A ventilator pipeline monitoring device and a monitoring method

Abstract

The present invention relates to the field of medical technologies, and particularly relates to a monitoring device and a monitoring method for a ventilator pipeline, including: obtaining the confidence level of variation data for each sampled respiratory cycle segment according to the distribution of flow rate data in the ventilator pipeline and the degree of pipeline variation in each sampled respiratory cycle segment; obtaining the influence degree of variation for each sampled respiratory cycle segment according to the confidence levels of variation data for all sampled respiratory cycle segments before each sampled respiratory cycle segment; adjusting the angle of the pipeline control clamp according to the influence degree of variation to obtain the optimal angle of the pipeline control clamp. The present invention reduces the possibility of the ventilator becoming an important infection source of ventilator-associated pneumonia and improves the operating quality of the ventilator equipment.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a monitoring device and method for a ventilator pipeline. Background Art

[0002] Ventilator equipment plays an important role in hospital clinical treatment. However, in actual applications, a large amount of condensate will be generated in the ventilator pipeline. This is mainly because the gas after the ventilator humidifier is heated and the exhaled gas of the patient are often higher than the indoor temperature. Therefore, part of the gas is condensed in the pipeline, and the humid environment is prone to bacterial reproduction. Therefore, if the angle of the pipeline control clamp of the ventilator is set improperly, the bacteria-containing condensate may directly flow back into the patient's respiratory tract, or reflux into the humidification tank and then contaminate the humidifying liquid, ultimately causing the ventilator to become an important infection source of ventilator-associated pneumonia. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a monitoring device and method for a ventilator pipeline.

[0004] An embodiment of the present invention provides a monitoring method for a ventilator pipeline, which includes the following steps:

[0005] Obtain the position height data sequence of the ventilator pipeline and the flow velocity time series sequence in the ventilator pipeline at a preset angle of the pipeline control clamp;

[0006] According to the change of the height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, obtain the pipeline change degree at each sampling moment; divide the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series sequence of the ventilator pipeline into multiple sampling respiratory cycle segments; according to the distribution of the flow velocity data in the ventilator pipeline in each sampling respiratory cycle segment and the pipeline change degree, obtain the change data confidence level of each sampling respiratory cycle segment;

[0007] According to the change data confidence levels of all sampling respiratory cycle segments before each sampling respiratory cycle segment, obtain the change influence degree of each sampling respiratory cycle segment; adjust the angle of the pipeline control clamp according to the change influence degree to obtain the optimal angle of the pipeline control clamp.

[0008] Preferably, the method for obtaining the pipeline change degree at each sampling moment according to the change of the height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline includes the following specific method:

[0009] Denote the normalized value of the mean of the height data at all positions on the ventilator pipeline at the th sampling moment as the external mechanical influence value; denote the The ratio of the variance of the height data at all positions on the ventilator pipeline at a sampling moment to the external mechanical influence value is used as the pipeline variation degree at the

[0010] Preferably, the method for dividing the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series of the ventilator pipeline into multiple sampling respiratory cycle segments includes the following specific steps:

[0011] Use the least squares method to perform curve fitting on the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series of the ventilator pipeline to obtain the time-flow velocity curve in the ventilator pipeline;

[0012] Preset a segmentation parameter , and mark the data points on the time-flow velocity curve in the ventilator pipeline where the flow velocity data is as segmentation points; divide the time-flow velocity curve in the ventilator pipeline into multiple curve segments according to all segmentation points; sequentially form data segments from the flow velocity data at all sampling moments in every two curve segments as a sampling respiratory cycle segment.

[0013] Preferably, the method for obtaining the variation data confidence level of each sampling respiratory cycle segment according to the distribution of the flow velocity data in the ventilator pipeline in each sampling respiratory cycle segment and the pipeline variation degree includes the following specific steps:

[0014] Obtain the variation confidence factor at each sampling moment according to the pipeline variation degree;

[0015] Obtain the intermediate distribution degree of the flow velocity data in the pipeline in each sampling respiratory cycle segment according to the distribution of the flow velocity data in the ventilator pipeline in each sampling respiratory cycle segment;

[0016] Take the normalization value of the ratio between the sum of the variation confidence factors at all sampling moments in the th sampling respiratory cycle segment and the intermediate distribution degree of the flow velocity data in the pipeline in the th sampling respiratory cycle segment as the variation data confidence level of the th sampling respiratory cycle segment.

[0017] Preferably, the method for obtaining the variation confidence factor at each sampling moment according to the pipeline variation degree includes the following specific steps:

[0018] Take the ratio of the pipeline variation degree at the th sampling moment to the flow velocity data in the ventilator pipeline at the th sampling moment as the variation confidence factor at the th sampling moment.

[0019] Preferably, the method for obtaining the intermediate distribution degree of the flow rate data in the ventilator pipeline for each sampling respiratory cycle segment according to the distribution of the flow rate data in the ventilator pipeline is as follows:

[0020] Denote the sequence composed of the flow rate data in the ventilator pipeline at all sampling moments in the th sampling respiratory cycle segment as the flow rate data sequence of the th sampling respiratory cycle segment; Denote the cosine similarity between the flow rate data sequence of the th sampling respiratory cycle segment and the standard Gaussian distribution curve as the intermediate distribution degree of the flow rate data in the th sampling respiratory cycle segment.

[0021] Preferably, the method for obtaining the change influence degree of each sampling respiratory cycle segment according to the confidence level of the change data of all sampling respiratory cycle segments before each sampling respiratory cycle segment is as follows:

[0022] Obtain the change influence factor of each sampling respiratory cycle segment according to the confidence level of the change data of each sampling respiratory cycle segment;

[0023] [[ID=2\0]]Denote the normalized value of the sum of the change influence factors of all sampling respiratory cycle segments before the th sampling respiratory cycle segment as the change influence degree of the th sampling respiratory cycle segment.

[0024] Preferably, the method for obtaining the change influence factor of each sampling respiratory cycle segment according to the confidence level of the change data of each sampling respiratory cycle segment is as follows:

[0025] Denote the mean value of the flow rate data in the ventilator pipeline at all sampling moments in the th sampling respiratory cycle segment as the mean value of the flow rate data of the th sampling respiratory cycle segment; Denote the ratio of the confidence level of the change data of the th sampling respiratory cycle segment to the mean value of the flow rate data of the th sampling respiratory cycle segment as the change influence factor of the th sampling respiratory cycle segment.

[0026] Preferably, the method for adjusting the angle of the pipeline control clamp according to the change influence degree to obtain the optimal angle of the pipeline control clamp is as follows:

[0027] Preset a step size parameter , and use the sum of the change influence degrees of all sampling respiratory cycle segments and the step size parameter The product is denoted as the adjustment value; the integer value of the sum of the angle of the pipeline control clamp and the adjustment value is used as the angle of the optimal pipeline control clamp.

[0028] The present invention also provides a ventilator pipeline monitoring device, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned ventilator pipeline monitoring methods are implemented.

[0029] The beneficial effects of the technical solution of the present invention are as follows: According to the distribution of the flow rate data in the ventilator pipeline in each sampled respiratory cycle segment and the degree of pipeline change, the confidence level of the change data in each sampled respiratory cycle segment is obtained; according to the confidence level of the change data in all sampled respiratory cycle segments before each sampled respiratory cycle segment, the influence degree of the change in each sampled respiratory cycle segment is obtained; according to the influence degree of the change, the angle of the pipeline control clamp is adjusted to obtain the angle of the optimal pipeline control clamp. In this way, the bacteria-containing condensed water will not directly flow back into the patient's respiratory tract, or flow back into the humidification tank to contaminate the humidifying liquid, thereby reducing the possibility that the ventilator becomes an important infection source of ventilator-associated pneumonia and improving the operation quality of the ventilator equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 is the flowchart of the steps of a ventilator pipeline monitoring method of the present invention;

[0032] Figure 2 is the flowchart of the characteristic relationship of a ventilator pipeline monitoring method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and effects of a ventilator pipeline monitoring device and monitoring method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0035] The following specifically describes the specific solutions of a ventilator pipeline monitoring device and a monitoring method provided by the present invention in conjunction with the accompanying drawings.

[0036] Please refer to Figure 1 , which shows a flowchart of the steps of a ventilator pipeline monitoring method provided by an embodiment of the present invention. The method includes the following steps:

[0037] Step S001: Obtain the position height data sequence of the ventilator pipeline and the flow rate time series sequence inside the ventilator pipeline at a preset angle of the pipeline control clip.

[0038] It should be noted that monitoring the status of the ventilator pipeline is crucial in the medical environment, especially for the treatment of critically ill patients. By real-time monitoring of gas flow rate parameters, it can ensure unobstructed airways, optimize the treatment effect, and at the same time prevent the risk of infection caused by the accumulation of condensate and secretions. Correspondingly, for the actual body position changes of clinical patients and the possible short-term changes in the gas flow in the ventilator pipeline caused by coughing, it is necessary to real-time monitor the position changes on the ventilator pipeline.

[0039] Specifically, first, it is necessary to obtain the position height data sequence of the ventilator pipeline and the flow rate time series sequence inside the ventilator pipeline at a preset angle of the pipeline control clip. The specific process is as follows:

[0040] Preset an included angle parameter , where this embodiment takes as an example for description, and this embodiment does not make specific limitations, where depends on the specific implementation situation;

[0041] The ventilator pipeline is evenly divided into multiple positions every 7 cm, a flow rate sensor is placed inside the ventilator pipeline, and a position sensor is placed at each position; when the angle of the pipeline control clip is , taking every 1 second as a sampling moment, each time the height data of each position is collected in turn using the position sensor, and the flow rate data inside the ventilator pipeline is collected using the flow rate sensor. A total of 1 hour is collected. The sequence composed of the height data of each position on the ventilator pipeline at each sampling moment is used as the position height data sequence of the ventilator pipeline; the sequence composed of the flow rate data inside the ventilator pipeline at all sampling moments is denoted as the flow rate time series sequence inside the ventilator pipeline.

[0042] So far, through the above method, the position height data sequence of the ventilator pipeline and the flow velocity time series sequence in the ventilator pipeline are obtained at the preset angle of the pipeline control clamp.

[0043] Step S002: According to the change of the height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, obtain the pipeline change degree at each sampling moment; divide the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series sequence of the ventilator pipeline into multiple sampling respiratory cycle segments; according to the distribution of the flow velocity data in each sampling respiratory cycle segment and the pipeline change degree, obtain the change data confidence level of each sampling respiratory cycle segment.

[0044] It should be noted that the flow velocity time series sequence in the ventilator pipeline usually consists of several key stages, and each stage has unique morphological characteristics on the waveform: In the inhalation phase, at the beginning of the waveform, the flow velocity will rise rapidly, forming a steep rising slope, which indicates that gas is flowing rapidly into the patient's lungs; the goal of this stage is to quickly reach the set inhalation flow velocity, and some respiratory modes (such as pressure control mode) may have a stable plateau during the inhalation phase; when the flow velocity reaches the set value, the waveform shows a flat horizontal line for a period of time, meaning that the flow velocity remains constant, which helps to provide a stable tidal volume and gas exchange; In the switching phase, at the end of the inhalation phase, the waveform rapidly drops from the peak to zero, indicating a change in the gas flow direction. This conversion stage usually shows a rapid and steep descending slope, and a smooth switch is crucial for patient comfort and ventilator synchronization; In the exhalation phase, the waveform shows a trend of rapidly dropping from zero to a negative value, indicating that the gas in the lungs begins to be discharged at a high flow velocity. The waveform in this stage is usually relatively steep. As exhalation progresses, the flow velocity gradually decreases, and the waveform tends to be flat and finally approaches zero, indicating the end of the exhalation process and the basic discharge of the gas in the lungs; The morphological characteristics of these stages on the waveform intuitively reflect the gas flow characteristics during the respiratory cycle, thus helping clinical staff to monitor and adjust ventilator parameters to meet the needs of patients.

[0045] Preferably, in some implementation manners of the embodiments of the present invention, for the height data obtained by the position sensor at a single position on the ventilator pipeline, the fluctuation difference of the ventilator pipeline should be due to the actual body position change of the clinical patient and the possible transient change of the gas flow in the ventilator pipeline caused by coughing, resulting in mechanical movement changes at all positions on the ventilator pipeline. Therefore, the specific method for obtaining the pipeline change degree at each sampling moment according to the change of the height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline is as follows:

[0046] The The normalized value of the mean of the height data at all positions on the ventilator pipeline at a sampling moment is denoted as the external mechanical influence value; the ratio of the variance of the height data at all positions on the ventilator pipeline at the th sampling moment to the external mechanical influence value is taken as the pipeline variation degree at the th sampling moment;

[0047] The specific formula is as follows:

[0048]

[0049] In the formula, represents the pipeline variation degree at the th sampling moment; represents the variance of the height data at all positions on the ventilator pipeline at the th sampling moment; represents the mean of the height data at all positions on the ventilator pipeline at the th sampling moment; represents the linear normalization function.

[0050] It should be noted that represents the mechanical displacement condition of all positions on the ventilator pipeline caused by the external mechanical influence of the ventilator, that is, the mechanical variation condition. The larger this value, the greater the degree of influence of the patient's activity, and the greater the influence on the flow data change and condensate in the pipeline.

[0051] It should be noted that since the pipeline variation degree is the pipeline variation condition from the perspective of a single pipeline connection direction, in fact, with the change of the tidal volume in the pipeline, the condensate accumulates to different degrees. If the condensate in the pipeline of the ventilator equipment is not processed in time, it will cause the inner diameter of the pipeline to shrink and the airway resistance to increase; therefore, if there is condensate at a certain place in the pipeline, the lumen will suddenly become thinner when the air flow passes through, and then turbulence will occur, resulting in an increase in airway resistance; and any cause of airway resistance increase will affect the patient; among them, when the tidal volume decreases, the change characteristics shown on the time-flow velocity curve in the ventilator pipeline will present an overall phased change. Therefore, the extraction of this variation condition should be analyzed based on the overall variation condition of all acquisition moments.

[0052] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for dividing the flow velocity data in the ventilator pipeline at all sampling moments into multiple sampling respiratory cycle segments is as follows:

[0053] Preset a segmentation parameter , where in this embodiment, is taken as an example for description, and this embodiment does not make specific limitations, where is determined according to the specific implementation situation;

[0054] Using the least squares method, the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series in the ventilator pipeline is curve-fitted to obtain the time-flow velocity curve in the ventilator pipeline;

[0055] Among them, the least squares method is a prior art and will not be elaborated here in this embodiment.

[0056] The data points on the time-flow velocity curve in the ventilator pipeline with the flow velocity data of are recorded as segmentation points; according to all the segmentation points, the time-flow velocity curve in the ventilator pipeline is divided into multiple curve segments; the flow velocity data at all sampling moments in every two consecutive curve segments are sequentially formed into a data segment as a sampling breathing cycle segment.

[0057] It should be noted that for the flow velocity data in the pipeline of a single sampling breathing cycle segment, with the command ventilation of the ventilator and the patient's spontaneous breathing process, the waveform of the time-flow velocity curve will change to a certain extent, that is, the data fluctuation within the local breathing stage, such as reducing the peak value or increasing the fluctuation condition, while the influence of the condensed water is more inclined to the overall waveform shift.

[0058] Preferably, in some implementation manners of the embodiment of the present invention, according to the distribution of the flow velocity data in the ventilator pipeline in each sampling breathing cycle segment and the degree of pipeline change, the calculation method for obtaining the confidence level of the change data in each sampling breathing cycle segment is as follows:

[0059] The ratio of the degree of pipeline change at the th sampling moment to the flow velocity data in the ventilator pipeline at the th sampling moment is recorded as the change confidence factor at the th sampling moment;

[0060] The sequence formed by the flow velocity data in the ventilator pipeline at all sampling moments in the th sampling breathing cycle segment is recorded as the flow velocity data sequence in the pipeline of the th sampling breathing cycle segment; the cosine similarity between the flow velocity data sequence in the pipeline of the th sampling breathing cycle segment and the standard Gaussian distribution curve is recorded as the intermediate distribution degree of the flow velocity data in the pipeline of the th sampling breathing cycle segment;

[0061] The normalized value of the ratio between the sum of the change confidence factors at all sampling moments in the th sampling breathing cycle segment and the intermediate distribution degree of the flow velocity data in the pipeline of the th sampling breathing cycle segment is used as the confidence level of the change data in the th sampling breathing cycle segment;

[0062] The specific formula is as follows:

[0063]

[0064] In the formula, represents the confidence level of the variation data of the th sampling respiratory cycle segment; represents the number of all sampling moments in the th sampling respiratory cycle segment; represents the degree of pipeline variation at the th sampling moment in the th sampling respiratory cycle segment; represents the flow rate data in the ventilator pipeline at the th sampling moment in the th sampling respiratory cycle segment; represents the intermediate distribution degree of the flow rate data in the pipeline of the th sampling respiratory cycle segment; represents the linear normalization function.

[0065] It should be noted that the flow rate data sequence of the pipeline in the th sampling respiratory cycle segment is mainly determined by the fluctuation range of the flow rate data. When the deviation caused by the condensate in the pipeline is more obvious, the data values in the flow rate data sequence of the pipeline in the th sampling respiratory cycle segment tend to be more centered; while the change trend of the flow rate data in the flow rate data sequence of the pipeline in the th sampling respiratory cycle segment reflects the sensitivity of the current in-pipe flow rate data to the variation of the overall pipeline vibration condition. The larger the change amplitude, the more likely the pipeline is to cause flow rate changes of different degrees.

[0066] Thus, the confidence level of the variation data of each sampling respiratory cycle segment is obtained through the above method.

[0067] Step S003: Obtain the influence degree of each sampling respiratory cycle segment according to the confidence level of the variation data of all sampling respiratory cycle segments before each sampling respiratory cycle segment; adjust the angle of the pipeline control clamp according to the influence degree to obtain the optimal angle of the pipeline control clamp.

[0068] It should be noted that since the influence of the condensate on the pipeline is gradually accumulated, as the condensate accumulates gradually, when the angle of the pipeline control clamp cannot enable the condensate to be smoothly collected into the condensate collection device, it will show an obvious accumulation effect in the flow rate data, that is, the variation increases in multiple sampling respiratory cycle segments.

[0069] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for obtaining the change influence degree of each sampled breathing cycle segment according to the confidence degrees of the change data of all the sampled breathing cycle segments before each sampled breathing cycle segment is as follows:

[0070] Denote the mean value of the flow rate data in the ventilator pipeline at all sampling moments in the th sampled breathing cycle segment as the flow rate data mean value of the th sampled breathing cycle segment; Denote the ratio of the confidence degree of the change data of the th sampled breathing cycle segment to the flow rate data mean value of the th sampled breathing cycle segment as the change influence factor of the th sampled breathing cycle segment;

[0071] Take the normalized value of the sum of the change influence factors of all the sampled breathing cycle segments before the th sampled breathing cycle segment as the change influence degree of the th sampled breathing cycle segment;

[0072] The specific formula is as follows:

[0073]

[0074] In the formula, represents the change influence degree of the th sampled breathing cycle segment; represents the number of all the sampled breathing cycle segments before the th sampled breathing cycle segment; represents the confidence degree of the change data of the th sampled breathing cycle segment before the th sampled breathing cycle segment; represents the mean value of the flow rate data in the ventilator pipeline at all sampling moments in the th sampled breathing cycle segment before the th sampled breathing cycle segment; represents the linear normalization function.

[0075] It should be noted that since the adjustment of the condensate collection should be adjusted according to the flow influence condition of the device, when the flow influence conditions tend to be stable, the relevant parameters of the condensate collection need to be adjusted at this time. Here, the angle of the pipeline control clamp is selected for adjustment. For the adjustment of the flow influence condition of the device, the pipeline change condition in each sampled breathing cycle segment should tend to be stable.

[0076] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for adjusting the angle of the pipeline control clamp according to the degree of change influence to obtain the optimal angle of the pipeline control clamp is as follows:

[0077] Preset a step size parameter , where in this embodiment, is taken as an example for description, and this embodiment does not make specific limitations, where is determined according to the specific implementation situation;

[0078] Multiply the sum of the degrees of change influence of all sampled breathing cycle segments by the step size parameter , and record it as the adjustment value; take the integer value of the sum of the angle of the pipeline control clamp and the adjustment value as the optimal angle of the pipeline control clamp;

[0079] The specific formula is:

[0080]

[0081] In the formula, represents the optimal angle of the pipeline control clamp; represents the angle of the pipeline control clamp; represents the degree of change influence of the th sampled breathing cycle segment; represents the number of all sampled breathing cycle segments; represents the preset step size parameter; represents rounding up.

[0082] So far, the optimal angle of the pipeline control clamp is obtained through the above method.

[0083] Set the angle of the pipeline control clamp to the optimal angle of the pipeline control clamp, and then monitor the ventilator pipeline, so that the bacteria-containing condensate will not directly flow back into the patient's respiratory tract, or flow back into the humidification tank and contaminate the humidifying liquid.

[0084] Please refer to Figure 2 , which shows a characteristic relationship flowchart of a ventilator pipeline monitoring method.

[0085] Through the above steps, a ventilator pipeline monitoring method is completed.

[0086] The present invention also provides a ventilator pipeline monitoring device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the ventilator pipeline monitoring method described in steps S001 to S003 are implemented. The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring a ventilator pipeline, characterized in that, The method includes the following steps: At a preset angle of the pipeline control clamp, obtain the position height data sequence of the ventilator pipeline and the flow velocity time series sequence inside the ventilator pipeline; According to the change of the height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, obtain the pipeline change degree at each sampling moment; divide the flow velocity data inside the ventilator pipeline at all sampling moments in the flow velocity time series sequence of the ventilator pipeline into multiple sampling respiratory cycle segments; according to the distribution of the flow velocity data inside the ventilator pipeline in each sampling respiratory cycle segment and the pipeline change degree, obtain the change data confidence level of each sampling respiratory cycle segment; According to the change data confidence levels of all sampling respiratory cycle segments before each sampling respiratory cycle segment, obtain the change influence degree of each sampling respiratory cycle segment; adjust the angle of the pipeline control clamp according to the change influence degree to obtain the optimal angle of the pipeline control clamp.

2. The method for monitoring a ventilator pipeline according to claim 1, wherein The specific method for obtaining the pipeline change degree at each sampling moment according to the change of the height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline is as follows: Normalize the mean value of the height data at all positions on the ventilator pipeline at the th sampling moment, and denote it as the external mechanical influence value; take the ratio of the variance of the height data at all positions on the ventilator pipeline at the th sampling moment to the external mechanical influence value as the degree of pipeline variation at the th sampling moment.

3. The method for monitoring a ventilator pipeline according to claim 1, wherein The specific method for dividing the flow velocity data inside the ventilator pipeline at all sampling moments in the flow velocity time series sequence of the ventilator pipeline into multiple sampling respiratory cycle segments is as follows: Use the least squares method to perform curve fitting on the flow velocity data inside the ventilator pipeline at all sampling moments in the flow velocity time series sequence of the ventilator pipeline to obtain the time-flow velocity curve inside the ventilator pipeline; Preset a segmentation parameter , and mark the data points on the time-flow rate curve in the ventilator pipeline with a flow rate data of as segmentation points; divide the time-flow rate curve in the ventilator pipeline into multiple curve segments according to all the segmentation points; successively form data segments from the flow rate data at all sampling moments in every two curve segments as a sampling respiratory cycle segment.

4. The method for monitoring a ventilator pipeline according to claim 1, wherein, The specific method for obtaining the change data confidence level of each sampling respiratory cycle segment according to the distribution of the flow velocity data inside the ventilator pipeline in each sampling respiratory cycle segment and the pipeline change degree is as follows: According to the pipeline change degree, obtain the change confidence factor at each sampling moment; According to the distribution of the flow velocity data inside the ventilator pipeline in each sampling respiratory cycle segment, obtain the intermediate distribution degree of the flow velocity data inside the pipeline in each sampling respiratory cycle segment; The sum of the cumulative variation confidence factors at all sampling times in the th sampled breathing cycle segment is divided by the degree of intermediate distribution of the flow velocity data in the pipeline in the th sampled breathing cycle segment, and the normalized value of the ratio is used as the variation data confidence level of the th sampled breathing cycle segment.

5. The method for monitoring a ventilator pipeline according to claim 4, wherein The specific method for obtaining the change confidence factor at each sampling moment according to the pipeline change degree is as follows: The ratio of the degree of pipeline change at the th sampling moment to the flow velocity data in the ventilator pipeline at the th sampling moment is denoted as the change confidence factor at the th sampling moment.

6. The method for monitoring a ventilator pipeline according to claim 4, wherein The specific method for obtaining the intermediate distribution degree of the flow velocity data inside the pipeline in each sampling respiratory cycle segment according to the distribution of the flow velocity data inside the ventilator pipeline in each sampling respiratory cycle segment is as follows: Denote the sequence composed of the flow velocity data in the ventilator pipeline at all sampling moments in the th sampled respiratory cycle segment as the pipeline flow velocity data sequence of the th sampled respiratory cycle segment; Denote the cosine similarity between the pipeline flow velocity data sequence of the th sampled respiratory cycle segment and the standard Gaussian distribution curve as the intermediate distribution degree of the pipeline flow velocity data of the th sampled respiratory cycle segment.

7. The method for monitoring a ventilator pipeline according to claim 1, wherein The specific method for obtaining the change influence degree of each sampling respiratory cycle segment according to the change data confidence levels of all sampling respiratory cycle segments before each sampling respiratory cycle segment is as follows: According to the change data confidence level of each sampling respiratory cycle segment, obtain the change influence factor of each sampling respiratory cycle segment; Normalize the sum of the change impact factors of all sampling respiratory cycle segments before the th sampling respiratory cycle segment, and use it as the change impact degree of the th sampling respiratory cycle segment.

8. The method for monitoring a ventilator pipeline according to claim 7, characterized in that, The specific method for obtaining the change influence factor of each sampling respiratory cycle segment according to the change data confidence level of each sampling respiratory cycle segment is as follows: Denote the mean value of the flow rate data in the ventilator pipeline at all sampling moments in the th sampled respiratory cycle segment as the mean value of the flow rate data in the th sampled respiratory cycle segment; Denote the ratio of the confidence level of the variation data in the th sampled respiratory cycle segment to the mean value of the flow rate data in the th sampled respiratory cycle segment as the variation influence factor in the th sampled respiratory cycle segment.

9. The method for monitoring a ventilator pipeline according to claim 1, characterized in that The specific method for adjusting the angle of the pipeline control clamp according to the change influence degree to obtain the optimal angle of the pipeline control clamp is as follows: Preset a step parameter , and denote the product of the cumulative sum of the influence degrees of the changes in all sampled respiratory cycle segments and the step parameter as the adjustment value; take the integer value of the sum of the angle of the pipeline control clamp and the adjustment value as the angle of the optimal pipeline control clamp.

10. A ventilator pipeline monitoring device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a ventilator pipeline monitoring method as described in any one of claims 1-9.

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