Breathing machine pipeline monitoring device and monitoring method

Abstract

The invention relates to the technical field of medical treatment, in particular to a breathing machine pipeline monitoring device and method.The breathing machine pipeline monitoring method comprises the steps that according to the distribution condition of flow velocity data in a breathing machine pipeline in each sampling breathing period and the pipeline change degree, the change data confidence coefficient of each sampling breathing period is obtained; according to the change data confidence of all the sampling respiratory cycle segments before each sampling respiratory cycle segment, the change influence degree of each sampling respiratory cycle segment is obtained; the angle of the pipeline control clamp is adjusted according to the change influence degree, and the optimal angle of the pipeline control clamp is obtained. The possibility that the breathing machine becomes an important infection source of related pneumonia of a patient is reduced, and the running quality of breathing machine equipment is improved.

Description

Technical Field

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

[0002] Ventilator equipment plays an important role in clinical treatment in hospitals, but in actual applications, a large amount of condensed water will be generated in the ventilator pipeline. This is mainly because the gas heated by the ventilator humidifier and the patient's exhaled gas is often higher than the room temperature, so some of the gas is condensed in the pipeline, and the humidified environment is prone to bacterial reproduction; therefore, if the angle of the ventilator's pipeline control clamp is improperly set, the bacterial condensed water may directly flow back into the patient's respiratory tract, or flow back into the humidification tank and contaminate the humidification fluid, eventually causing the ventilator to become an important source of infection for patient-related pneumonia. Summary of the invention

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

[0004] An embodiment of the present invention provides a ventilator circuit monitoring method, the method comprising the following steps: At a preset angle of the pipeline control clamp, obtain a position height data sequence of the ventilator pipeline and a flow rate time sequence in the ventilator pipeline; According to the height data changes at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, the pipeline change degree at each sampling moment is obtained; the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series sequence in the ventilator pipeline is divided into multiple sampling breathing cycle segments; according to the distribution of the flow velocity data in the ventilator pipeline in each sampling breathing cycle segment and the pipeline change degree, the change data confidence of each sampling breathing cycle segment is obtained; According to the confidence of the change data of all the sampling breathing cycle segments before each sampling breathing cycle segment, the change impact degree of each sampling breathing cycle segment is obtained; according to the change impact degree, the angle of the pipeline control clamp is adjusted to obtain the optimal angle of the pipeline control clamp.

[0005] Preferably, the method of obtaining the pipeline change degree at each sampling moment according to the height data change 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 methods: The first The normalized value of the mean of the height data of all positions on the ventilator pipeline at the sampling time is recorded as the external mechanical influence value; The ratio of the variance of the height data at all positions on the ventilator pipeline at the sampling time to the external mechanical influence value is taken as the The degree of pipeline change at each sampling time.

[0006] Preferably, the flow rate data in the ventilator pipeline at all sampling moments in the flow rate time series in the ventilator pipeline is divided into a plurality of sampling breathing cycle segments, including the specific method of: The flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series in the ventilator pipeline are curve-fitted using the least squares method to obtain a time flow velocity curve in the ventilator pipeline; Preset a segment parameter , the flow rate data on the time-flow rate curve in the ventilator circuit is The data points are recorded as segmentation points; the time flow velocity curve in the ventilator pipeline is divided into multiple curve segments according to all segmentation points; the flow velocity data at all sampling moments in every two curve segments are sequentially formed into a data segment as a sampling breathing cycle segment.

[0007] Preferably, the specific method of obtaining the change data confidence of each sampling breathing cycle segment according to the distribution of flow rate data in the ventilator pipeline in each sampling breathing cycle segment and the degree of pipeline change includes: According to the degree of pipeline change, obtain the change confidence factor at each sampling time; According to the distribution of the flow velocity data in the ventilator pipeline in each sampling breathing cycle segment, the intermediate distribution degree of the flow velocity data in the pipeline of each sampling breathing cycle segment is obtained; The first The cumulative sum of the variable confidence factors at all sampling moments in the sampling breathing cycle is equal to the The normalized value of the ratio between the intermediate distribution of the flow velocity data in the pipeline of the first sampling breathing cycle segment is used as the The confidence level of the variation data of each sampling breathing cycle segment.

[0008] Preferably, the specific method of obtaining the change confidence factor at each sampling time according to the pipeline change degree is as follows: The first The degree of pipeline change at the sampling time is The ratio of the flow rate data in the ventilator circuit at the sampling time is recorded as The confidence factor of the change at each sampling moment.

[0009] Preferably, the method of obtaining the intermediate distribution degree of the flow velocity data in the pipeline of each sampling breathing cycle segment according to the distribution of the flow velocity data in the ventilator pipeline in each sampling breathing cycle segment includes the following specific methods: The first The sequence of flow rate data in the ventilator circuit at all sampling moments in the sampling breathing cycle is recorded as The data sequence of the flow rate in the pipeline of the sampling breathing cycle segment; The cosine similarity between the in-pipe velocity data sequence of the 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 during a sampling breathing cycle.

[0010] Preferably, the variation influence degree of each sampling breathing cycle segment is obtained according to the variation data confidence of all sampling breathing cycle segments before each sampling breathing cycle segment, and the specific method includes: According to the confidence level of the change data of each sampling breathing cycle segment, the change impact factor of each sampling breathing cycle segment is obtained; The first The normalized value of the cumulative sum of the change influence factors of all sampling breathing cycle segments before the first sampling breathing cycle segment is taken as the first The degree of influence of changes in the sampling breathing cycle segment.

[0011] Preferably, the specific method of obtaining the change impact factor of each sampling breathing cycle segment according to the change data confidence of each sampling breathing cycle segment is as follows: The first The mean value of the flow rate data in the ventilator circuit at all sampling times in the sampling breathing cycle is recorded as The average flow rate data of the sampling breathing cycle segment; The confidence of the variation data of the first sampling breathing cycle segment is The ratio of the mean flow rate data of the sampling breathing cycle segment is recorded as The influence factor of the variation of each sampling breathing cycle segment.

[0012] Preferably, the adjusting the angle of the pipeline control clamp according to the degree of influence of the change to obtain the optimal angle of the pipeline control clamp includes the following specific methods: Preset a step size parameter , the cumulative sum of the influence of changes in all sampling breathing cycle segments and the step parameter The product of is recorded as the adjustment value; the integer value of the sum of the angle of the pipeline control clamp and the adjustment value is taken as the optimal angle of the pipeline control clamp.

[0013] The present invention also proposes a ventilator circuit monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the steps of the ventilator circuit monitoring method when executing the computer program.

[0014] The beneficial effects of the technical solution of the present invention are as follows: the present invention obtains the confidence of the change data of each sampling breathing cycle segment according to the distribution of the flow rate data in the ventilator pipeline in each sampling breathing cycle segment and the degree of pipeline change; obtains the degree of change influence of each sampling breathing cycle segment according to the confidence of the change data of all sampling breathing cycle segments before each sampling breathing cycle segment; adjusts the angle of the pipeline control clamp according to the degree of change influence to obtain the optimal angle of the 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 and then contaminate the humidification liquid, thereby reducing the possibility of the ventilator becoming an important source of infection for patient-related pneumonia and improving the operation quality of the ventilator equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of the steps of a ventilator circuit monitoring method of the present invention; Figure 2 The present invention is a characteristic relationship flow chart of a ventilator pipeline monitoring method. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a ventilator circuit monitoring device and monitoring method proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of a ventilator pipeline monitoring device and a monitoring method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a method for monitoring a ventilator circuit provided by an embodiment of the present invention, the method comprising the following steps: Step S001: Under a preset angle of the pipeline control clamp, obtain a position height data sequence of the ventilator pipeline and a flow rate time series sequence in the ventilator pipeline.

[0021] It should be noted that monitoring the status of ventilator circuits is crucial in medical environments, especially for the treatment of critically ill patients. Real-time monitoring of gas flow rate parameters can ensure airway patency and optimize treatment outcomes, while preventing infection risks caused by accumulation of condensed water and secretions. Correspondingly, actual changes in clinical patient position and possible wheezing and coughing can lead to temporary changes in gas flow in the ventilator circuit, so real-time monitoring of position changes on the ventilator circuit is required.

[0022] Specifically, it is first necessary to obtain the position height data sequence of the ventilator pipeline and the flow rate time sequence in the ventilator pipeline at the preset angle of the pipeline control clamp. The specific process is as follows: Preset an angle parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; The ventilator pipeline is evenly divided into multiple positions every 7 cm, and a flow rate sensor is placed in the ventilator pipeline, and a position sensor is placed at each position; the angle of the pipeline control clamp is With every 1 second as a sampling moment, the position sensor is used to collect the height data of each position, and the flow rate sensor is used to collect the flow rate data in the ventilator pipeline. The sampling is done for 1 hour in total. The sequence composed of the height data of each position on the ventilator pipeline at each sampling moment is recorded as the position height data sequence of the ventilator pipeline; the sequence composed of the flow rate data in the ventilator pipeline at all sampling moments is recorded as the flow rate time series sequence in the ventilator pipeline.

[0023] So far, the above method is used to obtain the position height data sequence of the ventilator pipeline and the flow rate time sequence in the ventilator pipeline at the preset angle of the pipeline control clamp.

[0024] Step S002: According to the change of height data at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, the degree of pipeline change at each sampling moment is obtained; the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series sequence in the ventilator pipeline is divided into multiple sampling breathing cycle segments; according to the distribution of flow velocity data in the ventilator pipeline in each sampling breathing cycle segment, and the degree of pipeline change, the confidence of the change data in each sampling breathing cycle segment is obtained.

[0025] It should be noted that the flow rate timing sequence in the ventilator circuit is usually composed of several key stages, each of which has unique morphological characteristics in the waveform: during the inspiratory phase, at the beginning of the waveform, the flow rate will rise rapidly, forming a steep upward slope, which indicates that the gas is flowing into the patient's lungs rapidly; the goal of this stage is to quickly reach the set inspiratory flow rate, and some breathing modes (such as pressure control mode) may have a stable platform during the inspiratory phase; when the flow rate reaches the set value, the waveform presents a flat horizontal line for a period of time, which means that the flow rate remains constant, which helps to provide stable tidal volume and gas exchange; during the switching period, at the end of the inspiratory phase, the waveform rapidly decreases from the peak to the bottom. During the exhalation phase, the waveform shows a rapid downward trend from zero to a negative value, indicating that the gas in the lungs begins to be discharged at a higher flow rate. The waveform in this phase is usually steeper. As the exhalation proceeds, the flow rate gradually decreases, the waveform tends to be flat, and finally approaches zero, indicating that the exhalation process is over and the gas in the lungs is basically discharged. The waveforms of these stages intuitively reflect the gas flow characteristics in the respiratory cycle, thereby helping clinical staff monitor and adjust ventilator parameters to meet the needs of patients.

[0026] Preferably, in some implementations 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 position change of the clinical patient and the possible short-term change of the gas flow in the ventilator pipeline caused by wheezing and coughing, resulting in mechanical movement changes at all positions on the ventilator pipeline. Therefore, according to the height data changes at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, the specific method for obtaining the pipeline change degree at each sampling moment is: The first The normalized value of the mean of the height data of all positions on the ventilator pipeline at the sampling time is recorded as the external mechanical influence value; The ratio of the variance of the height data at all positions on the ventilator pipeline at the sampling time to the external mechanical influence value is taken as the The degree of pipeline change at each sampling time; The specific formula is:

[0027] In the formula, Indicates The degree of pipeline changes at each sampling time; Indicates The variance of the height data at all locations on the ventilator pipeline at each sampling time; Indicates The mean of the height data of all positions on the ventilator pipeline at the sampling time; represents the linear normalization function.

[0028] It should be noted that It indicates the mechanical displacement of all positions on the ventilator pipeline due to the external mechanical influence of the ventilator, that is, the mechanical change condition. The larger the value, the greater the degree of influence of patient activity, and the greater the impact on the flow data change and condensed water in the pipeline.

[0029] It should be noted that since the degree of pipeline change refers to the pipeline change condition from the perspective of a single pipeline connection direction, in fact, with the change of tidal volume in the pipeline, condensed water will accumulate to varying degrees. If the condensed water in the pipeline of the ventilator equipment is not dealt with in time, it will cause the inner diameter of the pipeline to shrink and the airway resistance to increase. Therefore, if there is condensed water at a certain point in the pipeline, the lumen will suddenly become thinner when the airflow passes through, and turbulence will occur, resulting in increased airway resistance. Any increase in airway resistance caused by any reason will affect the patient. Among them, the decrease in tidal volume, its change characteristics manifested in the time velocity curve in the ventilator pipeline will show a stage-by-stage overall change, so the extraction of this change condition should be based on the overall change condition at all acquisition times for analysis.

[0030] Preferably, in some implementations of the embodiments of the present invention, the specific method of dividing the flow rate data in the ventilator pipeline at all sampling moments into a plurality of sampling breathing cycle segments is: Preset a segment parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; The flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series in the ventilator pipeline are curve-fitted using the least squares method to obtain a time flow velocity curve in the ventilator pipeline; Among them, the least square method is an existing technology and will not be described in detail in this embodiment.

[0031] The flow rate data on the time-flow rate curve in the ventilator pipeline is The data points are recorded as segmentation points; the time flow velocity curve in the ventilator pipeline is divided into multiple curve segments according to all segmentation points; the flow velocity data at all sampling moments in every two curve segments are sequentially formed into a data segment as a sampling breathing cycle segment.

[0032] It should be noted that for the flow rate data in the pipeline of a single sampling breathing cycle segment, the waveform of the time flow rate curve will change to a certain extent with the command ventilation of the ventilator and the patient's spontaneous breathing process, that is, the data fluctuations in the local breathing stage, such as reducing the peak value or increasing the fluctuation condition, and the impact of condensed water is more inclined to the overall waveform deviation.

[0033] Preferably, in some implementations of the embodiments of the present invention, according to the distribution of flow rate data in the ventilator pipeline in each sampling breathing cycle segment and the degree of pipeline change, the calculation method for obtaining the confidence of the change data of each sampling breathing cycle segment is: The first The degree of pipeline change at the sampling time is The ratio of the flow rate data in the ventilator circuit at the sampling time is recorded as The confidence factor of the change at each sampling moment; The first The sequence of flow rate data in the ventilator circuit at all sampling moments in the sampling breathing cycle is recorded as The data sequence of the flow rate in the pipeline of the sampling breathing cycle segment; The cosine similarity between the in-pipe velocity data sequence of the 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 during each sampling breathing cycle; The first The cumulative sum of the variable confidence factors at all sampling moments in the sampling breathing cycle is equal to the The normalized value of the ratio between the intermediate distribution of the flow velocity data in the pipeline of the first sampling breathing cycle segment is used as the The confidence level of the variation data of each sampling breathing cycle segment; The specific formula is:

[0034] In the formula, Indicates The confidence level of the variation data of each sampling breathing cycle segment; Indicates The number of all sampling moments in a sampling breathing cycle segment; Indicates The first sampling breathing cycle The degree of pipeline changes at each sampling time; Indicates The first sampling breathing cycle Flow rate data in the ventilator circuit at each sampling moment; Indicates The intermediate distribution degree of the flow velocity data in the pipeline during each sampling breathing cycle; represents the linear normalization function.

[0035] It should be noted that The velocity data sequence in the pipeline of each sampling breathing cycle segment is mainly determined by the fluctuation range of the velocity data. When the deviation caused by condensed water in the pipeline is more obvious, the The data values ​​in the pipeline flow rate data sequence of the first sampling breathing cycle segment are more inclined to the middle distribution; The changing trend of the flow velocity data in the pipeline flow velocity data sequence of each sampling breathing cycle segment reflects that the flow velocity data in the pipeline at this time is sensitive to changes in the overall vibration condition of the pipeline. The larger the change amplitude, the more likely the pipeline is to cause changes in flow to varying degrees.

[0036] So far, the confidence of the change data of each sampling breathing cycle segment is obtained through the above method.

[0037] Step S003: Obtain the influence degree of the change of each sampling breathing cycle segment according to the confidence degree of the change data of all sampling breathing cycle segments before each sampling breathing cycle segment; adjust the angle of the pipeline control clamp according to the influence degree of the change to obtain the optimal angle of the pipeline control clamp.

[0038] It should be noted that since the impact of condensed water on the pipeline is gradually accumulated, as the condensed water gradually accumulates, when the angle of the pipeline control clamp cannot allow the condensed water to be smoothly collected into the condensed water collection device, it will show an obvious accumulation effect in the flow rate data, that is, the changes in multiple sampling breathing cycle segments will increase.

[0039] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the degree of influence of the change of each sampling breathing cycle segment according to the confidence of the change data of all sampling breathing cycle segments before each sampling breathing cycle segment is: The first The mean value of the flow rate data in the ventilator circuit at all sampling times in the sampling breathing cycle is recorded as The average flow rate data of the sampling breathing cycle segment; The confidence of the variation data of the first sampling breathing cycle segment is The ratio of the mean flow rate data of the sampling breathing cycle segment is recorded as The influence factor of the variation of each sampling breathing cycle segment; The first The normalized value of the cumulative sum of the change impact factors of all sampling breathing cycle segments before the sampling breathing cycle segment is taken as the first The degree of influence of changes in each sampling breathing cycle segment; The specific formula is:

[0040] In the formula, Indicates The degree of influence of changes in each sampling breathing cycle segment; Indicates The number of all sampling breathing cycle segments before the sampling breathing cycle segment; Indicates The first sampling breathing cycle before The confidence level of the variation data of each sampling breathing cycle segment; Indicates The first sampling breathing cycle before The mean value of the flow rate data in the ventilator circuit at all sampling times in a sampling breathing cycle segment; represents the linear normalization function.

[0041] It should be noted that since the adjustment of condensed water collection should be based on the flow influence conditions of the device, when the flow influence conditions tend to be stable, it is necessary to adjust the relevant parameters of condensed water collection. Here, we choose to adjust the angle of the pipeline control clamp. For the adjustment of the flow influence conditions of the device, the pipeline changes in each sampling breathing cycle segment should be stabilized.

[0042] Preferably, in some implementations of the embodiments of the present invention, the angle of the pipeline control clamp is adjusted according to the degree of influence of the change, and the specific method for obtaining the optimal angle of the pipeline control clamp is: Preset a step size parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; The cumulative sum of the influence of changes in all sampling breathing cycle segments and the step parameter The product of is recorded as the adjustment value; the integer value of the sum of the angle of the pipeline control clamp and the adjustment value is taken as the optimal angle of the pipeline control clamp; The specific formula is:

[0043] In the formula, Indicates the angle of the optimal pipe control clamp; Indicates the angle of the pipe control clamp; Indicates The degree of influence of changes in each sampling breathing cycle segment; Indicates the number of all sampled breathing cycle segments; Indicates the preset step size parameter; Indicates rounding up.

[0044] At this point, the optimal angle of the pipeline control clamp is obtained through the above method.

[0045] The angle of the tube control clamp is set to the optimal angle of the tube control clamp, and then the ventilator tube is monitored to ensure that the bacteria-containing condensed water does not directly flow back into the patient's respiratory tract, or flow back into the humidification tank and contaminate the humidification fluid.

[0046] See also Figure 2 , which shows a characteristic relationship flow chart of a ventilator circuit monitoring method.

[0047] Through the above steps, a ventilator circuit monitoring method is completed.

[0048] The present invention also provides a ventilator circuit monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a ventilator circuit monitoring method described in step S001 to step S003 when executing the computer program. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A ventilator circuit monitoring method, characterized in that: The method comprises the following steps: At a preset angle of the pipeline control clamp, obtain a position height data sequence of the ventilator pipeline and a flow rate time sequence in the ventilator pipeline; According to the height data changes at different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline, the pipeline change degree at each sampling moment is obtained; the flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series sequence in the ventilator pipeline is divided into multiple sampling breathing cycle segments; according to the distribution of the flow velocity data in the ventilator pipeline in each sampling breathing cycle segment and the pipeline change degree, the change data confidence of each sampling breathing cycle segment is obtained; According to the confidence of the change data of all the sampling breathing cycle segments before each sampling breathing cycle segment, the change impact degree of each sampling breathing cycle segment is obtained; according to the change impact degree, the angle of the pipeline control clamp is adjusted to obtain the optimal angle of the pipeline control clamp.

2. A ventilator circuit monitoring method according to claim 1, characterized in that: The method of obtaining the pipeline change degree at each sampling moment according to the height data change of different positions on the ventilator pipeline at each sampling moment in the position height data sequence of the ventilator pipeline includes: The first The normalized value of the mean of the height data of all positions on the ventilator pipeline at the sampling time is recorded as the external mechanical influence value; The ratio of the variance of the height data at all positions on the ventilator pipeline at the sampling time to the external mechanical influence value is taken as the The degree of pipeline change at each sampling time.

3. A ventilator circuit monitoring method according to claim 1, characterized in that: The specific method of dividing the flow rate data in the ventilator pipeline at all sampling moments in the flow rate time series in the ventilator pipeline into a plurality of sampling breathing cycle segments includes: The flow velocity data in the ventilator pipeline at all sampling moments in the flow velocity time series in the ventilator pipeline are curve-fitted using the least squares method to obtain a time flow velocity curve in the ventilator pipeline; Preset a segment parameter , the flow rate data on the time-flow rate curve in the ventilator circuit is The data points are recorded as segmentation points; the time flow velocity curve in the ventilator pipeline is divided into multiple curve segments according to all segmentation points; the flow velocity data at all sampling moments in every two curve segments are sequentially formed into a data segment as a sampling breathing cycle segment.

4. A ventilator circuit monitoring method according to claim 1, characterized in that: The specific method of obtaining the change data confidence of each sampling breathing cycle segment according to the distribution of the flow rate data in the ventilator pipeline in each sampling breathing cycle segment and the degree of pipeline change is as follows: According to the degree of pipeline change, obtain the change confidence factor at each sampling time; According to the distribution of the flow velocity data in the ventilator pipeline in each sampling breathing cycle segment, the intermediate distribution degree of the flow velocity data in the pipeline of each sampling breathing cycle segment is obtained; The first The cumulative sum of the variable confidence factors at all sampling moments in the sampling breathing cycle is equal to the The normalized value of the ratio between the intermediate distribution of the flow velocity data in the pipeline of the first sampling breathing cycle segment is used as the The confidence level of the variation data of each sampling breathing cycle segment.

5. A ventilator circuit monitoring method according to claim 4, characterized in that: The specific method of obtaining the change confidence factor at each sampling time according to the pipeline change degree is as follows: The first The degree of pipeline change at the sampling time is The ratio of the flow rate data in the ventilator circuit at the sampling time is recorded as The confidence factor of the change at each sampling moment.

6. A ventilator circuit monitoring method according to claim 4, characterized in that: The method of obtaining the intermediate distribution degree of the flow rate data in the pipeline of each sampling breathing cycle segment according to the distribution of the flow rate data in the ventilator pipeline in each sampling breathing cycle segment includes the following specific methods: The first The sequence of flow rate data in the ventilator circuit at all sampling moments in the sampling breathing cycle is recorded as The data sequence of the flow rate in the pipeline of the sampling breathing cycle segment; The cosine similarity between the in-pipe velocity data sequence of the 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 during a sampling breathing cycle.

7. A ventilator circuit monitoring method according to claim 1, characterized in that: The specific method of obtaining the influence degree of the change of each sampling breathing cycle segment according to the confidence of the change data of all sampling breathing cycle segments before each sampling breathing cycle segment is as follows: According to the confidence level of the change data of each sampling breathing cycle segment, the change impact factor of each sampling breathing cycle segment is obtained; The first The normalized value of the cumulative sum of the change impact factors of all sampling breathing cycle segments before the sampling breathing cycle segment is taken as the first The degree of influence of changes in the sampling breathing cycle segment.

8. A ventilator circuit monitoring method according to claim 7, characterized in that: The specific method of obtaining the change impact factor of each sampling breathing cycle segment according to the change data confidence of each sampling breathing cycle segment is as follows: The first The mean value of the flow rate data in the ventilator circuit at all sampling times in the sampling breathing cycle is recorded as The average flow rate data of the sampling breathing cycle segment; The confidence of the variation data of the first sampling breathing cycle segment is The ratio of the mean flow rate data of the sampling breathing cycle segment is recorded as The influence factor of the variation of each sampling breathing cycle segment.

9. A ventilator circuit monitoring method according to claim 1, characterized in that: The method of adjusting the angle of the pipeline control clamp according to the degree of influence of the change to obtain the optimal angle of the pipeline control clamp includes the following specific methods: Preset a step size parameter , the cumulative sum of the influence of changes in all sampling breathing cycle segments and the step parameter The product of is recorded as the adjustment value; the integer value of the sum of the angle of the pipeline control clamp and the adjustment value is taken as the optimal angle of the pipeline control clamp.

10. A ventilator circuit 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, the steps of a ventilator circuit monitoring method as described in any one of claims 1-9 are implemented.

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