Intelligent control system and method for hyperbaric oxygen chamber

Abstract

The application relates to the technical field of intelligent control, and discloses a high-pressure oxygen cabin intelligent control system and method, which comprises a collection module used for acquiring user breathing waveform data; a dynamic baseline establishment module used for establishing a dynamic baseline containing average breathing duration and peak flow; a phase detection and prediction module used for identifying a breathing phase, predicting an inhale / exhale starting point when real-time flow reaches a peak preset threshold, and generating a pre-trigger signal; a multi-stage execution module used for executing gas supply according to the phase and the pre-trigger signal; and a baseline self-adaption module used for calculating a deviation rate of an actual duration and an average duration, and triggering baseline reconstruction when the deviation rate continuously exceeds a threshold. The scheme overcomes the contradiction that gas supply response speed and oxygen utilization rate are difficult to balance, and realizes pre-judgment of the breathing phase and on-demand gas supply.

Description

Technical Field

[0001] This specification relates to the field of intelligent control technology, and in particular to an intelligent control system and method for a hyperbaric oxygen chamber. Background Technology

[0002] A hyperbaric oxygen chamber is a medical device that improves tissue hypoxia by providing an oxygen environment at a pressure higher than atmospheric pressure. It is widely used as an adjunct treatment for conditions such as carbon monoxide poisoning, decompression sickness, and diabetic foot. During hyperbaric oxygen therapy, the gas supply system needs to adjust the oxygen output in real time according to the user's breathing rhythm to ensure the oxygen concentration at the beginning of inhalation and avoid wasting oxygen during exhalation.

[0003] The existing hyperbaric oxygen chambers mainly use the following methods for gas supply control: First, a control method based on spatial concentration detection, which calculates the replenishment volume by monitoring the oxygen concentration at multiple points in the chamber and the distance to the air inlet; second, a control method based on model prediction, which uses algorithms such as deep learning to train historical data to predict gas demand; and third, a control method based on physiological parameter coupling, which introduces heart rate and respiratory rate as correction coefficients to adjust environmental parameters such as chamber pressure and temperature.

[0004] However, the above solutions have the following shortcomings: Spatial concentration detection-based methods cannot perceive the user's immediate inhalation and exhalation, resulting in a lag in air supply compared to the respiratory rhythm, leading to insufficient oxygen at the beginning of inhalation and wasted oxygen at the end of exhalation; model prediction-based methods rely on a large amount of pre-trained data, lacking generalization ability for users with different physical conditions and states, and are computationally complex and costly; physiological parameter coupling-based methods maintain control granularity at the environmental level, failing to precisely align air supply with the respiratory waveform phase. Furthermore, the user's respiratory state changes during treatment (e.g., transitioning from wakefulness to sleep), and existing systems often operate with fixed parameters, lacking the ability to adapt to individual respiratory rhythms.

[0005] Therefore, there is an urgent need for an intelligent control system and method for hyperbaric oxygen chambers to solve the above problems. Summary of the Invention

[0006] In view of this, the present invention aims to propose an intelligent control system and method for hyperbaric oxygen chambers, in order to solve the problems in the existing hyperbaric oxygen chamber gas supply control that make it difficult to balance gas supply response speed and oxygen utilization rate, and the inability to dynamically adjust the gas supply strategy according to the user's real-time breathing phase.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A hyperbaric oxygen chamber intelligent control system includes: The breathing waveform acquisition module is used to acquire breathing waveform data of users inside the hyperbaric oxygen chamber; A dynamic baseline establishment module is used to calculate and store a dynamic baseline based on the respiratory waveform data; the dynamic baseline includes the average respiratory duration and peak respiratory flow. The phase detection and prediction module is used to analyze the respiratory waveform data in real time to identify the current respiratory phase, and predict the start time of the next exhalation when the real-time respiratory flow rate drops to a preset percentage threshold of the peak inspiratory flow rate, or predict the start time of the next inhalation when the real-time respiratory flow rate rises to a preset percentage threshold of the peak expiratory flow rate, and generate a pre-trigger signal; the respiratory phase includes the inspiratory phase and the expiratory phase; A multi-level execution module is used to execute the corresponding gas supply strategy based on the identified current respiratory phase and the pre-trigger signal; The baseline adaptation module is used to calculate in real time the deviation rate between the actual respiratory duration in the current respiratory cycle and the average respiratory duration in the dynamic baseline, and to trigger the dynamic baseline establishment module to re-establish the baseline when the deviation rate continuously exceeds a preset threshold.

[0008] Furthermore, the respiratory waveform acquisition module includes a micro-flow sensor; The microflow sensor is installed at the breathing tube interface of the cabin and is used to output real-time flow data. Its positive and negative values ​​represent inhalation and exhalation airflow, respectively.

[0009] Furthermore, the dynamic baseline establishment module uses a peak detection algorithm to process the respiratory waveform data.

[0010] Furthermore, the phase detection and prediction module includes a finite state machine and a dynamic threshold comparator; the finite state machine defines an end-expiratory waiting state, an inspiratory preparation trigger state, an inspiratory phase supply state, and an expiratory waiting state. The triggering condition for the dynamic threshold comparator is:

[0011] In the formula, This is the absolute value of the expiratory flow rate in the real-time flow data. This serves as the baseline for peak expiratory flow. This is the trigger threshold coefficient.

[0012] Furthermore, the phase detection and prediction module is also configured with a prediction timeliness safety check: after triggering the pre-trigger signal, a timer is started, and the maximum waiting time is calculated based on the average exhalation duration. The calculation logic is as follows:

[0013] In the formula, For the maximum waiting time, This refers to the average exhalation time. If no inspiratory start signal is detected within the maximum waiting time, the prediction is determined to be a false positive, and the system returns to the expiratory phase waiting state without sending an inspiratory command.

[0014] Furthermore, the multi-stage execution module includes a gas supply pipeline and a high-speed electromagnetic proportional valve; the oxygen source is connected to the hyperbaric oxygen chamber through the gas supply pipeline, and the high-speed electromagnetic proportional valve is installed on the gas supply pipeline; During the intake phase, the opening of the high-speed electromagnetic proportional valve is adjusted proportionally according to the real-time intake flow rate to output compensated oxygen. During the expiratory phase, the high-speed electromagnetic proportional valve is closed or reduced to a low-flow state that maintains chamber pressure, wherein the low-flow is 5% to 10% of the peak expiratory flow rate.

[0015] Furthermore, the baseline adaptive module employs a sliding window algorithm to calculate the deviation rate between the actual respiratory duration in the current respiratory cycle and the average respiratory duration in the dynamic baseline. The sliding window has a length equal to a preset threshold number of respiratory cycles. At the end of each respiratory cycle, the average of the actual inspiratory and expiratory durations for each respiratory cycle within the current window is calculated as the current baseline value. The calculation logic is as follows:

[0016]

[0017] In the formula, For the first The sliding baseline value of inspiratory duration at the end of a respiratory cycle; For the first The sliding baseline value of expiratory duration at the end of a respiratory cycle; The length of the sliding window; For the first The actual inspiratory duration of each respiratory cycle; For the first The actual expiratory duration of each respiratory cycle; The inspiratory duration deviation rate and expiratory duration deviation rate of the current respiratory cycle are calculated separately, and the calculation logic is as follows:

[0018]

[0019] In the formula, This refers to the inhalation duration deviation rate; This refers to the deviation rate of expiratory duration. This refers to the actual inspiratory duration of the current respiratory cycle; This represents the actual expiratory duration of the current respiratory cycle.

[0020] Furthermore, a method for intelligent control of a hyperbaric oxygen chamber includes: S1. Real-time acquisition of respiratory waveform data of users in the hyperbaric oxygen chamber; S2. Based on the collected respiratory waveform data, a peak detection algorithm is used to calculate and store the dynamic baseline, which includes the average respiratory duration and peak respiratory flow. S3. Analyze respiratory waveform data in real time and identify the current respiratory phase; when the real-time respiratory flow is detected to drop to a preset percentage threshold of the peak inspiratory flow, predict the start time of the next exhalation, or when the real-time respiratory flow is detected to rise to a preset percentage threshold of the peak expiratory flow, predict the start time of the next inhalation and generate a pre-trigger signal. S4. Execute the corresponding gas supply strategy according to the identified current breathing phase and pre-trigger signal; during the inspiratory phase, adjust the opening of the high-speed electromagnetic proportional valve according to the real-time inspiratory flow rate to output compensated oxygen; during the expiratory phase, close the high-speed electromagnetic proportional valve or adjust it to a micro-underflow state to maintain chamber pressure. S5. Using a sliding window algorithm, the deviation rate between the actual respiratory duration of the current respiratory cycle and the average respiratory duration in the dynamic baseline is calculated in real time. When the deviation rate continuously exceeds the preset threshold, the dynamic baseline establishment module is triggered to re-establish the baseline in order to control the dynamic gas supply of the hyperbaric oxygen chamber.

[0021] The principles and advantages of this invention are: 1. The respiratory waveform acquisition module captures the user's respiratory waveform data in real time. Combined with the dynamic baseline establishment module, the average respiratory duration and peak respiratory flow are extracted as a personalized baseline. This breaks through the limitations of traditional solutions that rely on historical data training or fixed parameters, fully adapts to the differences in respiratory rhythms of different users, and the constructed control model is more in line with individual physiological characteristics. This solves the problems of insufficient adaptation to individual respiratory rhythms and poor generalization ability of existing hyperbaric oxygen chamber gas supply control.

[0022] 2. The phase detection and prediction module analyzes the respiratory waveform in real time to identify the current respiratory phase. It predicts the start of exhalation when the real-time respiratory flow drops to a preset percentage threshold of the peak inspiratory flow, or predicts the start of inhalation when it rises to a preset percentage threshold of the peak expiratory flow, generating a pre-trigger signal. This allows the gas supply action to respond in advance of the respiratory phase transition point, eliminating the gas supply lag caused by the traditional solution that only starts supplemental gas when the concentration drops. At the same time, through a multi-level execution module, oxygen is supplied during the inspiratory phase and the gas supply flow is cut off or reduced during the expiratory phase, achieving precise time-sharing control of inspiratory oxygen supply and expiratory oxygen conservation, thus resolving the contradiction between gas supply response speed and oxygen utilization rate.

[0023] 3. The baseline adaptive module uses a sliding window algorithm to calculate the deviation rate between the actual respiratory duration of the current respiratory cycle and the average respiratory duration in the dynamic baseline in real time. When the deviation rate continuously exceeds a preset threshold, dynamic baseline reconstruction is automatically triggered, enabling the system to dynamically follow changes in the user's respiratory state (such as from wakefulness to sleep), avoiding the decrease in control accuracy caused by respiratory rhythm drift, and solving the problem that the existing system lacks the ability to adapt to changes in respiratory state. Attached Figure Description

[0024] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is an exemplary system structure diagram of an intelligent control system for a hyperbaric oxygen chamber; Figure 2 This is an exemplary flowchart of a smart control method for a hyperbaric oxygen chamber. Detailed Implementation

[0025] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0026] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0027] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0028] The following detailed explanation illustrates the specific implementation methods: Example 1: like Figure 1 As shown, a smart control system for a hyperbaric oxygen chamber includes: The breathing waveform acquisition module is used to collect breathing waveform data of users inside the hyperbaric oxygen chamber.

[0029] Respiratory waveform data is a sequence of real-time flow data changing over time. Its positive and negative values ​​themselves constitute the waveform shape.

[0030] For example, the positive value segment represents the inspiratory phase, the negative value segment represents the expiratory phase, the peak value represents the peak inspiratory flow rate, and the absolute value of the trough represents the peak expiratory flow rate.

[0031] The respiratory waveform acquisition module includes a microflow sensor.

[0032] The microflow sensor is installed at the breathing tubing interface of the cabin to output real-time flow data. Its positive and negative values ​​represent inhalation and exhalation airflow, respectively.

[0033] The dynamic baseline establishment module is used to calculate and store dynamic baselines based on respiratory waveform data.

[0034] In this embodiment, the dynamic baseline includes at least the average respiratory duration and peak respiratory flow.

[0035] The average breathing duration refers to the sum of the average inhalation duration and exhalation duration within a preset number of breathing cycles, including the average inhalation duration and the average exhalation duration.

[0036] Peak respiratory flow refers to the combined peak inspiratory and peak expiratory flow rates within a preset number of respiratory cycles. Specifically, peak inspiratory flow rate is the average of the maximum positive flow rate during the inspiratory phase of each respiratory cycle within the preset number of respiratory cycles; peak expiratory flow rate is the average of the absolute values ​​of the maximum negative flow rate during the expiratory phase of each respiratory cycle within the preset number of respiratory cycles.

[0037] The dynamic baseline establishment module uses a peak detection algorithm to process respiratory waveform data.

[0038] Peak detection algorithms are classic algorithms in the field of respiratory signal processing used to identify inspiratory peaks and expiratory troughs from waveform data. In this embodiment, the peak detection algorithm specifically includes: A-1. Detect the maximum value in the respiratory waveform data as the inspiratory peak value and the minimum value as the expiratory valley value.

[0039] A-2. The time interval between adjacent inspiratory peaks is defined as a respiratory cycle. The time from the inspiratory peak to the adjacent expiratory trough is defined as the expiratory duration, and the time from the expiratory trough to the next inspiratory peak is defined as the inspiratory duration.

[0040] A-3. Take the average value of the preset number of breathing cycles in the initial stage, calculate the average inspiratory duration, average expiratory duration, peak inspiratory flow rate, and peak expiratory flow rate, and store the calculation results as a dynamic baseline.

[0041] The phase detection and prediction module is used to analyze respiratory waveform data in real time to identify the current respiratory phase, and predict the start time of the next exhalation when the real-time respiratory flow drops to a preset percentage threshold of the peak inspiratory flow, or predict the start time of the next inhalation when the real-time respiratory flow rises to a preset percentage threshold of the peak expiratory flow, and generate a pre-trigger signal; the respiratory phase includes the inspiratory phase and the expiratory phase.

[0042] Specifically, the phase detection and prediction module determines the current respiratory phase by checking the sign of the real-time flow data: when the real-time flow data is positive, it is determined that the current phase is inspiratory; when the real-time flow data is negative, it is determined that the current phase is expiratory. Based on this, the phase detection and prediction module further detects the transition points of the respiratory phase.

[0043] When it is determined that the current inspiratory phase is in progress and the real-time flow data continues to decline from the peak, the phase detection and prediction module compares the real-time inspiratory flow value with the peak inspiratory flow in the dynamic baseline, and calculates the ratio of the current inspiratory flow value to the peak inspiratory flow in real time. When the ratio drops to a preset percentage threshold, it indicates that the inspiratory action is nearing completion and the expiratory phase is about to begin. At this time, the phase detection and prediction module predicts the start time of the next exhalation and generates a pre-trigger signal for exhalation.

[0044] The preset percentage threshold can be adaptively set according to the breathing characteristics of different users.

[0045] In the field of mechanical ventilation, expiratory trigger sensitivity (ETS) is a key parameter determining when to switch from the inspiratory phase to the expiratory phase. It is defined as the percentage of inspiratory flow rate that decreases to peak flow rate. In clinical practice, the initial setting of this parameter is usually 25%, that is, 25% for invasive ventilation and 15%-30% for non-invasive ventilation. For patients with high expiratory resistance, this value can be increased to ensure adequate expiratory time; for patients with airway obstruction, the threshold needs to be lowered to avoid delayed switching.

[0046] Based on the above clinical evidence, the preset percentage threshold for the expiratory flow rate value in this embodiment can be set to 15% to 25%, with a recommended initial value of 15%.

[0047] In this embodiment, the phase detection and prediction module includes a finite state machine and a dynamic threshold comparator; it is used to predict the start time of the next exhalation or inhalation.

[0048] A finite state machine is a mathematical model used to describe the transition logic of a system between a finite number of states. When applied to respiratory phase detection, four states are defined: end-expiratory waiting state, inspiratory preparation trigger state, inspiratory phase supply state, and expiratory waiting state.

[0049] In this embodiment, the ratio of the absolute value of the expiratory flow rate in the real-time flow data to the peak expiratory flow rate is the core basis for the dynamic threshold comparator to determine the state transition. The phase detection and prediction module continuously receives the real-time flow data output by the respiratory waveform acquisition module. When it is determined that the current phase is expiratory, the dynamic threshold comparator calculates in real time the ratio of the absolute value of the expiratory flow rate in the real-time flow data to the peak expiratory flow rate in the dynamic baseline, and compares this ratio with the trigger threshold coefficient α.

[0050] The trigger condition for the dynamic threshold comparator is:

[0051] In the formula, This is the absolute value of the expiratory flow rate in the real-time flow data. Peak expiratory flow rate; The trigger threshold coefficient ranges from 0.05 to 0.25, and is set to 0.15 in this embodiment.

[0052] When the above conditions are met, it indicates that the user's exhalation has been completed by approximately 85%, the intrapulmonary pressure has been largely restored, and the inspiratory phase is about to begin. At this point, the finite state machine switches from the expiratory phase waiting state to the inspiratory preparation trigger state. The phase detection and prediction module generates a pre-trigger signal and transmits it to the multi-level execution module, enabling the multi-level execution module to prepare oxygen supply in advance before the actual start of inspiration, thereby eliminating oxygen supply lag.

[0053] The phase detection and prediction module is also equipped with a prediction timeliness safety check: after the pre-trigger signal is triggered, a timer is started to calculate the maximum waiting time based on the average exhalation duration. The calculation logic is as follows:

[0054] In the formula, For the maximum waiting time, This represents the average exhalation time.

[0055] Among them, average exhalation time The data is calculated by the dynamic baseline establishment module. Within a preset number of respiratory cycles after system startup, the phase detection and prediction module identifies the inspiratory peak and expiratory trough points in the respiratory waveform using a peak detection algorithm. The time from the inspiratory peak to the adjacent expiratory trough is taken as the expiratory duration. The actual expiratory duration of each respiratory cycle is recorded, and then its arithmetic mean is calculated.

[0056] Maximum waiting time The prediction timeliness safety check unit of the phase detection and prediction module calculates the result. Specifically, after triggering the pre-trigger signal, the average expiratory duration in the dynamic baseline is read. Multiply by the preset scaling factor of 0.3 to obtain the maximum waiting time.

[0057] The proportionality coefficient is set based on the principles of respiratory physiology. When the expiratory flow rate drops to 15% of the peak flow rate, the expiratory action is about 85% complete, and the remaining expiratory time accounts for about 15% of the total expiratory duration. Considering the sensor sampling delay (usually 5-10ms) and individual respiratory rhythm fluctuations, the waiting window is extended to 30% of the average expiratory duration. This provides sufficient safety margin to cover normal respiratory interval fluctuations while avoiding sluggish control response due to excessively long waiting times.

[0058] If no inspiratory start signal is detected within the maximum waiting time, the prediction is determined to be a false positive, and the system returns to the expiratory phase waiting state without sending an inspiratory command.

[0059] This formula is used to calculate the maximum waiting time after the pre-trigger signal is issued, in order to avoid false predictions caused by abnormal user breathing rhythms or sensor noise.

[0060] Specifically, when the phase detection and prediction module generates a pre-trigger signal, the prediction timeliness safety check simultaneously starts a timer. The timer is based on the average exhalation duration. Based on this, multiply by a coefficient of 0.3 to calculate the maximum waiting time. The average expiratory duration reflects the average duration of the user's expiratory phase. The coefficient of 0.3 is set based on the fact that when the expiratory flow rate drops to 15% of the peak flow rate, the expiratory action is about 85% complete, and the remaining incomplete expiratory time accounts for about 15% of the total expiratory duration. Considering the fluctuations caused by sensor sampling delay and individual differences, the waiting window is extended to 30% of the average expiratory duration, which not only provides sufficient safety margin but also avoids sluggish control response due to excessively long waiting time.

[0061] If in Within the specified timeframe, if the phase detection and prediction module detects that the real-time flow data has flipped from a negative value to a positive value, the prediction is deemed valid, and the multi-stage execution module executes the intake phase gas supply strategy normally. If in If no zero-crossing positive flow is detected within the specified time, the prediction is considered a false positive. The phase detection and prediction module then reverts the finite state machine to the expiratory phase waiting state and does not send any air supply commands to the multi-stage execution module. This safety verification mechanism effectively filters out false triggers caused by abnormal respiratory events such as coughing, body movement, and transient sensor noise, improving the system's anti-interference capability and control stability.

[0062] The multi-level execution module is used to execute the corresponding gas supply strategy based on the identified current breathing phase and pre-trigger signal.

[0063] The multi-stage execution module includes a gas supply pipeline and a high-speed electromagnetic proportional valve; the oxygen source is connected to the hyperbaric oxygen chamber through the gas supply pipeline, and the high-speed electromagnetic proportional valve is located on the gas supply pipeline.

[0064] During the intake phase, the opening of the high-speed electromagnetic proportional valve is adjusted proportionally according to the real-time intake flow rate to output compensated oxygen.

[0065] During the expiratory phase, the high-speed electromagnetic proportional valve is closed or reduced to a low-flow state that maintains chamber pressure, wherein the low-flow is 5% to 10% of the peak expiratory flow rate.

[0066] The baseline adaptation module is used to calculate the deviation rate between the actual respiratory duration in the current respiratory cycle and the average respiratory duration in the dynamic baseline in real time, and to trigger the dynamic baseline establishment module to re-establish the baseline when the deviation rate continuously exceeds a preset threshold.

[0067] The baseline adaptation module uses a sliding window algorithm to calculate the deviation rate between the actual respiratory duration in the current respiratory cycle and the average respiratory duration in the dynamic baseline.

[0068] The sliding window has a length equal to a preset threshold number of respiratory cycles. At the end of each respiratory cycle, the average of the actual inspiratory and expiratory durations for each respiratory cycle within the current window is calculated as the current baseline value. The calculation logic is as follows:

[0069]

[0070] In the formula, For the first The sliding baseline value of inspiratory duration at the end of a respiratory cycle; For the first The sliding baseline value of expiratory duration at the end of a respiratory cycle; The length of the sliding window; For the first The actual inspiratory duration of each respiratory cycle; For the first The actual expiratory duration of a respiratory cycle.

[0071] in, It can be calculated from the peak detection results of the phase detection and prediction module.

[0072] The window length of the sliding window can be set according to the user's breathing stability. When the user's breathing rhythm is relatively regular and the periodic fluctuations are small, a shorter window length (such as 3 respiratory cycles) can be selected to quickly respond to changes in breathing status; when the user's breathing rhythm fluctuates greatly or there are occasional irregular breaths, a longer window length (such as 5 respiratory cycles) can be selected to improve baseline stability and avoid frequent baseline fluctuations caused by a single abnormal breath. In this embodiment, the window length of the sliding window... The value is taken as 3 respiratory cycles.

[0073] The inspiratory duration deviation rate and expiratory duration deviation rate of the current respiratory cycle are calculated separately, and the calculation logic is as follows:

[0074]

[0075] In the formula, This refers to the inhalation duration deviation rate; This refers to the deviation rate of expiratory duration. This refers to the actual inspiratory duration of the current respiratory cycle; This represents the actual expiratory duration of the current respiratory cycle.

[0076] Upon obtaining the inspiratory duration deviation rate and expiratory duration deviation rate, the baseline adaptation module compares each deviation rate with preset thresholds. When both the inspiratory duration deviation rate and expiratory duration deviation rate continuously exceed the preset thresholds, it indicates that the user's inspiratory and expiratory durations have changed significantly simultaneously, meaning that the user's overall respiratory rhythm has undergone a substantial change. At this point, the baseline adaptation module sends a trigger reconstruction signal to the dynamic baseline establishment module, which then re-acquires waveform data for a preset number of respiratory cycles, calculates, and updates the dynamic baseline.

[0077] If only one deviation rate exceeds the threshold while another deviation rate is normal, it is determined to be an occasional respiratory irregularity event (such as coughing, talking, body movement, etc.), and baseline reconstruction is not triggered to avoid frequent baseline fluctuations due to non-persistent interference. In this embodiment, the preset threshold is set to 15%.

[0078] In this embodiment, the above-mentioned deviation rate calculation formula achieves refined monitoring of respiratory rhythm changes by calculating the degree of deviation between the inspiratory and expiratory durations separately. Baseline reconstruction is triggered when both exceed the threshold consecutively, while a single exceedance of the threshold is determined to be an occasional interference, thus balancing adaptive tracking and anti-interference capabilities.

[0079] like Figure 2 As shown, this embodiment also provides an intelligent control method for a hyperbaric oxygen chamber, including: S1. Real-time acquisition of respiratory waveform data of users in hyperbaric oxygen chamber.

[0080] S2. Based on the collected respiratory waveform data, a peak detection algorithm is used to calculate and store the dynamic baseline, which includes the average respiratory duration and peak respiratory flow.

[0081] S3. Analyze respiratory waveform data in real time and identify the current respiratory phase; when the real-time respiratory flow is detected to drop to a preset percentage threshold of the peak inspiratory flow, predict the start time of the next exhalation, or when the real-time respiratory flow is detected to rise to a preset percentage threshold of the peak expiratory flow, predict the start time of the next inhalation and generate a pre-trigger signal.

[0082] S4. Execute the corresponding gas supply strategy according to the identified current breathing phase and pre-trigger signal; during the inspiratory phase, adjust the opening of the high-speed electromagnetic proportional valve according to the real-time inspiratory flow rate to output compensated oxygen; during the expiratory phase, close the high-speed electromagnetic proportional valve or adjust it to a micro-underflow state to maintain chamber pressure.

[0083] S5. Using a sliding window algorithm, the deviation rate between the actual respiratory duration of the current respiratory cycle and the average respiratory duration in the dynamic baseline is calculated in real time. When the deviation rate continuously exceeds the preset threshold, the dynamic baseline establishment module is triggered to re-establish the baseline in order to control the dynamic gas supply of the hyperbaric oxygen chamber.

[0084] In this embodiment, the method acquires the user's respiratory waveform in real time and establishes a dynamic baseline. Combined with a respiratory phase recognition and prediction mechanism, it predicts the inhalation or exhalation start point in advance and generates a pre-trigger signal when the real-time respiratory flow reaches a preset threshold. This ensures that the gas supply action is precisely synchronized with the user's respiratory rhythm. Oxygen is supplied on demand during the inhalation phase and the gas supply flow is cut off or reduced during the exhalation phase. This effectively overcomes the contradiction between the gas supply response lag and low oxygen utilization rate in traditional solutions. At the same time, the method uses a sliding window algorithm to calculate the deviation rate between the actual respiratory duration and the average respiratory duration in real time and automatically reconstructs the baseline when the threshold is exceeded continuously. This achieves dynamic adaptation to changes in the user's respiratory state and significantly improves the real-time performance and accuracy of hyperbaric oxygen chamber gas supply.

[0085] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0086] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0087] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0088] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0089] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0090] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. An intelligent control system for a hyperbaric oxygen chamber, characterized in that, include: The breathing waveform acquisition module is used to acquire breathing waveform data of users inside the hyperbaric oxygen chamber; A dynamic baseline establishment module is used to calculate and store a dynamic baseline based on the respiratory waveform data; the dynamic baseline includes the average respiratory duration and peak respiratory flow. The phase detection and prediction module is used to analyze the respiratory waveform data in real time to identify the current respiratory phase, and predict the start time of the next exhalation when the real-time respiratory flow rate drops to a preset percentage threshold of the peak inspiratory flow rate, or predict the start time of the next inhalation when the real-time respiratory flow rate rises to a preset percentage threshold of the peak expiratory flow rate, and generate a pre-trigger signal; the respiratory phase includes the inspiratory phase and the expiratory phase; A multi-level execution module is used to execute the corresponding gas supply strategy based on the identified current respiratory phase and the pre-trigger signal; The baseline adaptation module is used to calculate in real time the deviation rate between the actual respiratory duration in the current respiratory cycle and the average respiratory duration in the dynamic baseline, and to trigger the dynamic baseline establishment module to re-establish the baseline when the deviation rate continuously exceeds a preset threshold.

2. The intelligent control system for a hyperbaric oxygen chamber according to claim 1, characterized in that, The respiratory waveform acquisition module includes a micro-flow sensor; The microflow sensor is installed at the breathing tube interface of the cabin and is used to output real-time flow data. Its positive and negative values ​​represent inhalation and exhalation airflow, respectively.

3. The intelligent control system for a hyperbaric oxygen chamber according to claim 1, characterized in that, The dynamic baseline establishment module uses a peak detection algorithm to process the respiratory waveform data.

4. The intelligent control system for a hyperbaric oxygen chamber according to claim 1, characterized in that, The phase detection and prediction module includes a finite state machine and a dynamic threshold comparator; the finite state machine defines the end-expiratory waiting state, the inspiratory preparation trigger state, the inspiratory phase supply state, and the expiratory waiting state. The triggering condition for the dynamic threshold comparator is: In the formula, This is the absolute value of the expiratory flow rate in the real-time flow data. Peak expiratory flow rate at the dynamic baseline; This is the trigger threshold coefficient.

5. The intelligent control system for a hyperbaric oxygen chamber according to claim 4, characterized in that, The phase detection and prediction module is also equipped with a prediction timeliness safety check: after triggering the pre-trigger signal, a timer is started, and the maximum waiting time is calculated based on the average exhalation duration. The calculation logic is as follows: In the formula, For the maximum waiting time, This refers to the average exhalation time. If no inspiratory start signal is detected within the maximum waiting time, the prediction is determined to be a false positive, and the system returns to the expiratory phase waiting state without sending an inspiratory command.

6. The intelligent control system for a hyperbaric oxygen chamber according to claim 1, characterized in that, The multi-stage execution module includes a gas supply pipeline and a high-speed electromagnetic proportional valve; the oxygen source is connected to the hyperbaric oxygen chamber through the gas supply pipeline, and the high-speed electromagnetic proportional valve is installed on the gas supply pipeline. During the intake phase, the opening of the high-speed electromagnetic proportional valve is adjusted proportionally according to the real-time intake flow rate to output compensated oxygen. During the expiratory phase, the high-speed electromagnetic proportional valve is closed or reduced to a low-flow state that maintains chamber pressure, wherein the low-flow is 5% to 10% of the peak expiratory flow rate.

7. The intelligent control system for a hyperbaric oxygen chamber according to claim 1, characterized in that, The baseline adaptive module uses a sliding window algorithm to calculate the deviation rate between the actual respiratory duration in the current respiratory cycle and the average respiratory duration in the dynamic baseline. The sliding window has a length equal to a preset threshold number of respiratory cycles. At the end of each respiratory cycle, the average of the actual inspiratory and expiratory durations for each respiratory cycle within the current window is calculated as the current baseline value. The calculation logic is as follows: In the formula, For the first The sliding baseline value of inspiratory duration at the end of a respiratory cycle; For the first The sliding baseline value of expiratory duration at the end of a respiratory cycle; The length of the sliding window; For the first The actual inspiratory duration of each respiratory cycle; For the first The actual expiratory duration of each respiratory cycle; The inspiratory duration deviation rate and expiratory duration deviation rate of the current respiratory cycle are calculated separately, and the calculation logic is as follows: In the formula, This refers to the inhalation duration deviation rate; This refers to the deviation rate of expiratory duration. This refers to the actual inspiratory duration of the current respiratory cycle; This represents the actual expiratory duration of the current respiratory cycle.

8. A method for intelligent control of a hyperbaric oxygen chamber, characterized in that, include: S1. Real-time acquisition of respiratory waveform data of users in the hyperbaric oxygen chamber; S2. Based on the collected respiratory waveform data, a peak detection algorithm is used to calculate and store the dynamic baseline, which includes the average respiratory duration and peak respiratory flow. S3. Real-time analysis of respiratory waveform data to identify the current respiratory phase; When the real-time respiratory flow is detected to drop to a preset percentage threshold of the peak inspiratory flow, the start time of the next exhalation is predicted; or when the real-time respiratory flow is detected to rise to a preset percentage threshold of the peak expiratory flow, the start time of the next inhalation is predicted, and a pre-trigger signal is generated. S4. Execute the corresponding gas supply strategy according to the identified current breathing phase and pre-trigger signal; during the inspiratory phase, adjust the opening of the high-speed electromagnetic proportional valve according to the real-time inspiratory flow rate to output compensated oxygen; during the expiratory phase, close the high-speed electromagnetic proportional valve or adjust it to a micro-underflow state to maintain chamber pressure. S5. Using a sliding window algorithm, the deviation rate between the actual respiratory duration of the current respiratory cycle and the average respiratory duration in the dynamic baseline is calculated in real time. When the deviation rate continuously exceeds the preset threshold, the dynamic baseline establishment module is triggered to re-establish the baseline in order to control the dynamic gas supply of the hyperbaric oxygen chamber.

Images