[0110]

[0111] Output Gate:

[0112] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0113] h t =o t ·tanh(C t )

[0114] Among them, W f W i W c W o and b f ,b i ,bc ,b o Here, σ represents the weight matrix and bias vector, respectively, σ is the sigmoid activation function, and tanh is the hyperbolic tangent activation function.

[0115] The output of the LSTM layer is a new sequence with the shape (n-k+1, units), where units is the number of neurons in the LSTM.

[0116] LSTM layer formula:

[0117] LSTM out =LSTM(Z1,units=128,return_sequences=False)

[0118] Here, units=128 indicates that the number of neurons in the LSTM is 128, and return_sequences=False indicates that the LSTM only outputs the final hidden state, rather than the hidden state at each time step.

[0119] The output of the LSTM layer is connected to the fully connected layer (Dense layer) to generate the final prediction result. The fully connected layer maps the output of the LSTM layer to one or more target variables through the activation function, including the predicted water level, the rate of wetting water consumption, and the remaining available time.

[0120] The prediction formula for fully connected layers is:

[0121]

[0122] Here, units=1 indicates that a single predicted value is output, and activation='linear' indicates that the output is a continuous value.

[0123] Advanced output functions are added to the output of the 1D-CNN-LSTM network to further meet the needs of medical scenarios, including 24-hour water level change prediction curves and abnormal pattern early warning.

[0124] a) Using 1D-CNN-LSTM models (such as...) Figure 4 As shown in the figure, the system predicts the trend of water level change in the humidification tank over the next 24 hours and outputs the water level change curve to help medical staff analyze the water level dynamics in advance.

[0125] The 1D-CNN-LSTM model outputs the predicted water level for the next 24 hours:

[0126]

[0127] in, Let N be the predicted water level at time t+1, N be the number of time steps in the next 24 hours, and t be the time step in minutes (set to 5 minutes, which can be adjusted according to specific circumstances).

[0128] Predicted water level value Plot the water level change curve and output a water level change trend chart for the next 24 hours (e.g.) Figure 5 As shown in the image, the data is uploaded to the user terminal display module, where it is updated in real time and embedded into the interactive interface of medical staff.

[0129] b) Real-time detection of water level changes in the humidification tank and abnormal humidification water delivery patterns, triggering early warnings so that medical staff can promptly handle equipment malfunctions or replace the humidification water with a backup.

[0130] The formula for calculating the rate of water level change is:

[0131]

[0132] Where Δt represents the time interval. The water level value at the current time t is output by the 1D-CNN-LSTM model. The water level value at the previous time t-1 is output by the 1D-CNN-LSTM model;

[0133] The criteria for determining abnormal modes are set as follows:

[0134] Abnormal rate of water level drop Abnormal rate of water level rise Abnormal humidification water delivery Q add =0

[0135] Where, r 正常上限 The normal upper limit for the rate of water level drop is set to 3 mm / 5 minutes. 正常下限 The lower limit of the normal rate of water level rise is set at 1.5 mm / 5 minutes; the specific data can be adjusted according to actual conditions. Q add Add flow rate (unit: ml / s) to the humidification water;

[0136] When an abnormal pattern is detected, an alarm signal is generated and transmitted to the main control module.

[0137] 3) The main control module executes the corresponding humidification water flow command to the flow control module based on the signal received from the processing module. This module intelligently controls the flow regulating valve to automatically add humidification water to the humidification water tank to the safe water level range. At the same time, the main control module also coordinates with the real-time temperature and humidity feedback from the patient to ensure that the humidification water volume is sufficient to meet the patient's water volume requirements to achieve the desired temperature and humidity after heating. When an abnormal signal is received, an alarm command is sent to the alarm feedback module.

[0138] Based on real-time temperature and humidity feedback from the patient, coordinated adjustments are made, and the flow regulation valve is intelligently controlled every 10 minutes (adjustable according to specific circumstances). Specifically, this includes:

[0139] Water level conversion in humidification tank:

[0140]

[0141] Among them: W level This represents the relative position of the current water level in the humidification tank within the safe water level range, which is from 0 to 1. H represents the current water level in the humidification tank (unit: mm). max The maximum water level in the humidification tank (unit: mm, upper limit of the humidification tank);

[0142] W level =0 indicates that the water level in the humidification tank has dropped to the minimum safe threshold; W level =1 indicates that the water level in the humidification tank has reached the maximum safe water level limit; A value between 0 and 1 indicates that the water level is within a safe range, and the ratio reflects the relative relationship between the current water level and the safe water level.

[0143] Patient-side temperature difference: Represents the difference between the patient-side temperature and the target temperature, ΔT = T p -T target When ΔT>0, the temperature is too high; when ΔT=0, the temperature is suitable; when ΔT<0, the temperature is too low.

[0144] Patient-side humidity difference: Represents the difference between the patient-side humidity and the target humidity, ΔH = H p -H target When ΔH>0, the humidity is too high; when ΔH=0, the humidity is suitable; when ΔH<0, the humidity is too low.

[0145] Add humidification water flow calculation formula

[0146] Q add =k×(ΔT·w T +ΔH·w H )×(1-W level )

[0147] Among them, Q add Add the flow rate of humidifying water (unit: ml / s); ΔT = T p -T target The temperature difference; ΔH = H p -H target For humidity difference; w T (0.6~0.7) and w H(0.4~0.5) are the weighting coefficients for temperature and humidity, respectively, used to adjust the relative influence of temperature and humidity on the humidification water flow rate; W level The current water level ratio of the humidification tank (between 0 and 1); k (0.8 to 1.2) is the adjustment coefficient.

[0148] 4) When the processing module detects abnormal data, the alarm feedback module receives instructions from the main control module and issues an alarm sound to indicate that the water level is abnormal or that backup humidification water needs to be added in time. The main control module uploads the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning signal, and audible and visual alarm information to the user terminal display module through the wireless module to prompt medical staff to take action.

[0149] When this invention is working, the data acquisition module collects temperature and humidity data inside the humidification tank, ambient temperature and humidity data, air source flow rate data, humidification water infusion flow rate data, and patient end temperature and humidity data and sends them to the processing and identification module.

[0150] The processing and identification module receives temperature, humidity, and flow data from the data acquisition module, calculates key differences and change rate indicators, and trains functions on the monitored temperature and humidity data using the constructed water level monitoring data model; it generates the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, and abnormal mode early warning signal, and transmits the corresponding signals to the main control module.

[0151] The main control module receives signals from the processing and identification module and executes corresponding humidification water flow commands to the flow control module (flow regulating valve), automatically adding humidification water to the humidification water tank to the safe water level range. When the processing and identification module identifies that the patient's temperature is too high and the humidity is suitable, the system will reduce the humidification water flow; when the patient's temperature is too high and the humidity is too low, the system will increase the humidification water flow to increase humidity and maintain temperature; when the patient's temperature is suitable and the humidity is too high, the system will increase the humidification water flow to replenish moisture and prevent the water level from becoming too low; when the patient's temperature is suitable and the humidity is too low, the system will increase the humidification water flow to increase humidity and meet heating requirements; when the patient's temperature is too low and the humidity is too high, the system will increase the humidification water flow to increase humidity and replenish the water level; when the patient's temperature is too low and the humidity is too low, the system will increase the humidification water flow to meet both temperature and humidity requirements; when the patient's temperature and humidity are suitable, the system will maintain the humidification water flow to stabilize the water level. The intelligent control flow regulating valve ensures that the supply of humidifying water meets the patient's temperature and humidity requirements while maintaining the water level in the humidification tank within a safe range. The alarm feedback module issues an audible and visual alarm and provides real-time updates on the remaining available time when the processing module detects a stop infusion of humidifying water. When the processing module detects abnormal data, it receives instructions from the main control module, issues an alarm sound, and indicates an abnormal water level or the need to add backup humidifying water promptly.

[0152] The user-side display module is located on the medical staff's computer. The information displayed on the user page includes, but is not limited to, the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warnings, and audible and visual alarm information, prompting medical staff to take action and improving the safety and reliability of medical equipment.

[0153] This invention provides an intelligent water level monitoring and control system and method based on a respiratory humidifier. It employs a 1D-CNN-LSTM structure to effectively extract features such as the temperature and humidity difference inside and outside the humidifier tank, air flow rate, and its rate of change, while significantly reducing the number of network parameters and computational complexity, thus ensuring real-time water level monitoring. Subsequently, the LSTM network learns from the time-series information to predict water level, water consumption rate, and remaining available time. An alarm is issued when abnormalities occur, providing medical staff with accurate and safe operating time, improving the safety and reliability of the respiratory humidifier. Finally, combined with real-time temperature and humidity feedback from the patient, the system intelligently controls the flow regulating valve to automatically add humidified water to the humidifier tank to a safe level, while simultaneously meeting the water volume requirements for the patient to achieve their desired temperature and humidity after heating, thus providing the patient with the most appropriate airway humidification solution.

[0154] This invention is not limited to the above embodiments. Based on the technical solutions disclosed in this invention, those skilled in the art can make some substitutions and modifications to some of the technical features without creative effort, and all such substitutions and modifications are within the protection scope of this invention.

Claims

1. A water level intelligent monitoring and control system based on a breathing humidifier, characterized in that, It includes a data acquisition module, a processing and identification module, a main control module, a flow control module, an alarm feedback module, a wireless module, and a user terminal display module; The data acquisition module is used to monitor the temperature and humidity data inside the humidification tank, the ambient temperature and humidity data, the gas source flow rate data, the humidification water infusion flow rate data, and the temperature and humidity data at the patient end in real time. The collected data is then transmitted to the processing and recognition module. The processing and identification module is used to process and calculate the collected data to obtain key difference and rate of change indicators. Based on historical and real-time data, it uses a constructed water level monitoring data model to predict the water level change trend for the next 24 hours and sends real-time signals to the main control module according to the water level status in the tank and the temperature and humidity at the user end. The processing and identification module receives the temperature, humidity, and flow data from the data acquisition module and calculates the key difference and rate of change indicators, specifically including: Temperature difference ΔT between the humidification tank and the ambient temperature: ΔT = T 罐 - T 环 ΔT reflects the difference between the temperature inside the humidification tank and the ambient temperature; The humidity difference between the humidification tank and the ambient environment, ΔRH: ΔRH = RH 罐 - RH 环 The ΔRH difference represents the difference between the humidity inside the humidification tank and the ambient humidity. 10s Temperature Difference Change Rate ΔT' / Δt: First, record the temperature difference values ​​ΔT1 and ΔT2 at two adjacent 10-second time points, corresponding to the temperature difference between the humidification tank and the environment at times t1 and t2, respectively. Then, calculate the change between these two temperature differences ΔT' = ΔT2 - ΔT1. The 10s temperature difference change rate is calculated using the formula: ΔT' / Δt = ΔT' / 10s. The ΔT' / Δt change rate reflects the rate of change of the temperature difference between the humidification tank and the environment within 10 seconds. 10s Humidity Difference Change Rate ΔRH' / Δt: Record the humidity difference ΔRH1 and ΔRH2 at two adjacent 10-second time points, then calculate the change between these two humidity differences ΔRH' = ΔRH2 - ΔRH1. Finally, obtain the 10s humidity difference change rate using the formula: ΔRH' / Δt = ΔRH' / 10s. The ΔRH' / Δt change rate indicates how quickly the humidity difference between the humidification tank and the environment changes within 10 seconds. 10s gas source flow rate change rate ΔQ / Δt: Record the gas source flow rates Q1 and Q2 at two adjacent 10-second time points, and calculate the change between these two flow rates ΔQ = Q2 - Q1; calculate the 10s gas source flow rate change rate using the formula: ΔQ / Δt = ΔQ / 10s. The ΔQ / Δt change rate reveals the change in gas source flow rate within 10 seconds. The main control module receives signals from the processing and identification module and executes corresponding humidification water flow commands to the flow control module; when an abnormal signal is received, an alarm command is sent to the alarm feedback module; the main control module, in conjunction with real-time temperature and humidity feedback from the patient, performs coordinated adjustments, specifically including: Water level conversion in humidification tank: ； in: This represents the relative position of the current water level in the humidification tank within the safe water level range, ranging from 0 to 1. The current water level in the humidification tank is shown in mm. The maximum water level in the humidification tank, in mm; the upper limit water level of the humidification tank. This indicates that the water level in the humidification tank has dropped to the minimum safe threshold; This indicates that the water level in the humidification tank has reached the maximum safe water level limit; A value between 0 and 1 indicates that the water level is within a safe range, and the ratio reflects the relative relationship between the current water level and the safe water level. Patient-side temperature difference: Represents the difference between the patient-side temperature and the target temperature. , This indicates that the temperature is too high; This indicates that the temperature is suitable; This indicates that the temperature is too low; Patient-side humidity difference: Represents the difference between the humidity at the patient's end and the target humidity. , This indicates that the humidity is too high; This indicates that the humidity is suitable; This indicates that the humidity is too low; Add the formula for calculating the humidification water flow rate: ； in, Add flow rate to the humidification water, unit: ml / s; For temperature difference; Humidity difference; =0.6~0.7 and =0.4~0.5 are the weighting coefficients for temperature and humidity, respectively, used to adjust the relative influence of temperature and humidity on the humidification water flow rate; This represents the current water level ratio in the humidification tank, between 0 and 1. =0.8 ~ 1.2 is the adjustment coefficient; The alarm feedback module is used to receive instructions from the main control module and issue an alarm sound when the data is abnormal, indicating that the water level is abnormal or that backup humidifying water needs to be added in time. The wireless module is used to upload data from the main control module and the alarm feedback module to the user terminal display module; The flow control module is connected to the flow regulating valve installed at the inlet of the humidification water tank. It is used to receive instructions from the main control module, adjust the infusion rate of humidification water, keep the humidification water level within a safe range, and ensure that the water volume is sufficient for the patient to achieve the target temperature and humidity required by the patient. The user terminal display module is used to display the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning and audible and visual alarm information to prompt medical staff to take action. The data acquisition module, processing and identification module, flow control module, alarm feedback module, wireless module and main control module are electrically connected; The data acquisition module is electrically connected to the internal temperature and humidity sensor, the ambient temperature and humidity sensor, the gas source flow sensor, the humidification water infusion flow sensor, and the patient-side temperature and humidity sensor.

2. The intelligent water level monitoring and control system based on a breathing humidifier according to claim 1, characterized in that, The internal temperature and humidity sensor is installed inside the humidification tank; the ambient humidity sensor is installed on the base of the humidification tank; the air source flow sensor is installed on the inner wall of the air inlet of the humidification tank; the humidification water infusion flow sensor is installed on the inner wall of the liquid inlet of the humidification tank; and the patient-side temperature and humidity sensor is located near the patient end of the ventilator tubing.

3. A method for intelligent monitoring and control of water level based on a breathing humidifier, characterized in that, Includes the following steps: 1) The data acquisition module monitors the real-time temperature and humidity inside the humidification tank in real time through internal temperature and humidity sensors, ambient temperature and humidity sensors, air source flow sensors, humidification water infusion flow sensors, and patient-side temperature and humidity sensors. The units are: degrees Celsius °C and relative humidity % RH; external ambient temperature and humidity, the units are: degrees Celsius °C and relative humidity % RH. Gas source flow rate, unit: liters per minute (L / min); humidifying water infusion flow rate, unit: milliliters per minute (ml / min); and patient end temperature and humidity, unit: degrees Celsius (°C) and relative humidity (% RH), and the collected data is transmitted to the processing and identification module; 2) The processing and identification module receives temperature, humidity and flow data from the data acquisition module, calculates key differences and change rate indicators, and trains functions on the monitored temperature and humidity data through the constructed water level monitoring data model; generates current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve and abnormal mode early warning signal, and transmits the corresponding signals to the main control module. The processing and identification module receives data from the data acquisition module and performs calculations to obtain key difference and rate of change indicators, specifically including: Temperature difference ΔT between the humidification tank and the ambient temperature: ΔT = T 罐 - T 环 ΔT reflects the difference between the temperature inside the humidification tank and the ambient temperature; The humidity difference between the humidification tank and the ambient environment, ΔRH: ΔRH = RH 罐 - RH 环 The ΔRH difference represents the difference between the humidity inside the humidification tank and the ambient humidity. 10s Temperature Difference Change Rate ΔT' / Δt: First, record the temperature difference values ​​ΔT1 and ΔT2 at two adjacent 10-second time points, corresponding to the temperature difference between the humidification tank and the environment at times t1 and t2, respectively. Then, calculate the change between these two temperature differences ΔT' = ΔT2 - ΔT1. The 10s temperature difference change rate is calculated using the formula: ΔT' / Δt = ΔT' / 10s. The ΔT' / Δt change rate reflects the rate of change of the temperature difference between the humidification tank and the environment within 10 seconds. 10s Humidity Difference Change Rate ΔRH' / Δt: Record the humidity difference ΔRH1 and ΔRH2 at two adjacent 10-second time points, then calculate the change between these two humidity differences ΔRH' = ΔRH2 - ΔRH1. Finally, obtain the 10s humidity difference change rate using the formula: ΔRH' / Δt = ΔRH' / 10s. The ΔRH' / Δt change rate indicates how quickly the humidity difference between the humidification tank and the environment changes within 10 seconds. 10s gas source flow rate change rate ΔQ / Δt: Record the gas source flow rates Q1 and Q2 at two adjacent 10-second time points, and calculate the change between these two flow rates ΔQ = Q2 - Q1; calculate the 10s gas source flow rate change rate using the formula: ΔQ / Δt = ΔQ / 10s. The ΔQ / Δt change rate reveals the change in gas source flow rate within 10 seconds. The specific steps of training the monitored temperature and humidity data using the constructed water level monitoring data model include: using a one-dimensional convolutional 1D-CNN to extract the temperature and humidity inside the humidification tank, the temperature and humidity of the external environment, the temperature and humidity difference between the inside and outside of the humidification tank, the air source flow rate and its rate of change, and then using an LSTM network to learn the time series information to complete the prediction of water level, humidification water consumption rate, and remaining available time. Before using a one-dimensional convolutional 1D-CNN to extract the temperature and humidity inside the humidification tank, the temperature and humidity of the external environment, the temperature and humidity difference between the inside and outside of the humidification tank, the air source flow rate and its rate of change, preprocessing is required, specifically including: Input data includes the temperature data T inside the humidification tank. 罐 Humidity data (RH) inside the humidification tank 罐 Ambient temperature data T 环 Ambient temperature data RH 环 The data for each time step t are represented as a one-dimensional vector containing multiple features: air source flow rate Q, temperature difference between the humidification tank and the environment ΔT, humidity difference between the humidification tank and the environment ΔRH, 10s temperature difference change rate ΔT' / Δt, 10s humidity difference change rate ΔRH' / Δt, and 10s air source flow rate change rate ΔQ / Δt. Before being input into the 1D-CNN-LSTM model, the data needs to be weighted, with different data items assigned different weight coefficients according to their importance: Primary weights 0.7~0.9 is suitable for raw collected data. , ; Intermediate weight 0.9~1.1 is suitable for difference data. ; Advanced weights Sections 1.1 to 1.3 apply to rate of change data. ; The features at each time step are weighted to form a weighted data matrix, represented as follows: ； Weighted data matrix Its shape is ,in It is the number of time steps. This represents the number of features at each time step; the weighted data matrix will serve as the input to subsequent 1D-CNN and LSTM layers for further analysis and prediction. 3) The main control module executes the corresponding humidification water flow command to the flow control module based on the signal received from the processing module. The flow control module intelligently controls the flow regulating valve to automatically add humidification water to the humidification water tank to the safe water level range. At the same time, the main control module combines the real-time temperature and humidity feedback from the patient's end to perform coordinated adjustment. When an abnormal signal is received, an alarm command is sent to the alarm feedback module. The main control module, in conjunction with real-time temperature and humidity feedback from the patient, performs coordinated adjustments, specifically including: Water level conversion in humidification tank: ； in: This represents the relative position of the current water level in the humidification tank within the safe water level range, ranging from 0 to 1. The current water level in the humidification tank is shown in mm. The maximum water level in the humidification tank, in mm; the upper limit water level of the humidification tank. This indicates that the water level in the humidification tank has dropped to the minimum safe threshold; This indicates that the water level in the humidification tank has reached the maximum safe water level limit; A value between 0 and 1 indicates that the water level is within a safe range, and the ratio reflects the relative relationship between the current water level and the safe water level. Patient-side temperature difference: Represents the difference between the patient-side temperature and the target temperature. , This indicates that the temperature is too high; This indicates that the temperature is suitable; This indicates that the temperature is too low; Patient-side humidity difference: Represents the difference between the humidity at the patient's end and the target humidity. , This indicates that the humidity is too high; This indicates that the humidity is suitable; This indicates that the humidity is too low; Add the formula for calculating the humidification water flow rate: ； in, Add flow rate to the humidification water, unit: ml / s; For temperature difference; Humidity difference; =0.6~0.7 and =0.4~0.5 are the weighting coefficients for temperature and humidity, respectively, used to adjust the relative influence of temperature and humidity on the humidification water flow rate; This represents the current water level ratio in the humidification tank, between 0 and 1. =0.8 ~ 1.2 is the adjustment coefficient; 4) When the processing module detects an abnormal signal, the alarm feedback module receives an alarm command from the main control module and issues an alarm sound to indicate that the water level is abnormal or that backup humidification water needs to be added in time. The main control module uploads the current water level, water consumption rate, remaining available time, 24-hour water level change prediction curve, abnormal mode warning signal, and audible and visual alarm information to the user terminal display module through the wireless module to prompt medical staff to take action.

4. The intelligent water level monitoring and control method based on a breathing humidifier according to claim 3, characterized in that, Step 2) specifically includes generating the 24-hour water level change prediction curve: The 1D-CNN-LSTM model is used to predict the water level in the humidification tank over the next 24 hours and output the water level change curve. The 1D-CNN-LSTM model outputs predicted water levels for the next 24 hours. ； in, For the first The predicted water level at any given time, where 𝑁 represents the time step over the next 24 hours. The time step is in minutes. Predicted water level value The water level change curve is plotted, and a trend chart of water level change over the next 24 hours is output. This chart is then uploaded to the user terminal display module for real-time updates and embedded into the interactive interface of medical staff.

5. The intelligent water level monitoring and control method based on a breathing humidifier according to claim 3, characterized in that, The abnormal mode warning signal mentioned in step 2) specifically includes: Real-time detection of water level changes in the humidification tank and abnormal patterns of humidification water delivery can trigger an early warning so that medical staff can promptly handle equipment malfunctions or replace the humidification water with a backup. The formula for calculating the rate of water level change is: ； in, Indicates time interval, The current time output by the 1D-CNN-LSTM model water level value, The previous time for the output of the 1D-CNN-LSTM model Water level value; The criteria for determining abnormal modes are set as follows: Abnormal rate of water level drop The rate of water level rise is abnormal. Abnormal humidification water delivery ; in, This represents the normal upper limit of the rate of water level drop. This represents the normal lower limit of the rate of water level rise. Add flow rate to the humidification water, unit: ml / s; When an abnormal pattern is detected, an alarm signal is generated and transmitted to the main control module.

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