CN111737855A - Air respirator air consumption prediction method based on activity intensity - Google Patents

Air respirator air consumption prediction method based on activity intensity Download PDF

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CN111737855A
CN111737855A CN202010440199.5A CN202010440199A CN111737855A CN 111737855 A CN111737855 A CN 111737855A CN 202010440199 A CN202010440199 A CN 202010440199A CN 111737855 A CN111737855 A CN 111737855A
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张宏远
李梦琪
王颖辉
晏国辉
李思维
李亚林
梁延松
毕坤鹏
刘学程
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Insititute Of Nbc Defence
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Abstract

The invention discloses an air respirator air consumption prediction method based on activity intensity, which comprises the following steps: step 1, determining the activity intensity level of a person wearing the air pager; step 2, collecting and processing data, determining the air breathing rate variation and the dynamic-static heart rate ratio, and removing out-of-range data according to different activity intensity grade division tables; step 3, constructing a correlation coefficient of activity intensity influence factors of the rescuers, and determining main influence factors of the air consumption rate variation of different activity intensities; step 4, constructing an air breathing gas consumption rate regression model with different levels of activity intensity; and 5, predicting the air-breathing gas consumption time of the activity intensity of different levels. According to the method, the gas consumption rate condition of the same batch of people is predicted through the gas consumption rate change rule summarized by the data of the sample personnel, and the obtained result is reliable and is in accordance with the reality; the method can predict the use time of the personal idle call in advance by combining different personal conditions, and provides reference basis for rescue action task allocation and command decision.

Description

Air respirator air consumption prediction method based on activity intensity
Technical Field
The invention relates to an activity intensity-based air respirator air consumption prediction method, in particular to a method for calculating air respirator air consumption amounts with different activity intensities by considering individual differences of people, and provides a basis for predicting the service time of an individual air respirator. Particularly, the individual difference of the rescue workers is considered aiming at the prediction of the use time of the air respirator caused by the body difference and the different intensity of the rescue activities of the rescue workers, and the method belongs to the technical field of the management of rescue equipment.
Background
A positive pressure air respirator (hereinafter referred to as air breathing) is an equipment for rescuers in special use environments of toxic and harmful gases, smoke, dust and the like. The emergency rescue environment has high danger, and rescuers must make a judgment on how long the worn air exhalant can work and whether the worn air exhalant can work enough according to the size of a task site, the consumption speed of the air exhalant, the residual gas amount and the like, so that the air exhaustion is prevented, and the protection effect is lost.
At present, the air quantity of the air respirator is provided for people through the display of the residual air pressure in an air bottle, and the actual empty breathing capacity is reflected through the change of the display number of an empty breathing pressure gauge. However, air respirators of equal capacity can be used for a wide variety of different rescuers and rescue missions. For example: heart rate (pulse), height, weight, physical fitness and the like of different rescuers are different to a certain extent; in the rescue activities, there are various situations such as running, erecting a ladder, saving people with a stretcher, assisting and saving people, and the like. These are all differentiating factors in the use of air respirators. How to predict the service time of the personal air respirator according to the influence relationship of the differentiation factors has important significance for evaluating the rescue ability of rescuers.
Disclosure of Invention
The invention aims to provide an air respirator air consumption prediction method based on activity intensity, aiming at the problem that the existing air respirator cannot distinguish different rescuers and different use differences of rescue tasks; the invention finds that whether different rescuers are physically qualified or different rescue strengths are finally reflected in the gas consumption rate of the air respirator for a person using the air respirator, and provides a concept for the following reasons: the air breathing rate represents the amount of air consumed by a user in a working range in unit time in MPa/min (megapascal per minute) by the pressure of the air respirator.
The invention can be realized by the following method, which mainly comprises the following steps:
step 1, determining the activity intensity level of the person wearing the air pager. The load and the terrain are main factors influencing the activities of the personnel, the two factors are combined into one factor of activity intensity, the activity intensity is classified according to the difference of the load and the terrain, and the rescue personnel work under the state of protecting certain and carrying the air respirator, so the working intensity is sequentially classified into four grades of medium, heavy, very heavy and extremely heavy, and the four working activities of bare-handed travel on the flat ground, heavy-load travel on the flat ground, bare-handed stair ascending and descending, and heavy-load stair ascending and descending are respectively corresponding to the air breathing and full-length protection.
And 2, collecting and processing data, determining the air breathing rate variation and the dynamic-static heart rate ratio, and removing the data beyond the range according to different activity intensity grade division tables.
In defining the data items: according to the characteristics of heart rate rise and respiration increase during work, in order to eliminate individual difference, a heart rate factor is converted into a dynamic and static heart rate ratio which is defined as the ratio of the heart rate of the rescuer during activity to the heart rate of the rescuer during rest (namely, the dynamic and static heart rate ratio is the heart rate during activity/the heart rate during rest); setting the parameters of the gas consumption rate of individual activity of the personnel as the gas consumption rate variable quantity,is defined as the difference between the air breathing rate of the rescuer during activity and the air consumption rate of the rescuer in a resting state. Air breathing rate Y for measuring static state of person wearing protective clothing during air breathing0The unit MPa/min is different from person to person, and the reference value of each person is different.
Collecting the values of various influencing factors A, B, … and X and the gas consumption rate Y of a tester under the current activity intensityj(j is the activity intensity code). Wherein, A, B, … and X respectively correspond to each influencing factor and comprise: the person's heart rate (pulse), height, weight, physical fitness, weight bearing, speed of travel, terrain on site, cylinder filling pressure, etc., are among the factors of interest to the test. Calculating the air consumption rate YjChange Y of gas consumption rate relative to static statej-0. The measured data are processed according to the activity intensity level (see table 1) to remove data that are outside the range corresponding to each activity intensity.
Figure BDA0002503860420000021
TABLE 1 activity intensity grading chart for person wearing empty call
Step 3, constructing the activity intensity influence factor correlation coefficient of the rescuers, and determining the main influence factors of the air consumption rate variation of different activity intensities
Establishing an activity intensity level corresponding influence factor and an air breathing rate variation Y by constructing an activity intensity influence factor correlation coefficient matrixj-0The table of correlation coefficients of (2) is shown in table 2.
Figure BDA0002503860420000031
TABLE 2 correlation of air consumption rate of certain activity intensity
Step 4, constructing a regression model of the air breathing gas consumption rate of different levels of activity intensity
And (4) processing the data by using SPSS25.0 software and Excel according to the correlation result of the influence factors obtained in the step (3) and the correlation theory of regression modeling, and constructing a regression model of the variation of the air breathing rate under the current activity intensity by taking the main influence factors with larger influence degree as independent variables and the variation of the air breathing rate as dependent variables.
when the independent variable is single variable X, X is the independent variable and Y isj-0And fitting a multi-class function equation of the independent variable and the dependent variable by using software such as SPSS (software specification system) or MATLAB (matrix laboratory) and the like, selecting a constructed model with the best fitting degree from the fitting equations, and further determining the quality of the constructed model according to the tests such as significance, variance, complex correlation coefficient and the like. And if the test is not passed, selecting the model type with suboptimal fitting degree to reconstruct. When the constructed model passes the inspection, writing a corresponding regression equation according to the model coefficient table
Figure BDA0002503860420000032
② when the independent variable of the main influencing factor has two or more, using the influencing factor as independent variable, Yj-0And (3) judging whether the data is suitable for linear regression or not by performing Debinwatson test, drawing a histogram or a normal probability graph and the like on the standardized residual error of the sample by using software such as SPSS (software-programmable system) or MATLAB (matrix laboratory) and the like as a dependent variable. If the data is suitable, carrying out linear regression modeling on the sample data, and writing a regression equation
Figure BDA0002503860420000033
If not, carrying out standardization processing on the data, carrying out principal component analysis, calculating the weight of each influence factor, further establishing a model, and writing an equation according to the weight of each influence factor
Figure BDA0002503860420000034
Step 5, predicting the air-breathing gas consumption time of the activity intensity of different levels
The air breathing gas consumption rate Y of the static state when the person wears the air breathing with the protective clothing at the current gas pressure P in the bottle0In MPa/min. The available remaining time (min) can thus be predicted as:
Figure BDA0002503860420000035
the invention relates to an activity intensity-based air respirator air consumption prediction method, which has the advantages and effects that: and (3) obtaining a calculation formula of the air breathing gas consumption of different activity strengths of a batch of personnel according to the test data of different activity strength grades. The air consumption rate of the person wearing the air breathing under the activity intensity can be calculated by utilizing the stable speed or the dynamic-static heart rate ratio of the rescue person under different activity intensities during daily training. The air-breathing gas consumption rate value can represent the air-breathing gas consumption rate value of a single person, and a judgment reference basis is provided for the air-breathing service time of the rescue worker in the actual operation decision. By adopting the method, the gas consumption rate condition of the same batch of people is predicted through the gas consumption rate change rule summarized by the data of the sample personnel, and the obtained result is more reliable and is in practical fit.
The invention provides four activity intensity levels in combination with the working practice, provides air breathing rate variation and a dynamic-static heart rate ratio in consideration of personal differences, provides a method for calculating the air breathing rate variation with different activity intensities in consideration of personal differences on the basis of a regression modeling theory, and provides a method for predicting the service time of a personal air respirator. The method can predict the use time of the personal idle call in advance by combining different personal conditions, and provides reference basis for rescue action task allocation and command decision.
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FIG. 1 is a block diagram of the process of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example (b):
based on the method, experimental data of a certain fire fighter is selected as a data sample.
Firstly, determining the activity intensity level of the air caller. The rescue personnel work under the state of certain protection and carrying the air respirator, the working intensity grades are sequentially set as four grades of medium, heavy, very heavy and extremely heavy, and the four working activities of bare-handed travel on the flat ground, load travel on the flat ground, bare-handed up and down stairs and load up and down stairs which are protected by wearing the air respirator and the whole body are respectively corresponded.
Determining and collecting influence factor data samples, specifically comprising collecting and processing data, determining air breathing rate variation and dynamic-static heart rate ratio, and removing out-of-range data according to different activity intensity grade division tables.
1) A reference experimenter A was determined to be 174.3 + -2 cm in height and 66.4 + -2 kg in weight. Heart rate, speed, activity intensity, air consumption rate variation, resting air consumption rate and the like are influence factors.
2) The pressure of the air expiration bottle with the capacity of 6.8L is 30MPa when the air expiration bottle is filled with gas, the experimenter A uses the battle uniform for 68min in a static state, and the static gas consumption rate is calculated to be 0.441 MPa/min.
3) The experimenter a was allowed to measure under different exercise conditions to obtain the dynamic-static heart rate ratio range and the air consumption rate range in table 3 below.
Figure BDA0002503860420000041
Remarking: the number of people is related to the data selected in each experiment
TABLE 3 Experimental personnel Activity data
Thirdly, determining main factors influencing the variation of the gas consumption rate under the activity intensity of four levels
Establishing the influence factors corresponding to the activity intensity grade and the air consumption rate variable quantity Y by constructing the activity intensity influence factor correlation coefficient matrixj-0The correlation coefficient table is analyzed and researched through a correlation coefficient matrix of the air consumption rate variation and other influence factors. And judging which are main influence factors according to the degree of the correlation coefficient indicated by the output result. If no, representing that the corresponding two variables have no correlation; if one variable exists, the corresponding two variables are obviously correlated at the level of 0.05, namely the probability of the error of the correlation obvious judgment is five percent; if there are two, it means that the two corresponding variables are significantly correlated at the level of 0.01, i.e. the probability of a significant judgment error of the correlation is one percent.
Figure BDA0002503860420000051
Correlation coefficient of intensity of rank Activity in Table 4
According to table 4 above, at the middle-level activity intensity, the main factor with significant correlation is the speed.
Figure BDA0002503860420000052
TABLE 5 intensity of activity correlation coefficient of weight scale
According to the above table 5, under the intensity of heavy-grade activities, the main factors with significant relevance are speed and ratio of static heart rate and static heart rate.
Figure BDA0002503860420000053
TABLE 6 very heavy-ranking Activity Strength correlation coefficients
According to the above table 6, the main factor with significant correlation at the intensity of very heavy-grade activities is the ratio of the static heart rate to the static heart rate.
Figure BDA0002503860420000054
TABLE 7 extreme gravity level Activity Strength correlation coefficient
According to the above table 7, the main factor with significant correlation at the intensity of extremely heavy-grade activities is the ratio of the static heart rate to the static heart rate.
When correlation analysis is carried out, the speed and the ratio of the dynamic heart rate to the static heart rate are closely related to the variation of the air consumption rate under different levels of activity intensity. This correlation may be due to the fact that weight loading has less effect on the activity of the person at lower activity intensity levels, and the change in gas consumption rate is mainly caused by the change in velocity. As the intensity of activity increases, the energy that a person needs to consume to maintain the current exercise state correspondingly increases, and the effect of the heart rate on the change of the air consumption rate becomes obvious.
Fourthly, constructing an air breathing gas consumption rate regression model according to different activity intensity influence factors
Regression model for air breathing gas consumption rate variation of medium-grade activity intensity
The factor that middle level activity intensity has a large impact on gas consumption rate is velocity, and thus velocity (v)1) As independent variable, the air consumption rate variation (Y)1) Model fitting was performed for the dependent variable. The summary of 11 models such as the linear model and the logarithmic model are shown in the following table 8.
Figure BDA0002503860420000061
Summary of fitting models for rank Activity Strength in Table 8
In table 8, the significance of each model is 0.00 < 0.05, which indicates that all model relationships fitted from the speed and the change amount of the gas consumption rate are preliminarily established. And selecting the S model with the maximum R square value to model the data according to the R square value. The fitted S model coefficient is shown in a table 9, t represents the test of whether the independent variable has a significant effect, the significance level of the t test in the table is 0.00 < 0.05, and the model coefficient is proved to have statistical significance and can be used for describing the relation between the speed and the air breathing rate variation.
Figure BDA0002503860420000062
Table 9 set of regression coefficients of the S-model for intensity of activity
Constructing a regression equation of the gas consumption rate variation under the middle-level activity intensity as follows:
Figure BDA0002503860420000071
v1-the speed of travel of the person in m/s at medium level activity intensity;
Y1the variation of the air breathing gas consumption rate of the middle-level activity intensity, the difference between the gas consumption rate of the person in activity and the gas consumption rate of the person in a static state under the middle-level activity intensity, and the unit is MPa/min.
(II) constructing regression model of air breathing gas consumption rate variation of activity intensity of other levels
And establishing a regression model of the air breathing rate variation under heavy, heavy and extremely heavy level activity strengths according to the same method and steps of the regression model of the air breathing rate variation under the medium level activity strength.
At a high activity level, a velocity (v) with a significant correlation2) Ratio of dynamic and static heart rates (r)2) As independent variable, the air consumption rate variation (Y)2) For the dependent variable, a suitable regression model is obtained as a linear regression model, and according to table 10, a regression equation of the activity intensity at the heavy level is constructed as follows:
Y2＝-0.959+0.468v2+0.488r2(2)
v2-the speed of travel of the person, in m/s;
r2-the ratio of the dynamic to static heart rate at the intensity of the heavy-grade activity;
Y2the variation of the air consumption rate of the personnel in MPa/min.
Figure BDA0002503860420000072
TABLE 10 weight class Activity Strength Linear model coefficient Table
At very heavy level of activity intensity, the ratio of the dynamic heart rate to the static heart rate (r) is significant in correlation3) As independent variable, gas consumption rate (Y)3) For the dependent variable, the appropriate regression model is obtained as a cubic model, and according to table 11, a regression equation of the activity intensity of the heavy level is constructed as follows:
Y3＝1.782-1.396r3 2+0.654r3 3(3)
r3-ratio of static to static heart rate at very high level of activity intensity;
Y3the variation of the air consumption rate of the personnel in MPa/min.
Figure BDA0002503860420000073
TABLE 11 triple model coefficient Table for very heavy class Activity Strength
At the intensity of extremely heavy grade activity, the ratio of the dynamic heart rate to the static heart rate (r) with obvious correlation4) As independent variable, gas consumption rate (Y)4) For the dependent variable, a suitable regression model was obtained as the composite model, and according to table 12, a regression equation was constructed as:
Figure BDA0002503860420000074
r4-the ratio of the dynamic to static heart rates at extreme levels of activity intensity;
Y4the air breathing rate variation of the person in heavy activity in MPa/min.
Figure BDA0002503860420000081
TABLE 12 coefficient Table of extreme gravity grade intensity regression model
According to four regression equations with different intensities, the air consumption rate of the person wearing the air exhaling under the activity intensity can be calculated by utilizing the stable speed or the ratio of the dynamic heart rate to the static heart rate under different activity intensities during daily training of the firefighter. The air-breathing rate value can represent the air-breathing rate value of a single person, and a judgment reference basis is provided for the firemen to judge the air-breathing service time in the actual operation decision.
Fifthly, predicting the air-breathing gas consumption time of the activity intensity of different levels
The current intra-bottle gas pressure P is 20Mpa, the heart rate of the firefighter a is 2 times the heart rate in a resting state, and then under extremely heavy grade activity intensity, the available remaining time for predicting the empty breath of the firefighter is:
Figure BDA0002503860420000083
Figure BDA0002503860420000082

Claims (2)

1. an activity intensity-based air respirator air consumption prediction method is characterized by comprising the following steps: the method comprises the following steps:
step 1, determining the activity intensity level of a person wearing the air pager;
because the load and the terrain are main factors influencing the activities of the personnel, the two factors are combined into the activity strength, and the activity strength is classified according to the difference of the load and the terrain, the working strength grades are sequentially set into four grades, and the four working activities of flat ground free-hand advancing, flat ground load advancing, free-hand stair ascending and descending, and load ascending and descending stair are respectively correspondingly worn and protected by the whole body;
step 2, collecting and processing data, determining the air breathing rate variation and the dynamic-static heart rate ratio, and removing out-of-range data according to different activity intensity grade division tables;
collecting the numerical value and the gas consumption rate Y of each influencing factor of the tester under the current activity intensityjJ is the activity intensity code; wherein, each influencing factor comprises: the heart rate, the height, the weight, the physical fitness, the load bearing, the advancing speed, the site terrain and the gas cylinder filling pressure of the person are mainly tested and concerned factors are considered; calculating the air consumption rate YjChange Y of gas consumption rate relative to static statej-0(ii) a Processing the measured data according to the activity intensity level, and removing the data beyond the corresponding range of each activity intensity;
step 3, constructing a correlation coefficient of activity intensity influence factors of the rescuers, and determining main influence factors of the air breathing rate variation of different activity intensities;
establishing an activity intensity level corresponding influence factor and an air breathing rate variation Y by constructing an activity intensity influence factor correlation coefficient matrixj-0Table of correlation coefficients
Step 4, constructing a regression model of the air breathing gas consumption rate of different levels of activity intensity
According to the influence factor correlation result obtained in the step 3 and the correlation theory of regression modeling, constructing a regression model of the variation of the air breathing rate under the current activity intensity by taking the main influence factor with larger influence degree as an independent variable and the variation of the air breathing rate as a dependent variable;
step 5, predicting the air-breathing gas consumption time of the activity intensity of different levels
The air breathing gas consumption rate Y of the static state when the person wears the air breathing with the protective clothing at the current gas pressure P in the bottle0Unit MPa/min; the available remaining time (min) can thus be predicted as:
Figure FDA0002503860410000011
2. the activity intensity-based air respirator air consumption prediction method according to claim 1, characterized in that: the specific process of the step 4 is as follows:
when the independent variable is single variable X, X is the independent variable and Y isj-0Fitting a multi-class function equation of the independent variable and the dependent variable for the dependent variable, selecting a constructed model with the best fitting degree, and further checking and determining the quality of the constructed model according to the significance, the variance and the complex correlation coefficient; if the test is not passed, selecting a model type with suboptimal fitting degree to reconstruct; when the constructed model passes the inspection, writing a corresponding regression equation according to the model coefficient table
Figure FDA0002503860410000021
② when the independent variable of the main influencing factor has two or more, using the influencing factor as independent variable, Yj-0As a dependent variable, judging whether the data is suitable for linear regression by carrying out Debin-Watson test on the standardized residual error of the sample and drawing a histogram or a normal probability chart; if the data is suitable, carrying out linear regression modeling on the sample data, and writing a regression equation
Figure FDA0002503860410000022
If not, standardizing the data, performing principal component analysis, calculating the weight of each influence factor, further establishing a model, and analyzing the influence factors according to the weightWeight value write equation
Figure FDA0002503860410000023
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CN113648557A (en) * 2021-09-15 2021-11-16 深圳市奥瑞那智慧科技有限公司 Intelligent modification device for firefighting rescue breathing mask and application method of intelligent modification device

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CN113648557A (en) * 2021-09-15 2021-11-16 深圳市奥瑞那智慧科技有限公司 Intelligent modification device for firefighting rescue breathing mask and application method of intelligent modification device

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