CN-121994672-A - Mask respiratory resistance monitoring method based on intelligent sensor

CN121994672ACN 121994672 ACN121994672 ACN 121994672ACN-121994672-A

Abstract

The invention discloses a mask respiratory resistance monitoring method based on an intelligent sensor, which relates to the technical field of fluid resistance monitoring and comprises the steps of collecting main pressure measuring cavity pressure, environment reference pressure, damping reference cavity pressure and auxiliary airflow signals, generating a synchronous multichannel pressure flow signal frame sequence through time stamp alignment and sensor static bias correction, constructing a differential pressure decoupling model based on the synchronous multichannel pressure flow signal frame sequence by adopting a total differential pressure layer, an internal reference differential pressure layer and a reference differential pressure layer, outputting a smooth differential pressure sequence and an auxiliary flow sequence through combined disturbance inhibition filtering treatment, extracting a steady-state pressure section from the smooth differential pressure sequence based on the auxiliary flow sequence, and performing leakage compensation by calculating a seal offset index to generate an effective differential pressure sequence. According to the invention, through differential pressure layered decoupling and nonlinear flow resistance inversion, stability, accuracy and interpretability of pressure measurement and resistance monitoring are improved.

Inventors

Ye Shangyou
HUANG YIBIN

Assignees

江苏汉盾安防科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260126

Claims (10)

1. A mask respiratory resistance monitoring method based on an intelligent sensor is characterized by comprising the following steps of, Collecting main pressure measuring cavity pressure, environment reference pressure, damping reference cavity pressure and auxiliary airflow signals, and generating a synchronous multichannel pressure flow signal frame sequence through time stamp alignment and sensor static bias correction; Based on the synchronous multichannel pressure flow signal frame sequence, a differential pressure decoupling model is built by adopting a total differential pressure layer, an internal reference differential pressure layer and a reference differential pressure layer, and a smooth differential pressure sequence and an auxiliary flow sequence are output through combined disturbance suppression filtering treatment; based on the auxiliary flow sequence, a steady-state pressure section is extracted from the smooth differential pressure sequence, leakage compensation is carried out by calculating a seal offset index, and an effective differential pressure sequence is generated; Pairing the effective differential pressure sequence with the auxiliary flow sequence, inverting the equivalent flow resistance through a nonlinear differential pressure flow mapping method, obtaining flow resistance parameters, and generating a pressure measurement value through dynamically correcting a cross-domain nonlinear error; and performing error source decoupling on the pressure measured value based on the seal deviation index and the flow resistance parameter to generate a multi-dimensional pressure monitoring report.
2. The method for monitoring respiratory resistance of mask based on intelligent sensor according to claim 1, wherein the main pressure measuring chamber pressure, the environment reference pressure, the damping reference chamber pressure and the auxiliary air flow signal are collected, and the method is corrected by aligning a time stamp and static offset of the sensor, specifically comprising the following steps of, Collecting main pressure measuring cavity pressure, environment reference pressure, damping reference cavity pressure and auxiliary airflow signals, and aligning through a time stamp to generate a multichannel alignment sampling sequence; And analyzing the relative change relation of the multichannel aligned sampling sequences under the same time stamp, and constructing an offset reference segment.
3. The method for monitoring respiratory resistance of a mask based on an intelligent sensor as set forth in claim 1, wherein said generating a synchronized multichannel pressure flow signal frame sequence is calculating a static bias amount based on a bias reference segment, and performing bias correction on a multichannel aligned sampling sequence to generate the synchronized multichannel pressure flow signal frame sequence.
4. The method for monitoring respiratory resistance of mask based on intelligent sensor according to claim 1, wherein the method for monitoring respiratory resistance of mask based on synchronous multichannel pressure flow signal frame sequence comprises the following steps of constructing differential pressure decoupling model by adopting total differential pressure layer, internal reference differential pressure layer and reference differential pressure layer, The total differential pressure layer carries out moment-by-moment differential construction on the pressure of the main pressure measuring cavity and the environmental reference pressure in the synchronous multichannel pressure flow signal frame sequence to obtain a total differential pressure sequence; The internal reference differential pressure layer takes the damping reference cavity pressure as an internal pressure reference, and carries out internal reference mapping on the main pressure measuring cavity pressure to obtain a pressure change component; the reference differential pressure layer performs joint characterization on the damping reference cavity pressure and the environment reference pressure, and extracts drift influence reference differential pressure components; and (3) separating and reconstructing the total differential pressure layer, the internal reference differential pressure layer and the reference differential pressure layer to generate a differential pressure decoupling model.
5. The method for monitoring respiratory resistance of mask based on intelligent sensor according to claim 4, wherein the output smooth differential pressure sequence and auxiliary flow sequence comprises the following steps, Based on the differential pressure decoupling model, performing combined disturbance rejection filtering processing on the total differential pressure sequence, the pressure variation component and the drift-affected reference differential pressure component to generate a smooth differential pressure sequence; And extracting parameter configuration of the combined disturbance rejection filtering process according to the smooth differential pressure sequence, and carrying out consistency filtering process on the auxiliary airflow signal to generate an auxiliary flow sequence.
6. The method for monitoring respiratory resistance of mask based on intelligent sensor as set forth in claim 1, wherein extracting steady-state pressure segment from the smoothed differential pressure sequence based on the auxiliary flow sequence is based on the auxiliary flow sequence and the smoothed differential pressure sequence, and the steady-state pressure segment is obtained by identifying a time segment that no longer maintains synchronous response relationship through a change association consistency analysis.
7. The method for monitoring respiratory resistance of mask based on intelligent sensor according to claim 6, wherein the generating effective differential pressure sequence comprises the following steps, Based on a differential pressure decoupling model, extracting a pressure change component corresponding to a steady-state pressure section and a drift influence reference differential pressure component, and calculating a seal offset index; and performing deviation compensation on the smooth differential pressure sequence according to the seal deviation index to generate an effective differential pressure sequence.
8. The method for monitoring respiratory resistance of mask based on intelligent sensor as set forth in claim 1, wherein the steps of pairing the effective differential pressure sequence with the auxiliary flow sequence, inverting the equivalent flow resistance by a nonlinear differential pressure flow mapping method, obtaining flow resistance parameters, generating pressure measurement values by dynamically correcting cross-domain nonlinear errors, Pairing the effective differential pressure sequence with the auxiliary flow sequence, and establishing a differential pressure flow relation; inverting the equivalent flow resistance by a nonlinear differential pressure flow mapping method based on the differential pressure flow relation to obtain flow resistance parameters; based on the flow resistance parameter, executing sequence consistency constraint processing on the differential pressure flow relation to acquire a differential pressure correction value; Continuously splicing and eliminating the differential pressure correction value by adopting a segmentation continuous mapping correction method to generate a corrected differential pressure sequence; Based on the flow resistance parameter, a back-substitution calculation is performed on the corrected differential pressure sequence to obtain a pressure measurement response, and a consistency correction is performed to generate a pressure measurement.
9. The method for monitoring respiratory resistance of mask based on intelligent sensor according to claim 7, wherein the error source decoupling of the pressure measurement is performed based on the seal deviation index and the flow resistance parameter, specifically comprising the following steps, Based on the seal deviation index and the flow resistance parameter, carrying out component correlation analysis on the pressure measured value to obtain a pressure deviation component, and arranging the pressure deviation component in time sequence to generate a seal related pressure component sequence; calculating a seal contribution coefficient according to the seal related pressure component sequence and the pressure measurement value, and carrying out parallel comparison by combining the seal offset index to generate a seal characterization standing book; performing a change direction consistency separation on the pressure measurement values according to the flow resistance parameters to generate a pressure drift component sequence; Based on the pressure drift component sequence, quantifying a drift characterization quantity of the pressure measurement value, and recording the drift characterization quantity as a drift characterization quantity standing book.
10. The method for monitoring respiratory resistance of a mask based on an intelligent sensor according to claim 9, wherein the multi-dimensional pressure monitoring report comprises a multi-dimensional measurement quality assessment dataset of drift error, leakage deviation, corrected pressure value; The step of generating the multidimensional pressure monitoring report is to organize the pressure measured values in parallel based on the seal characterization standing book and the drift characterization quantity standing book to generate the multidimensional pressure monitoring report.

Description

Mask respiratory resistance monitoring method based on intelligent sensor Technical Field The invention relates to the technical field of fluid resistance monitoring, in particular to a mask respiratory resistance monitoring method based on an intelligent sensor. Background In recent years, pressure acquisition and data processing technologies based on intelligent sensors are mature continuously, and multichannel pressure sensors, miniature airflow sensors and embedded signal processing are introduced into mask respiratory resistance monitoring methods, so that continuous pressure data acquisition under dynamic respiratory conditions is possible. The related method generally comprises the steps of arranging a pressure measuring cavity in the mask, and carrying out sampling analysis on pressure change in the breathing process by combining environmental reference pressure or flow information, so as to estimate the ventilation resistance characteristics of the whole mask or part of the mask. Early mechanical or single point measurement approaches have evolved in terms of hardware integration, real-time performance, and data acquisition density. However, the existing mask respiratory resistance monitoring method based on the intelligent sensor still has certain limitations in engineering application. On one hand, most schemes mainly rely on a single differential pressure structure or a simple pressure-flow mapping relation, the measured pressure signals often overlap real respiratory resistance change and non-target disturbance factors at the same time, so that the stability of a monitoring result is insufficient, on the other hand, under a continuous respiratory working condition, the sensor zero drift, the environment pressure slow change and the airflow disturbance factors easily introduce accumulated errors, the existing method mostly adopts static calibration for correction, and effective distinction and decoupling of different error sources are difficult to realize, so that the consistency and the reliability of pressure measurement values under long-term monitoring or multi-working condition switching are influenced. Disclosure of Invention The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the invention provides a mask respiratory resistance monitoring method based on an intelligent sensor, which solves the problems that an error source is difficult to decouple and a pressure measurement result is easily affected by working condition change. In order to solve the technical problems, the invention provides the following technical scheme: the invention provides a mask respiratory resistance monitoring method based on an intelligent sensor, which comprises the steps of collecting main pressure measuring cavity pressure, environment reference pressure, damping reference cavity pressure and auxiliary airflow signals, aligning and correcting static bias of the sensor through a time stamp to generate a synchronous multichannel pressure flow signal frame sequence, constructing a differential pressure decoupling model by adopting a total differential pressure layer, an internal reference differential pressure layer and the reference differential pressure layer based on the synchronous multichannel pressure flow signal frame sequence, outputting a smooth differential pressure sequence and an auxiliary flow sequence through combined disturbance suppression filtering processing, extracting a steady-state pressure section from the smooth differential pressure sequence based on the auxiliary flow sequence, performing leakage compensation through calculating a seal offset index to generate an effective differential pressure sequence, matching the effective differential pressure sequence with the auxiliary flow sequence, inverting equivalent flow resistance through a nonlinear differential pressure flow mapping method, acquiring flow resistance parameters, dynamically correcting a cross-domain nonlinear error to generate a pressure measurement value, and performing error source decoupling on the pressure measurement value based on the seal offset index and the flow resistance parameters to generate a multidimensional pressure monitoring report. As a preferable scheme of the mask respiratory resistance monitoring method based on the intelligent sensor, the method comprises the steps of collecting main pressure measuring cavity pressure, environment reference pressure, damping reference cavity pressure and auxiliary airflow signals, aligning with a time stamp, correcting static offset of the sensor, specifically comprising the following steps of, Collecting main pressure measuring cavity pressure, environment reference pressure, damping reference cavity pressure and auxiliary airflow signals, and aligning through a time stamp to generate a multichannel alignment sampling sequence; And analyzing the relative change relation of the multichannel aligned samplin