CN116821633A - Atmospheric pollution cause analysis method, apparatus, device and storage medium - Google Patents

Atmospheric pollution cause analysis method, apparatus, device and storage medium Download PDF

Info

Publication number
CN116821633A
CN116821633A CN202310969920.3A CN202310969920A CN116821633A CN 116821633 A CN116821633 A CN 116821633A CN 202310969920 A CN202310969920 A CN 202310969920A CN 116821633 A CN116821633 A CN 116821633A
Authority
CN
China
Prior art keywords
data
voc
target area
analysis
characteristic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310969920.3A
Other languages
Chinese (zh)
Inventor
邓英豪
黎臣
付湘鄂
戈云飞
胡超杰
张洁琼
李静
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Bowo Wisdom Technology Co ltd
Original Assignee
Shenzhen Bowo Wisdom Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Bowo Wisdom Technology Co ltd filed Critical Shenzhen Bowo Wisdom Technology Co ltd
Priority to CN202310969920.3A priority Critical patent/CN116821633A/en
Publication of CN116821633A publication Critical patent/CN116821633A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21Design, administration or maintenance of databases
    • G06F16/215Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25Integrating or interfacing systems involving database management systems
    • G06F16/252Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28Databases characterised by their database models, e.g. relational or object models
    • G06F16/284Relational databases
    • G06F16/285Clustering or classification
    • G06F16/287Visualization; Browsing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Quality & Reliability (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of environmental monitoring and discloses an atmospheric pollution cause analysis method, an atmospheric pollution cause analysis device, atmospheric pollution cause analysis equipment and a storage medium, wherein the method comprises the steps of receiving a user instruction and determining a target area; acquiring real-time monitoring data of a target area, and generating a related data set of the target area by combining a historical database; respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set, and displaying a corresponding analysis result in a chart form; feature correlation screening is carried out on the analysis result to obtain related feature data, and prediction emission reduction information is generated according to the related feature data; and combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area. According to the invention, the real-time data and the historical data are combined, and the analysis result is output in an intuitive chart form according to the selection condition of the user, so that the user can conveniently and rapidly acquire the atmospheric pollution cause information of the target area.

Description

Atmospheric pollution cause analysis method, apparatus, device and storage medium
Technical Field
The present invention relates to the field of environmental monitoring technologies, and in particular, to a method, an apparatus, a device, and a storage medium for analyzing atmospheric pollution factors.
Background
At present, with the development of technology, the problem of atmospheric pollution is increasingly prominent, the existing cause analysis of atmospheric pollution is mainly based on monitoring data of different sites, however, due to the fact that related detection data such as an air station, a weather station and a super station are numerous in sources, the problems of complex structure and large time span exist, the problems of complex calculation and long time consumption exist when data analysis is carried out, and the requirement that a user accurately and intuitively acquires the cause information of the atmospheric pollution in a certain area cannot be met immediately.
The foregoing is provided merely for the purpose of facilitating understanding of the technical scheme of the present invention and is not intended to represent an admission that the foregoing is related art.
Disclosure of Invention
The invention mainly aims to provide an atmospheric pollution cause analysis method, an atmospheric pollution cause analysis device, atmospheric pollution cause analysis equipment and a storage medium, and aims to solve the technical problem that when an regional atmospheric pollution analysis task is executed, the obtained analysis conclusion is complex because the obtained data sources are wide in types and various, and a user is difficult to quickly and accurately know the atmospheric pollution cause of a target region.
To achieve the above object, the present invention provides an atmospheric pollution cause analysis method comprising the steps of:
Receiving a user instruction and determining a target area;
acquiring real-time monitoring data of the target area, and generating a related data set of the target area by combining a historical database;
respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set, and displaying a corresponding analysis result in a chart form;
screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data;
and combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area.
Optionally, the data analysis based on the transmission process, the feature type and the feature source is performed on the related data set, and the corresponding analysis result is displayed in a chart form, which includes:
judging whether the region transmission phenomenon exists in the target region or not by adopting a preset algorithm in the related data set, and obtaining an analysis result based on region transmission;
classifying the data of the related data sets according to the characteristic pollutant types to generate regional characteristic table sets of corresponding characteristic types;
Extracting VOC data in the related data set and obtaining a target VOC type, and obtaining source information of a target area according to the target VOC type matching;
and visually displaying the analysis result based on the regional transmission, the regional characteristic table set and the source information.
Optionally, the extracting the VOC data in the related data set and obtaining a target VOC type, and obtaining source information of a target area according to the target VOC type matching includes:
acquiring VOC component factor concentration data and VOC related factor concentration data in the related data set, determining the relativity of each VOC related factor and the VOC component factor, and generating a relativity table;
selecting VOC component factors at preset positions in the correlation degree table as target VOC factors;
and obtaining a pollution source of the target area according to the target VOC factor matching, and obtaining source information.
Optionally, the screening for feature correlation of the analysis result to obtain feature related data, generating predicted emission reduction information according to the feature related data, including:
acquiring VOC data in a first preset time period based on the first preset time period;
And extracting active VOC factors in the VOC data, and obtaining predicted emission reduction information according to the active VOC factors and the target VOC factors.
Optionally, in the related data set, a preset algorithm is adopted to determine whether a region transmission phenomenon exists in the target region, so as to obtain an analysis result based on region transmission, including:
extracting regional history data in the related data set to determine a background value interval;
acquiring a current reference mean value according to real-time monitoring data by adopting a preset algorithm, and comparing the current reference mean value with the background value interval;
judging whether the region transmission phenomenon exists in the target region according to the comparison result, and obtaining an analysis result based on region transmission.
Optionally, the classifying the related data set according to the characteristic pollutant category to generate a regional characteristic table set corresponding to the characteristic category includes:
classifying the data of the related data set according to the characteristic pollutant category to obtain conventional monitoring data and VOC component data;
generating a weather analysis chart, a pollutant change characteristic chart and a site analysis chart of the target area according to the conventional monitoring data;
and generating a VOC component change trend table according to the VOC component data based on a second preset time period.
Optionally, the acquiring the real-time monitoring data of the target area and combining the historical database to generate a related data set of the target area includes:
based on a preset template, carrying out data standardization on the real-time monitoring data of the target area and a historical database to obtain an initial data set;
and cleaning the data of the initial data set for abnormality screening to obtain a related data set of the target area.
In addition, in order to achieve the above object, the present invention also provides an atmospheric pollution cause analysis device comprising:
the area determining module is used for receiving a user instruction and determining a target area;
the data management module is used for acquiring real-time monitoring data of the target area and generating a related data set of the target area by combining a historical database;
the chart display module is used for respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set and displaying a corresponding analysis result in a chart form;
the effect evaluation module is used for screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data;
And the cause report module is used for combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area.
In addition, in order to achieve the above object, the present invention also proposes an atmospheric pollution cause analysis apparatus comprising: a memory, a processor, and an atmospheric pollution cause analysis program stored on the memory and executable on the processor, the atmospheric pollution cause analysis program configured to implement the steps of the atmospheric pollution cause analysis method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon an atmospheric pollution cause analysis program which, when executed by a processor, implements the steps of the atmospheric pollution cause analysis method as described above.
Firstly, receiving a user instruction and determining a target area; acquiring real-time monitoring data of the target area, and generating a related data set of the target area by combining a historical database; then, respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set, and displaying a corresponding analysis result in a chart form; further, screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data; and finally, combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area. According to the invention, the real-time data and the historical data are combined to be used as a data set, the required analysis statistical data is rapidly output according to the selection condition of the user, and is displayed in an intuitive chart form, and the comprehensive area analysis report is obtained, so that the efficiency of data carding statistics is improved, and meanwhile, the capability of data analysis is improved, and the user can conveniently and rapidly acquire the atmospheric pollution cause information of the target area.
Drawings
FIG. 1 is a schematic diagram of an atmospheric pollution cause analysis device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the method for analyzing atmospheric pollution cause according to the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of the method for analyzing atmospheric pollution cause according to the present invention;
FIG. 4 is a table showing the correlation degree between each VOC constituent factor and each VOC-related factor in the second embodiment of the method for analyzing atmospheric pollution cause according to the present invention;
FIG. 5 is a schematic flow chart of a third embodiment of an atmospheric pollution cause analysis method according to the present invention;
FIG. 6 is a schematic diagram of an atmospheric pollution cause analysis based on the classification of regional transport, pollution characteristics, and pollution sources in a third embodiment of the atmospheric pollution cause analysis method of the present invention;
FIG. 7 is a block diagram showing the construction of a first embodiment of an apparatus for analyzing an atmospheric pollution cause according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an atmospheric pollution cause analysis device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the atmospheric pollution cause analysis apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is not limiting of the atmospheric pollution cause analysis device and may include more or fewer components than shown, or certain components in combination, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and an atmospheric pollution cause analysis program may be included in the memory 1005 as one type of storage medium.
In the atmospheric pollution cause analysis device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the atmospheric pollution cause analysis apparatus of the present invention may be provided in an atmospheric pollution cause analysis apparatus which calls an atmospheric pollution cause analysis program stored in the memory 1005 through the processor 1001 and executes the atmospheric pollution cause analysis method provided by the embodiment of the present invention.
An embodiment of the invention provides an atmospheric pollution cause analysis method, referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the atmospheric pollution cause analysis method of the invention.
In this embodiment, the method for analyzing the atmospheric pollution cause includes the following steps:
step S10: and receiving a user instruction and determining a target area.
It should be noted that, the execution body of the method of the present embodiment may be a computer service device with functions of screen display, data processing, network communication and program operation, for example, a mobile phone, a tablet computer, a personal computer, an atmosphere analyzer, an atmosphere pollution detection device, or other electronic devices capable of implementing the same or similar functions and implementing an atmosphere pollution cause analysis method, which is not limited in this embodiment. The embodiments of the method for analyzing the atmospheric pollution cause of the present invention will be described by taking an atmospheric pollution cause analysis device (hereinafter referred to as an analysis device) as an example.
It can be understood that the user instruction may be an operation instruction triggered by the user on the display interface of the current analysis device, where the user may select a target area where the atmospheric pollution cause information needs to be acquired and set the time range.
It should be understood that the target area may be selected in units of provinces and cities divided by administrative regions, or may be further located to a specific area based on the administrative divided regions, which is not limited in this embodiment.
Step S20: and acquiring real-time monitoring data of the target area, and generating a related data set of the target area by combining a historical database.
It will be appreciated that the real-time monitoring data of the target area may be data obtained from atmospheric monitoring data stations of different levels or different pollution types, such as urban stations, meteorological stations, super stations, etc., where the data from different sources may be associated by the city, county or directly by geographic location, based on longitude and latitude, and associated with the air station corresponding to the target area. Wherein the monitoring data obtained from the city station may include, but is not limited to: SO (SO) 2 、NO 2 、PM10、PM2.5、CO、O 3 Common atmospheric pollutants are equal; the monitoring data obtained from the weather station may include, but is not limited to: temperature, humidity, air pressure, wind speed, wind direction, etc.; the monitoring data obtained from the superstation may include volatile organic compounds (Volatile Organic Compounds, VOCs) composition data.
When the real-time monitoring data is obtained from each site, the historical data in recent years can be obtained, and the relevant data set with richer and more reliable target area can be obtained through the combination of the real-time monitoring data and the historical database and can be stored in a data warehouse so as to be directly called when the area analysis of other areas related to the current target area is carried out later.
Further, considering the diversity of sources of the acquired data, the time consuming for performing the analysis of the multi-source data is long, and the acquired multi-source data may be cleaned and standardized, step S20 includes:
step S201: and based on a preset template, carrying out data standardization on the real-time monitoring data of the target area and a historical database to obtain an initial data set.
It can be appreciated that the preset template can be a unified form template, and when multi-source data is imported, data is standardized through the preset template, so that different data can be classified based on sources and types, and further processing of the data can be performed later.
Step S202: and cleaning the data of the initial data set for abnormality screening to obtain a related data set of the target area.
It should be understood that after receiving the standardized initial data set, the data set may be screened for repeated data, abnormal data, non-digital data and other messy code data, so as to realize preliminary verification and quality inspection of the data and facilitate subsequent further analysis and processing of the data.
Step S30: and respectively carrying out data analysis based on the transmission process, the characteristic type and the characteristic source on the related data set, and displaying the corresponding analysis result in a chart form.
It should be noted that the analysis based on the transmission process may be an analysis of whether the pollution phenomenon existing in the target area is mainly transmitted in the area or mainly discharged locally; the analysis based on the characteristic types can be respectively based on the pollutant types of different types to carry out analysis of the combination time characteristic and the climate characteristic; the analysis based on the characteristic sources can be pollution influence correlation analysis according to different pollutant types, and particularly can be used for carrying out correlation screening on VOCs components and matching the analysis of corresponding pollution source industries.
Further, considering that the analysis results obtained by performing different analyses on the data set may be displayed in a plurality of different forms, in order to facilitate the user to obtain the analysis results more intuitively, step S30 includes:
step S301: and in the related data set, judging whether the region transmission phenomenon exists in the target region by adopting a preset algorithm, and obtaining an analysis result based on region transmission.
It will be appreciated that the data of a specific contaminant type may be selected to determine whether there is a zone transport phenomenon in the target zone, and thus to derive whether the contamination in the target zone is primarily zone transport or is primarily local emission, where the contaminant type may be O 3 Xylene, SO 2 And the like, and selecting a corresponding preset algorithm based on the selected pollutant type.
The preset algorithm may be a texas environmental quality committee (Texas Commission on Environmental Quality, TCEQ) method, an ozone background value method, and a characteristic pollutant method.
It should be appreciated that the TCEQ method is based on background point measurement and may be used to correlate data set with O 3 The related minimum value of the moving average of the maximum of the ozone day and the eight hours is taken as the external transmission ozone, the maximum average value of the moving average of the ozone day and the eight hours is taken as the maximum ozone concentration of the area, and the difference between the maximum value and the minimum value of the moving average of the ozone day and the eight hours can obtain the local emission capability of the current area, so as to judge whether the pollution in the target area is mainly transmitted by the area or mainly emitted by the local area.
It can be understood that the ozone background value method can be to extract historical ozone data in a related data set, calculate to obtain an ozone background value, compare the ozone background value with ozone data monitored in real time, and obtain a contribution ratio of regional transmission and local emission to pollution in a target region based on time; the characteristic pollutant ratio method can select hours or days as a minimum unit, count the overall situation in a certain period of time, and set a standard ratio threshold value number, so as to obtain the contribution degree of regional transmission situations or local emission situations to pollution in a target region, for example: if the characteristic pollutant is xylene or benzene, the contribution of local emission to the pollution in the target area can be considered to be larger when the ratio of the xylene to the benzene is larger than 1.1 in a certain time, and the contribution of area transmission to the pollution in the target area can be considered to be larger when the ratio of the xylene to the benzene is smaller than 1.1.
Step S302: and carrying out data classification on the related data set according to the characteristic pollutant category to generate a regional characteristic table set corresponding to the characteristic category.
It can be appreciated that, considering the source and variety of data in the data set, the multi-dimensional analysis based on time, space, place, climate and the like can be performed on different types of data based on the intrinsic characteristics of the characteristic pollutants corresponding to the data, and various different types of visual display charts such as a histogram, a table, a line graph, a rose graph and the like are generated, so as to obtain a characteristic table set of a target region.
Step S303: and extracting the VOC data in the related data set, obtaining a target VOC type, and obtaining source information of a target area according to the target VOC type matching.
It will be appreciated that the VOC data is data related to volatile organic compounds in the relevant data set, and the target VOC species for matching the source of the contaminant can be determined by the correlation between each specific VOC component factor in the VOC data and other characteristic contaminants in the relevant data set, such as ozone or nitrogen oxides, so as to match the source of the contaminant in the target area.
It can be appreciated that a matching table of VOC types and pollutant sources can be pre-established, the matching table can include mapping relations between different VOC types and pollutant source industries, and the mapping relation can be determined by summarizing based on social experience values. For example, when the VOC type is formaldehyde, the pollution source industry corresponding to formaldehyde is the decoration industry.
Step S304: and visually displaying the analysis result based on the regional transmission, the regional characteristic table set and the source information.
In a specific implementation, the analysis results of each type obtained above may be used as a tag index by means of transmission analysis, pollution feature analysis and pollution source analysis, and the analysis results based on regional transmission, the regional feature table set and the source information are respectively displayed under the corresponding tags, so that the user can learn the atmospheric pollution cause information of the target region from multiple dimensions based on personalized selection.
Further, in order to facilitate the user to learn the trend of the atmospheric pollution cause of the target area within a certain time period, the daily air quality condition can be displayed in the form of an air calendar based on a time range preset by the user, the display grid with the day as the minimum unit in the calendar can contain the daily air quality level, the atmospheric pollutant type with the highest contribution degree, and the like, so that the user can intuitively learn the overall atmospheric pollution profile of the target area within the selected time period.
Further, the selection can be based on the personalized selection of the user on the basis of the generated air calendarInstructions for further extracting site monitoring data from the relevant data sets, generating an air quality index (Air Quality Index, AQI) ranking ring map, a primary pollutant ring map, a common six pollutants (SO) in combination with air quality index (Air Quality Index, AQI) ranking for a selected time period 2 、NO 2 、PM10、PM2.5、CO、O 3 ) A variation histogram and a station-to-station variation histogram.
Step S40: and screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data.
It can be understood that when the above-mentioned multi-dimensional analysis data of the atmospheric pollution of the target area displayed in different forms are obtained, various pollutants forming the atmospheric pollution of the target area can be subjected to correlation screening, multiple types of characteristic pollutants with high contribution to the atmospheric pollution of the area can be obtained as main pollutants of the target area, the common six pollutants and components of VOCs can be included, and the numerical data of the main characteristic pollutants of the target area can be specifically obtained as related characteristic data, so as to determine the implementation effect of the emission reduction measure for the main characteristic pollutants, and generate the predicted emission reduction information.
Step S50: and combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area.
In a specific implementation, the analysis device integrates the air pollution analysis data of the multi-dimensional target area with the implementation effect of the emission reduction measure adopted by the current target area, so that a detailed air pollution cause analysis report table covering all the dimensional analysis data and emission reduction information of the target area can be obtained, and a general comprehensive analysis conclusion of the target area can be obtained, for example: in the area A, the main characteristic pollutant is ozone in a selected time period, the total ozone exceeds the standard for 4 days, and under the condition that the meteorological conditions do not change greatly, the relevant pollution emission sources corresponding to the active species are accurately controlled, and when the emission reduction ratio is 100%, the peak of the ozone can be reduced for 4 days theoretically.
Firstly, receiving a user instruction and determining a target area; acquiring real-time monitoring data of the target area, and generating a related data set of the target area by combining a historical database; then, respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set, and displaying a corresponding analysis result in a chart form; further, screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data; and finally, combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area. In the embodiment, the real-time data and the historical data are combined to be used as the data set, the required analysis statistical data is rapidly output according to the selection condition of the user, and is displayed in an intuitive chart form, the comprehensive area analysis report is obtained, the efficiency of data carding statistics is improved, the data analysis capability is improved, and the user can conveniently and rapidly acquire the atmospheric pollution cause information of the target area.
Referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the method for analyzing atmospheric pollution cause according to the present invention.
Based on the above embodiment, step S303, for further analysis according to the pollution source, includes:
step S3031: and acquiring the VOC component factor concentration data and the VOC related factor concentration data in the related data set, determining the correlation of each VOC related factor and the VOC component factor, and generating a correlation degree table.
It is understood that the VOC component factor may include alkanes, alkenes, aromatics, halogenated hydrocarbons, alkynes, natural sources, and others, and the VOC related factor may be ozone (O 3 ) Or Nitrogen Oxides (NO) x ) Etc.
Step S3032: and selecting the VOC component factors at preset positions in the correlation degree table as target VOC factors.
It should be noted that each VOC component factor and VOC related factor (O 3 And NO x ) Concentration within a user selected time periodData, counting concentration average values of all factors according to the hour data group, and calculating daily average concentration; then, based on the concentration data, each VOC component factor and O are calculated 3 And NO x Screening out high correlation (|R|correlation)>0.5 A correlation table is obtained, and the daily variation concentration difference of the VOCs species of each VOC component factor can be further calculated.
It should be understood that referring to FIG. 4, FIG. 4 is a table of the correlation of VOC component factors with VOC related factors, where A in FIG. 4 is a specific individual VOC component factor with O 3 In FIG. 4, B is a table of correlation between the VOC component factors and NO x Is related to the degree of correlation table of (2); and screening out VOC component factors with high correlation with ozone and high correlation with nitrogen oxides to obtain active VOCs species, namely target VOC factors. Referring to table 1, table 1 is a table of target VOC factors shown in graphic form, and based on the correlation in fig. 4, the target VOC factors in table 1 are propane, ethylene, n-butane, isobutane, ethane, toluene, propylene, and benzene.
TABLE 1 target VOC factor tables
Step S3033: and obtaining a pollution source of the target area according to the target VOC factor matching, and obtaining source information.
It is understood that the source of pollution may be the industry of possible sources of atmospheric pollution including, but not limited to: motor vehicles, oil volatilization, petrochemical industry, fire coal, printing, packaging industry and the like.
In a specific implementation, after the target VOC factor is obtained, the source industry corresponding to the target VOC factor can be determined based on a matching table of the VOC type and the pollutant source, and source information can be obtained.
Further, when the target VOC factor, i.e., the active VOCs species and the daily variation difference of the VOCs species are obtained, the abatement concentration may be calculated according to the abatement ratio, and thus, step S40 includes:
step S401: and acquiring VOC data in a first preset time period based on the first preset time period.
It will be appreciated that the first predetermined period of time may be O in a period of time that is selectively set by the user on the analysis device 3 The VOC data in the first period may be data corresponding to the VOC composition factor and the VOC related factor described above.
Step S402: and extracting active VOC factors in the VOC data, and obtaining predicted emission reduction information according to the active VOC factors and the target VOC factors.
It will be appreciated that in the screening to obtain O 3 After the data of specific days with the concentration exceeding the standard, the data of daily control time, active components, reduction amount, expected effect and the like can be calculated and obtained, and the remarks of peak clipping or good preservation are correspondingly carried out.
The control time range calculation method comprises the following steps: calculating average value of 24 hours from the past year synchronization history data of the selected time range, calculating change amount of each hour, namely change amount of current hour compared with the last hour, (current hour concentration-last hour concentration) ×100/last hour concentration, and judging O 3 The hour value is larger than the hour average value and the hour variation is larger than the hour corresponding to the variation average value, and the hour value is taken as the starting time of control; the moment when the radiation intensity starts to decrease and when the radiation intensity at a later moment no longer increases, this time serves as the end time of the control.
The screening method of the active components, namely the active VOC factors, can be as follows: selecting a specific day in the display interface of the analysis device, and acquiring the VOC component factors of the day and the VOC related factors (O 3 、NO X ) And filtering out high-correlation component factors and taking intersections as active VOC factors, and displaying the high-correlation component factors in a chart form in a display interface of analysis equipment so that a user can intuitively obtain the correlation among the factors.
Wherein, the reduction amount can be ozone reduction amount, is the difference of active components and is emission reduction ratio, and the expected effect is the value of the concentration of ozone in the day-ozone reduction amount. The final remark is to determine if the expected outcome is greater than 160 (national secondary standard), if so, peak clipping, otherwise, warranty.
In a specific implementation, the analysis device is at O 3 And (3) obtaining VOC data in specific days with the concentration exceeding the standard, calculating the reduction concentration according to parameters such as the control time and the reduction amount based on the active VOC factors to obtain expected results, comparing the expected results with the national standard, further judging peak clipping or quality assurance, and finally counting specific days of peak clipping and quality assurance in a time period selected and set by a user to obtain predicted emission reduction information, so that the control effect on different atmospheric pollution causes of a target area can be obtained.
In the embodiment, the VOC component factor concentration data and the VOC related factor concentration data in the related data set are obtained, the relativity of each VOC related factor and the VOC component factor is determined, and a relativity degree table is generated; selecting VOC component factors at preset positions in the correlation degree table as target VOC factors; obtaining a pollution source of the target area according to the target VOC factor matching, obtaining source information, and obtaining VOC data in a first preset time period based on the first preset time period; and extracting active VOC factors in the VOC data, and obtaining predicted emission reduction information according to the active VOC factors and the target VOC factors. The analysis of the target area based on the pollution source can be realized, and the evaluation of the emission reduction effect of the target area can be realized based on specific VOC component factors in the pollution source, so that a user can intuitively acquire the atmospheric pollution information of the target area from multiple angles.
Referring to fig. 5, fig. 5 is a schematic flow chart of a third embodiment of the method for analyzing atmospheric pollution cause according to the present invention.
Based on the above embodiment, it is determined whether or not there is a zone transport phenomenon in the target zone in consideration of the data that can select a specific contaminant type, wherein O 3 For a significantly representative contaminant species, the ozone background value method may be specifically selected to analyze the relevant data set based on the angle of regional transmission, step S301 includes:
step S3011: and extracting the area history data in the related data set to determine a background value interval.
Step S3012: and obtaining a current reference mean value according to the real-time monitoring data by adopting a preset algorithm, and comparing the current reference mean value with the background value interval.
Step S3013: judging whether the region transmission phenomenon exists in the target region according to the comparison result, and obtaining an analysis result based on region transmission.
In a specific implementation, firstly, an O3 background value interval of a corresponding level can be found in the area history data through AQI data corresponding to a target day, and then O corresponding to the target day is calculated 3 The background value average value is used as a current reference average value; and comparing the current reference mean value with the acquired background value interval, and if the current reference mean value is larger than the upper limit of the background value interval, possibly causing an area transmission phenomenon in the target day.
Further, considering the source and variety of the data in the data set, the data of different types may be classified according to the intrinsic characteristics of the characteristic contaminant corresponding to the data, so that the user obtains a clearer classified analysis result, step S302 includes:
Step S3021: and classifying the data of the related data set according to the characteristic pollutant category to obtain conventional monitoring data and VOC component data.
Step S3022: and generating a weather analysis graph, a pollutant change characteristic graph and a site analysis graph of the target area according to the conventional monitoring data.
Step S3023: and generating a VOC component change trend table according to the VOC component data based on a second preset time period.
It is understood that the second preset time period may be a time period set secondarily by the user on the display interface of the analysis apparatus, or may be a continuous time automatically acquired based on a time range set when the user initially selects a target area in which the atmospheric pollution cause information needs to be acquired.
In particular, reference may be made to fig. 6, which is a schematic diagram of an atmospheric pollution cause analysis based on regional transport, pollution characteristics, and categories of pollution sources.
The area transmission analysis can adopt a TCEQ method, an ozone background value method and a characteristic pollutant ratio method for judgment; the pollution characteristic analysis can be further divided into conventional monitoring data analysis and superstation data analysis, wherein the conventional monitoring data analysis can be displayed from the angles of weather analysis (shown by wind rose diagram and weather change diagram), change characteristics (primary pollutant and fine rate analysis; 6 conventional pollutant concentration, daily change and homonymous change diagrams), station analysis and ozone and weather parameter correlation analysis tables, and the superstation analysis can be displayed from the angles of VOCs concentration composition, overall change and dominant species screening and daily change analysis; the pollution source analysis can be selected from the group consisting of active VOCs (SOA potential analysis, ozone and precursor VOCs and NO) 2 Correlation analysis (screening of species with higher correlation, determination of active species, judgment of industry source according to active species, evaluation of emission reduction effect), characteristic pollutant ratio traceability analysis, and remote sensing inversion of ozone precursors, and can generate ozone VOCs and NO 2 Pollution rose diagram.
The embodiment determines a background value interval by extracting the area history data in the related data set; acquiring a current reference mean value according to real-time monitoring data by adopting a preset algorithm, and comparing the current reference mean value with the background value interval; whether the region transmission phenomenon exists in the target region is judged according to the comparison result, an analysis result based on the region transmission is obtained, and whether the atmospheric pollution in the target region is mainly transmitted in the region or mainly discharged locally can be accurately judged. Data classification is carried out on the related data sets according to the characteristic pollutant types, and conventional monitoring data and VOC component data are obtained; generating a weather analysis chart, a pollutant change characteristic chart and a site analysis chart of the target area according to the conventional monitoring data; based on a second preset time period, the VOC component change trend table is generated according to the VOC component data, so that different types of data can be gathered and correlated, analysis of different dimensions can be performed, and the air pollution cause information of a target area can be conveniently and rapidly acquired by a user while multi-source data integration is realized.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon an atmospheric pollution cause analysis program which, when executed by a processor, implements the steps of the atmospheric pollution cause analysis method as described above.
Because the storage medium adopts all the technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are not described in detail herein.
Referring to fig. 7, fig. 7 is a block diagram showing the construction of a first embodiment of the atmospheric pollution cause analyzing apparatus of the present invention.
As shown in fig. 7, the atmospheric pollution cause analysis device according to the embodiment of the present invention includes:
the area determining module 701 is configured to receive a user instruction, and determine a target area;
the data management module 702 is configured to obtain real-time monitoring data of the target area, and combine the real-time monitoring data with a history database to generate a related data set of the target area;
the chart display module 703 is configured to perform data analysis based on a transmission process, a feature type, and a feature source on the related data set, and display a corresponding analysis result in a chart form;
The effect evaluation module 704 is configured to perform feature correlation screening on the analysis result to obtain feature related data, and generate predicted emission reduction information according to the feature related data;
and the cause report module 705 is configured to combine the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area.
Firstly, receiving a user instruction and determining a target area; acquiring real-time monitoring data of the target area, and generating a related data set of the target area by combining a historical database; then, respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set, and displaying a corresponding analysis result in a chart form; further, screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data; and finally, combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area. In the embodiment, the real-time data and the historical data are combined to be used as the data set, the required analysis statistical data is rapidly output according to the selection condition of the user, and is displayed in an intuitive chart form, the comprehensive area analysis report is obtained, the efficiency of data carding statistics is improved, the data analysis capability is improved, and the user can conveniently and rapidly acquire the atmospheric pollution cause information of the target area.
Based on the first embodiment of the atmospheric pollution cause analysis device according to the present invention, a second embodiment of the atmospheric pollution cause analysis device according to the present invention is proposed.
In this embodiment, the chart display module 703 is configured to determine, in the relevant data set, whether a region transmission phenomenon exists in the target region by using a preset algorithm, so as to obtain an analysis result based on region transmission; classifying the data of the related data sets according to the characteristic pollutant types to generate regional characteristic table sets of corresponding characteristic types; extracting VOC data in the related data set and obtaining a target VOC type, and obtaining source information of a target area according to the target VOC type matching; and visually displaying the analysis result based on the regional transmission, the regional characteristic table set and the source information.
Further, the chart display module 703 is further configured to obtain VOC component factor concentration data and VOC related factor concentration data in the related data set, determine a correlation between each VOC related factor and the VOC component factor, and generate a correlation degree table; selecting VOC component factors at preset positions in the correlation degree table as target VOC factors; and obtaining a pollution source of the target area according to the target VOC factor matching, and obtaining source information.
An effect evaluation module 704, configured to obtain VOC data in a first preset time period based on the first preset time period; and extracting active VOC factors in the VOC data, and obtaining predicted emission reduction information according to the active VOC factors and the target VOC factors.
Further, the chart presentation module 703 is further configured to extract the region history data in the related dataset to determine a background value interval; acquiring a current reference mean value according to real-time monitoring data by adopting a preset algorithm, and comparing the current reference mean value with the background value interval; judging whether the region transmission phenomenon exists in the target region according to the comparison result, and obtaining an analysis result based on region transmission.
Further, the chart display module 703 is further configured to perform data classification on the related data set according to the characteristic contaminant category, so as to obtain conventional monitoring data and VOC component data; generating a weather analysis chart, a pollutant change characteristic chart and a site analysis chart of the target area according to the conventional monitoring data; and generating a VOC component change trend table according to the VOC component data based on a second preset time period.
The data management module 702 is configured to perform data standardization on the real-time monitoring data of the target area and the historical database based on a preset template, so as to obtain an initial data set; and cleaning the data of the initial data set for abnormality screening to obtain a related data set of the target area.
Other embodiments or specific implementation manners of the atmospheric pollution cause analysis device of the present invention may refer to the above method embodiments, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (10)

1. A method of analyzing atmospheric pollution causes, the method comprising:
receiving a user instruction and determining a target area;
acquiring real-time monitoring data of the target area, and generating a related data set of the target area by combining a historical database;
respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set, and displaying a corresponding analysis result in a chart form;
screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data;
and combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area.
2. The atmospheric pollution cause analysis method according to claim 1, wherein the performing data analysis on the related data sets based on a transmission process, a feature type, and a feature source, respectively, graphically displaying a corresponding analysis result, comprises:
Judging whether the region transmission phenomenon exists in the target region or not by adopting a preset algorithm in the related data set, and obtaining an analysis result based on region transmission;
classifying the data of the related data sets according to the characteristic pollutant types to generate regional characteristic table sets of corresponding characteristic types;
extracting VOC data in the related data set and obtaining a target VOC type, and obtaining source information of a target area according to the target VOC type matching;
and visually displaying the analysis result based on the regional transmission, the regional characteristic table set and the source information.
3. The atmospheric pollution cause analysis method according to claim 2, wherein the extracting the VOC data in the related dataset and obtaining a target VOC class, and obtaining source information of a target area according to the target VOC class matching comprises:
acquiring VOC component factor concentration data and VOC related factor concentration data in the related data set, determining the relativity of each VOC related factor and the VOC component factor, and generating a relativity table;
selecting VOC component factors at preset positions in the correlation degree table as target VOC factors;
And obtaining a pollution source of the target area according to the target VOC factor matching, and obtaining source information.
4. The method of claim 3, wherein the step of performing feature correlation screening on the analysis result to obtain feature-related data, and generating predicted emission reduction information based on the feature-related data comprises:
acquiring VOC data in a first preset time period based on the first preset time period;
and extracting active VOC factors in the VOC data, and obtaining predicted emission reduction information according to the active VOC factors and the target VOC factors.
5. The method of claim 2, wherein the determining whether the zone transmission phenomenon exists in the target zone in the relevant dataset by using a preset algorithm to obtain the analysis result based on zone transmission comprises:
extracting regional history data in the related data set to determine a background value interval;
acquiring a current reference mean value according to real-time monitoring data by adopting a preset algorithm, and comparing the current reference mean value with the background value interval;
judging whether the region transmission phenomenon exists in the target region according to the comparison result, and obtaining an analysis result based on region transmission.
6. The atmospheric pollution cause analysis method of claim 2, wherein the data classifying the related data set according to the characteristic pollutant class to generate a regional characteristic table set of corresponding characteristic class comprises:
classifying the data of the related data set according to the characteristic pollutant category to obtain conventional monitoring data and VOC component data;
generating a weather analysis chart, a pollutant change characteristic chart and a site analysis chart of the target area according to the conventional monitoring data;
and generating a VOC component change trend table according to the VOC component data based on a second preset time period.
7. The method of claim 1, wherein the acquiring real-time monitoring data of the target area and combining a history database to generate a related dataset of the target area comprises:
based on a preset template, carrying out data standardization on the real-time monitoring data of the target area and a historical database to obtain an initial data set;
and cleaning the data of the initial data set for abnormality screening to obtain a related data set of the target area.
8. An atmospheric pollution cause analysis device, the device comprising:
The area determining module is used for receiving a user instruction and determining a target area;
the data management module is used for acquiring real-time monitoring data of the target area and generating a related data set of the target area by combining a historical database;
the chart display module is used for respectively carrying out data analysis based on a transmission process, a characteristic type and a characteristic source on the related data set and displaying a corresponding analysis result in a chart form;
the effect evaluation module is used for screening the characteristic correlation of the analysis result to obtain related characteristic data, and generating prediction emission reduction information according to the related characteristic data;
and the cause report module is used for combining the analysis result with the predicted emission reduction information to generate an atmospheric pollution cause analysis report of the target area.
9. An atmospheric pollution cause analysis apparatus, the apparatus comprising: a memory, a processor and an atmospheric pollution cause analysis program stored on the memory and executable on the processor, the atmospheric pollution cause analysis program configured to implement the steps of the atmospheric pollution cause analysis method of any one of claims 1 to 7.
10. A storage medium having stored thereon an atmospheric pollution cause analysis program which, when executed by a processor, implements the steps of the atmospheric pollution cause analysis method according to any one of claims 1 to 7.
CN202310969920.3A 2023-08-01 2023-08-01 Atmospheric pollution cause analysis method, apparatus, device and storage medium Pending CN116821633A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310969920.3A CN116821633A (en) 2023-08-01 2023-08-01 Atmospheric pollution cause analysis method, apparatus, device and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310969920.3A CN116821633A (en) 2023-08-01 2023-08-01 Atmospheric pollution cause analysis method, apparatus, device and storage medium

Publications (1)

Publication Number Publication Date
CN116821633A true CN116821633A (en) 2023-09-29

Family

ID=88114654

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310969920.3A Pending CN116821633A (en) 2023-08-01 2023-08-01 Atmospheric pollution cause analysis method, apparatus, device and storage medium

Country Status (1)

Country Link
CN (1) CN116821633A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117875576A (en) * 2024-03-13 2024-04-12 四川国蓝中天环境科技集团有限公司 Urban atmosphere pollution analysis method based on structured case library

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117875576A (en) * 2024-03-13 2024-04-12 四川国蓝中天环境科技集团有限公司 Urban atmosphere pollution analysis method based on structured case library
CN117875576B (en) * 2024-03-13 2024-05-24 四川国蓝中天环境科技集团有限公司 Urban atmosphere pollution analysis method based on structured case library

Similar Documents

Publication Publication Date Title
CN111614690B (en) Abnormal behavior detection method and device
US10515308B2 (en) System, method and cloud-based platform for predicting energy consumption
CN116821633A (en) Atmospheric pollution cause analysis method, apparatus, device and storage medium
CN111474307A (en) Pollutant tracing method and device, computer equipment and storage medium
CN107436277A (en) The single index data quality control method differentiated based on similarity distance
CN111930864B (en) Grid list optimization method and device based on monitoring data
Sancho et al. New methodology to determine air quality in urban areas based on runs rules for functional data
CN113537563B (en) Pollution emergency management and control effect evaluation method and device
US20180165845A1 (en) Method of Analysis of Visualised Data
CN113155939A (en) Online volatile organic compound source analysis method, system, equipment and medium
CN116739388B (en) Emission reduction measure evaluation method, device and storage medium
CN117007476B (en) Environment-friendly intelligent terminal data acquisition system based on Internet of things
Hodoli et al. Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana
CN110942319A (en) Method and device for modeling analysis and prediction of product claim data
CN113225391B (en) Atmospheric environment monitoring quality monitoring method based on sliding window anomaly detection and computing equipment
CN116205528A (en) Illegal construction identification method and system based on construction site power data
Morsi Electronic noses for monitoring environmental pollution and building regression model
Gómez-Losada Forecasting ozone threshold exceedances in urban background areas using supervised classification and easy-access information
CN112711911B (en) Rapid pollution tracing method applied to boundary observation based on pollution source spectrum library
CN114169794A (en) Method and device for evaluating contamination possibility, computer device and storage medium
CN113362069A (en) Dynamic adjustment method, device and equipment of wind control model and readable storage medium
CN114136342A (en) Mileage tampering judgment method and system
CN111931414A (en) Enterprise smoke emission data detection method and system based on big data analysis
CN114090411B (en) Application data analysis method, device and equipment and readable storage medium
CN115271548B (en) Secondary pollutant source analysis method and device and electronic equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination