Intelligent control method, system and storage medium for artificial airway airbag pressure

Abstract

The present application relates to the field of artificial airway pressure control technology, and discloses an intelligent control method, system and storage medium for artificial airway airbag pressure. The method includes: obtaining the real-time pressure of the airbag within a first time period based on a first monitoring frequency; determining the deviation value between the real-time pressure and the standard range, and if the real-time pressure is judged to be abnormal based on the deviation value, selecting the target pressure within the standard range, and determining the pressure difference between the real-time pressure and the target pressure; converting the pressure difference into a gas adjustment amount, inflating or deflation of the airbag based on the gas adjustment amount, and dynamically adjusting the gas injection speed based on the adjustment strategy until the real-time pressure reaches the target pressure; generating a pressure change curve based on the real-time pressure, analyzing the pressure change curve to generate an analysis result; adjusting the first monitoring frequency to the second monitoring frequency based on the analysis result. The present invention can dynamically adjust the inflation or deflation speed according to the actual situation of the airbag pressure difference, thereby ensuring safer use of the artificial airbag.

Description

Technical Field

[0001] The present application relates to the technical field of artificial airway pressure control, and in particular to an intelligent control method, system and storage medium for artificial airway airbag pressure. Background Art

[0002] In artificial airway devices, the sealing performance of the airbag is crucial to ensure effective ventilation and prevent gas leakage. If the pressure of the airbag is too high, it may damage the patient's tracheal wall; while if the pressure is too low, it may lead to poor sealing, causing problems such as insufficient ventilation or aspiration.

[0003] In order to solve the above problems, a variety of methods have been proposed in the prior art to monitor the airbag pressure. For example, the Chinese patent document with publication number CN111956937A discloses a real-time pressure monitoring system and method for the airbag of an artificial airway. The method detects the pressure of the airbag of the artificial airway in real time through a pressure sensing module, and controls the pressure of the airbag of the artificial airway within a reasonable range through a pressure control module. The airbag pressure data is also transmitted to the doctor's computer through short-frequency data and forms relevant record documents, which is convenient for the doctor to analyze and study the patient's condition through the data in the later stage. For another example, the Chinese patent document with publication number CN118987428A discloses an artificial airway airbag pressure control method, system and control method thereof. The method dynamically updates the target range value of the airbag pressure, thereby adapting to the differences in laryngeal tissues and anesthesia levels between patients, achieving better monitoring effects and reducing the degree of pressure fluctuation.

[0004] However, the above method does not take into account the control of the inflation or deflation speed of the airbag. Too fast inflation or deflation speed will cause over-pressure, making it impossible to maintain the pressure at the optimal level. Too low speed will lead to slow pressure control speed. Therefore, a more intelligent artificial airway airbag pressure intelligent control method is needed. Summary of the invention

[0005] To achieve the above objectives, the present application provides an intelligent control method, system and storage medium for artificial airway bag pressure.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention proposes an intelligent control method for artificial airway airbag pressure, comprising:

[0007] A first monitoring frequency is set based on the patient's physical data, and a pressure sensor obtains the real-time pressure of the airbag within a first time period based on the first monitoring frequency;

[0008] Setting a standard range, determining a deviation value between the real-time pressure and the standard range, and if the real-time pressure is judged to be abnormal based on the deviation value, selecting a target pressure within the standard range, and determining a pressure difference between the real-time pressure and the target pressure;

[0009] converting the pressure difference into a gas adjustment amount based on a gas pressure conversion formula, and inflating or deflating the airbag based on the gas adjustment amount;

[0010] During the regulation process, the gas injection speed is dynamically adjusted based on the adjustment strategy until the real-time pressure reaches the target pressure;

[0011] generating a pressure change curve of the first duration based on the real-time pressure, and analyzing the pressure change curve to generate an analysis result;

[0012] The first monitoring frequency is adjusted to a second monitoring frequency based on the analysis result, and the pressure sensor obtains the real-time pressure of the airbag within a second time period based on the second monitoring frequency.

[0013] Further, selecting the target pressure within the standard range comprises the following steps:

[0014] The deviation value is the difference between the real-time pressure and the boundary value of the standard range, and a first intermediate value of the standard range is determined. Within the first time period, when it is determined based on the deviation value that the real-time pressure is outside the standard range, the real-time pressure is judged to be abnormal, and the first intermediate value is set as the target pressure;

[0015] After entering the second time period, the standard range is divided into multiple sub-intervals, the probability distribution of the real-time pressure in the sub-intervals during the first time period is counted, and the sub-intervals are divided into a safe zone and a dangerous zone by analyzing the probability distribution. In the second time period, when it is determined that the real-time pressure is outside the standard range based on the deviation value, the target pressure is set to the second intermediate value of the safe zone. After entering the third time period, this step is repeated until a stop command is received.

[0016] Further, after the second duration has passed, the subsequent durations are adjusted based on the following steps:

[0017] Setting an adjustment condition, after the second time period has passed, obtaining a pressure fluctuation parameter within the second time period, determining an initial proportionality coefficient based on the adjustment condition satisfied by the pressure fluctuation parameter, and calculating a smoothing adjustment factor, and adjusting the second time period in combination with the initial proportionality coefficient and the smoothing adjustment factor to obtain the third time period;

[0018] A locking value is set. If the value of the third time length is greater than the locking value, then after the third time length ends, the initial shortening proportional coefficient is adjusted according to the pressure fluctuation parameters within the third time length, and the third time length is adjusted to obtain the fourth time length. If the value of the third time length is less than the locking value, the third time length is not adjusted.

[0019] Further, adjusting the gas injection speed comprises the following steps:

[0020] The gas adjustment amount includes the inflation amount and the deflation amount, and the adjustment strategy includes a first critical value. Within the first time length, the gas adjustment process is divided into a fast stage and a slow stage based on the first critical value, and the gas adjustment rate in the fast stage is smaller than that in the slow stage. During the second time length, the first critical value is corrected based on the pressure fluctuation parameter within the first time length to obtain a second critical value, and the gas adjustment process within the second time length is divided into the fast stage and the slow stage based on the second critical value.

[0021] Further, analyzing the pressure change curve comprises the following steps:

[0022] Establishing a coordinate system, drawing a first boundary line, a second boundary line, and a standard pressure line in the coordinate system based on the upper boundary, the lower boundary, and the target pressure of the standard range, respectively; defining an area enclosed by a section of the pressure change curve located above the first boundary line and the first boundary line as a first area; defining an area enclosed by a section of the pressure change curve located below the second boundary line and the second boundary line as a second area; and defining an area enclosed by a section of the pressure change curve located between the first boundary line and the second boundary line and the standard pressure line as a third area;

[0023] An imbalance score is calculated based on the first area, the second area, the third area, and the number of intersections of the pressure change curve with the first boundary line and the second boundary line. When the imbalance score is greater than a first threshold, the adjusted second monitoring frequency is greater than the first monitoring frequency; when it is less than a second threshold, the second monitoring frequency is less than the first monitoring frequency.

[0024] Further, setting the first monitoring frequency includes the following steps:

[0025] The physical data includes disease data and multiple physical indicators. Each of the physical indicators is divided into multiple numerical intervals. A health score is set for the multiple disease data and each of the numerical intervals. The patient's disease data and the health score corresponding to each of the physical indicators are obtained. The health scores are weighted and summed to obtain a comprehensive score. The comprehensive score is linearly scaled to obtain a scaled value, a growth factor is constructed, and the comprehensive score is mapped to the corresponding first monitoring frequency based on the growth factor.

[0026] Furthermore, the physical indicators include age, weight, and lung function parameters.

[0027] Furthermore, the pressure fluctuation parameters include pressure mean, pressure fluctuation amplitude, pressure trend and pressure deviation.

[0028] The present application also provides an intelligent control system for artificial airway bag pressure, which is used to implement the above-mentioned intelligent control method for artificial airway bag pressure, and the system includes:

[0029] An acquisition module, which sets a first monitoring frequency based on the patient's physical data, and a pressure sensor obtains the real-time pressure of the airbag within a first time period based on the first monitoring frequency;

[0030] an adjustment module, setting a standard range, determining a deviation value between the real-time pressure and the standard range, and if the real-time pressure is judged to be abnormal based on the deviation value, selecting a target pressure within the standard range, determining a pressure difference between the real-time pressure and the target pressure, converting the pressure difference into a gas adjustment amount based on a gas pressure conversion formula, inflating or deflating the airbag based on the gas adjustment amount, and dynamically adjusting the gas injection speed based on the adjustment strategy during the adjustment process until the real-time pressure reaches the target pressure;

[0031] An analysis module, generating a pressure change curve of the first duration based on the real-time pressure, and analyzing the pressure change curve to generate an analysis result;

[0032] The optimization module adjusts the first monitoring frequency to a second monitoring frequency based on the analysis result, and the pressure sensor obtains the real-time pressure of the airbag within a second time period based on the second monitoring frequency.

[0033] The present application also provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the above-mentioned intelligent control method for artificial airway bag pressure is implemented.

[0034] The present invention first sets a first monitoring frequency according to the patient's physical data, so as to accurately obtain the airbag pressure according to the actual conditions of different patients; in the pressure monitoring process, it is judged whether the pressure is abnormal by the deviation value between the real-time pressure and the standard range. If it is abnormal, the target pressure is selected within the standard range, and the pressure difference is determined based on the target pressure. The gas adjustment amount to compensate for the pressure difference is calculated through the air pressure conversion formula, and combined with the dynamic adjustment strategy, the segmented control of the pressure regulation speed is realized, thereby effectively reducing the risk of pressure overcharging or insufficient regulation during the pressure regulation process.

[0035] The present invention generates a pressure change curve based on the pressure change data within the first time period for in-depth analysis, which can fully understand the changing trend of the airbag pressure, thereby optimizing the monitoring frequency and further improving the adaptability of the pressure monitoring frequency to the pressure change situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0037] Figure 1 A schematic diagram of an intelligent control method for artificial airway bag pressure in this application;

[0038] Figure 2 A schematic diagram of the safe and dangerous areas for this application;

[0039] Figure 3 A schematic diagram of the pressure variation curve of this application;

[0040] Figure 4 This is a schematic structural diagram of an intelligent control system for artificial airway bag pressure in the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0043] First, the airbag of the artificial airway is introduced. When the patient uses a ventilator, in order to prevent food from refluxing into the airway through the esophagus, the airbag needs to be placed in the airway, and the airbag is inflated and stuck in the airway, so as to prevent the food from entering the airway when it refluxes. Therefore, if the pressure of the airbag is too large, the squeezing force on the tracheal wall will be too large, causing damage to the tracheal wall. If the pressure is too small, it may not be able to prevent esophageal reflux; the airbag pressure is different in different body positions, and the order of pressure from low to high is semi-recumbent position → supine position → left side lying position → right side lying position. In addition, suctioning sputum can easily cause the patient to choke and cough, causing the airbag pressure to fluctuate greatly. The complex clinical environment of artificial airway patients will also affect the airbag pressure, such as temperature and altitude. In order to accurately monitor and adjust airway pressure, the present invention proposes the following method.

[0044] like Figure 1 As shown, an intelligent control method for artificial airway airbag pressure includes:

[0045] S1: A first monitoring frequency is set based on the patient's physical data, and the pressure sensor obtains the real-time pressure of the airbag within a first time period based on the first monitoring frequency.

[0046] In this embodiment, the physical indicators include age, weight, and lung function parameters.

[0047] The specific method of setting the first monitoring frequency according to the body data will be introduced later. The first monitoring frequency is, for example, 5s / time, and the pressure sensor collects the airbag pressure once every 5s and uses it as the real-time pressure.

[0048] S2: Set a standard range and determine the deviation value between the real-time pressure and the standard range. If the real-time pressure is judged to be abnormal based on the deviation value, select a target pressure within the standard range and determine the pressure difference between the real-time pressure and the target pressure.

[0049] S3: Convert the pressure difference into a gas adjustment amount based on a gas pressure conversion formula, and inflate or deflate the airbag based on the gas adjustment amount.

[0050] The standard range in this embodiment is 25-30 cmH2O. The first duration is the timing starting after the artificial airway is first established. The first duration can be set to 30 minutes, 1 hour or 2 hours. During the first duration, whenever the real-time pressure is obtained, the deviation value between the real-time pressure and the boundary value of the standard range is calculated. When the real-time pressure is 24 cmH2O, the deviation value from the lower boundary is +1 cmH2O, indicating that the real-time pressure is lower than the standard range, and the real-time pressure is abnormal, and the pressure of the airbag needs to be adjusted. When adjusting, a value is selected within the standard range as the target pressure to be adjusted, which is the middle value of the standard range, 27.5 cmH2O. The pressure difference between the real-time pressure and the target pressure is 27.5-24=3.5 cmH2O.

[0051] The air pressure conversion formula in this embodiment is V=k·ΔP, where V is the gas adjustment amount and k is the conversion coefficient. The conversion coefficient can be determined experimentally. The conversion coefficient in this embodiment is 2, and the calculated gas adjustment amount is 7ml. After the calculation is completed, the airbag is inflated, and the pressure change is monitored in real time during the inflation process to avoid over-inflation or under-inflation.

[0052] S4: During the regulation process, the gas injection speed is dynamically adjusted based on the adjustment strategy until the real-time pressure reaches the target pressure.

[0053] Assuming that the airbag pressure does not fluctuate due to external influences during the inflation process, the pressure adjustment speed is dynamically adjusted based on the adjustment strategy, such as using a faster speed for inflation in the first half and a slower speed for inflation in the second half. In particular, if the real-time pressure fluctuates abnormally during the inflation process, the inflation or deflation speed is readjusted based on the real-time pressure after the fluctuation.

[0054] S5: Generate a pressure change curve of a first duration based on the real-time pressure, and analyze the pressure change curve to generate an analysis result.

[0055] S6: Based on the analysis result, the first monitoring frequency is adjusted to the second monitoring frequency, and the pressure sensor obtains the real-time pressure of the airbag within the second time period based on the second monitoring frequency.

[0056] After the first time period, a corresponding pressure change curve is generated according to the change of the airbag pressure during the first time period. By analyzing the pressure change curve, the fluctuation of the airbag pressure during the first time period can be determined. If the pressure fluctuation is frequent, the first monitoring frequency is increased. The increased first monitoring frequency is defined as the second monitoring frequency. In the subsequent second time period, the real-time pressure of the airbag is collected at the second monitoring frequency. If the pressure fluctuation is moderate, the first monitoring frequency is kept unchanged. If the pressure fluctuation is very small, the first monitoring frequency is reduced to extend the life of the pressure sensor. In particular, the monitoring frequency can also be adjusted manually.

[0057] The present invention first sets a first monitoring frequency according to the patient's physical data, so as to accurately obtain the airbag pressure according to the actual conditions of different patients; in the pressure monitoring process, it is judged whether the pressure is abnormal by the deviation value between the real-time pressure and the standard range. If it is abnormal, the target pressure is selected within the standard range, and the pressure difference is determined based on the target pressure. The gas adjustment amount to compensate for the pressure difference is calculated through the air pressure conversion formula, and combined with the dynamic adjustment strategy, the segmented control of the pressure regulation speed is realized, thereby effectively reducing the risk of pressure overcharging or insufficient regulation during the pressure regulation process.

[0058] The present invention generates a pressure change curve based on the pressure change data within the first time period for in-depth analysis, which can fully understand the changing trend of the airbag pressure, thereby optimizing the monitoring frequency and further improving the adaptability of the pressure monitoring frequency to the pressure change situation.

[0059] It is particularly noteworthy that the present invention can dynamically adjust the inflation or deflation speed according to the actual situation of the airbag pressure difference, thereby ensuring safer use of the artificial airbag.

[0060] In this embodiment, selecting the target pressure within the standard range includes the following steps:

[0061] The deviation value is the difference between the real-time pressure and the boundary value of the standard range. The first middle value of the standard range is determined. Within the first time period, when the real-time pressure is determined to be outside the standard range based on the deviation value, the real-time pressure is judged to be abnormal, and the first middle value is set as the target pressure.

[0062] After entering the second time period, the standard range is divided into multiple sub-intervals, the probability distribution of the real-time pressure in the sub-intervals during the first time period is counted, and the probability distribution is analyzed to divide the sub-intervals into a safe zone and a dangerous zone. In the second time period, when the real-time pressure is determined to be outside the standard range based on the deviation value, the target pressure is set to the second middle value of the safe zone. After entering the third time period, this step is repeated until a stop command is received.

[0063] As mentioned above, the deviation value is the difference between the upper boundary or lower boundary of the standard range and the real-time pressure. When the real-time pressure is determined to be outside the standard range according to the deviation value, the first middle value of the standard range, 27.5 cmH2O, is used as the target pressure in the first time period. When entering the second time period, the standard range of 25-30 cmH2O is divided into 3 sub-intervals, such as Figure 2 As shown, they are [25, 27), [27, 28), [28, 30] respectively, and the probability of the real-time pressure falling in each sub-interval within the first time period is calculated based on the first formula. The first formula is: ,in, is the probability that the real-time pressure is in the i-th subinterval, is the number of occurrences of the real-time pressure in the i-th subinterval within the first time period, and M is the total number of measurements of the real-time pressure within the first time period.

[0064] Through probability analysis, it is determined that the real-time pressure has the highest probability of occurring in [25, 27), which means that the airbag can be easily and stably maintained in the sub-interval of [25, 27) in the patient's body. Therefore, this sub-interval is used as a safe zone. Figure 2 In the interval F in the standard range, the airbag pressure can be easily kept stable by moving the airbag pressure closer to the safety zone within the standard range in the subsequent adjustment. Therefore, in the second time period, when the real-time pressure is outside the standard range, the second intermediate value (26cmH2O) in the safety zone is used as the target pressure to be adjusted. After entering the third time period, the safety zone and the danger zone are re-divided according to the probability distribution of the real-time pressure in the second time period, and the target pressure is selected from them until the system receives a stop command.

[0065] In this embodiment, after the second duration has passed, the subsequent durations are adjusted based on the following steps:

[0066] Set adjustment conditions, and after the second time period has passed, obtain the pressure fluctuation parameters within the second time period, determine the initial proportional coefficient based on the adjustment conditions satisfied by the pressure fluctuation parameters, and calculate the smoothing adjustment factor, and adjust the second time period in combination with the initial proportional coefficient and the smoothing adjustment factor to obtain a third time period.

[0067] In this embodiment, the pressure fluctuation parameters include pressure mean, pressure fluctuation amplitude, pressure trend and pressure deviation.

[0068] The pressure mean is the average value of the real-time pressure in the second time period, the pressure fluctuation amplitude is the standard deviation of the real-time pressure in the second time period, and the pressure trend is calculated based on the second formula, which is: , where R is the pressure trend, and are the real-time pressures at the beginning and end of the second time period respectively, T is the length of the second time period, and the pressure deviation is the absolute value of the pressure mean value minus the target pressure.

[0069] The adjustment conditions include fluctuation conditions, deviation conditions and trend conditions, which correspond to the first range, the second range and the third range respectively. When the pressure fluctuation amplitude is higher than the first range, the fluctuation condition is met and the second duration needs to be shortened. When the pressure fluctuation amplitude is lower than the first range, the fluctuation condition is met and the second duration needs to be extended. When the pressure fluctuation amplitude is within the first range, the fluctuation condition is not met and there is no need to adjust the second duration. The deviation condition and trend condition judgment process are the same as above and will not be introduced here.

[0070] This implementation obtains the third duration according to the third formula, which is: ,in, is the value of the third duration, is the value of the second duration, K is the initial proportional coefficient, and S is the smoothing adjustment factor. The smoothing adjustment factor is calculated according to the fourth formula, which is:

[0071] Wherein, e is the base of the natural logarithm, r is the sensitivity coefficient, which is 2 in this embodiment, and h is the difference between the pressure mean and the upper limit of the first range. The initial proportional coefficient is determined based on the fifth formula, which is: , The base value of the jth adjustment condition, in this embodiment, when the pressure fluctuation amplitude is higher than the first range, the base value is -0.2, when the pressure fluctuation amplitude is lower than the first range, the base value is 0.2, and when it is within the first range, the base value is 0. The other two adjustment conditions are determined in the same principle. For example, when the three adjustment conditions are met and all need to shorten the second duration, the value of K is -0.6. According to the third formula, the calculated result is less than the original second duration, thereby shortening the second duration.

[0072] Set a locking value. If the value of the third time length is greater than the locking value, then after the third time length ends, adjust the initial shortening ratio coefficient according to the pressure fluctuation parameters within the third time length, and adjust the third time length to obtain the fourth time length. If the value of the third time length is less than the locking value, do not adjust the third time length.

[0073] This step can be used to prevent the subsequent monitoring time from being too short to observe the pressure fluctuation trend under a certain monitoring frequency. The locked value is, for example, 30 minutes. If the third time period is determined to be 28 minutes through calculation, when the length of the fourth time period needs to be determined after the third time period, 28 minutes will still be used as the length of the fourth time period, that is, the third time period will not be adjusted.

[0074] In this embodiment, adjusting the gas injection speed includes the following steps:

[0075] The gas adjustment amount includes the inflation amount and the deflation amount. The adjustment strategy includes a first critical value. Within a first time length, the gas adjustment process is divided into a fast stage and a slow stage based on the first critical value. The gas adjustment rate in the fast stage is smaller than that in the slow stage. During a second time length, the first critical value is corrected based on the pressure fluctuation parameters within the first time length and a second critical value is obtained. Based on the second critical value, the gas adjustment process within the second time length is divided into a fast stage and a slow stage.

[0076] The first critical value is, for example, 3ml. When the gas adjustment volume is 7ml, the first critical value divides the gas adjustment process into a fast stage (the first 4ml) and a slow stage (the last 3ml). In the fast stage, the gas is inflated at 1ml / s, and in the slow stage, the gas is inflated at 0.5ml / s. After the first duration ends, by analyzing the pressure fluctuation parameters, it is found that the fluctuation of the airbag pressure is large and frequent during the first duration, so the first critical value is expanded to 4ml. For example, the second critical value divides the gas adjustment process into a fast stage (the first 3ml) and a slow stage (the last 4ml). This shortens the inflation volume in the fast stage and avoids overfilling.

[0077] In this embodiment, analyzing the pressure change curve includes the following steps:

[0078] A coordinate system is established, and a first boundary line, a second boundary line, and a standard pressure line are drawn in the coordinate system based on the upper boundary, the lower boundary, and the target pressure of the standard range, respectively; the area enclosed by a segment of the pressure change curve above the first boundary line and the first boundary line is defined as a first area; the area enclosed by a segment of the pressure change curve below the second boundary line and the second boundary line is defined as a second area; and the area enclosed by a segment of the pressure change curve between the first boundary line and the second boundary line and the standard pressure line is defined as a third area.

[0079] The imbalance score is calculated based on the first area, the second area, the third area, and the number of intersections between the pressure change curve and the first boundary line and the second boundary line. When the imbalance score is greater than the first threshold, the adjusted second monitoring frequency is greater than the first monitoring frequency; when it is less than the second threshold, the second monitoring frequency is less than the first monitoring frequency.

[0080] like Figure 3 As shown, it includes the first boundary line L1, the second boundary line L2 and the standard pressure line L3, and the first area, the second area and the third area enclosed by the pressure change curve and the first boundary line L1, the second boundary line L2 and the standard pressure line L3 are shown as A1, A2 and A3 respectively, and A1, A2 and A3 only represent Figure 3 The first area, the second area and the third area of ​​the middle part; this embodiment calculates the imbalance score based on the sixth formula, and the sixth formula is: , where Q is the imbalance score, and are the first weight and the second weight respectively, C1 is the sum of the first area and the second area, C2 is the third area, D is the number of intersections between the pressure change curve and the first boundary line and the second boundary line, Figure 3 The number of intersections is 13. The larger the sum of the first area and the second area, the smaller the third area, and the more intersections there are, indicating that the greater the fluctuation, the greater the calculated imbalance score. In this embodiment, the first threshold and the second threshold are set to 0.4 and 0.1 respectively.

[0081] In this embodiment, setting the first monitoring frequency includes the following steps:

[0082] The physical data includes disease data and multiple physical indicators. Each physical indicator is divided into multiple numerical intervals. A health score is set for the multiple disease data and each numerical interval. The patient's disease data and the health score corresponding to each physical indicator are obtained. The health scores are weighted and summed to obtain a comprehensive score. The comprehensive score is linearly scaled to obtain a scaled value, a growth factor is constructed, and the comprehensive score is mapped to the corresponding first monitoring frequency based on the growth factor.

[0083] For example, for weight data, it is divided into 40kg-60kg, 60kg-80kg, 80kg-100kg, and the health score of each numerical range is 5 points, 4 points, and 3 points respectively. The weights of age, weight, and lung function parameters are assigned 0.1, 0.2, and 0.5 respectively. In addition, different weights are assigned to different diseases, such as the health score of disease 1 is 3, the corresponding weight is 0.8, and the health score of disease 2 is 6, and the corresponding weight is 0.55. Then, the health score of each of the above-mentioned physical indicators and diseases is weighted and summed according to the weight to obtain a comprehensive score. In order to accurately correspond each comprehensive score to a first monitoring frequency, the present invention proposes the following method: first, according to the set health score and weight, the minimum and maximum values ​​that the comprehensive score can reach are obtained, that is, the range of the comprehensive score is determined, for example, [10,30], and then the minimum and maximum value normalization method is used to linearly scale the actual comprehensive score and map it to the interval [0,1].

[0084] When the comprehensive score is 20, the normalized value is 0.5, and then the mapping function is constructed: , where y is the first monitoring frequency corresponding to the comprehensive score, ROU is the rounding function, x is the comprehensive score, and u is the growth factor, whose value is set according to experimental measurements and is generally 2 or 3. When the growth factor is 2, the corresponding first monitoring frequency is ROU (0.82) = 1, and the first monitoring frequency is once per second.

[0085] like Figure 4 As shown, the present application also provides an intelligent control system for artificial airway bag pressure, which is used to implement the above-mentioned intelligent control method for artificial airway bag pressure, and the system includes:

[0086] The acquisition module sets a first monitoring frequency based on the patient's physical data, and the pressure sensor obtains the real-time pressure of the airbag within a first time period based on the first monitoring frequency;

[0087] The adjustment module sets the standard range and determines the deviation value between the real-time pressure and the standard range. If the real-time pressure is abnormal based on the deviation value, the target pressure is selected within the standard range, the pressure difference between the real-time pressure and the target pressure is determined, the pressure difference is converted into a gas adjustment amount based on the gas pressure conversion formula, and the airbag is inflated or deflated based on the gas adjustment amount. During the adjustment process, the gas injection speed is dynamically adjusted based on the adjustment strategy until the real-time pressure reaches the target pressure;

[0088] An analysis module, generating a pressure change curve of a first duration based on the real-time pressure, and analyzing the pressure change curve to generate an analysis result;

[0089] The optimization module adjusts the first monitoring frequency to the second monitoring frequency based on the analysis result, and the pressure sensor obtains the real-time pressure of the airbag within the second time period based on the second monitoring frequency.

[0090] The present application also provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the above-mentioned intelligent control method for artificial airway bag pressure is implemented.

[0091] It should be understood that the various technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control system for artificial airway bag pressure, characterized in that: An acquisition module, which sets a first monitoring frequency based on the patient's physical data, and a pressure sensor obtains the real-time pressure of the airbag within a first time period based on the first monitoring frequency; The adjustment module sets a standard range, determines a deviation value between the real-time pressure and the standard range, and if the real-time pressure is judged to be abnormal based on the deviation value, selects a target pressure within the standard range, the deviation value is the difference between the real-time pressure and the boundary value of the standard range, determines a first intermediate value of the standard range, and within the first time period, when the real-time pressure is determined to be outside the standard range based on the deviation value, judges that the real-time pressure is abnormal, and sets the first intermediate value as the target pressure. After entering the second time period, the standard range is divided into multiple sub-intervals, and the probability distribution of the real-time pressure in the sub-intervals within the first time period is counted, and the sub-intervals are divided into a safe area and a dangerous area by analyzing the probability distribution. During the time period, when it is determined based on the deviation value that the real-time pressure is outside the standard range, the target pressure is set to the second intermediate value of the safety zone. After entering the third time period, the safety zone and the danger zone are redivided according to the probability distribution of the real-time pressure in the second time period, and the target pressure is selected therefrom until a stop command is received, the pressure difference between the real-time pressure and the target pressure is determined, the pressure difference is converted into a gas adjustment amount based on a gas pressure conversion formula, the airbag is inflated or deflated based on the gas adjustment amount, and the gas injection speed is dynamically adjusted based on the adjustment strategy during the adjustment process until the real-time pressure reaches the target pressure; an analysis module generates a pressure change curve for the first time period based on the real-time pressure, and analyzes the pressure change curve to generate an analysis result; The optimization module adjusts the first monitoring frequency to a second monitoring frequency based on the analysis result, and the pressure sensor obtains the real-time pressure of the airbag within the second time period based on the second monitoring frequency.

2. The system according to claim 1, characterized in that After the second duration has elapsed, the subsequent durations are adjusted based on the following steps: Setting an adjustment condition, after the second time period has passed, obtaining a pressure fluctuation parameter within the second time period, determining an initial proportionality coefficient based on the adjustment condition satisfied by the pressure fluctuation parameter, and calculating a smoothing adjustment factor, and adjusting the second time period in combination with the initial proportionality coefficient and the smoothing adjustment factor to obtain the third time period; Set a locking value. If the value of the third time duration is greater than the locking value, then after the third time duration ends, adjust the initial proportional coefficient according to the pressure fluctuation parameters within the third time duration, and adjust the third time duration to obtain the fourth time duration. If the value of the third time duration is less than the locking value, do not adjust the third time duration.

3. The system according to claim 2, characterized in that Adjusting the gas injection speed comprises the following steps: The gas adjustment amount includes the inflation amount and the deflation amount, and the adjustment strategy includes a first critical value. Within the first time length, the gas adjustment process is divided into a fast stage and a slow stage based on the first critical value, and the gas adjustment rate in the fast stage is smaller than that in the slow stage. During the second time length, the first critical value is corrected based on the pressure fluctuation parameter within the first time length to obtain a second critical value, and the gas adjustment process within the second time length is divided into the fast stage and the slow stage based on the second critical value.

4. The system according to claim 2, characterized in that Analyzing the pressure change curve comprises the following steps: Establishing a coordinate system, drawing a first boundary line, a second boundary line, and a standard pressure line in the coordinate system based on the upper boundary, the lower boundary, and the target pressure of the standard range, respectively; defining an area enclosed by a section of the pressure change curve located above the first boundary line and the first boundary line as a first area; defining an area enclosed by a section of the pressure change curve located below the second boundary line and the second boundary line as a second area; and defining an area enclosed by a section of the pressure change curve located between the first boundary line and the second boundary line and the standard pressure line as a third area; An imbalance score is calculated based on the first area, the second area, the third area, and the number of intersections of the pressure change curve with the first boundary line and the second boundary line. When the imbalance score is greater than a first threshold, the adjusted second monitoring frequency is greater than the first monitoring frequency; when it is less than a second threshold, the second monitoring frequency is less than the first monitoring frequency.

5. The system according to claim 1, characterized in that Setting the first monitoring frequency comprises the following steps: The physical data includes disease data and multiple physical indicators. Each of the physical indicators is divided into multiple numerical intervals. A health score is set for the multiple disease data and each of the numerical intervals. The patient's disease data and the health score corresponding to each of the physical indicators are obtained. The health scores are weighted and summed to obtain a comprehensive score. The comprehensive score is linearly scaled to obtain a scaled value, a growth factor is constructed, and the comprehensive score is mapped to the corresponding first monitoring frequency based on the growth factor.

6. The system according to claim 5, characterized in that The physical indicators include age, weight, and lung function parameters.

7. The system according to claim 2, characterized in that The pressure fluctuation parameters include pressure mean, pressure fluctuation amplitude, pressure trend and pressure deviation.

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