Microfluidic enrichment and rapid detection system for microorganism in internal gas circuit of breathing machine

Abstract

The application provides a ventilator internal gas path microorganism microfluidic enrichment and rapid detection system, and belongs to the technical field of ventilators. The system comprises a gas path sampling module, which is used for obtaining gas path sample information; a microfluidic enrichment module, which is used for outputting corresponding enrichment detection signals; a signal preprocessing module, which is used for generating microorganism state data of a target ventilator internal gas path; a multi-parameter data acquisition module, which is used for constructing a microorganism concentration feature data set; a gas path microorganism analysis module, which is used for obtaining a microorganism pollution state coefficient of the target ventilator through comprehensive calculation, and constructing a gas path risk situation coefficient of the target ventilator based on the microorganism pollution state coefficient of the target ventilator; and a microorganism risk analysis module, which is used for calculating a microorganism risk evaluation coefficient of the target ventilator. The application can improve the operation safety and stability of the ventilator.

Description

Technical Field

[0001] This invention relates to the field of ventilator technology, and more specifically, to a microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator. Background Technology

[0002] As a key piece of equipment in clinical intensive care and respiratory support therapy, ventilators are widely used in medical scenarios such as intensive care units (ICUs), emergency resuscitation, and anesthesia recovery. During long-term mechanical ventilation, the internal airway system of the ventilator is constantly exposed to the patient's exhaled air and the external air medium, which can easily lead to the formation of condensate deposits on the inner wall of the airway tubing. This provides a suitable environment for the growth of microorganisms and the accumulation of aerosols, resulting in the risk of gradual microbial contamination inside the airway. This can easily increase the probability of ventilator-associated infections and pose a potential threat to patient safety.

[0003] In existing technologies, the ventilator airway is often monitored by periodic disassembly and disinfection or by monitoring based on single-point sensors. However, these methods generally suffer from long detection cycles, poor real-time performance, and inability to reflect changes in the dynamic ventilation process. In particular, they are difficult to continuously monitor the microbial status of the airway at different stages before, during, and after use. Furthermore, traditional methods often focus on detecting a single indicator, such as only detecting the microbial content in the gas or condensate, lacking the ability to comprehensively analyze changes in airway fluid dynamics, condensate migration behavior, and the spatial distribution characteristics of microorganisms, resulting in insufficient accuracy of risk assessment results. Summary of the Invention

[0004] To overcome the above deficiencies, the present invention provides a microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator that overcomes or at least partially solves the above technical problems.

[0005] This invention is implemented as follows:

[0006] This invention provides a microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator, comprising:

[0007] The gas sampling module is used to collect gas and condensate in the gas path inside the ventilator before, during, or after use of the target ventilator, and to mark the collection location to obtain gas path sample information.

[0008] The microfluidic enrichment module is used to introduce gas path sample information into the microfluidic chip, enrich the microorganisms in the sample through the microchannel device, and output the corresponding enriched detection signal.

[0009] The signal preprocessing module is used to preprocess the enriched detection signal, remove background noise, and generate microbial status data of the target ventilator's internal airway by uniformly dividing the detection area and setting several detection units and marking the signals of several detection units.

[0010] The multi-parameter data acquisition module is used to identify and monitor the microbial state dataset, obtain the transient flow velocity offset, condensate interface migration speed and equivalent wet film thickness of the enrichment area in the microbial microfluidic channel of the target ventilator's internal airway, and construct a microbial concentration feature dataset.

[0011] The airway microbial analysis module is used to perform structured analysis of the microbial state data of the target ventilator's internal airway based on preset rules, extract features from microbial concentration data, and combine the microbial concentration feature dataset to obtain the microbial contamination state coefficient of the target ventilator through comprehensive calculation. And based on the microbial contamination status coefficient of the target ventilator Construct the airway risk situation coefficient of the target ventilator ;

[0012] The microbial risk analysis module is used to determine the microbial contamination status coefficient of the target ventilator. Gas route risk situation coefficient Correlation analysis was performed to calculate the microbial risk assessment coefficient of the target ventilator. And conduct evaluation and optimization.

[0013] In a preferred embodiment, the gas sampling module includes a gas acquisition unit and a condensate acquisition unit;

[0014] The gas sampling unit is used to collect residual gas in the airway before the target ventilator is used and before the ventilator is connected to the patient. This is done through a gas sampling interface and bypass sampling channel set in the airway and connected to a negative pressure drainer, so as to obtain a gas sample in the initial state of the airway.

[0015] During the use of the target ventilator, during the stable operation of the ventilator, the sampling flow rate in the bypass sampling channel is limited by the microflow controller. According to the ventilation cycle of the ventilator, negative pressure drainage devices are used to collect samples during the inspiratory and expiratory phases to obtain gas samples that reflect the dynamic microenvironment characteristics in the airway during the ventilation process.

[0016] After the target ventilator is used, and after ventilation ends and the patient is disconnected, the gas remaining in the airway is drained and collected again using a negative pressure drainage device to obtain a gas sample that reflects the residual state in the airway after ventilation ends.

[0017] The condensate collection unit is used to collect residual condensate in the airway before the target ventilator is used. Specifically, it includes: when the ventilator is not activated, guiding the condensate adhering to the inner wall of the airway or remaining in the airway to the condensate collection chamber located in the low area of ​​the airway, and outputting the condensate sample in the condensate collection chamber when the condensate reaches the preset collection conditions, so as to obtain a condensate sample reflecting the residual state of the airway before the ventilator is used.

[0018] During the use of the target ventilator, a condensate diversion channel located in the lower part of the airway continuously guides the condensate that forms on the inner wall of the airway during ventilation. The condensate is collected along the diversion channel under the action of gravity into the condensate collection chamber. The condensate collection chamber is connected to the condensate collection interface, which is equipped with a one-way isolation valve. This allows the condensate to be continuously discharged without affecting the normal ventilation of the airway, so as to obtain a condensate sample that reflects the gradual enrichment of microorganisms during ventilation.

[0019] After the target ventilator is used, and the patient connection is disconnected after ventilation, the condensate drainage path set in the low area of ​​the airway is opened. This allows the condensate adhering to the inner wall of the airway and remaining in the low area to flow into the condensate collection chamber under the action of gravity along the condensate guide channel. Subsequently, the condensate in the condensate collection chamber is exported in one go through the condensate collection interface to obtain a condensate sample reflecting the degree of residual contamination in the airway after use, thereby obtaining airway sample information.

[0020] In a preferred embodiment, the microfluidic enrichment module includes a gas enrichment processing unit and a condensate enrichment processing unit;

[0021] The gas enrichment processing unit is used to introduce gas samples collected from the internal gas path of the target ventilator before, during, or after use into the gas enrichment channel within the microfluidic chip via a gas sample inlet interface. A microscale flow-limiting device is installed at the front end of the gas enrichment channel. As the gas sample flows within the channel, it passes sequentially through a periodically bending device, causing the microbial aerosol particles in the gas to undergo radial displacement under inertial force. A gas-liquid conversion zone is located downstream of the gas enrichment channel, connected to a pre-placed capture liquid, allowing the gas sample to... Microbial particles in the gas sample enter the capture liquid through inertial impaction, interfacial adsorption, or diffusion deposition during the flow process, realizing the transfer of microorganisms from the gas phase to the liquid phase. After the gas-liquid conversion is completed, the liquid sample enters the microbial enrichment zone. The enrichment zone is equipped with microcolumn arrays or high-density microstructures to limit the liquid flow rate and prolong the residence time of microorganisms, so that the microorganisms transferred to the liquid phase gradually concentrate in a local area to form a stable high-concentration enrichment zone. When the microorganisms in the enrichment zone reach the preset enrichment conditions, the corresponding gas sample enrichment detection signal is output from the enrichment zone.

[0022] The condensate enrichment processing unit is used to collect condensate samples from the internal airway of the target ventilator before, during, or after use. These samples are introduced into the condensate enrichment channel within the microfluidic chip via a condensate sample inlet interface. A liquid rectifier is installed at the inlet of the condensate enrichment channel. The condensate sample flows along a progressively narrowing microchannel within the enrichment channel, reducing the sample volume without turbulence. A size-selective retention zone is located downstream of the enrichment channel. This zone consists of microscale sieving devices with controlled spacing, selectively retaining microorganisms and microbial aggregates in the condensate, while the liquid carrier continues to flow downstream, separating the microorganisms from the liquid. The retained microorganisms accumulate within the retention zone and, under geometric constraints, form a high-density distribution area, constituting the microbial enrichment zone of the condensate sample. When the microorganisms in the enrichment zone reach the preset enrichment conditions, a corresponding condensate sample enrichment detection signal is output from the enrichment zone.

[0023] In a preferred embodiment, the signal preprocessing module includes a gas sample enrichment detection signal denoising processing unit, a condensate sample enrichment detection signal denoising processing unit, and a labeling unit.

[0024] The gas sample enrichment detection signal denoising unit is used to acquire the background baseline signal of the detection channel at the corresponding sampling stage before, during, and after the use of the target ventilator. This signal is used to characterize the detection system's own noise, gas path environmental disturbances, and non-specific interference signals. After the gas sample enters the microfluidic enrichment area and generates a detection response, the original detection signal is aligned and corrected with the background baseline signal. Background components unrelated to microbial enrichment are canceled out. Simultaneously, the detection signal is time-synchronized and segmented according to the ventilator's ventilation cycle parameters, eliminating abnormal fluctuation signals generated during unstable ventilation stages. Based on this, the corrected detection signal is smoothed and fluctuation suppressed to reduce random noise components caused by gas flow changes, electrical noise, or transient disturbances. Finally, the effective signal component corresponding to the microbial enrichment area is extracted from the processed detection signal to generate a gas sample enrichment detection signal with improved signal-to-noise ratio and enhanced stability.

[0025] The noise reduction processing unit for the detection signal after condensate sample enrichment is used to, before, during, or after the use of the target ventilator, identify stable segments of the detection signal to distinguish between continuous flow and discontinuous disturbance stages, addressing transient interference signals introduced by unstable liquid flow, interface disturbances, and bubble entrainment in the condensate sample. It then suppresses the identified discontinuous disturbance stage signals to reduce transient spike noise caused by bubble bursting, droplet merging, or liquid surface oscillation. Simultaneously, based on the flow damping characteristics of the condensate in the microfluidic channel, it filters high-frequency random fluctuations in the detection signal that are unrelated to changes in liquid inertia. Furthermore, by comparing the background signal of the non-enriched region in the microfluidic chip with a reference, it eliminates background noise caused by detector background drift, spontaneous signals from the chip material, and changes in ambient temperature and humidity. After the above processing, a noise-reduced detection signal that primarily reflects the microbial enrichment behavior in the condensate is obtained.

[0026] The marking unit is used to assign a corresponding detection unit identifier to each detection unit after uniformly dividing the detection area and setting up several detection units, and to associate and store the detection unit identifier of each detection unit with its corresponding detection signal when collecting enriched detection signals, thereby realizing the spatial location marking of the detection signals of several detection units and generating microbial status data of the internal airway of the target ventilator.

[0027] In a preferred embodiment, the multi-parameter data acquisition module includes a transient flow velocity offset acquisition unit in the microfluidic channel, a condensate interface migration velocity acquisition unit, and an equivalent wet film thickness acquisition unit in the enrichment region.

[0028] The transient flow velocity offset acquisition unit in the microfluidic channel is used to set up a micro-flow velocity sensing component at a preset monitoring position in the microfluidic channel. When the sample fluid passes through the microfluidic channel, it continuously acquires the local flow velocity signal at different times in the channel. The continuously acquired flow velocity signal is used to construct a transient flow velocity sequence and compared with the reference flow velocity sequence of the corresponding channel under a preset baseline state to obtain the transient flow velocity offset in the microbial microfluidic channel of the target ventilator internal airway caused by microbial enrichment and changes in local channel resistance.

[0029] The condensate interface migration velocity acquisition unit is used to set an interface sensing component at a preset position in the condensate enrichment channel or condensate collection area, and to perform continuous imaging or optical detection of the liquid interface formed by the condensate and the surrounding medium; by identifying the interface position change at adjacent acquisition times, the displacement of the condensate interface in the microfluidic channel is obtained, and combined with the acquisition time interval, the migration velocity of the microbial condensate interface in the airway inside the target ventilator is calculated.

[0030] The equivalent wet film thickness acquisition unit for the enriched area is used to set up optical or electrical sensing components at a preset monitoring position in the enriched area of ​​the microfluidic chip to acquire the signal response characteristics of the liquid-covered area in the enriched area; by detecting the signal attenuation degree or reflection / resistance change characteristics of the enriched area at different sampling times, the change in the coverage area of ​​the liquid on the microstructure surface is indirectly characterized; and by combining the preset geometric parameters of the enriched area and the signal response coefficient corresponding to the unit area, the equivalent wet film thickness of the microbial enriched area in the airway of the target ventilator is calculated.

[0031] A dataset of microbial concentration characteristics is constructed based on the transient flow velocity offset, condensate interface migration velocity, and equivalent wet film thickness in the microbial microfluidic channel within the internal airway of the target ventilator.

[0032] In a preferred embodiment, the gas path microbial analysis module includes a feature extraction unit, a microbial contamination status analysis unit, a microbial assessment unit, a gas path risk situation analysis unit, and a gas path risk situation assessment unit.

[0033] The feature extraction unit is used to extract features from the microbial state data of the airway inside the target ventilator, including extracting the cumulative growth rate of microorganisms in the airway inside the target ventilator based on the changing trend of the microbial state data over time.

[0034] The spatial distribution dispersion of microorganisms in the internal airway of the target ventilator was extracted based on the signal differences between different detection units.

[0035] Extracting the microbial migration response delay in the internal airway of a target ventilator based on changes in fluid dynamics within a microfluidic channel;

[0036] The microbial contamination status analysis unit is used to analyze the transient flow rate offset within the microbial microfluidic channel of the target ventilator's internal airway. Condensate interface migration rate Equivalent wet film thickness in enriched areas Cumulative growth rate of microorganisms Spatial distribution dispersion of microorganisms and the delay in microbial migration response The microbial contamination status coefficient of the target ventilator was obtained through the following methods. ;

[0037] First, by measuring the transient flow rate shift within the microfluidic channels of the target ventilator's internal airway. The contribution of microbial flow velocity disturbance in the airway of the target ventilator was calculated. ;

[0038] In the formula Indicates the reference flow rate of the microfluidic channel;

[0039] Migration rate of condensate interface within the target ventilator's internal airway The contribution of condensate interface migration in the internal airway of the target ventilator was calculated. ;

[0040] In the formula Indicates the length of the detection channel feature;

[0041] Equivalent wet film thickness in the enrichment area of ​​the target ventilator's internal airway The contribution of the wet film to the internal airway of the target ventilator was calculated. ;

[0042] In the formula This indicates the preset maximum wet film reference value;

[0043] Then, based on the contribution of microbial flow velocity disturbance in the target ventilator's internal airway. Contribution of condensate interface migration in the internal airway of the target ventilator and the contribution of the wet membrane to the internal airway of the target ventilator By combining and normalizing the data, the environmental disturbance factor of the target ventilator's internal airway is obtained through calculation. ;

[0044] ;

[0045] Subsequently, the cumulative growth rate of microorganisms in the airway inside the target ventilator was used. The cumulative microbial growth response of the target ventilator was calculated. ;

[0046] In the formula Indicates the initial growth benchmark value;

[0047] Spatial distribution dispersion of microorganisms in the internal airway of the target ventilator The spatial uniformity of microbial response of the target ventilator was obtained through calculation. ;

[0048] In the formula Indicates standard deviation, Indicates the baseline value of the reference distribution;

[0049] Utilizing the delay in microbial migration response within the target ventilator's internal airway The contribution of the microbial response delay of the target ventilator was calculated. ;

[0050] In the formula Indicates the reference response time;

[0051] Next, based on the cumulative microbial growth response of the target ventilator Microbial spatial uniformity response of the target ventilator Contribution of microbial response delay to the target ventilator By combining and normalizing the data, the microbial response factors of the target ventilator were calculated. ;

[0052] ;

[0053] Finally, based on the environmental disturbance factors of the target ventilator's internal airway. Microbial response factors of the target ventilator By combining the analysis, the microbial contamination status coefficient of the target ventilator was calculated using a formula. ;

[0054] .

[0055] In a preferred embodiment, the microbial assessment unit is used to preset a microbial contamination state threshold ZC and to set the microbial contamination state coefficient of the target ventilator. Comparison with the microbial contamination state threshold ZC, including:

[0056] when When the value is >ZC, it indicates that the microbial contamination status of the airway inside the target ventilator is abnormal. It is necessary to reduce the ventilation drive flow rate by 5%-20%, increase the airway flushing frequency by 2-3 times, and start the local circulation purification treatment of the airway to reduce the microbial enrichment level in the airway.

[0057] when When the value is ≤ZC, it indicates that the microbial contamination status of the internal airway of the target ventilator is normal.

[0058] In a preferred embodiment, the airway risk situation analysis unit is used to analyze the microbial contamination status coefficient of the target ventilator. The airway risk situation coefficient of the target ventilator is calculated using a formula. ;

[0059] .

[0060] In a preferred embodiment, the airway risk situation assessment unit is used to preset the airway risk situation threshold AW and to set the airway risk situation coefficient of the target ventilator. Comparison with the gas path risk situation threshold (AW), including:

[0061] when When the value is >AW, it indicates that the microbial risk situation of the target ventilator's airway is abnormal. It is necessary to reduce the airway flow rate by 10%-30%, increase the airway flushing flow rate by 20%-50%, and start the internal circulation flushing and disinfection procedure of the airway to reduce the probability of microbial spread in the airway.

[0062] when When the value is ≤AW, it indicates that the microbial risk status of the target ventilator's airway is normal, and the current ventilation parameters should be maintained.

[0063] In a preferred embodiment, the microbial risk analysis module includes an association unit and a microbial risk assessment unit;

[0064] The association unit is used to determine the microbial contamination status coefficient of the target ventilator. airway risk profile coefficient relative to the target ventilator Correlation, after normalization, and calculation, yielded the microbial risk assessment coefficient for the target ventilator. ;

[0065] ;

[0066] The microbial risk assessment unit is used to preset the microbial risk threshold SK and to set the microbial risk assessment coefficient of the target ventilator. Comparison with the microbial risk threshold SK, including:

[0067] when When the value is >SK, it indicates that the microbial contamination of the airway inside the target ventilator is in an abnormal state. It is necessary to reduce the tidal volume by 5%-20%, reduce the airway drive flow rate by 10%-30%, and at the same time increase the airway flushing frequency by 1.5-3 times.

[0068] when When the value is ≤SK, it indicates that the microbial contamination of the airway inside the target ventilator is in a normal state, and the current ventilation parameters remain unchanged.

[0069] This invention provides a microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator, the beneficial effects of which include:

[0070] 1. By collecting gas and condensate samples from the airway before, during, and after use of the target ventilator, and combining this with sampling location marking, microbial sample information from different operating stages and airway locations can be distinguished and traced, thereby improving the spatial and temporal resolution of microbial detection. Microbial enrichment is performed on the airway samples using microfluidic chips and microchannel devices, and combined with the output of enriched detection signals, low-concentration microorganisms can be effectively enriched in a short time, thereby improving the sensitivity and rapid response capability of microbial detection. By denoising the enriched detection signals and combining them with uniform division of the detection area and detection unit signal marking, a structured expression of detection signals at different spatial locations can be achieved, thereby reducing background interference and improving the accuracy and stability of microbial state data.

[0071] 2. By extracting transient flow velocity offset, condensate interface migration velocity, and equivalent wet film thickness in the enrichment zone of the microfluidic channel, and constructing a microbial concentration feature dataset, a multi-dimensional quantitative characterization of the fluid state and microbial enrichment process of the gas path microenvironment is achieved. A microbial contamination state coefficient is constructed through a rule-based feature analysis method, and a gas path risk situation coefficient is further constructed to achieve a hierarchical characterization of the gas path microbial contamination state and risk evolution trend, thereby improving the accuracy and stability of risk assessment. Based on this coefficient, dynamic assessment and optimized control of the ventilator gas path microbial risk are achieved, thereby improving the safety and stability of ventilator operation. Attached Figure Description

[0072] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0073] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Example 1, referring to Figure 1 This invention provides a technical solution: a microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator, comprising:

[0076] The gas sampling module is used to collect gas and condensate in the gas path inside the ventilator before, during, or after use of the target ventilator, and to mark the collection location to obtain gas path sample information.

[0077] The microfluidic enrichment module is used to introduce gas path sample information into the microfluidic chip, enrich the microorganisms in the sample through the microchannel device, and output the corresponding enriched detection signal.

[0078] The signal preprocessing module is used to preprocess the enriched detection signal, remove background noise, and generate microbial status data of the target ventilator's internal airway by uniformly dividing the detection area and setting several detection units and marking the signals of several detection units.

[0079] The multi-parameter data acquisition module is used to identify and monitor the microbial state dataset, obtain the transient flow velocity offset, condensate interface migration speed and equivalent wet film thickness of the enrichment area in the microbial microfluidic channel of the target ventilator's internal airway, and construct a microbial concentration feature dataset.

[0080] The airway microbial analysis module is used to perform structured analysis of the microbial state data of the target ventilator's internal airway based on preset rules, extract features from microbial concentration data, and combine the microbial concentration feature dataset to obtain the microbial contamination state coefficient of the target ventilator through comprehensive calculation. And based on the microbial contamination status coefficient of the target ventilator Construct the airway risk situation coefficient of the target ventilator ;

[0081] The microbial risk analysis module is used to determine the microbial contamination status coefficient of the target ventilator. Gas route risk situation coefficient Correlation analysis was performed to calculate the microbial risk assessment coefficient of the target ventilator. And conduct evaluation and optimization.

[0082] In this embodiment, by collecting gas and condensate in the airway before, during, and after use of the target ventilator, and combining the collection location markers, microbial sample information at different operating stages and different airway locations can be distinguished and traced, thereby improving the spatial and temporal resolution of microbial detection. Microbial enrichment processing of the airway samples is performed using microfluidic chips and microchannel devices, and combined with the output of enriched detection signals, low-concentration microorganisms can be effectively enriched in a short time, thereby improving the sensitivity and rapid response capability of microbial detection. By denoising the enriched detection signals and combining uniform division of the detection area and detection unit signal marking, a structured expression of detection signals at different spatial locations is achieved, thereby reducing background interference and improving the accuracy and stability of microbial state data.

[0083] By extracting multiple parameters such as transient flow velocity offset of microfluidic channels, condensate interface migration velocity, and equivalent wet film thickness of enrichment areas, and constructing a microbial concentration feature dataset, a multi-dimensional quantitative characterization of the fluid state and microbial enrichment process of the gas path microenvironment is achieved. A microbial contamination state coefficient is constructed through a rule-based feature analysis method, and a gas path risk situation coefficient is further constructed to achieve a hierarchical characterization of the gas path microbial contamination state and risk evolution trend, thereby improving the accuracy and stability of risk assessment. Based on this coefficient, dynamic assessment and optimized control of ventilator gas path microbial risk are achieved, thereby improving the safety and stability of ventilator operation.

[0084] Example 2 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the gas sampling module includes a gas collection unit and a condensate collection unit;

[0085] The gas sampling unit is used to collect residual gas in the airway before the target ventilator is used and before the ventilator is connected to the patient. This is done through a gas sampling interface and bypass sampling channel set in the airway and connected to a negative pressure drainer, so as to obtain a gas sample in the initial state of the airway.

[0086] During the use of the target ventilator, during the stable operation of the ventilator, the sampling flow rate in the bypass sampling channel is limited by the microflow controller. According to the ventilation cycle of the ventilator, negative pressure drainage devices are used to collect samples during the inspiratory and expiratory phases to obtain gas samples that reflect the dynamic microenvironment characteristics in the airway during the ventilation process.

[0087] After the target ventilator is used, and after ventilation ends and the patient is disconnected, the gas remaining in the airway is drained and collected again using a negative pressure drainage device to obtain a gas sample that reflects the residual state in the airway after ventilation ends.

[0088] The condensate collection unit is used to collect residual condensate in the airway before the target ventilator is used. Specifically, it includes: when the ventilator is not activated, guiding the condensate adhering to the inner wall of the airway or remaining in the airway to the condensate collection chamber located in the low area of ​​the airway, and outputting the condensate sample in the condensate collection chamber when the condensate reaches the preset collection conditions, so as to obtain a condensate sample reflecting the residual state of the airway before the ventilator is used.

[0089] During the use of the target ventilator, a condensate diversion channel located in the lower part of the airway continuously guides the condensate that forms on the inner wall of the airway during ventilation. The condensate is collected along the diversion channel under the action of gravity into the condensate collection chamber. The condensate collection chamber is connected to the condensate collection interface, which is equipped with a one-way isolation valve. This allows the condensate to be continuously discharged without affecting the normal ventilation of the airway, so as to obtain a condensate sample that reflects the gradual enrichment of microorganisms during ventilation.

[0090] After the target ventilator is used, and the patient connection is disconnected after ventilation, the condensate drainage path set in the low area of ​​the airway is opened. This allows the condensate adhering to the inner wall of the airway and remaining in the low area to flow into the condensate collection chamber under the action of gravity along the condensate guide channel. Subsequently, the condensate in the condensate collection chamber is exported in one go through the condensate collection interface to obtain a condensate sample reflecting the degree of residual contamination in the airway after use, thereby obtaining airway sample information.

[0091] In this embodiment, gas and condensate in the gas path are collected in three stages: before, during, and after use of the target ventilator. This ensures that the samples cover the entire life cycle of the device, reflecting the dynamic changes of microorganisms in the gas path and improving the completeness and traceability of contamination analysis. By setting up a gas sampling interface, a bypass sampling channel, and a negative pressure drain, stable acquisition of gas samples under different ventilation conditions is achieved, improving the representativeness of the samples. By setting up a condensate collection chamber and a guide channel in the low-level area of ​​the gas path, the condensate can be continuously collected and concentrated under gravity, improving the continuous capture capability of condensate samples. At the same time, a one-way isolation valve isolates the condensate collection from the gas path ventilation process, avoiding interference with the normal ventilation process and ensuring the stability of system operation.

[0092] Example 3 is an explanation of Example 1; please refer to the provided text. Figure 1Specifically, the microfluidic enrichment module includes a gas enrichment processing unit and a condensate enrichment processing unit.

[0093] The gas enrichment processing unit is used to introduce gas samples collected from the internal gas path of the target ventilator before, during, or after use into the gas enrichment channel within the microfluidic chip via a gas sample inlet interface. A microscale flow-limiting device is installed at the front end of the gas enrichment channel. As the gas sample flows within the channel, it passes sequentially through a periodically bending device, causing the microbial aerosol particles in the gas to undergo radial displacement under inertial force. A gas-liquid conversion zone is located downstream of the gas enrichment channel, connected to a pre-placed capture liquid, allowing the gas sample to... Microbial particles in the gas sample enter the capture liquid through inertial impaction, interfacial adsorption, or diffusion deposition during the flow process, realizing the transfer of microorganisms from the gas phase to the liquid phase. After the gas-liquid conversion is completed, the liquid sample enters the microbial enrichment zone. The enrichment zone is equipped with microcolumn arrays or high-density microstructures to limit the liquid flow rate and prolong the residence time of microorganisms, so that the microorganisms transferred to the liquid phase gradually concentrate in a local area to form a stable high-concentration enrichment zone. When the microorganisms in the enrichment zone reach the preset enrichment conditions, the corresponding gas sample enrichment detection signal is output from the enrichment zone.

[0094] The condensate enrichment processing unit is used to collect condensate samples from the internal airway of the target ventilator before, during, or after use. These samples are introduced into the condensate enrichment channel within the microfluidic chip via a condensate sample inlet interface. A liquid rectifier is installed at the inlet of the condensate enrichment channel. The condensate sample flows along a progressively narrowing microchannel within the enrichment channel, reducing the sample volume without turbulence. A size-selective retention zone is located downstream of the enrichment channel. This zone consists of microscale sieving devices with controlled spacing, selectively retaining microorganisms and microbial aggregates in the condensate, while the liquid carrier continues to flow downstream, separating the microorganisms from the liquid. The retained microorganisms accumulate within the retention zone and, under geometric constraints, form a high-density distribution area, constituting the microbial enrichment zone of the condensate sample. When the microorganisms in the enrichment zone reach the preset enrichment conditions, a corresponding condensate sample enrichment detection signal is output from the enrichment zone.

[0095] In this embodiment, gas and condensate samples collected by the ventilator's airway are introduced into corresponding enrichment channels within the microfluidic chip, respectively, to achieve separate processing of gas and liquid phase samples, thereby improving the adaptability and enrichment capability of microbial samples of different morphologies. Specifically, the gas enrichment unit uses a microscale flow-limiting structure and periodically bent channels to cause microbial aerosol particles to spatially shift under inertial action, achieving efficient transfer from the gas phase to the liquid phase in the gas-liquid conversion zone. Combined with a microcolumn array enrichment structure, microorganisms are locally confined and aggregated, significantly improving the capture efficiency and enrichment concentration of low-concentration aerosol microorganisms. The condensate enrichment unit uses a progressively contracting microchannel and liquid rectification structure to achieve stable laminar flow transport of samples, and utilizes a size-selective retention zone to screen and retain microorganisms and aggregates, allowing microorganisms to continuously accumulate in local areas to form a high-density enrichment zone, thereby improving the separation efficiency and enrichment effect of microorganisms in the condensate.

[0096] Example 4 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the signal preprocessing module includes a gas sample enrichment signal denoising processing unit, a condensate sample enrichment signal denoising processing unit, and a labeling unit.

[0097] The gas sample enrichment detection signal denoising unit is used to acquire the background baseline signal of the detection channel at the corresponding sampling stage before, during, and after the use of the target ventilator. This signal is used to characterize the detection system's own noise, gas path environmental disturbances, and non-specific interference signals. After the gas sample enters the microfluidic enrichment area and generates a detection response, the original detection signal is aligned and corrected with the background baseline signal. Background components unrelated to microbial enrichment are canceled out. Simultaneously, the detection signal is time-synchronized and segmented according to the ventilator's ventilation cycle parameters, eliminating abnormal fluctuation signals generated during unstable ventilation stages. Based on this, the corrected detection signal is smoothed and fluctuation suppressed to reduce random noise components caused by gas flow changes, electrical noise, or transient disturbances. Finally, the effective signal component corresponding to the microbial enrichment area is extracted from the processed detection signal to generate a gas sample enrichment detection signal with improved signal-to-noise ratio and enhanced stability.

[0098] The noise reduction processing unit for the detection signal after condensate sample enrichment is used to, before, during, or after the use of the target ventilator, identify stable segments of the detection signal to distinguish between continuous flow and discontinuous disturbance stages, addressing transient interference signals introduced by unstable liquid flow, interface disturbances, and bubble entrainment in the condensate sample. It then suppresses the identified discontinuous disturbance stage signals to reduce transient spike noise caused by bubble bursting, droplet merging, or liquid surface oscillation. Simultaneously, based on the flow damping characteristics of the condensate in the microfluidic channel, it filters high-frequency random fluctuations in the detection signal that are unrelated to changes in liquid inertia. Furthermore, by comparing the background signal of the non-enriched region in the microfluidic chip with a reference, it eliminates background noise caused by detector background drift, spontaneous signals from the chip material, and changes in ambient temperature and humidity. After the above processing, a noise-reduced detection signal that primarily reflects the microbial enrichment behavior in the condensate is obtained.

[0099] The marking unit is used to assign a corresponding detection unit identifier to each detection unit after uniformly dividing the detection area and setting up several detection units, and to associate and store the detection unit identifier of each detection unit with its corresponding detection signal when collecting enriched detection signals, thereby realizing the spatial location marking of the detection signals of several detection units and generating microbial status data of the internal airway of the target ventilator.

[0100] In this embodiment, targeted denoising processing is performed on the detection signals after enrichment of gas samples and condensate samples, respectively, improving the effectiveness and stability of the detection signals. Specifically, in gas detection signal processing, a background baseline signal is introduced to align and correct the original detection signal. Time synchronization and segmentation are performed in conjunction with the ventilator ventilation cycle, effectively eliminating irrelevant fluctuations caused by unstable ventilation phases and environmental disturbances. High-frequency random noise is also smoothed and suppressed, thereby improving the signal-to-noise ratio and reliability of the gas sample microbial enrichment signal. In condensate detection signal processing, the transient interference signals caused by discontinuous liquid flow phases are identified and suppressed by bubbles, droplets, and interface oscillations. The background reference signal is used to eliminate detector drift and the influence of environmental factors, thereby improving the stability and accuracy of the condensate microbial detection signal. Furthermore, by uniformly dividing the detection area and setting detection unit identifiers, precise correlation and storage of detection signals from different spatial locations are achieved, enabling spatial traceability of microbial state data and improving the accuracy and completeness of subsequent microbial distribution analysis.

[0101] Example 5 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the multi-parameter data acquisition module includes a transient flow velocity offset acquisition unit, a condensate interface migration velocity acquisition unit, and an equivalent wet film thickness acquisition unit in the enrichment region within the microfluidic channel.

[0102] The transient flow velocity offset acquisition unit in the microfluidic channel is used to set up a micro-flow velocity sensing component at a preset monitoring position in the microfluidic channel. When the sample fluid passes through the microfluidic channel, it continuously acquires the local flow velocity signal at different times in the channel. The continuously acquired flow velocity signal is used to construct a transient flow velocity sequence and compared with the reference flow velocity sequence of the corresponding channel under a preset baseline state to obtain the transient flow velocity offset in the microbial microfluidic channel of the target ventilator internal airway caused by microbial enrichment and changes in local channel resistance.

[0103] The condensate interface migration velocity acquisition unit is used to set an interface sensing component at a preset position in the condensate enrichment channel or condensate collection area, and to perform continuous imaging or optical detection of the liquid interface formed by the condensate and the surrounding medium; by identifying the interface position change at adjacent acquisition times, the displacement of the condensate interface in the microfluidic channel is obtained, and combined with the acquisition time interval, the migration velocity of the microbial condensate interface in the airway inside the target ventilator is calculated.

[0104] The equivalent wet film thickness acquisition unit for the enriched area is used to set up optical or electrical sensing components at a preset monitoring position in the enriched area of ​​the microfluidic chip to acquire the signal response characteristics of the liquid-covered area in the enriched area; by detecting the signal attenuation degree or reflection / resistance change characteristics of the enriched area at different sampling times, the change in the coverage area of ​​the liquid on the microstructure surface is indirectly characterized; and by combining the preset geometric parameters of the enriched area and the signal response coefficient corresponding to the unit area, the equivalent wet film thickness of the microbial enriched area in the airway of the target ventilator is calculated.

[0105] A dataset of microbial concentration characteristics is constructed based on the transient flow velocity offset, condensate interface migration velocity, and equivalent wet film thickness in the microbial microfluidic channel within the internal airway of the target ventilator.

[0106] In this embodiment, by setting up transient flow velocity offset acquisition units, condensate interface migration velocity acquisition units, and equivalent wet film thickness acquisition units in the enrichment zone within the microfluidic channel, multi-dimensional synchronous characterization of fluid dynamics changes and microbial enrichment states in the ventilator's airway microenvironment is achieved. Specifically, by continuously acquiring transient flow velocity signals within the microfluidic channel and comparing them with a baseline flow velocity sequence, the characteristics of flow disturbances caused by microbial enrichment and changes in local channel resistance can be effectively reflected, improving the sensitivity to microscopic blockages and enrichment states in the airway. By continuously identifying and time-correlatedly calculating changes in condensate interface displacement, a quantitative characterization of the condensate migration process in the airway is achieved, reflecting the dynamic characteristics of microbial propagation and aggregation with the condensate. By detecting changes in the liquid coverage signal response in the enrichment zone and calculating the equivalent wet film thickness using structural parameters, an indirect quantitative description of the liquid distribution state in the microfluidic enrichment region is achieved, thereby improving the spatial characterization capability of the microbial enrichment environment.

[0107] Example 6 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the gas path microbial analysis module includes a feature extraction unit, a microbial contamination status analysis unit, a microbial assessment unit, a gas path risk situation analysis unit, and a gas path risk situation assessment unit.

[0108] The feature extraction unit is used to extract features from the microbial state data of the airway inside the target ventilator, including extracting the cumulative growth rate of microorganisms in the airway inside the target ventilator based on the changing trend of the microbial state data over time.

[0109] The spatial distribution dispersion of microorganisms in the internal airway of the target ventilator was extracted based on the signal differences between different detection units.

[0110] Extracting the microbial migration response delay in the internal airway of a target ventilator based on changes in fluid dynamics within a microfluidic channel;

[0111] The microbial contamination status analysis unit is used to analyze the transient flow rate offset within the microbial microfluidic channel of the target ventilator's internal airway. Condensate interface migration rate Equivalent wet film thickness in enriched areas Cumulative growth rate of microorganisms Spatial distribution dispersion of microorganisms and the delay in microbial migration response The microbial contamination status coefficient of the target ventilator was obtained through the following methods. ;

[0112] First, by measuring the transient flow rate shift within the microfluidic channels of the target ventilator's internal airway. The contribution of microbial flow velocity disturbance in the airway of the target ventilator was calculated. ;

[0113] In the formula This indicates the reference flow rate of the microfluidic channel, which is obtained by pre-calibrating the microfluidic channel under clean, unloaded or standard reference conditions.

[0114] Migration rate of condensate interface within the target ventilator's internal airway The contribution of condensate interface migration in the internal airway of the target ventilator was calculated. ;

[0115] In the formula The length of the detection channel characteristic is determined directly by the chip design dimensions or obtained through pre-calibration.

[0116] Equivalent wet film thickness in the enrichment area of ​​the target ventilator's internal airway The contribution of the wet film to the internal airway of the target ventilator was calculated. ;

[0117] In the formula This represents the preset maximum wet film reference value, which is determined by the experimental calibration value or design limit value of the microfluidic enrichment region under the maximum liquid saturation state.

[0118] Then, based on the contribution of microbial flow velocity disturbance in the target ventilator's internal airway. Contribution of condensate interface migration in the internal airway of the target ventilator and the contribution of the wet membrane to the internal airway of the target ventilator By combining and normalizing the data, the environmental disturbance factor of the target ventilator's internal airway is obtained through calculation. ;

[0119] ;

[0120] The following are environmental disturbance factors in the internal airway of the target ventilator. The sample data table is shown below:

[0121] Sampling time number Transient flow velocity offset within the microfluidic channel of the target ventilator's internal airway Microfluidic channel reference flow rate Contribution of microbial flow velocity disturbance in the target ventilator's internal airway Target ventilator internal airway condensate interface migration rate Detection channel feature length Contribution of condensate interface migration in the internal airway of the target ventilator Equivalent wet film thickness of the enrichment area in the internal airway of the target ventilator Preset maximum wet film reference value The contribution of the wet membrane to the internal airway of the target ventilator Environmental disturbance factors in the internal airway of the target ventilator 1 0.52 0.48 0.04 0.31 1.00 0.31 0.22 0.45 0.489 0.839 2 0.61 0.48 0.13 0.36 1.00 0.36 0.28 0.45 0.622 1.112 3 0.44 0.48 0.04 0.29 1.00 0.29 0.18 0.45 0.400 0.739 4 0.67 0.48 0.19 0.41 1.00 0.41 0.35 0.45 0.778 1.378 5 0.50 0.48 0.02 0.33 1.00 0.33 0.25 0.45 0.556 0.906

[0122] Subsequently, the cumulative growth rate of microorganisms in the airway inside the target ventilator was used. The cumulative microbial growth response of the target ventilator was calculated. ;

[0123] In the formula This represents the initial growth benchmark value, obtained through statistical analysis of historical benchmark data.

[0124] Spatial distribution dispersion of microorganisms in the internal airway of the target ventilator The spatial uniformity of microbial response of the target ventilator was obtained through calculation. ;

[0125] In the formula Indicates standard deviation, This represents the reference distribution baseline value, which is obtained from historically measured spatial distribution reference values.

[0126] Utilizing the delay in microbial migration response within the target ventilator's internal airway The contribution of the microbial response delay of the target ventilator was calculated. ;

[0127] In the formula The reference response time is obtained from the baseline value of the microbial response time pre-calibrated under standard operating conditions.

[0128] Next, based on the cumulative microbial growth response of the target ventilator Microbial spatial uniformity response of the target ventilator Contribution of microbial response delay to the target ventilator By combining and normalizing the data, the microbial response factors of the target ventilator were calculated. ;

[0129] ;

[0130] The following are the microbial response factors for the target ventilator. The sample data table is shown below:

[0131] Sampling time number Cumulative growth rate of microorganisms in the airway of the target ventilator Initial growth benchmark Microbial cumulative growth response of the target ventilator Spatial distribution dispersion of microorganisms in the internal airway of the target ventilator Reference distribution baseline value Microbial spatial uniformity response of the target ventilator Standard deviation Target ventilator internal airway microbial migration response delay Reference response time Contribution of microbial response delay to the target ventilator Microbial response factors of target ventilator 1 0.62 0.40 0.55 0.48 0.50 0.88 0.12 0.32 0.45 1.41 2.84 2 0.75 0.40 0.88 0.52 0.50 0.84 0.15 0.40 0.45 1.12 2.84 3 0.58 0.40 0.45 0.46 0.50 0.91 0.10 0.28 0.45 1.61 2.97 4 0.82 0.40 1.05 0.55 0.50 0.80 0.18 0.47 0.45 0.96 2.81 5 0.66 0.40 0.65 0.49 0.50 0.87 0.13 0.35 0.45 1.29 2.81

[0132] Finally, based on the environmental disturbance factors of the target ventilator's internal airway. Microbial response factors of the target ventilator By combining the analysis, the microbial contamination status coefficient of the target ventilator was calculated using a formula. ;

[0133] .

[0134] The following is the microbial contamination status coefficient of the target ventilator. The sample data table is shown below:

[0135] Sampling time number Environmental disturbance factors in the internal airway of the target ventilator Microbial response factors of target ventilator Microbial contamination status coefficient of the target ventilator 1 0.839 2.84 2.383 2 1.112 2.84 3.157 3 0.739 2.97 2.194 4 1.378 2.81 3.872 5 0.906 2.81 2.546

[0136] In this embodiment, multidimensional feature extraction is performed on the microbial state data of the internal airway of the target ventilator. The cumulative growth rate of microorganisms, spatial distribution dispersion, and migration response delay are extracted from three dimensions: temporal variation, spatial distribution, and hydrodynamic variation. This achieves a structured expression of the microbial state change patterns, thereby improving the completeness and resolvability of the microbial state characterization. Based on this, multi-source parameters such as transient flow velocity offset of the microfluidic channel, condensate interface migration velocity, and equivalent wet film thickness of the enrichment zone are introduced to construct an environmental disturbance factor. Combined with microbial growth, distribution, and response characteristics, a microbial response factor is constructed, achieving a synergistic quantitative description of changes in the airway environment and changes in microbial behavior. Furthermore, by coupling the environmental disturbance factor and the microbial response factor, a microbial contamination state coefficient is obtained, transforming the microbial contamination state from a single indicator representation to a multi-parameter comprehensive representation, thereby improving the accuracy and stability of the assessment of the degree of microbial contamination in the ventilator's airway.

[0137] Example 7 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the microbial assessment unit is used to preset the microbial contamination state threshold ZC;

[0138] When the target ventilator is in a clean initial state or a low-contamination baseline state, baseline data of the microbial contamination state coefficient corresponding to multiple stable operating cycles are collected, and the baseline data are statistically processed. The mean or quantile statistical results are used as the base value, and then corrected by a preset safety margin. The safety margin is used to cover the range of microbial fluctuations during normal operation, thereby determining the microbial contamination state threshold ZC.

[0139] And the microbial contamination status coefficient of the target ventilator Comparison with the microbial contamination state threshold ZC, including:

[0140] when When the value is >ZC, it indicates that the microbial contamination status of the airway inside the target ventilator is abnormal. It is necessary to reduce the ventilation drive flow rate by 5%-20%, increase the airway flushing frequency by 2-3 times, and start the local circulation purification treatment of the airway to reduce the microbial enrichment level in the airway.

[0141] when When the value is ≤ZC, it indicates that the microbial contamination status of the internal airway of the target ventilator is normal.

[0142] Microbial contamination status coefficient based on the target ventilator Example data table: The following is the microbial contamination status coefficient of the target ventilator. Example table of data comparing with the microbial contamination state threshold ZC;

[0143] Sampling time number Microbial contamination status coefficient of the target ventilator Microbial contamination state threshold ZC Comparison results 1 2.383 2.433 The microbial contamination status of the target ventilator's internal airway is normal. 2 3.157 2.433 The target ventilator has an abnormal microbial contamination status in its internal airway. 3 2.194 2.433 The microbial contamination status of the target ventilator's internal airway is normal. 4 3.872 2.433 The target ventilator has an abnormal microbial contamination status in its internal airway. 5 2.546 2.433 The target ventilator has an abnormal microbial contamination status in its internal airway.

[0144] In this embodiment, by setting a threshold for microbial contamination status and comparing the microbial contamination status coefficient of the target ventilator with the threshold in real time, the microbial contamination status of the airway inside the target ventilator can be quickly determined, thereby improving the timeliness and accuracy of contamination status identification.

[0145] Example 8 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the airway risk situation analysis unit is used to analyze the microbial contamination status coefficient of the target ventilator. The airway risk situation coefficient of the target ventilator is calculated using a formula. ;

[0146] .

[0147] The following are the airway risk profile coefficients for the target ventilator. The sample data table is shown below:

[0148] Sampling time number Microbial contamination status coefficient of the target ventilator airway risk profile coefficient of the target ventilator 1 2.383 0.704 2 3.157 0.759 3 2.194 0.687 4 3.872 0.795 5 2.546 0.718

[0149] The gas path risk situation assessment unit is used to preset the gas path risk situation threshold AW;

[0150] When the target ventilator is in an initial uncontaminated or low-contaminated baseline state, a baseline sequence of airway risk situation coefficients is collected within multiple stable ventilation cycles. The baseline sequence is statistically analyzed and processed, and its mean is taken and superimposed with a preset safety margin coefficient to determine the result. The safety margin coefficient is used to reflect the fluctuation range under different breathing conditions so that the airway risk situation threshold AW can cover the upper limit of risk fluctuation under normal operating conditions, and thus serve as the airway risk situation threshold AW.

[0151] And the airway risk profile coefficient of the target ventilator Comparison with the gas path risk situation threshold (AW), including:

[0152] when When the value is >AW, it indicates that the microbial risk situation of the target ventilator's airway is abnormal. It is necessary to reduce the airway flow rate by 10%-30%, increase the airway flushing flow rate by 20%-50%, and start the internal circulation flushing and disinfection procedure of the airway to reduce the probability of microbial spread in the airway.

[0153] when When the value is ≤AW, it indicates that the microbial risk status of the target ventilator's airway is normal, and the current ventilation parameters should be maintained.

[0154] Based on the airway risk situation coefficient of the target ventilator The following is a sample data table showing the airway risk profile coefficients for the target ventilator. Example table of data comparing the risk situation threshold (AW) of the gas path;

[0155] Sampling time number airway risk profile coefficient of the target ventilator Gas path risk situation threshold AW Comparison results 1 0.704 0.759 The microbial risk profile of the target ventilator's airway is normal. 2 0.759 0.759 The microbial risk profile of the target ventilator's airway is normal. 3 0.687 0.759 The microbial risk profile of the target ventilator's airway is normal. 4 0.795 0.759 The microbial risk profile of the target ventilator's airway is abnormal. 5 0.718 0.759 The microbial risk profile of the target ventilator's airway is normal.

[0156] In this embodiment, the microbial contamination state coefficient based on the target ventilator is used. Constructing the airway risk situation coefficient of the target ventilator The system employs a nonlinear mapping relationship for calculation, enabling a further transformation and enhanced expression of gastrointestinal microbial risk from "contamination state" to "risk status," thereby improving the sensitivity and discriminative power of risk changes. By presetting a gastrointestinal risk status threshold and setting the gastrointestinal risk status coefficient of the target ventilator... This allows for real-time comparison, enabling rapid assessment of the risk status of the ventilator's airway and improving the timeliness and accuracy of risk identification.

[0157] Example 9, this example is an explanation of Example 1, please refer to it. Figure 1 Specifically, the microbial risk analysis module includes an association unit and a microbial risk assessment unit;

[0158] The association unit is used to determine the microbial contamination status coefficient of the target ventilator. airway risk profile coefficient relative to the target ventilator Correlation, after normalization, and calculation, yielded the microbial risk assessment coefficient for the target ventilator. ;

[0159] ;

[0160] The following are the microbial risk assessment coefficients for the target ventilator. The sample data table is shown below:

[0161] Sampling time number Microbial contamination status coefficient of the target ventilator airway risk profile coefficient of the target ventilator Microbial risk assessment coefficient of the target ventilator 1 2.383 0.704 0.508 2 3.157 0.759 0.626 3 2.194 0.687 0.457 4 3.872 0.795 0.715 5 2.546 0.718 0.543

[0162] The microbial risk assessment unit is used to preset the microbial risk threshold SK;

[0163] Based on historical data of the microbial risk assessment coefficient of the target ventilator under clean baseline and normal operating conditions, the statistical baseline value is taken and combined with the preset safety margin correction to obtain the microbial risk threshold SK.

[0164] And the microbial risk assessment coefficient of the target ventilator. Comparison with the microbial risk threshold SK, including:

[0165] when When the value is >SK, it indicates that the microbial contamination of the airway inside the target ventilator is in an abnormal state. It is necessary to reduce the tidal volume by 5%-20%, reduce the airway drive flow rate by 10%-30%, and at the same time increase the airway flushing frequency by 1.5-3 times.

[0166] when When the value is ≤SK, it indicates that the microbial contamination of the airway inside the target ventilator is in a normal state, and the current ventilation parameters remain unchanged.

[0167] Microbial risk assessment coefficient based on the target ventilator The following is a sample data table showing the microbial risk assessment coefficients for the target ventilator. Example table of data comparing with the microbial risk threshold SK;

[0168] Sampling time number Microbial risk assessment coefficient of the target ventilator Microbial risk threshold SK Comparison results 1 0.508 0.57 The microbial contamination of the target ventilator's internal airway is within normal limits. 2 0.626 0.57 The target ventilator's internal airway is in an abnormal state of microbial contamination. 3 0.457 0.57 The microbial contamination of the target ventilator's internal airway is within normal limits. 4 0.715 0.57 The target ventilator's internal airway is in an abnormal state of microbial contamination. 5 0.543 0.57 The microbial contamination of the target ventilator's internal airway is within normal limits.

[0169] In this embodiment, the microbial contamination status coefficient of the target ventilator is used... airway risk profile coefficient relative to the target ventilator Correlation was performed, and a normalized coupling method was used to construct the microbial risk assessment coefficient for the target ventilator. This approach achieves a comprehensive and integrated characterization of the microbial risk in the ventilator's airway, thereby avoiding the one-sidedness of single-indicator assessments and improving the comprehensiveness and accuracy of risk assessment. By uniformly quantifying contamination status information and risk situation information, microbial risk assessment is upgraded from a single-dimensional assessment to a multi-parameter coupled assessment, improving the stability and discriminative ability of risk characterization. Furthermore, by preset a microbial risk threshold SK, the microbial risk assessment coefficient of the target ventilator is... By comparing with it, we can achieve a graded assessment of the risk of microorganisms in the gas tract.

[0170] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0171] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator, characterized in that, include: The gas sampling module is used to collect gas and condensate in the gas path inside the ventilator before, during, or after use of the target ventilator, and to mark the collection location to obtain gas path sample information. The microfluidic enrichment module is used to introduce gas path sample information into the microfluidic chip, enrich the microorganisms in the sample through the microchannel device, and output the corresponding enriched detection signal. The signal preprocessing module is used to preprocess the enriched detection signal, remove background noise, and generate microbial status data of the target ventilator's internal airway by uniformly dividing the detection area and setting several detection units and marking the signals of several detection units. The multi-parameter data acquisition module is used to identify and monitor the microbial state dataset, obtain the transient flow velocity offset, condensate interface migration speed and equivalent wet film thickness of the enrichment area in the microbial microfluidic channel of the target ventilator's internal airway, and construct a microbial concentration feature dataset. The airway microbial analysis module is used to perform structured analysis of the microbial state data of the target ventilator's internal airway based on preset rules, extract features from microbial concentration data, and combine the microbial concentration feature dataset to obtain the microbial contamination state coefficient of the target ventilator through comprehensive calculation. And based on the microbial contamination status coefficient of the target ventilator Construct the airway risk situation coefficient of the target ventilator ; The microbial risk analysis module is used to determine the microbial contamination status coefficient of the target ventilator. Gas route risk situation coefficient Correlation analysis was performed to calculate the microbial risk assessment coefficient of the target ventilator. And conduct evaluation and optimization.

2. The microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 1, characterized in that, The gas sampling module includes a gas acquisition unit and a condensate acquisition unit; The gas sampling unit is used to collect residual gas in the airway before the target ventilator is used and before the ventilator is connected to the patient. This is done through a gas sampling interface and bypass sampling channel set in the airway and connected to a negative pressure drainer, so as to obtain a gas sample in the initial state of the airway. During the use of the target ventilator, during the stable operation of the ventilator, the sampling flow rate in the bypass sampling channel is limited by the microflow controller. According to the ventilation cycle of the ventilator, negative pressure drainage devices are used to collect samples during the inspiratory and expiratory phases to obtain gas samples that reflect the dynamic microenvironment characteristics in the airway during the ventilation process. After the target ventilator is used, and after ventilation ends and the patient is disconnected, the gas remaining in the airway is drained and collected again using a negative pressure drainage device to obtain a gas sample that reflects the residual state in the airway after ventilation ends. The condensate collection unit is used to collect residual condensate in the airway before the target ventilator is used. Specifically, it includes: when the ventilator is not activated, guiding the condensate adhering to the inner wall of the airway or remaining in the airway to the condensate collection chamber located in the low area of ​​the airway, and outputting the condensate sample in the condensate collection chamber when the condensate reaches the preset collection conditions, so as to obtain a condensate sample reflecting the residual state of the airway before the ventilator is used. During the use of the target ventilator, a condensate diversion channel located in the lower part of the airway continuously guides the condensate that forms on the inner wall of the airway during ventilation. The condensate is collected along the diversion channel under the action of gravity into the condensate collection chamber. The condensate collection chamber is connected to the condensate collection interface, which is equipped with a one-way isolation valve. This allows the condensate to be continuously discharged without affecting the normal ventilation of the airway, so as to obtain a condensate sample that reflects the gradual enrichment of microorganisms during ventilation. After the target ventilator is used, and the patient connection is disconnected after ventilation, the condensate drainage path set in the low area of ​​the airway is opened. This allows the condensate adhering to the inner wall of the airway and remaining in the low area to flow into the condensate collection chamber under the action of gravity along the condensate guide channel. Subsequently, the condensate in the condensate collection chamber is exported in one go through the condensate collection interface to obtain a condensate sample reflecting the degree of residual contamination in the airway after use, thereby obtaining airway sample information.

3. The microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator according to claim 2, characterized in that, The microfluidic enrichment module includes a gas enrichment processing unit and a condensate enrichment processing unit. The gas enrichment processing unit is used to introduce gas samples collected from the internal gas path of the target ventilator before, during, or after use into the gas enrichment channel within the microfluidic chip via a gas sample inlet interface. A microscale flow-limiting device is installed at the front end of the gas enrichment channel. As the gas sample flows within the channel, it passes sequentially through a periodically bending device, causing the microbial aerosol particles in the gas to undergo radial displacement under inertial force. A gas-liquid conversion zone is located downstream of the gas enrichment channel, connected to a pre-placed capture liquid, allowing the gas sample to... Microbial particles in the gas sample enter the capture liquid through inertial impaction, interfacial adsorption, or diffusion deposition during the flow process, realizing the transfer of microorganisms from the gas phase to the liquid phase. After the gas-liquid conversion is completed, the liquid sample enters the microbial enrichment zone. The enrichment zone is equipped with microcolumn arrays or high-density microstructures to limit the liquid flow rate and prolong the residence time of microorganisms, so that the microorganisms transferred to the liquid phase gradually concentrate in a local area to form a stable high-concentration enrichment zone. When the microorganisms in the enrichment zone reach the preset enrichment conditions, the corresponding gas sample enrichment detection signal is output from the enrichment zone. The condensate enrichment processing unit is used to collect condensate samples from the internal airway of the target ventilator before, during, or after use. These samples are introduced into the condensate enrichment channel within the microfluidic chip via a condensate sample inlet interface. A liquid rectifier is installed at the inlet of the condensate enrichment channel. The condensate sample flows along a progressively narrowing microchannel within the enrichment channel, reducing the sample volume without turbulence. A size-selective retention zone is located downstream of the enrichment channel. This zone consists of microscale sieving devices with controlled spacing, selectively retaining microorganisms and microbial aggregates in the condensate, while the liquid carrier continues to flow downstream, separating the microorganisms from the liquid. The retained microorganisms accumulate within the retention zone and, under geometric constraints, form a high-density distribution area, constituting the microbial enrichment zone of the condensate sample. When the microorganisms in the enrichment zone reach the preset enrichment conditions, a corresponding condensate sample enrichment detection signal is output from the enrichment zone.

4. The microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 3, characterized in that, The signal preprocessing module includes a gas sample enrichment signal denoising processing unit, a condensate sample enrichment signal denoising processing unit, and a labeling unit. The gas sample enrichment detection signal denoising unit is used to acquire the background baseline signal of the detection channel at the corresponding sampling stage before, during, and after the use of the target ventilator. This signal is used to characterize the detection system's own noise, gas path environmental disturbances, and non-specific interference signals. After the gas sample enters the microfluidic enrichment area and generates a detection response, the original detection signal is aligned and corrected with the background baseline signal. Background components unrelated to microbial enrichment are canceled out. Simultaneously, the detection signal is time-synchronized and segmented according to the ventilator's ventilation cycle parameters, eliminating abnormal fluctuation signals generated during unstable ventilation stages. Based on this, the corrected detection signal is smoothed and fluctuation suppressed to reduce random noise components caused by gas flow changes, electrical noise, or transient disturbances. Finally, the effective signal component corresponding to the microbial enrichment area is extracted from the processed detection signal to generate a gas sample enrichment detection signal with improved signal-to-noise ratio and enhanced stability. The noise reduction processing unit for the detection signal after condensate sample enrichment is used to, before, during, or after the use of the target ventilator, identify stable segments of the detection signal to distinguish between continuous flow and discontinuous disturbance stages, addressing transient interference signals introduced by unstable liquid flow, interface disturbances, and bubble entrainment in the condensate sample. It then suppresses the identified discontinuous disturbance stage signals to reduce transient spike noise caused by bubble bursting, droplet merging, or liquid surface oscillation. Simultaneously, based on the flow damping characteristics of the condensate in the microfluidic channel, it filters high-frequency random fluctuations in the detection signal that are unrelated to changes in liquid inertia. Furthermore, by comparing the background signal of the non-enriched region in the microfluidic chip with a reference, it eliminates background noise caused by detector background drift, spontaneous signals from the chip material, and changes in ambient temperature and humidity. After the above processing, a noise-reduced detection signal that primarily reflects the microbial enrichment behavior in the condensate is obtained. The marking unit is used to assign a corresponding detection unit identifier to each detection unit after uniformly dividing the detection area and setting up several detection units, and to associate and store the detection unit identifier of each detection unit with its corresponding detection signal when collecting enriched detection signals, thereby realizing the spatial location marking of the detection signals of several detection units and generating microbial status data of the internal airway of the target ventilator.

5. The microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 4, characterized in that, The multi-parameter data acquisition module includes a transient flow velocity offset acquisition unit in the microfluidic channel, a condensate interface migration velocity acquisition unit, and an equivalent wet film thickness acquisition unit in the enrichment region. The transient flow velocity offset acquisition unit in the microfluidic channel is used to set up a micro-flow velocity sensing component at a preset monitoring position in the microfluidic channel. When the sample fluid passes through the microfluidic channel, it continuously acquires the local flow velocity signal at different times in the channel. The continuously acquired flow velocity signal is used to construct a transient flow velocity sequence and compared with the reference flow velocity sequence of the corresponding channel under a preset baseline state to obtain the transient flow velocity offset in the microbial microfluidic channel of the target ventilator internal airway caused by microbial enrichment and changes in local channel resistance. The condensate interface migration velocity acquisition unit is used to set an interface sensing component at a preset position in the condensate enrichment channel or condensate collection area, and to perform continuous imaging or optical detection of the liquid interface formed by the condensate and the surrounding medium; by identifying the interface position change at adjacent acquisition times, the displacement of the condensate interface in the microfluidic channel is obtained, and combined with the acquisition time interval, the migration velocity of the microbial condensate interface in the airway inside the target ventilator is calculated. The equivalent wet film thickness acquisition unit for the enriched area is used to set up optical or electrical sensing components at a preset monitoring position in the enriched area of ​​the microfluidic chip to acquire the signal response characteristics of the liquid-covered area in the enriched area; by detecting the signal attenuation degree or reflection / resistance change characteristics of the enriched area at different sampling times, the change in the coverage area of ​​the liquid on the microstructure surface is indirectly characterized; and by combining the preset geometric parameters of the enriched area and the signal response coefficient corresponding to the unit area, the equivalent wet film thickness of the microbial enriched area in the airway of the target ventilator is calculated. A dataset of microbial concentration characteristics is constructed based on the transient flow velocity offset, condensate interface migration velocity, and equivalent wet film thickness in the microbial microfluidic channel within the internal airway of the target ventilator.

6. The microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 5, characterized in that, The gas path microbial analysis module includes a feature extraction unit, a microbial contamination status analysis unit, a microbial assessment unit, a gas path risk situation analysis unit, and a gas path risk situation assessment unit. The feature extraction unit is used to extract features from the microbial state data of the airway inside the target ventilator, including extracting the cumulative growth rate of microorganisms in the airway inside the target ventilator based on the changing trend of the microbial state data over time. The spatial distribution dispersion of microorganisms in the internal airway of the target ventilator was extracted based on the signal differences between different detection units. Extracting the microbial migration response delay in the internal airway of a target ventilator based on changes in fluid dynamics within a microfluidic channel; The microbial contamination status analysis unit is used to analyze the transient flow rate offset within the microbial microfluidic channel of the target ventilator's internal airway. Condensate interface migration rate Equivalent wet film thickness in enriched areas Cumulative growth rate of microorganisms Spatial distribution dispersion of microorganisms and the delay in microbial migration response The microbial contamination status coefficient of the target ventilator was obtained through the following methods. ; First, by measuring the transient flow rate shift within the microfluidic channels of the target ventilator's internal airway. The contribution of microbial flow velocity disturbance in the airway of the target ventilator was calculated. ; Migration rate of condensate interface within the target ventilator's internal airway The contribution of condensate interface migration in the internal airway of the target ventilator was calculated. ; Equivalent wet film thickness in the enrichment area of ​​the target ventilator's internal airway The contribution of the wet film to the internal airway of the target ventilator was calculated. ; Then, based on the contribution of microbial flow velocity disturbance in the target ventilator's internal airway. Contribution of condensate interface migration in the internal airway of the target ventilator and the contribution of the wet membrane to the internal airway of the target ventilator By combining and normalizing the data, the environmental disturbance factor of the target ventilator's internal airway is obtained through calculation. ; ； Subsequently, the cumulative growth rate of microorganisms in the airway inside the target ventilator was used. The cumulative microbial growth response of the target ventilator was calculated. ; Spatial distribution dispersion of microorganisms in the internal airway of the target ventilator The spatial uniformity of microbial response of the target ventilator was obtained through calculation. ; Utilizing the delay in microbial migration response within the target ventilator's internal airway The contribution of the microbial response delay of the target ventilator was calculated. ; Next, based on the cumulative microbial growth response of the target ventilator Microbial spatial uniformity response of the target ventilator Contribution of microbial response delay to the target ventilator By combining and normalizing the data, the microbial response factors of the target ventilator were calculated. ; ； Finally, based on the environmental disturbance factors of the target ventilator's internal airway. Microbial response factors of the target ventilator By combining the analysis, the microbial contamination status coefficient of the target ventilator was calculated using a formula. ; 。 7. A microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 6, characterized in that, The microbial assessment unit is used to preset the microbial contamination status threshold ZC and to set the microbial contamination status coefficient of the target ventilator. Comparison with the microbial contamination state threshold ZC, including: when When the value is >ZC, it indicates that the microbial contamination status of the airway inside the target ventilator is abnormal. It is necessary to reduce the ventilation drive flow rate by 5%-20%, increase the airway flushing frequency by 2-3 times, and start the local circulation purification treatment of the airway. when When the value is ≤ZC, it indicates that the microbial contamination status of the internal airway of the target ventilator is normal.

8. A microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 7, characterized in that, The gas path risk situation analysis unit is used to analyze the microbial contamination status coefficient of the target ventilator. The airway risk situation coefficient of the target ventilator is calculated using a formula. ; 。 9. A microfluidic enrichment and rapid detection system for microorganisms in the internal airway of a ventilator according to claim 8, characterized in that, The gas path risk situation assessment unit is used to preset the gas path risk situation threshold AW and to set the gas path risk situation coefficient of the target ventilator. Comparison with the gas path risk situation threshold (AW), including: when When the value is >AW, it indicates that the microbial risk situation of the target ventilator's airway is abnormal. It is necessary to reduce the airway flow rate by 10%-30%, increase the airway flushing flow rate by 20%-50%, and start the internal circulation flushing and disinfection procedure of the airway. when When the value is ≤AW, it indicates that the microbial risk status of the target ventilator's airway is normal, and the current ventilation parameters should be maintained.

10. A microfluidic enrichment and rapid detection system for microorganisms in the airway of a ventilator according to claim 9, characterized in that, The microbial risk analysis module includes an association unit and a microbial risk assessment unit; The association unit is used to determine the microbial contamination status coefficient of the target ventilator. airway risk profile coefficient relative to the target ventilator Correlation, after normalization, and calculation, yielded the microbial risk assessment coefficient for the target ventilator. ; ； The microbial risk assessment unit is used to preset the microbial risk threshold SK and to set the microbial risk assessment coefficient of the target ventilator. Comparison with the microbial risk threshold SK, including: when When the value is >SK, it indicates that the microbial contamination of the airway inside the target ventilator is in an abnormal state. It is necessary to reduce the tidal volume by 5%-20%, reduce the airway drive flow rate by 10%-30%, and at the same time increase the airway flushing frequency by 1.5-3 times. when When the value is ≤SK, it indicates that the microbial contamination of the airway inside the target ventilator is in a normal state, and the current ventilation parameters remain unchanged.

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