Intelligent temperature and humidity identification and anti-blocking and anti-pipe-dropping tracheal tube monitoring system

Abstract

The application provides a tracheal tube monitoring system for intelligent temperature and humidity identification and anti-blocking and anti-pipe falling, and relates to the technical field of medical devices. The system comprises a multi-dimensional sensing and collecting module, a central processing module, an interactive feedback module and a remote communication module. The multi-dimensional sensing and collecting module drives a sensing component to synchronously detect the micro-environment of the airway and the physical state of the catheter, acquires a multi-dimensional characteristic signal set and locks the running state type of the tracheal tube. The central processing module matches and processes the signal set and performs logical operation based on the state type, generates a monitoring state parameter set and determines a hierarchical alarm control instruction. The interactive feedback module generates a visual monitoring interface and executes sound and light alarm action switching accordingly. The remote communication module transmits the parameter set to an external monitoring terminal. Through in-situ synchronous detection, characteristic decoupling algorithm and multi-level risk arbitration mechanism, the application effectively solves the problems of airway temperature and humidity monitoring distortion, sputum blocking early warning lag and catheter displacement difficult to be detected in real time in the prior art.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to an intelligent endotracheal intubation monitoring system that identifies temperature and humidity and prevents blockage and dislodgement of the tube. Background Technology

[0002] Endotracheal intubation is a crucial method for establishing an artificial airway and rescuing patients with respiratory failure. Currently, the most commonly used endotracheal intubation systems in clinical practice typically consist of the intubation tube itself, a sealing balloon, and an externally connected humidifier for the ventilator. In routine treatment, healthcare professionals use the heating and humidification device on the ventilator tubing (such as the VADI series) to set the gas temperature and humidity, and rely on the markings on the intubation tube and the balloon inflation valve to confirm the intubation depth and seal. This modular management approach has long provided basic ventilatory support for patients and is the standard airway management configuration in ICUs and surgical anesthesia.

[0003] However, existing airway management technologies suffer from significant monitoring blind spots and lags. First, humidification monitoring and control points are located at the ventilator end, failing to detect the true temperature and humidity inside the patient's trachea after traveling through a long tube. This makes them highly susceptible to airway imbalance due to the "rain effect" or insufficient humidification, leading to thickened sputum or airway damage. Second, current endotracheal intubation systems lack the ability to sense the patency of the lumen, failing to provide early warnings in the initial stages of sputum accumulation (such as when it reaches 1 / 3 of the lumen). Often, it is only discovered when severe obstruction or even suffocation occurs, forcing healthcare workers to perform high-risk repeat intubation. Furthermore, catheter position monitoring relies primarily on periodic imaging examinations or external calibration, lacking a real-time dynamic monitoring mechanism for intubation depth displacement or dislodgement risks, hindering precise and visualized airway care. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide an intelligent endotracheal intubation monitoring system with intelligent temperature and humidification identification, anti-blockage and anti-dislodgement functions. This invention solves the problems of airway temperature and humidification monitoring distortion, lack of real-time early warning for sputum blockage and catheter displacement, and low integration of monitoring data in the prior art.

[0005] To achieve the above objectives, the present invention provides the following solution: An intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system includes: The central processing module, as well as the multi-dimensional sensing and acquisition module, interactive feedback module, remote communication module, and endotracheal tube body, all connected to the central processing module; The multidimensional sensing and acquisition module is used to drive the sensing components installed on the endotracheal tube body to synchronously detect the airway microenvironment and the physical state of the tube, obtain a multidimensional feature signal set, and determine the current intubation operation state type based on the multidimensional feature signal set. The central processing module is used to perform logical operations on the multi-dimensional feature signal set according to the intubation operation status type to obtain the monitoring status parameter set, and to determine the corresponding hierarchical alarm control command according to the monitoring status parameter set. The interactive feedback module is used to generate a visual monitoring interface based on the monitoring status parameter set and to switch between audible and visual alarm actions according to the hierarchical alarm control command. The remote communication module is used to transmit the monitoring status parameter set to an external monitoring terminal according to a preset communication protocol.

[0006] The present invention discloses the following technical effects: This invention provides an intelligent endotracheal intubation monitoring system for temperature and humidification identification, as well as anti-blockage and anti-dislodgement. By integrating a multi-dimensional sensing and acquisition module within the endotracheal tube, this invention effectively solves the problems of blind spots in airway temperature and humidification monitoring, delayed warnings of sputum blockage, and difficulty in real-time detection of tube displacement in existing technologies. Compared with existing technologies, this system utilizes in-situ synchronous detection technology to directly acquire real temperature and humidification data within the airway, eliminating the "rain effect" error caused by remote monitoring and effectively preventing ventilator-associated pneumonia. Simultaneously, through a sliding window change rate and baseline deviation algorithm, the system can issue graded warnings at the initial stage of sputum accumulation (such as 1 / 3 blockage of the lumen) and capture minute axial displacement of the tube in real time. This intelligent mechanism, transforming "post-event remediation" into "pre-event proactive intervention," significantly improves the safety of the artificial airway, reduces the patient's pain from secondary intubation, and alleviates the monitoring pressure on medical staff. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a schematic diagram of an intelligent temperature and humidity identification and anti-blockage and anti-dislodgement endotracheal intubation monitoring system provided in an embodiment of the present invention.

[0009] Figure label: 1-Central processing module, 2-Multi-dimensional sensing and acquisition module, 3-Interactive feedback module, 4-Remote communication module, 5-Tracheal tube body. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] like Figure 1 As shown, this invention provides an intelligent endotracheal intubation monitoring system with intelligent temperature and humidification identification, anti-blockage, and anti-dislodgement capabilities, comprising: The central processing module 1, as well as the multi-dimensional sensing and acquisition module 2, the interactive feedback module 3, the remote communication module 4, and the endotracheal tube body 5, all connected to the central processing module 1; The multidimensional sensing and acquisition module 2 is used to drive the sensing components installed on the endotracheal tube body 5 to synchronously detect the airway microenvironment and the physical state of the tube, obtain a multidimensional feature signal set, and determine the current intubation operation state type based on the multidimensional feature signal set. The central processing module 1 is used to perform logical operations on the multi-dimensional feature signal set according to the intubation operation status type to obtain the monitoring status parameter set, and to determine the corresponding hierarchical alarm control command according to the monitoring status parameter set. The interactive feedback module 3 is used to generate a visual monitoring interface based on the monitoring status parameter set and to switch between audible and visual alarm actions according to the hierarchical alarm control command. The remote communication module 4 is used to transmit the monitoring status parameter set to an external monitoring terminal according to a preset communication protocol.

[0013] Specifically, this embodiment provides an intelligent endotracheal intubation monitoring system with intelligent temperature and humidity recognition and anti-blockage and anti-dislodgement capabilities. Its physical structure is based on an improved endotracheal intubation tube body 5, including a medical PVC catheter, a distal sealing balloon, and a proximal standard connector. The core monitoring functions are achieved through multi-dimensional sensing components integrated into the inner wall of the catheter and the balloon: a miniature MEMS temperature and humidity sensor is attached to the proximal and distal ends of the inner wall of the catheter to directly contact the inhaled airflow and obtain real microenvironment data; an infrared through-beam or ultrasonic transceiver array is arranged along the catheter axis as a patency recognition sensor to detect the thickness of the medium buildup in the lumen in real time; a magnetic induction coil or acoustic ranging probe is used as a displacement sensor to monitor the axial position of the catheter relative to an anatomical reference point; and a pressure sensor is installed in the balloon inflation tubing to monitor the balloon inflation status. All of the above sensing components are connected to a miniature control box fixed to the proximal connector of the catheter via flexible circuitry. This control box integrates a central processing module 1, a multi-dimensional sensing and acquisition module 2, an interactive feedback module 3, and a remote communication module 4, and is independently powered by a built-in miniature lithium battery, forming an integrated intelligent monitoring terminal.

[0014] During system operation, the multi-dimensional sensing and acquisition module 2 first drives each sensing component to synchronously sample at a frequency of 10Hz via a DAC circuit, acquiring a multi-dimensional feature signal set including temperature, humidity, light intensity attenuation, magnetic field strength, and air pressure. Based on abrupt changes in signal characteristics, such as a sharp decrease in light intensity or a step change in magnetic field, it predicts and locks the current intubation operation status type, such as a high-risk physical intervention status or an environmental regulation response status. Subsequently, the central processing module 1 retrieves a targeted algorithm model to perform in-depth processing on the signal set according to the locked status type: it uses the light intensity attenuation model to calculate the lumen blockage percentage, such as 35%, uses the magnetic field change to calculate the axial displacement deviation, such as outward slippage of 1.5cm, and combines it with a thermal compensation algorithm to obtain the airway relative humidity saturation. The central processing module 1 further compares these calculated monitoring status parameter sets with a built-in risk classification table for multi-level threshold comparison. When the blockage rate or displacement exceeds the preset safety range, it immediately generates a graded alarm control command containing a specific drive code stream. This command includes specific drive signals such as red flashing and high-frequency buzzing.

[0015] The interactive feedback module 3 and the remote communication module 4 execute synchronized local and remote responses according to the instructions of the central processing module 1. Locally, the interactive feedback module 3 drives the OLED micro-display on the surface of the control box to refresh and display key values ​​such as blockage rate, displacement, temperature, and humidity in real time. Based on the tiered alarm control instructions, it drives the LED indicator light to switch from a steady green light to a red flashing mode, accompanied by a buzzer emitting a rapid alarm sound, achieving immediate visual and auditory warning. Simultaneously, the remote communication module 4, using a low-power Bluetooth BLE protocol, calls a preset communication protocol, such as the HL7 standard, to encapsulate the monitored status parameter set into telemetry data frames, which are then wirelessly transmitted after establishing a secure handshake channel with the external monitoring terminal at the nurse station. This mechanism ensures the synchronized triggering of bedside physical alarms and remote monitoring pop-ups, thus realizing a complete closed loop from the perception of the airway microenvironment to dual clinical nursing interventions.

[0016] Furthermore, the specific implementation process of the multi-dimensional sensing and acquisition module 2 is as follows: In this embodiment, the synchronous sampling submodule serves as the core of the data acquisition front-end. Through its internally integrated high-precision clock generator, it sends unified synchronization trigger commands to various sensing components, including those for temperature, humidity, patency, location depth, and balloon pressure. This ensures strict alignment of multi-source signals in the time dimension, eliminating data heterogeneity issues caused by sampling time differences. Upon receiving the analog electrical signals from each sensing component, this embodiment utilizes a multi-channel parallel analog-to-digital converter circuit for high-speed quantization processing. This converts the continuous analog waveform into a discrete digital sequence and encapsulates the data from each channel according to the sampling timing. Finally, it outputs a raw digital signal stream containing the original information from all channels and synchronized with the timing sequence.

[0017] Subsequently, the feature decoupling submodule of this embodiment receives the original digital signal stream and, according to a preset signal separation protocol, accurately decomposes and reconstructs the mixed signal stream into two independent feature vectors by identifying the channel identifier code in the data frame header: an environmental monitoring feature vector composed of temperature and humidity data, and a mechanical safety feature vector composed of accessibility, location depth, and balloon pressure data. Based on this, this embodiment performs depth calculations on both sets of vectors. On one hand, a sliding window algorithm is used to perform time-series difference analysis on the environmental monitoring feature vector to obtain the rate of change value. On the other hand, a pre-stored ideal model is called to perform normalized difference comparison on the mechanical safety feature vector, thereby quantifying and calculating the benchmark deviation value reflecting the degree of deviation from the current physical state.

[0018] Finally, the state arbitration submodule of this embodiment performs logical judgment on the calculated numerical indicators, comparing the rate of change of the environmental monitoring feature vector with a preset environmental fluctuation threshold, and simultaneously comparing the baseline deviation of the mechanical safety feature vector with a preset safety baseline threshold. Based on the comparison results, this embodiment uses a priority arbitration strategy to lock the current operating state. If the baseline deviation exceeds the standard, it is preferentially determined to be a high-risk physical intervention state; if only the rate of change exceeds the standard, it is determined to be an environmental regulation response state; if neither exceeds the standard, it is determined to be a stable maintenance monitoring state. Thus, it outputs the accurate intubation operation state type to the central processing module 1, providing a decision basis for subsequent graded alarms.

[0019] Specifically, upon receiving the original digital signal stream, the protocol parsing and distribution unit in this embodiment immediately initiates a parsing program based on a preset frame structure. It scans and locates the start sign of the data frame bit by bit, and then reads the channel identifier code that follows. Based on the specific binary sequence of this channel identifier code, this embodiment maps data frames carrying temperature and humidity information to the environmental monitoring buffer, and data frames carrying accessibility, location, and pressure information to the mechanical safety buffer. Subsequently, by removing frame headers and trailers and performing CRC checks, it reconstructs and generates environmental monitoring feature vectors and mechanical safety feature vectors containing the latest time-series data, thereby achieving logical splitting of multi-source heterogeneous data.

[0020] Next, the environmental change rate calculation unit in this embodiment retrieves historical data from the environmental monitoring feature vector, constructs a time sliding window of a preset length, and performs multi-order temporal difference operations on the temperature and humidity components within the window. This embodiment calculates the Euclidean distance between the feature value at the current moment and the feature value at the previous moment, and performs normalization processing in conjunction with the sampling time interval, thereby accurately quantifying the fluctuation amplitude of environmental parameters per unit time. Finally, it outputs a change rate value that can sensitively reflect the stability of the airway microclimate, providing a dynamic basis for subsequent judgment on whether humidification or heating adjustment needs to be initiated.

[0021] Finally, the safety deviation calculation unit in this embodiment calls the preset ideal state benchmark in the memory. This benchmark represents the theoretical characteristic vector when the catheter is at optimal patency, standard insertion depth, and standard cuff pressure. This embodiment performs vector subtraction between the real-time acquired mechanical safety characteristic vector and the ideal state benchmark to obtain a difference vector. The modulus of this difference vector is then calculated using a L2 norm algorithm, thereby quantifying the multi-dimensional physical state deviation into a single scalar index, namely the benchmark deviation value. This intuitively represents the degree of deviation of the current airway physical state from the ideal safe state, achieving a comprehensive quantitative assessment of blockage, dislodgement, and pressure abnormalities.

[0022] Furthermore, the expression for calculating the rate of change is as follows: ; The formula for calculating the baseline deviation value is: ; in, For a moment The environmental change rate value; For a moment The deviation value from the safety benchmark; For a moment Environmental monitoring feature vectors; For a moment Mechanical safety feature vector; The baseline vector represents the ideal state. The sliding window length calculated for the rate of change, for the most recent Statistical analysis was performed on the next synchronous sampling points; The time interval between two consecutive synchronous samplings; It is a norm 2; To prevent extremely small positive numbers with a denominator of zero.

[0023] Specifically, in this embodiment, in order to accurately quantify the dynamic changes in airway status and identify potential risks, the environmental change rate calculation unit and the safety deviation calculation unit use specific mathematical logic based on vector space for data processing. First, for the environmental monitoring feature vector, this embodiment uses the average Euclidean distance algorithm based on a sliding window to calculate the change rate value. The specific processing logic is as follows: This embodiment retrieves the environmental monitoring feature vector within the most recent several consecutive sampling points, calculates the L2 distance between the feature vectors at two adjacent time points one by one, sums up all the calculated adjacent distances within the time window, and divides the summation result by the total duration of the time window to obtain the average change rate per unit time, thereby characterizing the degree of fluctuation in temperature and humidity. In this process, the sliding window length refers to the number of historical sampling points participating in the statistics, and its function is to smooth random noise; the synchronous sampling time interval refers to the physical time difference between two adjacent data acquisitions. Secondly, regarding the mechanical safety feature vector; This embodiment uses a normalized relative error algorithm to calculate the baseline deviation value. The specific processing logic is as follows: the mechanical safety feature vector collected at the current moment is subtracted from the pre-stored ideal state baseline vector to obtain the difference vector between the two, and the L2 norm of the difference vector is calculated. Then, the L2 norm is divided by the denominator, which is composed of the L2 norm of the ideal state baseline vector and a preset minimum positive number. The ideal state baseline vector refers to the set of standard parameters pre-calibrated by the system, representing that the catheter is fully patent, the insertion depth is standard, and the balloon pressure is normal, which serves as the "zero point" for safety comparison.

[0024] More specifically, in this embodiment, the threshold logic comparison unit first receives the rate of change value and the benchmark deviation value output by the preceding unit, and simultaneously retrieves the pre-configured preset environmental fluctuation threshold and preset safety benchmark threshold from the internal memory. This embodiment utilizes a hardware comparator or logic operation circuit to import these two dynamically quantized indicators, calculated in real time, into their respective comparison channels. This prepares the data for subsequently converting continuously changing analog features into discrete binary logic states, ensuring the real-time performance of the judgment and the stability of the benchmark.

[0025] Next, this embodiment executes specific numerical magnitude determination logic to generate a status flag. On one hand, this embodiment compares the rate of change value with a preset environmental fluctuation threshold. Once the rate of change value exceeds the environmental fluctuation threshold, it is determined that the airway microenvironment is experiencing unstable oscillations, thereby triggering the generation of a logic-level valid environmental abnormality flag. On the other hand, this embodiment simultaneously compares the baseline deviation value with a preset safety baseline threshold. If the baseline deviation value exceeds the safety baseline threshold, it indicates that the physical state of the catheter has deviated from the ideal safety range, and a logic-level valid physical risk flag is then triggered.

[0026] Finally, the state mapping and locking unit of this embodiment performs hierarchical scanning and state locking on the generated flag bits according to a preset priority arbitration strategy. This embodiment first detects the physical risk flag bit. If the flag bit is detected, the state of the environmental anomaly flag bit is directly ignored according to the safety first principle, and the current intubation operation state type is forcibly locked as the highest priority high-risk physical intervention state. If the physical risk flag bit is not detected, this embodiment further detects the environmental anomaly flag bit. If the flag bit is detected, the intubation operation state type is locked as the second priority environmental regulation response state. If neither of the above two flag bits is detected, this embodiment determines that the current airway is in a safe range, locks the intubation operation state type as a stable maintenance monitoring state, and outputs the finally determined state type to the central processing module 1.

[0027] Furthermore, the specific implementation process of the central processing module 1 is as follows: In this embodiment, the parameter fusion submodule serves as the core computing unit. Through its internal multi-core parallel processing architecture, it receives a set of multi-dimensional feature signals and calls a pre-stored feature quantization algorithm library to map various raw signals to physical quantities. This embodiment utilizes a thermodynamic compensation model to jointly calculate temperature and humidity signals, eliminating the cooling effect error of airflow velocity on the sensor, thereby outputting accurate airway humidification saturation. Simultaneously, this embodiment applies a signal attenuation integral algorithm to process the patency signal, converting the attenuation of light intensity or sound waves into a quantified percentage of lumen blockage. Combined with the magnetic field differential calculation results of the position and depth signals, it derives the axial displacement deviation. Finally, these physically quantified data are encapsulated into a monitoring status parameter set, providing a standardized data foundation for subsequent logical judgments.

[0028] Subsequently, the threshold comparison submodule and risk decision submodule of this embodiment work together to complete the closed-loop judgment of the safety logic. This embodiment first imports each value in the monitored status parameter set into a preset standard safety range model for traversal comparison. This model defines the normal fluctuation range of various physiological and physical parameters. Once a parameter value is found to exceed this range, it is marked as an abnormal item, and an abnormal feature vector composed of binary status codes is generated. Next, this embodiment performs a weighted evaluation of the abnormal feature vector based on a clinical risk weight matrix, assigning higher weight coefficients to physical risks such as blockage and tube dislodgement, and lower weight coefficients to environmental risks such as temperature and humidity fluctuations. The current risk score is calculated through weighted summation, and the corresponding risk classification label, such as a Level 1 high-risk alarm or a Level 2 warning alarm, is matched accordingly.

[0029] Finally, the instruction encoding submodule of this embodiment performs protocol conversion of the hardware control layer based on the output risk classification label. This embodiment retrieves the corresponding hardware control strategy from the driver protocol library according to the level index of the risk classification label, generating a composite graded alarm control instruction. This instruction contains two core code streams: one is an interface rendering code stream for driving the micro-display, specifying the parameter area to be highlighted and the warning color; the other is an audio-visual driving code stream for controlling the buzzer frequency and LED blinking duty cycle, ensuring that the alarm signal can be accurately transmitted to medical personnel in a form that conforms to medical device industry standards, achieving precise execution from data calculation to physical feedback.

[0030] Furthermore, the specific implementation process of the interactive feedback module 3 is as follows: The interface rendering submodule of this embodiment first loads a pre-set graphical user interface template library from non-volatile memory. This template library contains UI layout files and chart control objects defined for different monitoring scenarios, such as dynamic waveforms, circular progress bars, and digital dashboards. This embodiment utilizes direct memory access technology or a video memory mapping mechanism to bind and map the values ​​of airway humidification saturation, lumen obstruction percentage, and axial displacement deviation from the real-time received monitoring status parameter set to the corresponding chart control data source on a one-to-one basis. Subsequently, this embodiment uses a display controller to refresh the screen buffer at a fixed frame rate, driving the pixel array on the micro-display to update its status, thereby dynamically generating a digital visualization monitoring interface that intuitively reflects minute changes within the airway, ensuring that medical personnel can obtain critical vital sign information with zero latency.

[0031] Next, the alarm decoding submodule of this embodiment performs bit-level parsing on the received hierarchical alarm control commands. This embodiment extracts specific risk level encoded fields from the command frame using bitmasking technology and uses them as index keys for rapid retrieval and matching in a preset warning strategy library. This warning strategy library stores target alarm execution strategies corresponding one-to-one with different risk levels. Each strategy defines in detail the color parameters and flashing frequency parameters of the LED indicator, as well as the beeping tone and interval period parameters of the buzzer. Through this lookup table matching mechanism, this embodiment transforms abstract digital encoded commands into a specific set of hardware driver parameters, providing precise control basis for subsequent physical alarm execution.

[0032] Finally, the state switching submodule of this embodiment, based on the parsed target alarm execution strategy, controls the working state of the audio-visual hardware circuit through pulse width modulation signals to achieve a smooth transition of monitoring modes. When a low-risk command is received, the driving circuit of this embodiment maintains the silent monitoring mode, only illuminating the green indicator light while turning off the buzzer; when a medium-risk command is received, the control circuit of this embodiment transitions to the visual flashing prompt mode, driving the yellow or red indicator light to work in a low-frequency breathing flashing mode; when a high-risk command is received, this embodiment immediately switches the circuit to the dual audio-visual alarm mode, simultaneously driving the red indicator light to flash at a high frequency and activating the buzzer to emit a rapid alarm sound. This embodiment, through this state machine logic based on the hardware-level driver, accurately executes the switching of audio-visual alarm actions, ensuring that the warning intensity is matched to different levels of urgency.

[0033] Furthermore, the specific implementation process of the remote communication module 4 is as follows: Upon receiving the monitoring status parameter set, the protocol encapsulation submodule of this embodiment immediately invokes the communication protocol stack stored in the firmware to assemble data packets according to the predefined telemetry frame structure standard. This embodiment first constructs a frame header field, writing a synchronization word and frame length information to identify the start and size of the data packet. Then, key data from the monitoring status parameter set, such as airway humidification saturation and lumen blockage percentage, are mapped to specified offset addresses in the payload field and filled in. To ensure data integrity during transmission, this embodiment performs a cyclic redundancy check (CRC) operation on the filled payload data, generating a checksum and appending it to the frame tail, thus ultimately encapsulating and generating a telemetry data frame that conforms to the transmission standard. This data frame possesses self-describing and self-correcting capabilities.

[0034] Next, the link management submodule of this embodiment initiates an active handshake procedure, sending a connection request beacon to the external monitoring terminal and simultaneously activating the channel eavesdropping mechanism. This embodiment detects the channel quality of the current communication band by reading the received signal strength indicator and signal-to-noise ratio parameters, automatically avoiding frequencies with severe interference to select the optimal carrier frequency, and completing two-way authentication with the external monitoring terminal based on an asymmetric encryption algorithm, thereby establishing a secure transmission channel that is resistant to eavesdropping and interference. Once the handshake is successful and the channel quality meets the preset communication threshold, this embodiment immediately sets the internal register, generating a high-level active transmit enable signal, indicating that the communication link has entered a ready state.

[0035] Finally, the wireless transmission submodule of this embodiment is in standby mode, monitoring the level changes of the transmission permission signal in real time. When the signal is detected to be valid, this embodiment reads the telemetry data frame to be transmitted from the buffer, and uses an RF modulation circuit to modulate the digital baseband signal onto a high-frequency carrier, for example, using Gaussian frequency shift keying modulation to reduce out-of-band radiation. This embodiment then amplifies the modulated RF signal through a power amplifier and drives the onboard antenna through an impedance matching network, directionally radiating the RF signal carrying the monitoring information to the external monitoring terminal through the aforementioned established secure transmission channel, completing the remote wireless data upload.

[0036] This embodiment also provides an intelligent temperature and humidification identification and anti-blockage and anti-dislodgement method for endotracheal intubation monitoring, including: The sensor components installed on the endotracheal tube body 5 are driven to synchronously detect the airway microenvironment and the physical state of the tube, obtain a multi-dimensional feature signal set, and determine the current intubation operation state type based on the multi-dimensional feature signal set. Logical operations are performed on the multidimensional feature signal set to obtain a monitoring status parameter set, and corresponding hierarchical alarm control instructions are determined based on the monitoring status parameter set. A visual monitoring interface is generated based on the monitoring status parameter set, and the sound and light alarm actions are switched according to the hierarchical alarm control instructions. The set of monitoring status parameters is transmitted to an external monitoring terminal according to a preset communication protocol.

[0037] This embodiment also provides an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform one of the endotracheal intubation monitoring methods.

[0038] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0039] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A smart endotracheal intubation monitoring system with temperature and humidification recognition, and anti-blockage and anti-dislodgement functions, characterized in that, include: The central processing module, as well as the multi-dimensional sensing and acquisition module, interactive feedback module, remote communication module, and endotracheal tube body, all connected to the central processing module; The multidimensional sensing and acquisition module is used to drive the sensing components installed on the endotracheal tube body to synchronously detect the airway microenvironment and the physical state of the tube, obtain a multidimensional feature signal set, and determine the current intubation operation state type based on the multidimensional feature signal set. The central processing module is used to perform logical operations on the multi-dimensional feature signal set according to the intubation operation status type to obtain the monitoring status parameter set, and to determine the corresponding hierarchical alarm control command according to the monitoring status parameter set. The interactive feedback module is used to generate a visual monitoring interface based on the monitoring status parameter set and to switch between audible and visual alarm actions according to the hierarchical alarm control command. The remote communication module is used to transmit the monitoring status parameter set to an external monitoring terminal according to a preset communication protocol.

2. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 1, characterized in that, The multidimensional feature signal set includes: Temperature signal, humidity signal, patency signal, location and depth signal, and balloon pressure signal.

3. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 1, characterized in that, The multi-dimensional sensing and acquisition module includes: The synchronous sampling submodule is used to send a synchronous trigger command to the sensing component and convert the analog electrical signal fed back by the sensing component into a digital signal to output the original digital signal stream; The feature decoupling submodule is used to separate the original digital signal stream into environmental monitoring feature vectors and mechanical safety feature vectors using a preset signal separation protocol, and to calculate the rate of change of the environmental monitoring feature vectors and the reference deviation of the mechanical safety feature vectors, respectively. The status arbitration submodule is used to compare the change rate value with a preset environmental fluctuation threshold and the benchmark deviation value with a preset safety benchmark threshold, so as to determine and output the corresponding intubation operation status type based on the comparison results.

4. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 3, characterized in that, The feature decoupling submodule includes: The protocol parsing and distribution unit is used to identify the channel identifier code in the original digital signal stream using the preset signal separation protocol, and to classify and extract the original digital signal stream according to the channel identifier code, so as to reconstruct and generate the environmental monitoring feature vector and the mechanical safety feature vector; An environmental change rate calculation unit is used to perform time-series difference operations on the environmental monitoring feature vector to calculate the change rate value; The safety deviation calculation unit is used to compare the mechanical safety feature vector with the preset ideal state benchmark to quantitatively calculate the benchmark deviation value. The expression for calculating the rate of change is: ； The formula for calculating the baseline deviation value is: ； in, For a moment The environmental change rate value; For a moment The deviation value from the safety benchmark; For a moment Environmental monitoring feature vectors; For a moment Mechanical safety feature vector; The baseline vector represents the ideal state. The sliding window length calculated for the rate of change, for the most recent Statistical analysis was performed on the next synchronous sampling points; The time interval between two consecutive synchronous samplings; It is a norm 2; To prevent extremely small positive numbers with a denominator of zero.

5. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 3, characterized in that, The status arbitration submodule includes: The threshold logic comparison unit is used to retrieve the preset environmental fluctuation threshold and the preset safety benchmark threshold, and execute the numerical magnitude determination logic respectively: when the change rate exceeds the preset environmental fluctuation threshold, an environmental anomaly flag is generated; when the benchmark deviation exceeds the preset safety benchmark threshold, a physical risk flag is generated. The state mapping locking unit is used to find the corresponding state tag according to a preset priority arbitration strategy and output the intubation operation state type; wherein, the priority arbitration strategy is: if the physical risk flag is identified, the output of high-risk physical intervention state is locked; if the physical risk flag is not identified but the environmental abnormality flag is identified, the output of environmental adjustment response state is locked; if neither the physical risk flag nor the environmental abnormality flag is identified, the output of stable maintenance monitoring state is locked.

6. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 1, characterized in that, The central processing module includes: The parameter fusion submodule is used to call a preset feature quantization algorithm to perform parallel operations on the multi-dimensional feature signal set to generate a monitoring state parameter set; The threshold comparison submodule is used to compare the parameters in the monitoring status parameter set with the preset standard safety interval model to identify abnormal items that exceed the standard safety interval model and generate abnormal feature vectors that characterize the deviation of each parameter. The risk decision-making submodule is used to perform weighted evaluation of the abnormal feature vector based on a preset clinical risk weight matrix, determine the current overall risk level, and output the corresponding risk classification label. The instruction encoding submodule is configured to receive the risk classification label and use it to retrieve the corresponding hardware driver protocol according to the risk classification label to generate the graded alarm control instruction containing the audio-visual driver code stream and the interface rendering code stream.

7. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 1, characterized in that, The interactive feedback module includes: The interface rendering submodule is used to call the preset graphical user interface template library and map the values ​​in the monitoring status parameter set to the corresponding chart controls in the graphical user interface template library in real time, so as to dynamically refresh and generate the visual monitoring interface. The alarm decoding submodule is used to parse the risk level code carried in the hierarchical alarm control command, and match the corresponding target alarm execution strategy from the preset warning strategy library according to the risk level code; The state switching submodule is used to drive the audio-visual hardware circuit to transition between silent monitoring mode, visual flashing prompt mode and dual audio-visual alarm mode according to the target alarm execution strategy, so as to execute the audio-visual alarm action switching.

8. The intelligent temperature and humidification identification and anti-blockage / anti-dislodgement endotracheal intubation monitoring system according to claim 1, characterized in that, The remote communication module includes: The protocol encapsulation submodule is used to perform header verification and payload filling on the monitoring status parameter set according to a predetermined frame structure format to generate telemetry data frames that conform to the transmission standard. The link management submodule is configured to perform a handshake interaction with the external monitoring terminal to detect the channel quality of the current communication frequency band and establish a secure transmission channel, while generating a transmission permission signal that characterizes the channel's ready state. The wireless transmission submodule is configured to receive the telemetry data frame and the transmit permission signal, and to modulate the telemetry data frame into a radio frequency signal in response to the transmit permission signal, and transmit the radio frequency signal to the external monitoring terminal through the secure transmission channel.

9. A method for monitoring endotracheal intubation with intelligent temperature and humidification identification and anti-blockage and anti-dislodgement technology, characterized in that, include: The sensor components installed on the endotracheal tube body are driven to synchronously detect the airway microenvironment and the physical state of the tube, obtain a multi-dimensional feature signal set, and determine the current intubation operation state type based on the multi-dimensional feature signal set. Logical operations are performed on the multidimensional feature signal set to obtain a monitoring status parameter set, and corresponding hierarchical alarm control instructions are determined based on the monitoring status parameter set. A visual monitoring interface is generated based on the monitoring status parameter set, and the sound and light alarm actions are switched according to the hierarchical alarm control instructions. The set of monitoring status parameters is transmitted to an external monitoring terminal according to a preset communication protocol.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the endotracheal intubation monitoring method according to any one of claims 9.

Images