Respiratory device adaptive pressure control method in sleep intervention scenarios

Abstract

This invention provides an adaptive pressure regulation control method for respiratory equipment in sleep intervention scenarios, relating to the field of respiratory equipment data processing technology. The method acquires a broadband air pressure change data sequence inside the mask and a mechanical vibration data sequence of the mask shell; extracts high-frequency hysteresis coefficients; and performs cross-data channel temporal logic alignment between the air pressure temporal change rate characteristics and the vibration sequence to calculate the confidence level of physical spasm interference. Both are input into a comprehensive weighted decision module to perform dynamic weighted penalty calculation, outputting a comprehensive pressure regulation decision value. Subsequently, a basic air pressure intervention command is issued in response to this decision value, and the false trigger interception judgment threshold is dynamically updated. This invention effectively solves the technical pain point of physical spasms causing transient oscillations in the pipeline network being misjudged as precursors to airway collapse, avoids the conflict between high-pressure output from the fan and air supply, and improves the safety and robustness of the adaptive pressure regulation method.

Description

Technical Field

[0001] This invention relates to the field of respiratory equipment data processing technology, specifically to an adaptive pressure regulation control method for respiratory equipment in a sleep intervention scenario. Background Technology

[0002] With the development of medical IoT and intelligent sensing technologies, sleep apnea intervention devices (continuous positive airway pressure ventilation devices) are gradually evolving towards intelligent control based on real-time closed-loop feedback of multimodal physiological data. In this type of macro-level operational flow, the system needs to collect various physiological characteristics of patients at high frequency to adaptively adjust the airway intervention pressure, thereby ensuring ventilation quality and stable vital signs during the sleep cycle.

[0003] Existing technologies still face challenges and limitations in data feature decoupling and pressure regulation decision-making mechanisms under complex dynamic sleep scenarios. For example, an existing patent with publication number CN121265927B proposes a real-time closed-loop adaptive airway pressure regulation system, which identifies airway collapse risk windows and adaptively increases pressure by preprocessing monitoring data such as airflow, pressure, snoring, and electromyography. Although this solution has some effectiveness in reducing the false alarm rate of specific sleep apnea events, it still has significant spatiotemporal decoupling blind spots when facing the nonlinear physical resistance changes during the actual sleep cycle: its judgment logic, which relies solely on fluid-side or superficial physiological electrical signals, cannot effectively isolate the transient mechanical oscillations of the mask and tubing caused by the patient's physical twitching (such as sleep startle or turning over). This derivative high-frequency air pressure fluctuation caused by physical conduction is easily misjudged by existing algorithms as "precursors to actual airway collapse," leading to flow delays and miscalculations of pressure regulation commands, ultimately inducing the device to generate high-pressure output, causing a conflict between air supply and actual ventilation demand. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive pressure regulation control method for respiratory devices in sleep intervention scenarios, introducing a dual-channel sensing link for both the air pressure inside the mask and the mechanical vibration of the outer shell. By performing cross-channel timestamp backward alignment, the time difference of physical mechanical stress transmission to the fluid network is objectively eliminated. The confidence level of physical spasm interference is extracted, and a nonlinear weighted penalty network is used to dynamically correct and fuse the equivalent resistance characteristics of the microfluidic system. This addresses the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The adaptive pressure regulation control method for respiratory equipment in sleep intervention scenarios includes the following steps:

[0007] Step S1: Acquire the broadband air pressure change data sequence inside the mask of the breathing device and the synchronously acquired mechanical vibration data sequence of the mask shell;

[0008] Step S2: Based on the preset micro-perturbation feature analysis rules, the broadband air pressure change data sequence inside the mask is processed to extract the high-frequency hysteresis coefficient; at the same time, gradient operation is performed on the broadband air pressure change data sequence inside the mask to extract the air pressure time series change rate feature, and the air pressure time series change rate feature is aligned and matched with the mechanical vibration data sequence of the mask shell across data channels to extract the confidence level of physical spasm interference;

[0009] The high-frequency blocking coefficient and the confidence level of physical spasm interference are input into the pre-configured comprehensive weighted decision module to perform dynamic weighted penalty calculation and output the comprehensive voltage regulation decision value;

[0010] Step S3: In response to the integrated voltage regulation decision value satisfying the preset command triggering rule, the following actions are executed in parallel:

[0011] A baseline air pressure intervention command is generated and sent to the ventilator fan control terminal. This baseline air pressure intervention command is configured to drive the ventilator fan control terminal to perform a pre-pressurization output action or maintain the current air pressure dwell action; and

[0012] A sleep stage status update log is generated, and the comprehensive voltage regulation decision value is written into a preset sleep status verification benchmark database to dynamically update the false trigger interception judgment threshold for subsequent detection cycles.

[0013] Compared with the prior art, the beneficial effects of the present invention are:

[0014] This invention extracts the confidence level of physical spasm interference by acquiring the air pressure sequence inside the mask and the mechanical vibration sequence of the outer shell, and performing cross-channel timing logic alignment based on asynchronous time windows on the air pressure time-series change rate and vibration sequence. This eliminates the physical transmission delay between heterogeneous data, decouples non-pathological air pressure pulsations caused by startles or turning over during sleep, and reduces the frequency of erroneous pressurization commands triggered by the device.

[0015] This invention synchronously inputs the extracted high-frequency hysteresis coefficient and the confidence level of physical spasm interference into the comprehensive weighted decision module, performing nonlinear dynamic weighted penalty calculation. This mechanism utilizes the confidence level to generate a dynamically weakening weight factor, forcibly implementing multiplicative logic circuit breaking on false alarm peaks, and outputs a comprehensive voltage regulation decision value under a logarithmic mapping model. This ensures that while maintaining a high-sensitivity response to real, subtle collapses, it also achieves slope convergence and flexible blocking of physical noise, improving the robustness of control flow and the safety of equipment operation under complex sleep conditions.

[0016] This invention generates a sleep stage status update log while issuing basic wind pressure intervention instructions, and writes the decision judgment value into a benchmark database to dynamically update the false trigger interception judgment threshold for subsequent detection cycles. This closed-loop learning mechanism can adaptively perform state machine game and threshold optimization between "high-frequency anti-interference during light sleep" and "high-sensitivity anti-missed detection during deep sleep" based on global physiological metabolism and rhythm fluctuation trends, thereby providing patients with personalized and precise ventilation support that closely matches their individual evolution patterns throughout the night. Attached Figure Description

[0017] Figure 1 This is a dynamic topology diagram of the present invention;

[0018] Figure 2 A schematic diagram showing the results of a numerical verification experiment on a dynamic weighted penalty mechanism for multi-source heterogeneous data.

[0019] Figure 3 This is a flowchart illustrating the technical logic of the present invention.

[0020] Figure 4 This is a schematic diagram comparing the baseline waveforms of clinical polysomnography (PSG) in cases of early airway collapse and interference from false-positive physical spasms. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various elements, but unless otherwise stated, these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0023] Example 1:

[0024] Please see Figures 1 to 4 The present invention provides a technical solution:

[0025] An adaptive pressure regulation control method for a respiratory device in a sleep intervention scenario, executed by the processor of a computing device, includes the following steps:

[0026] The broadband air pressure variation data sequence inside the mask is denoted as Its characterization is a set of raw airway fluid pressure temporal fluctuation data acquired from a pre-configured pneumatic sensing access link, containing a wide frequency distribution. The mask shell mechanical vibration data sequence is denoted as... It represents the set of discontinuous mechanical forces or transient physical impact states experienced by the physical boundaries of the mask within the synchronization time window. The high-frequency hysteresis coefficient is denoted as... It characterizes the degree of variation in the equivalent acoustic drag of the microscopic network of the airway during the initial stage of physical cross-sectional collapse. The confidence level of physical spasm interference is denoted as... Its characterization indicates that the currently captured transient airflow events do not originate from actual metabolic ventilatory failure, but rather from the objective logical probability of physical tics (such as sleep startles). The sleep stage stability index is denoted as... It represents a time-stationary characteristic quantity of the macroscopic respiratory rhythm fluctuation pattern of patients within the current detection period, extracted from historical logs. The penalty decay control factor is denoted as... It is a control parameter used to adjust the nonlinear curvature inside the anti-interference strength mapping network. In this embodiment, the value range is defined as [0.15, 0.85].

[0027] The core algorithm model of the logical matching network is pre-constructed and calibrated to solidify the baseline spasm feature distribution matrix (denoted as...). The specific construction process is as follows: A historical benchmark multimodal dataset is acquired through a pre-configured clinical polysomnography (PSG) data synchronization gateway. This dataset contains positive samples of "real early airway collapse events" labeled with the medical gold standard, and negative samples of "false positive physical interference events" including mask-induced physical turning collisions and hypnic jerks. The extracted normalized basic feature state vectors are input into the initial unsupervised topological space network, using intra-class feature dispersion and inter-class cross-entropy as the joint loss function. A pre-defined adaptive gradient descent algorithm is invoked to calculate the feature gradient of each sample to the current cluster center during each data batch transfer, and the benchmark spasticity feature distribution matrix in the network is iteratively adjusted. The internal coordinate parameters of the baseline cluster centers in each dimension. Throughout the entire evolution cycle, when the false positive rate (false positive percentage) of the joint loss function on the independent cross-validation set converges to a preset stable threshold (preferably calibrated to be below 3% in this embodiment) for ten consecutive training epochs, a training circuit breaker instruction is triggered to stop the topology space reconstruction action and extract the set of coordinates of each feature cluster center in the current convergence state, which is then persistently solidified into a baseline spasm feature distribution matrix. It is deployed and distributed in the format of a standard data dictionary to online logical matching network storage nodes for real-time querying and retrieval.

[0028] Step S1: Acquire the broadband air pressure change data sequence inside the mask of the breathing device and the synchronously acquired mechanical vibration data sequence of the mask shell;

[0029] Further explanation of step S1: The process of acquiring a broadband pressure change data sequence inside the mask of the breathing device includes:

[0030] Raw composite pressure time-series data is acquired using a pre-configured pressure sensor access link;

[0031] The original composite pressure time series data is input into the pre-configured frequency domain separation algorithm module to perform feature stripping operation. Specifically, based on the preset frequency domain separation benchmark threshold, the low-frequency pressure fluctuation features that characterize the normal respiratory cycle are filtered out, and the high-frequency micro-fluctuation sequence that exceeds the frequency domain separation benchmark threshold and characterizes the airflow impacting the airway wall reflection echo is extracted as the broadband pressure change data sequence inside the mask.

[0032] Further explanation of step S1: The mechanical vibration data sequence of the mask shell is obtained through the following logic:

[0033] The system acquires discontinuous, localized sudden vibration intensity data through a pre-set vibration sensor acquisition link.

[0034] Based on a preset transient peak determination threshold, background feature screening is performed on the discontinuous local sudden vibration intensity data, and signal amplitude accumulation and summation processing exceeding the transient peak determination threshold is performed within a preset transient vibration feature extraction time period to extract a set of vibration peak morphology features characterizing short-term body twitching intensity, and the set of vibration peak morphology features is encapsulated into the mechanical vibration data sequence of the mask shell.

[0035] Specific implementation instructions for step S1: The input data includes two subsets: airflow inside the mask and vibration outside the mask. For the data inside the mask, raw composite pressure time-series data is acquired using a pre-configured pressure sensor access link. The pre-configured pressure sensor access link is configured as a data aggregation node, logically mapped to the airflow convergence area inside the mask cavity. By calling a preset analog-to-digital converter (ADC) service component, the link is driven to perform a transition from continuous physical quantities to the discrete digital domain. The following parameters are introduced:

[0036] The basic sampling frequency is denoted as It characterizes the macroscopic time density of discrete data capture performed by the barometric pressure sensor access link. The barometric pressure quantization bit depth is denoted as... Its characterization represents the logical analytical resolution of pressure amplitude changes at a single sampling point, ensuring that weak high-frequency airflow flutter is captured without loss. During the acquisition of raw composite pressure time-series data, the pressure sensor access link is invoked to perform the following scheduling actions: using the base sampling frequency... The transient air pressure fluctuation values ​​inside the mask cavity are continuously read with a step size, and the data is then quantized based on the air pressure depth. The process involves performing analog-to-digital conversion to generate discrete pressure amplitude feature sets; retrieving a globally unified time synchronization server to append an absolute timestamp parameter to each discrete pressure amplitude feature; serializing and encapsulating the feature pairs containing the absolute timestamp and corresponding pressure amplitude; and outputting raw composite pressure time-series data with time-series alignment attributes. The basic sampling frequency is... The preferred value range is defined as [100, 250] Hz. During a normal, stable respiratory cycle, the base sampling frequency in this embodiment is lowered to approximately 100 Hz to reduce concurrent communication load; in response to the detection of early signs of body sway, the system instantly switches to a high-frequency, high-sensitivity mode, raising the base sampling frequency to approximately the upper limit of 250 Hz to enhance the ability to detect deep features of sudden changes in airway resistance.

[0037] The raw composite barometric time-series data is input into a pre-configured frequency domain separation algorithm module to perform feature stripping. Specifically, a preset high-frequency filtering separation logic is invoked, and a frequency domain separation benchmark threshold is introduced to define the extraction boundary of the effective data stream. A pre-configured high-pass digital filter is used to truncate the input raw composite barometric time-series data. In this embodiment, the high-pass digital filter preferably adopts a fourth-order Butterworth infinite impulse response (IIR) filter architecture. At the physical execution level, normalized digital frequency conversion is performed based on the sampling rate of the barometric sensor's hardware front end (preferably set to not less than 100Hz) to construct the corresponding difference equation. By calling this filter architecture, low-frequency barometric pressure fluctuation features characterizing the normal respiratory cycle are filtered out (the tidal volume fluctuation background band with a frequency lower than the frequency domain separation benchmark threshold is removed). This embodiment employs an IIR architecture, which ensures the complete stripping of extremely narrow, weak high-frequency characteristics (greater than 2Hz) while avoiding the logic timing delay overflow problem caused by high-order FIR filters. It then separates and extracts high-frequency micro-fluctuation sequences that exceed the frequency domain separation threshold, characterizing the reflected echoes generated by airflow impacting the airway wall. Finally, this sequence is encapsulated and output as a broadband air pressure change data sequence within the mask. The frequency domain separation benchmark threshold characterizes the physical frequency boundary that distinguishes the pleural metabolic ventilation waveform from the high-frequency flutter of microscopic airway wall deformation. In this embodiment, the preferred value for the frequency domain separation benchmark threshold is set to 2Hz. If this parameter is set below the lower limit (below 0.5Hz), it will not be able to effectively filter out the patient's normal tidal volume fluctuation waveform, causing subsequent weak high-frequency blockage features to be completely masked by low-frequency airflow energy, resulting in false negative omissions. If it exceeds the upper limit (set above 10Hz), it will produce an allergic filtering effect, treating the real early weak collapse echo signal as high-frequency noise and causing the best time for disaster prevention and pressure regulation intervention to be missed. In actual macroscopic respiratory monitoring workflow, in response to the patient entering a shallow and rapid breathing state with a sharp increase in respiratory rhythm frequency, the frequency domain separation benchmark threshold will be adaptively increased to smoothly shift to 3Hz to ensure the objective purity of high-frequency air pressure waveform feature stripping.

[0038] For the mask shell data, discontinuous, localized sudden vibration intensity data is acquired through a pre-defined vibration sensor acquisition link. This pre-defined vibration sensor acquisition link is configured as an asynchronous event listening node independent of the fluid side, logically positioned at the force transmission center of the rigid mask shell. This link employs an event-driven sleep / wake-up architecture to reduce normal power consumption. The following core characteristic parameters are introduced at this stage:

[0039] The spatial triaxial acceleration components are denoted as It characterizes the mask displacement and impact features captured in real time by the vibration sensor acquisition link in a three-dimensional physical coordinate system. The spatially synthesized vibration scalar is denoted as... It represents the absolute stress intensity characterization quantity generated after dimensionality reduction and aggregation of the three-dimensional heterogeneous stress features in this embodiment. During the process of acquiring discontinuous local sudden vibration intensity data, the following gate-based filtering action is performed: specifically, the spatial triaxial acceleration components under the current time slice are acquired in parallel through the vibration sensor acquisition link. The component values ​​of the above three dimensions are squared respectively, and the three squared terms are aggregated and summed. The arithmetic square root of the sum is extracted and used to generate a spatially synthesized vibration scalar without direction dimension through spatial mapping operation. The scalar is pre-checked using a pre-configured sleep / wake threshold. If the scalar does not exceed the sleep / wake threshold, the data is silently discarded to maintain the network's silent state. If the scalar goes out of bounds, a trigger timestamp is immediately added, and the scalar value at that moment is encapsulated and output to obtain discontinuous local sudden vibration intensity data that is only triggered by physical jerking or rolling collisions.

[0040] The sleep-wake threshold represents the minimum spatially synthesized force critical blocking line that triggers full caching of vibration data, appending timestamps, and activating cross-channel alignment links. In this embodiment, the quantization range of the sleep-wake threshold is calibrated as [0.05G, 0.20G] (G is the equivalent unit of gravitational acceleration), and its baseline initialization is preferably calibrated to 0.10G.

[0041] The system invokes preset macroscopic feature filtering rules: specifically, it removes background noise from the vibration intensity data based on a preset transient peak determination threshold; for valid oscillation pulse nodes exceeding this threshold, it initiates a preset transient vibration feature extraction time period starting from this threshold and introduces a discrete mapping mechanism after analog-to-digital conversion. Specifically, it uses a fixed hardware sampling period as the step size and performs aggregation and summation deduction based on discrete state sequences within this period window. The discretized aggregation model is defined as follows: Traverse the J effective discrete sampling nodes within the periodic window and extract the instantaneous digital acceleration amplitude of each effective discrete sampling node j. Compare it with the preset transient peak determination threshold. Perform interpolation to remove noise floor; compare the net effective amplitude with a fixed time interval parameter between two adjacent sampling points. Weighted multiplication is performed, and all product terms are finally summed and aggregated. The total number of valid discrete sampling nodes, J, represents the number of hardware interrupts that meet the triggering conditions within a preset periodic window; the specific number is determined by both the window length and the sampling rate. The fixed time interval parameter... The physical sampling resolution of the sensor is characterized, preferably 10ms at a sampling rate of 100Hz in this embodiment, and configured within the range of [5ms, 20ms] in practical applications. Through this aggregation and summation operation based on a controlled time window, a set of vibration peak morphology features characterizing the intensity of short-term body twitching is extracted, and this set of vibration peak morphology features is encapsulated and output as a mechanical vibration data sequence of the mask shell. .

[0042] The transient peak determination threshold represents the minimum mechanical force extreme value that can be identified as an effective physical twitching impact. In this embodiment, its preferred value is set to 0.5G. If this parameter is set to deviate from the lower limit (below 0.1G), all normal subtle resonances caused by the operation of the built-in micro fan will be regarded as startle features and integrated, leading to false positive interception in subsequent judgments. If this parameter exceeds the upper limit, the patient's real slight limb twitching will be directly missed (false negative). The transient vibration feature extraction time period represents the macroscopic time span used for integrating and calculating the mechanical energy burst of a single physical twitching. Its preferred configuration is 300 milliseconds. This period length can completely cover and encompass all the mask mechanical aftershock attenuation waveforms caused by a typical sleep startle, thereby ensuring the completeness of local energy accumulation statistics.

[0043] Step S2: Based on the preset micro-perturbation feature analysis rules, the broadband air pressure change data sequence inside the mask is processed to extract the high-frequency hysteresis coefficient; at the same time, gradient operation is performed on the broadband air pressure change data sequence inside the mask to extract the air pressure time series change rate feature, and the air pressure time series change rate feature is aligned and matched with the mechanical vibration data sequence of the mask shell across data channels to extract the confidence level of physical spasm interference;

[0044] The high-frequency blocking coefficient and the confidence level of physical spasm interference are input into the pre-configured comprehensive weighted decision module to perform dynamic weighted penalty calculation and output the comprehensive voltage regulation decision value;

[0045] Further explanation of step S2: The process of performing cross-data-channel temporal logical alignment and matching on the air pressure time-series change rate characteristics and the mask shell mechanical vibration data sequence, and extracting the confidence level of physical spasm interference, includes:

[0046] Based on a preset asynchronous time sliding window, a backward delayed backtracking scan is performed with the peak trigger timestamp of the mechanical vibration data sequence of the mask shell as the anchor point. The time sequence logic alignment based on the timestamp feature is performed on the air pressure time sequence change rate feature and the mechanical vibration data sequence of the mask shell. The first derivative peak feature representing the air pressure time sequence change rate feature and the instantaneous spasmodic oscillation intensity feature representing the mechanical vibration data sequence of the mask shell are extracted respectively.

[0047] The peak feature of the first derivative and the instantaneous spasm oscillation intensity feature are input into a preset logical matching network to perform similarity calculation, so as to generate and output the confidence level of the physical spasm interference.

[0048] The step of inputting the peak feature of the first derivative and the instantaneous spasmodic oscillation intensity feature into a preset logical matching network to perform similarity calculation includes:

[0049] Numerical normalization processing based on preset value range boundaries is performed on the first derivative peak feature and the instantaneous spasmodic oscillation intensity feature respectively to eliminate the dimensional differences of heterogeneous business data;

[0050] The normalized first derivative peak feature and the instantaneous spasmodic oscillation intensity feature are combined by a dimension concatenation operation to construct a cross-modal feature state vector representing the current abrupt change in airflow and mechanical mixing.

[0051] The baseline spasm feature distribution matrix is ​​obtained from the preset storage node of the logical matching network, and the preset spatial distance matching algorithm is called to calculate the feature space Euclidean distance parameter between the cross-modal feature state vector and the baseline spasm feature distribution matrix.

[0052] Based on a preset confidence transformation rule, an inverse proportional mapping process is performed on the Euclidean distance parameter of the feature space to generate and output the confidence level of the physical spasm interference.

[0053] Further explanation of step S2: The process of performing dynamic weighted penalty calculation and outputting the comprehensive voltage regulation decision value includes:

[0054] The physical spasm interference confidence level is mapped to a preset penalty inverse function to generate a dynamic weakening weight factor characterizing the anti-interference strength.

[0055] The high-frequency hysteresis coefficient is multiplied by the dynamic attenuation weighting factor to obtain the smoothing suppression parameter;

[0056] The smoothing suppression parameters are subjected to nonlinear mapping and control quantity conversion operations with the preset basic fluid dynamics mapping model to generate the comprehensive pressure regulation decision value.

[0057] The process of mapping the physical spasm interference confidence level to a preset penalty inverse function to generate a dynamic weakening weight factor characterizing the anti-interference strength specifically includes:

[0058] Obtain historical time-series features recorded in the sleep state verification benchmark database, and extract the sleep stage stability index that characterizes the fluctuation pattern of the respiratory rhythm in the current stage;

[0059] In response to the sleep stage stability index meeting the preset first light sleep overdose condition, the quantification value of the penalty decay control factor inside the penalty inverse function is increased to construct a first mapping curvature network with strong inhibition properties.

[0060] In response to the sleep stage stability index meeting the preset second deep sleep stability condition, the quantification value of the penalty decay control factor is reduced to construct a second mapping curvature network with weak inhibition properties.

[0061] The confidence level of the physical spasm interference is input into the currently active first or second mapped curvature network to perform dynamic control quantity reduction in order to generate the dynamic weakening weight factor.

[0062] Specific implementation instructions for step S2:

[0063] The first stage involves feature extraction and spatiotemporal alignment: based on pre-defined micro-perturbation feature analysis rules, a broadband air pressure change data sequence inside the shield is analyzed. Continuous data stream parsing and processing are performed. A preset time sliding window is opened for the extracted high-frequency micro-fluctuation sequence, and a short-time Fourier transform is performed within the window to construct a multi-dimensional frequency domain feature map;

[0064] The resting reference network amplitude is defined as the baseline amplitude reference value of the inherent acoustic high-frequency flutter waveform within the initial calibration period, under resting fluid conditions where the mask is not worn or there is absolutely no air leakage or airway collapse.

[0065] The inherent physical resonance frequency of a standard open airway represents the center point of the inherent hydrodynamic resonance core frequency excited by fluid passing through the respiratory tract at high speed when the airway cross section of a standard healthy organism is fully expanded and no hidden collapse has occurred.

[0066] By traversing and retrieving the frequency domain feature map, extreme value tracing logic is executed for all high-frequency components within the current time sliding window to locate the core resonant frequency point where high-frequency energy is maximized. The amplitude attenuation feature is extracted by calculating the ratio of the difference between the actual signal amplitude extreme value at this core resonant frequency point and the amplitude of the initialized resting reference network. It characterizes the macroscopic collapse span of the fluid-equivalent acoustic drag; simultaneously, by comparing the frequency shift deviation between the absolute frequency parameter of this core resonant frequency point and the inherent physical resonant frequency of a standard open airway, the frequency shift characteristic is extracted and denoted as... It characterizes the degree of secondary frequency domain variation caused by airway physical deformation.

[0067] In this embodiment, the calibration range of the resting reference network amplitude is determined by the full-scale linear projection region of the hardware sensor. The preferred range for the inherent physical resonant frequency of the standard open airway is calibrated as [6.5, 12.5] Hz.

[0068] The pre-configured acoustic feature calculation mechanism is invoked, and its built-in model is as follows: ;

[0069] Obtain the amplitude decay feature of each local feature node i within the current sliding window. and compare it with the preset amplitude-sensitive weighting coefficient. Perform a weighted multiplication operation; simultaneously acquire the frequency offset feature values ​​of each corresponding local feature node i. and compare it with the preset frequency offset sensitive weighting coefficient. Perform a weighted multiplication operation; perform feature fusion summation on the two sets of weighted reference values, and divide by the total number of local feature nodes N within the window to perform dimensionality reduction and normalization, outputting the final high-frequency hysteresis coefficient. Among them, the amplitude-sensitive weighting coefficient The value range is defined as [0.65, 0.75], and the frequency offset sensitive weighting coefficient is... The value range is defined as [0.25, 0.35], and it satisfies the frequency offset sensitive weighting coefficient. With amplitude-sensitive weighting coefficient The sum is limited to a normalized boundary condition of 1. If the amplitude-sensitive weighting coefficients... Setting a lower limit will cause this embodiment to react sluggishly to the weakening of high-frequency airflow caused by slight collapse, triggering false negatives and causing the lower-level execution side to miss the micro-pressurization intervention window; if the parameter exceeds the upper limit, it will easily amplify the normal airflow disturbance of the mask's normal exhaust port, leading to frequent false positives. When determining a high-risk supine position, the amplitude sensitivity weighting coefficient will be adjusted. The weight is increased to the maximum of 0.75 to maintain the highest interception and defense effectiveness; when the target is determined to be in a safe lying position, the weight is smoothly reduced to achieve a balance between detection sensitivity and resistance to environmental noise.

[0070] Broadband air pressure variation data sequence inside the shield Gradient calculations were performed to extract temporal pressure change rate features. This was based on the broadband pressure change data sequence within the mask. It is represented as a discrete time-series numerical sequence, and a preset discrete difference operation engine is invoked to perform gradient calculations. Specifically, the following core computational parameters are introduced:

[0071] The characteristic sequence of the rate of change of air pressure over time is denoted as: It characterizes the steepness of the abrupt change in fluid pressure within the airway per unit physical time. Specifically, it is based on the following discrete difference mapping model: ;

[0072] Traverse the current discrete sampling node r in the characteristic sequence of the rate of change of air pressure over time to obtain its air pressure amplitude. And its air pressure amplitude at the kth preceding historical sampling node. Perform a subtraction operation to obtain the net pressure difference; divide the net pressure difference by the preset span time parameter, which is determined by the number of span nodes k and the physical sampling step size. The product is obtained by performing a multiplication operation; after completing the cross-data channel timing logic alignment and delayed backtracking scan, the extreme value is directly extracted from the locked sequence interval as the first derivative peak feature.

[0073] Based on a preset asynchronous time sliding window, delay feature compensation and alignment operations are performed to eliminate the delayed abrupt change in the internal air pressure waveform after the outer surface of the mask has experienced physical vibration impact first. The specific steps are configured as follows: activating the timing extreme value screening mechanism and applying it to the mechanical vibration data sequence of the mask shell. Within the current evaluation period, all discontinuous oscillation pulses undergo amplitude traversal and comparison sorting. Secondary oscillation aftershocks are filtered out, and the single maximum force peak representing the peak of physical kinetic energy burst is extracted. The trigger timestamp corresponding to this extracted single maximum force peak is then set as the absolute retrieval anchor point, and a backward delay backtracking scan with a preset asynchronous compensation duration is initiated using this anchor point. By traversing and retrieving the highest peak value of the air pressure temporal change rate characteristic within the scan window period and erasing its physical delay, the logical timestamps of the two are bound and merged, thus completing the temporal logic alignment based on timestamp features. After alignment, the first derivative peak feature representing the steepness of airflow abrupt change and the instantaneous spasmodic oscillation intensity feature representing short-term force are extracted, which are completely synchronized in time and space. The following core architecture parameters are introduced when performing the extraction and locking of the single maximum force peak:

[0074] The intensity characteristics of transient spasmodic oscillations are denoted as It represents the absolute highest extreme value of external mechanical impact energy captured in the digital domain in this embodiment during a single physical twitching event. (This refers to the mechanical vibration data sequence of the mask shell.) The process involves performing a traversal and filtering operation based on discrete time steps. It initializes the local maximum cache parameter and sets its baseline to absolute zero. Then, it reads the effective net oscillation amplitude of each discrete node within the current scan window and performs a logical comparison and verification operation between the effective net oscillation amplitude of the current node and the local maximum cache parameter. If the effective net oscillation amplitude of the current node is greater than the current value of the local maximum cache parameter, a dynamic overwrite operation is triggered, updating and replacing the local maximum cache parameter with the effective net oscillation amplitude data of the current node. If the effective net oscillation amplitude is less than or equal to the current value of the local maximum cache parameter, it is silently allowed to proceed, maintaining the original value of the local maximum cache parameter until the next node is evaluated. After exhaustively traversing all effective discrete nodes within the current evaluation period, it extracts the final extreme value data residing in the local maximum cache parameter, persists it, and outputs it as a transient spasmodic oscillation intensity feature. .

[0075] The asynchronous compensation duration represents the buffer period used in this embodiment to actively compensate for the delay in the conversion of mechanical stress from the mask to the internal fluid network. Its preferred value range is defined as [50, 150] milliseconds. If this parameter is set below the lower limit, the system will incorrectly determine that the delayed pressure fluctuations and physical oscillations are independent events that are completely unrelated, directly causing false negatives. If this parameter exceeds the upper limit, the scanning window will easily capture the normal peak of adjacent real respiratory collapse cycles, causing unrelated independent physiological events to be forcibly associated and recombined, leading to high-frequency false positives.

[0076] The second stage involves feature space mapping and similarity calculation. The first-order derivative peak feature and the instantaneous spasmodic oscillation intensity feature are respectively subjected to linear mapping operations based on preset value range boundaries, scaling their values ​​to a uniform dimensionless preset interval [0,1]. The normalization operation is defined as: performing space mapping separately for the two independent datasets. Taking any feature dimension V as an example, the current sliding window is traversed to extract the local maximum extrema of that dimension. and local minimum And perform processing based on the following transformation logic: In practice, the currently acquired real-time feature value V is compared with local minima. The subtraction operation is performed to obtain the feature offset, which is the range difference between the local maximum and minimum extrema, and then compared with the preset anti-deadlock micro-perturbation constant. Perform a summation operation to construct an effective divisor parameter; divide the feature offset by this effective divisor parameter to generate normalized feature values. Among them, the anti-deadlock micro-perturbation constant The smallest positive real number used to force the processor to truncate a divide-by-zero hardware exception is preferably calibrated to 1 × 10^2 in this embodiment. -6 The above operations eliminate the dimensional difference between the fluid pressure derivative and the mechanical vibration amplitude.

[0077] Subsequently, the two sets of normalized features are concatenated to construct a cross-modal feature state vector that represents the current abrupt change in the mixing of airflow and machinery.

[0078] Specifically, for the dimension concatenation operation, a spatial arrangement order rule is established: the peak feature of the first derivative after normalization is taken as the first business dimension component, and the instantaneous spasmodic oscillation intensity feature after normalization is taken as the second business dimension component; the first and second business dimension components are sequentially concatenated according to the order rule to construct a two-dimensional first-order column vector as the cross-modal feature state vector. Its characteristics are:

[0079] in The normalized first-order derivative peak feature, after extreme value linear scaling, represents the first spatial dimension of the cross-modal feature state vector. The normalized instantaneous spasmodic oscillation intensity feature, after extreme value linear scaling, is characterized in the second spatial dimension of the cross-modal characteristic state vector; T represents the matrix transpose operator, which is configured to forcibly replace a one-dimensional row vector with a one-dimensional column vector that conforms to the input interface standard of the spatial topology calculation network.

[0080] Extract the baseline spasm feature distribution matrix for online deployment from the preset storage nodes of the logical matching network. And invoke the preset multi-dimensional space topology calculation logic. Specifically, this is done by extracting cross-modal feature state vectors (denoted as...). ) and the baseline spasticity characteristic distribution matrix The corresponding dimension parameter is used to calculate the Euclidean distance parameter in the feature space (denoted as ) based on the following logic. ): ;

[0081] Iterate through the K feature dimensions one by one to obtain the component value of the current cross-modal feature state vector in the k1-th dimension. And compare it with the reference coordinate components of the cluster centers of the reference matrix in the same dimension. Perform a subtraction operation to obtain the dimension bias parameter; square each dimension bias parameter and introduce a preset dimension importance attenuation coefficient. A weighted multiplication operation is performed to suppress numerical drift caused by edge features; an aggregation summation operation is performed on the weighted squared terms of all K dimensions; a square root operation is performed on the high-dimensional spatial volume parameter generated by the aggregation summation to reduce dimensionality, and the output is the absolute quantized spatial span representing the deviation of the current input data from the baseline startle noise, which in turn represents the Euclidean distance parameter of the feature space. Among them, the dimensionality importance decay coefficient The confidence weight of heterogeneous physical quantities in joint anti-counterfeiting determination is represented. For the dimension representing the peak value of the first derivative of sudden airflow obstruction, its corresponding weight parameter is calibrated as [0.7, 0.8]; for the dimension representing the intensity of instantaneous spasmodic oscillations caused by external forces, its corresponding weight parameter is calibrated as [0.2, 0.3]. In this embodiment, abrupt changes in air pressure sequence are the primary feature determining ventilation quality, while mask mechanical vibration is an auxiliary criterion; differential weighting and dimensionality reduction effectively filter out false positive spikes in local features caused by passive collisions.

[0082] Based on a preset confidence transformation rule, the Euclidean distance parameter in the feature space is... Perform an exponential inverse conversion derivation. Specifically, the following standardized mapping function is used: The deductive logic is as follows: obtain the Euclidean distance parameter of the feature space generated by the current comparison operation. And subtract the preset cluster discrete reference distance from it. Obtain the distance deviation parameter; then map the distance deviation parameter to a preset spatial smoothing mapping coefficient. Perform a multiplication operation; construct the exponential term parameter by using the multiplication result as the exponential component of the natural base e, and after summing it with the unit constant, perform an inversion operation to finally generate the physical spasm interference confidence score whose value converges to the closed interval [0,1]. Among them, the cluster discrete benchmark distance The minimum boundary radius for determining a valid deviation in the historical feature space is quantized to 0.5 in the current standardized spatial system; the spatial smoothing mapping coefficient... The nonlinear slope characterizing the transformation from distance physical quantities to logical probabilities is preferably within the range of [8, 12]. If the spatial smoothing mapping coefficient... If the setting deviates from the lower limit, the mapping relationship will become flat, and even if the current feature is far away from the startle reference feature cluster, it will still be given a high false confidence, causing real airway collapse to be mistakenly intercepted as physical spasm. If the upper limit function is exceeded, it will degenerate into a rigid step function, losing the probability tolerance for blurred edge features and easily causing false negatives. In response to the increase in vibration noise level output by the environmental noise perception module, the spatial smoothing mapping coefficient is actively increased in the upper limit region of 12 to construct a steep interception boundary and prevent low-quality distance parameters caused by slight turning from being mapped as high-confidence interference.

[0083] The third stage involves implementing an adaptive penalty mechanism for execution status and comprehensive decision-making calculation. Specifically, it addresses the issue of physical spasms interfering with confidence levels. With high-frequency hysteresis coefficient The input is fed into the pre-configured comprehensive weighted adjudication module. Specifically, the following feedback-based dynamic weighted penalty calculation is performed: historical time-series features recorded in the sleep state verification benchmark database are obtained through the pre-configured log probe service. The set of tidal volume peak time intervals for M consecutive respiratory cycles is extracted at a preset sampling frequency. The number of sample cycles M represents the time depth of macroscopic rhythm stability assessment performed in this embodiment. In this embodiment, the preferred value is 15 complete respiratory cycles, and its value range is limited to [10, 30]. If this number is lower than the lower limit, it is easily affected by local mutations caused by a single turning over and holding one's breath, resulting in distortion of variance calculation. If it exceeds the upper limit, the ability to sensitively track changes in sleep state will be lost.

[0084] Furthermore, this embodiment intervenes in the boundary flypoint interception and filtering process before feature parsing to prevent transient missed reports from physical sensor probes during wearing offset, which could lead to false doubling extreme values ​​in the collected peak spacing and thus completely contaminate the variance calculation center. The extracted M original interval samples are sorted and the median benchmark is extracted. Using a preset limit offset tolerance (the area exceeding ±50% of the median) as the legal business boundary, abnormal time interval samples exceeding this boundary are removed, and the mean of adjacent normal samples is used for smooth interpolation reconstruction. A secondary feature cascade parsing action is performed on the purified tidal volume peak time interval set: specifically, summation and arithmetic mean operations are performed on all interval samples within the current statistical window to obtain the dynamic mean parameter representing the current local respiratory rhythm center point; the difference between each discrete interval sample and the dynamic mean parameter is calculated one by one, and the squared terms of all differences are aggregated, summed, and averaged to obtain discrete variance feature values ​​representing the degree of rhythm disorder. Furthermore, the sleep stage stability index is extracted based on the following normalization mapping mechanism. : The specific derivation logic is as follows: perform variance calculation on the extracted time interval set to obtain discrete variance eigenvalues ​​that characterize the degree of rhythm disorder. ; and compare it with the square of the preset global steady breathing baseline duration parameter. Perform a ratio division operation to obtain the relative discrete fluctuation ratio; subtract the relative discrete fluctuation ratio from the unit value 1, and simultaneously activate the value range protection mechanism: if the result of the above subtraction falls below the logical lower limit of 0, a boundary clamping action is forcibly executed to lock its value to 0; if the result does not exceed the limit, the original calculated value is maintained; thus, an objective quantitative parameter that absolutely converges to the closed interval [0,1] and represents the current sleep in a steady state is generated, which in turn represents the sleep stage stability index. Among them, the global steady breathing baseline duration parameter The extreme value of the standard healthy respiratory span captured during the current patient's awake calibration period in this embodiment is characterized, and its preferred initial value in this embodiment is 4.5 seconds. The global steady respiratory baseline duration parameter is used to eliminate individual absolute duration differences caused by different patients' basal metabolic rates, and to achieve normalization of physiological parameters for different patients.

[0085] According to the sleep stage stability index The penalty network is adaptively reconstructed based on the judgment state. Specifically, quantification criteria are established for the first light sleep over-condition and the second deep sleep stability condition. The following calibration parameters are pre-introduced into the global configuration:

[0086] The light sleep interception control threshold is denoted as Its characteristic is the upper limit of macroscopic stability, defined as the threshold for identifying patients entering a stage of highly disordered rhythms and frequent muscle twitches. The deep sleep release control threshold is denoted as... The lower limit of macroscopic stability (green line) is used to identify patients who have entered a highly stable rhythm and are in a high-risk stage of airway collapse.

[0087] The sleep stage stability index will be calculated in real time. Respectively compared with the light sleep interception control threshold and the deep sleep release control threshold The specific network reconstruction branch routing rule configuration for performing dual-line logical comparison and verification is as follows:

[0088] Working condition branch one (activating the first light sleep transition condition): Response to determining that the sleep stage stability index satisfies the relation. The system determines that the respiratory rhythm disturbance level within the current local time frame has crossed the high-sensitivity red line, triggering the first condition for excessive light sleep. At this point, the penalty attenuation control factor is applied. The direct clamping assignment is set to the preset upper limit of 0.85, activating the first mapping curvature network with strong suppression properties.

[0089] Working condition branch two (activating the second deep sleep stabilization condition): Response to the determination that the sleep stage stability index satisfies the relation. The current macroeconomic time series has been determined to have fully entered a deep steady-state state, triggering the second deep steady-state condition. At this point, the penalty decay control factor will be applied. The value is directly downgraded to the preset lower limit extreme value of 0.15, which forcibly activates the second mapping curvature network with weak suppression properties.

[0090] Working condition branch three (activating the dynamic transition smoothing game mechanism): In response to determining that the sleep stage stability index is in the gray transition range between the two, the relation is satisfied. The system determines that the current physiological state is in a dynamic transition window from light sleep to deep sleep. To avoid abrupt and violent oscillations in the control variable between 0.15 and 0.85 that could contaminate subsequent fan output, this embodiment employs a nonlinear curvature smoothing reconstruction model based on linear interpolation in this branch. This transition mechanism allows for a dynamic balance between the fluid detection end and the mechanical interception end. In this embodiment, the shallow sleep interception control threshold... The preferred calibration value is 0.40, which is the deep sleep release control threshold. The preferred calibration value is 0.75.

[0091] Response to Sleep Stage Stability Index If the preset first light sleep transition condition is met (sleep scenario A: hypersensitivity period with frequent muscle twitches), increase the punishment decay control factor. The value approaches the upper limit of 0.85 to construct a first-mapped curvature network with inhibitory properties, thereby ensuring ultimate interception stability against physical spasm interference; in response to the sleep stage stability index To meet the preset conditions for second deep sleep stability (sleep scenario B: period of extreme muscle relaxation and high incidence of airway collapse), the penalty decay control factor is lowered. The value falls back to the lower limit of 0.15 to construct a second mapping curvature network with weak suppression properties, so as to smoothly transfer control to the airway deformation detection end.

[0092] When performing the above-mentioned dynamic mapping curvature network reconstruction, this embodiment introduces a boundary circuit breaker protection mechanism to constrain the penalty decay control factor. In this embodiment, the constraint penalty attenuation control factor range of [0.15, 0.85] directly determines the nonlinear scaling ratio of the control quantity conversion. If this parameter is set off from the lower limit (e.g., incorrectly lowered to below 0.15 during deep sleep), it will cause the mapping curvature to flatten, resulting in the omission of false negatives in this embodiment due to a sluggish response to physical vibrations. This will prevent the effective interception of interference caused by slight movements, leading to the high-pressure airflow output from the fan control terminal of the lower-level respiratory equipment waking the patient up incorrectly. If this parameter exceeds the upper limit (e.g., incorrectly raised to 0.85 during light sleep), it will cause excessive interference penalty, resulting in frequent false positives due to allergic reactions. This will cause the real life-saving baseline pressure compensation to be excessively weakened and the critical micro-pressure opportunity to be missed when facing real airway collapse with slight limb tremors. This embodiment introduces a sleep stage stability index. The transition mechanism. In the scenario of light sleep transition, the constraint penalty decay control factor will be used. Approaching the upper limit of 0.85 to ensure the absolute stability of the core anti-false alarm interception link; in the extremely relaxed business scenario of deep sleep, the constraint penalty decay control factor will be adjusted. It falls back to 0.15, smoothly transferring control to the airway deformation detection end.

[0093] In this embodiment, physical spasm interference confidence is selected by choosing a specific mapped curvature network. The input performs a nonlinear curvature reduction operation. Specifically, the execution... It obtains the confidence parameter of physical spasm interference in the current cycle. And use it as the base; invoke the penalty decay control factor that is currently dynamically activated based on sleep stage. The curvature distortion parameter is obtained by exponentiation of the parameter and then subtracted from it by the unit constant 1. The result is a dynamic weighting factor whose value converges to the interval [0,1]. When physical spasms interfere with confidence. When it approaches 1, regardless of the penalty decay control factor The value of the generated dynamic weakening weight factor All of them will rapidly drop to a minimum value close to 0, thus breaking the multiplication logic in subsequent calculations and directly erasing the compensation command for boosting the wind turbine, achieving a hard anti-interference interception mechanism.

[0094] Further, the high-frequency hysteresis coefficient of the variable With dynamic weakening weight factor The product of these two factors is input into a preset exponential moving average time filter to perform anti-shake and noise reduction processing. The specific formula is as follows:

[0095] Specifically, it calculates the high-frequency hysteresis coefficient in real time for the current time slice (t). With dynamic weakening weight factor Perform a multiplication operation to obtain the current transient suppression reference value; then multiply this transient suppression reference value with a preset time smoothing tracking coefficient. Perform a weighted multiplication operation; synchronously read the historical smoothing suppression parameters output in the previous time slice (t-1). Combine it with (1 minus the time smoothing follower coefficient) The weighted difference between the two sets of weighted reference values ​​is multiplied by a weighted multiplication operation; the two sets of weighted reference values ​​are then fused and summed to generate the smoothing suppression parameter for the current period. ).

[0096] Among them, the time smoothing following coefficient This parameter represents the trust weight for the latest data. In this embodiment, its preferred value is set to 0.35. If this parameter is set close to 0, it will lead to a sluggish response, causing the latest real airway resistance surge event to be over-diluted by historical data, missing the micro-inflation intervention window; if this parameter is close to 1, the effectiveness of the time filtering function will be reduced, and it will easily cause high-frequency pulsation fluctuations in the fan control terminal of the breathing equipment due to transient noise.

[0097] This embodiment calls a preset basic fluid dynamics mapping model, targeting the smoothing suppression parameter. Perform cross-dimensional nonlinear control quantity conversion. Specifically, a logarithmic pressure ramping curve is used, defined as follows:

[0098] This embodiment obtains the input smoothing suppression parameters. This is compared with the preset aerodynamic drag response constant. Perform a multiplication operation; then perform aggregation summation with a constant 1 and calculate the natural logarithm of the sum; compare the extracted logarithm result with the preset pressure compensation gain coefficient. Perform multiplication and conversion to directly output a comprehensive pressure regulation decision value with physical pressure dimensions (denoted as...). The pressure compensation gain coefficient is among them. The dimensional conversion reference parameter characterizing the conversion to the physical output of the equipment (unit: cmH2O) has a preferred calibration value of 2.5; aerodynamic drag response constant. This is a dimensionless amplification factor, set to 10.

[0099] Step S3: In response to the integrated voltage regulation decision value satisfying the preset command triggering rule, the following actions are executed in parallel:

[0100] A baseline air pressure intervention command is generated and sent to the ventilator fan control terminal. This baseline air pressure intervention command is configured to drive the ventilator fan control terminal to perform a pre-pressurization output action or maintain the current air pressure dwell action; and

[0101] A sleep stage status update log is generated, and the comprehensive voltage regulation decision value is written into a preset sleep status verification benchmark database to dynamically update the false trigger interception judgment threshold for subsequent detection cycles.

[0102] Further explanation of step S3: The action of generating a basic wind pressure intervention command and sending it to the ventilation equipment fan control terminal is specifically configured as follows:

[0103] Within the pre-configured pressure regulation command issuance buffer time window, in response to the comprehensive pressure regulation decision judgment value not exceeding the preset false trigger interception judgment threshold, the basic wind pressure intervention command containing the pressure compensation parameter with the preset gain step size is generated to drive the ventilation equipment fan control terminal to execute the pre-pressurization output action.

[0104] In response to the comprehensive pressure regulation decision judgment value being greater than or equal to the false trigger interception judgment threshold, a pressure regulation command silent locking mechanism is triggered, generating the basic air pressure intervention command containing the current reference pressure maintenance parameters, locking the current reference air pressure state to drive the breathing equipment fan control terminal to execute the action of maintaining the current air pressure residence.

[0105] The method also includes the following fault-tolerant backup voltage regulation steps:

[0106] A time-window-based coherence availability analysis is performed on the mechanical vibration data sequence of the mask shell. In response to the extracted characteristic quality assessment parameter representing the continuous availability of the time-series waveform falling below the preset sensor signal safety lower limit, the call instructions for the cross-data channel time-series logic alignment and matching link are actively blocked, and the path for sending the physical spasm interference confidence to the pre-configured comprehensive weighted decision module is forcibly blocked.

[0107] The air pressure output control unit is triggered to switch to the reference air pressure following control logic based on the low-frequency tidal volume characteristics to ensure that the air supply operation executed by the ventilation equipment fan control terminal is not interrupted by logic or loses air pressure control.

[0108] For step S3, the closed-loop feedback instruction generation and global state machine update are performed: specifically, the comprehensive weighted decision module outputs the comprehensive voltage regulation decision value. Immediately afterwards, the system enters the closed-loop feedback command issuance and global state machine control phase. The execution logic of this control phase depends on the following core parameters:

[0109] The threshold for determining accidental triggering of interception is denoted as: This embodiment characterizes the logical confidence boundary that distinguishes between genuine physiological airway collapse and non-pathological, purely physical tics at the macro-decision level.

[0110] The buffer time window for issuing voltage regulation commands is denoted as... This represents the delay period reserved in this embodiment after capturing anomaly precursors to aggregate multi-dimensional feature criteria and prevent high-frequency network jitter. Its initial default state is directly fixed in the global time control configuration library of this embodiment, and its absolute time scale is preferably set to 200 milliseconds. The trigger condition is: in response to the output of the abnormal decision judgment value of the comprehensive weighted adjudication module that breaks through the normal resting state, the buffer timer is immediately triggered and activated. Specifically, when the judgment state remains consistent across multiple frames within the 200-millisecond active state period, the main control flow is driven to transition to the release / execution state, and a pre-pressurization output action is issued; if the judgment state flips within the period, the timing is directly reset and the out-of-bounds instruction is silently intercepted, thereby configuring the logic to achieve efficient debouncing at the instruction level.

[0111] The first branch action involves the parallel issuance of basic wind pressure intervention commands: within the pre-configured buffer time window for issuing pressure regulation commands. Internally, the comprehensive pressure regulation decision judgment value will be determined. Threshold for determining false triggering of interception Perform a logical comparison operation and execute the determined instruction branch based on the comparison result:

[0112] Operating condition A1 (True physiological precursor response): Response to the comprehensive voltage regulation decision value The preset threshold for false triggering interception was not exceeded. The current increase in high-frequency resistance is determined to be a precursor to true respiratory failure. Under this condition, a fixed step size is not used; instead, a comprehensive pressure regulation decision is made based on the overall pressure regulation value. Perform a threshold mapping operation to the hardware driver domain. Invoke the pre-configured step-size quantization resolver, which internally performs the following transformations:

[0113] Obtain the comprehensive voltage regulation decision value Then, subtract it from the hardware response dead zone parameter of the equipment fan to obtain the net effective decision value;

[0114] Hardware response dead time parameter, denoted as It represents the minimum logical pressure fluctuation boundary required for the impeller of the actuator motor to overcome normal static friction and the initial background back pressure of the pipeline network, thereby generating a non-zero actual airflow boost output. In this embodiment, its preferred calibration range is [0.05, 0.15] cmH2O, and the solidified configuration value is 0.1 cmH2O. The net effective decision value is obtained... In the arithmetic flow, the following deadlock-prevention and amplitude-limiting mapping model is used: Perform a logical comparison and verification operation between the initial decision net value and the absolute zero value: In response to the determination that the initial decision net value has not broken through the absolute zero value, it is determined that the currently issued micro-boost request is within the physical dead zone where the motor hardware cannot respond. At this time, a boundary clamp overwrite operation is performed to update the net effective decision value. Forced assignment to zero; in response to the determination that the absolute zero value has been breached, the process is allowed to proceed without loss and the preliminary decision net value is directly assigned to the net effective decision value. .

[0115] Perform the control quantity dimensionality reduction projection action: through Perform cross-physical domain transformation; specifically, extract the net effective decision value. Compared with the preset wind turbine analog-to-digital conversion gain scaling factor Perform a multiplication operation to obtain the compensation offset, and then multiply it by the steady-state duty cycle indicator that maintains the current reference pressure. The aggregation and summation operation is performed to generate pulse width modulation (PWM) duty cycle command parameters that can be directly parsed by the hardware control board. Among them, the wind turbine analog-to-digital conversion gain scaling factor This characterizes the conversion between physical air pressure units (cmH2O) and hardware speed control drive signal strength. In this embodiment, based on the aerodynamic parameters of the fan impeller, the preferred value is 4.5% / cmH2O, and the actual matching range is set to [2,8] / cmH2O; steady-state duty cycle indicator. The initial command value is to maintain the patient's current baseline positive expiratory pressure.

[0116] This embodiment further performs a logical comparison and verification operation between the basic dynamic gain step size and the preset single intervention physical extreme value (hardly clamped to 1.5 cmH2O): if the calculated value exceeds the extreme value, a threshold truncation is applied and the final command is locked to the physical extreme value; if it does not exceed the extreme value, it is directly released. Finally, a basic wind pressure intervention command containing the above-mentioned controlled "preset gain step size" pressure compensation parameters is generated and issued.

[0117] Operating condition B1 (physical spasm interference interception): Response to the integrated voltage regulation decision value Exceeding the threshold for false triggering of interception The system determines that the current data fluctuation originates from physical disturbances. At this point, the "pressure regulation command silent interlock mechanism" is triggered, generating a baseline air pressure intervention command containing the "current baseline pressure maintenance parameters." This locks the current baseline air pressure state, driving the respiratory equipment's fan control to execute the "maintain current air pressure dwell action." Specifically, in response to the triggering of the pressure regulation command silent interlock mechanism, the net effective decision value in the projected model from the front-end hardware driver is... The link is forcibly cleared and suspended. At this time, a pulse width modulation command is sent to the electromechanical control board. It will be identified solely by the steady-state duty cycle. Independent support and control. This embodiment uses the average low-frequency tidal volume of the previous safe respiratory cycle as the reference source for the steady-state duty cycle, ensuring that the fan impeller maintains a constant rotational speed inertia that meets the patient's basal metabolic needs while shielding against transient spasm interference. This physically avoids motor stoppage or sudden airflow caused by closed-loop disconnection.

[0118] This embodiment has a threshold for determining false triggering of interception. The initial benchmark calibration is established through a pre-defined offline business boundary detection mechanism. An offline historical data playback environment is pre-built, continuously injecting a set of extreme physical twitching noise features, including frequent turning over and violent sleep startles. During playback, the extreme fluctuation points of the decision value output by the comprehensive weighted adjudication module are continuously monitored, and the critical feature vectors that trigger system-level false alarm blocking are recorded. Using the convergence rule of "maximizing the extreme physical noise interception rate while ensuring 99% timely release of genuine pathological collapse micro-precursors" as the convergence rule, the system continuously approaches the extreme value lowering boundary. Finally, this logical confidence boundary point is extracted and solidified as the false triggering interception judgment threshold deployed online. .

[0119] When the patient is in the first stage of light sleep, lower the threshold for detecting false triggers. The value and extend the buffer time window for issuing voltage regulation commands. To ensure the stability of the anti-interference link's interception mechanism; once the patient enters a stable second deep sleep state, this embodiment smoothly transitions control to the highly sensitive detection end and raises the threshold for determining false trigger interception. This aims to enhance the ability to deeply mine subtle airway deformations and intervene without delay.

[0120] The second branch action, sleep stage status update and benchmark reconstruction: In parallel with the above instruction issuance, the log processing module is invoked to generate a "sleep stage status update log". The comprehensive voltage regulation decision value of the current timestamp is extracted. It is then persistently written to a pre-defined sleep state verification benchmark database. A dynamic self-learning update logic based on historical sequences is executed, specifically introducing a macroscopic penalty convergence ratio, denoted as... This is characterized by the quantitative ratio of the number of commands triggering the "silent locking mechanism" to the total number of comprehensive voltage regulation commands issued within a preset retrospective time window (past 30 minutes). Based on the preset retrospective time window, historical comprehensive voltage regulation decision judgment values ​​in the sleep state verification benchmark database are statistically analyzed. Distribution, calculation and extraction of the current macroscopic penalty convergence ratio. Based on a preset threshold-based step-compensation logic, the threshold for preventing false triggering in the next detection cycle is determined. Perform dynamic correction: Specifically set the baseline interception threshold. In response to determining the macroscopic penalty convergence ratio. If the frequency of startle reflexes exceeds the preset upper limit (greater than 40%), the patient is determined to be in a period of high-frequency physical agitation and negative step compensation is triggered, setting the threshold for intercepting false triggers for the next cycle. Corrected downwards ;in The preset fine-tuning safety step size proactively lowers the anti-counterfeiting interception trigger threshold, ensuring robustness against false positives under extreme volatility; responding to the determination of the macroscopic penalty convergence ratio. If the sleep rate falls below the preset lower limit of resting stability (less than 5%), the patient is determined to have entered a high-risk period of deep sleep collapse. At this point, positive step-compensation is triggered, setting the threshold for intercepting false triggers in the next cycle. Corrected upwards This allows the anti-counterfeiting threshold to be proactively tightened while the device is in a deep, resting state.

[0121] Baseline interception threshold This embodiment characterizes the initial confidence threshold for preventing false triggering under standard waking calibration or static steady-state breathing scenarios. In this embodiment, its preferred initial calibration value is fixed at 0.80.

[0122] Fine-tuning the safety step size This characterizes the single-step finite element compensation increment implemented in this embodiment for frequent agitation or extreme deep sleep states during a single detection cycle. In this embodiment, the value is fixed at 0.05.

[0123] Further execute the fault-tolerant backup voltage regulation control steps:

[0124] Characteristic quality assessment parameters, denoted as It represents the quantization score of the currently captured data sequence in terms of continuous availability waveform ratio or signal-to-noise ratio. The lower safety limit of the sensor signal is denoted as... This parameter represents the minimum data integrity threshold required to maintain multimodal high-sensitivity joint computing in this embodiment. It is read from a preset security control registry during initialization and, in this embodiment, is quantified as a waveform ratio greater than 85% within a continuous time window. The background watchdog service component continuously and frequently reads the characteristic quality assessment parameters of the current channel; when the characteristic quality assessment parameters are determined to be within the lower limit of the sensor signal safety... When the comparison result is "effective / safe" (current quality is better than or equal to 85%), the main business flow is allowed to continue to flow in the multimodal joint computing link; when the comparison result is determined to fall below the red line and enter the "failure / blockage" state, the highest priority hardware decoupling interrupt is triggered unconditionally, driving the global control flow to transition to the single-channel backup baseline mode.

[0125] This embodiment invokes a preset coherence detector to continuously perform usability analysis based on a sliding time window on the mechanical vibration data sequence of the mask shell. Specifically, the following evaluation actions are performed:

[0126] Within the currently set detection window (the past 5 seconds), count the total number of sampling points containing valid non-zero waveform digital identifiers, divide this total number by the theoretical total number of sampling points within the window, and perform a normalization mapping operation to generate characteristic quality assessment parameters representing the continuous availability of the current link timing waveform. The real-time generated feature quality assessment parameters Compared with the preset sensor signal safety lower limit value Perform logic comparison and verification. Respond to the determination of feature quality assessment parameters. If the threshold value is breached (the waveform ratio is consistently below 85%, indicating a timing disruption due to sensor detachment, poor contact, or strong external electromagnetic interference), this embodiment immediately triggers a degraded routing control command. Driven by this command, the cross-data channel comparison command output results within the pre-configured integrated weighted decision module are actively isolated and shielded. Simultaneously, the gas pressure output control unit is triggered, switching the current main control flow to "reference gas pressure following control logic based on low-frequency tidal volume characteristics." This ensures that the gas supply service flow executed by the terminal does not experience logic deadlock or gas pressure runaway.

[0127] The core output parameter in this embodiment is the comprehensive voltage regulation decision value. After undergoing nonlinear conversion of the logarithmic pressure easing curve and hardware-level clamping verification of the physical extreme value of a single intervention, the effective value range of this judgment value is converged and locked within the interval [0, 1.5] (unit: cmH2O).

[0128] When the comprehensive pressure regulation decision judgment value When approaching the minimum value of 0: This indicates that the comprehensive weighted decision module has determined with high probability that the high-frequency pressure fluctuations currently captured in the fluid pipeline network are essentially caused by non-pathological, purely physical twitching disturbances (such as mechanical force transmission caused by startle reflexes during sleep) or are in a stable, normal, and safe resting state. Under this trend, a hard anti-spoofing interception mechanism is triggered, and the command output is reduced to maintain the baseline dwell state.

[0129] When the comprehensive pressure regulation decision judgment value Approaching the maximum value of 1.5: The high-frequency flutter characteristics of the acquired airflow are not accompanied by significant mechanical vibration of the outer shell, and its frequency domain variation pattern precisely matches the temporal evolution law of early physiological airway collapse. Under this extreme trend, the lower-level hardware communication link is driven to instantly activate the pre-pressurization output action, providing zero-delay forward aerodynamic kinetic energy support with a limited single intervention physical extreme value (1.5 cmH2O). This prevents the biological airway from evolving into irreversible complete physical closure, achieving hard interception of malignant sleep apnea events.

[0130] Physical spasm interferes with confidence level The increase in the value will trigger the comprehensive pressure regulation decision-making value. It exhibits a nonlinear, cliff-like attenuation. Pressure surges detected within the mask's airway in a physical fluid dynamics environment have a definite objective probability of being passively generated echoes caused by external bodily pressure compressing the airway wall, rather than being primary airway collapse or blockage. This confidence level is generated by performing an exponential inverse proportional reduction on the Euclidean distance parameter across the cross-channel characteristic space; a higher value indicates a greater physical probability that the fluid surge is caused by external rolling or startling. In this embodiment, the parameter and the integrated decision output link are designed as a negative correlation mapping based on a penalized inverse function. A dynamically weakened weighting factor is constructed by introducing a power operation between the base and the exponent. This ensures that when external mechanical and physical interference increases sharply, an absolute "multiplication logic circuit breaker" can be created to erase the boost compensation value of subsequent computing nodes.

[0131] High-frequency hysteresis coefficient The increase in the value will drive the overall voltage regulation decision judgment value. It exhibits a positive upward climb with a logarithmically gradual slope. High-frequency hysteresis coefficient. This study quantifies the variation in equivalent acoustic drag during the initial stage of microscopic pipe network collapse. Based on Bernoulli's principle in fluid mechanics and the characteristics of the pipe network, a slight reduction in the physical cross-section of the pipe diameter will trigger an exponential explosive jump in the drag parameter at the end of the collapse. This parameter is designed to be positively correlated with the final output command, ensuring that micro-pressure compensation is triggered as soon as the initial signs of pipe network drag appear. Simultaneously, a cross-dimensional conversion is performed using a preset basic fluid mechanics mapping model, rather than employing a dangerous linear amplification function. This allows this embodiment to automatically converge the control output slope when dealing with the extreme drag burst zone.

[0132] Sleep stage stability index Characterizing the degree of rhythm disorder, its state transitions directly determine the penalty decay control factor. The index represents a wandering calibration point within the closed interval [0.15, 0.85]. In objective clinical biological evolution, patients experience high-frequency muscle twitching during the light sleep transition phase, while facing an extremely high probability of actual airway physical collapse during the deep relaxation phase. This index quantifies this macroscopic temporal stationarity by extracting the discrete variance characteristics of the tidal volume peak time interval. In response to the index characterizing the light sleep state, a penalty decay control factor is applied. Approaching the upper limit of 0.85, a first-mapped curvature network with strong suppression properties is constructed to achieve extreme interception and filtering of frequent physical twitching noise; in response to the exponential representation of the deep sleep state, the penalty decay control factor is applied. The curve falls back to near the lower limit of 0.15 in order to construct a second mapping curvature network with weak suppression properties, smoothly transferring control to the fluid high-sensitivity detection end.

[0133] In another embodiment, this embodiment is configured in a digital twin verification and monitoring environment for a sleep apnea intervention scenario, aiming to demonstrate the flow process and dynamic interception efficiency of the system under multimodal data input conditions through mathematical deduction. The pre-configured constant boundary settings in this digital twin application scenario are as follows: aerodynamic drag response constant. Configured to a constant dimensionless value of 10; pressure compensation gain coefficient Curing time is 2.5; time smoothing follow-through factor. In the single-step real-time calculation logic, a value of 1 is forcibly assigned; this is to exclude the historical moving average window buffer and demonstrate the ability to determine single-step transient extremes, in order to conduct bare-bones extreme pressure tests during the deduction. The hard clamping threshold for single-intervention physical extreme values ​​is set to 1.5 cmH2O. The system receives external multi-source interactive timing waveforms through simulation, extracts features, and performs the following rigorously quantized closed-loop logic deduction by the comprehensive weighted decision module.

[0134] Table 1: Examples of voltage regulation response calculation verification under different physiological evolution and external physical action conditions

[0135] Application scenarios High-frequency hysteresis coefficient Physical spasm interferes with confidence level Penalty decay control factor Dynamically weakening weight factors Aerodynamic compensation interception rate Comprehensive pressure regulation decision judgment value Scenario 1: Severe startle reflex during light sleep 0.5 0.95 0.85 (strongly suppressed state) 0.043 95.70% 0.49 (significantly weakened state) Scenario 2: Severe airway obstruction during deep sleep 0.5 0.02 0.15 (weakly inhibited state) 0.47 53.00% 1.50 (Touching extreme value clamping) Scenario 3: During light sleep, a routine turning over causes a collision with a face mask. 0.2 0.8 0.85 (strongly suppressed state) 0.173 82.70% 0.74 (smooth suppression state) Scenario 4: Slight airway deformation in the early stage of deep sleep 0.1 0.01 0.15 (weakly inhibited state) 0.5 50.00% 1.01 (Compensation for Gao Min's Release) Scenario 5: Mild obstruction accompanied by tics during the reconciliation period 0.3 0.4 0.50 (transitional equilibrium state) 0.368 63.20% 1.86→1.50 (Clamping Lock) Scenario 6: Standard safe resting tidal volume band 0.02 0.01 0.50 (transitional equilibrium state) 0.9 10.00% 0.41 (Baseline Safety Quiet)

[0136] Aerodynamically compensated interception rate, denoted as By extracting the dynamically weakened weighting factor generated in the current operation. The net interception coefficient reference value is obtained by subtracting it from the unit constant 1; the net interception coefficient reference value is then multiplied by the constant 100% to generate this quantitative parameter with percentage properties. This parameter quantitatively characterizes the ratio of active elimination and reduction intensity implemented by the current computing node after capturing high-frequency variations in the fluid pipeline network, based on cross-modal analysis of the external mechanical impact force state, and in response to the basic pure fluid dynamics pressurization intervention command. The closer this value is to 100%, the more certain it is that the current fluctuation originates from purely physical external noise, and the more resolute the soft blocking interception force is.

[0137] Based on the measurement matrix output in Table 1 above, this embodiment obtains direct quantitative evidence to support the technical effectiveness of the multimodal noise feature isolation and hard interception mechanism. The data stream of scenario one (severe limb startle reflexes during light sleep) is compared with the traditional single-dimensional benchmark technology using extreme theoretical methods:

[0138] When faced with the complex working conditions of Scenario 1, the violent twitching of the simulated test object's limbs causes enormous mechanical forces that are transmitted upwards, inevitably leading to passive, derivative high-frequency oscillations within the fluid pipeline network. In this case, this embodiment accurately extracts a high-frequency hysteresis coefficient of 0.5. If the existing benchmark technology that does not yet incorporate the cross-modal comparison architecture of this embodiment is adopted (stripping the mechanical force acquisition channel of the mask shell, blindly assuming the confidence level of physical spasm interference is 0, resulting in the dynamic weakening weight factor being a constant value of unit constant 1), the following logical conversion will be performed: multiply the high-frequency hysteresis coefficient 0.50 with the assumed weight 1 to obtain the benchmark smoothing parameter 0.50; multiply it with the aerodynamic drag response constant 10 and perform a fusion summation with the unit constant 1 to generate the proper basis 6 of the logarithmic function; finally, after calling the natural logarithm mapping (calculated value approximately 1.792), multiply it with the pressure compensation gain coefficient 2.5 for conversion. Under this flawed architecture, an incorrect judgment value of 4.48 cmH2O will be output, forcing the fan to instantly output a full-load high-pressure airflow of 1.50 cmH2O into the mask, causing the object in the nerve sensitive period to be subjected to a violent physical airflow impact.

[0139] Compared to the aforementioned baseline defects, this invention blocks this malicious transmission link. As shown in Table 1, the measured simulation yielded a physical spasm interference confidence level of 0.95 through parallel vibration capture pathways. This triggers a strong interception network flow: using the penalty decay control factor of 0.85 activated under the current light sleep condition as the exponent, and the physical spasm interference confidence level of 0.95 as the base, a nonlinear power operation is performed to obtain the curvature parameter 0.957; this parameter is then separated from the unit value of 1 and subtracted to generate a dynamic weakening weight factor of only 0.043. This results in the subsequent logarithmic reduction network outputting only a moderate overall voltage regulation decision value of 0.49 cmH2O. .

[0140] for Figure 1Explanation: PS1 represents the internal pressure sensing link of the mask, which is the physical hardware access layer. Utilizing a pre-configured pressure sensing link, it continuously acquires raw composite pressure time-series data, providing a native underlying data stream for fluid micro-deformation analysis. VS1 represents the outer shell vibration sensing link of the mask, which is also a physical hardware access layer. It independently acquires discontinuous, localized sudden vibration intensity data through a preset vibration sensor acquisition link to capture the physical spatial force characteristics caused by startles during sleep or turning over. FSD1 represents the frequency domain separation algorithm module, which filters out low-frequency tidal volume bands based on a preset frequency domain separation benchmark threshold, purifying the high-frequency micro-fluctuation sequence characterizing the micro-impedance of the pipeline network. CTA1 represents the cross-data channel time-series logic alignment module, which is the core logic alignment gateway. Based on an asynchronous time sliding window, it performs a backward delayed backtracking scan with the vibration peak trigger timestamp as the anchor point, eliminating the physical conduction time difference of heterogeneous media and generating a confidence parameter characterizing the objective probability of spasm. DWP1 represents the comprehensive weighted decision module, which is the core calculation unit for nonlinear control quantity conversion and performs dynamic weighted penalty calculation. This node uses the confidence level of physical spasm interference and the penalty inverse function to generate a dynamic weakening weight factor, which is multiplied by the high-frequency hysteresis coefficient and then input into the fluid dynamics mapping model to generate a comprehensive pressure regulation decision value. FCD1 represents the ventilation equipment fan control terminal, which is the physical electromechanical execution hardware base. Responding to the instruction rules triggered by the comprehensive pressure regulation decision value, it executes the pre-pressurization action to implement active ventilation protection, or executes the current reference pressure maintenance action to silently avoid air supply resistance. SBR1 represents the sleep state verification benchmark database, which is the global feedback business state registration center. It continuously absorbs and writes the comprehensive pressure regulation decision value and generates a sleep stage status update log, providing a persistent benchmark basis for the false trigger interception judgment threshold of subsequent cycles in this method's adaptive dynamic evolution.

[0141] Figure 1The core execution steps of the data flow closed-loop mapping are as follows: Broadband air pressure change data sequences within the mask of the breathing device and synchronously acquired mechanical vibration data sequences of the mask shell are acquired in parallel through dual-channel sensing facilities (PS1 and VS1) located at the top layer. The data is then injected into the central computing cluster. On one hand, high-frequency hysteresis coefficients are extracted via frequency domain separation logic (FSD1). On the other hand, the core alignment gateway (CTA1) performs cross-data channel temporal logic alignment and matching between the air pressure temporal change rate characteristics and the vibration sequence, extracting the confidence level of physical spasm interference. Based on this, the above two key features are integrated into the comprehensive weighted decision module (DWP1) to perform dynamic weighted penalty calculation, and output a comprehensive pressure regulation decision value with absolute physical isolation and anti-interference properties after crossing the fluid dynamics mapping model. Based on whether the judgment value breaks through the preset interception rule, a dual-line parallel response is triggered: the downstream hardware drives the physical breathing device fan control terminal (FCD1) to execute the pre-pressurization or silent dwell maintenance command; at the same time, the side feedback link updates the global life cycle log of the sleep state verification benchmark database (SBR1) to complete the dynamic evolution of the intelligent adaptive threshold.

[0142] Figure 2 Numerical verification aims to test the theoretical response characteristics of this embodiment under preset standardized boundary conditions. In the figure, the horizontal axis represents the standardized test case set, and the vertical axis represents the comprehensive voltage regulation decision value calculated based on the underlying control mapping formula. Different bar filling patterns represent the calculated numerical results of the theoretical baseline no-filter command and the dynamic penalty command of this method, respectively. Figure 2 Numerical calculation results clearly reveal that this method has the advantages of definite spatial isolation and intelligent diversion control when dealing with heterogeneous physical noise and physiological deformation under different physiological stages.

[0143] Under the conditions representing the extreme values ​​of mechanical force, namely "severe startle reflex during light sleep (Scenario 1)" and "routine turning over and colliding with the mask during light sleep (Scenario 3)," the calculated dynamic penalty command is subject to a step-by-step hard suppression due to the accompanying violent external vibration excitation, converging to the low safety levels of 0.49 and 0.74, respectively. This represents a quantitative reduction difference from the theoretically direct triggering of the baseline unfiltered command for air supply countermeasures, verifying the interception rate of physical jerking noise in this embodiment.

[0144] In stark contrast, in the scenarios of "severe airway obstruction during deep sleep (Scenario 2)" and "mild obstruction with twitching during the complex phase (Scenario 5)" during the muscle relaxation phase, the two sets of calculated data are paralleled or locked, touching and maintaining the limit release clamp position of 1.50, forcibly providing sufficient pneumatic support to avoid the risk of suffocation.

[0145] In the case of “weak airway deformation in the early stage of deep sleep (Scenario 4)”, the instruction of this invention exhibits a high-sensitivity release characteristic of 1.01, ensuring that weak pathological precursors are not missed; while in the case of “normal safe resting tidal volume band (Scenario 6)”, the output definitively falls back and remains silent at a safe baseline of 0.41.

[0146] Figure 4 This characterizes the objective physical time series of historical benchmark multimodal datasets acquired through a clinical polysomnography (PSG) data synchronization gateway before performing offline multidimensional spatial topology calculations. The horizontal axis of the graph represents continuous physical time slices, and the vertical axis represents the normalized amplitude parameters of each heterogeneous sensor channel.

[0147] For pathological precursors (positive sample flow characteristics): In the broadband pneumatic time series of the first channel, after a stable low-frequency tidal volume fluctuation phase, a high-frequency micro-fluidic sequence appears (the peak amplitude decays sharply and is accompanied by high-frequency flutter). Comparing this to the second channel (mechanical vibration sequence) and the fourth channel (electromyography), the waveform maintains an absolute resting background without any sudden oscillatory pulses. This high-frequency blockade variation on the purely fluid side is labeled as a "true early airway collapse event" by the medical gold standard system.

[0148] Physical startle noise (negative sample flow characteristic): A maximum force peak that breaks through the transient peak judgment threshold appears in the second channel (mechanical vibration sequence) at the time anchor point, and is simultaneously accompanied by muscle contraction discharge in the fourth channel (electromyography). At this time, due to the derivative effect of the internal conduction of this physical mechanical kinetic energy, the first channel (broadband air pressure) also produces passive hysteresis oscillation. This kind of "false positive physical interference event" caused by body turning over or sleep jerking is the core target object of the hard anti-counterfeiting interception required by the comprehensive weighted judgment module of this embodiment.

[0149] The computational logic involved in this application can be constructed using algorithms such as regression analysis in machine learning, establishing a mathematical model by analyzing the inherent trends and interrelationships of the collected parameters. This process can be implemented using specialized computational tools (such as Python's Scikit-learn library or the R language environment). Throughout all calculations, to eliminate the influence of different physical dimensions and ensure that data is compared and analyzed on the same scale, the input parameters in each formula are dimensionless. The dimensionless techniques used include, but are not limited to, max-min normalization or Z-score standardization.

[0150] The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0151] It should be emphasized that the foregoing embodiments are merely illustrative of preferred implementations of the present invention and are not intended to limit the scope of protection of the present invention. This application also provides a computer-readable storage medium having computer program instructions stored thereon.

Claims

1. An adaptive pressure regulation control method for respiratory equipment in sleep intervention scenarios, characterized in that, The specific steps include: Step S1: Acquire the broadband air pressure change data sequence inside the mask of the breathing device and the synchronously acquired mechanical vibration data sequence of the mask shell; Step S2: Based on the preset micro-perturbation feature analysis rules, the broadband air pressure change data sequence inside the mask is processed to extract the high-frequency hysteresis coefficient; at the same time, gradient operation is performed on the broadband air pressure change data sequence inside the mask to extract the air pressure time series change rate feature, and the air pressure time series change rate feature is aligned and matched with the mechanical vibration data sequence of the mask shell across data channels to extract the confidence level of physical spasm interference; The high-frequency blocking coefficient and the confidence level of physical spasm interference are input into the pre-configured comprehensive weighted decision module to perform dynamic weighted penalty calculation and output the comprehensive voltage regulation decision value; Step S3: In response to the integrated voltage regulation decision value satisfying the preset command triggering rule, the following actions are executed in parallel: A baseline air pressure intervention command is generated and sent to the ventilator fan control terminal. This baseline air pressure intervention command is configured to drive the ventilator fan control terminal to perform a pre-pressurization output action or maintain the current air pressure dwell action; and A sleep stage status update log is generated, and the comprehensive voltage regulation decision value is written into a preset sleep status verification benchmark database to dynamically update the false trigger interception judgment threshold for subsequent detection cycles.

2. The adaptive pressure regulation control method for respiratory devices in sleep intervention scenarios according to claim 1, characterized in that: The process of acquiring broadband pressure change data sequences inside the mask of a breathing device includes: Raw composite pressure time-series data is acquired using a pre-configured pressure sensor access link; The original composite pressure time series data is input into the pre-configured frequency domain separation algorithm module to perform feature stripping operation. Specifically, based on the preset frequency domain separation benchmark threshold, the low-frequency pressure fluctuation features that characterize the normal respiratory cycle are filtered out, and the high-frequency micro-fluctuation sequence that exceeds the frequency domain separation benchmark threshold and characterizes the airflow impacting the airway wall reflection echo is extracted as the broadband pressure change data sequence inside the mask.

3. The adaptive pressure control method for respiratory devices in sleep intervention scenarios according to claim 2, characterized in that: The mechanical vibration data sequence of the mask shell is obtained through the following logic: The system acquires discontinuous, localized sudden vibration intensity data through a pre-set vibration sensor acquisition link. Based on a preset transient peak determination threshold, background feature screening is performed on the discontinuous local sudden vibration intensity data, and signal amplitude accumulation and summation processing exceeding the transient peak determination threshold is performed within a preset transient vibration feature extraction time period to extract a set of vibration peak morphology features characterizing short-term body twitching intensity, and the set of vibration peak morphology features is encapsulated into the mechanical vibration data sequence of the mask shell.

4. The adaptive pressure control method for respiratory devices in sleep intervention scenarios according to claim 3, characterized in that: The process of performing cross-data-channel temporal logical alignment and matching on the time-series change rate characteristics of air pressure and the mechanical vibration data sequence of the mask shell, and extracting the confidence level of physical spasm interference, includes: Based on a preset asynchronous time sliding window, a backward delayed backtracking scan is performed with the peak trigger timestamp of the mechanical vibration data sequence of the mask shell as the anchor point. The time sequence logic alignment based on the timestamp feature is performed on the air pressure time sequence change rate feature and the mechanical vibration data sequence of the mask shell. The first derivative peak feature representing the air pressure time sequence change rate feature and the instantaneous spasmodic oscillation intensity feature representing the mechanical vibration data sequence of the mask shell are extracted respectively. The peak feature of the first derivative and the instantaneous spasm oscillation intensity feature are input into a preset logical matching network to perform similarity calculation, so as to generate and output the confidence level of the physical spasm interference.

5. The adaptive pressure regulation control method for respiratory devices in sleep intervention scenarios according to claim 4, characterized in that: The process of performing dynamic weighted penalty calculation and outputting a comprehensive voltage regulation decision value includes: The physical spasm interference confidence level is mapped to a preset penalty inverse function to generate a dynamic weakening weight factor characterizing the anti-interference strength. The high-frequency hysteresis coefficient is multiplied by the dynamic attenuation weighting factor to obtain the smoothing suppression parameter; The smoothing suppression parameters are subjected to nonlinear mapping and control quantity conversion operations with the preset basic fluid dynamics mapping model to generate the comprehensive pressure regulation decision value.

6. The adaptive pressure regulation control method for respiratory devices in sleep intervention scenarios according to claim 5, characterized in that: The action of generating a basic air pressure intervention command and sending it to the ventilator fan control terminal is specifically configured as follows: Within the pre-configured pressure regulation command issuance buffer time window, in response to the comprehensive pressure regulation decision judgment value not exceeding the preset false trigger interception judgment threshold, the basic wind pressure intervention command containing the pressure compensation parameter with the preset gain step size is generated to drive the ventilation equipment fan control terminal to execute the pre-pressurization output action. In response to the comprehensive pressure regulation decision judgment value being greater than or equal to the false trigger interception judgment threshold, a pressure regulation command silent locking mechanism is triggered, generating the basic air pressure intervention command containing the current reference pressure maintenance parameters, locking the current reference air pressure state to drive the breathing equipment fan control terminal to execute the action of maintaining the current air pressure residence.

7. The adaptive pressure regulation control method for respiratory devices in sleep intervention scenarios according to claim 6, characterized in that: The method also includes the following fault-tolerant backup voltage regulation steps: A time-window-based coherence availability analysis is performed on the mechanical vibration data sequence of the mask shell. In response to the extracted characteristic quality assessment parameter representing the continuous availability of the time-series waveform falling below the preset sensor signal safety lower limit, the call instructions for the cross-data channel time-series logic alignment and matching link are actively blocked, and the path for sending the physical spasm interference confidence to the pre-configured comprehensive weighted decision module is forcibly blocked. The air pressure output control unit is triggered to switch to the reference air pressure following control logic based on the low-frequency tidal volume characteristics to ensure that the air supply operation executed by the ventilation equipment fan control terminal is not interrupted by logic or loses air pressure control.

8. The adaptive pressure regulation control method for respiratory devices in sleep intervention scenarios according to claim 7, characterized in that: The step of inputting the peak feature of the first derivative and the instantaneous spasmodic oscillation intensity feature into a preset logical matching network to perform similarity calculation includes: Numerical normalization processing based on preset value range boundaries is performed on the first derivative peak value feature and the instantaneous spasmodic oscillation intensity feature, respectively; The normalized first derivative peak feature and the instantaneous spasmodic oscillation intensity feature are combined by a dimension concatenation operation to construct a cross-modal feature state vector representing the current abrupt change in airflow and mechanical mixing. The baseline spasm feature distribution matrix is ​​obtained from the preset storage node of the logical matching network, and the preset spatial distance matching algorithm is called to calculate the feature space Euclidean distance parameter between the cross-modal feature state vector and the baseline spasm feature distribution matrix. Based on a preset confidence transformation rule, an inverse proportional mapping process is performed on the Euclidean distance parameter of the feature space to generate and output the confidence level of the physical spasm interference.

9. The adaptive pressure regulation control method for respiratory devices in a sleep intervention scenario according to claim 8, characterized in that: The process of mapping the physical spasm interference confidence level to a preset penalty inverse function to generate a dynamic weakening weight factor characterizing the anti-interference strength specifically includes: Obtain historical time-series features recorded in the sleep state verification benchmark database, and extract the sleep stage stability index that characterizes the fluctuation pattern of the respiratory rhythm in the current stage; In response to the sleep stage stability index meeting the preset first light sleep overdose condition, the quantification value of the penalty decay control factor inside the penalty inverse function is increased to construct a first mapping curvature network with strong inhibition properties. In response to the sleep stage stability index meeting the preset second deep sleep stability condition, the quantification value of the penalty decay control factor is reduced to construct a second mapping curvature network with weak inhibition properties. The confidence level of the physical spasm interference is input into the currently active first or second mapped curvature network to perform dynamic control quantity reduction in order to generate the dynamic weakening weight factor.

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