A highly sensitive measurement method and system for humidity of air-oxygen mixed gas

Abstract

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

[0001] The invention relates to the technical field of physical measurement, and in particular to a highly sensitive humidity measurement method and system for air-oxygen mixed gas. Background Art

[0002] In many industrial, environmental and scientific research fields, humidity measurement of air-oxygen mixed gas is an important parameter that affects the operation of equipment, product quality and the accuracy of experiments. Especially 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 changes and adjust the system in time, it is particularly important to develop highly sensitive humidity measurement methods and systems.

[0003] At present, high-sensitivity measurement systems for the humidity of air-oxygen mixed gases usually rely on the characteristics of certain specific materials whose resistance or capacitance changes with humidity. However, in practical applications, the gas composition (such as oxygen, nitrogen, etc.) may fluctuate due to changes in environmental conditions or industrial processes, which will affect the electrical properties of the sensor material, resulting in a deviation in the relationship between resistance or capacitance and actual humidity, which in turn affects the measurement accuracy and causes a large deviation between the measured humidity and the actual humidity. Summary of the invention

[0004] In order to solve the technical problem that the measured humidity of air-oxygen mixed gas measured by the humidity sensor deviates greatly from the actual humidity, the purpose of the present invention is to provide a highly sensitive humidity measurement method and system for air-oxygen mixed gas. The technical solution adopted is as follows:

[0005] The present invention provides a highly sensitive method for measuring the humidity of an air-oxygen mixed gas, the method comprising:

[0006] Obtain historical humidity measurement data of air-oxygen mixed gas, and determine a fitting curve corresponding to the historical humidity measurement data;

[0007] Determine each extreme value in the fitting curve, and use the extreme value to determine the cumulative offset value of the historical error at each historical moment;

[0008] Determine the data fluctuation coefficient of each current humidity measurement data in the current measurement phase, and use the data fluctuation coefficient to determine the similar data fluctuation characteristic area;

[0009] Determine the mean value of the data fluctuation coefficient of all current humidity measurement data in the area with similar data fluctuation characteristics, and determine the humidity fluctuation scale at each moment in the current measurement stage;

[0010] The real-time distortion value at each moment of the current measurement phase is determined by using the mean value of the data fluctuation coefficient and the humidity fluctuation scale;

[0011] The corrected humidity measurement value at the current moment is determined by using the historical error cumulative offset value and the real-time distortion degree value.

[0012] Furthermore, the extreme value includes a maximum value and a minimum value; the step of using the extreme value to determine the historical error cumulative offset value at each historical moment includes:

[0013] Determine the upper envelope corresponding to the maximum point and the lower envelope corresponding to the minimum point;

[0014] Using the upper and lower envelopes, determine the cumulative coefficient of historical errors at each historical moment;

[0015] Taking the maximum point as the starting point, setting a preset number of extreme point steps, and determining the window area corresponding to the maximum point;

[0016] Using the maximum point set and each window area, determine the historical error offset compensation index at each historical moment;

[0017] The historical error cumulative offset value at each historical moment is calculated using the historical error cumulative coefficient and the historical error offset compensation index.

[0018] Furthermore, the step of determining the historical error offset compensation index at each historical moment by using the maximum value point set and each window area includes:

[0019] Determine the set of extreme value points in the window area, the slope of the extreme value points at the fitting curve, and the frequency of occurrence of the extreme value points in the fitting curve;

[0020] The historical error offset compensation index at each historical moment is calculated using the maximum point set and the extreme point set in the window area, the slope, and the frequency of occurrence.

[0021] Furthermore, the step of determining the data fluctuation coefficient of each current humidity measurement data in the current measurement phase includes:

[0022] Taking any current humidity measurement data in the current measurement phase as a starting point, setting a preset number of current humidity measurement data step lengths, and determining a window area corresponding to the current humidity measurement data;

[0023] The standard deviation of the current humidity measurement data in the window area is determined as the data fluctuation coefficient of the starting point, and the data fluctuation coefficient of each current humidity measurement data is obtained.

[0024] Furthermore, the step of determining the similar data fluctuation characteristic area by using the data fluctuation coefficient includes:

[0025] Determine the absolute value of the difference between the data fluctuation coefficients of adjacent current humidity measurement data;

[0026] The current humidity measurement data whose absolute value of the difference is less than a preset threshold value is divided into regions with similar data fluctuation characteristics.

[0027] Further, the step of determining the real-time distortion degree value at each moment of the current measurement stage by using the data fluctuation coefficient mean and the humidity fluctuation scale includes:

[0028] Using the mean value of data fluctuation coefficient, determine the degree of smoothing distortion influence of each similar data fluctuation characteristic area;

[0029] The real-time distortion level at each moment of the current measurement phase is determined by using the smoothing distortion influence degree and humidity fluctuation scale.

[0030] Furthermore, the step of determining the smoothing distortion influence degree of each similar data fluctuation characteristic region by using the mean value of the data fluctuation coefficient includes:

[0031] Determine any similar data fluctuation feature region as a target feature region;

[0032] Determine a left-side similar data fluctuation feature region and a right-side similar data fluctuation feature region respectively adjacent to both sides of the target feature region;

[0033] By using the data fluctuation difference of the mean values ​​of the data fluctuation coefficients between the target feature region, the similar data fluctuation feature region on the left, and the similar data fluctuation feature region on the right, the smoothing distortion influence degree of each similar data fluctuation feature region is obtained.

[0034] Furthermore, the step of determining the humidity fluctuation scale at each moment in the current measurement phase includes:

[0035] Determine the variance of the current humidity measurement data in the current measurement phase;

[0036] Determine the difference between the maximum humidity value and the minimum humidity value in the current humidity measurement data;

[0037] The humidity fluctuation scale of the current measurement phase is obtained by using the variance and difference calculation;

[0038] The humidity fluctuation scale is used as the humidity fluctuation scale at each moment.

[0039] Further, the step of determining the corrected humidity measurement value at the current moment by using the historical error cumulative offset value and the real-time distortion degree value includes:

[0040] Determine the first environmental factor data of the air-oxygen mixed gas at the current moment;

[0041] Determine the second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement phase;

[0042] Determine the third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;

[0043] Determine the third environmental factor data having the highest similarity to the first environmental factor data, and obtain the historical error cumulative offset value corresponding to the current moment;

[0044] Determine the second environmental factor data having the highest similarity to the first environmental factor data, and obtain the real-time distortion degree value corresponding to the current moment;

[0045] Determine the corrected humidity measurement value at the current moment by using the historical error cumulative offset value, the real-time distortion value, and the initial humidity measurement value corresponding to the current moment;

[0046] The environmental factor data include various gas contents and gas temperature of the air-oxygen mixed gas; the environmental factor data at each moment are associated and stored with the humidity measurement data.

[0047] The present invention also provides a highly sensitive humidity measurement system for air-oxygen mixed gas, the system being used to implement the highly sensitive humidity measurement method for air-oxygen mixed gas as described above; the system comprising:

[0048] A data acquisition module, used to obtain historical humidity measurement data of air-oxygen mixed gas and determine a fitting curve corresponding to the historical humidity measurement data;

[0049] The data analysis module is used to determine each extreme value in the fitting curve, and use the extreme value to determine the historical error cumulative offset value at each historical moment; determine the data fluctuation coefficient of each current humidity measurement data in the current measurement stage, and use the data fluctuation coefficient to determine the similar data fluctuation characteristic area; determine the mean value of the data fluctuation coefficient of all current humidity measurement data in the similar data fluctuation characteristic area, and determine the humidity fluctuation scale at each moment in the current measurement stage; use the mean value of the data fluctuation coefficient and the humidity fluctuation scale to determine the real-time distortion value at each moment in the current measurement stage;

[0050] The correction compensation module uses the historical error cumulative offset value and the real-time distortion degree value to determine the corrected humidity measurement value at the current moment.

[0051] The present invention has the following beneficial effects:

[0052] The present invention aims at the existing high-sensitivity measurement scheme of air-oxygen mixed gas. The gas components (such as oxygen, nitrogen, etc.) are affected by environmental conditions or changes in industrial processes, which may cause the electrical properties of the sensor material to fluctuate, thereby causing the relationship between the resistance or capacitance and the actual humidity to deviate, affecting the measurement accuracy. In order to solve this problem, the method analyzes the historical data of the sensor to extract the error accumulation function and the corresponding value at each moment, the real-time distortion function and the corresponding value at each moment. The error accumulation function is used to describe the degree of deviation between humidity and the electrical properties of the sensor material, and the real-time distortion function is used to adjust the measurement error in real time. Through the combined effect of these two functions and the corresponding values ​​at each moment, the intelligent correction and optimization of the measurement system is realized, and the measurement accuracy of the humidity of the air-oxygen mixed gas is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flowchart of a method for highly sensitively measuring humidity of an air-oxygen mixed gas provided by an embodiment of the present invention;

[0055] Figure 2 A detailed flow chart of step S2 in a method for highly sensitively measuring humidity of an air-oxygen mixed gas provided by one embodiment of the present invention;

[0056] Figure 3 A detailed flow chart of step S5 in a method for highly sensitively measuring humidity of an air-oxygen mixed gas provided by one embodiment of the present invention;

[0057] Figure 4 A detailed flow chart of step S6 in a method for highly sensitively measuring humidity of an air-oxygen mixed gas provided by one embodiment of the present invention;

[0058] Figure 5 A schematic diagram of a fitting curve involved in a highly sensitive humidity measurement method for an air-oxygen mixed gas provided by an embodiment of the present invention;

[0059] Figure 6 It is a schematic diagram of the structure of the hardware operating environment of the highly sensitive humidity measuring device of the air-oxygen mixed gas involved in the embodiment of the present invention;

[0060] Figure 7 It is a module schematic diagram of a highly sensitive humidity measurement system of air-oxygen mixed gas involved in an embodiment of the present invention;

[0061] Figure 8 The present invention is a schematic diagram of the framework structure of a highly sensitive humidity measurement system for air-oxygen mixed gas according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the highly sensitive humidity measurement method of an air-oxygen mixed gas proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0063] 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.

[0064] The specific scheme of the highly sensitive humidity measurement method of air-oxygen mixed gas provided by the present invention is described in detail below with reference to the accompanying drawings.

[0065] Embodiment 1:

[0066] First, before developing the following embodiments, it is necessary to briefly describe the specific hardware system and related concepts preferably applicable to the high-sensitivity measurement method for humidity of air-oxygen mixed gas in order to facilitate understanding of the following embodiments:

[0067] Highly sensitive humidity measurement system for air-oxygen mixed gas, please refer to Figure 8 , which 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.;

[0068] In the humidity measurement system, the sensor data acquisition module is its core component, which is responsible for connecting with multiple humidity sensors through the data acquisition card and collecting signals;

[0069] To ensure the accuracy and coverage of the measurement results, humidity sensors (such as capacitive and impedance humidity sensors) are installed in the main air flow paths or key nodes of the measurement gas system, such as the air flow inlet and outlet, different positions of the pipeline, etc. These sensors form a humidity sensing network to ensure humidity monitoring at multiple points simultaneously;

[0070] Considering the need for high-sensitivity measurements, the system can use a high-frequency data acquisition method of 10 times per second to capture subtle fluctuations in humidity changes;

[0071] The data acquisition card further processes the data collected from each sensor, including signal amplification, filtering and correction, to ensure that the signal meets the accuracy requirements of subsequent data processing;

[0072] The central processing unit receives and analyzes sensor data from the data acquisition card in real time. Through built-in algorithms and analysis modules, the central processing unit can not only perform real-time data analysis and processing, as well as calibrate and compensate the measured data, but also automatically transmit the results to the cloud for storage, providing support for subsequent analysis and monitoring.

[0073] The display and feedback unit can output the result data processed by the central processing unit and provide result feedback.

[0074] In the process of measuring the humidity of air-oxygen mixed gas, "air-oxygen mixed gas" refers to a mixture of oxygen and other gases. In practical applications, the concentration of the mixed gas is affected by environmental changes or fluctuations in industrial processes, which causes changes in the conductivity and resistance of the capacitive humidity sensor material. This causes the changes in electrical properties that should have been caused by humidity changes to be disturbed by fluctuations in gas composition, resulting in errors in the measurement results.

[0075] The core of the system humidity measurement calibration controller is the calibration and compensation module and the data analysis and algorithm processing module, which aims to perform precision correction on the mixed gas humidity data collected by the humidity sensor. The humidity measurement calibration controller consists of two main component functions: error accumulation function and real-time distortion function. The two functions correspond to the two purposes of: the degree of deviation of the relationship between humidity and the electrical characteristics of the sensor material and real-time error adjustment, and intelligently optimize the measurement strategy of the measurement system.

[0076] The component function generation process is generated by the data analysis module through big data analysis, and then the initial humidity data is corrected and fed back to the system humidity monitoring platform;

[0077] The error accumulation function describes the cumulative deviation of the linear relationship between humidity and the electrical characteristics of the sensing material caused by the change in the composition of the mixed gas when each humidity sensor measures the humidity of the mixed gas.

[0078] When each gas environment factor is abnormal, it may trigger the linear deviation of the electrical characteristics of the sensing material. For humidity sensors installed in different areas, the optimization correction scale for humidity measurement may be different.

[0079] For the highly sensitive humidity measurement method of air-oxygen mixed gas provided by the present invention, please refer to Figure 1 , which shows a flow chart of steps of a highly sensitive method for measuring humidity of an air-oxygen mixed gas provided by an embodiment of the present invention.

[0080] The method comprises:

[0081] Step S1, obtaining historical humidity measurement data of air-oxygen mixed gas, and determining a fitting curve corresponding to the historical humidity measurement data;

[0082] The central processing unit of the system can retrieve all humidity measurement data records of air-oxygen mixed gas within the historical 24 hours (or other time periods, which can be set according to actual needs) of each humidity sensor to obtain historical humidity measurement data with time series; then these historical humidity measurement data are fitted to obtain the corresponding fitting curve, and the upper envelope and lower envelope can be given respectively according to the maximum and minimum values ​​in the fitting curve, refer to Figure 5 shown.

[0083] With the dynamic change of the mixed gas composition, the electrical characteristics of the humidity sensor change nonlinearly, causing errors or drifts in the gas humidity data fitting curve, that is, the maximum and minimum values ​​of the fitting curve are offset;

[0084] In the process of mixed gas humidity measurement, since the dynamic changes of gas composition are random and these changes usually affect the properties of the sensing material slowly, the errors in the historical data will gradually accumulate. As time goes by, the phase difference between the upper and lower envelopes of the fitting curve will gradually accumulate and increase;

[0085] The process of obtaining the fitting curve and the upper and lower envelopes can be briefly described as follows:

[0086] Perform polynomial fitting on all historical humidity measurement data in the time series to obtain the fitting function curve ;

[0087] Get the fitting curve The set of all local extreme values ​​(maxima and minima);

[0088] Connect all local maxima and local minima respectively to obtain the fitting curve The upper and lower envelopes of the .

[0089] Step S2, determining each extreme value in the fitting curve, and using the extreme value to determine the historical error cumulative offset value at each historical moment;

[0090] In a specific embodiment, please refer to Figure 2 , the extreme value includes a maximum value and a minimum value; the step of using the extreme value to determine the historical error cumulative offset value at each historical moment includes:

[0091] Step S21, determining an upper envelope corresponding to a maximum value point and a lower envelope corresponding to a minimum value point;

[0092] Step S22, using the upper envelope and the lower envelope to determine the historical error accumulation coefficient at each historical moment;

[0093] The upper envelope and lower envelope curve data in the time domain are converted into frequency domain data using the fast Fourier transform (FFT) algorithm;

[0094] The phase information of the upper and lower envelope frequency domain data is obtained respectively; at the same time, the phase information is converted into data using trigonometric functions to facilitate subsequent data calculation and analysis;

[0095]

[0096] In the formula, Represents the fitting curve The phase information of the upper envelope, Represents the fitting curve The phase information of the lower envelope, Represents the historical error accumulation coefficient in the measurement process;

[0097] Step S23, taking the maximum point as the starting point, setting a preset number of extreme point steps, and determining the window area corresponding to the maximum point;

[0098] Step S24, using the maximum point set and each window area, determining the historical error offset compensation index at each historical moment;

[0099] Specifically, the step S24 includes:

[0100] Determine the set of extreme value points in the window area, the slope of the extreme value points at the fitting curve, and the frequency of occurrence of the extreme value points in the fitting curve;

[0101] The historical error offset compensation index at each historical moment is calculated using the maximum point set and the extreme point set in the window area, the slope, and the frequency of occurrence.

[0102] Step S25, using the historical error accumulation coefficient and the historical error offset compensation index, calculate and obtain the historical error accumulation offset value at each historical moment.

[0103] When the humidity of the gas changes rapidly, along with the chaotic changes in the composition of the mixed gas, the characteristics of the sensor material may change unstably, which will aggravate the cumulative error of the sensor measurement data.

[0104] Fit the curve Window division is performed at any maximum point in the data, and local window area characteristics are obtained to measure the characteristics of data extreme points and evaluate the error offset of the final humidity data.

[0105] The local window starts with the maximum point and the step length is 10 (or other preset number) data extreme points to construct the window area; (when there are insufficient data points, all remaining data points are regarded as the same area).

[0106] Get the frequency of occurrence of all extreme points (including maximum and minimum values) in the window area in the overall historical humidity measurement data , and the extreme points in the fitting curve The slope at ;

[0107]

[0108] In the formula, Represents the curve The number of maximum points in the set, Represents The collection A maximum point, Represents the The maximum point corresponds to the set of extreme points in the window area. Represents The first in the collection An extreme point, Represents the The extreme point on the curve The slope at Represents the The frequency of occurrence of extreme points in historical data, Represents the error offset compensation index during the measurement process.

[0109] A larger value means that the mixed gas humidity data changes more dramatically (i.e., the slope of the data point is larger, reflecting a larger change), and the extreme points corresponding to these changes rarely appear in the historical humidity measurement data (i.e., the frequency is lower, belonging to "outliers"). 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.

[0110] In the complete measurement process, the humidity measurement error of the system begins to accumulate from the beginning of the measurement until the end of the measurement. Therefore, it is necessary to obtain the error accumulation curve of the system at any time during the overall measurement process and the corresponding error compensation function;

[0111] Get any time during the measurement process The historical error accumulation coefficient is used to fit the error accumulation curve ;

[0112] Similarly, get the value at any time during the measurement process The error compensation function is fitted based on the error offset compensation index. ;

[0113] According to the curve during the measurement , Get the final error accumulation offset function (same as "error accumulation function"):

[0114]

[0115] The error accumulation function is calculated from historical humidity measurement data. The function of each humidity sensor is unique to adapt to the gas conditions at its location. The error accumulation curve and compensation function of each sensor are fitted by the measurement optimization strategy to estimate the error accumulation offset function. These functions are used to analyze historical error accumulation to optimize current measurement accuracy.

[0116] The error accumulation offset function is generated by the central processing module after processing the big data. The function data is sent to the sensor measurement control module and can be set to be updated every 24 hours.

[0117] Furthermore, according to the above error accumulation function, the historical error accumulation offset value at each historical moment can be obtained.

[0118] Step S3, determining the data fluctuation coefficient of each current humidity measurement data in the current measurement phase, and determining a similar data fluctuation feature area using the data fluctuation coefficient;

[0119] When the composition of the mixed gas changes dynamically, or the gas humidity changes rapidly and the amplitude of the change is small, the humidity-material property relationship fluctuates multiple times in a short period of time, making the sensor's response to the actual humidity of the gas unstable, thereby reducing the sensor's sensitivity. In the local measurement process, this phenomenon will cause the measurement data output by the sensor to be over-smoothed, resulting in data distortion. Therefore, the distortion of the measurement data needs to be estimated next.

[0120] Specifically, the step of determining the data fluctuation coefficient of each current humidity measurement data in the current measurement phase includes:

[0121] Taking any current humidity measurement data in the current measurement phase as a starting point, setting a preset number of current humidity measurement data step lengths, and determining a window area corresponding to the current humidity measurement data;

[0122] The standard deviation of the current humidity measurement data in the window area is determined as the data fluctuation coefficient of the starting point, and the data fluctuation coefficient of each current humidity measurement data is obtained.

[0123] Retrieve all humidity measurement data records within the past 1 hour of each humidity sensor (this is taken as an example, not limited to 1 hour), which can be used as the current measurement stage of the corresponding humidity sensor within the past 1 hour;

[0124] Taking any data point (current humidity measurement data) in the current measurement phase as the starting point, the step length can be 9 data points, and constructing the window area corresponding to the any data point; the data features in the window area are regarded as the data features of the starting point.

[0125] Calculate the standard deviation of the data in the window area , is regarded as the data fluctuation coefficient of the corresponding starting point, thereby obtaining the data fluctuation coefficient of each current humidity measurement data.

[0126] Specifically, the step of determining the similar data fluctuation characteristic area by using the data fluctuation coefficient includes:

[0127] Determine the absolute value of the difference between the data fluctuation coefficients of adjacent current humidity measurement data;

[0128] The current humidity measurement data whose absolute value of the difference is less than a preset threshold value is divided into regions with similar data fluctuation characteristics.

[0129] Calculate the absolute value of the difference in data fluctuation coefficients between adjacent data points ;

[0130] Thresholds can be set , perform regional threshold division, Data points smaller than the threshold are divided into regions with similar data fluctuation characteristics. Otherwise, the region division is interrupted and restarted until the entire historical data record in the current measurement phase is traversed, thereby obtaining various regions with similar data fluctuation characteristics.

[0131] Step S4, determining the mean value of the data fluctuation coefficient of all current humidity measurement data in the region with similar data fluctuation characteristics, and determining the humidity fluctuation scale at each moment in the current measurement stage;

[0132] Specifically, the steps of determining the humidity fluctuation scale at each moment in the current measurement phase include:

[0133] Determine the variance of the current humidity measurement data in the current measurement phase;

[0134] Determine the difference between the maximum humidity value and the minimum humidity value in the current humidity measurement data;

[0135] The humidity fluctuation scale of the current measurement phase is obtained by using the variance and difference calculation;

[0136] The humidity fluctuation scale is used as the humidity fluctuation scale at each moment.

[0137] Calculate the variance of the humidity measurement data for the current measurement period and the difference between the maximum and minimum humidity values ;

[0138] The ratio between the two , recorded as the fluctuation scale of the mixed gas humidity at the current measurement stage and the current moment The larger the value, the more dramatic the humidity fluctuation of the mixed gas is and the smaller the fluctuation amplitude is during the current measurement phase;

[0139] For the measurement stage with larger fluctuation scale, the possibility of data smoothing due to decreased sensor sensitivity is greater, and the impact caused is greater.

[0140] Step S5, using the data fluctuation coefficient mean and humidity fluctuation scale, determining the real-time distortion degree value at each moment in the current measurement phase;

[0141] For details, please refer to Figure 3 , the step S5 comprises:

[0142] Step S51, using the mean value of the data fluctuation coefficient, determining the smoothing distortion influence degree of each similar data fluctuation characteristic region;

[0143] More specifically, the step S51 includes:

[0144] Determine any similar data fluctuation feature region as a target feature region;

[0145] Determine a left-side similar data fluctuation feature region and a right-side similar data fluctuation feature region respectively adjacent to both sides of the target feature region;

[0146] By using the data fluctuation difference of the mean values ​​of the data fluctuation coefficients between the target feature region, the similar data fluctuation feature region on the left, and the similar data fluctuation feature region on the right, the smoothing distortion influence degree of each similar data fluctuation feature region is obtained.

[0147] Calculate the mean of the data fluctuation coefficient of all data points in all similar data fluctuation characteristic areas respectively , regarded as the data fluctuation characteristics corresponding to the area with similar data fluctuation characteristics;

[0148]

[0149] In the formula, Represents the sensor’s historical data (referring to the current measurement phase). similar data fluctuation feature regions (as target feature regions), Represents the The area with similar data fluctuation characteristics adjacent to the right side of the area, Represents the The area with similar data fluctuation characteristics adjacent to the left side of the area, Represents the The data fluctuation characteristics of the regions with similar data fluctuation characteristics, Represents the The data fluctuation characteristics of the regions with similar data fluctuation characteristics, Represents the The data fluctuation characteristics of the regions with similar data fluctuation characteristics, Represents the The degree of smoothing distortion influence in each area.

[0150] The larger the value, the greater the humidity measurement. Region and The greater the regional data fluctuation difference, and Region and The smaller the data fluctuation difference between regions, the smaller the The data fluctuations in the area may appear in abnormal data forms, that is, the data in the area may have a decrease in sensor sensitivity due to drastic changes in gas humidity or composition, resulting in large-scale data smoothing or loss.

[0151] Step S52, using the smoothing distortion influence degree and the humidity fluctuation scale, determine the real-time distortion degree value at each moment in the current measurement stage.

[0152] The average of the smoothing distortion influence of all regions in the humidity data in the current measurement phase is taken as the measurement distortion influence value of the sensor at each moment, denoted as .

[0153]

[0154] In the formula, Represents the The fluctuation scale of the mixed gas humidity at the moment, Represents the The sensor measurement distortion impact value at the moment, represents the real-time distortion function; is a natural constant.

[0155] The smaller the The overall measurement fluctuation scale of the mixed gas humidity at each moment is small. The larger the There is abnormal data smoothing in the local measurement stage of the mixed gas at this moment; the greater the multiplication of the two, the greater the possibility that the sensor's measurement sensitivity decreases due to instantaneous slight changes in the mixed gas composition or humidity during the measurement process, that is, the smoothing distortion of the humidity measurement data at the current moment becomes more serious;

[0156] Then, the real-time distortion degree value at each moment is determined according to the real-time distortion function.

[0157] Step S6, using the historical error cumulative offset value and the real-time distortion value, determine the corrected humidity measurement value at the current moment.

[0158] For details, please refer to Figure 4 , the step S6 comprises:

[0159] Step S61, determining the first environmental factor data of the air-oxygen mixed gas at the current moment;

[0160] Step S62, determining the second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement phase;

[0161] Step S63, determining the third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;

[0162] Step S64, determining the third environmental factor data having the highest similarity to the first environmental factor data, and obtaining the historical error cumulative offset value corresponding to the current moment;

[0163] Step S65, determining the second environmental factor data having the highest similarity to the first environmental factor data, and obtaining a real-time distortion degree value corresponding to the current moment;

[0164] Step S66, using the historical error cumulative offset value corresponding to the current moment, the real-time distortion value and the initial humidity measurement value, to determine the corrected humidity measurement value at the current moment;

[0165] The environmental factor data include various gas contents and gas temperature of the air-oxygen mixed gas; the environmental factor data at each moment are stored in association with the humidity measurement data.

[0166] For the historical error cumulative offset value:

[0167] Obtain all environmental factor data (first environmental factor data) of the air-oxygen mixed gas detected by the humidity sensor at the current moment, and perform similarity comparison with the environmental factor data (third environmental factor data) corresponding to the historical humidity measurement data. Here, the historical humidity measurement data can be 24 hours as mentioned in the previous embodiment, and the time length can also be further extended. For example, the environmental factor data within one month can also be compared for similarity; the similarity can be calculated by common methods such as cosine similarity;

[0168] The time at which a control record with the highest similarity is obtained, thereby obtaining the historical error cumulative offset function value at that time as the error cumulative offset value at the current time.

[0169] Similarly, for the real-time distortion value:

[0170] Get all gas environment factor data (first environment factor data) of the humidity sensor at the current moment, and compare the similarity with all environment factors (second environment factor data) recorded in each control during the current measurement phase (such as the historical 1 hour). The similarity can be calculated using common methods such as cosine similarity;

[0171] The time of the most recent control record with the closest similarity is obtained, and the real-time distortion function value at the time is obtained as the real-time distortion degree value at the current time.

[0172] In the process of measuring the humidity of air-oxygen mixed gas, the humidity measurement value of the sensor may deviate due to the historical accumulated error and the distortion in the real-time smoothing process of the data. Through big data processing technology, the historical accumulated error and real-time smoothing distortion degree are obtained, so as to accurately correct the initial measurement value of the humidity sensor.

[0173] Obtain the initial humidity measurement value of the humidity sensor based on the traditional linear relationship between gas humidity and the electrical properties of the sensing material ;

[0174]

[0175] In the formula, Represents the current timestamp measured by the system (i.e. the current moment), Represents the The error accumulation offset value of the timestamp measurement data, Represents the The real-time distortion value of the timestamp measurement data, Represents the The initial humidity measurement value of the timestamped measurement data, Represents the Corrected humidity measurement value for each time-stamped measurement data.

[0176] The system continuously monitors changes in humidity data and, combined with internal control algorithms, adjusts the working status of the sensor in real time to avoid errors.

[0177] When the humidity value exceeds the set threshold, the feedback module will trigger an alarm signal (such as a light alarm) to remind the operator.

[0178] The processed humidity data will eventually be stored in a database for subsequent analysis and traceability, and the storage method will ensure the integrity and security of the data. Through these measures, the system can effectively eliminate interference factors and compensate for environmental changes, thereby providing accurate and reliable humidity data.

[0179] The present invention aims at the existing high-sensitivity measurement scheme of air-oxygen mixed gas. The gas components (such as oxygen, nitrogen, etc.) are affected by environmental conditions or changes in industrial processes, which may cause the electrical properties of the sensor material to fluctuate, thereby causing the relationship between the resistance or capacitance and the actual humidity to deviate, affecting the measurement accuracy. In order to solve this problem, the method analyzes the historical data of the sensor to extract the error accumulation function and the corresponding value at each moment, the real-time distortion function and the corresponding value at each moment. The error accumulation function is used to describe the degree of deviation between humidity and the electrical properties of the sensor material, and the real-time distortion function is used to adjust the measurement error in real time. Through the combined effect of these two functions and the corresponding values ​​at each moment, the intelligent correction and optimization of the measurement system is realized, and the measurement accuracy of the humidity of the air-oxygen mixed gas is improved.

[0180] Embodiment 2:

[0181] The embodiment of the present invention also provides a highly sensitive humidity measurement device for air-oxygen mixed gas. The highly sensitive humidity measurement device for air-oxygen mixed gas can be a data calculation and processing device such as a computer, a server, or a combination of multiple devices.

[0182] like Figure 6 As shown, Figure 6 It is a structural schematic diagram of the hardware operating environment of the highly sensitive humidity measuring device for air-oxygen mixed gas involved in the embodiment of the present invention.

[0183] like Figure 6 As shown, the highly sensitive humidity measurement device for the air-oxygen mixed gas 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. Among them, the communication bus 1002 is used to realize the connection and 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 and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as 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 be a storage device independent of the aforementioned processor 1001. The memory 1005 as a computer storage medium may include a highly sensitive humidity measurement program for the air-oxygen mixed gas.

[0184] Those skilled in the art will understand that Figure 6 The hardware structure shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0185] Continue to refer to Figure 6 , Figure 6 The memory 1005 as a computer-readable storage medium may include an operating system, a user interface module, a network communication module, and a highly sensitive humidity measurement program for air-oxygen mixed gas.

[0186] exist Figure 6 In the embodiment, the network communication module is mainly used to connect to the server and can communicate data with the server; and the processor 1001 can call the high-sensitivity measurement program of the humidity of the air-oxygen mixed gas stored in the memory 1005 and execute the steps in the above embodiments.

[0187] The hardware structure of the above-mentioned highly sensitive humidity measurement device for air-oxygen mixed gas is used to implement various embodiments of the highly sensitive humidity measurement method for air-oxygen mixed gas of the present invention.

[0188] In addition, the present invention also provides a highly sensitive humidity measurement system for air-oxygen mixed gas, please refer to Figure 7 , the highly sensitive humidity measurement system of the air-oxygen mixed gas comprises:

[0189] The data acquisition module A10 is used to obtain historical humidity measurement data of the air-oxygen mixed gas and determine a fitting curve corresponding to the historical humidity measurement data;

[0190] The data analysis module A20 is used to determine each extreme value in the fitting curve, and use the extreme value to determine the historical error cumulative offset value at each historical moment; determine the data fluctuation coefficient of each current humidity measurement data in the current measurement stage, and use the data fluctuation coefficient to determine the similar data fluctuation characteristic area; determine the data fluctuation coefficient mean of all current humidity measurement data in the similar data fluctuation characteristic area, and determine the humidity fluctuation scale at each moment in the current measurement stage; use the data fluctuation coefficient mean and the humidity fluctuation scale to determine the real-time distortion degree value at each moment in the current measurement stage;

[0191] The correction and compensation module A30 uses the historical error cumulative offset value and the real-time distortion degree value to determine the corrected humidity measurement value at the current moment.

[0192] Furthermore, the data analysis module A20 is also used for:

[0193] Determine the upper envelope corresponding to the maximum point and the lower envelope corresponding to the minimum point;

[0194] Using the upper and lower envelopes, determine the cumulative coefficient of historical errors at each historical moment;

[0195] Taking the maximum point as the starting point, setting a preset number of extreme point steps, and determining the window area corresponding to the maximum point;

[0196] Using the maximum point set and each window area, determine the historical error offset compensation index at each historical moment;

[0197] The historical error cumulative offset value at each historical moment is calculated using the historical error cumulative coefficient and the historical error offset compensation index.

[0198] Furthermore, the data analysis module A20 is also used for:

[0199] Determine the set of extreme value points in the window area, the slope of the extreme value points at the fitting curve, and the frequency of occurrence of the extreme value points in the fitting curve;

[0200] The historical error offset compensation index at each historical moment is calculated using the maximum point set and the extreme point set in the window area, the slope, and the frequency of occurrence.

[0201] Furthermore, the data analysis module A20 is also used for:

[0202] Taking any current humidity measurement data in the current measurement phase as a starting point, setting a preset number of current humidity measurement data step lengths, and determining a window area corresponding to the current humidity measurement data;

[0203] The standard deviation of the current humidity measurement data in the window area is determined as the data fluctuation coefficient of the starting point, and the data fluctuation coefficient of each current humidity measurement data is obtained.

[0204] Furthermore, the data analysis module A20 is also used for:

[0205] Determine the absolute value of the difference between the data fluctuation coefficients of adjacent current humidity measurement data;

[0206] The current humidity measurement data whose absolute value of the difference is less than a preset threshold value is divided into regions with similar data fluctuation characteristics.

[0207] Furthermore, the data analysis module A20 is also used for:

[0208] Using the mean value of data fluctuation coefficient, determine the degree of smoothing distortion influence of each similar data fluctuation characteristic area;

[0209] The real-time distortion level at each moment of the current measurement phase is determined by using the smoothing distortion influence degree and humidity fluctuation scale.

[0210] Furthermore, the data analysis module A20 is also used for:

[0211] Determine any similar data fluctuation feature region as a target feature region;

[0212] Determine a left-side similar data fluctuation feature region and a right-side similar data fluctuation feature region respectively adjacent to both sides of the target feature region;

[0213] By using the data fluctuation difference of the mean values ​​of the data fluctuation coefficients between the target feature region, the similar data fluctuation feature region on the left, and the similar data fluctuation feature region on the right, the smoothing distortion influence degree of each similar data fluctuation feature region is obtained.

[0214] Furthermore, the data analysis module A20 is also used for:

[0215] Determine the variance of the current humidity measurement data in the current measurement phase;

[0216] Determine the difference between the maximum humidity value and the minimum humidity value in the current humidity measurement data;

[0217] The humidity fluctuation scale of the current measurement phase is obtained by using the variance and difference calculation;

[0218] The humidity fluctuation scale is used as the humidity fluctuation scale at each moment.

[0219] Furthermore, the correction and compensation module A30 is also used for:

[0220] Determine the first environmental factor data of the air-oxygen mixed gas at the current moment;

[0221] Determine the second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement phase;

[0222] Determine the third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data;

[0223] Determine the third environmental factor data having the highest similarity to the first environmental factor data, and obtain the historical error cumulative offset value corresponding to the current moment;

[0224] Determine the second environmental factor data having the highest similarity to the first environmental factor data, and obtain the real-time distortion degree value corresponding to the current moment;

[0225] Determine the corrected humidity measurement value at the current moment by using the historical error cumulative offset value, the real-time distortion value, and the initial humidity measurement value corresponding to the current moment;

[0226] The environmental factor data include various gas contents and gas temperature of the air-oxygen mixed gas; the environmental factor data at each moment are associated and stored with the humidity measurement data.

[0227] In addition, for a more specific high-sensitivity measurement system for humidity of air-oxygen mixed gas, reference may also be made to the specific hardware system and Figure 8 , I will not go into details here.

[0228] The specific implementation of the highly sensitive humidity measurement system for air-oxygen mixed gas of the present invention is substantially the same as the above-mentioned embodiments of the highly sensitive humidity measurement method for air-oxygen mixed gas, and will not be described in detail herein.

[0229] In addition, the present invention also provides a computer-readable storage medium. The computer-readable storage medium of the present invention stores a highly sensitive humidity measurement program for air-oxygen mixed gas, wherein when the highly sensitive humidity measurement program for air-oxygen mixed gas is executed by a processor, the steps of the highly sensitive humidity measurement method for air-oxygen mixed gas as described above are implemented.

[0230] The method implemented when the high-sensitivity measurement program for the humidity of an air-oxygen mixed gas is executed can refer to the various embodiments of the high-sensitivity measurement method for the humidity of an air-oxygen mixed gas of the present invention, and will not be described in detail here.

[0231] It should be noted that the sequence of the above 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 specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0232] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0233] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] The above description is only a preferred embodiment of the present invention, and does not limit the protection scope of the present invention. All equivalent structural / method transformations made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the protection scope of the present invention.

Claims

1. A highly sensitive method for measuring humidity of air-oxygen mixed gas, characterized in that: The method comprises: Obtain historical humidity measurement data of air-oxygen mixed gas, and determine a fitting curve corresponding to the historical humidity measurement data; Determine each extreme value in the fitting curve, and use the extreme value to determine the cumulative offset value of the historical error at each historical moment; The method for obtaining the historical error cumulative offset value is as follows: using the upper envelope and the lower envelope corresponding to the maximum point to determine the historical error cumulative coefficient at each historical moment; taking the maximum point as the starting point, setting a preset number of extreme point steps, and determining the window area corresponding to the maximum point; using the maximum point set and each window area to determine the historical error offset compensation index at each historical moment; using the historical error cumulative coefficient and the historical error offset compensation index, calculate the historical error cumulative offset value at each historical moment; Determine the data fluctuation coefficient of each current humidity measurement data in the current measurement phase, and use the data fluctuation coefficient to determine the similar data fluctuation characteristic area; The method for obtaining the data fluctuation coefficient is as follows: taking any current humidity measurement data in the current measurement phase as the starting point, setting a preset number of current humidity measurement data steps, and determining the window area corresponding to the current humidity measurement data; determining the standard deviation of the current humidity measurement data in the window area as the data fluctuation coefficient of the current humidity measurement data; Determine the mean value of the data fluctuation coefficient of all current humidity measurement data in the area with similar data fluctuation characteristics, and determine the humidity fluctuation scale at each moment in the current measurement stage; The method for obtaining the humidity fluctuation scale is as follows: determining the variance of the current humidity measurement data in the current measurement phase; determining the difference 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 phase using the variance and the difference; The real-time distortion value at each moment of the current measurement phase is determined by using the mean value of the data fluctuation coefficient and the humidity fluctuation scale; The method for obtaining the real-time distortion degree value is as follows: using the mean value of the data fluctuation coefficient to determine the smoothing distortion influence degree of each similar data fluctuation characteristic area; using the smoothing distortion influence degree and the humidity fluctuation scale to determine the real-time distortion degree value at each moment of the current measurement stage; The corrected humidity measurement value at the current moment is determined by using the historical error cumulative offset value and the real-time distortion degree value.

2. The highly sensitive humidity measurement method of air-oxygen mixed gas according to claim 1, characterized in that: The steps of determining the historical error offset compensation index at each historical moment by using the maximum point set and each window area include: Determine the set of extreme value points in the window area, the slope of the extreme value points at the fitting curve, and the frequency of occurrence of the extreme value points in the fitting curve; The historical error offset compensation index at each historical moment is calculated using the maximum point set and the extreme point set in the window area, the slope, and the frequency of occurrence.

3. The highly sensitive humidity measurement method of air-oxygen mixed gas according to claim 1, characterized in that: The steps of determining the similar data fluctuation characteristic area by using the data fluctuation coefficient include: Determine the absolute value of the difference between the data fluctuation coefficients of adjacent current humidity measurement data; The current humidity measurement data whose absolute value of the difference is less than a preset threshold value are divided into regions with similar data fluctuation characteristics.

4. The highly sensitive humidity measurement method of air-oxygen mixed gas according to claim 1, characterized in that: The steps of determining the smoothing distortion influence degree of each similar data fluctuation characteristic region by using the mean value of the data fluctuation coefficient include: Determine any similar data fluctuation feature region as a target feature region; Determine a left-side similar data fluctuation feature region and a right-side similar data fluctuation feature region respectively adjacent to both sides of the target feature region; By using the data fluctuation difference of the mean values ​​of the data fluctuation coefficients between the target feature region, the similar data fluctuation feature region on the left, and the similar data fluctuation feature region on the right, the smoothing distortion influence degree of each similar data fluctuation feature region is obtained.

5. The highly sensitive humidity measurement method of air-oxygen mixed gas according to claim 1, characterized in that: The steps of determining the corrected humidity measurement value at the current moment by using the historical error cumulative offset value and the real-time distortion degree value include: Determine the first environmental factor data of the air-oxygen mixed gas at the current moment; Determine the second environmental factor data of the air-oxygen mixed gas at each moment in the current measurement phase; Determine the third environmental factor data of the air-oxygen mixed gas at each moment corresponding to the historical humidity measurement data; Determine the third environmental factor data having the highest similarity to the first environmental factor data, and obtain the historical error cumulative offset value corresponding to the current moment; Determine the second environmental factor data having the highest similarity to the first environmental factor data, and obtain the real-time distortion degree value corresponding to the current moment; Determine the corrected humidity measurement value at the current moment by using the historical error cumulative offset value, the real-time distortion value, and the initial humidity measurement value corresponding to the current moment; The environmental factor data include various gas contents and gas temperature of the air-oxygen mixed gas; the environmental factor data at each moment are stored in association with the humidity measurement data.

6. A highly sensitive humidity measurement system for air-oxygen mixed gas, characterized in that: The system is used to implement the highly sensitive humidity measurement method of the air-oxygen mixed gas according to any one of claims 1 to 5; the system comprises: A data acquisition module, used to obtain historical humidity measurement data of air-oxygen mixed gas and determine a fitting curve corresponding to the historical humidity measurement data; The data analysis module is used to determine each extreme value in the fitting curve, and use the extreme value to determine the historical error cumulative offset value at each historical moment; determine the data fluctuation coefficient of each current humidity measurement data in the current measurement stage, and use the data fluctuation coefficient to determine the similar data fluctuation characteristic area; determine the mean value of the data fluctuation coefficient of all current humidity measurement data in the similar data fluctuation characteristic area, and determine the humidity fluctuation scale at each moment in the current measurement stage; use the mean value of the data fluctuation coefficient and the humidity fluctuation scale to determine the real-time distortion value at each moment in the current measurement stage; The correction compensation module uses the historical error cumulative offset value and the real-time distortion degree value to determine the corrected humidity measurement value at the current moment.

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