Trachea cannula temperature and humidity monitoring control system

Abstract

The invention discloses a trachea cannula temperature and humidity monitoring control system, and relates to the technical field of medical instruments. The equipment control terminal is used for establishing communication connection with the trachea monitoring terminal and the medical monitoring station, acquiring the acquired real-time temperature and humidity data, judging whether temperature and humidity abnormity exists in the trachea cannula or not through data analysis, triggering airway temperature and humidity abnormity early warning and controlling airway temperature and humidity adjustment according to a judgment result; provided is a medical monitoring station. By monitoring temperature and humidity changes in the trachea cannula in real time and combining physiological signs and environmental data of a patient, personalized temperature and humidity adjustment and abnormity early warning functions are provided; the system has intelligent data analysis and prediction capabilities, can predict future temperature and humidity trends, automatically adjust regulation and control parameters, perform dynamic optimization according to physical signs and environment states of patients, ensure that the temperature and the humidity are within the most suitable range, and improve the comfort level and the experience effect of the patients.

Description

Technical Field

[0001] The invention relates to the technical field of medical devices, and in particular to a tracheal intubation temperature and humidity monitoring and control system. Background Art

[0002] With the continuous improvement of medical technology and treatment level, the demand for respiratory management equipment in clinical practice is increasing. Especially in operations such as endotracheal intubation and tracheotomy, these devices play a vital role in rescuing patients with respiratory dysfunction and maintaining the normal operation of vital signs. However, as patients' demands for medical equipment become increasingly complex, traditional endotracheal intubation and tracheotomy tube products can no longer fully meet the requirements of modern medical care for high-precision and high-stability monitoring, especially for the monitoring of respiratory temperature and humidity.

[0003] Endotracheal intubation and tracheotomy are widely used in critically ill patients, especially in patients with anesthesia, respiratory failure or inability to breathe on their own. These two operations can ensure that the airway is open and provide effective ventilation support for patients. Endotracheal intubation is usually used for acute conditions or short-term treatment, while tracheotomy is mostly used for patients who require long-term mechanical ventilation, such as chronic respiratory failure, critically ill patients or patients who require long-term ventilation management.

[0004] However, whether it is endotracheal intubation or tracheotomy, postoperative airway management of patients is crucial. The humidity and temperature of the patient's airway not only directly affect the patency of the airway, but also have a profound impact on the rehabilitation process, the prevention of complications, and the effectiveness of treatment. For example, during endotracheal intubation or tracheotomy, the airway is often exposed to the external environment, and drastic changes in airway temperature and humidity may trigger a series of adverse reactions, such as airway dryness, sputum viscosity, and upper respiratory tract damage.

[0005] Although endotracheal intubation and tracheotomy have been widely used, most endotracheal intubation and tracheotomy tube products on the market lack real-time temperature and humidity monitoring functions. Currently, existing products on the market are usually limited to monitoring traditional parameters such as airway patency, airflow, and air pressure, and the real-time tracking and control of respiratory temperature and humidity are relatively weak. This makes it difficult for medical staff to grasp the changes in the patient's respiratory temperature and humidity in the first place after surgery, and they are unable to take timely intervention measures.

[0006] In addition, although some existing temperature and humidity monitoring devices have certain monitoring functions, their accuracy and stability still cannot meet the requirements of clinical applications. Some devices have problems such as inaccurate monitoring, data delays, and insufficient accuracy, making it difficult to accurately reflect the true condition of the patient's respiratory tract. For patients with complex or large changes, the lag and inaccuracy of monitoring results may lead to delayed treatment and even complications, increasing the risk of patients. Summary of the invention

[0007] Based on this, it is necessary to provide a tracheal tube temperature and humidity monitoring and control system to address the above technical issues.

[0008] The present invention provides a tracheal intubation temperature and humidity monitoring and control system, the system comprising: The airway monitoring terminal is used to be installed on the wall of the endotracheal tube to monitor the temperature and humidity changes inside the endotracheal tube in real time, obtain real-time temperature and humidity data, and integrate data display and transmission functions to synchronously display and transmit real-time temperature and humidity data information; The equipment control terminal is used to establish a communication connection with the tracheal monitoring terminal and the medical monitoring station, obtain the collected real-time temperature and humidity data, and judge whether there is abnormal temperature and humidity in the tracheal tube through data analysis. According to the judgment result, the airway temperature and humidity abnormality warning is triggered and the airway temperature and humidity adjustment is controlled; The medical monitoring station is used to establish a communication connection with the equipment control terminal, obtain the monitoring data and operating status of all airway monitoring terminals and equipment control terminals in the monitoring area, display them in real time through visualization equipment, and centrally store and manage the monitoring data; Further, the airway monitoring terminal includes: a waterproof coating module, a detection sensor module, a fixed protection module, an integrated display module, a power supply module and an external interface module; Among them, the waterproof coating module is used to protect electronic components from moisture and liquid damage, and maintain the long-term stable operation of electronic components in the humid environment of the endotracheal tube; The detection sensor module is used to integrate the temperature sensor and the humidity sensor to monitor the temperature and humidity status and changes inside the endotracheal tube in real time and obtain real-time temperature and humidity data; A fixed protection module is used to construct and install a fixed bracket using biocompatible materials, attach the electronic components to the wall of the endotracheal tube, and provide a locking function to prevent falling off or deviation; An integrated display module is used to obtain the real-time temperature and humidity data collected by the detection sensor module and display the values ​​of the real-time temperature and humidity data through an external display; The power supply module is used to provide an independent power supply, monitor the battery power of the power supply in real time, display it using the integrated display module, and can be connected to the power line for continuous power supply; The external interface module is used to provide wireless communication components and a connection interface with the device control terminal, supporting data transmission and control signal exchange.

[0009] Furthermore, the equipment control terminal includes: a communication interface module, a data processing module, an initial setting module, a temperature and humidity prediction module, an abnormal warning module and a control and adjustment module; Among them, the communication interface module is used to provide data communication and signal exchange channels with the airway monitoring terminal and the medical monitoring station, obtain the real-time temperature and humidity data collected by the airway monitoring terminal, and upload the data analysis results and control signals to the medical monitoring station; Data processing module, used for preprocessing real-time temperature and humidity data; An initial setting module is used to manually or automatically select different control modes based on the patient's identity information input in advance, and set the temperature and humidity thresholds that meet the patient's personalized needs; The temperature and humidity prediction module is used to predict the temperature and humidity change trend inside the endotracheal tube in the future time period based on the recorded historical temperature and humidity data combined with the patient's physiological signs and environmental data; The abnormal warning module is used to monitor real-time temperature and humidity data and temperature and humidity forecast data based on temperature and humidity thresholds, analyze potential abnormal phenomena, and provide multi-level intelligent early warning reminders; The control and adjustment module is used to calculate the control parameters of the airway temperature and humidity control equipment in the future time period based on the prediction results and abnormal analysis results of the real-time temperature and humidity data, and to perform personalized dynamic optimization of the control parameters based on environmental data, patient physiological signs, and the error between the real-time temperature and humidity data and the target data, so as to adjust the temperature and humidity values ​​in the endotracheal tube in the future time period.

[0010] Further, the initial setting module includes: an information input unit, a mode selection unit, a threshold setting unit, a learning optimization unit and an interactive operation unit; The information input unit is used to input the patient's identity information and physiological signs, and match them in the historical archive. If there is a historical archive of the patient, the historical control mode and temperature and humidity thresholds are selected. If there is no historical archive of the patient, the manual or automatic control mode is selected for the first time. A mode selection unit is used to select a corresponding control mode according to the patient's identity information and physiological signs. When in manual mode, the temperature and humidity threshold range is manually input; when in automatic mode, the historical archive that is most similar to the patient's identity information and physiological signs is matched in the historical archive library, and the temperature and humidity threshold range corresponding to the historical archive is selected and set; The learning optimization unit is used to continuously record the historical temperature and humidity data and feedback information of patients during use, store them in the historical archive, model the temperature and humidity requirements of patients through machine learning, and adjust the temperature and humidity thresholds based on the historical temperature and humidity data, feedback information and real-time temperature and humidity data; The interactive operation unit is used to provide an intuitive operation interface, supporting information input, control mode selection, and temperature and humidity threshold display and adjustment functions.

[0011] Furthermore, the temperature and humidity prediction module includes: a variable setting unit, a model building unit, a training optimization unit and a model prediction unit; Among them, the variable setting unit is used to set the input variables of the temperature and humidity prediction model. The input variables are normalized by the minimum-maximum normative method and divided into a training set and a test set. The input variables include historical temperature and humidity data, physiological signs and environmental data; The model building unit is used to create a back propagation neural network including an input layer, a hidden layer, and an output layer, calculate the number of nodes in the hidden layer using an empirical method, and build a model architecture; A training optimization unit is used to combine the dung beetle optimization algorithm with the back propagation neural network, and use the global search ability and fast convergence ability of the dung beetle optimization algorithm to update and optimize the initial weights and thresholds of the back propagation neural network, and build a temperature and humidity prediction model of the combined neural network. The temperature and humidity prediction model includes a humidity prediction model and a temperature prediction model; The model prediction unit is used to set the prediction period of temperature and humidity prediction, input the real-time temperature and humidity data, environmental data and physiological signs at the current moment into the temperature and humidity prediction model, predict the temperature and humidity prediction data in the future time period, and draw the change curve of temperature and humidity data over time.

[0012] Furthermore, the dung beetle optimization algorithm is combined with the back propagation neural network. The global search ability and fast convergence ability of the dung beetle optimization algorithm are used to update and optimize the initial weights and thresholds of the back propagation neural network. The temperature and humidity prediction model of the combined neural network is constructed, including: Initialize the structural parameters of the dung beetle algorithm and the back propagation neural network, obtain the normalized data of historical temperature and humidity data, physiological signs and environmental data, each dung beetle represents a solution vector of weights and thresholds, and each solution vector contains the weights and biases of all layers of the back propagation neural network; When the dung beetle rolls the ball forward without any obstacles, the individual position of the dung beetle after the ball is rolled forward is updated. When the dung beetle encounters an obstacle and cannot move forward, it repositions itself by dancing, and the individual position of the dung beetle after dancing is updated. A boundary selection strategy was used to simulate the female breeding area, and the upper and lower limits of the breeding area were set; Set the upper and lower limits of the foraging area, and when the foraging dung beetle starts foraging, update the individual position of the dung beetle after foraging; When the thieving dung beetle starts stealing, the individual position of the dung beetle after stealing is updated; Find the best position and fitness value of the current dung beetle, and determine whether the preset termination condition is met. If so, execute the next step. If not, continue to find the best position of the dung beetle; The back propagation neural network is trained, and the global optimal weights and thresholds optimized by the dung beetle optimization algorithm are transferred to the back propagation neural network to obtain the temperature and humidity prediction model.

[0013] Furthermore, the calculation formula for updating the position of the individual dung beetle after the ball moves forward is: ; In the formula, For the i The position of the dung beetle is t Position information at the iteration; a is the natural coefficient; k is a constant; t is the number of iterations; b is a random number; is the global worst position.

[0014] Furthermore, the abnormal warning module includes: a data comparison unit, an abnormality detection unit, an early warning generation unit and a feedback adjustment unit; Among them, the data comparison unit is used to compare the real-time temperature and humidity data with the upper and lower limits of the temperature and humidity threshold range. If it exceeds the temperature and humidity threshold range, it is marked as abnormal, and the real-time temperature and humidity data is compared with the temperature and humidity prediction data to determine whether the deviation between the real-time temperature and humidity data and the temperature and humidity prediction data exceeds the set range. If it exceeds, it is marked as abnormal; The anomaly detection unit is used to check potential anomalies and determine the type and cause of anomalies based on the comparison results of real-time temperature and humidity data with the predicted temperature and humidity values ​​using an anomaly rule engine; An early warning generation unit is used to generate multi-level early warning information based on the abnormal detection results. The multi-level early warning information includes green early warning, yellow early warning and red early warning. When in green early warning, the current control parameters are maintained. When in yellow early warning, the control and adjustment module is used to perform automatic control and adjustment. When in red early warning, a manual intervention early warning is triggered; The feedback adjustment unit is used to trigger corresponding temperature and humidity intervention measures according to the warning level.

[0015] Furthermore, the control and regulation module includes: a control parameter calculation unit, a personalized dynamic optimization unit, a multivariable control unit and an evaluation feedback unit; Among them, the control parameter calculation unit is used to set the standard target of temperature and humidity according to the current temperature and humidity threshold range, calculate the actual error between the real-time temperature and humidity data and the standard target, and the predicted error between the temperature and humidity prediction data and the standard target, and calculate the control parameters of the airway temperature and humidity adjustment device required to maintain the temperature and humidity at the standard target through the PID control algorithm; Personalized dynamic optimization unit, which is used to optimize the control parameters based on the patient's physiological signs and environmental data to meet the temperature and humidity requirements of different patients in different environments; The equipment control output unit is used to output the optimized control parameters to the corresponding airway temperature and humidity control equipment to control the operation model and operation status at the future moment; The evaluation feedback unit is used to continuously monitor the operating status of the airway temperature and humidity adjustment equipment and the real-time temperature and humidity data in the endotracheal tube, and to evaluate the adjustment effect in real time. If there is an expected error in the adjustment effect, the control parameters are optimized through the feedback results. If there is no error in the adjustment effect, no adjustment is required.

[0016] Furthermore, based on the patient's physiological signs and environmental data, the control parameters are optimized in a personalized manner to meet the temperature and humidity requirements of different patients in different environments, including: Based on the patient's physiological signs, a model for evaluating the patient's tolerance to temperature and humidity changes is established. By real-time monitoring of the numerical changes in physiological signs, the patient's current physiological state is dynamically evaluated and the patient's tolerance to temperature and humidity changes at the current moment is analyzed. Based on the tolerance assessment results of the patient's physiological signs and combined with environmental data, the temperature and humidity threshold range is optimized, personalized temperature and humidity targets are calculated, and the control parameters predicted for future moments are optimized. The airway temperature and humidity adjustment equipment is controlled to adjust the inside of the endotracheal tube to the temperature and humidity targets.

[0017] Furthermore, the personalized temperature and humidity targets are calculated including the temperature value to be adjusted and the humidity value to be adjusted; The calculation formula for the temperature value to be adjusted is: ; In the formula, ΔT adjust To adjust the temperature value; K T is the temperature adjustment factor; T target To set the temperature standard target; T current is the current temperature; T normal is the patient's current body temperature; The calculation formula for adjusting the humidity value is: ; ΔH adjust To adjust the humidity value; K H is the humidity adjustment factor; H target To set the humidity standard target; Hcurrent is the current humidity; HumidityFactor is the humidity adjustment factor.

[0018] The beneficial effects of the present invention are: 1. By real-time monitoring of the temperature and humidity changes in the endotracheal tube, combined with the patient's physiological signs and environmental data, it provides personalized temperature and humidity adjustment and abnormal warning functions; it has intelligent data analysis and prediction capabilities, can predict future temperature and humidity trends, automatically adjust the control parameters, and dynamically optimize according to the patient's physical signs and environmental conditions to ensure that the temperature and humidity are within the most suitable range, thereby improving the patient's comfort and experience effect; the present invention combines the temperature and humidity prediction module, the abnormal warning module and the personalized dynamic optimization control to achieve high-precision, high-stability and personalized temperature and humidity management, significantly improving the accuracy and safety of clinical treatment and nursing.

[0019] 2. By combining the dung beetle optimization algorithm with the back propagation neural network, the advantages of the dung beetle algorithm in global search and rapid convergence are fully utilized, and the weights and thresholds of the back propagation neural network are accurately optimized, thereby constructing a high-precision and high-stability temperature and humidity prediction model. It can perform personalized temperature and humidity prediction and adjustment based on the patient's physiological signs and environmental data, and automatically adjust the temperature and humidity parameters to ensure that the temperature and humidity in the endotracheal tube are maintained within the most suitable range, effectively improving the patient's comfort and experience, optimizing the performance of the temperature and humidity prediction model, and improving the system's adaptability and prediction accuracy in complex environments, significantly enhancing the intelligence and personalization of endotracheal intubation temperature and humidity monitoring and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 The present invention is a block diagram of a tracheal tube temperature and humidity monitoring and control system according to an embodiment of the present invention.

[0021] Figure numbers: 1. Airway monitoring terminal; 2. Equipment control terminal; 3. Medical monitoring station. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] See also Figure 1 , a tracheal intubation temperature and humidity monitoring and control system, the system comprising: The airway monitoring terminal 1 is used to be installed on the wall of the endotracheal tube to monitor the temperature and humidity changes inside the endotracheal tube in real time, obtain real-time temperature and humidity data, and integrate data display and transmission functions to synchronously display and transmit data information of real-time temperature and humidity data.

[0024] In the description of the present invention, the airway monitoring terminal 1 includes: a waterproof coating module (not shown in the figure), a detection sensor module (not shown in the figure), a fixed protection module (not shown in the figure), an integrated display module (not shown in the figure), a power supply module (not shown in the figure) and an external interface module (not shown in the figure).

[0025] The waterproof coating module is used to protect electronic components from moisture and liquids, and maintain the long-term stable operation of electronic components in the humid environment of the endotracheal tube.

[0026] Specifically, the airway monitoring terminal 1 will be directly exposed to the airway and endotracheal tube, and will come into contact with humid gases (such as oxygen, humidified air, etc.), so waterproofing is very critical. The waterproof coating module can effectively protect the sensor and circuit from moisture and liquid, ensuring that the equipment can work stably and long-term in a humid environment. It is necessary to select a coating material with good air permeability and waterproofness to ensure the safety of internal electronic components without affecting the working accuracy of the temperature and humidity sensor. At the same time, precise sealing technology is used to ensure the waterproof performance of the connection points and interface parts to prevent the external environment from damaging the sensor and circuit board.

[0027] The detection sensor module is used to integrate the temperature sensor and the humidity sensor to monitor the temperature and humidity status and changes inside the endotracheal tube in real time and obtain real-time temperature and humidity data.

[0028] Specifically, the temperature sensor is used to monitor the air temperature in the endotracheal tube in real time. Common temperature sensors include thermistors, thermocouples, etc. The selection of sensors needs to consider their response time, accuracy, and stability. The humidity sensor is used to monitor the air humidity in the endotracheal tube. Humidity sensors usually use capacitive, resistive, or electronic sensors. When selecting, attention should be paid to their measurement range, accuracy, and anti-interference ability.

[0029] The fixed protection module is used to construct and install a fixed bracket using biocompatible materials, attach the electronic components to the wall of the endotracheal tube, and provide a locking function to prevent falling off or deviation.

[0030] Specifically, the fixed protection module is used to fix and protect the airway monitoring terminal so that it is firmly installed in the endotracheal tube to prevent the device from being displaced or damaged due to the patient's activities or external factors. The fixed protection module can ensure the stability of the monitoring terminal in the endotracheal tube and guarantee the accuracy of data in long-term use.

[0031] The integrated display module is used to obtain the real-time temperature and humidity data collected by the detection sensor module, and display the values ​​of the real-time temperature and humidity data through an external display.

[0032] The power supply module is used to provide an independent power supply, monitor the battery power of the power supply in real time, display it using the integrated display module, and can be connected to the power line for continuous power supply.

[0033] Specifically, the power required by the airway monitoring terminal 1 is provided to ensure that the device can continue to operate. The power module needs to have the characteristics of high efficiency and long battery life, especially in the ward environment, to reduce the maintenance frequency of medical equipment. High-performance, long-life rechargeable batteries (such as lithium batteries) are required to power the device. The battery should have a large capacity to extend the service life.

[0034] The external interface module is used to provide wireless communication components and a connection interface with the device control terminal 2, supporting data transmission and control signal exchange.

[0035] The equipment control terminal 2 is used to establish a communication connection with the tracheal monitoring terminal and the medical monitoring station, obtain the collected real-time temperature and humidity data, and judge whether there are abnormal temperature and humidity in the tracheal tube through data analysis. According to the judgment result, the airway temperature and humidity abnormality warning is triggered and the airway temperature and humidity adjustment is controlled.

[0036] In the description of the present invention, the device control terminal 2 includes: a communication interface module (not shown in the figure), a data processing module (not shown in the figure), an initial setting module (not shown in the figure), a temperature and humidity prediction module (not shown in the figure), an abnormal warning module (not shown in the figure) and a control and adjustment module (not shown in the figure).

[0037] In addition, in the device control terminal 2, the communication interface module, the data processing module, the initial setting module, the temperature and humidity prediction module, the abnormal warning module and the control and adjustment module are connected in sequence.

[0038] The communication interface module is used to provide data communication and signal exchange channels with the airway monitoring terminal 1 and the medical monitoring station 3, obtain real-time temperature and humidity data collected by the airway monitoring terminal 1, and upload data analysis results and control signals to the medical monitoring station 3.

[0039] Specifically, the communication interface module is responsible for data communication and signal exchange between the device control terminal 2 and the airway monitoring terminal 1, the medical monitoring station 3 and other related devices. Through this module, the device control terminal can receive real-time data from the airway monitoring terminal, and can also feed back the processing results to other systems (such as the medical monitoring station) or control the airway temperature and humidity adjustment equipment.

[0040] The data processing module is used to pre-process the real-time temperature and humidity data.

[0041] Specifically, the data processing module is responsible for real-time processing of the temperature and humidity data received from the airway monitoring terminal 1, including data cleaning, denoising, smoothing, etc. The data processing module provides high-quality input data for subsequent analysis and decision-making, and dynamically adjusts the control strategy according to the actual needs of the patient. Remove influencing factors such as sensor errors and signal interference to ensure data accuracy, convert the original temperature and humidity data into a form that is easy to analyze, and perform data preprocessing and storage.

[0042] The initial setting module is used to manually or automatically select different control modes based on the patient's identity information input in advance, and set the temperature and humidity thresholds that meet the patient's personalized needs.

[0043] In the description of the present invention, the initial setting module includes: an information input unit (not shown in the figure), a mode selection unit (not shown in the figure), a threshold setting unit (not shown in the figure), a learning optimization unit (not shown in the figure) and an interactive operation unit (not shown in the figure).

[0044] In addition, in the initial setting module, the information input unit, the mode selection unit, the threshold setting unit, the learning optimization unit and the interactive operation unit are connected in sequence.

[0045] The information input unit is used to input the patient's identity information and physiological signs and match them in the historical archive. If the patient's historical archive exists, the historical control mode and temperature and humidity thresholds are selected. If the patient's historical archive does not exist, the manual or automatic control mode is selected for the first time.

[0046] The mode selection unit is used to select the corresponding control mode according to the patient's identity information and physiological signs. When in manual mode, the temperature and humidity threshold range is manually input. When in automatic mode, the historical archive that is most similar to the patient's identity information and physiological signs is matched in the historical archive library, and the temperature and humidity threshold range corresponding to the historical archive is selected and set.

[0047] Specifically, the control mode refers to different temperature and humidity setting schemes to meet the needs of different types of patients. According to the patient's personal information and clinical needs, the mode setting module should provide two options.

[0048] Automatic mode selection: In this mode, the system automatically selects a suitable temperature and humidity control mode based on the patient's basic information (such as age, weight, disease type, etc.). Through a pre-established database or model, this mode can recommend the optimal temperature and humidity thresholds and control strategies for different patient groups. In the mode setting module, a temperature and humidity control data model corresponding to different patient groups (such as children, adults, the elderly, critically ill patients, etc.) is established. For example, children may require higher humidity to prevent airway dryness, while the elderly may be more sensitive to temperature changes. Based on the specific characteristics of the patient, the system will select a suitable temperature and humidity control mode from the database and automatically set the temperature and humidity thresholds (such as temperature range, humidity range). This automatic selection process can be continuously optimized through data mining or machine learning algorithms, enabling the system to adjust the control strategy in real time.

[0049] Manual mode selection: For patients who require personalized adjustments, medical staff can manually select the mode and enter the specific temperature and humidity range suitable for the patient. For example, for certain specific diseases (such as COPD patients), lower temperatures and higher humidity may need to be set, while for other types of respiratory failure patients, higher temperatures and moderate humidity may be required. The manual mode selection interface allows medical staff to enter or select a specific control mode through a user-friendly interface (such as a touch screen, PC, or mobile terminal). The interface should provide different preset modes (such as "child mode", "adult mode", "critical mode", etc.) and allow adjustment of the temperature and humidity settings in the mode. Custom settings allow doctors or nurses to enter personalized temperature and humidity ranges based on the patient's real-time physiological state, and the system will generate real-time control strategies based on these inputs.

[0050] In addition, the threshold setting is based on age and weight, and patients of different age groups and weights have different requirements for temperature and humidity. For example, children's respiratory tract is more fragile and usually requires higher humidity; while the elderly may be intolerant to excessive humidity. The type of disease must also be considered. For example, patients with chronic obstructive pulmonary disease (COPD) usually require low-temperature, humidified air to reduce respiratory irritation; patients with acute respiratory failure may require higher temperatures and humidity to help reduce airway discomfort and increase oxygen inhalation. In addition, there is the influence of environmental data, and the indoor temperature and humidity environment also need to be considered. For example, dry indoor air may require an increase in the humidity output of the humidifier.

[0051] The learning optimization unit is used to continuously record the historical temperature and humidity data and feedback information during the patient's use, store them in the historical archive, model the patient's temperature and humidity requirements through machine learning, and adjust the temperature and humidity thresholds based on historical temperature and humidity data, feedback information and real-time temperature and humidity data.

[0052] Specifically, by real-time monitoring of the patient's physiological data (such as body temperature, respiratory rate, etc.), the temperature and humidity thresholds can be dynamically adjusted based on real-time feedback. If the patient's body temperature is found to be too low or too high, the system can automatically adjust the temperature and humidity settings to avoid discomfort to the patient due to temperature and humidity discomfort.

[0053] The learning optimization unit can model the temperature and humidity requirements of each patient through machine learning (such as a regression model based on a neural network), and gradually optimize the temperature and humidity control thresholds based on the patient's historical data, feedback information, and real-time monitoring data.

[0054] The interactive operation unit is used to provide an intuitive operation interface, supporting information input, control mode selection, and temperature and humidity threshold display and adjustment functions.

[0055] The temperature and humidity prediction module is used to predict the temperature and humidity change trend inside the endotracheal tube in the future time period based on the recorded historical temperature and humidity data combined with the patient's physiological signs and environmental data.

[0056] In the description of the present invention, the temperature and humidity prediction module includes: a variable setting unit (not shown in the figure), a model building unit (not shown in the figure), a training optimization unit (not shown in the figure) and a model prediction unit (not shown in the figure).

[0057] In addition, in the temperature and humidity prediction module, the variable setting unit, the model building unit, the training optimization unit and the model prediction unit are connected in sequence.

[0058] The variable setting unit is used to set the input variables of the temperature and humidity prediction model. The input variables are normalized using the minimum-maximum standard method and divided into training sets and test sets. The input variables include historical temperature and humidity data, physiological signs and environmental data.

[0059] The model building unit is used to create a back propagation neural network including an input layer, a hidden layer, and an output layer, calculate the number of nodes in the hidden layer using an empirical method, and build a model architecture.

[0060] The training optimization unit is used to combine the dung beetle optimization algorithm with the back propagation neural network, and utilize the global search capability and fast convergence capability of the dung beetle optimization algorithm to update and optimize the initial weights and thresholds of the back propagation neural network, and construct a temperature and humidity prediction model of the combined neural network. The temperature and humidity prediction model includes a humidity prediction model and a temperature prediction model.

[0061] Specifically, the Dung beetle optimizer (DBO) simulates the rolling, dancing, foraging, stealing, and breeding behaviors of dung beetles. It is a heuristic global optimization algorithm that simulates the rolling, dancing, foraging, stealing, and breeding behaviors of dung beetles (dung beetles). The DBO algorithm takes into account both exploration and development capabilities, with fast convergence, high accuracy, and strong stability.

[0062] Dung beetles roll their dung balls backward quickly to avoid competition with other dung beetles. The ball rolling behavior uses celestial clues (sunlight, moon, polarized light) for navigation. More precisely, it uses light sources to roll in a straight line. If there is no light and it is completely dark, the ball rolling path is curved. Other natural factors may cause the dung beetle's ball rolling path to deviate from the original direction, such as wind speed, ground flatness, etc. When encountering obstacles, the dung beetle may stop rolling and not move forward or be unable to move forward. The dung beetle may dance on the dung ball (spin, pause, etc.) and then decide which direction to move. The purpose of the dung beetle rolling ball is to lay eggs and reproduce the next generation on the one hand, and to provide food for the larvae on the other hand. Therefore, the dung ball is very important to dung beetles.

[0063] Dung beetle behaviors include rolling behavior, dancing behavior, breeding behavior, foraging behavior, and stealing behavior.

[0064] 1. Dung beetle ball-rolling behavior: Dung beetles use celestial clues to navigate and make the dung ball move in a straight line in a specified direction, simulating the ball-rolling behavior. Dung beetles move in a given direction in the search domain. Considering sunlight navigation, the intensity of sunlight affects the ball-rolling path of dung beetles.

[0065] 2. Dancing behavior of dung beetles: When encountering obstacles, dung beetles dance to reposition themselves and obtain a new route. Dancing is very important for dung beetles. i ], the dung beetle determines the new direction and rolls the ball backwards. 3. Reproductive behavior of dung beetles: Dung beetles roll their dung balls to a safe place and hide them, select egg-laying sites, and use boundary selection strategies to simulate the egg-laying areas of female dung beetles.

[0066] 4. Dung beetle foraging behavior: Adult dung beetles (foraging dung beetles) will crawl out to look for food. Establish a good foraging area to guide the small dung beetles to forage. First, define the boundaries of the best foraging area.

[0067] 5. Dung beetle stealing behavior: Some dung beetles will steal the dung balls of other dung beetles. They are called thief dung beetles or thieving dung beetles.

[0068] In the description of the present invention, the dung beetle optimization algorithm is combined with the back propagation neural network, and the global search ability and fast convergence ability of the dung beetle optimization algorithm are used to update and optimize the initial weights and thresholds of the back propagation neural network. The temperature and humidity prediction model of the combined neural network is constructed, including: Step S201, initialize the structural parameters of the dung beetle algorithm and the back propagation neural network, obtain the normalized data of historical temperature and humidity data, physiological signs and environmental data, each dung beetle represents a solution vector of weights and thresholds, and each solution vector contains the weights and biases of all layers of the back propagation neural network.

[0069] Step S202: When the dung beetle rolls the ball forward without any obstacles, the individual position of the dung beetle after the ball rolls forward is updated; when the dung beetle encounters an obstacle and cannot move forward, it repositions itself by dancing, and the individual position of the dung beetle after dancing is updated.

[0070] In the description of the present invention, the calculation formula for updating the position of the individual dung beetle after the rolling ball moves forward is: ; In the formula, For the i The position of the dung beetle is t Position information at the iteration; a is the natural coefficient; k is a constant; t is the number of iterations; b is a random number; is the global worst position.

[0071] Step S203: Use boundary selection strategy to simulate the female breeding area, and set the upper and lower limits of the breeding area.

[0072] Step S204, setting the upper limit and the lower limit of the foraging area, when the foraging dung beetle starts foraging, updating the individual position of the dung beetle after foraging.

[0073] Step S205: When the stealing dung beetle starts stealing, the individual position of the dung beetle after stealing is updated.

[0074] Step S206, find the current best position and fitness value of the dung beetle, and determine whether the preset termination condition is met. If so, execute the next step. If not, continue to find the best position of the dung beetle and repeat steps S202-S206.

[0075] Step S207: training the back propagation neural network, transferring the global optimal weights and thresholds optimized by the dung beetle optimization algorithm to the back propagation neural network, and obtaining a temperature and humidity prediction model.

[0076] The model prediction unit is used to set the prediction period of temperature and humidity prediction, input the real-time temperature and humidity data, environmental data and physiological signs at the current moment into the temperature and humidity prediction model, predict the temperature and humidity prediction data in the future time period, and draw the change curve of temperature and humidity data over time.

[0077] The abnormal warning module is used to monitor real-time temperature and humidity data and temperature and humidity forecast data based on temperature and humidity thresholds, analyze potential abnormal phenomena, and provide multi-level intelligent early warning reminders.

[0078] In the description of the present invention, the abnormal warning module includes: a data comparison unit (not shown in the figure), an abnormality detection unit (not shown in the figure), a warning generation unit (not shown in the figure) and a feedback adjustment unit (not shown in the figure).

[0079] In addition, in the abnormal warning module, the data comparison unit, the abnormality detection unit, the warning generation unit and the feedback adjustment unit are connected in sequence.

[0080] The data comparison unit is used to compare the real-time temperature and humidity data with the upper and lower limits of the temperature and humidity threshold range. If the temperature and humidity threshold range is exceeded, it is marked as abnormal. The real-time temperature and humidity data are compared with the temperature and humidity forecast data to determine whether the deviation between the real-time temperature and humidity data and the temperature and humidity forecast data exceeds the set range. If it exceeds, it is marked as abnormal.

[0081] The anomaly detection unit is used to check potential anomalies and determine the type and cause of anomalies based on the comparison results of real-time temperature and humidity data with temperature and humidity forecast values ​​using an anomaly rule engine.

[0082] Specifically, based on the comparison of the threshold comparison results and the predicted data, detect whether there are potential abnormalities. Common types of abnormalities include: temperature is too high, too low, humidity is too high, too low, sudden changes in temperature and humidity, and excessive fluctuations in temperature and humidity. The abnormal rule engine can detect abnormalities based on set rules (such as temperature and humidity exceeding the standard, excessive fluctuations, etc.). For example, if the real-time data exceeds the threshold for more than a certain period of time, it is judged as abnormal; if the deviation between the real-time data and the predicted data exceeds the set error range, an abnormal warning is triggered.

[0083] By comparing with the preset threshold and historical data, check whether the current temperature and humidity data are abnormal. For example, assuming that the set temperature threshold is [36.0°C, 37.5°C], if the real-time data is 38.0°C, the temperature exceeding the standard warning will be triggered. Use the future data output by the temperature and humidity prediction model to compare with the real-time data to analyze the degree to which the current data deviates from the predicted value. If the real-time temperature is 38.0°C and the predicted temperature is 36.5°C, the deviation is 1.5°C. If it exceeds the set allowable deviation range, the warning will be triggered.

[0084] The warning generation unit is used to generate multi-level warning information based on the abnormal detection results. The multi-level warning information includes green warning, yellow warning and red warning. When it is in the green warning, the current control parameters are maintained. When it is in the yellow warning, the control and adjustment module is used to perform automatic control and adjustment. When it is in the red warning, the manual intervention warning is triggered.

[0085] The feedback adjustment unit is used to trigger corresponding temperature and humidity intervention measures according to the warning level.

[0086] The control and adjustment module is used to calculate the control parameters of the airway temperature and humidity control equipment in the future time period based on the prediction results and abnormal analysis results of the real-time temperature and humidity data, and to perform personalized dynamic optimization of the control parameters based on environmental data, patient physiological signs, and the error between the real-time temperature and humidity data and the target data, so as to adjust the temperature and humidity values ​​in the endotracheal tube in the future time period.

[0087] In the description of the present invention, the control and adjustment module includes: a control parameter calculation unit (not shown in the figure), a personalized dynamic optimization unit (not shown in the figure), a multivariable control unit (not shown in the figure) and an evaluation feedback unit (not shown in the figure).

[0088] In addition, in the control and regulation module, the control parameter calculation unit, the personalized dynamic optimization unit, the multivariable control unit and the evaluation feedback unit are connected in sequence.

[0089] The control parameter calculation unit is used to set the standard target of temperature and humidity according to the current temperature and humidity threshold range, calculate the actual error between the real-time temperature and humidity data and the standard target, and the predicted error between the temperature and humidity prediction data and the standard target, and calculate the control parameters required by the airway temperature and humidity adjustment equipment to maintain the temperature and humidity at the standard target through the PID control algorithm.

[0090] The personalized dynamic optimization unit is used to optimize the control parameters based on the patient's physiological signs and environmental data to meet the temperature and humidity requirements of different patients in different environments.

[0091] In the description of the present invention, based on the patient's physiological signs and environmental data, the control parameters are personalized and optimized to meet the temperature and humidity requirements of different patients in different environments, including: Step S211: Based on the patient's physiological signs, a model for evaluating the patient's tolerance to changes in temperature and humidity is established. By real-time monitoring of the numerical changes in the physiological signs, the patient's current physiological state is dynamically evaluated to analyze the patient's tolerance to changes in temperature and humidity at the current moment.

[0092] Specifically, the patient's physiological signs include: age (Age), weight (Weight), underlying disease conditions (such as respiratory diseases, heart disease, etc.), current body temperature (Body Temperature), respiratory rate (Respiratory Rate) and heart rate (Heart Rate), etc.

[0093] Conduct a preliminary analysis of the patient's physiological data to assess the patient's tolerance. For example, patients with a higher body weight or chronic diseases may have a lower tolerance to changes in temperature and humidity and therefore require slower adjustments. A tolerance model can be designed, such as: Tolerance = f ( Age , Weight , Diseases ), which is used to calculate the patient's overall tolerance based on age, weight, and disease status. Different patients can be assigned a tolerance factor (such as 0.8 for low tolerance and 1.2 for high tolerance).

[0094] Step S212: Based on the tolerance assessment results of the patient's physiological signs and in combination with environmental data, the temperature and humidity threshold range is optimized, the personalized temperature and humidity targets are calculated, and the control parameters predicted for future moments are optimized to control the airway temperature and humidity adjustment device to adjust the inside of the endotracheal tube to the temperature and humidity targets.

[0095] Specifically, by real-time monitoring of physiological signs data (such as body temperature, heart rate, respiratory rate, etc.), the patient's current physiological state is dynamically evaluated to determine his tolerance to temperature and humidity changes. Based on the monitored physiological signs data, the patient's current physiological condition is evaluated. For example, when the body temperature is too high or too low, the power output of the temperature and humidity adjustment equipment needs to be adjusted; when the heart rate is too fast or too slow, it may mean that the patient is under high stress and the rate of temperature and humidity change also needs to be adjusted.

[0096] Therefore, according to the patient's physiological data and environmental conditions, a personalized temperature and humidity target range can be set to optimize the control parameters of the temperature and humidity control device. The target temperature and humidity can be achieved by adjusting the output power of the airway temperature and humidity control device. When the temperature is too high, the control device should reduce the heating power and increase the humidification output of the humidity control device; when the temperature is too low, the reverse adjustment is required.

[0097] In the description of the present invention, calculating the personalized temperature and humidity target includes the temperature value to be adjusted and the humidity value to be adjusted.

[0098] The calculation formula for the temperature value to be adjusted is: ; In the formula, ΔT adjust To adjust the temperature value; K T is the temperature adjustment factor; T target To set the temperature standard target; T current is the current temperature; T normal is the patient's current body temperature; The calculation formula for adjusting the humidity value is: ; ΔH adjust To adjust the humidity value; K H is the humidity adjustment factor; H target To set the humidity standard target; H current is the current humidity; HumidityFactor is the humidity adjustment factor.

[0099] The equipment control output unit is used to output the optimized control parameters to the corresponding airway temperature and humidity control equipment to control the operation model and operation status at future times.

[0100] Specifically, the main task of the device control output unit is to pass the optimized control parameters to the airway temperature and humidity control device to control the operation mode and status of the device in the future. The optimized control parameters are calculated based on real-time data, predicted data and personalized optimization results. By adjusting the output power of the temperature and humidity control device, the working status of the heater or humidifier, etc., the temperature and humidity in the endotracheal tube are always kept within the set optimal range.

[0101] In the device control output unit, the optimized control parameters are transmitted to the airway temperature and humidity control device through the communication interface, and the device will adjust the operating status according to these parameters. For example, if the control result requires increasing the humidifier output power, the system will tell the humidifier to increase the output through a control signal, thereby increasing the humidity; if the temperature is too high, the control signal will adjust the heater power to reduce the temperature.

[0102] The evaluation feedback unit is used to continuously monitor the operating status of the airway temperature and humidity adjustment equipment and the real-time temperature and humidity data in the endotracheal tube, and to evaluate the adjustment effect in real time. If there is an expected error in the adjustment effect, the control parameters are optimized through the feedback results. If there is no error in the adjustment effect, no adjustment is required.

[0103] Specifically, the unit continuously monitors the operating status of the airway temperature and humidity control equipment and the real-time temperature and humidity data in the endotracheal tube, and evaluates the adjustment effect in real time to ensure that the error in the adjustment process is controlled within a reasonable range. If the adjustment effect is found to be inconsistent with expectations, the evaluation feedback unit will optimize the control parameters through the feedback mechanism based on the error between the real-time data and the target data to ensure that the system always maintains the most suitable temperature and humidity range.

[0104] The medical monitoring station 3 is used to establish a communication connection with the equipment control terminal 2, obtain the monitoring data and operating status of all airway monitoring terminals 1 and equipment control terminals 2 in the monitoring area, display them in real time through visualization equipment, and centrally store and manage the monitoring data.

[0105] Specifically, the Medical Monitoring Station (MMS) plays a core role in centralized management, data display and monitoring in the endotracheal intubation temperature and humidity monitoring and control system. Specifically, it is mainly responsible for establishing a communication connection with the equipment control terminal, obtaining and displaying the monitoring data and operating status of all airway monitoring terminals and equipment control terminals in the monitoring area, ensuring that medical staff can grasp the temperature and humidity status of patients in real time and adjust the treatment plan in time.

[0106] After receiving monitoring data from the equipment control terminal, the medical monitoring station will store and manage the data in a centralized manner. Historical data, real-time data, and warning information will be stored in local or cloud databases for subsequent analysis and query. Data storage uses relational databases (such as MySQL, PostgreSQL) or non-relational databases (such as MongoDB), and is optimized based on data volume, query requirements, and storage methods. In order to ensure data security and reliability, hybrid storage can be combined with cloud storage and local storage to ensure that data is not lost.

[0107] To sum up, with the help of the above-mentioned technical scheme of the present invention, by real-time monitoring of the temperature and humidity changes in the endotracheal tube, combined with the patient's physiological signs and environmental data, personalized temperature and humidity adjustment and abnormal warning functions are provided; it has intelligent data analysis and prediction capabilities, can predict future temperature and humidity trends, automatically adjust the control parameters, and dynamically optimize according to the patient's physical signs and environmental conditions to ensure that the temperature and humidity are within the most suitable range, thereby improving the patient's comfort and experience effect; the present invention combines the temperature and humidity prediction module, the abnormal warning module and the personalized dynamic optimization control to achieve high-precision, high-stability and personalized temperature and humidity management, which significantly improves the accuracy and safety of clinical treatment and nursing. By combining the dung beetle optimization algorithm with the back-propagation neural network, the advantages of the dung beetle algorithm in global search and rapid convergence are fully utilized, and the weights and thresholds of the back-propagation neural network are accurately optimized, thereby constructing a high-precision and high-stability temperature and humidity prediction model. It can perform personalized temperature and humidity prediction and adjustment based on the patient's physiological signs and environmental data, and automatically adjust the temperature and humidity parameters to ensure that the temperature and humidity in the endotracheal tube are maintained within the most suitable range, effectively improving the patient's comfort and experience, optimizing the performance of the temperature and humidity prediction model, and improving the system's adaptability and prediction accuracy in complex environments, significantly enhancing the intelligence and personalization of endotracheal intubation temperature and humidity monitoring and control.

[0108] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

Claims

1. A tracheal intubation temperature and humidity monitoring and control system, characterized in that: include: Airway monitoring terminal; The equipment control terminal is used to establish a communication connection with the tracheal monitoring terminal and the medical monitoring station, obtain the collected real-time temperature and humidity data, and judge whether there is abnormal temperature and humidity in the tracheal tube through data analysis. According to the judgment result, the airway temperature and humidity abnormality warning is triggered and the airway temperature and humidity adjustment is controlled; Medical monitoring stations; The device control terminal comprises: The temperature and humidity prediction module is used to predict the temperature and humidity change trend inside the endotracheal tube in the future time period based on the recorded historical temperature and humidity data combined with the patient's physiological signs and environmental data; The control and adjustment module is used to calculate the control parameters of the airway temperature and humidity control equipment in the future time period based on the prediction results and abnormal analysis results of the real-time temperature and humidity data, and to perform personalized dynamic optimization of the control parameters based on environmental data, patient physiological signs, and the error between the real-time temperature and humidity data and the target data, so as to adjust the temperature and humidity values ​​in the endotracheal tube in the future time period.

2. A tracheal intubation temperature and humidity monitoring and control system according to claim 1, characterized in that: The airway monitoring terminal comprises: Waterproof coating module, used to protect electronic components from moisture and liquid damage, and maintain long-term stable operation of electronic components in the humid environment of the endotracheal tube; The detection sensor module is used to integrate the temperature sensor and the humidity sensor to monitor the temperature and humidity status and changes inside the endotracheal tube in real time and obtain real-time temperature and humidity data; A fixed protection module is used to construct and install a fixed bracket using biocompatible materials, attach the electronic components to the wall of the endotracheal tube, and provide a locking function to prevent falling off or deviation; An integrated display module is used to obtain the real-time temperature and humidity data collected by the detection sensor module and display the values ​​of the real-time temperature and humidity data through an external display; The power supply module is used to provide an independent power supply, monitor the battery power of the power supply in real time, display it using the integrated display module, and can be connected to the power line for continuous power supply; The external interface module is used to provide wireless communication components and a connection interface with the device control terminal, supporting data transmission and control signal exchange.

3. A tracheal intubation temperature and humidity monitoring and control system according to claim 1, characterized in that: The device control terminal also includes: The communication interface module is used to provide data communication and signal exchange channels with the airway monitoring terminal and the medical monitoring station, obtain the real-time temperature and humidity data collected by the airway monitoring terminal, and upload the data analysis results and control signals to the medical monitoring station; Data processing module, used for preprocessing real-time temperature and humidity data; An initial setting module is used to manually or automatically select different control modes based on the patient's identity information input in advance, and set the temperature and humidity thresholds that meet the patient's personalized needs; The abnormal warning module is used to monitor real-time temperature and humidity data and temperature and humidity forecast data based on temperature and humidity thresholds, analyze potential abnormal phenomena, and provide multi-level intelligent early warning reminders.

4. A tracheal intubation temperature and humidity monitoring and control system according to claim 3, characterized in that: The initial setting module includes: An information input unit is used to input the patient's identity information and physiological signs, and match them in the historical archive. If the patient's historical archive exists, the historical control mode and temperature and humidity thresholds are selected. If the patient's historical archive does not exist, the manual or automatic control mode is selected for the first time. A mode selection unit is used to select a corresponding control mode according to the patient's identity information and physiological signs. When in manual mode, the temperature and humidity threshold range is manually input; when in automatic mode, the historical archive that is most similar to the patient's identity information and physiological signs is matched in the historical archive library, and the temperature and humidity threshold range corresponding to the historical archive is selected and set; The learning optimization unit is used to continuously record the historical temperature and humidity data and feedback information of patients during use, store them in the historical archive, model the temperature and humidity requirements of patients through machine learning, and adjust the temperature and humidity thresholds based on the historical temperature and humidity data, feedback information and real-time temperature and humidity data; The interactive operation unit is used to provide an intuitive operation interface, supporting information input, control mode selection, and temperature and humidity threshold display and adjustment functions.

5. A tracheal intubation temperature and humidity monitoring and control system according to claim 3, characterized in that: The temperature and humidity prediction module includes: The variable setting unit is used to set the input variables of the temperature and humidity prediction model. The input variables are normalized by the minimum-maximum standard method and divided into a training set and a test set. The input variables include historical temperature and humidity data, physiological signs and environmental data. The model building unit is used to create a back propagation neural network including an input layer, a hidden layer, and an output layer, calculate the number of nodes in the hidden layer using an empirical method, and build a model architecture; A training optimization unit is used to combine the dung beetle optimization algorithm with the back propagation neural network, and use the global search ability and fast convergence ability of the dung beetle optimization algorithm to update and optimize the initial weights and thresholds of the back propagation neural network, and build a temperature and humidity prediction model of the combined neural network. The temperature and humidity prediction model includes a humidity prediction model and a temperature prediction model; The model prediction unit is used to set the prediction period of temperature and humidity prediction, input the real-time temperature and humidity data, environmental data and physiological signs at the current moment into the temperature and humidity prediction model, predict the temperature and humidity prediction data in the future time period, and draw the change curve of temperature and humidity data over time.

6. A tracheal tube temperature and humidity monitoring and control system according to claim 5, characterized in that: The method combines the dung beetle optimization algorithm with the back propagation neural network, utilizes the global search capability and fast convergence capability of the dung beetle optimization algorithm, updates and optimizes the initial weights and thresholds of the back propagation neural network, and constructs a temperature and humidity prediction model of the combined neural network, including: Initialize the structural parameters of the dung beetle algorithm and the back propagation neural network, obtain the normalized data of historical temperature and humidity data, physiological signs and environmental data, each dung beetle represents a solution vector of weights and thresholds, and each solution vector contains the weights and biases of all layers of the back propagation neural network; When the dung beetle rolls the ball forward without any obstacles, the individual position of the dung beetle after the ball is rolled forward is updated. When the dung beetle encounters an obstacle and cannot move forward, it repositions itself by dancing, and the individual position of the dung beetle after dancing is updated. A boundary selection strategy was used to simulate the female breeding area, and the upper and lower limits of the breeding area were set; Set the upper and lower limits of the foraging area, and when the foraging dung beetle starts foraging, update the individual position of the dung beetle after foraging; When the thieving dung beetle starts stealing, the individual position of the dung beetle after stealing is updated; Find the best position and fitness value of the current dung beetle, and determine whether the preset termination condition is met. If so, execute the next step. If not, continue to find the best position of the dung beetle; The back propagation neural network is trained, and the global optimal weights and thresholds optimized by the dung beetle optimization algorithm are transferred to the back propagation neural network to obtain the temperature and humidity prediction model.

7. A tracheal intubation temperature and humidity monitoring and control system according to claim 3, characterized in that: The abnormal warning module includes: A data comparison unit is used to compare the real-time temperature and humidity data with the upper and lower limits of the temperature and humidity threshold range. If the temperature and humidity threshold range is exceeded, it is marked as abnormal, and the real-time temperature and humidity data is compared with the temperature and humidity forecast data to determine whether the deviation between the real-time temperature and humidity data and the temperature and humidity forecast data exceeds the set range. If it exceeds, it is marked as abnormal; The anomaly detection unit is used to check potential anomalies and determine the type and cause of anomalies based on the comparison results of real-time temperature and humidity data with the predicted temperature and humidity values ​​using an anomaly rule engine; An early warning generation unit is used to generate multi-level early warning information based on the abnormal detection results. The multi-level early warning information includes green early warning, yellow early warning and red early warning. When in green early warning, the current control parameters are maintained. When in yellow early warning, the control and adjustment module is used to perform automatic control and adjustment. When in red early warning, a manual intervention early warning is triggered; The feedback adjustment unit is used to trigger corresponding temperature and humidity intervention measures according to the warning level.

8. The endotracheal tube temperature and humidity monitoring and control system according to claim 3, characterized in that: The control and regulation module comprises: A control parameter calculation unit is used to set a standard target for temperature and humidity according to the current temperature and humidity threshold range, calculate the actual error between the real-time temperature and humidity data and the standard target, and the predicted error between the temperature and humidity prediction data and the standard target, and calculate the control parameters required by the airway temperature and humidity control device to maintain the temperature and humidity at the standard target through a PID control algorithm; Personalized dynamic optimization unit, which is used to optimize the control parameters based on the patient's physiological signs and environmental data to meet the temperature and humidity requirements of different patients in different environments; The equipment control output unit is used to output the optimized control parameters to the corresponding airway temperature and humidity control equipment to control the operation model and operation status at the future moment; The evaluation feedback unit is used to continuously monitor the operating status of the airway temperature and humidity adjustment equipment and the real-time temperature and humidity data in the endotracheal tube, and to evaluate the adjustment effect in real time. If there is an expected error in the adjustment effect, the control parameters are optimized through the feedback results. If there is no error in the adjustment effect, no adjustment is required.

9. A tracheal tube temperature and humidity monitoring and control system according to claim 8, characterized in that: The personalized optimization of the control parameters based on the patient's physiological signs and environmental data to adapt to the temperature and humidity requirements of different patients in different environments includes: Based on the patient's physiological signs, a model for evaluating the patient's tolerance to temperature and humidity changes is established. By real-time monitoring of the numerical changes in physiological signs, the patient's current physiological state is dynamically evaluated and the patient's tolerance to temperature and humidity changes at the current moment is analyzed. Based on the tolerance assessment results of the patient's physiological signs and combined with environmental data, the temperature and humidity threshold range is optimized, personalized temperature and humidity targets are calculated, and the control parameters predicted for future moments are optimized. The airway temperature and humidity adjustment equipment is controlled to adjust the inside of the endotracheal tube to the temperature and humidity targets.

10. A tracheal intubation temperature and humidity monitoring and control system according to claim 9, characterized in that: The calculated personalized temperature and humidity targets include a temperature value to be adjusted and a humidity value to be adjusted; The calculation formula for the temperature value to be adjusted is: ； In the formula, ΔT adjust To adjust the temperature value; K T is the temperature adjustment factor; T target To set the temperature standard target; T current is the current temperature; T normal is the patient's current body temperature; The calculation formula for the humidity value to be adjusted is: ； ΔH adjust To adjust the humidity value; K H is the humidity adjustment factor; H target To set the humidity standard target; H current is the current humidity; HumidityFactor is the humidity adjustment factor.

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