CN119619237B - A highly sensitive measurement method and system for humidity of air-oxygen mixed gas - Google Patents
A highly sensitive measurement method and system for humidity of air-oxygen mixed gas Download PDFInfo
- Publication number
- CN119619237B CN119619237B CN202510168545.1A CN202510168545A CN119619237B CN 119619237 B CN119619237 B CN 119619237B CN 202510168545 A CN202510168545 A CN 202510168545A CN 119619237 B CN119619237 B CN 119619237B
- Authority
- CN
- China
- Prior art keywords
- data
- humidity
- determining
- value
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/048—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance for determining moisture content of the material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/22—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating capacitance
- G01N27/223—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating capacitance for determining moisture content, e.g. humidity
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Electrochemistry (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Pathology (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Algebra (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
本发明涉及物理测量技术领域，具体涉及一种空氧混合气体的湿度高灵敏测量方法及系统，所述方法包括：获取空氧混合气体的历史湿度测量数据，确定其拟合曲线和其中的极值，利用极值确定各个历史时刻的误差累计偏移值；确定当前测量阶段中数据的数据波动系数，进而确定相似数据波动特征区域和数据的数据波动系数均值，确定当前测量阶段各个时刻的湿度波动尺度；利用数据波动系数均值和湿度波动尺度，确定当前测量阶段各个时刻的实时失真程度值；利用历史误差累计偏移值和实时失真程度值，确定当前时刻的修正湿度测量值。通过本发明的所述方法，实现对测量系统的智能校正和优化，提升对空氧混合气体湿度的测量精度。
The present invention relates to the field of physical measurement technology, and specifically to a highly sensitive humidity measurement method and system for air-oxygen mixed gas, the method comprising: obtaining historical humidity measurement data of air-oxygen mixed gas, determining its fitting curve and the extreme value thereof, and using the extreme value to determine the error cumulative offset value at each historical moment; determining the data fluctuation coefficient of the data in the current measurement phase, and then determining the data fluctuation coefficient mean value of the similar data fluctuation characteristic area and the data, and determining the humidity fluctuation scale at each moment of the current measurement phase; using the data fluctuation coefficient mean value and the humidity fluctuation scale, determining the real-time distortion degree value at each moment of the current measurement phase; using the historical error cumulative offset value and the real-time distortion degree value, determining the corrected humidity measurement value at the current moment. Through the method of the present invention, intelligent correction and optimization of the measurement system are achieved, and the measurement accuracy of the humidity of the air-oxygen mixed gas is improved.
Description
Technical Field
The invention relates to the technical field of physical measurement, in particular to a humidity high-sensitivity measurement method and system for air-oxygen mixed gas.
Background
In many industrial, environmental and scientific fields, humidity measurement of air-oxygen mixture is an important parameter, which affects the operation of the equipment, the quality of the product and the accuracy of the experiment. Particularly in some special environments, the humidity of the gas not only directly affects the characteristics of the gas, but is also closely related to many chemical and physical processes. In order to accurately grasp the humidity change and adjust the system in time, it becomes particularly important to develop a humidity measurement method and system with high sensitivity.
Currently, high sensitivity measurement systems for air-oxygen mixture humidity generally rely on the property of the resistance or capacitance of certain specific materials as a function of humidity. However, in practical applications, the gas components (such as oxygen, nitrogen, etc.) may fluctuate due to changes in environmental conditions or industrial processes, which may affect the electrical characteristics of the sensor material, and thus cause a deviation in the relationship between the resistance or capacitance and the actual humidity, and thus affect the measurement accuracy, so that the measured humidity deviates greatly from the actual humidity.
Disclosure of Invention
In order to solve the technical problem that the measured humidity and the actual humidity of the air-oxygen mixed gas measured by the humidity sensor deviate greatly, the invention aims to provide a high-sensitivity humidity measuring method and a high-sensitivity humidity measuring system of the air-oxygen mixed gas, and the adopted technical scheme is as follows:
The invention provides a high-sensitivity humidity measurement method of an air-oxygen mixed gas, which comprises the following steps:
acquiring historical humidity measurement data of the air-oxygen mixed gas, and determining a fitting curve corresponding to the historical humidity measurement data;
determining each extremum in the fitted curve, and determining a historical error accumulated offset value at each historical moment by using the extremum;
determining a data fluctuation coefficient of each current humidity measurement data in the current measurement stage, and determining a similar data fluctuation characteristic region by using the data fluctuation coefficient;
Determining the average value of data fluctuation coefficients of all current humidity measurement data in the similar data fluctuation characteristic area, and determining the humidity fluctuation scale at each moment in the current measurement stage;
Determining a real-time distortion degree value of each moment in the current measurement stage by utilizing the data fluctuation coefficient mean value and the humidity fluctuation scale;
And determining the corrected humidity measured value at the current moment by utilizing the historical error accumulated offset value and the real-time distortion degree value.
Further, the step of determining a historical error accumulation offset value for each historical time using the extremum comprises:
determining an upper envelope corresponding to a maximum value point and a lower envelope corresponding to a minimum value point;
Determining historical error accumulation coefficients of all historical moments by using the upper envelope curve and the lower envelope curve;
Setting a preset number of extreme point step sizes by taking a maximum point as a starting point, and determining a window area corresponding to the maximum point;
Determining a historical error offset compensation index at each historical moment by utilizing the maximum value point set and each window area;
and calculating to obtain the historical error accumulation offset value of each historical moment by using the historical error accumulation coefficient and the historical error offset compensation index.
Further, the step of determining a history error offset compensation index for each history time using the maximum point set and each window area includes:
Determining an extreme point set in the window area and the slope of the extreme point at the fitting curve, and the occurrence frequency of the extreme point in the fitting curve;
And calculating to obtain the historical error offset compensation index at each historical moment by using the maximum point set, the extreme point set, the slope and the occurrence frequency in the window area.
Further, the step of determining the data fluctuation coefficient of each current humidity measurement data in the current measurement phase includes:
Setting a preset number of current humidity measurement data step sizes by taking any one of the current humidity measurement data in the current measurement stage as a starting point, and determining a window area corresponding to the current humidity measurement data;
and determining the standard deviation of the current humidity measurement data in the window area as the data fluctuation coefficient of the starting point to obtain the data fluctuation coefficient of each current humidity measurement data.
Further, the step of determining the similar data fluctuation feature region using the data fluctuation coefficient includes:
determining the absolute value of the difference value of the data fluctuation coefficient between adjacent current humidity measurement data;
And dividing the current humidity measurement data with the absolute value of the difference value smaller than a preset threshold value into similar data fluctuation characteristic areas.
Further, the step of determining the real-time distortion degree value at each moment in the current measurement stage by using the mean value of the data fluctuation coefficient and the humidity fluctuation scale comprises the following steps:
Determining the smooth distortion influence degree of each similar data fluctuation characteristic region by utilizing the data fluctuation coefficient mean value;
and determining the real-time distortion degree value of each moment in the current measurement stage by using the smooth distortion influence degree and the humidity fluctuation scale.
Further, the step of determining the degree of influence of the smooth distortion of each similar data fluctuation feature region by using the mean value of the data fluctuation coefficients includes:
determining any similar data fluctuation feature area as a target feature area;
Determining a left similar data fluctuation feature area and a right similar data fluctuation feature area which are respectively adjacent to two sides of the target feature area;
And obtaining the smooth distortion influence degree of each similar data fluctuation characteristic region by utilizing the data fluctuation difference of the data fluctuation coefficient mean value among the target characteristic region, the left similar data fluctuation characteristic region and the right similar data fluctuation characteristic region.
Further, the step of determining the humidity fluctuation scale at each moment in the current measurement stage includes:
determining a variance of current humidity measurement data in a current measurement phase;
determining a difference value between a humidity maximum value and a humidity minimum value in the current humidity measurement data;
calculating by using the variance and the difference to obtain the humidity fluctuation scale of the current measurement stage;
And taking the humidity fluctuation scale as the humidity fluctuation scale of each moment.
Further, the step of determining the corrected humidity measurement value at the current time by using the historical error accumulated offset value and the real-time distortion degree value includes:
determining first environmental factor data of the air-oxygen mixed gas at the current moment;
Determining second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement stage;
determining third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;
Determining third environmental factor data with highest similarity with the first environmental factor data to obtain a historical error accumulation offset value corresponding to the current moment;
determining second environmental factor data with highest similarity with the first environmental factor data to obtain a real-time distortion degree value corresponding to the current moment;
determining a corrected humidity measurement value at the current moment by utilizing the historical error accumulated offset value, the real-time distortion degree value and the initial humidity measurement value corresponding to the current moment;
The environmental factor data comprise various gas contents and gas temperatures of the air-oxygen mixed gas, and the environmental factor data at various moments are stored in association with the humidity measurement data.
The invention also discloses a humidity high-sensitivity measurement system of the air-oxygen mixed gas, which is used for realizing the humidity high-sensitivity measurement method of the air-oxygen mixed gas, and comprises the following steps:
The data acquisition module is used for acquiring historical humidity measurement data of the air-oxygen mixed gas and determining a fitting curve corresponding to the historical humidity measurement data;
The system comprises a data analysis module, a data fluctuation characteristic region, a humidity fluctuation scale and a real-time distortion degree value, wherein the data analysis module is used for determining each extreme value in a fitting curve, determining a historical error accumulation offset value at each historical moment by using the extreme value, determining a data fluctuation coefficient of each current humidity measurement data in a current measurement stage, determining a similar data fluctuation characteristic region by using the data fluctuation coefficient, determining a data fluctuation coefficient mean value of all current humidity measurement data in the similar data fluctuation characteristic region, and determining a humidity fluctuation scale at each moment in the current measurement stage;
and the correction compensation module is used for determining a correction humidity measurement value at the current moment by utilizing the historical error accumulated offset value and the real-time distortion degree value.
The invention has the following beneficial effects:
The invention aims at solving the problem that in the existing air-oxygen mixed gas high-sensitivity measurement scheme, the influence of environmental conditions or industrial process changes on gas components (such as oxygen, nitrogen and the like) can cause fluctuation of the electrical characteristics of sensor materials, so that the relation between resistance or capacitance and actual humidity is deviated, and the measurement accuracy is influenced. In order to solve the problem, the method extracts an error accumulation function and corresponding values of all moments, a real-time distortion function and corresponding values of all moments respectively by analyzing historical data of a sensor. The error accumulation function is used to describe the degree of deviation between the humidity and the sensor material electrical characteristics, and the real-time distortion function is used to adjust the measurement error in real time. Through the comprehensive actions of the two functions and the corresponding time values, the intelligent correction and optimization of the measurement system are realized, and the measurement accuracy of the air-oxygen mixed gas humidity is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a method for measuring the humidity of an air-oxygen mixture according to an embodiment of the present invention;
FIG. 2 is a detailed flow chart of step S2 in a method for measuring humidity and sensitivity of air-oxygen mixture according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S5 in a method for measuring humidity and sensitivity of an air-oxygen mixture according to an embodiment of the present invention;
FIG. 4 is a detailed flowchart of step S6 in a method for measuring humidity and sensitivity of an air-oxygen mixture according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a fitting curve involved in a method for measuring humidity and sensitivity of an air-oxygen mixture according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a hardware operation environment of a humidity-sensitive measurement device for air-oxygen mixture according to an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a humidity-sensitive measurement system for air-oxygen mixture according to an embodiment of the present invention;
Fig. 8 is a schematic diagram of a frame structure of a humidity-sensitive measurement system for air-oxygen mixture according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of a method for measuring the humidity of an air-oxygen mixture according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The specific scheme of the humidity high-sensitivity measurement method for the air-oxygen mixed gas provided by the invention is specifically described below with reference to the accompanying drawings.
Embodiment one:
First, before the following embodiments are developed, a specific hardware system and related concepts to which the humidity high sensitivity measurement method of air-oxygen mixture is preferably applied need to be briefly described to facilitate understanding the following embodiments:
Referring to fig. 8, the humidity high sensitivity measurement system of the air-oxygen mixed gas may include a humidity sensor, a signal processing unit, a calibration and compensation module, a data analysis and algorithm processing module, a display and feedback unit, etc.;
In the humidity measurement system, a sensor data acquisition module is a core component part of the humidity measurement system and is responsible for being connected with a plurality of humidity sensors through a data acquisition card and acquiring signals;
To ensure accuracy and coverage of the measurement results, humidity sensors (e.g., capacitive, resistive, etc.) are installed at the primary air flow path or critical nodes of the measurement gas system, e.g., at the gas flow inlet and outlet, at different locations of the duct, etc. The sensors form a humidity sensing network, so that humidity monitoring is ensured to be carried out at multiple points at the same time;
considering the requirement of high sensitivity measurement, the system can adopt a high-frequency data acquisition mode of 10 times per second to capture the fine fluctuation of humidity change;
the data acquisition card further processes the data acquired from each sensor, including signal amplification, filtering, correction and the like, so as to ensure that the signals meet the precision requirements of subsequent data processing;
The central processing unit receives and analyzes the sensor data from the data acquisition card in real time. Through built-in algorithm and analysis module, central processing unit not only can carry out data real-time analysis processing and correct and compensate measurement data, can also transmit the result to the high in the clouds automatically and store, provides support for subsequent analysis and control.
The display and feedback unit can output and feed back the result data obtained by the processing of the central processing unit.
In the air-oxygen mixed gas humidity measurement process, "air-oxygen mixed gas" refers to a mixture of oxygen and other gases. In practice, the concentration of the mixed gas is affected by environmental changes or industrial process fluctuations, resulting in changes in the electrical conductivity and resistance of the capacitive humidity sensor material. This causes the change in electrical characteristics that would otherwise be caused by the change in humidity to be disturbed by the fluctuation in the gas composition, causing an error in the measurement results.
The system humidity measurement calibration controller is characterized by comprising a calibration and compensation module and a data analysis and algorithm processing module, wherein the calibration and compensation module and the data analysis and algorithm processing module aim at carrying out precision correction on the mixed gas humidity data acquired by a humidity sensor;
The generation process of the component function is generated by a data analysis module through big data analysis, and then the initial humidity data is corrected and fed back to a system humidity monitoring platform;
the error accumulation function describes the accumulated deviation degree of the linear relation of the electrical characteristics of the humidity-sensing materials, which is generated by the variation of the components of the mixed gas when each humidity sensor measures the humidity of the mixed gas;
when each gas environment factor is abnormal, linear deviation of the electrical characteristics of the sensing material can be triggered, and the optimized correction scale in the humidity measurement can be different for humidity sensors installed in different areas.
The high-sensitivity humidity measuring method for the air-oxygen mixed gas provided by the invention. Referring to fig. 1, a flowchart of a method for measuring humidity and high sensitivity of an air-oxygen mixture according to an embodiment of the invention is shown.
The method comprises the following steps:
step S1, acquiring historical humidity measurement data of air-oxygen mixed gas, and determining a fitting curve corresponding to the historical humidity measurement data;
The central processing unit of the system can be used for calling the history of each humidity sensor for 24 hours (other time length can be set according to actual needs), recording all humidity measurement data of the air-oxygen mixed gas so as to obtain time sequence history humidity measurement data, fitting the history humidity measurement data to obtain corresponding fitting curves, and respectively giving the corresponding fitting curves to an upper envelope and a lower envelope according to the maximum value and the minimum value in the fitting curves, as shown in fig. 5.
Along with the dynamic change of the components of the mixed gas, the electrical characteristics of the humidity sensor are nonlinear changed, so that errors or drift exists in a fitting curve of gas humidity data, namely, the maximum value and the minimum value of the fitting curve are offset;
During the measurement of the humidity of the mixed gas, the dynamic changes of the gas components have randomness, and the influence of the changes on the characteristics of the sensing materials is usually slow, so that the errors in the historical data are gradually accumulated. The phase difference between the upper envelope curve and the lower envelope curve of the fitting curve can be gradually accumulated and increased along with the time;
The process of obtaining the fitting curve and the upper and lower envelopes can be briefly described as follows:
polynomial fitting is carried out on all historical humidity measurement data on time sequence, and a fitting function curve is obtained ;
Obtaining a fitting curveAll local extrema (maxima and minima) set;
Respectively connecting all local maximum values and local minimum values to obtain a fitting curve Upper envelope and lower envelope of (c).
S2, determining each extremum in the fitting curve, and determining a history error accumulation offset value at each history moment by using the extremum;
in one embodiment, referring to FIG. 2, the extremum includes a maximum value and a minimum value, and the step of determining the historical error accumulated offset value at each historical time using the extremum includes:
step S21, determining an upper envelope corresponding to a maximum value point and a lower envelope corresponding to a minimum value point;
step S22, utilizing the upper envelope line and the lower envelope line to determine the history error accumulation coefficient of each history moment;
converting the upper envelope curve data and the lower envelope curve data on the time domain into frequency domain data by using a Fast Fourier Transform (FFT) algorithm;
simultaneously, the phase information is subjected to data conversion by utilizing a trigonometric function so as to facilitate the subsequent data calculation and analysis;
in the formula, Represents a fitted curveThe phase information of the upper envelope is calculated,Represents a fitted curveThe phase information of the lower envelope curve,Representing the historical error accumulation coefficient in the measuring process;
Step S23, setting a preset number of extreme point step sizes by taking a maximum point as a starting point, and determining a window area corresponding to the maximum point;
Step S24, utilizing the maximum value point set and each window area to determine a history error offset compensation index of each history moment;
specifically, the step S24 includes:
Determining an extreme point set in the window area and the slope of the extreme point at the fitting curve, and the occurrence frequency of the extreme point in the fitting curve;
And calculating to obtain the historical error offset compensation index at each historical moment by using the maximum point set, the extreme point set, the slope and the occurrence frequency in the window area.
Step S25, calculating the historical error accumulation offset value of each historical moment by using the historical error accumulation coefficient and the historical error offset compensation index.
Under the condition of rapid change of gas humidity, along with disturbance and variation of mixed gas components, unstable change of characteristics of sensor materials can occur, so that the influence of accumulated errors generated by sensor measurement data is aggravated;
Will fit a curve Window division is carried out on any maximum point in the data, local window area characteristics are obtained to measure the data extreme point characteristics, and error offset of final humidity data is estimated;
The local window takes the maximum value point as a starting point, the step length is 10 (other preset number of data extreme points can be also adopted) to construct a window area, and when the data points are insufficient, all the rest data points are regarded as the same area.
The occurrence frequencies of all extreme points (including maximum and minimum) in the window area in the whole historical humidity measurement data are respectively obtainedAnd extreme points are in a fitting curveSlope at;
In the formula,Represents a curveA set of medium maximum point numbers,RepresentsThe first in the collectionThe point of the maximum value of the number,Represents the firstEach maximum point corresponds to a set of extreme points within the window region,RepresentsThe first in the collectionThe number of extreme points is chosen,Represents the firstAt the extreme point of the curveThe slope at which the slope is to be determined,Represents the firstThe frequency of occurrence of the extreme points in the history data,Representing an error offset compensation indicator during the measurement.
The larger the value, the more severe the change of the humidity data of the mixed gas (namely, the larger the slope of the data point, the larger the change amplitude is reflected), and the extreme points corresponding to the changes are less appeared in the historical humidity measurement data (namely, the frequency is lower, belonging to 'abnormal value'). In this case, the sensor may not be able to quickly adapt to the dynamic relationship between humidity and material properties, resulting in an increase in data offset errors.
In the whole measurement process, starting from the measurement starting time, the humidity measurement errors of the system are continuously accumulated until the measurement is finished, so that an error accumulation curve of the system at any time in the whole measurement process and a corresponding error compensation function are required to be acquired;
acquiring any time in the measuring process Is fitted to the historical error accumulation coefficient of (a) to form an error accumulation curve;
Similarly, any time in the measurement process is acquiredIs used for fitting an error compensation function;
From curves during the measurement、A final error accumulation offset function (also "error accumulation function") is obtained:
The error accumulation function is calculated from historical humidity measurement data, and the function of each humidity sensor is unique to adapt to the gas conditions at the location. And (3) fitting an error accumulation curve and a compensation function of each sensor through measuring an optimization strategy regulation record, and further estimating an error accumulation offset function. These functions are used to analyze historical error accumulation to optimize current measurement accuracy;
the error accumulation offset function is generated after the central processing module processes big data, and the function data is sent to the sensor measurement control module, so that the function data can be updated every 24 hours.
Further, the history error accumulation offset value at each history time can be obtained from the error accumulation function.
Step S3, determining the data fluctuation coefficient of each piece of current humidity measurement data in the current measurement stage, and determining similar data fluctuation characteristic areas by using the data fluctuation coefficients;
when the composition of the mixed gas changes dynamically, or the gas humidity changes faster and the change amplitude is smaller, the humidity-material characteristic relationship fluctuates many times in a short time, so that the response of the sensor to the actual humidity of the gas becomes unstable, thereby reducing the sensitivity of the sensor. This phenomenon can cause excessive smoothing of the measured data output by the sensor during local measurements, thereby distorting the data. Therefore, it is necessary to estimate distortion of the measurement data next.
Specifically, the step of determining the data fluctuation coefficient of each current humidity measurement data in the current measurement phase includes:
Setting a preset number of current humidity measurement data step sizes by taking any one of the current humidity measurement data in the current measurement stage as a starting point, and determining a window area corresponding to the current humidity measurement data;
and determining the standard deviation of the current humidity measurement data in the window area as the data fluctuation coefficient of the starting point to obtain the data fluctuation coefficient of each current humidity measurement data.
Taking the history of each humidity sensor for 1 hour (for example, not limited to 1 hour), and taking all humidity measurement data records as the current measurement stage of the corresponding humidity sensor within 1 hour;
taking any data point (current humidity measurement data) in the current measurement stage as a starting point, wherein the step length can be 9 data points, constructing a window area corresponding to the any data point, and taking the data characteristics in the window area as the data characteristics of the starting point.
Calculating standard deviation of data in window areaAnd regarding the data fluctuation coefficient corresponding to the starting point, thereby obtaining the data fluctuation coefficient of each piece of current humidity measurement data.
Specifically, the step of determining the similar data fluctuation feature region using the data fluctuation coefficient includes:
determining the absolute value of the difference value of the data fluctuation coefficient between adjacent current humidity measurement data;
And dividing the current humidity measurement data with the absolute value of the difference value smaller than a preset threshold value into similar data fluctuation characteristic areas.
Calculating absolute value of difference of data fluctuation coefficients between adjacent data points;
A threshold value can be setDividing the region threshold valueAnd dividing the data points smaller than the threshold value into similar data fluctuation characteristic areas, otherwise, interrupting the area division, and restarting the area division until the whole historical data record in the current measurement stage is traversed, so as to obtain each similar data fluctuation characteristic area.
Step S4, determining the average value of data fluctuation coefficients of all current humidity measurement data in the similar data fluctuation characteristic area, and determining the humidity fluctuation scale at each moment in the current measurement stage;
specifically, the step of determining the humidity fluctuation scale at each moment in the current measurement stage includes:
determining a variance of current humidity measurement data in a current measurement phase;
determining a difference value between a humidity maximum value and a humidity minimum value in the current humidity measurement data;
calculating by using the variance and the difference to obtain the humidity fluctuation scale of the current measurement stage;
And taking the humidity fluctuation scale as the humidity fluctuation scale of each moment.
Calculating the variance of the humidity measurement data of the current measurement phaseAnd the difference between the maximum and minimum humidity values;
The ratio between the twoRecorded as fluctuation scale of mixed gas humidity at current measuring stage and current moment. The larger the value is, the more severe the fluctuation of the humidity of the mixed gas and the smaller the fluctuation range are in the current measurement stage;
For measurement phases with larger fluctuation scales, the greater the likelihood that they occur that the data will be smoothed due to reduced sensor sensitivity and the greater the impact that will be caused.
S5, determining real-time distortion degree values of all moments in the current measurement stage by utilizing the mean value of the data fluctuation coefficient and the humidity fluctuation scale;
specifically, referring to fig. 3, the step S5 includes:
step S51, determining the smooth distortion influence degree of each similar data fluctuation characteristic area by utilizing the mean value of the data fluctuation coefficients;
more specifically, the step S51 includes:
determining any similar data fluctuation feature area as a target feature area;
Determining a left similar data fluctuation feature area and a right similar data fluctuation feature area which are respectively adjacent to two sides of the target feature area;
And obtaining the smooth distortion influence degree of each similar data fluctuation characteristic region by utilizing the data fluctuation difference of the data fluctuation coefficient mean value among the target characteristic region, the left similar data fluctuation characteristic region and the right similar data fluctuation characteristic region.
Respectively calculating the mean value of the data fluctuation coefficients of all data points in all similar data fluctuation characteristic areasThe data fluctuation features corresponding to the similar data fluctuation feature areas are considered;
in the formula, Represents the first of the sensor history data (referring to the current measurement phase)A similar data fluctuation feature region (as a target feature region),Represents the firstAdjacent similar data fluctuation feature region to the right of each region,Represents the firstAdjacent similar data fluctuation feature regions to the left of the individual regions,Represents the firstData fluctuation features of the similar data fluctuation feature areas,Represents the firstData fluctuation features of the similar data fluctuation feature areas,Represents the firstData fluctuation features of the similar data fluctuation feature areas,Represents the firstThe degree of smooth distortion of the individual regions.
The larger the value, i.e. during the humidity measurement,Region and areaThe larger the difference in data fluctuation of the region, andRegion and areaThe smaller the data fluctuation difference between the areas, meaningAbnormal data forms may occur in the data fluctuation of the area, that is, the data in the area may have the sensitivity of the sensor reduced due to the severe variation of the humidity or the composition of the gas, and further, the data of a larger scale may be smoothed or lost.
Step S52, determining the real-time distortion degree value of each moment in the current measuring stage by using the smooth distortion influence degree and the humidity fluctuation scale.
Taking the average value of the smooth distortion influence degree of all areas in the humidity data in the current measurement stage as the measurement distortion influence value of the sensor at each moment, and recording as。
In the formula,Represents the firstThe fluctuation scale of the humidity of the mixed gas at the moment,Represents the firstThe sensor at the moment measures the distortion impact value,Representing a real-time distortion function; Is a natural constant.
The smaller the firstThe fluctuation scale of the whole measurement of the humidity of the mixed gas at the moment is smaller,The larger the firstMultiplying the two values, wherein the larger the multiplication value is, the greater the possibility that the sensor has the condition of reduced measurement sensitivity due to the instantaneous tiny change of the mixed gas component or the humidity in the measurement process, namely the more serious the smooth distortion of the humidity measurement data at the current moment is;
And then, according to the real-time distortion function, determining the real-time distortion degree value of each moment.
And S6, determining a corrected humidity measured value at the current moment by utilizing the historical error accumulated offset value and the real-time distortion degree value.
Specifically, referring to fig. 4, the step S6 includes:
step S61, determining first environmental factor data of the air-oxygen mixture at the current moment;
step S62, determining second environmental factor data of the air-oxygen mixture at each moment in the current measurement stage;
Step S63, determining third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;
step S64, determining third environmental factor data with highest similarity with the first environmental factor data to obtain a historical error accumulation offset value corresponding to the current moment;
Step S65, determining second environmental factor data with highest similarity with the first environmental factor data to obtain a real-time distortion degree value corresponding to the current moment;
step S66, utilizing the historical error accumulated offset value, the real-time distortion degree value and the initial humidity measured value corresponding to the current moment to determine the corrected humidity measured value at the current moment;
The environmental factor data comprise various gas contents and gas temperatures of the air-oxygen mixed gas, and the environmental factor data at various moments are stored in association with the humidity measurement data.
Accumulating offset values for historical errors:
Acquiring all environmental factor data (first environmental factor data) of the air-oxygen mixed gas detected by the humidity sensor at the current moment, and comparing the similarity with environmental factor data (third environmental factor data) corresponding to historical humidity measurement data, wherein the historical humidity measurement data can be 24 hours in the previous embodiment, can extend the duration further, and can compare the similarity with the environmental factor data in one month, and the similarity can be calculated through common methods such as cosine similarity;
and acquiring the time at which one regulation record with the highest similarity is positioned, thereby acquiring a historical error accumulation offset function value of the time as an error accumulation offset value of the current time.
Similarly, for the real-time distortion level value:
and acquiring all gas environment factor data (first environment factor data) of the humidity sensor at the current moment, and comparing the gas environment factor data with all environment factors (second environment factor data) of each regulation record in the current measurement stage (such as 1 hour in history) in a similarity mode. The similarity can be calculated by common methods such as cosine similarity and the like;
And acquiring the moment of a regulation record with the nearest similarity, and acquiring a real-time distortion function value of the moment to serve as a real-time distortion degree value of the current moment.
In the air-oxygen mixture humidity measurement process, the humidity measurement value of the sensor may deviate due to the history accumulated error and distortion in the data real-time smoothing process. By the big data processing technology, the obtained historical accumulated error and the real-time smooth distortion degree are adopted, so that the initial measured value of the humidity sensor is accurately corrected.
Acquiring initial humidity measurement value of humidity sensor based on linear relation of electrical characteristics of traditional gas humidity-sensing material;
In the formula,Representing the system measuring the current timestamp (i.e. the current time),Represents the firstThe error of the individual timestamp measurement data accumulates an offset value,Represents the firstThe individual time stamps measure the real-time distortion level values of the data,Represents the firstInitial humidity measurements of the data are measured with a time stamp,Represents the firstCorrected humidity measurements of the individual time stamp measurement data.
The system continuously monitors the change of the humidity data, and combines an internal control algorithm to adjust the working state of the sensor in real time so as to avoid error generation.
When the humidity value exceeds a set threshold, the feedback module triggers an alarm signal (such as a lighting alarm) to remind an operator.
Finally, the processed humidity data are stored in a database for subsequent analysis and tracing, and the storage mode ensures the integrity and the safety of the data. By means of the measures, the system can effectively eliminate interference factors and compensate environmental changes, so that accurate and reliable humidity data are provided.
The invention aims at solving the problem that in the existing air-oxygen mixed gas high-sensitivity measurement scheme, the influence of environmental conditions or industrial process changes on gas components (such as oxygen, nitrogen and the like) can cause fluctuation of the electrical characteristics of sensor materials, so that the relation between resistance or capacitance and actual humidity is deviated, and the measurement accuracy is influenced. In order to solve the problem, the method extracts an error accumulation function and corresponding values of all moments, a real-time distortion function and corresponding values of all moments respectively by analyzing historical data of a sensor. The error accumulation function is used to describe the degree of deviation between the humidity and the sensor material electrical characteristics, and the real-time distortion function is used to adjust the measurement error in real time. Through the comprehensive actions of the two functions and the corresponding time values, the intelligent correction and optimization of the measurement system are realized, and the measurement accuracy of the air-oxygen mixed gas humidity is improved.
Embodiment two:
The embodiment of the invention also provides a high-sensitivity humidity measuring device for the air-oxygen mixed gas. The high-sensitivity humidity measuring equipment of the air-oxygen mixed gas can be a data computing processing device such as a computer, a server and the like or a combination of a plurality of devices.
As shown in fig. 6, fig. 6 is a schematic structural diagram of a hardware operation environment of the humidity-sensitive measurement device for air-oxygen mixture according to the embodiment of the present invention.
As shown in fig. 6, the humidity high sensitivity measuring device of the air-oxygen mixture may include a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display (Display), an input unit such as a control panel, and the optional user interface 1003 may also include a standard wired interface, a wireless interface. Network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a WIFI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above. A humidity highly sensitive measurement program of the air-oxygen mixture gas may be included in the memory 1005 as a computer storage medium.
Those skilled in the art will appreciate that the hardware configuration shown in fig. 6 is not limiting of the device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
With continued reference to fig. 6, the memory 1005 of fig. 6, which is a computer readable storage medium, may include an operating system, a user interface module, a network communication module, and a humidity-sensitive measurement program for an air-oxygen mixture.
In fig. 6, the network communication module is mainly used for connecting with a server and can perform data communication with the server, and the processor 1001 can call the humidity high sensitivity measurement program of the air-oxygen mixture stored in the memory 1005 and perform the steps in the above embodiments.
The hardware structure of the humidity high-sensitivity measuring equipment based on the air-oxygen mixed gas is used for realizing each embodiment of the humidity high-sensitivity measuring method of the air-oxygen mixed gas.
In addition, the present invention also provides a humidity high-sensitivity measurement system of air-oxygen mixed gas, please refer to fig. 7, the humidity high-sensitivity measurement system of air-oxygen mixed gas includes:
The data acquisition module A10 is used for acquiring historical humidity measurement data of the air-oxygen mixed gas and determining a fitting curve corresponding to the historical humidity measurement data;
The data analysis module A20 is used for determining each extreme value in the fitting curve, determining a historical error accumulation offset value at each historical moment by using the extreme value, determining a data fluctuation coefficient of each current humidity measurement data in the current measurement stage, determining a similar data fluctuation characteristic region by using the data fluctuation coefficient, determining a data fluctuation coefficient mean value of all current humidity measurement data in the similar data fluctuation characteristic region, and determining a humidity fluctuation scale at each moment in the current measurement stage;
the correction compensation module A30 utilizes the historical error accumulated offset value and the real-time distortion degree value to determine a correction humidity measurement value at the current moment.
Further, the data analysis module a20 is further configured to:
determining an upper envelope corresponding to a maximum value point and a lower envelope corresponding to a minimum value point;
Determining historical error accumulation coefficients of all historical moments by using the upper envelope curve and the lower envelope curve;
Setting a preset number of extreme point step sizes by taking a maximum point as a starting point, and determining a window area corresponding to the maximum point;
Determining a historical error offset compensation index at each historical moment by utilizing the maximum value point set and each window area;
and calculating to obtain the historical error accumulation offset value of each historical moment by using the historical error accumulation coefficient and the historical error offset compensation index.
Further, the data analysis module a20 is further configured to:
Determining an extreme point set in the window area and the slope of the extreme point at the fitting curve, and the occurrence frequency of the extreme point in the fitting curve;
And calculating to obtain the historical error offset compensation index at each historical moment by using the maximum point set, the extreme point set, the slope and the occurrence frequency in the window area.
Further, the data analysis module a20 is further configured to:
Setting a preset number of current humidity measurement data step sizes by taking any one of the current humidity measurement data in the current measurement stage as a starting point, and determining a window area corresponding to the current humidity measurement data;
and determining the standard deviation of the current humidity measurement data in the window area as the data fluctuation coefficient of the starting point to obtain the data fluctuation coefficient of each current humidity measurement data.
Further, the data analysis module a20 is further configured to:
determining the absolute value of the difference value of the data fluctuation coefficient between adjacent current humidity measurement data;
And dividing the current humidity measurement data with the absolute value of the difference value smaller than a preset threshold value into similar data fluctuation characteristic areas.
Further, the data analysis module a20 is further configured to:
Determining the smooth distortion influence degree of each similar data fluctuation characteristic region by utilizing the data fluctuation coefficient mean value;
and determining the real-time distortion degree value of each moment in the current measurement stage by using the smooth distortion influence degree and the humidity fluctuation scale.
Further, the data analysis module a20 is further configured to:
determining any similar data fluctuation feature area as a target feature area;
Determining a left similar data fluctuation feature area and a right similar data fluctuation feature area which are respectively adjacent to two sides of the target feature area;
And obtaining the smooth distortion influence degree of each similar data fluctuation characteristic region by utilizing the data fluctuation difference of the data fluctuation coefficient mean value among the target characteristic region, the left similar data fluctuation characteristic region and the right similar data fluctuation characteristic region.
Further, the data analysis module a20 is further configured to:
determining a variance of current humidity measurement data in a current measurement phase;
determining a difference value between a humidity maximum value and a humidity minimum value in the current humidity measurement data;
calculating by using the variance and the difference to obtain the humidity fluctuation scale of the current measurement stage;
And taking the humidity fluctuation scale as the humidity fluctuation scale of each moment.
Further, the correction compensation module a30 is further configured to:
determining first environmental factor data of the air-oxygen mixed gas at the current moment;
Determining second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement stage;
determining third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;
Determining third environmental factor data with highest similarity with the first environmental factor data to obtain a historical error accumulation offset value corresponding to the current moment;
determining second environmental factor data with highest similarity with the first environmental factor data to obtain a real-time distortion degree value corresponding to the current moment;
determining a corrected humidity measurement value at the current moment by utilizing the historical error accumulated offset value, the real-time distortion degree value and the initial humidity measurement value corresponding to the current moment;
The environmental factor data comprise various gas contents and gas temperatures of the air-oxygen mixed gas, and the environmental factor data at various moments are stored in association with the humidity measurement data.
In addition, for a more specific humidity high sensitivity measurement system of the air-oxygen mixture, reference may also be made to the specific hardware system and fig. 8 related to the above-mentioned method embodiment (embodiment one), which are not described herein again.
The specific implementation of the humidity high-sensitivity measurement system of the air-oxygen mixed gas is basically the same as that of each embodiment of the humidity high-sensitivity measurement method of the air-oxygen mixed gas, and is not repeated here.
Furthermore, the invention also provides a computer readable storage medium. The computer readable storage medium of the invention stores a humidity high-sensitivity measurement program of the air-oxygen mixed gas, wherein when the humidity high-sensitivity measurement program of the air-oxygen mixed gas is executed by a processor, the steps of the humidity high-sensitivity measurement method of the air-oxygen mixed gas are realized.
The method implemented when the humidity high sensitivity measurement procedure of the air-oxygen mixture is executed may refer to various embodiments of the humidity high sensitivity measurement method of the air-oxygen mixture according to the present invention, and will not be described herein.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, and all equivalent structural/method modifications made by the present invention and the accompanying drawings, or direct/indirect application in other related technical fields are included in the scope of the present invention.
Claims (6)
1. A method for highly sensitive measurement of humidity of an air-oxygen mixture, the method comprising:
acquiring historical humidity measurement data of the air-oxygen mixed gas, and determining a fitting curve corresponding to the historical humidity measurement data;
determining each extremum in the fitted curve, and determining a historical error accumulated offset value at each historical moment by using the extremum;
The method for acquiring the historical error accumulation offset value comprises the steps of determining the historical error accumulation coefficient of each historical moment by using an upper envelope curve and a lower envelope curve corresponding to a maximum value point, setting a preset number of extreme point step sizes by using the maximum value point as a starting point, determining a window area corresponding to the maximum value point, determining the historical error offset compensation index of each historical moment by using a maximum value point set and each window area, and calculating to obtain the historical error accumulation offset value of each historical moment by using the historical error accumulation coefficient and the historical error offset compensation index;
determining a data fluctuation coefficient of each current humidity measurement data in the current measurement stage, and determining a similar data fluctuation characteristic region by using the data fluctuation coefficient;
The method for acquiring the data fluctuation coefficient comprises the steps of taking any current humidity measurement data in a current measurement stage as a starting point, setting a preset number of current humidity measurement data step sizes, and determining a window area corresponding to the current humidity measurement data;
Determining the average value of data fluctuation coefficients of all current humidity measurement data in the similar data fluctuation characteristic area, and determining the humidity fluctuation scale at each moment in the current measurement stage;
The method for acquiring the humidity fluctuation scale comprises the steps of determining the variance of current humidity measurement data in a current measurement stage, determining the difference value between the maximum humidity value and the minimum humidity value in the current humidity measurement data, and calculating the humidity fluctuation scale at each moment in the current measurement stage by using the variance and the difference value;
Determining a real-time distortion degree value of each moment in the current measurement stage by utilizing the data fluctuation coefficient mean value and the humidity fluctuation scale;
The method for acquiring the real-time distortion degree value comprises the steps of determining the smooth distortion influence degree of each similar data fluctuation characteristic area by utilizing a data fluctuation coefficient mean value;
And determining the corrected humidity measured value at the current moment by utilizing the historical error accumulated offset value and the real-time distortion degree value.
2. The method for highly sensitive measurement of humidity of air-oxygen mixture according to claim 1, wherein the step of determining the history error offset compensation index at each history time using the maximum point set and each window area comprises:
Determining an extreme point set in the window area and the slope of the extreme point at the fitting curve, and the occurrence frequency of the extreme point in the fitting curve;
And calculating to obtain the historical error offset compensation index at each historical moment by using the maximum point set, the extreme point set, the slope and the occurrence frequency in the window area.
3. The method for highly sensitive measurement of humidity of air-oxygen mixture according to claim 1, wherein the step of determining the similar data fluctuation feature region using the data fluctuation coefficient comprises:
determining the absolute value of the difference value of the data fluctuation coefficient between adjacent current humidity measurement data;
And dividing the current humidity measurement data with the absolute value of the difference value smaller than a preset threshold value into similar data fluctuation characteristic areas.
4. The method for highly sensitive measurement of humidity of air-oxygen mixture according to claim 1, wherein the step of determining the degree of influence of smooth distortion in each similar data fluctuation feature region using the mean value of the data fluctuation coefficients comprises:
determining any similar data fluctuation feature area as a target feature area;
Determining a left similar data fluctuation feature area and a right similar data fluctuation feature area which are respectively adjacent to two sides of the target feature area;
And obtaining the smooth distortion influence degree of each similar data fluctuation characteristic region by utilizing the data fluctuation difference of the data fluctuation coefficient mean value among the target characteristic region, the left similar data fluctuation characteristic region and the right similar data fluctuation characteristic region.
5. The method for highly sensitive measurement of humidity of air-oxygen mixture according to claim 1, wherein the step of determining the corrected humidity measurement value at the present time by using the history error accumulated offset value and the real-time distortion degree value comprises:
determining first environmental factor data of the air-oxygen mixed gas at the current moment;
Determining second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement stage;
determining third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;
Determining third environmental factor data with highest similarity with the first environmental factor data to obtain a historical error accumulation offset value corresponding to the current moment;
determining second environmental factor data with highest similarity with the first environmental factor data to obtain a real-time distortion degree value corresponding to the current moment;
determining a corrected humidity measurement value at the current moment by utilizing the historical error accumulated offset value, the real-time distortion degree value and the initial humidity measurement value corresponding to the current moment;
The environmental factor data comprise various gas contents and gas temperatures of the air-oxygen mixed gas, and the environmental factor data at various moments are stored in association with the humidity measurement data.
6. The system for measuring the humidity of the air-oxygen mixed gas with high sensitivity is characterized by being used for realizing the method for measuring the humidity of the air-oxygen mixed gas with high sensitivity according to any one of claims 1-5, and comprises the following steps:
The data acquisition module is used for acquiring historical humidity measurement data of the air-oxygen mixed gas and determining a fitting curve corresponding to the historical humidity measurement data;
The system comprises a data analysis module, a data fluctuation characteristic region, a humidity fluctuation scale and a real-time distortion degree value, wherein the data analysis module is used for determining each extreme value in a fitting curve, determining a historical error accumulation offset value at each historical moment by using the extreme value, determining a data fluctuation coefficient of each current humidity measurement data in a current measurement stage, determining a similar data fluctuation characteristic region by using the data fluctuation coefficient, determining a data fluctuation coefficient mean value of all current humidity measurement data in the similar data fluctuation characteristic region, and determining a humidity fluctuation scale at each moment in the current measurement stage;
and the correction compensation module is used for determining a correction humidity measurement value at the current moment by utilizing the historical error accumulated offset value and the real-time distortion degree value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510168545.1A CN119619237B (en) | 2025-02-17 | 2025-02-17 | A highly sensitive measurement method and system for humidity of air-oxygen mixed gas |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510168545.1A CN119619237B (en) | 2025-02-17 | 2025-02-17 | A highly sensitive measurement method and system for humidity of air-oxygen mixed gas |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119619237A CN119619237A (en) | 2025-03-14 |
| CN119619237B true CN119619237B (en) | 2025-05-16 |
Family
ID=94897250
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510168545.1A Active CN119619237B (en) | 2025-02-17 | 2025-02-17 | A highly sensitive measurement method and system for humidity of air-oxygen mixed gas |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119619237B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984503A (en) * | 2020-08-17 | 2020-11-24 | 网宿科技股份有限公司 | A method and device for identifying abnormal data of monitoring index data |
| CN118362820A (en) * | 2024-06-20 | 2024-07-19 | 国网甘肃省电力公司电力科学研究院 | A method and system for intelligent diagnosis of energy storage equipment fault status |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3231614B2 (en) * | 1996-02-16 | 2001-11-26 | 松下電器産業株式会社 | Optical information reproducing apparatus and adjustment method thereof |
| US5792938A (en) * | 1996-12-13 | 1998-08-11 | Panametrics, Inc. | Humidity sensor with differential thermal detection and method of sensing |
| CN111338878A (en) * | 2020-02-21 | 2020-06-26 | 平安科技（深圳）有限公司 | Anomaly detection method and device, terminal device and storage medium |
| US20240272130A1 (en) * | 2023-02-15 | 2024-08-15 | Honeywell International Inc. | Gas sensor dynamic environmental compensation using auto-calibration method |
| CN119112155B (en) * | 2024-11-13 | 2025-02-07 | 大连广顺源发计算机科技有限公司 | Method, device and system for monitoring respiratory signals of patient |
-
2025
- 2025-02-17 CN CN202510168545.1A patent/CN119619237B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984503A (en) * | 2020-08-17 | 2020-11-24 | 网宿科技股份有限公司 | A method and device for identifying abnormal data of monitoring index data |
| CN118362820A (en) * | 2024-06-20 | 2024-07-19 | 国网甘肃省电力公司电力科学研究院 | A method and system for intelligent diagnosis of energy storage equipment fault status |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119619237A (en) | 2025-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119958763B (en) | Calibration method and system for metering data of pressure sensor | |
| CN101087993B (en) | Method for monitoring sensor function | |
| CN107621279B (en) | Data processing method, sensor data calibration method and device | |
| CN113607248B (en) | Hydraulic weighing method and hydraulic weighing device | |
| CN118938112A (en) | A multimeter calibration detection method, device, product and storage medium | |
| CN118689866A (en) | A data optimization method and system for ultrasonic flowmeter | |
| WO2022183976A1 (en) | Coulombmeter-based method and system for measuring remaining electric quantity of battery | |
| CN113670986B (en) | Moisture evaluation method, device and equipment for transformer and storage medium | |
| CN119619237B (en) | A highly sensitive measurement method and system for humidity of air-oxygen mixed gas | |
| CN116414160A (en) | Temperature control method, temperature control device, computer apparatus, storage medium, and program product | |
| CN114343629B (en) | Method and related device for automatically tracking and correcting sensor signal drift | |
| CN119438478B (en) | Oil chromatography online device fault identification and correction method and system based on accurate regulation and control of chromatographic column air resistance | |
| CN119394336B (en) | A Gsensor automated testing method for IC environmental deviation | |
| WO2018081945A1 (en) | Method for detecting background noise of sensor, and device thereof | |
| CN111707867B (en) | Alternating current measuring method and device, electronic equipment and storage medium | |
| CN119846043A (en) | Oxygen concentration continuous monitoring device and method | |
| CN111340647B (en) | Power distribution network data adjustment method and system | |
| CN119394233A (en) | A method, device, equipment and medium for detecting coal cake height on a tamping machine | |
| CN110020000A (en) | Determine the method and apparatus of abnormal air speed data | |
| WO2024235216A1 (en) | Nitrogen content measurement method and apparatus, and storage medium | |
| CN114460360B (en) | Detection method, system and device based on ammeter measurement current time integral | |
| CN120970790A (en) | Drift data processing method, system and storage medium for electronic scale | |
| CN120446852A (en) | A voltage transformer measurement method based on multi-source data | |
| CN118913388B (en) | A civil gas measurement method based on ultrasonic measurement | |
| CN119469481B (en) | High temperature energy storage monitoring method and device based on capacitive pressure sensor, medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |