CN109612895B - Air quality monitoring method in urban forest environment - Google Patents
Air quality monitoring method in urban forest environment Download PDFInfo
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- CN109612895B CN109612895B CN201910118261.6A CN201910118261A CN109612895B CN 109612895 B CN109612895 B CN 109612895B CN 201910118261 A CN201910118261 A CN 201910118261A CN 109612895 B CN109612895 B CN 109612895B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
- G01N2033/0068—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital using a computer specifically programmed
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
Abstract
The invention discloses an air quality monitoring method in an urban forest environment, which comprises the following steps: s1, arranging a sensor group in the target urban forest environment through a tree climbing robot; s2, collecting concentration parameters of air particles and concentration parameters of gas pollutants in the target urban forest environment based on the sensor group, and sending the parameters to a monitoring terminal through a Beidou module; s3, the monitoring terminal carries out evaluation on the current air quality based on a preset BP neural network model; and S4, the sensor group is recovered through the tree climbing robot. The invention arranges and recovers the sensors through the crawling robot, is convenient to use and high in flexibility, and greatly improves the accuracy of monitoring results.
Description
Technical Field
The invention relates to the field of air quality monitoring, in particular to an air quality monitoring method in an urban forest environment.
Background
The environmental protection problem is various actions taken to coordinate the relationship between human beings and the environment, protect the living environment of human beings and guarantee the sustainable development of the economic society; the urban forest is a measurement scale for people and environment in the city, so that the urban forest environment protection is particularly important, wherein the air quality in the urban forest environment is the important factor in the urban forest environment protection.
At present, air quality in urban forest environment is researched more, but in the past, the base station needs to be set up through the research method, time and labor are wasted, the influence of weather is not considered when the position of the sensor is not considered through monitored data, and the accuracy of a monitoring result is reduced to a certain extent.
Disclosure of Invention
In order to solve the problems, the invention provides the air quality monitoring method in the urban forest environment, which greatly improves the accuracy of the monitoring result, and is convenient to operate and high in flexibility.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for monitoring air quality in an urban forest environment comprises the following steps:
s1, arranging a sensor group in the target urban forest environment through a tree climbing robot;
s2, collecting concentration parameters of air particles and concentration parameters of gas pollutants in the target urban forest environment based on the sensor group, and sending the parameters to a monitoring terminal through a Beidou module;
s3, the monitoring terminal carries out evaluation on the current air quality based on a preset BP neural network model;
and S4, the sensor group is recovered through the tree climbing robot.
Further, the step S1 specifically includes the following steps:
s11, installing a clamping mechanical arm with a servo device on the tree climbing robot, and realizing the communication between a driver of the servo device and a remote controller through a wireless communication module;
s12, mounting a detection head with a sensor inside at the head end of the first serpentine mechanical arm;
s13, the clamping operation of the first serpentine mechanical arm is completed by remotely controlling the clamping mechanical arm;
s14, the tree climbing robot is controlled through a remote control to carry a first snake-shaped mechanical arm to perform crawling operation until the tree climbing robot reaches a designated position, the clamping mechanical arm drives the first snake-shaped mechanical arm to reach the side of a designated branch, the first snake-shaped mechanical arm is started to wind a target branch, after winding is completed, the manual remote control clamping mechanical arm loosens the first snake-shaped mechanical arm and resets, and then the manual remote control first snake-shaped mechanical arm drives a detection head to reach a target detection point;
and S15, repeating the steps S13 and S14 until the arrangement of all the sensors is completed.
Further, carry big dipper module and solar charging panel in detecting the head.
Furthermore, the centre gripping arm comprises second snakelike arm and the centre gripping manipulator of installing at second snakelike arm head end, first snakelike arm comprises a plurality of mechanical unit that are end to end connection and the steering wheel subassembly that is located between the mechanical unit.
Further, the step S4 specifically includes the following steps:
the tree climbing robot is controlled through a remote control to carry the clamping mechanical arm to carry out crawling operation, after the tree climbing robot reaches a designated position, the clamping mechanical arm of the clamping mechanical arm is controlled to move to the first snake-shaped mechanical arm of a target, the clamping operation of the first snake-shaped mechanical arm is completed, the first snake-shaped mechanical arm is controlled to be started, the winding operation of branches is loosened, the clamping mechanical arm resets, and then the crawling robot is controlled to reset.
Further, the step S3 specifically includes the following steps:
s31, the monitoring terminal receives the data sent by the sensor group, and the data are marked by the corresponding Beidou positioning data and then stored in the corresponding database;
s32, mining real-time weather data on each weather forecast base station through a web crawler module;
and S33, inputting the marked data and the corresponding real-time weather data into a preset BP neural network model for evaluating the current air quality.
Further, monitor terminal adopts cell-phone APP's form, establishes:
the data viewing module is used for carrying out historical/current acquired data of each sensor group and past air quality evaluation results;
the air quality evaluation module is internally provided with a BP neural network model and is used for realizing the evaluation of the current control quality according to the input monitoring data;
the graph drawing module is used for drawing various curve graphs obtained according to the monitoring data;
the regression calculation module is used for carrying out regression calculation on the actually measured data curve through different functions;
and the simulation analysis module is used for building a simulation analysis model of the related air quality data through Simulink to perform simulation analysis on the monitored data.
Furthermore, the graph drawing module generates a time-space effect curve, namely a temporal curve and a spatial curve, which change along with time and space according to the input monitoring data, wherein the temporal curve displays the change situation of the original data or the transferred data of each monitoring point along with time, and the spatial curve highlights the change rule of the monitoring results of different measuring points at the same time along with the spatial geographic position of the detection head.
The invention has the following beneficial effects:
1) the sensors are arranged and recovered through the crawling robot, so that the crawling robot is convenient to use and high in flexibility; the Beidou short message communication technology is adopted for data transmission, and a communication line does not need to be erected;
2) monitoring data with Beidou positioning and weather data are used as evaluation references, so that the accuracy of detection results is greatly improved;
3) the air quality condition in the target urban forest environment can be known more comprehensively.
Drawings
Fig. 1 is a flow chart of an air quality monitoring method in an urban forest environment according to an embodiment of the present invention.
Fig. 2 is a flowchart of step S1 in a method for monitoring air quality in an urban forest environment according to an embodiment of the present invention.
Fig. 3 is a flowchart of step S3 in a method for monitoring air quality in an urban forest environment according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described in detail below with reference to examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1 to 3, an embodiment of the present invention provides an air quality monitoring method in an urban forest environment, including the following steps:
s1, arranging a sensor group in the target urban forest environment through a tree climbing robot;
s11, installing a clamping mechanical arm with a servo device on the tree climbing robot, and realizing the communication between a driver of the servo device and a remote controller through a wireless communication module;
s12, mounting a detection head with a sensor inside at the head end of the first serpentine mechanical arm; the Beidou module and the solar charging panel are loaded in the detection head;
s13, the clamping operation of the first serpentine mechanical arm is completed by remotely controlling the clamping mechanical arm;
s14, the tree climbing robot is controlled through a remote control to carry a first snake-shaped mechanical arm to perform crawling operation until the tree climbing robot reaches a designated position, the clamping mechanical arm drives the first snake-shaped mechanical arm to reach the side of a designated branch, the first snake-shaped mechanical arm is started to wind a target branch, after winding is completed, the manual remote control clamping mechanical arm loosens the first snake-shaped mechanical arm and resets, and then the manual remote control first snake-shaped mechanical arm drives a detection head to reach a target detection point;
s15, repeating the steps S13 and S14 until the arrangement of all the sensors is completed;
s2, collecting concentration parameters of air particles and concentration parameters of gas pollutants in the target urban forest environment based on the sensor group, and sending the parameters to a monitoring terminal through a Beidou module;
s3, the monitoring terminal carries out evaluation on the current air quality based on a preset BP neural network model;
s31, the monitoring terminal receives the data sent by the sensor group, and the data are marked by the corresponding Beidou positioning data and then stored in the corresponding database;
s32, mining real-time weather data on each weather forecast base station through a web crawler module;
s33, inputting the marked data and the corresponding real-time weather data into a preset BP neural network model for evaluating the current air quality;
s4, recycling the sensor group through the tree climbing robot;
the tree climbing robot is controlled through a remote control to carry the clamping mechanical arm to carry out crawling operation, after the tree climbing robot reaches a designated position, the clamping mechanical arm of the clamping mechanical arm is controlled to move to the first snake-shaped mechanical arm of a target, the clamping operation of the first snake-shaped mechanical arm is completed, the first snake-shaped mechanical arm is controlled to be started, the winding operation of branches is loosened, the clamping mechanical arm resets, and then the crawling robot is controlled to reset.
In this embodiment, the centre gripping arm comprises second snakelike arm and the centre gripping manipulator of installing at second snakelike arm head end, first snakelike arm comprises a plurality of mechanical unit that are end to end connection and the steering wheel subassembly that is located between the mechanical unit.
In this embodiment, the monitoring terminal adopts the form of cell-phone APP, establishes:
the data viewing module is used for carrying out historical/current acquired data of each sensor group and past air quality evaluation results;
the air quality evaluation module is internally provided with a BP neural network model and is used for realizing the evaluation of the current control quality according to the input monitoring data;
the graph drawing module is used for drawing various curve graphs obtained according to the monitoring data;
the regression calculation module is used for carrying out regression calculation on the actually measured data curve through different functions;
and the simulation analysis module is used for building a simulation analysis model of the related air quality data through Simulink to perform simulation analysis on the monitored data.
The graph drawing module generates a time-space effect curve, namely a dynamic curve and a space effect curve, which change along with time and space according to the input monitoring data, wherein the dynamic curve displays the change situation of the original data or the transferred data of each monitoring point along with the time, and the space effect curve highlights the change rule of the monitoring results of different measuring points along with the space geographic position of the detection head at the same time.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (7)
1. A method for monitoring air quality in an urban forest environment is characterized by comprising the following steps:
s1, arranging a sensor group in the target urban forest environment through a tree climbing robot;
s11, installing a clamping mechanical arm with a servo device on the tree climbing robot, and realizing the communication between a driver of the servo device and a remote controller through a wireless communication module;
s12, mounting a detection head with a sensor inside at the head end of the first serpentine mechanical arm;
s13, the clamping operation of the first serpentine mechanical arm is completed by remotely controlling the clamping mechanical arm;
s14, the tree climbing robot is controlled through a remote control to carry a first snake-shaped mechanical arm to perform crawling operation until the tree climbing robot reaches a designated position, the clamping mechanical arm drives the first snake-shaped mechanical arm to reach the side of a designated branch, the first snake-shaped mechanical arm is started to wind a target branch, after winding is completed, the manual remote control clamping mechanical arm loosens the first snake-shaped mechanical arm and resets, and then the manual remote control first snake-shaped mechanical arm drives a detection head to reach a target detection point;
s15, repeating the steps S13 and S14 until the arrangement of all the sensors is completed;
s2, collecting concentration parameters of air particles and concentration parameters of gas pollutants in the target urban forest environment based on the sensor group, and sending the parameters to a monitoring terminal through a Beidou module;
s3, the monitoring terminal carries out evaluation on the current air quality based on a preset BP neural network model;
and S4, the sensor group is recovered through the tree climbing robot.
2. The method as claimed in claim 1, wherein the detector head carries a Beidou module and a solar charging panel.
3. The method for monitoring the air quality in the urban forest environment according to claim 1, wherein the clamping mechanical arm is composed of a second serpentine mechanical arm and a clamping mechanical arm arranged at the head end of the second serpentine mechanical arm, and the first serpentine mechanical arm is composed of a plurality of mechanical units which are connected end to end and a steering engine assembly positioned between the mechanical units.
4. The method for monitoring the air quality in the urban forest environment according to claim 1, wherein the step S4 specifically comprises the steps of:
the tree climbing robot is controlled through a remote control to carry the clamping mechanical arm to carry out crawling operation, after the tree climbing robot reaches a designated position, the clamping mechanical arm of the clamping mechanical arm is controlled to move to the first snake-shaped mechanical arm of a target, the clamping operation of the first snake-shaped mechanical arm is completed, the first snake-shaped mechanical arm is controlled to be started, the winding operation of branches is loosened, the clamping mechanical arm resets, and then the crawling robot is controlled to reset.
5. The method for monitoring the air quality in the urban forest environment according to claim 1, wherein the step S3 specifically comprises the steps of:
s31, the monitoring terminal receives the data sent by the sensor group, and the data are marked by the corresponding Beidou positioning data and then stored in the corresponding database;
s32, mining real-time weather data on each weather forecast base station through a web crawler module;
and S33, inputting the marked data and the corresponding real-time weather data into a preset BP neural network model for evaluating the current air quality.
6. The method for monitoring the air quality in the urban forest environment according to claim 1, wherein the monitoring terminal is in the form of a mobile phone APP and is internally provided with:
the data viewing module is used for carrying out historical/current acquired data of each sensor group and past air quality evaluation results;
the air quality evaluation module is internally provided with a BP neural network model and is used for realizing the evaluation of the current control quality according to the input monitoring data;
the graph drawing module is used for drawing various curve graphs obtained according to the monitoring data;
the regression calculation module is used for carrying out regression calculation on the actually measured data curve through different functions;
and the simulation analysis module is used for building a simulation analysis model of the related air quality data through Simulink to perform simulation analysis on the monitored data.
7. The method as claimed in claim 6, wherein the graph plotting module generates a time-space effect curve, a temporal curve and a spatial curve, which change with time and space according to the input monitoring data, the temporal curve shows the change of the original data or the transferred data of each monitoring point with time, and the spatial curve highlights the change rule of the monitoring results of different measuring points with the spatial geographical position of the detection head at the same time.
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| CN113970511A (en) * | 2021-10-21 | 2022-01-25 | 天津大学 | Air particulate matter data monitoring system and method based on BP neural network |
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| CN106442881A (en) * | 2016-09-20 | 2017-02-22 | 北京市农林科学院 | Air quality monitoring method in urban forest environment |
| CN106483258A (en) * | 2016-12-14 | 2017-03-08 | 北京林业大学 | Air quality data collection and radio transmitting device under a kind of forest environment |
| CN107150729A (en) * | 2017-03-24 | 2017-09-12 | 广西大学 | One kind automation step-by-step movement becomes born of the same parents' robot capable of climbing trees |
| CN108053071A (en) * | 2017-12-21 | 2018-05-18 | 宇星科技发展(深圳)有限公司 | Regional air pollutant concentration Forecasting Methodology, terminal and readable storage medium storing program for executing |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US11092582B2 (en) * | 2016-11-28 | 2021-08-17 | International Business Machines Corporation | Dust emission forecasting |
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| CN106442881A (en) * | 2016-09-20 | 2017-02-22 | 北京市农林科学院 | Air quality monitoring method in urban forest environment |
| CN106483258A (en) * | 2016-12-14 | 2017-03-08 | 北京林业大学 | Air quality data collection and radio transmitting device under a kind of forest environment |
| CN107150729A (en) * | 2017-03-24 | 2017-09-12 | 广西大学 | One kind automation step-by-step movement becomes born of the same parents' robot capable of climbing trees |
| CN108053071A (en) * | 2017-12-21 | 2018-05-18 | 宇星科技发展(深圳)有限公司 | Regional air pollutant concentration Forecasting Methodology, terminal and readable storage medium storing program for executing |
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