CN117129638A - Regional air environment quality monitoring method and system - Google Patents

Regional air environment quality monitoring method and system Download PDF

Info

Publication number
CN117129638A
CN117129638A CN202311396620.7A CN202311396620A CN117129638A CN 117129638 A CN117129638 A CN 117129638A CN 202311396620 A CN202311396620 A CN 202311396620A CN 117129638 A CN117129638 A CN 117129638A
Authority
CN
China
Prior art keywords
monitoring
data
area
target
air
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.)
Granted
Application number
CN202311396620.7A
Other languages
Chinese (zh)
Other versions
CN117129638B (en
Inventor
冷健雄
彭戈
吴敏
黄庆发
陶靓
张初华
熊群群
朱海
吴启峰
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.)
Jiangxi Esun Environmental Protection Co ltd
Original Assignee
Jiangxi Esun Environmental Protection 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 Jiangxi Esun Environmental Protection Co ltd filed Critical Jiangxi Esun Environmental Protection Co ltd
Priority to CN202311396620.7A priority Critical patent/CN117129638B/en
Publication of CN117129638A publication Critical patent/CN117129638A/en
Application granted granted Critical
Publication of CN117129638B publication Critical patent/CN117129638B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • G01N33/0009General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0062General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06Investigating concentration of particle suspensions
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01WMETEOROLOGY
    • G01W1/00Meteorology

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Medicinal Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Dispersion Chemistry (AREA)
  • Combustion & Propulsion (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a regional air environment quality monitoring method and a regional air environment quality monitoring system, wherein the method comprises the following steps: when an input target monitoring area is received, historical monitoring data generated by the target monitoring area in a preset time period is obtained in a preset meteorological database, and a change curve graph corresponding to the air environment quality in the target monitoring area is drawn according to the historical monitoring data; extracting a plurality of maximum value points and a plurality of minimum value points which are respectively contained in the change curve graph, and detecting target generation sites of each maximum value point and each minimum value point in a target monitoring area one by one; the air monitoring device is arranged in the target generation place so as to monitor the target monitoring area in real time through the air monitoring device. The invention can effectively reduce the monitoring cost of the air environment quality and correspondingly improve the use experience of users.

Description

Regional air environment quality monitoring method and system
Technical Field
The invention relates to the technical field of data processing, in particular to a regional air environment quality monitoring method and system.
Background
The air quality reflects the pollution degree of the air, and is judged according to the concentration of pollutants in the air, and because the air pollution is a complex phenomenon, the concentration of the existing air pollutants can be influenced by various factors at specific time and place.
When the air quality of a certain area is monitored in the prior art, a plurality of monitoring devices are arranged in the monitoring area at the same time, so that the current monitoring area can be covered by the monitoring range of the current plurality of monitoring devices, however, the monitoring mode needs to consume a plurality of monitoring devices at the same time, and a large amount of redundant monitoring data can be correspondingly generated, so that the cost of air quality monitoring and the cost of monitoring data processing are correspondingly increased, and meanwhile, the efficiency of air quality monitoring is reduced.
Disclosure of Invention
Based on the above, the invention aims to provide a regional air environment quality monitoring method and a regional air environment quality monitoring system, so as to solve the technical problem of high air environment quality monitoring cost in the prior art.
The first aspect of the embodiment of the invention provides:
a method of regional air environmental quality monitoring, wherein the method comprises:
when an input target monitoring area is received, historical monitoring data generated by the target monitoring area in a preset time period is obtained in a preset meteorological database, and a change curve graph corresponding to the air environment quality in the target monitoring area is drawn according to the historical monitoring data;
Extracting a plurality of maximum value points and a plurality of minimum value points respectively contained in the change curve graph, and detecting target generation sites of each maximum value point and each minimum value point in the target monitoring area one by one;
and arranging air monitoring equipment in the target generation place so as to monitor the target monitoring area in real time through the air monitoring equipment.
The beneficial effects of the invention are as follows: by acquiring the historical monitoring data of the target monitoring area in real time, a change curve corresponding to the air environment quality in the current target monitoring area can be correspondingly drawn, and the change curve can directly reflect the historical air pollution degree of the current target monitoring area. Further, a plurality of maximum value points and a plurality of minimum value points respectively contained in the current change curve graph are extracted so as to correspondingly obtain a required target generation place, based on the maximum value points and the minimum value points, the existing air monitoring equipment is arranged in the current specific target generation place, so that a large number of air monitoring equipment can be omitted, the monitoring cost of air environment quality is reduced, and meanwhile, the use experience of a user is improved.
Further, the step of drawing a change graph corresponding to the air environment quality in the target monitoring area according to the historical monitoring data includes:
when the historical monitoring data are obtained, extracting pollutant concentration data and monitoring time data contained in the historical monitoring data, wherein the pollutant concentration data and the monitoring time data contain specific numerical values;
identifying a pollutant concentration value corresponding to each moment in the preset time period according to the pollutant concentration data and the monitoring time data, and constructing a mapping relation between each moment and each pollutant concentration value;
and generating a corresponding monitoring data chain according to the mapping relation, and generating the change curve graph according to the monitoring data chain.
Further, the step of generating a corresponding monitoring data chain according to the mapping relation includes:
creating a monitoring template based on a first preset program, and generating a corresponding monitoring time sequence according to the monitoring time data, wherein the monitoring template comprises a first area and a second area, and the first area corresponds to the second area;
Mapping the monitoring time sequence to the first area correspondingly, and mapping the pollutant concentration value to the second area correspondingly according to the monitoring time sequence;
and sequentially integrating the pollutant concentration values in the second area to generate a corresponding pollutant concentration sequence, and arranging and combining the monitoring time sequence and the pollutant concentration sequence based on the monitoring template to correspondingly generate the monitoring data chain, wherein the monitoring data chain has uniqueness.
Further, the step of generating the variation graph according to the monitoring data chain includes:
when the monitoring data chain is acquired, a two-dimensional space is created through a second preset program, and a two-dimensional reference surface is randomly created in the two-dimensional space;
randomly creating a coordinate origin in the two-dimensional reference plane, and extending a corresponding two-dimensional coordinate system based on the coordinate origin, wherein an x axis and a y axis of the two-dimensional coordinate system are parallel to the two-dimensional reference plane;
setting the monitoring time sequence as an x-axis of the two-dimensional coordinate system, and setting the pollutant concentration sequence as a y-axis of the two-dimensional coordinate system;
Inputting the monitoring data chain into the two-dimensional coordinate system to generate a plurality of corresponding monitoring points in the two-dimensional coordinate system, and sequentially connecting the plurality of monitoring points in the two-dimensional coordinate system to correspondingly generate the change curve graph.
Further, the step of detecting each maximum value point and each minimum value point in the target monitoring area respectively includes:
dividing the target monitoring area into a plurality of corresponding monitoring subareas, acquiring monitoring data sets corresponding to each monitoring subarea respectively according to the historical monitoring data, wherein the size of each monitoring subarea is equal, and each monitoring data set contains a specific numerical value;
detecting whether each monitoring data set contains the numerical value corresponding to the maximum value point or the minimum value point one by one;
and if the monitoring data set is detected to contain the numerical value corresponding to the maximum value point or the minimum value point, setting the monitoring subarea corresponding to the current monitoring data set as the target generation place.
Further, the method further comprises:
Receiving a plurality of monitoring reports respectively generated by each air monitoring device in real time, and identifying a project column and a data column contained in the monitoring reports, wherein the project column contains a plurality of project elements, and the data column contains a plurality of data elements;
storing each monitoring report into a storage folder correspondingly, and generating a corresponding air monitoring data chain according to a plurality of project elements and a plurality of data elements;
and setting each air monitoring data chain as the folder name of each storage folder, wherein the air monitoring data chain has uniqueness.
Further, the step of generating a corresponding air monitoring data chain according to the plurality of item elements and the plurality of data elements comprises:
extracting monitoring time and monitoring places contained in a plurality of item elements, and extracting particle diameters contained in a plurality of data elements;
and arranging and combining the monitoring time, the monitoring place and the particle diameter according to a preset rule so as to correspondingly generate the air monitoring data chain, wherein the particle diameter comprises various specifications.
A second aspect of an embodiment of the present invention proposes:
a regional air environmental quality monitoring system, wherein the system comprises:
the acquisition module is used for acquiring historical monitoring data generated by the target monitoring area in a preset time period in a preset weather database when an input target monitoring area is received, and drawing a change curve graph corresponding to the air environment quality in the target monitoring area according to the historical monitoring data;
the extraction module is used for extracting a plurality of maximum value points and a plurality of minimum value points which are respectively contained in the change curve graph, and detecting the target generation sites of each maximum value point and each minimum value point in the target monitoring area one by one;
and the execution module is used for arranging the air monitoring equipment in the target generation place so as to monitor the target monitoring area in real time through the air monitoring equipment.
Further, the obtaining module is specifically configured to:
when the historical monitoring data are obtained, extracting pollutant concentration data and monitoring time data contained in the historical monitoring data, wherein the pollutant concentration data and the monitoring time data contain specific numerical values;
Identifying a pollutant concentration value corresponding to each moment in the preset time period according to the pollutant concentration data and the monitoring time data, and constructing a mapping relation between each moment and each pollutant concentration value;
and generating a corresponding monitoring data chain according to the mapping relation, and generating the change curve graph according to the monitoring data chain.
Further, the obtaining module is specifically further configured to:
creating a monitoring template based on a first preset program, and generating a corresponding monitoring time sequence according to the monitoring time data, wherein the monitoring template comprises a first area and a second area, and the first area corresponds to the second area;
mapping the monitoring time sequence to the first area correspondingly, and mapping the pollutant concentration value to the second area correspondingly according to the monitoring time sequence;
and sequentially integrating the pollutant concentration values in the second area to generate a corresponding pollutant concentration sequence, and arranging and combining the monitoring time sequence and the pollutant concentration sequence based on the monitoring template to correspondingly generate the monitoring data chain, wherein the monitoring data chain has uniqueness.
Further, the obtaining module is specifically further configured to:
when the monitoring data chain is acquired, a two-dimensional space is created through a second preset program, and a two-dimensional reference surface is randomly created in the two-dimensional space;
randomly creating a coordinate origin in the two-dimensional reference plane, and extending a corresponding two-dimensional coordinate system based on the coordinate origin, wherein an x axis and a y axis of the two-dimensional coordinate system are parallel to the two-dimensional reference plane;
setting the monitoring time sequence as an x-axis of the two-dimensional coordinate system, and setting the pollutant concentration sequence as a y-axis of the two-dimensional coordinate system;
inputting the monitoring data chain into the two-dimensional coordinate system to generate a plurality of corresponding monitoring points in the two-dimensional coordinate system, and sequentially connecting the plurality of monitoring points in the two-dimensional coordinate system to correspondingly generate the change curve graph.
Further, the extraction module is specifically configured to:
dividing the target monitoring area into a plurality of corresponding monitoring subareas, acquiring monitoring data sets corresponding to each monitoring subarea respectively according to the historical monitoring data, wherein the size of each monitoring subarea is equal, and each monitoring data set contains a specific numerical value;
Detecting whether each monitoring data set contains the numerical value corresponding to the maximum value point or the minimum value point one by one;
and if the monitoring data set is detected to contain the numerical value corresponding to the maximum value point or the minimum value point, setting the monitoring subarea corresponding to the current monitoring data set as the target generation place.
Further, the regional air environmental quality monitoring system further comprises a storage module, wherein the storage module is specifically configured to:
receiving a plurality of monitoring reports respectively generated by each air monitoring device in real time, and identifying a project column and a data column contained in the monitoring reports, wherein the project column contains a plurality of project elements, and the data column contains a plurality of data elements;
storing each monitoring report into a storage folder correspondingly, and generating a corresponding air monitoring data chain according to a plurality of project elements and a plurality of data elements;
and setting each air monitoring data chain as the folder name of each storage folder, wherein the air monitoring data chain has uniqueness.
Further, the storage module is specifically configured to:
Extracting monitoring time and monitoring places contained in a plurality of item elements, and extracting particle diameters contained in a plurality of data elements;
and arranging and combining the monitoring time, the monitoring place and the particle diameter according to a preset rule so as to correspondingly generate the air monitoring data chain, wherein the particle diameter comprises various specifications.
A third aspect of an embodiment of the present invention proposes:
a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the regional air environmental quality monitoring method as described above when the computer program is executed by the processor.
A fourth aspect of the embodiment of the present invention proposes:
a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the regional air environment quality monitoring method as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a method for monitoring regional air environmental quality according to a first embodiment of the present invention;
Fig. 2 is a block diagram of a regional air environment quality monitoring system according to a sixth embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a method for monitoring regional air environmental quality according to a first embodiment of the present invention is shown, and the method for monitoring regional air environmental quality according to the present embodiment can omit a large number of air monitoring devices, thereby reducing the monitoring cost of air environmental quality and improving the user experience.
Specifically, the method for monitoring the regional air environment quality provided by the embodiment specifically includes the following steps:
step S10, when an input target monitoring area is received, acquiring historical meteorological data generated by the target monitoring area in a preset time period from a preset meteorological database, and drawing a change curve graph corresponding to the air environment quality in the target monitoring area according to the historical meteorological data;
step S20, extracting a plurality of maximum points and a plurality of minimum points respectively contained in the change curve graph, and detecting target generation sites of each maximum point and each minimum point respectively corresponding to the target monitoring region one by one;
and step S30, arranging air monitoring equipment in the target generation place so as to monitor the target monitoring area in real time through the air monitoring equipment.
In particular, in this embodiment, it should be noted first that the method for monitoring air environmental quality in an area is only used for monitoring air environmental quality in a certain area in real time, and specifically, the area may be, for example, a county, a village, or a city, so as to accurately monitor air environmental quality in a current area. In addition, it should be noted that the regional air environment quality monitoring method is implemented based on a server provided in the background, which is capable of receiving data in real time and efficiently processing the data. Based on this, it should be noted that, when the server receives the target monitoring area input by the user through the user terminal in real time, specifically, for example, the air environment quality is monitored in "a county", further, historical weather data that has been generated in a preset time period with the current target monitoring area is obtained in the existing weather database, specifically, for example, the historical weather data that has been generated in the current target monitoring area in the last seven days may be obtained, and at the same time, the current historical weather data is processed to correspondingly draw a change graph corresponding to the air environment quality in the current target monitoring area.
Furthermore, since the variation graph has a plurality of peaks and troughs, a plurality of maximum points and minimum points are correspondingly generated, and based on the peak and trough, the target generation point of each current maximum point and minimum point in the target monitoring area, namely the position corresponding to the target monitoring area, is further detected one by one. On the basis, the existing air monitoring equipment is correspondingly arranged in each detected target generation place, so that the use quantity of the air monitoring equipment can be reduced on the premise that the detection range of the air monitoring equipment covers the target monitoring areas, and meanwhile, each key area can be monitored, on one hand, the monitoring cost is reduced, and on the other hand, the use experience of a user is improved.
Second embodiment
Specifically, in this embodiment, it should be noted that the step of drawing the change graph corresponding to the air environment quality in the target monitoring area according to the historical meteorological data includes:
when the historical meteorological data are obtained, extracting pollutant concentration data and monitoring time data contained in the historical meteorological data, wherein the pollutant concentration data and the monitoring time data contain specific numerical values;
Identifying a pollutant concentration value corresponding to each moment in the preset time period according to the pollutant concentration data and the monitoring time data, and constructing a mapping relation between each moment and each pollutant concentration value;
and generating a corresponding monitoring data chain according to the mapping relation, and generating the change curve graph according to the monitoring data chain.
Specifically, in this embodiment, after the required historical meteorological data is obtained through the above steps, in order to further generate the required change graph, at this time, the pollutant concentration data and the monitoring time data respectively included in the current historical meteorological data need to be further extracted, where the pollutant concentration data may specifically include data of pollutant concentrations such as dust, carbon monoxide and sulfur dioxide, and the monitoring time data may specifically include a monitoring schedule, specifically, may use days as a unit, or may use hours as a unit.
Further, after the required pollutant concentration data and the monitoring time data are obtained, the pollutant concentration value corresponding to each moment can be further identified in the preset time period, specifically, for example, "the pollutant concentration value corresponding to each day is identified in seven days" or "the pollutant concentration value corresponding to each 6 hours is identified in seven days", and the like, based on this, the mapping relationship between each moment and each pollutant concentration value can be further constructed, and on this basis, the corresponding monitoring data chain can be generated according to the current mapping relationship, and the change graph can be further generated according to the current monitoring data chain.
Specifically, in this embodiment, it should be further noted that the step of generating the corresponding monitoring data chain according to the mapping relationship includes:
creating a monitoring template based on a first preset program, and generating a corresponding monitoring time sequence according to the monitoring time data, wherein the monitoring template comprises a first area and a second area, and the first area corresponds to the second area;
mapping the monitoring time sequence to the first area correspondingly, and mapping the pollutant concentration value to the second area correspondingly according to the monitoring time sequence;
and sequentially integrating the pollutant concentration values in the second area to generate a corresponding pollutant concentration sequence, and arranging and combining the monitoring time sequence and the pollutant concentration sequence based on the monitoring template to correspondingly generate the monitoring data chain, wherein the monitoring data chain has uniqueness.
Specifically, in this embodiment, it should be further described that, in order to simply and quickly generate the above-mentioned monitoring data chain, a monitoring template for recording monitoring data needs to be further created randomly by using existing EXCEL software, and meanwhile, a corresponding monitoring time sequence can be generated according to the above-mentioned monitoring time data, specifically, for example, "10-16-22 hours of 09/2023", further, the recording area in the above-mentioned monitoring template is correspondingly divided into a first area and a second area, preferably, the first area may be longitudinally arranged in the current monitoring template, and the corresponding second area may be transversely arranged in the current monitoring template. Based on this, the monitoring time series is mapped to the current first region, and the contaminant concentration value is mapped to the current second region. Furthermore, the pollutant concentration value in the current second area is integrated, so that a corresponding pollutant concentration sequence can be generated, and the monitoring data link is finally generated, specifically, for example, the monitoring data link generated in real time can be '2023, 09, 10 days-10 PPM carbon monoxide concentration-15 PPM sulfur dioxide', and the like, so as to facilitate subsequent further processing.
Third embodiment
In addition, in this embodiment, it should be noted that the step of generating the variation graph according to the monitoring data chain includes:
when the monitoring data chain is acquired, a two-dimensional space is created through a second preset program, and a two-dimensional reference surface is randomly created in the two-dimensional space;
randomly creating a coordinate origin in the two-dimensional reference plane, and extending a corresponding two-dimensional coordinate system based on the coordinate origin, wherein an x axis and a y axis of the two-dimensional coordinate system are parallel to the two-dimensional reference plane;
setting the monitoring time sequence as an x-axis of the two-dimensional coordinate system, and setting the pollutant concentration sequence as a y-axis of the two-dimensional coordinate system;
inputting the monitoring data chain into the two-dimensional coordinate system to generate a plurality of corresponding monitoring points in the two-dimensional coordinate system, and sequentially connecting the plurality of monitoring points in the two-dimensional coordinate system to correspondingly generate the change curve graph.
In addition, in this embodiment, after the required monitoring data chain is obtained through the above steps, an adaptive two-dimensional space needs to be created randomly in the virtual space by further using existing drawing software such as ug, and further, a two-dimensional reference plane is created randomly in the two-dimensional space, and at the same time, a coordinate origin is created randomly in the two-dimensional reference plane, so that a required two-dimensional coordinate system can be further extended according to the coordinate origin.
Further, in order to generate a change graph adapted to the monitoring data chain, the monitoring time sequence needs to be set to the x-axis of the current two-dimensional coordinate system, and correspondingly, the contaminant concentration sequence needs to be set to the y-axis of the current two-dimensional coordinate system, so that each monitoring data chain can be correspondingly input into the current two-dimensional coordinate system, and meanwhile, a plurality of corresponding monitoring points are synchronously generated in the two-dimensional coordinate system, namely, one monitoring data chain correspondingly generates one monitoring point, and further, the current plurality of monitoring points are sequentially connected, so that the change graph can be finally generated.
In this embodiment, the step of detecting the target generation point corresponding to each of the maximum point and the minimum point in the target monitoring area one by one includes:
dividing the target monitoring area into a plurality of corresponding monitoring subareas, acquiring monitoring data sets corresponding to each monitoring subarea respectively according to the historical meteorological data, wherein the size of each monitoring subarea is equal, and each monitoring data set contains a specific numerical value;
Detecting whether each monitoring data set contains the numerical value corresponding to the maximum value point or the minimum value point one by one;
and if the monitoring data set is detected to contain the numerical value corresponding to the maximum value point or the minimum value point, setting the monitoring subarea corresponding to the current monitoring data set as the target generation place.
In addition, in this embodiment, it should be further noted that, after the required maximum point and minimum point are obtained through the above steps, in order to further obtain the locations generated by each of the current maximum point and minimum point, specifically, the current target monitoring area needs to be further divided into a plurality of monitoring subareas with equal areas, and further, the corresponding monitoring data sets in each of the current monitoring subareas are obtained synchronously according to the historical meteorological data. Further, whether each current monitoring data set contains a numerical value corresponding to the maximum value point or the minimum value point is detected in real time, specifically, if yes, the monitoring subarea corresponding to the current monitoring data set is indicated to be a required target generation place, and if no, the monitoring subarea corresponding to the current monitoring data set is indicated to be not a required target generation place, so that subsequent processing is facilitated.
Fourth embodiment
In this embodiment, it should be noted that, the method further includes:
receiving a plurality of monitoring reports respectively generated by each air monitoring device in real time, and identifying a project column and a data column contained in the monitoring reports, wherein the project column contains a plurality of project elements, and the data column contains a plurality of data elements;
storing each monitoring report into a storage folder correspondingly, and generating a corresponding air monitoring data chain according to a plurality of project elements and a plurality of data elements;
and setting each air monitoring data chain as the folder name of each storage folder, wherein the air monitoring data chain has uniqueness.
In this embodiment, it should be noted that, in order to effectively store the monitoring report generated by the air monitoring device in real time, so as to protect the integrity of data, specifically, the server may extract, in real time, a project column and a data column respectively included in each monitoring report, where the project column includes a plurality of project elements, and the data column includes a plurality of data elements corresponding to the project column. Furthermore, each monitoring report is correspondingly stored in a storage folder, so that classification processing can be effectively performed. Based on the method, corresponding air monitoring data chains are generated in real time according to the project elements and the data elements, and finally each air monitoring data chain is correspondingly set as the folder name of each storage folder, so that on one hand, the storage of the monitoring report is effectively completed, on the other hand, a worker can intuitively observe which monitoring report is respectively stored in each folder, and the searching efficiency of the subsequent worker is correspondingly improved.
Fifth embodiment
In this embodiment, it should be noted that the step of generating the corresponding air monitoring data chain according to the plurality of item elements and the plurality of data elements includes:
extracting monitoring time and monitoring places contained in a plurality of item elements, and extracting particle diameters contained in a plurality of data elements;
and arranging and combining the monitoring time, the monitoring place and the particle diameter according to a preset rule so as to correspondingly generate the air monitoring data chain, wherein the particle diameter comprises various specifications.
In this embodiment, it should be noted that, in order to simply and quickly generate the air monitoring data chain, the monitoring time and the monitoring place included in the plurality of item elements are further extracted in real time, and at the same time, the particle diameters included in the plurality of data elements are extracted.
Further, the air monitoring data chain is generated according to the arrangement rules of the monitoring time, the monitoring place and the particle diameter. More specifically, for example, the air monitoring data chain generated in real time may be "2023, 09, B county-sulfur dioxide 8 PPM-carbon monoxide 16PPM" or the like, so as to facilitate subsequent processing.
Referring to fig. 2, a sixth embodiment of the present invention provides:
a regional air environmental quality monitoring system, wherein the system comprises:
the acquisition module is used for acquiring historical meteorological data generated by the target monitoring area in a preset time period in a preset meteorological database when an input target monitoring area is received, and drawing a change curve graph corresponding to the air environment quality in the target monitoring area according to the historical meteorological data;
the extraction module is used for extracting a plurality of maximum value points and a plurality of minimum value points which are respectively contained in the change curve graph, and detecting the target generation sites of each maximum value point and each minimum value point in the target monitoring area one by one;
and the execution module is used for arranging the air monitoring equipment in the target generation place so as to monitor the target monitoring area in real time through the air monitoring equipment.
In the above regional air environment quality monitoring system, the acquiring module is specifically configured to:
when the historical meteorological data are obtained, extracting pollutant concentration data and monitoring time data contained in the historical meteorological data, wherein the pollutant concentration data and the monitoring time data contain specific numerical values;
Identifying a pollutant concentration value corresponding to each moment in the preset time period according to the pollutant concentration data and the monitoring time data, and constructing a mapping relation between each moment and each pollutant concentration value;
and generating a corresponding monitoring data chain according to the mapping relation, and generating the change curve graph according to the monitoring data chain.
In the above regional air environment quality monitoring system, the acquiring module is further specifically configured to:
creating a monitoring template based on a first preset program, and generating a corresponding monitoring time sequence according to the monitoring time data, wherein the monitoring template comprises a first area and a second area, and the first area corresponds to the second area;
mapping the monitoring time sequence to the first area correspondingly, and mapping the pollutant concentration value to the second area correspondingly according to the monitoring time sequence;
and sequentially integrating the pollutant concentration values in the second area to generate a corresponding pollutant concentration sequence, and arranging and combining the monitoring time sequence and the pollutant concentration sequence based on the monitoring template to correspondingly generate the monitoring data chain, wherein the monitoring data chain has uniqueness.
In the above regional air environment quality monitoring system, the acquiring module is further specifically configured to:
when the monitoring data chain is acquired, a two-dimensional space is created through a second preset program, and a two-dimensional reference surface is randomly created in the two-dimensional space;
randomly creating a coordinate origin in the two-dimensional reference plane, and extending a corresponding two-dimensional coordinate system based on the coordinate origin, wherein an x axis and a y axis of the two-dimensional coordinate system are parallel to the two-dimensional reference plane;
setting the monitoring time sequence as an x-axis of the two-dimensional coordinate system, and setting the pollutant concentration sequence as a y-axis of the two-dimensional coordinate system;
inputting the monitoring data chain into the two-dimensional coordinate system to generate a plurality of corresponding monitoring points in the two-dimensional coordinate system, and sequentially connecting the plurality of monitoring points in the two-dimensional coordinate system to correspondingly generate the change curve graph.
In the above regional air environment quality monitoring system, the extraction module is specifically configured to:
dividing the target monitoring area into a plurality of corresponding monitoring subareas, acquiring monitoring data sets corresponding to each monitoring subarea respectively according to the historical meteorological data, wherein the size of each monitoring subarea is equal, and each monitoring data set contains a specific numerical value;
Detecting whether each monitoring data set contains the numerical value corresponding to the maximum value point or the minimum value point one by one;
and if the monitoring data set is detected to contain the numerical value corresponding to the maximum value point or the minimum value point, setting the monitoring subarea corresponding to the current monitoring data set as the target generation place.
Among the above-mentioned regional air environmental quality monitoring system, regional air environmental quality monitoring system still includes storage module, storage module specifically is used for:
receiving a plurality of monitoring reports respectively generated by each air monitoring device in real time, and identifying a project column and a data column contained in the monitoring reports, wherein the project column contains a plurality of project elements, and the data column contains a plurality of data elements;
storing each monitoring report into a storage folder correspondingly, and generating a corresponding air monitoring data chain according to a plurality of project elements and a plurality of data elements;
and setting each air monitoring data chain as the folder name of each storage folder, wherein the air monitoring data chain has uniqueness.
In the above regional air environment quality monitoring system, the storage module is further specifically configured to:
extracting monitoring time and monitoring places contained in a plurality of item elements, and extracting particle diameters contained in a plurality of data elements;
and arranging and combining the monitoring time, the monitoring place and the particle diameter according to a preset rule so as to correspondingly generate the air monitoring data chain, wherein the particle diameter comprises various specifications.
A seventh embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for monitoring regional air environmental quality provided in the above embodiments when the processor executes the computer program.
An eighth embodiment of the present invention provides a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the regional air environment quality monitoring method provided by the above embodiments.
In summary, the method and the system for monitoring the regional air environment quality provided by the embodiment of the invention can omit a large number of air monitoring devices, thereby reducing the monitoring cost of the air environment quality and improving the use experience of users.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (10)

1. A method for monitoring regional air environmental quality, the method comprising:
When an input target monitoring area is received, historical monitoring data generated by the target monitoring area in a preset time period is obtained in a preset meteorological database, and a change curve graph corresponding to the air environment quality in the target monitoring area is drawn according to the historical monitoring data;
extracting a plurality of maximum value points and a plurality of minimum value points respectively contained in the change curve graph, and detecting target generation sites of each maximum value point and each minimum value point in the target monitoring area one by one;
and arranging air monitoring equipment in the target generation place so as to monitor the target monitoring area in real time through the air monitoring equipment.
2. The regional air environmental quality monitoring method of claim 1, wherein: the step of drawing a change curve graph corresponding to the air environment quality in the target monitoring area according to the historical monitoring data comprises the following steps:
when the historical monitoring data are obtained, extracting pollutant concentration data and monitoring time data contained in the historical monitoring data, wherein the pollutant concentration data and the monitoring time data contain specific numerical values;
Identifying a pollutant concentration value corresponding to each moment in the preset time period according to the pollutant concentration data and the monitoring time data, and constructing a mapping relation between each moment and each pollutant concentration value;
and generating a corresponding monitoring data chain according to the mapping relation, and generating the change curve graph according to the monitoring data chain.
3. The regional air environmental quality monitoring method of claim 2, wherein: the step of generating the corresponding monitoring data chain according to the mapping relation comprises the following steps:
creating a monitoring template based on a first preset program, and generating a corresponding monitoring time sequence according to the monitoring time data, wherein the monitoring template comprises a first area and a second area, and the first area corresponds to the second area;
mapping the monitoring time sequence to the first area correspondingly, and mapping the pollutant concentration value to the second area correspondingly according to the monitoring time sequence;
and sequentially integrating the pollutant concentration values in the second area to generate a corresponding pollutant concentration sequence, and arranging and combining the monitoring time sequence and the pollutant concentration sequence based on the monitoring template to correspondingly generate the monitoring data chain, wherein the monitoring data chain has uniqueness.
4. A method of regional air environmental quality monitoring as claimed in claim 3, wherein: the step of generating the variation graph from the monitoring data chain comprises:
when the monitoring data chain is acquired, a two-dimensional space is created through a second preset program, and a two-dimensional reference surface is randomly created in the two-dimensional space;
randomly creating a coordinate origin in the two-dimensional reference plane, and extending a corresponding two-dimensional coordinate system based on the coordinate origin, wherein an x axis and a y axis of the two-dimensional coordinate system are parallel to the two-dimensional reference plane;
setting the monitoring time sequence as an x-axis of the two-dimensional coordinate system, and setting the pollutant concentration sequence as a y-axis of the two-dimensional coordinate system;
inputting the monitoring data chain into the two-dimensional coordinate system to generate a plurality of corresponding monitoring points in the two-dimensional coordinate system, and sequentially connecting the plurality of monitoring points in the two-dimensional coordinate system to correspondingly generate the change curve graph.
5. The regional air environmental quality monitoring method of claim 1, wherein: the step of detecting the target generation sites corresponding to each maximum value point and each minimum value point in the target monitoring area one by one includes:
Dividing the target monitoring area into a plurality of corresponding monitoring subareas, acquiring monitoring data sets corresponding to each monitoring subarea respectively according to the historical monitoring data, wherein the size of each monitoring subarea is equal, and each monitoring data set contains a specific numerical value;
detecting whether each monitoring data set contains the numerical value corresponding to the maximum value point or the minimum value point one by one;
and if the monitoring data set is detected to contain the numerical value corresponding to the maximum value point or the minimum value point, setting the monitoring subarea corresponding to the current monitoring data set as the target generation place.
6. The regional air environmental quality monitoring method of claim 1, wherein: the method further comprises the steps of:
receiving a plurality of monitoring reports respectively generated by each air monitoring device in real time, and identifying a project column and a data column contained in the monitoring reports, wherein the project column contains a plurality of project elements, and the data column contains a plurality of data elements;
storing each monitoring report into a storage folder correspondingly, and generating a corresponding air monitoring data chain according to a plurality of project elements and a plurality of data elements;
And setting each air monitoring data chain as the folder name of each storage folder, wherein the air monitoring data chain has uniqueness.
7. The regional air environmental quality monitoring method of claim 6, wherein: the step of generating a corresponding air monitoring data chain from the plurality of item elements and the plurality of data elements comprises:
extracting monitoring time and monitoring places contained in a plurality of item elements, and extracting particle diameters contained in a plurality of data elements;
and arranging and combining the monitoring time, the monitoring place and the particle diameter according to a preset rule so as to correspondingly generate the air monitoring data chain, wherein the particle diameter comprises various specifications.
8. A regional air environmental quality monitoring system, the system comprising:
the acquisition module is used for acquiring historical meteorological data generated by the target monitoring area in a preset time period in a preset meteorological database when an input target monitoring area is received, and drawing a change curve graph corresponding to the air environment quality in the target monitoring area according to the historical meteorological data;
The extraction module is used for extracting a plurality of maximum value points and a plurality of minimum value points which are respectively contained in the change curve graph, and detecting the target generation sites of each maximum value point and each minimum value point in the target monitoring area one by one;
and the execution module is used for arranging the air monitoring equipment in the target generation place so as to monitor the target monitoring area in real time through the air monitoring equipment.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of regional air environmental quality monitoring of any one of claims 1 to 7 when the computer program is executed.
10. A readable storage medium having stored thereon a computer program, which when executed by a processor implements the regional air environment quality monitoring method of any one of claims 1 to 7.
CN202311396620.7A 2023-10-26 2023-10-26 Regional air environment quality monitoring method and system Active CN117129638B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311396620.7A CN117129638B (en) 2023-10-26 2023-10-26 Regional air environment quality monitoring method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311396620.7A CN117129638B (en) 2023-10-26 2023-10-26 Regional air environment quality monitoring method and system

Publications (2)

Publication Number Publication Date
CN117129638A true CN117129638A (en) 2023-11-28
CN117129638B CN117129638B (en) 2024-01-12

Family

ID=88854905

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311396620.7A Active CN117129638B (en) 2023-10-26 2023-10-26 Regional air environment quality monitoring method and system

Country Status (1)

Country Link
CN (1) CN117129638B (en)

Citations (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1042274A (en) * 1996-07-25 1998-02-13 Babcock Hitachi Kk Abnormality monitoring method and device
CN102967689A (en) * 2012-11-22 2013-03-13 天津大学 Pollution source identification method based on correlation coefficient and monitoring stationing method
CN103679610A (en) * 2013-12-12 2014-03-26 北京航空航天大学 Visualization system for atmospheric environmental monitoring
CN104200075A (en) * 2014-08-20 2014-12-10 浙江中控软件技术有限公司 Trend chart drawing method and device applied to industrial monitoring system
KR20150031577A (en) * 2013-09-16 2015-03-25 주식회사 에니텍 Method of Inverse Calculation for The Amount of Air Pollutants Emissions
CN107193056A (en) * 2017-05-09 2017-09-22 西南石油大学 Air pollutants monitoring and pre-alarming method and cloud platform
CN107632111A (en) * 2016-07-19 2018-01-26 高奎峰 A kind of method and system for monitoring atmosphere pollution on-line
WO2018214060A1 (en) * 2017-05-24 2018-11-29 北京质享科技有限公司 Small-scale air quality index prediction method and system for city
CN109523066A (en) * 2018-10-29 2019-03-26 东华理工大学 A kind of newly-increased mobile site site selecting method of the PM2.5 based on Kriging regression
CN109613182A (en) * 2018-12-21 2019-04-12 北京英视睿达科技有限公司 Monitoring location site selecting method and device based on atmosphere pollution
CN110334828A (en) * 2019-03-07 2019-10-15 北京融链科技有限公司 Site selecting method and device, storage medium, processor
CN110766191A (en) * 2019-08-27 2020-02-07 东华理工大学 Newly-added PM2.5 fixed monitoring station site selection method based on space-time kriging interpolation
CN111157682A (en) * 2020-01-06 2020-05-15 上海应用技术大学 Air quality monitoring and predicting system and method
CN111175446A (en) * 2019-12-25 2020-05-19 嘉兴恒云数据科技有限公司 Gas tracing method and device
CN111768038A (en) * 2020-06-30 2020-10-13 平安国际智慧城市科技股份有限公司 Pollutant monitoring method and device, terminal equipment and storage medium
CN111983143A (en) * 2020-08-05 2020-11-24 宁夏无线互通信息技术有限公司 Air quality movement supervision system
CN112465243A (en) * 2020-12-02 2021-03-09 南通大学 Air quality forecasting method and system
CN112505254A (en) * 2020-12-03 2021-03-16 中科三清科技有限公司 Method and device for analyzing atmospheric pollution source, storage medium and terminal
KR20210032808A (en) * 2019-09-17 2021-03-25 한국과학기술연구원 Diurnal Pattern Automated Analysis Method From Air Quality Measurement Data
CN112905560A (en) * 2021-02-02 2021-06-04 中国科学院地理科学与资源研究所 Air pollution prediction method based on multi-source time-space big data deep fusion
CN113222328A (en) * 2021-03-25 2021-08-06 中国科学技术大学先进技术研究院 Air quality monitoring equipment point arrangement and site selection method based on road section pollution similarity
US20210396729A1 (en) * 2020-06-23 2021-12-23 Dataa Development Co., Ltd. Small area real-time air pollution assessment system and method
CN113946718A (en) * 2021-10-21 2022-01-18 河北先河环保科技股份有限公司 Air monitoring data storage method, equipment and storage system
CN113988348A (en) * 2020-07-10 2022-01-28 中国科学院沈阳计算技术研究所有限公司 Air quality prediction method based on grid monitoring
CN114858976A (en) * 2022-04-27 2022-08-05 浙江索思科技有限公司 Intelligent analysis method and system for atmospheric quality of industrial park
CN115018348A (en) * 2022-06-20 2022-09-06 北京北投生态环境有限公司 Environment analysis method, system, equipment and storage medium based on artificial intelligence
US20220316734A1 (en) * 2021-04-14 2022-10-06 Jiangnan University Deep Spatial-Temporal Similarity Method for Air Quality Prediction
CN115327041A (en) * 2022-08-09 2022-11-11 南京邮电大学 Air pollutant concentration prediction method based on correlation analysis
CN115358904A (en) * 2022-10-20 2022-11-18 四川国蓝中天环境科技集团有限公司 Dynamic and static combined urban area air quality monitoring station site selection method
CN115586304A (en) * 2022-10-04 2023-01-10 昆明理工大学 Environmental area air monitoring system
CN115656446A (en) * 2022-12-26 2023-01-31 沃客森信息科技(常州)有限公司 Air quality detection system and method based on Internet of things
CN115718169A (en) * 2022-11-14 2023-02-28 河北先河环保科技股份有限公司 Method, device and equipment for positioning high-value area with atmospheric pollution and storage medium
CN116484195A (en) * 2023-05-30 2023-07-25 碧空环境科技有限公司 Method and system for abnormal alarm feedback of conventional pollutant data of air station
CN116680658A (en) * 2023-05-31 2023-09-01 华南理工大学 Heat wave monitoring station site selection method and system based on risk evaluation
CN116699072A (en) * 2023-06-08 2023-09-05 东莞市华复实业有限公司 Environment early warning method based on detection cruising
CN116735807A (en) * 2023-08-09 2023-09-12 山东优控智能技术有限公司 Air quality detection and evaluation method based on multi-sensor data
DE202023105203U1 (en) * 2023-09-09 2023-09-20 Vrushali Bahurupi Sustainable environment monitoring system

Patent Citations (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1042274A (en) * 1996-07-25 1998-02-13 Babcock Hitachi Kk Abnormality monitoring method and device
CN102967689A (en) * 2012-11-22 2013-03-13 天津大学 Pollution source identification method based on correlation coefficient and monitoring stationing method
KR20150031577A (en) * 2013-09-16 2015-03-25 주식회사 에니텍 Method of Inverse Calculation for The Amount of Air Pollutants Emissions
CN103679610A (en) * 2013-12-12 2014-03-26 北京航空航天大学 Visualization system for atmospheric environmental monitoring
CN104200075A (en) * 2014-08-20 2014-12-10 浙江中控软件技术有限公司 Trend chart drawing method and device applied to industrial monitoring system
CN107632111A (en) * 2016-07-19 2018-01-26 高奎峰 A kind of method and system for monitoring atmosphere pollution on-line
CN107193056A (en) * 2017-05-09 2017-09-22 西南石油大学 Air pollutants monitoring and pre-alarming method and cloud platform
WO2018214060A1 (en) * 2017-05-24 2018-11-29 北京质享科技有限公司 Small-scale air quality index prediction method and system for city
CN109523066A (en) * 2018-10-29 2019-03-26 东华理工大学 A kind of newly-increased mobile site site selecting method of the PM2.5 based on Kriging regression
CN109613182A (en) * 2018-12-21 2019-04-12 北京英视睿达科技有限公司 Monitoring location site selecting method and device based on atmosphere pollution
CN110334828A (en) * 2019-03-07 2019-10-15 北京融链科技有限公司 Site selecting method and device, storage medium, processor
CN110766191A (en) * 2019-08-27 2020-02-07 东华理工大学 Newly-added PM2.5 fixed monitoring station site selection method based on space-time kriging interpolation
KR20210032808A (en) * 2019-09-17 2021-03-25 한국과학기술연구원 Diurnal Pattern Automated Analysis Method From Air Quality Measurement Data
CN111175446A (en) * 2019-12-25 2020-05-19 嘉兴恒云数据科技有限公司 Gas tracing method and device
CN111157682A (en) * 2020-01-06 2020-05-15 上海应用技术大学 Air quality monitoring and predicting system and method
US20210396729A1 (en) * 2020-06-23 2021-12-23 Dataa Development Co., Ltd. Small area real-time air pollution assessment system and method
CN111768038A (en) * 2020-06-30 2020-10-13 平安国际智慧城市科技股份有限公司 Pollutant monitoring method and device, terminal equipment and storage medium
CN113988348A (en) * 2020-07-10 2022-01-28 中国科学院沈阳计算技术研究所有限公司 Air quality prediction method based on grid monitoring
CN111983143A (en) * 2020-08-05 2020-11-24 宁夏无线互通信息技术有限公司 Air quality movement supervision system
CN112465243A (en) * 2020-12-02 2021-03-09 南通大学 Air quality forecasting method and system
CN112505254A (en) * 2020-12-03 2021-03-16 中科三清科技有限公司 Method and device for analyzing atmospheric pollution source, storage medium and terminal
CN112905560A (en) * 2021-02-02 2021-06-04 中国科学院地理科学与资源研究所 Air pollution prediction method based on multi-source time-space big data deep fusion
CN113222328A (en) * 2021-03-25 2021-08-06 中国科学技术大学先进技术研究院 Air quality monitoring equipment point arrangement and site selection method based on road section pollution similarity
US20220316734A1 (en) * 2021-04-14 2022-10-06 Jiangnan University Deep Spatial-Temporal Similarity Method for Air Quality Prediction
CN113946718A (en) * 2021-10-21 2022-01-18 河北先河环保科技股份有限公司 Air monitoring data storage method, equipment and storage system
CN114858976A (en) * 2022-04-27 2022-08-05 浙江索思科技有限公司 Intelligent analysis method and system for atmospheric quality of industrial park
CN115018348A (en) * 2022-06-20 2022-09-06 北京北投生态环境有限公司 Environment analysis method, system, equipment and storage medium based on artificial intelligence
CN115327041A (en) * 2022-08-09 2022-11-11 南京邮电大学 Air pollutant concentration prediction method based on correlation analysis
CN115586304A (en) * 2022-10-04 2023-01-10 昆明理工大学 Environmental area air monitoring system
CN115358904A (en) * 2022-10-20 2022-11-18 四川国蓝中天环境科技集团有限公司 Dynamic and static combined urban area air quality monitoring station site selection method
CN115718169A (en) * 2022-11-14 2023-02-28 河北先河环保科技股份有限公司 Method, device and equipment for positioning high-value area with atmospheric pollution and storage medium
CN115656446A (en) * 2022-12-26 2023-01-31 沃客森信息科技(常州)有限公司 Air quality detection system and method based on Internet of things
CN116484195A (en) * 2023-05-30 2023-07-25 碧空环境科技有限公司 Method and system for abnormal alarm feedback of conventional pollutant data of air station
CN116680658A (en) * 2023-05-31 2023-09-01 华南理工大学 Heat wave monitoring station site selection method and system based on risk evaluation
CN116699072A (en) * 2023-06-08 2023-09-05 东莞市华复实业有限公司 Environment early warning method based on detection cruising
CN116735807A (en) * 2023-08-09 2023-09-12 山东优控智能技术有限公司 Air quality detection and evaluation method based on multi-sensor data
DE202023105203U1 (en) * 2023-09-09 2023-09-20 Vrushali Bahurupi Sustainable environment monitoring system

Non-Patent Citations (16)

* Cited by examiner, † Cited by third party
Title
侯瑞莲;贾秋亭;祝洪杰;乔新晓;: "济南市空气质量实时监测信息系统的开发", 山东轻工业学院学报, no. 04 *
侯锦;: "年产10万吨聚氯乙烯项目环境空气质量现状分析及评价", 科技信息, no. 21 *
刘秀萍;李新宇;赵松婷;王行;: "北京地区城市绿地内不同空气颗粒物质量浓度时间变化特征及相关性分析", 北京农学院学报, no. 03 *
吴对林;李美敏;陈丽华;罗晓虹;: "东莞市城市功能区噪声自动监测点位布设初探", 中国环境监测, no. 04 *
庄杰平;: "泉港石化工业区环境空气污染监控点选取研究与应用", 海峡科学, no. 06 *
张祥志, 张宁红, 司蔚: "江苏省环境空气质量趋势监测点的优选", 环境监测管理与技术, no. 03 *
张静林;: "环境空气监测数据分析与处理技术研究", 环境与发展, no. 11 *
彭荔红, 李祚泳: "应用BP神经网络实现环境监测的优化布点", 环境保护, no. 04 *
徐境;: "城市环境空气监测点位的设置探析", 科技创新与应用, no. 07 *
李文豪;叶掌斌;郭辉;戴超;史新发;: "基于云平台的空气质量监测系统", 电脑迷, no. 01 *
李燕丽;穆超;邓君俊;赵淑惠;杜可;: "厦门秋季近郊近地面CO_2浓度变化特征研究", 环境科学, no. 05 *
林鸿雁;张江山;温烨明;林少玲;: "熵权物元分析法在地下水水质监测点优选中的应用", 福建师范大学学报(自然科学版), no. 01 *
焦勇霞;: "试析大气环境监测布点的方法", 环境与生活, no. 22 *
甘茂林;吕王勇;符璐;: "基于改进Moran\'s I指数的成都市PM_(2.5)的空间统计分析", 环境科学与技术, no. 09 *
蔡旺华;: "运用机器学习方法预测空气中臭氧浓度", 中国环境管理, no. 02 *
邓琴;: "基于某市空气质量监测数据的处理方法研究", 科技创新导报, no. 23 *

Also Published As

Publication number Publication date
CN117129638B (en) 2024-01-12

Similar Documents

Publication Publication Date Title
CN107918382B (en) Automobile fault diagnosis method, automobile fault diagnosis device and electronic equipment
CN112684133B (en) Water quality monitoring and early warning method and system based on big data platform and storage medium
CN111652266A (en) User interface component identification method and device, electronic equipment and storage medium
CN113064835A (en) Computer software test system
CN117129638B (en) Regional air environment quality monitoring method and system
CN111523815A (en) Power grid engineering review method and device, electronic equipment and storage medium
CN107992402A (en) Blog management method and log management apparatus
CN111090593A (en) Method, device, electronic equipment and storage medium for determining crash attribution
CN109389972B (en) Quality testing method and device for semantic cloud function, storage medium and equipment
CN116228501B (en) Pollution discharge exceeding area industry determining method and device, storage medium and electronic equipment
CN116011998B (en) Retired battery recycling and classifying treatment method and device
CN111143424A (en) Characteristic scene data mining method and device and terminal
CN115408244A (en) Webpage performance testing method, device, equipment and storage medium
CN101882159A (en) Database detecting method of and device thereof
CN103761247A (en) Processing method and device for error files
CN111209322A (en) Financial information acquisition and processing system and method
CN111026744A (en) Data management method and device based on energy station system model framework
CN105022380A (en) Technology related to decoder fault code indexing
CN117610895B (en) Method and device for determining heavy point pollution source management and control time, electronic equipment and medium
CN112527679A (en) Code circle complexity detection method, device and storage medium
CN117455124B (en) Environment-friendly equipment monitoring method, system, medium and electronic equipment for enterprises
CN117171043B (en) Intelligent source code detection method, system, equipment and storage medium
CN117499887A (en) Data acquisition method and system based on multi-sensor fusion technology
CN110677310B (en) Traffic attribution method, device and terminal
CN114936113B (en) Task avalanche recovery method and device, electronic equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant