CN111551613A - Gas monitoring and calibrating method and system based on linear regression - Google Patents
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Abstract
The invention discloses a gas monitoring and calibrating method and system based on linear regression, wherein the related gas monitoring and calibrating method based on linear regression comprises the following steps: s11, acquiring a gas mixing monitoring value of the environment where the monitoring equipment is located; s12, converting the obtained mixed monitoring value into a component value of the gas based on linear regression; s13, comparing the gas component value obtained by conversion with standard source data, and generating a new gas component value according to a comparison result; and S14, calibrating the obtained new component value to obtain final component information. The invention greatly improves the mutual interference of common monitoring gas factors (sulfur dioxide, nitrogen dioxide, ozone and carbon monoxide) in the atmospheric environment, and obtains more accurate component information; and the problem of monitoring the impact response of single gas in sudden change in the atmospheric environment is solved, and the problem of monitoring accuracy of the impact peak is greatly improved.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a gas monitoring and calibrating method and system based on linear regression.
Background
At present, sensors based on an electrochemical principle are commonly used in atmospheric environment gridding monitoring, and the sensors cannot avoid interaction among gas factors when facing common monitoring factors (sulfur dioxide, nitrogen dioxide, ozone and carbon monoxide), so that the problem that the monitoring result is greatly different from the actual composition is caused.
Current calibration techniques to correct for such differences rely primarily on theoretical calculations in combination with manual experience, or by comparative calibration of measured values by non-electrochemical principles. However, the implementation of the current calibration scheme cannot avoid the involvement of subjective factors, and cannot properly deal with the temperature and time sensitivity of the electrochemical sensor. In practice, only the mode of writing parameters or a comparison table once can be adopted to deal with the external monitoring environment change.
The existing difficulty for solving the calibration of multiple gas factor components is as follows:
1. quantitative determination of two gas factors is easy to interfere with each other, but the result cannot be directly used for mixed interference of multiple gas factors.
2. The impact effect of the concentration mutation of the single gas factor in the monitoring environment cannot respond or has overlarge error.
For example, patent publication No. CN107490613A discloses a calibration method for an electrochemical gas sensor, which comprises the following steps: s100, when an instruction for calibrating the electrochemical gas sensor is received, closing a measuring box of the electrochemical gas sensor; s200, acquiring a gas concentration detection value in the measurement box; s300, updating a gas concentration reference value of the electrochemical gas sensor according to the gas concentration detection value; the detection accuracy of the electrochemical gas sensor can be improved. The scheme can provide the detection accuracy of the electrochemical gas sensor, but the scheme is an improvement on the sensor, and accurate component information cannot be obtained for two mutually interfered gas factors.
Therefore, in order to solve the above technical problems, the present invention provides a gas monitoring calibration method and system based on linear regression.
Disclosure of Invention
The invention aims to provide a gas monitoring and calibrating method and system based on linear regression aiming at the defects of the prior art, so that the mutual interference of common monitoring gas factors (sulfur dioxide, nitrogen dioxide, ozone and carbon monoxide) in the atmospheric environment is greatly improved, and more accurate component information is obtained; and the problem of monitoring the impact response of single gas in sudden change in the atmospheric environment is solved, and the problem of monitoring accuracy of the impact peak is greatly improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a gas monitoring and calibrating method based on linear regression comprises the following steps:
s1, acquiring a gas mixing monitoring value of an environment where monitoring equipment is located;
s2, converting the obtained mixed monitoring value into a component value of the gas based on linear regression;
s3, comparing the gas component value obtained by conversion with standard source data, and generating a new gas component value according to a comparison result;
and S4, calibrating the obtained new component value to obtain final component information.
Further, the step S2 specifically includes:
s21, constructing a regression parameter pool based on the obtained mixed monitoring value and the gradient direction with the minimum square sum of linear regression;
s22, carrying out variance detection and level detection on the parameters in the regression parameter pool to obtain the optimal parameters in the parameter pool; the optimal parameter is the composition value of the gas.
Further, in step S3, the obtained comparison result is recalculated by the regression parameter in the linear regression, so as to obtain a new gas component value.
Further, the processing manner in the calibration processing of the new component value in step S4 includes temperature and humidity calibration and time calibration.
Further, the step S4 is followed by:
and S5, storing the acquired body mixing monitoring value, the standard source data, the temperature and humidity calibration data, the time calibration data and the calibrated final component information in a database.
Further, the step S5 is followed by:
and S6, issuing the stored data information.
Correspondingly, a gas monitoring and calibrating system based on linear regression is also provided, which comprises:
the acquisition module is used for acquiring a gas mixing monitoring value of the environment where the monitoring equipment is located;
the conversion module is used for converting the obtained mixed monitoring value into a component value of the gas based on linear regression;
the comparison module is used for comparing the gas component value obtained by conversion with standard source data and generating a new gas component value according to a comparison result;
and the calibration module is used for carrying out calibration processing on the obtained new component value to obtain final component information.
Further, the gas mixing monitoring value of the environment where the monitoring device is located is obtained by the obtaining module through the internet of things server.
Further, the standard source data in the comparison module is standard source data of a national station; the comparison module also comprises an internet of things server for acquiring standard source data of the national station.
Furthermore, the processing mode of the calibration processing of the new component values in the calibration module includes temperature and humidity calibration and time calibration.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the mutual interference of common monitoring gas factors (sulfur dioxide, nitrogen dioxide, ozone and carbon monoxide) in the atmospheric environment is greatly improved by comparing the acquired data with the national standard data, and more accurate component information is obtained.
2. According to the invention, temperature, humidity and time sensitive error calibration is carried out on the obtained data through a laboratory, so that the problem of impact response of monitoring sudden change of a single gas in an atmospheric environment is solved, and the problem of monitoring accuracy of an impact peak value is greatly improved.
3. The invention solves the problem of consistency of field and remote data by adopting the communication connection technology of the Internet of things server and the field equipment.
4. The invention adopts the Internet of things server to process data, and solves the problem of insufficient computing capacity of field equipment.
5. The invention improves the calibration difficulty through laboratory data, reduces the errors caused by the temperature, humidity and time sensitivity of the electrochemical sensor
6. According to the invention, the system automatically acquires data, processes the data and generates the data, so that the manual maintenance frequency is reduced, and the cost is reduced.
Drawings
FIG. 1 is a block diagram of a linear regression-based gas monitoring calibration system according to an embodiment;
fig. 2 is a schematic structural diagram of an internet of things server provided in the first embodiment;
FIG. 3 is a diagram illustrating a structure of an algorithm module for processing data according to an embodiment;
fig. 4 is a flowchart of a gas monitoring calibration method based on linear regression according to the second embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to provide a gas monitoring and calibrating method and system based on linear regression aiming at the defects of the prior art.
Example one
The present embodiment provides a gas monitoring calibration system based on linear regression, as shown in fig. 1, including:
the acquisition module 11 is used for acquiring a gas mixing monitoring value of the environment where the monitoring equipment is located;
the conversion module 12 is configured to convert the obtained mixed monitoring value into a component value of the gas based on linear regression;
a comparison module 13, configured to compare the converted gas component value with standard source data, and generate a new gas component value according to a comparison result;
and the calibration module 14 is configured to perform calibration processing on the obtained new component value to obtain final component information.
In the embodiment, the hardware device is composed of a field device (i.e., a monitoring device, i.e., an electrochemical sensor), an internet of things server, a national station (a standard data source), and a laboratory.
The monitoring equipment is used for acquiring self environmental data and transmitting the acquired environmental data to the Internet of things server;
the national station data source is used for confirming the general situation (open environment and applicable to unorganized emission) of the surrounding environment of the monitoring equipment and providing standard data for the server of the Internet of things;
the laboratory data is used for providing calibration of temperature and humidity and time sensitive errors of the sensor for the server of the Internet of things;
the Internet of things server is respectively connected with the monitoring equipment, the national station data source and the laboratory data and is used for receiving the environmental data sent by the monitoring equipment and acquiring the data of the national station through public data or a VPN (virtual private network) line; laboratory data were acquired via a proprietary line.
In the acquisition module 11, a gas mixture monitoring value of an environment in which the monitoring device is located is acquired.
The gas mixing monitoring value of the environment where the monitoring equipment collector is located is sent to the Internet of things server, and the Internet of things server carries out further processing.
It should be noted that the execution subject of the embodiment is an internet of things server.
In the conversion module 12, the acquired mixed monitored values are converted into component values of the gas based on linear regression.
And the Internet of things server converts the obtained monitoring value into a gas component value based on a linear regression model.
The method comprises the following steps:
constructing a regression parameter pool according to the obtained mixed monitoring value based on the gradient direction with the minimum square sum of linear regression;
carrying out variance test and level test on the parameters in the regression parameter pool to obtain the optimal parameters in the parameter pool; wherein the optimal parameter is the composition value of the gas.
The processing method based on linear regression specifically comprises the following steps:
1. and selecting a standard source data time period (which can be selected automatically or manually according to preset conditions).
2. Matching historical data of the monitoring equipment according to the time period selected in the step 1, if the effective data is insufficient, returning to the step 1, and re-selecting a standard source data segment; and if the valid data are sufficient, entering the step 3.
3. For the selected data segment, a multivariate (quaternary) linear equation set is solved by applying a random disturbance mode according to the gradient direction with the minimum integral regression square sum to form a better parameter solution. The formula is as follows:
where x represents field device output, a represents a parameter, Y represents standard source data, and Y represents a standard source dataiRepresenting the optimal solution output.
4. For the better solution produced in step 3, the rationality of the parameters was determined by chemical principles (gas a is increasing in concentration with gas B; the parameters must be negative, whereas the parameters must be positive). If the current parameters do not conform to the chemical principle, the method jumps to step 6 to search for better solution again. And if the answer is yes, the next step is carried out.
5. And (3) performing singularity test, and constructing a numerical test set by combining all the numerical values in the range (based on the precision of the monitoring equipment). And substituting the parameters into a regression equation set, inputting the numerical test set, and checking whether singularities (results far beyond the range) occur. If so, go to step 6 and restart to find the better solution. No singularities are generated, step 7 is entered.
6. Judging whether the allowable times are exceeded or not, if the allowable times are exceeded, recording the selected data segment and the optimal solution generated at the last time, and prompting manual processing; otherwise, go back to step 3 to look for better solution again.
7. And outputting the optimal solution, and automatically/manually applying the optimal solution to the data calibration of the current monitoring equipment according to the setting.
In the comparing module 13, the converted gas component value is compared with the standard source data, and a new gas component value is generated according to the comparison result.
And the Internet of things server acquires standard source data of the national station through public data or a VPN line, compares the standard source data with the obtained component values of the gas to obtain a comparison result, and recalculates the comparison result through regression parameters in linear regression to obtain a new gas component value.
In the calibration module 14, the obtained new component values are calibrated to obtain final component information.
The internet of things server acquires laboratory data through a dedicated line, and new gas component values are calibrated through calibration of temperature, humidity and time sensitive errors of sensors provided by a laboratory, so that final component information is obtained.
In this embodiment, the system further comprises a storage module and a publishing module;
and the storage module is used for storing the acquired volume mixing monitoring value, the standard source data, the temperature and humidity calibration data, the time calibration data and the calibrated final component information in a database.
And the release module is used for releasing the stored data information.
The internet of things server of the embodiment bears a data interaction function. As shown in fig. 3, the internet of things server mainly includes: the system comprises modules such as a data communication module, a data storage module, a data release module and an algorithm module. The data communication is used for data interaction between the server of the Internet of things and monitoring equipment, a national station and a laboratory; the data storage is used for storing original data, algorithm generation parameters, parameter timeliness and the like; the data release is used for releasing field data, network data and the like; the algorithm is used for solving the calculation problem generally related to the server of the internet of things.
As shown in fig. 2, the flow directions of the original data and the standard data are both sent to the algorithm module and/or the data storage module through the data communication module, wherein the data in the algorithm module and the data storage module flow in two directions; and then the data storage module publishes the stored data through the data publishing module.
Fig. 3 is a block diagram showing the calculation of the involved data in the algorithm module. The algorithm is divided into three layers, and the first layer adopts linear regression to convert the mixed monitoring value returned by the field device into component values. The second layer compares the component data with standard data sources such as on-site national stations, and generates comparison data periodically to form new regression parameters. And the third layer calibrates the component values with temperature, humidity and time to form release data.
Compared with the prior art, the embodiment has the following beneficial effects:
1. according to the invention, the component concentration of common monitoring factors (sulfur dioxide, nitrogen dioxide, ozone and carbon monoxide) in an open atmospheric environment (unorganized emission) is effectively calibrated by comparing the acquired data with national standard data.
2. According to the invention, temperature, humidity and time sensitive error calibration is carried out on the obtained data through a laboratory, so that the problem of impact response of monitoring sudden change of a single gas in an atmospheric environment is solved, the problem of monitoring accuracy of an impact peak value is greatly improved, and measurement errors caused by three factors of temperature, humidity and time are reduced.
3. The data related in the calibration process is stored, namely the data such as original data, standard source data, laboratory data, regression parameters and the like are stored, so that the related data can be traced, and the calibration and correction processes are recorded.
Example two
The embodiment discloses a gas monitoring and calibrating method based on linear regression, as shown in fig. 4, comprising the steps of:
s11, acquiring a gas mixing monitoring value of the environment where the monitoring equipment is located;
s12, converting the obtained mixed monitoring value into a component value of the gas based on linear regression;
s13, comparing the gas component value obtained by conversion with standard source data, and generating a new gas component value according to a comparison result;
and S14, calibrating the obtained new component value to obtain final component information.
Further, the step S12 specifically includes:
s121, constructing a regression parameter pool based on the obtained mixed monitoring value and the gradient direction with the minimum square sum of linear regression;
s122, carrying out variance detection and level detection on the parameters in the regression parameter pool to obtain the optimal parameters in the parameter pool; the optimal parameter is the composition value of the gas.
Further, in step S13, the obtained comparison result is recalculated by the regression parameters in the linear regression to obtain a new gas component value.
Further, the processing manner in the calibration processing of the new component value in step S14 includes temperature and humidity calibration and time calibration.
Further, step S14 is followed by:
and S15, storing the acquired body mixing monitoring value, the standard source data, the temperature and humidity calibration data, the time calibration data and the calibrated final component information in a database.
Further, step S15 is followed by:
and S16, issuing the stored data information.
It should be noted that, the gas monitoring calibration method based on linear regression provided in this embodiment is similar to the embodiment, and will not be described herein again.
Compared with the prior art, the embodiment has the following beneficial effects:
1. according to the invention, the component concentration of common monitoring factors (sulfur dioxide, nitrogen dioxide, ozone and carbon monoxide) in an open atmospheric environment (unorganized emission) is effectively calibrated by comparing the acquired data with national standard data.
2. According to the invention, temperature, humidity and time sensitive error calibration is carried out on the obtained data through a laboratory, so that the problem of impact response of monitoring sudden change of a single gas in an atmospheric environment is solved, the problem of monitoring accuracy of an impact peak value is greatly improved, and measurement errors caused by three factors of temperature, humidity and time are reduced.
3. The data related in the calibration process is stored, namely the data such as original data, standard source data, laboratory data, regression parameters and the like are stored, so that the related data can be traced, and the calibration and correction processes are recorded.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A gas monitoring and calibrating method based on linear regression is characterized by comprising the following steps:
s1, acquiring a gas mixing monitoring value of an environment where monitoring equipment is located;
s2, converting the obtained mixed monitoring value into a component value of the gas based on linear regression;
s3, comparing the gas component value obtained by conversion with standard source data, and generating a new gas component value according to a comparison result;
and S4, calibrating the obtained new component value to obtain final component information.
2. The method according to claim 1, wherein the step S2 specifically includes:
s21, constructing a regression parameter pool based on the obtained mixed monitoring value and the gradient direction with the minimum square sum of linear regression;
s22, carrying out variance detection and level detection on the parameters in the regression parameter pool to obtain the optimal parameters in the parameter pool; the optimal parameter is the composition value of the gas.
3. The method for gas monitoring calibration based on linear regression as claimed in claim 2, wherein the comparison result obtained in step S3 is recalculated by regression parameters in linear regression to obtain new gas component values.
4. The linear regression-based gas monitoring calibration method as claimed in claim 3, wherein the calibration process for the new component values in step S4 includes temperature and humidity calibration and time calibration.
5. The linear regression-based gas monitoring calibration method according to claim 4, wherein said step S4 is followed by further comprising:
and S5, storing the acquired body mixing monitoring value, the standard source data, the temperature and humidity calibration data, the time calibration data and the calibrated final component information in a database.
6. The linear regression-based gas monitoring calibration method according to claim 5, wherein said step S5 is followed by further comprising:
and S6, issuing the stored data information.
7. A linear regression-based gas monitoring calibration system, comprising:
the acquisition module is used for acquiring a gas mixing monitoring value of the environment where the monitoring equipment is located;
the conversion module is used for converting the obtained mixed monitoring value into a component value of the gas based on linear regression;
the comparison module is used for comparing the gas component value obtained by conversion with standard source data and generating a new gas component value according to a comparison result;
and the calibration module is used for carrying out calibration processing on the obtained new component value to obtain final component information.
8. The linear regression-based gas monitoring and calibration system according to claim 7, wherein the acquiring module acquires the gas mixture monitoring value of the environment where the monitoring device is located through an internet of things server.
9. The linear regression-based gas monitoring calibration system according to claim 8, wherein the standard source data in the comparison module is standard source data of a national station; the comparison module also comprises an internet of things server for acquiring standard source data of the national station.
10. The linear regression-based gas monitoring calibration system of claim 7, wherein the calibration module performs calibration processing on the new component values in a manner including temperature and humidity calibration and time calibration.
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