CN117990660A - Gas content measuring device and method based on optical remote sensing mode - Google Patents
Gas content measuring device and method based on optical remote sensing mode Download PDFInfo
- Publication number
- CN117990660A CN117990660A CN202410394349.1A CN202410394349A CN117990660A CN 117990660 A CN117990660 A CN 117990660A CN 202410394349 A CN202410394349 A CN 202410394349A CN 117990660 A CN117990660 A CN 117990660A
- Authority
- CN
- China
- Prior art keywords
- remote sensing
- gas content
- greenhouse gas
- gps receiver
- control module
- 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
Links
- 230000003287 optical effect Effects 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 18
- 239000005431 greenhouse gas Substances 0.000 claims abstract description 93
- 239000007789 gas Substances 0.000 claims abstract description 28
- 238000010801 machine learning Methods 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000013507 mapping Methods 0.000 claims abstract description 12
- 210000002569 neuron Anatomy 0.000 claims description 26
- 238000012549 training Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 10
- 230000009466 transformation Effects 0.000 claims description 8
- 239000000443 aerosol Substances 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 abstract description 2
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/47—Scattering, i.e. diffuse reflection
- G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
-
- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
-
- 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
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Chemical & Material Sciences (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Electromagnetism (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The invention relates to the technical field of environmental monitoring, in particular to a gas content measuring device and method based on an optical remote sensing mode. The device comprises: the turntable device drives the micro-pulse laser radar to perform space scanning to obtain a remote sensing image; the first GPS receiver is used for acquiring a first position coordinate where the micropulse laser radar is located; the data processing module and the first GPS receiver respectively send the remote sensing image and the first position coordinate to the control module for mapping to obtain a remote sensing map; the second GPS receiver is used for acquiring second position coordinates of the greenhouse gas measuring instrument; the data acquisition device is connected with the second GPS receiver and the greenhouse gas measuring instrument, and sends the second position coordinates and the greenhouse gas content information measured by the greenhouse gas measuring instrument to the control module for mapping; the control module records continuously collected information to obtain a data set; through machine learning, the greenhouse gas content at each location is obtained. The advantage is that a spatial distribution of greenhouse gases can be obtained.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a gas content measuring device and method based on an optical remote sensing mode.
Background
Greenhouse gases refer to any gas that absorbs and releases infrared radiation and that is in the atmosphere, and once the greenhouse gases exceed the atmospheric standards, they cause a greenhouse effect that increases the global temperature and threatens human survival, so controlling greenhouse gas emissions has become a major problem facing all humans. The real-time detection of greenhouse gases in the atmosphere plays a key role in distinguishing the causes and development rules of the greenhouse gases. At present, the detection of greenhouse gases is measured according to the principle of spectral absorption, mainly by means of a greenhouse gas measuring instrument, such as a Gasboard-3000GHG greenhouse gas (CO 2/CH4/N2 O) emission analyzer, which can give the content of gases such as CO 2、CO、CH4, but can only give the content of greenhouse gases at a specific point. However, no good solution is available for the spatial distribution of greenhouse gases.
The micro-pulse laser radar can perform spatial scanning on a selected area, so that a radar signal echo diagram of the area, namely a remote sensing image, is obtained, and the remote sensing image comprises the influence of molecules such as aerosol, water molecules, CO 2、CO、CH4 and the like on echo signals. But no spatial distribution of greenhouse gas content can be obtained by using the micropulse lidar alone.
Therefore, there is no system or effective measurement method for processing and analyzing spatial distribution information of greenhouse gas content. Therefore, it is needed to develop a system capable of obtaining the spatial distribution characteristics of greenhouse gases, so as to distinguish the cause and development law of greenhouse gases.
Disclosure of Invention
The invention provides a device and a method for measuring gas content based on an optical remote sensing mode for solving the problems.
A first object of the present invention is to provide a gas content measuring device based on an optical remote sensing method, including: the system comprises a first GPS antenna, a first GPS receiver, a second GPS antenna, a second GPS receiver, a micropulse laser radar, a greenhouse gas measuring instrument, a turntable device, a data acquisition unit, a data processing module and a control module;
The micro-pulse laser radar is arranged on the turntable device, and the turntable device is connected with the control module; the turntable device drives the micro-pulse laser radar to perform space scanning, the micro-pulse laser radar receives laser echo signals, and the data processing module obtains remote sensing images based on the laser echo signals;
The first GPS antenna is connected with the first GPS receiver, and the first GPS receiver is used for acquiring a first position coordinate where the micropulse laser radar is located; the data processing module and the first GPS receiver respectively send the remote sensing image and the first position coordinate to the control module, and the remote sensing image and the first position coordinate are mapped on the control module to obtain a remote sensing map;
The second GPS antenna is connected with the second GPS receiver, and the second GPS receiver is used for acquiring second position coordinates of the greenhouse gas measuring instrument; the data acquisition device is connected with the second GPS receiver and the greenhouse gas measuring instrument, and is used for transmitting the second position coordinates and the greenhouse gas content information measured by the greenhouse gas measuring instrument to the control module, and mapping the second position coordinates on the remote sensing map on the control module;
The control module records a continuously collected remote sensing map and greenhouse gas content information to obtain a data set; and performing machine learning on the data set, and then applying a machine learning result to the whole remote sensing map to obtain the greenhouse gas content at each point position.
Preferably, the control module comprises a main control computer for controlling the rotation of the turntable device, receiving data and analyzing the data; the machine learning is a back propagation neural network algorithm.
Preferably, the turret means is a two-dimensional turret, and the spatial scanning is scanning in both the horizontal and elevation directions.
Preferably, the first GPS antenna and the second GPS antenna are patch antennas.
Preferably, the first GPS receiver and the second GPS receiver are measurement type GPS receivers.
Preferably, the micro-pulse laser radar is an aerosol laser radar.
Preferably, the greenhouse gases include CO 2, CO, and/or CH 4.
The second object of the present invention is to provide a method for measuring gas content based on optical remote sensing, which adopts a greenhouse gas content measuring device for measurement, and specifically comprises the following steps:
S1, performing space scanning by adopting a micro-pulse laser radar and receiving a laser echo signal, and obtaining a remote sensing image based on the laser echo signal by a data processing module;
s2, a first GPS receiver collects a first position coordinate where the micro-pulse laser radar is located;
s3, the data processing module and the first GPS receiver respectively send the remote sensing image and the first position coordinate to the control module, and mapping is carried out on plane map software of the control module to obtain a remote sensing map;
S4, acquiring a second position coordinate of the greenhouse gas measuring instrument by a second GPS receiver; starting up the greenhouse gas measuring instrument to obtain the content of greenhouse gas;
s5, the data collector collects second position coordinates and sends greenhouse gas content information to the control module, and the second position coordinates are mapped on the remote sensing map on the control module;
s6, the control module records continuously collected remote sensing map and greenhouse gas content information to obtain a data set; performing machine learning on the data set, establishing a corresponding relation between the remote sensing image and the greenhouse gas content, and outputting a machine learning result;
s7, applying the machine learning result to the whole remote sensing map to obtain the greenhouse gas content at each point position on the remote sensing map;
And S8, applying a machine learning result to the micro-pulse laser radar, and independently working the laser radar to obtain a remote sensing map, and obtaining the greenhouse gas content at different positions through the remote sensing map.
Preferably, in step S6, the GPS pulse per second is used as a synchronization reference.
Preferably, the machine learning specifically includes: performing multi-layer perceptron model training, establishing a mapping relation between an image brightness value of the remote sensing map and the greenhouse gas through continuous weighted summation and nonlinear transformation, and outputting a training model;
The multi-layer perceptron model training uses a random gradient descent algorithm;
The data set passes through nonlinear transformation of a plurality of hidden layers from an input layer and finally reaches an output layer, and the following formula is shown:
；
where x represents the input, y represents the value of the output layer, Representing the sum of the input weights of the output layer k,/>Representing the weights of the current neuron and the next layer neuron, x jk represents the output value of the j-th layer neuron and the input value of the k-th layer neuron,To activate the function.
Compared with the prior art, the invention has the following beneficial effects:
The device and the method for measuring the greenhouse gas content based on the optical remote sensing mode are characterized in that a laser radar and a greenhouse gas measuring instrument are combined to be used, and greenhouse gas content information at each position on a remote sensing image is obtained through a machine learning method, so that the spatial distribution characteristic of greenhouse gas is obtained, and the device and the method are beneficial to distinguishing the cause and the development rule of the greenhouse gas.
Drawings
FIG. 1 is a schematic diagram of a greenhouse gas content measuring apparatus according to an embodiment of the present invention.
Reference numerals:
1. A first GPS antenna; 2. a first GPS receiver; 3. a second GPS antenna; 4. a second GPS receiver; 5. a micro-pulse laser radar; 6. a greenhouse gas measuring instrument; 7. a data collector; 8. a main control computer; 9. a two-dimensional turntable.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, like modules are denoted by like reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, a detailed description thereof will not be repeated.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limiting the invention.
The invention provides a gas content measuring device based on an optical remote sensing mode, wherein fig. 1 shows a device structure and a signal transmission path, and the device comprises:
The system comprises a first GPS antenna 1, a first GPS receiver 2, a second GPS antenna 3, a second GPS receiver 4, a micro-pulse laser radar 5, a greenhouse gas measuring instrument 6, a data collector 7, a two-dimensional turntable 9 and a control module;
the micro-pulse laser radar 5 is arranged on the two-dimensional turntable 9, the two-dimensional turntable 9 is connected with the control module, and the control module is used for controlling the two-dimensional turntable 9 to rotate; the two-dimensional turntable 9 drives the micro-pulse laser radar 5 to perform space scanning in the horizontal direction and the pitching direction, the micro-pulse laser radar 5 receives laser echo signals, and the data processing module obtains remote sensing images based on the laser echo signals;
The first GPS antenna 1 is connected with a first GPS receiver 2, and the first GPS receiver 2 is used for collecting a first position coordinate where the micropulse laser radar 5 is located; the data processing module and the first GPS receiver 2 respectively send the remote sensing image and the first position coordinate to the control module, and map on the plane map software of the control module to obtain a remote sensing map;
the second GPS antenna 3 is connected with a second GPS receiver 4, and the second GPS receiver 4 is used for acquiring a second position coordinate of the greenhouse gas measuring instrument 6; the greenhouse gas measuring instrument 6 is started to work and measure to obtain the content of the CO 2、CO、CH4 isothermal chamber gas; the data collector 7 is connected with the second GPS receiver 4 and the greenhouse gas measuring instrument 6, collects second position coordinates and greenhouse gas content information and sends the information to the control module, and maps the second position coordinates of the greenhouse gas measuring instrument 6 on a remote sensing map on the control module;
The control module records the continuously collected remote sensing map and greenhouse gas content information to obtain a data set; performing machine learning on the data set, and establishing a corresponding relation between the remote sensing image and the greenhouse gas content; and then applying the machine learning result to the whole remote sensing map to obtain the gas content of CO 2、CO、CH4 and the like at each point position on the remote sensing map.
Specifically, the control module includes a main control computer 8 for controlling the two-dimensional turntable 9 and receiving and analyzing data.
Specifically, machine learning is a back propagation neural network algorithm.
Specifically, the GPS antenna is a patch antenna.
Specifically, the GPS receiver is a measurement type GPS receiver.
Specifically, the micro-pulse laser radar 5 is an Aiwok PMTracer aerosol laser radar.
Specifically, the greenhouse gas meter 6 is a GR2012 portable greenhouse gas detector.
The invention also provides a gas content measuring method, which adopts the measuring device and specifically comprises the following steps:
s1, a two-dimensional turntable 9 drives a micro-pulse laser radar 5 to perform space scanning in the horizontal direction and the pitching direction, and the micro-pulse laser radar 5 receives laser echo signals to obtain a remote sensing image;
S2, a first GPS receiver 2 collects a first position coordinate where the micro-pulse laser radar 5 is located;
S3, the data processing module and the first GPS receiver 2 respectively send the remote sensing image and the first position coordinate to the control module, and mapping is carried out on plane map software of the control module to obtain a remote sensing map;
S4, acquiring a second position coordinate of the greenhouse gas measuring instrument 6 by the second GPS receiver 4; starting up the greenhouse gas measuring instrument 6 to obtain the content of the CO 2、CO、CH4 isothermal chamber gas;
S5, the data collector 7 collects the position coordinates of the greenhouse gas measuring instrument 6 and the greenhouse gas content information and sends the information to the control module, and the control module maps the second position coordinates of the greenhouse gas measuring instrument 6 on a remote sensing map;
S6, using GPS second pulse as a synchronous reference, and recording continuously collected remote sensing map and greenhouse gas content information by a control module to obtain a data set; performing machine learning on the data set, establishing a corresponding relation between the remote sensing image and the greenhouse gas content, and outputting a machine learning result;
specifically, machine learning is to perform multi-layer perceptron (MLP) model training, establish the mapping relation between the remote sensing map image brightness value and greenhouse gas through continuous weighted summation and nonlinear transformation, and output a training model; the data set passes through nonlinear transformation of a plurality of hidden layers from an input layer and finally reaches an output layer, as shown in the following formula:
；
where x represents the input, y represents the value of the output layer, Representing the sum of the input weights of the output layer k,/>Represents the weights of the current neuron and the next-layer neuron, x jk represents the output value of the j-th-layer neuron and the input value of the k-th-layer neuron (j represents the subscript of the current-layer neuron, is a hidden-layer neuron, k represents the subscript of the next-layer neuron, is an output-layer neuron), and the index of the current-layer neuron is equal to the index of the next-layer neuronIs an activation function; preferably, a sigmoid activation function is selected;
in the process of training the multi-layer perceptron model, a random gradient descent algorithm is used for calculating the gradient of a loss function on network parameters, the parameters are updated according to the gradient so as to minimize the loss function, and finally, the accuracy of predicting the greenhouse gas content is improved according to the brightness value of the remote sensing map image; the machine learning result is a training model;
s7, applying the machine learning result to the whole remote sensing map to obtain the gas content of CO 2、CO、CH4 and the like at each point position on the remote sensing map;
And S8, applying a machine learning result to the micro-pulse laser radar, and independently working the laser radar to obtain a remote sensing map, and obtaining the greenhouse gas content at different positions through the remote sensing map.
Example 1
The gas content measuring method based on the optical remote sensing mode specifically comprises the following steps:
S1, performing space scanning on the horizontal direction and the pitching direction by adopting a micro-pulse laser radar 5 to obtain a remote sensing image;
S2, a first GPS receiver 2 collects a first position coordinate where the micro-pulse laser radar 5 is located;
S3, the data processing module and the first GPS receiver 2 respectively send the remote sensing image and the first position coordinate to the main control computer 8, and mapping is carried out on the plane map software of the main control computer 8 to obtain a remote sensing map;
S4, acquiring a second position coordinate of the greenhouse gas measuring instrument 6 by the second GPS receiver 4; starting up the greenhouse gas measuring instrument 6 to obtain the content of the CO 2、CO、CH4 isothermal chamber gas;
S5, the data collector 7 collects the second position coordinates of the greenhouse gas measuring instrument 6 and the greenhouse gas content information and sends the information to the main control computer 8, and the second position coordinates of the greenhouse gas measuring instrument 6 are mapped on a remote sensing map on the main control computer 8;
S6, using GPS second pulse as synchronous reference, the main control computer 8 records continuously collected remote sensing map and greenhouse gas content information to obtain a data set; performing multi-layer perceptron (MLP) model training on the data set, establishing a mapping relation between the brightness value of the remote sensing map image and greenhouse gas through continuous weighted summation and nonlinear transformation, and outputting a training model;
the data set passes through nonlinear transformation of a plurality of hidden layers from an input layer and finally reaches an output layer, as shown in the following formula:
；
where x represents the input, y represents the value of the output layer, Representing the sum of the input weights of the output layer k,/>Represents the weights of the current neuron and the next-layer neuron, x jk represents the output value of the j-th-layer neuron and the input value of the k-th-layer neuron (j represents the subscript of the current-layer neuron, is a hidden-layer neuron, k represents the subscript of the next-layer neuron, is an output-layer neuron), and the index of the current-layer neuron is equal to the index of the next-layer neuronActivating a function for sigmoid;
In the process of training the multi-layer perceptron model, a random gradient descent algorithm is used for calculating the gradient of a loss function on network parameters, the parameters are updated according to the gradient so as to minimize the loss function, and finally, the accuracy of predicting the greenhouse gas content is improved according to the brightness value of the remote sensing map image;
S7, applying the training model to the whole remote sensing map to obtain the gas content of CO 2、CO、CH4 and the like at each point position on the remote sensing map;
and S8, applying the training model to a micro-pulse laser radar, and independently working the laser radar to obtain a remote sensing map, and obtaining the greenhouse gas content at different positions through the remote sensing map.
The micropulse laser radar 5 is an Aiwok PMTracer aerosol laser radar, and the greenhouse gas measuring instrument 6 is a GR2012 portable greenhouse gas detector.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A gas content measuring device based on optical remote sensing mode, characterized by comprising: the system comprises a first GPS antenna, a first GPS receiver, a second GPS antenna, a second GPS receiver, a micropulse laser radar, a greenhouse gas measuring instrument, a turntable device, a data acquisition unit, a data processing module and a control module;
The micro-pulse laser radar is arranged on the turntable device, and the turntable device is connected with the control module; the turntable device drives the micro-pulse laser radar to perform space scanning, the micro-pulse laser radar receives laser echo signals, and the data processing module obtains remote sensing images based on the laser echo signals;
The first GPS antenna is connected with the first GPS receiver, and the first GPS receiver is used for acquiring a first position coordinate where the micropulse laser radar is located; the data processing module and the first GPS receiver respectively send the remote sensing image and the first position coordinate to the control module, and the remote sensing image and the first position coordinate are mapped on the control module to obtain a remote sensing map;
The second GPS antenna is connected with the second GPS receiver, and the second GPS receiver is used for acquiring second position coordinates of the greenhouse gas measuring instrument; the data acquisition device is connected with the second GPS receiver and the greenhouse gas measuring instrument, and is used for transmitting the second position coordinates and the greenhouse gas content information measured by the greenhouse gas measuring instrument to the control module, and mapping the second position coordinates on the remote sensing map on the control module;
The control module records a continuously collected remote sensing map and greenhouse gas content information to obtain a data set; and performing machine learning on the data set, and then applying a machine learning result to the whole remote sensing map to obtain the greenhouse gas content at each point position.
2. The gas content measuring device based on an optical remote sensing mode according to claim 1, wherein: the control module comprises a main control computer and is used for controlling the rotation of the turntable device, receiving data and analyzing the data; the machine learning method is a back propagation neural network algorithm.
3. The gas content measuring device based on an optical remote sensing mode according to claim 2, wherein: the turntable device is a two-dimensional turntable, and the space scanning is scanning in the horizontal direction and the pitching direction.
4. A gas content measuring device based on optical remote sensing as defined in claim 3, wherein: and the first GPS antenna and the second GPS antenna are patch antennas.
5. The gas content measuring device based on an optical remote sensing mode according to claim 4, wherein: the first GPS receiver and the second GPS receiver are measurement type GPS receivers.
6. The gas content measuring device based on an optical remote sensing mode according to claim 5, wherein: the micro-pulse laser radar is an aerosol laser radar.
7. The gas content measuring device based on an optical remote sensing mode according to claim 6, wherein: the greenhouse gases include CO 2, CO, and/or CH 4.
8. The method for measuring the gas content based on the optical remote sensing mode is characterized by adopting the device for measuring the gas content based on the optical remote sensing mode as claimed in claim 1, and specifically comprising the following steps of:
S1, performing space scanning by adopting a micro-pulse laser radar and receiving a laser echo signal, and obtaining a remote sensing image based on the laser echo signal by a data processing module;
s2, a first GPS receiver collects a first position coordinate where the micro-pulse laser radar is located;
s3, the data processing module and the first GPS receiver respectively send the remote sensing image and the first position coordinate to the control module, and mapping is carried out on plane map software of the control module to obtain a remote sensing map;
S4, acquiring a second position coordinate of the greenhouse gas measuring instrument by a second GPS receiver; starting up the greenhouse gas measuring instrument to obtain the content of greenhouse gas;
s5, the data collector collects second position coordinates and sends greenhouse gas content information to the control module, and the second position coordinates are mapped on the remote sensing map on the control module;
s6, the control module records continuously collected remote sensing map and greenhouse gas content information to obtain a data set; performing machine learning on the data set, establishing a corresponding relation between the remote sensing image and the greenhouse gas content, and outputting a machine learning result;
s7, applying the machine learning result to the whole remote sensing map to obtain the greenhouse gas content at each point position on the remote sensing map;
And S8, applying a machine learning result to the micro-pulse laser radar, and independently working the laser radar to obtain a remote sensing map, and obtaining the greenhouse gas content at different positions through the remote sensing map.
9. The method for measuring the gas content based on the optical remote sensing mode according to claim 8, wherein the method comprises the following steps: in the step S6, the GPS pulse per second is used as a synchronization reference.
10. The method for measuring the gas content based on the optical remote sensing mode according to claim 9, wherein the method comprises the following steps: the machine learning specifically includes: performing multi-layer perceptron model training, establishing a mapping relation between an image brightness value of the remote sensing map and the greenhouse gas through continuous weighted summation and nonlinear transformation, and outputting a training model;
The multi-layer perceptron model training uses a random gradient descent algorithm;
The data set passes through nonlinear transformation of a plurality of hidden layers from an input layer and finally reaches an output layer, and the following formula is shown:
；
where x represents the input, y represents the value of the output layer, Representing the sum of the input weights of the output layer k,/>Representing the weights of the current neuron and the next layer neuron, x jk represents the output value of the j-th layer neuron and the input value of the k-th layer neuron,/>To activate the function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410394349.1A CN117990660B (en) | 2024-04-02 | 2024-04-02 | Gas content measuring device and method based on optical remote sensing mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410394349.1A CN117990660B (en) | 2024-04-02 | 2024-04-02 | Gas content measuring device and method based on optical remote sensing mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117990660A true CN117990660A (en) | 2024-05-07 |
| CN117990660B CN117990660B (en) | 2024-07-23 |
Family
ID=90894565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410394349.1A Active CN117990660B (en) | 2024-04-02 | 2024-04-02 | Gas content measuring device and method based on optical remote sensing mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117990660B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2170922C1 (en) * | 2000-04-10 | 2001-07-20 | Казанский государственный технический университет им. А.Н. Туполева | Procedure determining spatial distribution of concentration of gas |
| US20020118352A1 (en) * | 2001-02-23 | 2002-08-29 | Japan Atomic Energy Research Institute | Fast gate scanning three-dimensional laser radar apparatus |
| JP2005077347A (en) * | 2003-09-03 | 2005-03-24 | Kokusai Kogyo Co Ltd | Laser radar system for atmospheric measurement |
| WO2020148897A1 (en) * | 2019-01-18 | 2020-07-23 | 三菱電機株式会社 | Air conditioner and control method |
| US20200301015A1 (en) * | 2019-03-21 | 2020-09-24 | Foresight Ai Inc. | Systems and methods for localization |
| CN115494523A (en) * | 2022-11-21 | 2022-12-20 | 珩辉光电测量技术(吉林)有限公司 | Atmospheric pollutant concentration detection device and detection method |
| CN117392539A (en) * | 2023-10-13 | 2024-01-12 | 哈尔滨师范大学 | River water body identification method based on deep learning, electronic equipment and storage medium |
| CN117392564A (en) * | 2023-10-13 | 2024-01-12 | 哈尔滨师范大学 | River water quality inversion method based on deep learning, electronic equipment and storage medium |
| US20240044783A1 (en) * | 2022-08-02 | 2024-02-08 | Palo Alto Research Center Incorporated | Generating high-resolution concentration maps for atmospheric gases using geography-informed machine learning |
-
2024
- 2024-04-02 CN CN202410394349.1A patent/CN117990660B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2170922C1 (en) * | 2000-04-10 | 2001-07-20 | Казанский государственный технический университет им. А.Н. Туполева | Procedure determining spatial distribution of concentration of gas |
| US20020118352A1 (en) * | 2001-02-23 | 2002-08-29 | Japan Atomic Energy Research Institute | Fast gate scanning three-dimensional laser radar apparatus |
| JP2005077347A (en) * | 2003-09-03 | 2005-03-24 | Kokusai Kogyo Co Ltd | Laser radar system for atmospheric measurement |
| WO2020148897A1 (en) * | 2019-01-18 | 2020-07-23 | 三菱電機株式会社 | Air conditioner and control method |
| US20200301015A1 (en) * | 2019-03-21 | 2020-09-24 | Foresight Ai Inc. | Systems and methods for localization |
| US20240044783A1 (en) * | 2022-08-02 | 2024-02-08 | Palo Alto Research Center Incorporated | Generating high-resolution concentration maps for atmospheric gases using geography-informed machine learning |
| CN115494523A (en) * | 2022-11-21 | 2022-12-20 | 珩辉光电测量技术(吉林)有限公司 | Atmospheric pollutant concentration detection device and detection method |
| CN117392539A (en) * | 2023-10-13 | 2024-01-12 | 哈尔滨师范大学 | River water body identification method based on deep learning, electronic equipment and storage medium |
| CN117392564A (en) * | 2023-10-13 | 2024-01-12 | 哈尔滨师范大学 | River water quality inversion method based on deep learning, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| JEREMY DOBLER ET AL.: "Spatial mapping of greenhouse gases using laser absorption spectrometers at local scales of interest", 《PROC. OF SPIE》, 31 December 2015 (2015-12-31), pages 1 - 12 * |
| S L LESHCHENKO: "Technologies for remote lidar sensing of the surface to detect low concentrations of hydrocarbons in Siberia and the far north", 《IOP CONF. SERIES: MATERIALS SCIENCE AND ENGINEERING》, 31 December 2020 (2020-12-31), pages 1 - 6 * |
| 岳斌等: "温室气体通量测量方法及进展", 《光学学报》, vol. 43, no. 18, 30 September 2023 (2023-09-30), pages 1 - 13 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117990660B (en) | 2024-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113064117B (en) | Radiation source positioning method and device based on deep learning | |
| CN109977813A (en) | A kind of crusing robot object localization method based on deep learning frame | |
| CN108307767B (en) | Detection of obstacles obstacle avoidance system and method suitable for full-automatic weeder | |
| CN108693403A (en) | A kind of virtual densification frequency spectrum situation generation method of wide area | |
| JP2018163096A (en) | Information processing method and information processing device | |
| CN110095784A (en) | A kind of ocean-lower atmosphere layer laser under the influence of complex environment transmits modeling method | |
| CN112146761A (en) | Human body temperature measurement compensation method based on machine learning | |
| CN113850228A (en) | Road crack detection method and system based on multi-mode fusion | |
| CN111401180B (en) | Neural network recognition model training method, device, server and storage medium | |
| CN114966899B (en) | Regional visibility prediction method based on multi-source multi-element remote sensing technology cooperation | |
| CN111050294A (en) | Indoor positioning system and method based on deep neural network | |
| CN107590514A (en) | Sample point optimization distribution method, system and the equipment of Classification in Remote Sensing Image precision evaluation | |
| CN116338628B (en) | Laser radar sounding method and device based on learning architecture and electronic equipment | |
| Li et al. | A four-component parameterized directional thermal radiance model for row canopies | |
| CN117990660B (en) | Gas content measuring device and method based on optical remote sensing mode | |
| CN111611921B (en) | Solar panel identification system based on remote sensing big data | |
| CN111652276B (en) | All-weather portable multifunctional bionic positioning and attitude-determining viewing system and method | |
| CN111239682A (en) | Electromagnetic emission source positioning system and method | |
| CN115564678A (en) | Low-altitude unmanned-machine spectral radiation correction method and system for optimizing BP neural network | |
| US7130805B2 (en) | Method and apparatus for generating progressive queries and models for decision support | |
| CN203444122U (en) | Underwater hyperspectral imaging system | |
| CN115797807A (en) | Ocean garbage monitoring method, system and medium based on data of unmanned aerial vehicle-mounted spectrometer | |
| CN116124716A (en) | Water quality monitoring method and device based on proximity hyperspectral | |
| CN109061769A (en) | Infrared system operating distance based on controllable target tests appraisal procedure | |
| CN105784626B (en) | A kind of atmosphere pollution self-adaptive identification method and system based on infrared spectroscopy imaging technique |
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 |