阅读说明：本技术 一种臭氧缺失数据的插补方法、装置及插补设备 (Interpolation method and device for ozone missing data and interpolation equipment ) 是由 闫增祥 黄典 尹凌 胡金星 冯圣中 于 2020-03-10 设计创作，主要内容包括：本申请适用于计算机技术领域,提供了一种臭氧缺失数据的插补方法,包括：获取第一空气质量监测站的待补全的第一臭氧数据；获取第二空气质量监测站的第二臭氧数据和第一气象监测站的第一气象数据；以所述第一臭氧数据为因变量,以所述第二臭氧数据和所述第一气象数据自变量,构建多元回归模型；基于所述多元回归模型对所述第一臭氧数据中缺失的臭氧监测数据进行插补。上述方案,以所述第一臭氧数据为因变量,以所述第二臭氧数据和所述第一气象数据自变量,建立多元回归模型,通过模型计算出第一臭氧数据中缺失的臭氧检测数据,在对臭氧数据进行插补时,考虑各监测站之间的相关联系和气象数据对臭氧数据的影响,能够提高臭氧缺失数据插补的准确度。(The application is suitable for the technical field of computers, and provides an interpolation method of ozone missing data, which comprises the following steps: acquiring first ozone data to be supplemented of a first air quality monitoring station; acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station; constructing a multivariate regression model by taking the first ozone data as a dependent variable and the second ozone data and the first meteorological data independent variable; and interpolating the missing ozone monitoring data in the first ozone data based on the multiple regression model. According to the scheme, the first ozone data is used as a dependent variable, the second ozone data and the first meteorological data independent variable are used for establishing a multiple regression model, missing ozone detection data in the first ozone data are calculated through the model, and when the ozone data are interpolated, the influence of correlation systems among monitoring stations and meteorological data on the ozone data is considered, so that the accuracy of interpolation of the missing ozone data can be improved.)

1. An interpolation method of ozone depletion data, comprising:

acquiring first ozone data to be supplemented of a first air quality monitoring station;

acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station;

constructing a multivariate regression model by taking the first ozone data as a dependent variable and the second ozone data and the first meteorological data independent variable;

and interpolating the missing ozone monitoring data in the first ozone data based on the multiple regression model to obtain the complemented first ozone data.

2. The method of interpolating ozone depletion data according to claim 1, wherein said obtaining second ozone data from a second air quality monitoring station and first weather data from a first weather monitoring station comprises:

and taking the air quality monitoring station which is less than a first distance threshold value from the first air quality monitoring station as a second air quality monitoring station, and taking the weather monitoring station which is less than a second distance threshold value from the first air quality monitoring station as a first weather monitoring station to acquire second ozone data of the second air quality monitoring station and first weather data of the first weather monitoring station.

3. The method of interpolating ozone depletion data according to claim 1, wherein said obtaining second ozone data from a second air quality monitoring station and first weather data from a first weather monitoring station comprises:

taking the air quality monitoring station with the distance from the first air quality monitoring station smaller than a first distance threshold value as a candidate air quality monitoring station;

taking the weather monitoring station with the distance from the first air quality monitoring station smaller than a second distance threshold value as a candidate weather monitoring station;

acquiring candidate ozone data of each candidate air quality monitoring station and candidate meteorological data of each candidate meteorological monitoring station;

taking candidate ozone data with the linear correlation degree of the first ozone data meeting a first preset correlation degree condition as second ozone data, and taking a candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station;

and taking the candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as second meteorological data, and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

4. The method as claimed in claim 3, wherein the step of using the candidate ozone data whose linear correlation with the first ozone data satisfies a first preset correlation condition as the second ozone data and using the candidate air quality monitoring station corresponding to the second ozone data as the second air quality monitoring station comprises:

taking candidate ozone data with linear correlation degree meeting a first preset correlation degree condition with the first ozone data as pre-selected ozone data;

establishing a first linear regression model according to the preselected ozone data and the first ozone data, and calculating utility information of the first linear regression model;

and using the pre-selected ozone data corresponding to the utility information meeting the first preset utility condition as second ozone data, and using the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station.

5. The method of interpolating ozone depletion data according to claim 3, wherein said step of using the candidate meteorological data having a linear correlation with said first ozone data satisfying a second predetermined correlation condition as the second meteorological data and using the candidate meteorological monitoring station corresponding to said second meteorological data as the second meteorological monitor comprises the steps of:

taking candidate meteorological data with linear correlation degree with the first ozone data meeting a second preset correlation degree condition as pre-selected meteorological data;

establishing a second linear regression model according to the preselected meteorological data and the first ozone data, and calculating utility information of the second linear regression model;

and using the pre-selected meteorological data corresponding to the utility information meeting the second preset utility condition as second meteorological data, and using the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

6. The method of claim 4, wherein the calculating utility information of the first linear regression model comprises:

error information of the first linear regression model is calculated according to the first ozone data.

7. The method of interpolating ozone depletion data according to claim 5, wherein said calculating utility information of said second linear regression model comprises:

error information of the second linear regression model is calculated from the first ozone data.

8. An interpolation device for ozone depletion data, comprising:

the first acquisition unit is used for acquiring first ozone data to be supplemented of a first air quality monitoring station;

the second acquisition unit is used for acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station;

the construction unit is used for constructing a multiple regression model by taking the first ozone data as a dependent variable and taking the second ozone data and the first meteorological data independent variable;

and the first processing unit is used for interpolating the ozone monitoring data missing in the first ozone data based on the multiple regression model to obtain the complemented first ozone data.

9. An apparatus for interpolating ozone depletion data comprising a memory, a processor and a computer program stored in said memory and executable on said processor, wherein said processor when executing said computer program implements a method according to any one of claims 1 to 7.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.

Technical Field

The application belongs to the technical field of computers, and particularly relates to an interpolation method and device of ozone missing data, interpolation equipment and a computer storage medium.

Background

Ozone is one of main pollutants in the air, the content and the variation trend of the air pollutants are mastered through air pollutant data, the air quality of each region is evaluated, and a certain guiding function is provided for adjusting and making a prevention and control strategy of air pollution for each region and continuously improving the air quality. Therefore, mastering the ozone change trend of each region and predicting the ozone change trend have great significance on pollution regulation and control of each region and sustainable development of society, but research on pollutants such as ozone and the like often needs more accurate monitoring data. The data missing situation in the ozone concentration analysis and prediction work is common, and if the data missing situation is not processed properly, the data missing situation can have adverse effects on the ozone concentration statistical prediction work.

The existing ozone missing data interpolation method mainly analyzes the statistical connection and rule among the existing ozone monitoring data from the statistical and mathematical angles, and finally interpolates the missing ozone data. However, the existing interpolation method only considers the change situation of the ozone monitoring data, thereby causing the accuracy of the interpolation of the ozone missing data to be low.

Disclosure of Invention

The embodiment of the application provides an interpolation method and device of ozone missing data, interpolation equipment and a computer storage medium, and can solve the problem of low accuracy of interpolation of ozone missing data.

In a first aspect, an embodiment of the present application provides an interpolation method for ozone missing data, including:

acquiring first ozone data to be supplemented of a first air quality monitoring station;

acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station;

constructing a multivariate regression model by taking the first ozone data as a dependent variable and the second ozone data and the first meteorological data independent variable;

and interpolating the missing ozone monitoring data in the first ozone data based on the multiple regression model to obtain the complemented first ozone data.

Further, the acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station comprises:

and taking the air quality monitoring station which is less than a first distance threshold value from the first air quality monitoring station as a second air quality monitoring station, and taking the weather monitoring station which is less than a second distance threshold value from the first air quality monitoring station as a first weather monitoring station to acquire second ozone data of the second air quality monitoring station and first weather data of the first weather monitoring station.

Further, the acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station comprises:

taking the air quality monitoring station with the distance from the first air quality monitoring station smaller than a first distance threshold value as a candidate air quality monitoring station;

taking the weather monitoring station with the distance from the first air quality monitoring station smaller than a second distance threshold value as a candidate weather monitoring station;

acquiring candidate ozone data of each candidate air quality monitoring station and candidate meteorological data of each candidate meteorological monitoring station;

taking candidate ozone data with the linear correlation degree of the first ozone data meeting a first preset correlation degree condition as second ozone data, and taking a candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station;

and taking the candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as second meteorological data, and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

Further, the step of using the candidate ozone data whose linear correlation with the first ozone data satisfies a first preset correlation condition as the second ozone data and using the candidate air quality monitoring station corresponding to the second ozone data as the second air quality monitoring station includes:

taking candidate ozone data with linear correlation degree meeting a first preset correlation degree condition with the first ozone data as pre-selected ozone data;

establishing a first linear regression model according to the preselected ozone data and the first ozone data, and calculating utility information of the first linear regression model;

and using the pre-selected ozone data corresponding to the utility information meeting the first preset utility condition as second ozone data, and using the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station. Further, the step of using the candidate meteorological data whose linear correlation with the first ozone data satisfies a second preset correlation condition as the second meteorological data, and the step of using the candidate meteorological monitoring station corresponding to the second meteorological data as the second meteorological monitoring station includes:

taking candidate meteorological data with linear correlation degree with the first ozone data meeting a second preset correlation degree condition as pre-selected meteorological data;

establishing a second linear regression model according to the preselected meteorological data and the first ozone data, and calculating utility information of the second linear regression model;

and using the pre-selected meteorological data corresponding to the utility information meeting the second preset utility condition as second meteorological data, and using the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

Further, the calculating utility information of the first linear regression model comprises:

error information of the first linear regression model is calculated according to the first ozone data.

Further, the calculating utility information of the second linear regression model comprises:

error information of the second linear regression model is calculated from the first ozone data.

In a second aspect, an embodiment of the present application provides an interpolation apparatus for missing ozone data, including:

the first acquisition unit is used for acquiring first ozone data to be supplemented of a first air quality monitoring station;

the second acquisition unit is used for acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station;

the construction unit is used for constructing a multiple regression model by taking the first ozone data as a dependent variable and taking the second ozone data and the first meteorological data independent variable;

and the first processing unit is used for interpolating the ozone monitoring data missing in the first ozone data based on the multiple regression model to obtain the complemented first ozone data.

Further, the second obtaining unit is specifically configured to:

and taking the air quality monitoring station which is less than a first distance threshold value from the first air quality monitoring station as a second air quality monitoring station, and taking the weather monitoring station which is less than a second distance threshold value from the first air quality monitoring station as a first weather monitoring station to acquire second ozone data of the second air quality monitoring station and first weather data of the first weather monitoring station.

Further, the second obtaining unit includes:

the second processing unit is used for taking the air quality monitoring station with the distance from the first air quality monitoring station smaller than a first distance threshold value as a candidate air quality monitoring station;

the third processing unit is used for taking the weather monitoring station with the distance from the first air quality monitoring station smaller than a second distance threshold value as a candidate weather monitoring station;

the third acquisition unit is used for acquiring candidate ozone data of each candidate air quality monitoring station and candidate meteorological data of each candidate meteorological monitoring station;

the fourth processing unit is used for taking candidate ozone data of which the linear correlation degree with the first ozone data meets a first preset correlation degree condition as second ozone data and taking a candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station;

and the fifth processing unit is used for taking the candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as second meteorological data and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

Further, the fourth processing unit includes:

a sixth processing unit, configured to take candidate ozone data whose linear correlation with the first ozone data satisfies a first preset correlation condition as pre-selected ozone data;

the first calculation unit is used for establishing a first linear regression model according to the pre-selected ozone data and the first ozone data and calculating the utility information of the first linear regression model;

and the seventh processing unit is used for taking the preselected ozone data corresponding to the utility information meeting the first preset utility condition as second ozone data and taking the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station.

Further, the fifth processing unit includes:

the eighth processing unit is used for taking candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as pre-selected meteorological data;

the second calculation unit is used for establishing a second linear regression model according to the preselected meteorological data and the first ozone data and calculating utility information of the second linear regression model;

and the ninth processing unit is used for taking the pre-selected meteorological data corresponding to the utility information meeting the second preset utility condition as second meteorological data and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

Further, the first calculating unit is specifically configured to:

error information of the first linear regression model is calculated according to the first ozone data.

Further, the second calculating unit is specifically configured to:

error information of the second linear regression model is calculated from the first ozone data.

In a third aspect, an embodiment of the present application provides an apparatus for interpolating ozone-deficiency data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for interpolating ozone-deficiency data according to the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method for interpolating ozone depletion data according to the first aspect.

In the embodiment of the application, first ozone data to be supplemented of a first air quality monitoring station is obtained; acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station; constructing a multivariate regression model by taking the first ozone data as a dependent variable and the second ozone data and the first meteorological data independent variable; and interpolating the missing ozone monitoring data in the first ozone data based on the multiple regression model to obtain the complemented first ozone data. According to the scheme, the second ozone data of the second air quality monitoring station and the first meteorological data of the first meteorological monitoring station are obtained, the first ozone data is used as a dependent variable, the second ozone data and the first meteorological data independent variable are used for establishing a multiple regression model, ozone detection data missing in the first ozone data are calculated through the model, and therefore interpolation is conducted.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

FIG. 1 is a schematic flow chart of a method for interpolating ozone depletion data according to a first embodiment of the present disclosure;

FIG. 2 is a schematic flow chart of a refinement of S102 in the interpolation method for ozone depletion data according to the first embodiment of the present application;

FIG. 3 is a schematic flow chart illustrating the refinement of S1024 in the interpolation method for missing ozone data according to the first embodiment of the present application;

FIG. 4 is a schematic flow chart of a refinement of S1025 in the interpolation method of ozone depletion data according to the first embodiment of the present application;

FIG. 5 is a schematic diagram of an interpolation device for ozone depletion data provided in a second embodiment of the present application;

fig. 6 is a schematic diagram of an interpolation apparatus for ozone depletion data according to a third embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".

Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

Referring to fig. 1, fig. 1 is a schematic flow chart of an interpolation method of ozone depletion data according to a first embodiment of the present application. In this embodiment, the main execution body of the method for interpolating ozone missing data is a device having an ozone missing data interpolation function, and the device includes, but is not limited to, a desktop computer, a server, and the like. The interpolation method of the ozone depletion data shown in fig. 1 may include:

s101: acquiring first ozone data to be supplemented of a first air quality monitoring station.

Ozone is one of main pollutants in the air, the content and the variation trend of the air pollutants are mastered through air pollutant data, the air quality of each region is evaluated, and a certain guiding function is provided for adjusting and making a prevention and control strategy of air pollution for each region and continuously improving the air quality. Therefore, mastering the ozone change trend of each region and predicting the ozone change trend have great significance on pollution regulation and control of each region and sustainable development of society. The air quality monitoring station can monitor ozone data in the air, and the air quality monitoring station generally measures air pollutant data through some equipment to provide data support for improving the air quality. The air quality monitoring equipment is sometimes influenced by the external environment, or when daily maintenance behaviors such as zero calibration and standard calibration of a monitoring instrument occur, or the conditions such as instrument failure, communication failure and power failure occur, the phenomenon of monitoring data loss of individual point positions can be caused.

The equipment acquires first ozone data to be supplemented of a first air quality monitoring station. The first air quality monitoring station is an air quality monitoring station with missing ozone data, the first ozone data to be supplemented are ozone data acquired by the first air quality monitoring station, the ozone data comprise ozone values and corresponding time information, and the first ozone data can be all ozone data acquired by the first air quality monitoring station in a specified time period.

S102: second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station are acquired.

The apparatus obtains second ozone data from a second air quality monitoring station and first weather data from a first weather monitoring station. The second air quality monitoring station is the associated air quality monitoring station of the first air quality monitoring station, that is, the second ozone data acquired by the second air quality monitoring station is referential to the ozone data missing from the first air quality monitoring station. The first weather monitoring station is an associated weather monitoring station of the first air quality monitoring station, that is, the first weather data acquired by the first weather monitoring station is referential to ozone data missing from the first air quality monitoring station. The first weather data may include, but is not limited to, temperature data, humidity data, wind speed data, wind direction data, and the like.

The second air quality monitoring station is a designated air quality monitoring station and can also be selected from all the air quality monitoring stations through a preset strategy; the first weather monitoring station may be a designated weather monitoring station, or may be selected from all weather monitoring stations through a preset policy, which is not limited herein.

In one embodiment, S102 may include: and taking the air quality monitoring station which is less than a first distance threshold value from the first air quality monitoring station as a second air quality monitoring station, and taking the weather monitoring station which is less than a second distance threshold value from the first air quality monitoring station as a first weather monitoring station to acquire second ozone data of the second air quality monitoring station and first weather data of the first weather monitoring station.

In this embodiment, the second air quality monitoring station is screened by the distance between the first air quality monitoring station and the other air quality monitoring stations, and the distance between the first air quality monitoring station and the other air quality monitoring stations can be determined by a geospatial distance calculation formula, which is as follows:

wherein the content of the first and second substances,the method is characterized in that the method is represented by radian systems of the latitudes of two air quality monitoring stations, delta lambda is represented by radian systems of longitude difference values of the two air quality monitoring stations, temp is an intermediate variable, R is an average value of the earth radius, and dis is the distance between the two air quality monitoring stations.

The first distance threshold value is prestored in the equipment and used for screening the second air quality monitoring station, and when the distance between the air quality monitoring station and the first air quality monitoring station is smaller than the first distance threshold value, the air quality monitoring station is used as the second air quality monitoring station.

In this embodiment, the first weather monitoring station is screened according to the distance between the first air quality monitoring station and other weather monitoring stations, and the distance between the first air quality monitoring station and other weather monitoring stations can be determined by the aforementioned geospatial distance calculation formula, which is not described herein again. The equipment is pre-stored with a second distance threshold value, the second distance threshold value is used for screening the first meteorological monitoring station, and when the distance between the meteorological monitoring station and the first air quality monitoring station is smaller than the second distance threshold value, the meteorological monitoring station is used as the first meteorological monitoring station.

After the equipment determines the second air quality monitoring station and the first weather monitoring station, second ozone data of the second air quality monitoring station and first weather data of the first weather monitoring station are obtained.

In another embodiment, on the basis of screening based on the station distance, the second air quality monitoring station and the first weather monitoring station are determined by calculating the data correlation of the station, so as to improve the accuracy of selection of the second air quality monitoring station and the first weather monitoring station, S102 may include S1021 to S1025, as shown in fig. 2, where S1021 to S1025 are specifically as follows:

s1021: and taking the air quality monitoring station with the distance from the first air quality monitoring station smaller than a first distance threshold value as a candidate air quality monitoring station.

In S1021, the method of selecting a candidate air quality monitoring station is the same as the method of determining a second air quality monitoring station in the step of "taking an air quality monitoring station whose distance from the first air quality monitoring station is smaller than the first distance threshold as the second air quality monitoring station", which may refer to the related description above, and is not repeated here.

S1022: and taking the weather monitoring station with the distance from the first air quality monitoring station smaller than a second distance threshold value as a candidate weather monitoring station.

In S1021, the method for selecting candidate weather monitoring stations is the same as the method for determining the first weather monitoring station in the step of "taking the weather monitoring station whose distance from the first air quality monitoring station is smaller than the second distance threshold as the first weather monitoring station", which may refer to the related description above specifically, and is not repeated here.

S1023: and acquiring candidate ozone data of each candidate air quality monitoring station and candidate meteorological data of each candidate meteorological monitoring station.

The equipment acquires ozone data of each candidate air quality monitoring station as candidate ozone data, and the equipment acquires meteorological data of each candidate meteorological monitoring station as candidate meteorological data.

S1024: and taking the candidate ozone data with the linear correlation degree meeting a first preset correlation degree condition with the first ozone data as second ozone data, and taking the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station.

In this embodiment, the device screens out second ozone data from the candidate ozone data, wherein a first preset correlation condition is stored in the device in advance, and the linear correlation between the second ozone data and the first ozone data needs to satisfy the first preset correlation condition. The apparatus calculates a linear correlation between the candidate ozone data and the first ozone, and uses the candidate ozone data whose linear correlation with the first ozone data satisfies a first preset correlation condition as the second ozone data. For example, the device may perform screening by a stepwise regression method, taking the first ozone data as a dependent variable and the candidate ozone data as an independent variable, introducing the independent variables one by one, checking the variables that have been introduced into the selected regression model one by one after each new independent variable is introduced, and deleting the independent variables that are not considered to be significant through checking to ensure that each variable in the obtained independent variable quantum set is significant. Wherein the significant and insignificant differences can be set to specific thresholds as desired and can be adjusted. This process goes through several steps until no more new variables can be introduced. All independent variables in the regression model are significant to the dependent variables at this time, i.e., the independent variables in the regression model are the second ozone data.

And after the second ozone data is acquired, taking the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station.

Further, in order to more accurately select the second ozone data and the second air quality monitoring station, so as to more accurately interpolate the first ozone data, S1024 may include S10241 to S10243, as shown in fig. 3, where S10241 to S10243 are specifically as follows:

s10241: and taking candidate ozone data with the linear correlation degree with the first ozone data meeting a first preset correlation degree condition as pre-selected ozone data.

In the embodiment, after multiple screening, the second ozone data and the second air quality monitoring station are finally obtained. The equipment screens out pre-selection ozone data from the candidate ozone data, wherein a first preset correlation condition is stored in the equipment in advance, and the linear correlation of the pre-selection ozone data and the first ozone data needs to meet the first preset correlation condition. The apparatus calculates a linear correlation between the candidate ozone data and the first ozone, and takes the candidate ozone data whose linear correlation with the first ozone data satisfies a first preset correlation condition as pre-selected ozone data.

S10242: and establishing a first linear regression model according to the preselected ozone data and the first ozone data, and calculating the utility information of the first linear regression model.

The equipment establishes a first linear regression model according to the pre-selection ozone data and the first ozone data, acquires the combination of the air quality monitoring stations corresponding to different pre-selection ozone data, and respectively establishes a first linear regression model according to the pre-selection ozone data and the first ozone data corresponding to each group of air quality monitoring stations, wherein the first linear regression model is used for interpolating data.

The utility information of the first linear regression model is calculated by the equipment, the interpolation effect is identified by the utility information of the first linear regression model, and whether the obtained interpolation data are accurate or not can be determined through the utility information. The utility information can be determined by calculating the ozone data of the first air quality monitoring station at the non-data-missing moment and the real value at the moment through the first linear regression model.

Further, for accurate computational utility information, calculating utility information of the first linear regression model may include: error information of the first linear regression model is calculated according to the first ozone data. The equipment calculates error information of the first linear regression model according to the first ozone data, and takes the error information as utility information, wherein the error information can comprise average absolute error and root mean square error:

the Mean Absolute Error (MAE) is calculated as follows:

is the complement of the ith missing value, y_iIs the corresponding true value, and n is the number of missing values of the missing variable.

The Root Mean Square Error (RMSE) is calculated as follows:

is the complement of the ith missing value, y_iIs the corresponding true value, and n is the number of missing values of the missing variable.

The average absolute error and the root mean square error reflect the error between the interpolation value and the true value, and the smaller the value is, the smaller the deviation between the interpolation value and the true value is, so that the better the interpolation effect is.

S10243: and using the pre-selected ozone data corresponding to the utility information meeting the first preset utility condition as second ozone data, and using the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station.

The method comprises the steps that a first preset effect condition is preset in the equipment and used for screening second ozone data, the equipment takes pre-selected ozone data corresponding to effect information meeting the first preset effect condition as the second ozone data, and a candidate air quality monitoring station corresponding to the second ozone data serves as a second air quality monitoring station.

S1025: and taking the candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as second meteorological data, and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

In this embodiment, the device screens out the second meteorological data from the candidate meteorological data, wherein a second preset correlation condition is pre-stored in the device, and the linear correlation between the second meteorological data and the first ozone data needs to satisfy the second preset correlation condition. The apparatus calculates a linear correlation between the candidate meteorological data and the first ozone, and uses the candidate meteorological data whose linear correlation with the first ozone data satisfies a first preset correlation condition as the second meteorological data. For example, the device may perform screening by a stepwise regression method, taking the first ozone data as a dependent variable and the candidate meteorological data as independent variables, introducing the independent variables one by one, checking the variables that have been introduced into the selected regression model one by one after introducing a new independent variable, and deleting the independent variables that are not considered significant through checking to ensure that each variable in the obtained independent variable quantum set is significant. Wherein the significant and insignificant differences can be set to specific thresholds as desired and can be adjusted. This process goes through several steps until no more new variables can be introduced. All independent variables in the regression model are significant to the dependent variables, i.e., the independent variables in the regression model are the second meteorological data.

And after the second meteorological data are obtained, the candidate meteorological monitoring station corresponding to the second meteorological data is used as a second meteorological monitoring station.

Further, in order to more accurately select the second weather data and the second weather monitoring station, so as to more accurately interpolate the first ozone data, S1025 may include S10251 to S10253, as shown in fig. 4, S10251 to S10253 specifically include the following:

s10251: and taking the candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as pre-selected meteorological data.

In this embodiment, the second weather data and the second weather monitoring station are finally obtained through multiple screening. And screening out pre-selected meteorological data from the candidate meteorological data by the equipment, wherein a second preset correlation condition is pre-stored in the equipment, and the linear correlation of the pre-selected meteorological data and the first ozone data needs to meet the second preset correlation condition. The equipment calculates the linear correlation degree between the candidate meteorological data and the first ozone, and takes the candidate meteorological data of which the linear correlation degree with the first ozone data meets a first preset correlation degree condition as the pre-selected meteorological data.

S10252: and establishing a second linear regression model according to the preselected meteorological data and the first ozone data, and calculating utility information of the second linear regression model.

The equipment establishes a second linear regression model according to the pre-selected meteorological data and the first ozone data, acquires the combination of the meteorological monitoring stations corresponding to different pre-selected meteorological data, establishes a second linear regression model respectively according to the pre-selected meteorological data and the first ozone data corresponding to each group of meteorological monitoring stations, and the second linear regression model is used for interpolating data, namely calculating the ozone data of the first air quality monitoring station through the pre-selected meteorological data corresponding to each group of meteorological monitoring stations.

The utility information of the second linear regression model is calculated by the equipment, the interpolation effect is identified by the utility information of the second linear regression model, and whether the obtained interpolation data are accurate or not can be determined through the utility information. The ozone data of the first air quality monitoring station at the non-data-missing moment and the true value at the moment can be calculated through the second linear regression model to determine the utility information.

Further, for accurate computational utility information, calculating utility information of the second linear regression model may include: error information of the second linear regression model is calculated from the first ozone data. The equipment calculates error information of the second linear regression model according to the first ozone data, and takes the error information as utility information, wherein the error information can comprise average absolute error and root mean square error:

the Mean Absolute Error (MAE) is calculated as follows:

is the complement of the ith missing value, y_iIs the corresponding true value, and n is the number of missing values of the missing variable.

The Root Mean Square Error (RMSE) is calculated as follows:

is the complement of the ith missing value, y_iIs the corresponding true value, and n is the number of missing values of the missing variable.

The average absolute error and the root mean square error reflect the error between the interpolation value and the true value, and the smaller the value is, the smaller the deviation between the interpolation value and the true value is, so that the better the interpolation effect is.

S10253: and using the pre-selected meteorological data corresponding to the utility information meeting the second preset utility condition as second meteorological data, and using the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

The method comprises the steps that a second preset utility condition is preset in the equipment, the second preset utility condition is used for screening second meteorological data, the equipment takes pre-selected meteorological data corresponding to utility information meeting the second preset utility condition as the second meteorological data, and candidate meteorological monitoring stations corresponding to the second meteorological data serve as second meteorological monitoring stations.

Further, another way of determining the second ozone data and the second meteorological data may be: establishing a third linear regression model according to the preselected ozone data, the preselected meteorological data and the first ozone data, namely calculating the ozone data of the first air quality monitoring station through the preselected meteorological data and the preselected ozone data which respectively correspond to each group of meteorological monitoring stations and air quality monitoring stations, calculating utility information of the third linear regression model, taking the preselected meteorological data corresponding to the utility information meeting a third preset utility condition as second meteorological data, and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station. And using the preselected ozone data corresponding to the utility information meeting the third preset utility condition as second ozone data, and using the candidate air quality monitoring station corresponding to the second ozone data as a second weather monitoring station.

S103: and constructing a multiple regression model by taking the first ozone data as a dependent variable and taking the second ozone data and the first meteorological data independent variable.

The equipment takes the first ozone data as a dependent variable and takes the second ozone data and the first meteorological data independent variable to construct a multiple regression model. The multiple regression model is a mathematical model (including correlation hypothesis) used for regression analysis, in which a regression model containing only one regression variable is called a unitary regression model, and is otherwise called a multiple regression model. For example, the multivariate regression model can be as follows:

Y＝aX₁+bX₂+cX₃+...+dX_m+eH₁+fH₂+...+gH_n+

y is the missing data of the first air quality monitoring station, X₁,X₂,X₃,...,X_mIs the second ozone data, H, of a second air monitoring station₁,H₂,...,H_nRespectively, the second meteorological data of the second meteorological station, and are constant terms, and a, b, c, d, e, f and g are coefficients of the terms.

S104: and interpolating the missing ozone monitoring data in the first ozone data based on the multiple regression model to obtain the complemented first ozone data.

The equipment interpolates the ozone monitoring data missing from the first ozone data based on the multiple regression model, and respectively supplements the calculation results of the corresponding moments to the first ozone data based on the moments corresponding to the ozone monitoring data missing from the first ozone data to obtain the supplemented first ozone data.

In the embodiment of the application, first ozone data to be supplemented of a first air quality monitoring station is obtained; acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station; constructing a multivariate regression model by taking the first ozone data as a dependent variable and the second ozone data and the first meteorological data independent variable; and interpolating the missing ozone monitoring data in the first ozone data based on the multiple regression model to obtain the complemented first ozone data. According to the scheme, the second ozone data of the second air quality monitoring station and the first meteorological data of the first meteorological monitoring station are obtained, the first ozone data is used as a dependent variable, the second ozone data and the first meteorological data independent variable are used for establishing a multiple regression model, ozone detection data missing in the first ozone data are calculated through the model, and therefore interpolation is conducted.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Referring to fig. 5, fig. 5 is a schematic diagram of an interpolation device for ozone depletion data according to a second embodiment of the present application. The units included are used to perform the steps in the embodiments corresponding to fig. 1-4. Please refer to the related description of the embodiments corresponding to fig. 1 to 4. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 5, the interpolation device 5 for ozone depletion data includes:

a first obtaining unit 510, configured to obtain first ozone data to be supplemented of a first air quality monitoring station;

a second obtaining unit 520, configured to obtain second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station;

a constructing unit 530, configured to construct a multiple regression model by using the first ozone data as a dependent variable and using the second ozone data and the first meteorological data independent variable;

the first processing unit 540 is configured to interpolate missing ozone monitoring data in the first ozone data based on the multiple regression model to obtain supplemented first ozone data.

Further, the second obtaining unit 520 is specifically configured to:

and taking the air quality monitoring station which is less than a first distance threshold value from the first air quality monitoring station as a second air quality monitoring station, and taking the weather monitoring station which is less than a second distance threshold value from the first air quality monitoring station as a first weather monitoring station to acquire second ozone data of the second air quality monitoring station and first weather data of the first weather monitoring station.

Further, the second obtaining unit 520 includes:

the second processing unit is used for taking the air quality monitoring station with the distance from the first air quality monitoring station smaller than a first distance threshold value as a candidate air quality monitoring station;

the third processing unit is used for taking the weather monitoring station with the distance from the first air quality monitoring station smaller than a second distance threshold value as a candidate weather monitoring station;

the third acquisition unit is used for acquiring candidate ozone data of each candidate air quality monitoring station and candidate meteorological data of each candidate meteorological monitoring station;

the fourth processing unit is used for taking candidate ozone data of which the linear correlation degree with the first ozone data meets a first preset correlation degree condition as second ozone data and taking a candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station;

and the fifth processing unit is used for taking the candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as second meteorological data and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

Further, the fourth processing unit includes:

a sixth processing unit, configured to take candidate ozone data whose linear correlation with the first ozone data satisfies a first preset correlation condition as pre-selected ozone data;

the first calculation unit is used for establishing a first linear regression model according to the pre-selected ozone data and the first ozone data and calculating the utility information of the first linear regression model;

and the seventh processing unit is used for taking the preselected ozone data corresponding to the utility information meeting the first preset utility condition as second ozone data and taking the candidate air quality monitoring station corresponding to the second ozone data as a second air quality monitoring station.

Further, the fifth processing unit includes:

the eighth processing unit is used for taking candidate meteorological data of which the linear correlation degree with the first ozone data meets a second preset correlation degree condition as pre-selected meteorological data;

the second calculation unit is used for establishing a second linear regression model according to the preselected meteorological data and the first ozone data and calculating utility information of the second linear regression model;

and the ninth processing unit is used for taking the pre-selected meteorological data corresponding to the utility information meeting the second preset utility condition as second meteorological data and taking the candidate meteorological monitoring station corresponding to the second meteorological data as a second meteorological monitoring station.

Further, the first calculating unit is specifically configured to:

error information of the first linear regression model is calculated according to the first ozone data.

Further, the second calculating unit is specifically configured to:

error information of the second linear regression model is calculated from the first ozone data.

Fig. 6 is a schematic diagram of an interpolation apparatus for ozone depletion data according to a third embodiment of the present application. As shown in fig. 6, the interpolation apparatus 6 of ozone depletion data of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and operable on said processor 60, such as an interpolation program for ozone depletion data. The processor 60 executes the computer program 62 to implement the steps in the above-mentioned interpolation method embodiment of ozone depletion data, such as steps 101 to 104 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 510 to 540 shown in fig. 5.

Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 62 in the interpolation device 6 for ozone depletion data. For example, the computer program 62 may be divided into a first acquisition unit, a second acquisition unit, a construction unit, and a first processing unit, and each unit specifically functions as follows:

the first acquisition unit is used for acquiring first ozone data to be supplemented of a first air quality monitoring station;

the second acquisition unit is used for acquiring second ozone data of a second air quality monitoring station and first weather data of a first weather monitoring station;

the construction unit is used for constructing a multiple regression model by taking the first ozone data as a dependent variable and taking the second ozone data and the first meteorological data independent variable;

and the first processing unit is used for interpolating the ozone monitoring data missing in the first ozone data based on the multiple regression model to obtain the complemented first ozone data.

The interpolation device for ozone missing data may include, but is not limited to, the processor 60 and the memory 61. It will be understood by those skilled in the art that fig. 6 is merely an example of the interpolation device 6 for ozone-missing data, and does not constitute a limitation to the interpolation device 6 for ozone-missing data, and may include more or less components than those shown, or some components may be combined, or different components, for example, the interpolation device for ozone-missing data may further include an input/output device, a network access device, a bus, etc.

The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 61 may be an internal storage unit of the interpolation apparatus 6 for ozone-missing data, such as a hard disk or a memory of the interpolation apparatus 6 for ozone-missing data. The memory 61 may also be an external storage device of the interpolation device 6 for ozone missing data, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the interpolation device 6 for ozone missing data. Further, the interpolation device 6 of the ozone deficiency data may also include both an internal storage unit and an external storage device of the interpolation device 6 of the ozone deficiency data. The memory 61 is used for storing the computer program and other programs and data required by the interpolation device of the ozone missing data. The memory 61 may also be used to temporarily store data that has been output or is to be output.

It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

An embodiment of the present application further provides a network device, where the network device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.

The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.

The embodiments of the present application provide a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), random-access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.