Method for calibrating detection precision of anesthetic gas concentration detector

Abstract

The invention relates to the technical field of gas concentration detection, in particular to a method for calibrating the detection precision of an anesthetic gas concentration detector. The method comprises the following steps: detecting anesthetic gas by using a gas concentration detector to obtain a spectrum curve; decomposing the spectrum curve to obtain each IMF component; acquiring overlapping peaks according to distribution of extreme points in the spectrum curve; according to a relative distance between a corresponding position of each overlapping peak in the IMF component and a peak point in the IMF component, screening a corresponding target component peak of each overlapping peak in all the IMF components; obtaining an overlapping factor corresponding to each overlapping peak according to curve distribution characteristics of the overlapping peaks on two sides of the target component peak corresponding to each overlapping peak in the spectrum curve and a curve change trend between wave crest points and wave trough points on two sides of each overlapping peak in the spectrum curve; and integrating the overlapping factor and the spectrum curve to obtain the gas concentration after the overlapping influence is removed. The accuracy of anesthetic gas concentration detection results is improved.

Description

Technical Field

[0001] The invention relates to the technical field of gas concentration detection, and in particular to a method for calibrating the detection accuracy of an anesthetic gas concentration detector. Background Art

[0002] The detection accuracy calibration of the anesthetic gas concentration detector refers to ensuring that the anesthetic gas concentration detector can accurately measure the concentration of anesthetic gas through a specific calibration method to ensure the safety of gas use during anesthesia. Detection accuracy calibration usually needs to be performed regularly so that the instrument can provide accurate values ​​in actual applications. Common calibration methods include chromatography to detect concentration. Chromatography accurately determines the concentration of anesthetic components in the gas by separating and analyzing gas components. The specific process is: first, use a gas chromatograph to detect the concentration of anesthetic components in the gas, and then calibrate the measurement data of the anesthetic gas concentration detector based on the test results to ensure the accuracy and reliability of its measurement data.

[0003] Anesthetic gases usually have low molecular weight and low boiling point, so they are more volatile and exist in the gas phase. This property helps to separate them quickly and efficiently through chromatographic columns, but it also easily leads to problems such as peak overlap and insufficient resolution, which in turn leads to large errors in the detection of concentration detectors.

[0004] Existing methods often avoid overlapping peaks by adjusting the mobile phase ratio. However, due to the wide concentration range of anesthetic gases, especially in clinical anesthesia monitoring, the gas concentration is usually low. Anesthetic gases with low concentrations may interfere with the baseline of other gases. Therefore, it is difficult to completely solve the overlapping problem by adjusting the mobile phase, which in turn affects the accuracy of the measured results. Summary of the invention

[0005] In order to solve the problem of low accuracy of detection results when using a gas concentration detector to detect the concentration of anesthetic gas in the existing method, the purpose of the present invention is to provide a method for calibrating the detection accuracy of an anesthetic gas concentration detector. The technical solution adopted is as follows: The present invention provides a method for calibrating the detection accuracy of an anesthetic gas concentration detector, the method comprising the following steps: A gas concentration detector is used to detect the anesthetic gas to obtain a spectrum curve; Decomposing the spectrum curve to obtain each IMF component; obtaining each overlapping peak according to the distribution of extreme value points in the spectrum curve; screening the target component peak corresponding to each overlapping peak in all IMF components according to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component; According to the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectrum curve, and the change trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectrum curve, the overlapping factor corresponding to each overlapping peak in the spectrum curve is obtained; The overlapping factors and spectral curves corresponding to all overlapping peaks are combined to obtain the gas concentration after removing the overlapping effect.

[0006] Preferably, the step of obtaining overlapping peaks according to the distribution of extreme value points in the spectrum curve includes: For any maximum point in the spectrum curve: If the ordinate value of at least one of the two minimum value points adjacent to any of the maximum value points is not 0, then the any of the maximum value points is recorded as an overlapping peak.

[0007] Preferably, screening the target component peak corresponding to each overlapping peak in all IMF components according to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component includes: The point in each IMF component having the same abscissa as the candidate overlapping peak is recorded as a reference point, and the maximum point in each IMF component having the smallest difference in abscissa from the reference point is determined as the reference peak point of the candidate overlapping peak in each IMF component; According to the amplitude distribution of the reference peak points of the candidate overlapping peaks in all IMF components, the target component peaks corresponding to the candidate overlapping peaks in all IMF components are determined; The candidate overlapping peak is any overlapping peak in the spectrum curve.

[0008] Preferably, determining the target component peak corresponding to the candidate overlapping peak in all IMF components according to the amplitude distribution of the reference peak point of the candidate overlapping peak in all IMF components includes: The reference peak point when the amplitude of the reference peak point of the candidate overlapping peak in all IMF components takes the maximum value is determined as the target component peak corresponding to the candidate overlapping peak in all IMF components.

[0009] Preferably, the overlapping factor corresponding to each overlapping peak in the spectrum curve is obtained according to the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectrum curve, and the change trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectrum curve, including: The two peak points on both sides of the target component peak corresponding to the candidate overlapping peak and which are closest to the target component peak corresponding to the candidate overlapping peak are recorded as reference peak points of the candidate overlapping peak; For any reference peak point: fitting a first curve between two trough points with the smallest difference in horizontal coordinates between the left and right sides of the any reference peak point and the any reference peak point to obtain a first fitting equation; extending the first curve by the first fitting equation until the vertical coordinate is 0, and recording the extended portion of the curve as an extension line; determining the influence probability corresponding to the any reference peak point according to the extension line and the first curve; The trough points with the smallest differences between the left and right sides of the candidate overlapping peak and its horizontal coordinate are respectively recorded as the left reference trough point and the right reference trough point. According to the change trend of the curve between the left reference trough point and the peak point on its left, the slope change degree corresponding to the left reference trough point is obtained; according to the change trend of the curve between the right reference trough point and the peak point on its right, the slope change degree corresponding to the right reference trough point is obtained; The overlapping factor corresponding to the candidate overlapping peak is obtained according to the influence probability corresponding to the reference peak point of the candidate overlapping peak, the slope change degree corresponding to the left reference trough point and the slope change degree corresponding to the right reference trough point.

[0010] Preferably, determining the influence probability corresponding to any reference peak point according to the extension line and the first curve includes: Draw a first vertical line perpendicular to the horizontal axis through two trough points on the left and right sides of any reference peak point with the smallest difference in horizontal coordinates with the any reference peak point; The extension line and the first curve form a complementary curve; the area of ​​a closed region enclosed by the first perpendicular line, the extension line and the straight line with a ordinate of 0 is recorded as a first area; the area of ​​a closed region enclosed by the complementary curve and the straight line with a ordinate of 0 is recorded as a second area; The influence probability corresponding to any reference peak point is determined by combining the first area and the second area.

[0011] Preferably, obtaining the degree of change of the slope corresponding to the left reference trough point according to the change trend of the curve between the left reference trough point and the peak point on its left side; obtaining the degree of change of the slope corresponding to the right reference trough point according to the change trend of the curve between the right reference trough point and the peak point on its right side includes: The peak point on the left side of the candidate overlapping peak that is closest to the candidate overlapping peak is recorded as the left peak point; the curve between the point on the spectrum curve that is adjacent to the left peak point and located on the right side of the left peak point and the point that is adjacent to the left reference trough point and located on the left side of the left reference trough point is fitted by the least square method to obtain a second fitting equation; according to the second fitting equation, a new slope value of the point on the right side adjacent to the left reference trough point is obtained; a first slope difference between the slope value of the point on the right side adjacent to the left reference trough point and the new slope value of the adjacent point on the right side is calculated, and the slope change degree corresponding to the left reference trough point is obtained according to the first slope difference; The peak point on the right side of the candidate overlapping peak that is closest to the candidate overlapping peak is recorded as the right peak point; the curve between the point on the spectrum curve that is adjacent to the right peak point and located on the left side of the right peak point and the point that is adjacent to the right reference trough point and located on the right side of the right reference trough point is fitted by the least squares method to obtain a third fitting equation; according to the third fitting equation, a new slope value of the point on the left side adjacent to the right reference trough point is obtained; the second slope difference between the slope value of the point on the left side adjacent to the right reference trough point and the new slope value of the adjacent point on the left side is calculated, and the degree of slope change corresponding to the right reference trough point is obtained according to the second slope difference.

[0012] Preferably, obtaining the overlapping factor corresponding to the candidate overlapping peak according to the influence probability corresponding to the reference peak point of the candidate overlapping peak, the slope change degree corresponding to the left reference trough point, and the slope change degree corresponding to the right reference trough point includes: Determine the product of the influence probability corresponding to the reference peak point on the left side of the target component peak corresponding to the candidate overlapping peak and the slope change degree corresponding to the left reference trough point as the first contributing factor corresponding to the candidate overlapping peak; Determine the product of the influence probability corresponding to the reference peak point on the right side of the target component peak corresponding to the candidate overlapping peak and the slope change degree corresponding to the right reference trough point as the second contributing factor corresponding to the candidate overlapping peak; The sum of the first contribution factor and the second contribution factor is determined as the overlap factor corresponding to the candidate overlap peak.

[0013] Preferably, the method of synthesizing the overlapping factors and spectral curves corresponding to all overlapping peaks to obtain the gas concentration after removing the influence of the overlapping peaks comprises: Input the overlapping factor sequence and the spectrum curve into the trained neural network to obtain the gas concentration after removing the overlapping effect; The overlapping factor sequence is composed of overlapping factors corresponding to all overlapping peaks in the spectrum curve.

[0014] Preferably, an EMD decomposition method is used when decomposing the spectrum curve.

[0015] The present invention has at least the following beneficial effects: The present invention firstly uses a gas concentration detector to detect anesthetic gas and obtains a spectrum curve. Considering that anesthetic gas usually has a low molecular weight and a low boiling point, the existing method cannot avoid the influence of overlapping peaks when calibrating the measurement data of the gas detector, resulting in low accuracy of the anesthetic gas detection result finally obtained. In this embodiment, multiple overlapping peaks are obtained according to the distribution of extreme points in the spectrum curve, and the spectrum curve is decomposed into multiple IMF components. According to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component, each overlapping peak in the spectrum curve is screened out from all IMF components. The corresponding target component peak is combined with the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectral curve and the changing trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectral curve, and the overlapping factor corresponding to each overlapping peak is obtained. The overlapping factor is used to reflect the probability of peak overlap caused by inappropriate component rates. The overlapping factors and spectral curves corresponding to all overlapping peaks are further combined to calibrate the data measured by the gas concentration detector, and the gas concentration after removing the overlapping effect is obtained, which eliminates the influence of overlapping peaks on the identification of anesthetic gas concentration and improves the accuracy of anesthetic gas concentration detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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.

[0017] Figure 1 A flow chart of a method for calibrating the detection accuracy of an anesthetic gas concentration detector provided by an embodiment of the present invention; Figure 2 A structural block diagram of a system for calibrating the detection accuracy of an anesthetic gas concentration detector provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a method for calibrating the detection accuracy of an anesthetic gas concentration detector proposed by the present invention is described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0019] 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.

[0020] The following is a detailed description of a method for calibrating the detection accuracy of an anesthetic gas concentration detector provided by the present invention in conjunction with the accompanying drawings.

[0021] A method for calibrating the detection accuracy of an anesthetic gas concentration detector: The specific scenario targeted by this embodiment is: in the process of detecting the concentration of anesthetic gas using a gas concentration detector, due to the wide concentration range of anesthetic gas, the gas concentration is often low, and the low-concentration anesthetic gas will have baseline interference with other gases. In this way, it is difficult to completely solve the problem of peak overlap by adjusting the mobile phase, which affects the accuracy of the anesthetic gas concentration detection result. The overlap of a peak may be caused by the abnormal flow rate of the component corresponding to the peak, or it may be caused by the flow rate of the component corresponding to the adjacent peak being similar to the flow rate of the peak. Therefore, by analyzing the peaks in the original spectrum curve and the peaks in the IMF component, the contribution of different components when each peak becomes a peak in the overlapping peak group can be obtained, that is, the degree to which a certain component plays a role in forming the overlapping peak group, and then the information is introduced into the concentration recognition network, which helps to obtain a concentration recognition network that is not affected by overlapping peaks, thereby eliminating the influence of overlapping peaks on concentration recognition.

[0022] This embodiment proposes a method for calibrating the detection accuracy of an anesthetic gas concentration detector, such as Figure 1 As shown, a method for calibrating the detection accuracy of an anesthetic gas concentration detector in this embodiment includes the following steps: Step S1, using a gas concentration detector to detect anesthetic gas to obtain a spectrum curve.

[0023] A gas concentration detector is an instrument used to detect the concentration of a specific gas in the air or other gases. Such instruments are widely used in environmental monitoring, industrial safety, laboratory research, gas leak detection and other fields. In this embodiment, first, the concentration of the anesthetic gas is detected by using the gas concentration detector to obtain the corresponding spectrum curve.

[0024] Step S2, decomposing the spectrum curve to obtain each IMF component; obtaining each overlapping peak according to the distribution of extreme points in the spectrum curve; and screening the target component peak corresponding to each overlapping peak in all IMF components according to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component.

[0025] Since the concentration range of anesthetic gases is relatively wide and the gas concentration is often low, anesthetic gases with low concentrations will interfere with the baseline of other gases. Therefore, it is difficult to completely solve the problem of peak overlap by adjusting the mobile phase.

[0026] Since the spectral curves of different components are different, and the spectral curves of the same component are often similar, this embodiment first uses the EMD decomposition method to decompose the spectral curve to obtain multiple IMF components, that is, to decompose the original spectral curve with overlapping peaks into curves of different frequencies, and each IMF component can roughly represent the spectrum of a component in the anesthetic gas. EMD decomposition is a prior art and will not be described in detail here.

[0027] When the concentration of certain components in the anesthetic gas is low, they pass through the chromatographic column faster, resulting in a shorter separation time, forming overlapping peaks. Therefore, the influence of the speed at which the components pass through the chromatographic column on the formation of overlapping peaks can be evaluated, and then this influence information can be added to the neural network that identifies the concentration through the chromatographic curve to eliminate the influence of overlapping peaks on the concentration identification of anesthetic gas.

[0028] Specifically, in the spectrum curve, the second-order derivative method is used to obtain the maximum and minimum points on the spectrum curve; for each IMF component, the second-order derivative method is also used to obtain the maximum and minimum points. The maximum point is the peak point or peak point, and the minimum point is the trough point. The method for obtaining the maximum and minimum points is a prior art and will not be described in detail here.

[0029] For any maximum point in the spectrum curve: if at least one of the two minimum points adjacent to the maximum point has a ordinate value that is not 0, the maximum point is recorded as an overlapping peak. Using this method, multiple overlapping peaks in the spectrum curve can be obtained.

[0030] For each overlapping peak, different peaks represent different components in the anesthetic gas. If a peak dominates a certain IMF component, then the IMF component can represent the spectrum of the component corresponding to the peak in the overlapping peak group.

[0031] The overlap of a peak may be caused by the abnormal flow rate of the component corresponding to the peak, or it may be caused by the flow rate of the component corresponding to the adjacent peak being close to the flow rate of the peak. Therefore, by analyzing the peaks in the original spectral curve and the peaks in the IMF component, the contribution of different components when each peak becomes a peak in the overlapping peak group can be obtained, that is, the extent to which a certain component contributes to the formation of the overlapping peak group, and then the original spectral curve can be combined to realize the calibration of the anesthetic gas concentration and obtain accurate concentration data.

[0032] Next, this embodiment is described by taking an overlapping peak in a spectrum curve as an example. Other overlapping peaks in the spectrum curve can be processed by the method provided in this embodiment.

[0033] Specifically, any overlapping peak in the spectrum curve is recorded as a candidate overlapping peak, and the point in each IMF component with the same horizontal coordinate as the candidate overlapping peak is recorded as a reference point, and there is a reference point in each IMF component for the candidate overlapping peak; the maximum point in each IMF component with the smallest difference in horizontal coordinate with the reference point is determined as the reference peak point of the candidate overlapping peak in each IMF component, and there is a reference peak point in each IMF component for the candidate overlapping peak. Further, the reference peak point when the amplitude of the reference peak point of the candidate overlapping peak in all IMF components takes the maximum value is determined as the target component peak corresponding to the candidate overlapping peak in all IMF components.

[0034] By adopting the above method, the target component peak corresponding to each overlapping peak in the spectrum curve can be obtained.

[0035] Step S3, obtaining the overlapping factor corresponding to each overlapping peak in the spectrum curve according to the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectrum curve, and the change trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectrum curve.

[0036] There are overlapping peaks on the left and right sides of the trough point. It is necessary to consider whether the formation of the trough point is more caused by the inappropriate flow rate of the anesthetic gas component corresponding to the overlapping peak on the left, or the inappropriate speed of the anesthetic gas component corresponding to the overlapping peak on the right. In this embodiment, the calculation is performed based on the degree of interruption of the changing trend of the left and right overlapping peaks by the trough point. The higher the peak shape integrity, the stronger the trend continuity of the overlapping peaks, and the more likely the trough point is caused by the inappropriate flow rate of the component corresponding to the overlapping peak with low peak shape integrity.

[0037] Next, this embodiment is still explained by taking the candidate overlapping peak as an example. Specifically, the two peak points on the left and right sides of the target component peak corresponding to the candidate overlapping peak that are closest to the target component peak corresponding to the candidate overlapping peak are recorded as reference peak points of the candidate overlapping peak; there is a reference peak point on the left side of the candidate overlapping peak, and there is also a reference peak point on the right side of the candidate overlapping peak. It should be noted that: the closest distance here means that the difference between the horizontal coordinates is the smallest; in this embodiment, the specific calculation process of the difference between the horizontal coordinates of two points is: the absolute value of the difference between the horizontal coordinates of the two points is taken as the difference between the horizontal coordinates of the two points.

[0038] For any reference peak point: the least square method is used to fit the first curve between the two trough points with the smallest horizontal coordinate difference between the left and right sides of the reference peak point and the reference peak point to obtain the first fitting equation; it should be noted that: the first curve is the corresponding curve segment on the IMF component signal. The curve is extended by the first fitting equation until the ordinate is 0, and the curve of the extended part is recorded as the extension line. Next, the influence probability corresponding to the reference peak point is determined according to the extension line and the first curve. Specifically, the first vertical line perpendicular to the horizontal axis is drawn through the two trough points with the smallest horizontal coordinate difference between the left and right sides of the reference peak point and the reference peak point; the area of ​​the closed area surrounded by the first vertical line, the extension line and the straight line with the ordinate of 0 is recorded as the first area; the extension line and the first curve constitute a supplementary curve, which represents the spectral curve of the anesthetic gas component corresponding to the overlapping peak when it is not affected, and the area of ​​the closed area surrounded by the supplementary curve and the straight line with the coordinate of 0 is recorded as the second area. By adopting the above method, the first area corresponding to each reference peak point of the candidate overlapping point and the second area corresponding to each reference peak point of the candidate overlapping point can be obtained.

[0039] Furthermore, the influence probability corresponding to each reference peak point of the candidate overlapping points is determined by combining the first area corresponding to each reference peak point of the candidate overlapping points and the second area corresponding to each reference peak point of the candidate overlapping points. Specifically, the reference peak point on the left side of the candidate overlapping peak is recorded as the left reference peak point, and the reference peak point on the right side of the candidate overlapping peak is recorded as the right reference peak point. These two reference peak points represent the spectra of two components with similar flow rates in the anesthetic gas; the ratio between the first area corresponding to the left reference peak point and the second area corresponding to the left reference peak point is recorded as the first ratio; the ratio between the first area corresponding to the right reference peak point and the second area corresponding to the right reference peak point is recorded as the second ratio; the sum of the first ratio and the second ratio is recorded as the first sum, and the ratio between the first ratio and the first sum is determined as the influence probability corresponding to the left reference peak point, and the influence probability corresponding to the left reference peak point is used to characterize the probability of a trough point being formed due to an inappropriate flow rate of the anesthetic component corresponding to the left reference peak point; the ratio between the second ratio and the first sum is determined as the influence probability corresponding to the right reference peak point, and the influence probability corresponding to the right reference peak point is used to characterize the probability of a trough point being formed due to an inappropriate flow rate of the anesthetic component corresponding to the right reference peak point.

[0040] On the spectrum curve, the trough point with the smallest difference between the left side of the candidate overlapping peak and its horizontal coordinate is recorded as the left reference trough point, and the trough point with the smallest difference between the right side of the candidate overlapping peak and its horizontal coordinate is recorded as the right reference trough point. On the spectrum curve, the peak point on the left side of the candidate overlapping peak that is closest to the candidate overlapping peak is recorded as the left peak point; the curve between the point adjacent to the left peak point and located on the right side of the left peak point on the spectrum curve and the point adjacent to the left reference trough point and located on the left side of the left reference trough point is fitted by the least squares method to obtain a second fitting equation; according to the second fitting equation, the horizontal coordinate of the point is substituted into the second fitting equation to obtain a new coordinate, and the new slope value of the point adjacent to the left reference trough point is obtained according to the new coordinate. The absolute value of the difference between the slope value of the point adjacent to the right side of the left reference trough point and the new slope value of the adjacent right side point is recorded as the first slope difference, the first slope difference is normalized, and the normalized result is used as the slope change degree corresponding to the left reference trough point. The greater the degree of change in the slope corresponding to the left reference trough point, the better the continuity of the left curve to the trough point, and the smaller the possibility of the generation of overlapping points caused by the anesthetic components corresponding to the left peak point.

[0041] The two peak points on the right side of the candidate overlapping peak that are closest to the candidate overlapping peak are recorded as right peak points; the curve between the point adjacent to the right peak point and located on the left side of the right peak point and the point adjacent to the right reference trough point and located on the right side of the right reference trough point on the spectrum curve is fitted by the least square method to obtain a third fitting equation; according to the second fitting equation, the abscissa of the point is substituted into the second fitting equation to obtain a new coordinate, and the new slope value of the point on the left side adjacent to the right reference trough point is obtained according to the new coordinate; the absolute value of the difference between the slope value of the point on the left side adjacent to the right reference trough point and the new slope value of the adjacent left side point is recorded as the second slope difference, and the normalized result of the second slope difference is used as the slope change degree corresponding to the right reference trough point. The greater the slope change degree corresponding to the right reference trough point, the better the continuity of the right curve to the trough point, and the smaller the possibility of the generation of overlapping points due to the anesthetic component corresponding to the right peak point.

[0042] It should be noted that the method of calculating the slope according to the coordinates is a prior art and will not be described in detail here. When normalizing the first slope difference and the second slope difference, there are many normalization methods. This embodiment uses the maximum and minimum value normalization method for processing. The maximum and minimum value normalization method is a prior art and will not be described in detail here. The least square method is a prior art and will not be described in detail here.

[0043] Furthermore, the product of the influence probability corresponding to the reference peak point on the left side of the target component peak corresponding to the candidate overlapping peak and the degree of slope change corresponding to the left reference trough point is determined as the first causing factor corresponding to the candidate overlapping peak; the product of the influence probability corresponding to the reference peak point on the right side of the target component peak corresponding to the candidate overlapping peak and the degree of slope change corresponding to the right reference trough point is determined as the second causing factor corresponding to the candidate overlapping peak. The sum of the first causing factor and the second causing factor is determined as the overlapping factor corresponding to the candidate overlapping peak. The overlapping factor is used to reflect the probability of peak overlap due to inappropriate component rates.

[0044] By adopting the above method, the overlapping factor corresponding to each overlapping peak in the spectrum curve can be obtained.

[0045] Step S4, combining the overlapping factors and spectrum curves corresponding to all overlapping peaks to obtain the gas concentration after removing the overlapping influence.

[0046] In step S3, the overlapping factor corresponding to each overlapping peak in the spectral curve is obtained. Next, the overlapping factors corresponding to all overlapping peaks and the spectral curve are combined to calibrate the measurement data of the anesthetic gas concentration detector with the help of a neural network to obtain accurate concentration data of the anesthetic gas.

[0047] Specifically, the overlapping factors corresponding to all overlapping peaks in the spectrum curve are sorted in order from small to large in the horizontal coordinate to obtain an overlapping factor sequence. The overlapping factor sequence and the spectrum curve are input into the trained neural network, and the neural network outputs the gas concentration after removing the overlapping effect. The training process of the neural network is a prior art and will not be described in detail here.

[0048] Thus, the method provided in this embodiment has been used to calibrate the gas concentration data detected by the anesthetic gas concentration detector, and to obtain accurate anesthetic gas concentration data.

[0049] In this embodiment, a gas concentration detector is first used to detect anesthetic gas to obtain a spectrum curve. Considering that anesthetic gas usually has a low molecular weight and a low boiling point, the existing method cannot avoid the influence of overlapping peaks when calibrating the measurement data of the gas detector, resulting in low accuracy of the anesthetic gas detection result. In this embodiment, multiple overlapping peaks are obtained according to the distribution of extreme points in the spectrum curve, and the spectrum curve is decomposed into multiple IMF components. Each overlapping peak in the spectrum curve is screened out from all IMF components according to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component. The target component peak corresponding to the peak is obtained by combining the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectral curve and the changing trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectral curve. The overlapping factor corresponding to each overlapping peak is used to reflect the probability of peak overlap caused by inappropriate component rates. The overlapping factors and spectral curves corresponding to all overlapping peaks are further combined to calibrate the data measured by the gas concentration detector, and the gas concentration after removing the overlapping influence is obtained, which eliminates the influence of overlapping peaks on the identification of anesthetic gas concentration and improves the accuracy of anesthetic gas concentration detection results.

[0050] A system embodiment for calibrating the detection accuracy of an anesthetic gas concentration detector: See also Figure 2 , which shows a structural block diagram of a system for calibrating the detection accuracy of an anesthetic gas concentration detector provided by an embodiment of the present invention, the system may include a data acquisition module, a target component peak determination module, an overlap factor calculation module and a concentration calibration module; The data acquisition module is used to detect the anesthetic gas using a gas concentration detector to obtain a spectrum curve; A target component peak determination module is used to decompose the spectrum curve to obtain each IMF component; obtain each overlapping peak according to the distribution of extreme value points in the spectrum curve; and screen the target component peak corresponding to each overlapping peak in all IMF components according to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component; The overlapping factor calculation module is used to obtain the overlapping factor corresponding to each overlapping peak in the spectrum curve according to the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectrum curve and the change trend of the curve between the peak point and the trough point on both sides of each overlapping peak in the spectrum curve; The concentration calibration module is used to integrate the overlapping factors and spectral curves corresponding to all overlapping peaks to obtain the gas concentration after removing the overlapping effects.

[0051] It should be understood that Figure 2The structural block diagram of a system for calibrating the detection accuracy of an anesthetic gas concentration detector and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0052] For more details about the above modules, please refer to other places in this manual and will not be repeated here.

[0053] In other embodiments, a medium is also provided, wherein the medium stores at least one program executable by a computer, and when the at least one program is executed by a computer, the computer executes the steps in the method for calibrating the detection accuracy of an anesthetic gas concentration detector in the above-mentioned embodiment, and the medium may be a computer-readable storage medium.

[0054] Among them, the provided devices, systems, and media are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0055] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calibrating the detection accuracy of an anesthetic gas concentration detector, characterized in that: The method comprises the following steps: A gas concentration detector is used to detect the anesthetic gas to obtain a spectrum curve; Decomposing the spectrum curve to obtain each IMF component; obtaining each overlapping peak according to the distribution of extreme value points in the spectrum curve; screening the target component peak corresponding to each overlapping peak in all IMF components according to the relative distance between the corresponding position of each overlapping peak in the IMF component and the peak point in the IMF component; According to the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectrum curve, and the change trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectrum curve, the overlapping factor corresponding to each overlapping peak in the spectrum curve is obtained; The overlapping factors and spectrum curves corresponding to all overlapping peaks are combined to obtain the gas concentration after removing the overlapping influence; The screening of the target component peak corresponding to each overlapping peak in all IMF components includes: The point in each IMF component having the same abscissa as the candidate overlapping peak is recorded as a reference point, and the maximum point in each IMF component having the smallest difference in abscissa from the reference point is determined as the reference peak point of the candidate overlapping peak in each IMF component; The reference peak point when the amplitude of the reference peak point of the candidate overlapping peak in all IMF components is the maximum is determined as the target component peak corresponding to the candidate overlapping peak in all IMF components; The candidate overlapping peak is any overlapping peak in the spectrum curve.

2. A method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 1, characterized in that: The step of obtaining each overlapping peak according to the distribution of extreme value points in the spectrum curve includes: For any maximum point in the spectrum curve: If the ordinate value of at least one of the two minimum value points adjacent to any of the maximum value points is not 0, then the any of the maximum value points is recorded as an overlapping peak.

3. A method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 1, characterized in that: The method of obtaining the overlapping factor corresponding to each overlapping peak in the spectrum curve according to the curve distribution characteristics of the overlapping peaks on both sides of the target component peak corresponding to each overlapping peak in the spectrum curve and the change trend of the curve between the peak points and the trough points on both sides of each overlapping peak in the spectrum curve comprises: The two peak points on both sides of the target component peak corresponding to the candidate overlapping peak and which are closest to the target component peak corresponding to the candidate overlapping peak are recorded as reference peak points of the candidate overlapping peak; For any reference peak point: fitting a first curve between two trough points with the smallest difference in horizontal coordinates between the left and right sides of the any reference peak point and the any reference peak point to obtain a first fitting equation; extending the first curve by the first fitting equation until the vertical coordinate is 0, and recording the extended portion of the curve as an extension line; determining the influence probability corresponding to the any reference peak point according to the extension line and the first curve; The trough points with the smallest differences between the left and right sides of the candidate overlapping peak and its horizontal coordinate are respectively recorded as the left reference trough point and the right reference trough point. According to the change trend of the curve between the left reference trough point and the peak point on its left, the slope change degree corresponding to the left reference trough point is obtained; according to the change trend of the curve between the right reference trough point and the peak point on its right, the slope change degree corresponding to the right reference trough point is obtained; The overlapping factor corresponding to the candidate overlapping peak is obtained according to the influence probability corresponding to the reference peak point of the candidate overlapping peak, the slope change degree corresponding to the left reference trough point and the slope change degree corresponding to the right reference trough point.

4. A method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 3, characterized in that: The determining, according to the extension line and the first curve, the influence probability corresponding to any reference peak point includes: Draw a first vertical line perpendicular to the horizontal axis through two trough points on the left and right sides of any reference peak point with the smallest difference in horizontal coordinates with the any reference peak point; The extension line and the first curve form a complementary curve; the area of ​​a closed region enclosed by the first perpendicular line, the extension line and the straight line with a ordinate of 0 is recorded as a first area; the area of ​​a closed region enclosed by the complementary curve and the straight line with a ordinate of 0 is recorded as a second area; The influence probability corresponding to any reference peak point is determined by combining the first area and the second area.

5. A method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 3, characterized in that: The step of obtaining the slope change degree corresponding to the left reference trough point according to the change trend of the curve between the left reference trough point and the peak point on its left side; and obtaining the slope change degree corresponding to the right reference trough point according to the change trend of the curve between the right reference trough point and the peak point on its right side, comprises: The peak point on the left side of the candidate overlapping peak that is closest to the candidate overlapping peak is recorded as the left peak point; the curve between the point on the spectrum curve that is adjacent to the left peak point and located on the right side of the left peak point and the point that is adjacent to the left reference trough point and located on the left side of the left reference trough point is fitted by the least square method to obtain a second fitting equation; according to the second fitting equation, a new slope value of the point on the right side adjacent to the left reference trough point is obtained; a first slope difference between the slope value of the point on the right side adjacent to the left reference trough point and the new slope value of the adjacent point on the right side is calculated, and the slope change degree corresponding to the left reference trough point is obtained according to the first slope difference; The peak point on the right side of the candidate overlapping peak that is closest to the candidate overlapping peak is recorded as the right peak point; the curve between the point on the spectrum curve that is adjacent to the right peak point and located on the left side of the right peak point and the point that is adjacent to the right reference trough point and located on the right side of the right reference trough point is fitted by the least squares method to obtain a third fitting equation; according to the third fitting equation, a new slope value of the point on the left side adjacent to the right reference trough point is obtained; the second slope difference between the slope value of the point on the left side adjacent to the right reference trough point and the new slope value of the adjacent point on the left side is calculated, and the slope change degree corresponding to the right reference trough point is obtained according to the second slope difference.

6. A method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 3, characterized in that: The obtaining of the overlapping factor corresponding to the candidate overlapping peak according to the influence probability corresponding to the reference peak point of the candidate overlapping peak, the slope change degree corresponding to the left reference trough point, and the slope change degree corresponding to the right reference trough point includes: Determine the product of the influence probability corresponding to the reference peak point on the left side of the target component peak corresponding to the candidate overlapping peak and the slope change degree corresponding to the left reference trough point as the first contributing factor corresponding to the candidate overlapping peak; Determine the product of the influence probability corresponding to the reference peak point on the right side of the target component peak corresponding to the candidate overlapping peak and the slope change degree corresponding to the right reference trough point as the second contributing factor corresponding to the candidate overlapping peak; The sum of the first contribution factor and the second contribution factor is determined as the overlap factor corresponding to the candidate overlap peak.

7. A method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 1, characterized in that: The method of synthesizing the overlapping factors and spectral curves corresponding to all overlapping peaks to obtain the gas concentration after removing the overlapping influence includes: Input the overlapping factor sequence and the spectrum curve into the trained neural network to obtain the gas concentration after removing the overlapping effect; The overlapping factor sequence is composed of overlapping factors corresponding to all overlapping peaks in the spectrum curve.

8. The method for calibrating the detection accuracy of an anesthetic gas concentration detector according to claim 1, characterized in that: The EMD decomposition method is used when decomposing the spectrum curve.

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