CN108090031A - Gas concentration lwevel inverting optimization method and system - Google Patents
Gas concentration lwevel inverting optimization method and system Download PDFInfo
- Publication number
- CN108090031A CN108090031A CN201711418084.0A CN201711418084A CN108090031A CN 108090031 A CN108090031 A CN 108090031A CN 201711418084 A CN201711418084 A CN 201711418084A CN 108090031 A CN108090031 A CN 108090031A
- Authority
- CN
- China
- Prior art keywords
- inverting
- gas concentration
- pixel
- concentration lwevel
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/85—Investigating moving fluids or granular solids
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/85—Investigating moving fluids or granular solids
- G01N2021/8578—Gaseous flow
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical & Material Sciences (AREA)
- Biochemistry (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention provides a kind of gas concentration lwevel inverting optimization method, comprises the following steps：Step 1：The data from greenhouse gases survey meter and cloud detection and aerosol detection instrument are obtained, and using having cloud pixel in L2 grades of cloud detection products of CAPI and aerosol product rejecting HS carbon dioxide L1B data, filter out the pixel available for inverting gas concentration lwevel；Step 2：For different carbon moonscope patterns, the pel data for screening different ground surface types carries out inverting；Step 3：The pixel that can be used to inverting gas concentration lwevel screened is averagely allocated to multiple calculate nodes and carries out inverting；Step 4：Single calculate node is carried out at the same time the inverting of gas concentration lwevel using several subprocess of multi-process concurrent technique startup, and inversion result is write Redis memory databases；Step 5：Inversion result in Redis memory databases is read, generates carbon dioxide retrieval products file and browse graph.
Description
Technical field
The present invention relates to a kind of gas concentration lwevel inverting optimization method and systems.
Background technology
The variation of greenhouse gas concentration is most important for prediction Future Climate Change, therefore global carbon dioxide concentration
Change in time and space is always the hot and difficult issue studied.In the global carbon dioxide monitoring scientific experiment satellite (abbreviation of China's transmitting
Carbon satellite) on to be equipped with EO-1 hyperion greenhouse gases survey meter (HS carbon dioxide), two, cloud ＆ aerosol detections instrument (CAPI) important
Load.Satellite data transmission is to ground data reception system, the processing through product generation system to data, and generation carbon dioxide is anti-
Product is drilled, so as to fulfill the real-time monitoring to gas concentration lwevel.
Carbon satellite carbon dioxide inverting obtains carbon dioxide with product generation method using full physics Iterative inversion algorithm
Spatial and temporal distributions, the algorithm need to simulate the physical process of radiation transmission in refutation process, consider steam, temperature, cloud, gas comprehensively
The influence factor of the air such as colloidal sol and Land Surface Parameters, it is usually extremely complex.EO-1 hyperion inverting data volume is big, is calculated using physics iteration
The inverse time that method completes gas concentration lwevel is long, therefore utilizes traditional serial computing, unicomputer node multi-threaded parallel
Mode is difficult to meet real-time business operation demand.
The present invention is very low for the timeliness of full physics iterative algorithm inverting gas concentration lwevel, it is difficult to meet real time business
The problem of changing operation demand, proposes a kind of parallel computation optimization method.
The content of the invention
For full physics iterative algorithm inverting gas concentration lwevel timeliness it is relatively low the problem of, the present invention provides a kind of dioxy
Change concentration of carbon inverting optimization method and system, solve the problems, such as that carbon dioxide inversion speed is slow, timeliness is very low, realize to dioxy
Change the real-time monitoring of concentration of carbon.
The invention is realized by the following technical scheme.
(1) a kind of gas concentration lwevel inverting optimization method, comprises the following steps：
Step 1：The data from greenhouse gases survey meter and cloud detection and aerosol detection instrument are obtained, and utilize CAPI
L2 grades of cloud detection products and aerosol product, which are rejected in HS carbon dioxide L1B data, cloud pixel, filters out available for inverting two
Aoxidize the pixel of concentration of carbon；
Step 2：For different carbon moonscope patterns, the pel data for screening different ground surface types carries out inverting；
Step 3：The pixel that can be used to inverting gas concentration lwevel screened is averagely allocated to multiple calculate nodes
Carry out inverting；
Step 4：Single calculate node starts several subprocess using multi-process concurrent technique and is carried out at the same time dense carbon dioxide
The inverting of degree, and inversion result is write into Redis memory databases；
Step 5：Inversion result in Redis memory databases is read, generates carbon dioxide retrieval products file and browse graph.
(2) in the gas concentration lwevel inverting optimization method described in above-mentioned (1),
The step 1 includes：
Step 11：COSS scheduling parameters are parsed, read HS carbon dioxide L1B files, and according to observation time Auto-matching
Corresponding CAPI L2 grades of cloud detection and aerosol product；With
Step 12：Being gone out using L2 grades of product screenings of CAPI in HS carbon dioxide L1B data can inverting gas concentration lwevel
Pixel.
(3) in the gas concentration lwevel inverting optimization method described in above-mentioned (2),
The step 2 includes：
Step 21：HS carbon dioxide L1B data observation patterns are parsed, and read the extra large land mask identifier of each pixel；
Step 22：Under solar flare observation mode, earth observation data are rejected；
Step 23：Under star under observation mode, target observation pattern, ocean overhead observation data are rejected；
(4) in the gas concentration lwevel inverting optimization method described in above-mentioned (3),
The step 3 includes：
Step 31：Read the total pixel number that can be used to inverting gas concentration lwevel；
Step 32：Each calculate node according to COSS scheduling parameters, calculate this node be assigned to can inverting pixel number
It and each can inverting pixel sounding id；.
(5) in the gas concentration lwevel inverting optimization method described in above-mentioned (4),
The step 4 includes：
Step 41：By the test screen of a variety of parallel processing strategies, multi-process parallel form is selected；
Step 42：Parent process creates several subprocess；
Step 43：Each subprocess is directed to the inverting pixel sounding id being assigned to and carries out inverting, and subprocess reads phase
Spectral calibration data, location data and the forecast field data answered, call physics Iterative inversion algorithm storehouse, complete gas concentration lwevel
Inverting；
Step 44：The inversion result data volume of single pixel is very big, and meets key-value types, therefore selects redis
Memory database carries out the read-write of inversion result；
Step 45：When completing the carbon dioxide inverting of single pixel, result is stored in Redis memory databases；
Step 46：When subprocess completes gas concentration lwevel inverting, exit functions is called to exit.Parent process withdraw son into
Journey, subprocess complete inverting task.
(6) in the gas concentration lwevel inverting optimization method described in above-mentioned (5),
The step 5 includes：
Step 51：Read the inversion result of single pixel in Redis memory databases, generation carbon dioxide retrieval products text
Part；
Step 52：Generate gas concentration lwevel distribution browse graph；
Step 53：Delete carbon dioxide inversion result in Redis memory databases.
(7) in the gas concentration lwevel inverting optimization method described in above-mentioned (1),
Step 6, the Performance tuning of cross-node multi-process Parallel Computation,
The step 6 includes：
Step 61：Pass through the operating status of top instruction analysis subprocess and computer hardware resource occupancy situation；
Step 62：According to the CPU core number and hardware resource of each calculate node, start optimal concurrent process number；
Step 63：Performance tuning is carried out using Intel's VTune Amplifier softwares, checking in program influences inverting speed
The key factor of degree；
Step 64：In the case of hardware resource abundance, static data is all read in memory.
(8) a kind of gas concentration lwevel inverting optimization system, including：
Data capture unit obtains the data from greenhouse gases survey meter and cloud detection and aerosol detection instrument；
Pixel screening unit rejects HS carbon dioxide L1B data using L2 grades of cloud detection products of CAPI and aerosol product
In have cloud pixel, filter out the pixel available for inverting gas concentration lwevel, and for different carbon moonscope patterns, filter out
The pel data of different ground surface types；
Inverting unit, the pixel average mark that can be used to inverting gas concentration lwevel that the pixel screening unit is filtered out
The multiple calculate nodes of dispensing carry out inverting, and single calculate node starts several subprocess using multi-process concurrent technique and is carried out at the same time
The inverting of gas concentration lwevel, and inversion result is write into Redis memory databases；
Data-reading unit reads the inversion result in the Redis memory databases, and generates carbon dioxide inverting production
Product file and browse graph.
(9) in the gas concentration lwevel inverting optimization system described in (8), the pixel screening unit includes：CAPI L2
Grade cloud detection and aerosol product selecting unit parse COSS scheduling parameters, read the HS carbon dioxide L1B files, according to
L2 grades of cloud detection of observation time Auto-matching CAPI corresponding with the observation time and aerosol product.
(10) in the gas concentration lwevel inverting optimization system described in (8), the pixel screening unit further includes：Observation
Pattern determining unit, parses the observation mode of the HS carbon dioxide L1B data, and reads the extra large land mask mark of each pixel
Know；Data culling unit under solar flare observation mode, rejects earth observation data, observation mode, target observation pattern under star
Under, reject ocean overhead observation data.
(11) in the gas concentration lwevel inverting optimization system described in (9), the inverting unit includes：Pixel read and
Computing unit reads the total pixel number that can be used to inverting gas concentration lwevel, and each calculate node is according to COSS scheduling parameters, meter
Calculate this node be assigned to can inverting pixel number and each can inverting pixel.
(12) in the gas concentration lwevel inverting optimization system described in (11), the inverting unit includes：Multi-process is simultaneously
Line mode selecting unit according to the test screen of a variety of parallel processing strategies, selects multi-process parallel form；Subprocess is created
Allocation unit is built, creates several subprocess, distributes each subprocess inverting pixel, each subprocess is directed to the image by inversion being assigned to
Member carries out inverting；The subprocess includes：Data capture unit reads corresponding spectral calibration data, location data and forecast
Field data；Subprocess inverting unit calls physics Iterative inversion algorithm storehouse, completes gas concentration lwevel inverting.
(13) in the gas concentration lwevel inverting optimization system described in (8), the data-reading unit is reading Redis
The inversion result of single pixel in memory database generates carbon dioxide retrieval products file and gas concentration lwevel distribution browsing
After figure, the carbon dioxide inversion result in Redis memory databases is deleted.
Invention effect
By means of the invention it is possible to improve carbon dioxide inversion speed, real time business operation demand is met.Ensureing instead
On the premise of drilling precision, gas concentration lwevel inverting efficiency can be effectively improved, therefore can be obtained based on cross-node multi-process simultaneously
The high-performance gas concentration lwevel inverting optimization processing system of row computing technique.
Description of the drawings
Fig. 1 be the present invention is based on carbon dioxide inverting optimization method FB(flow block).
Specific embodiment
In the following, the processing procedure of the present invention is described in detail with reference to Fig. 1.
The gas concentration lwevel inverting optimization method of the present invention comprises the following steps.
Step 1, being rejected using L2 grades of cloud detection products of CAPI and aerosol product in HSCO2L1B data has cloud picture dot,
Filter out the pixel available for inverting CO2 concentration.
(1) COSS scheduling parameters are parsed, read HSCO2L1B files, and according to the corresponding of observation time Auto-matching
L2 grades of cloud monitorings of CAPI and aerosol product；
(2) using having cloud pixel in L2 grades of cloud products of CAPI, aerosol product rejecting HSCO2L1B level data, obtaining can
For gas concentration lwevel inverting pixel sum and each can inverting pixel sounding id；
(3) by available for inverting gas concentration lwevel pixel sum, can inverting CO2 concentration pixel sounding id,
Forecast the write-in temporary file such as field data.
Step 2, to different carbon moonscope patterns, the pel data for screening different ground surface types carries out inverting.
(1) HSCO2L1B data observation patterns are parsed, and read the extra large land mask identifier of each pixel；
(2) under solar flare observation mode, earth observation data are rejected；
(3) under star under observation mode, target observation pattern, ocean overhead observation data are rejected.
Step 3, the pixel point that can be used to inverting CO2 concentration is averagely allocated to multiple calculate nodes and carries out inverting.
(1) the total pixel number that can be used to inverting CO2 concentration is read；
(2) each calculate node is according to COSS scheduling parameters, and calculate that this node is assigned to can inverting pixel number and picture
First sounding id.
Step 4, single calculate node starts multiple subprocess progress gas concentration lwevels using multi-process concurrent technique
Inverting, and inversion result is write into Redis memory databases.
(1) by the test screen of a variety of parallel processing strategies, multi-process parallel form is selected；
(2) parent process is several subprocess using fork () function creation；
(3) what the successful each subprocess calculating of establishment was assigned to can inverting pixel number and pixel sounding id；
(4) subprocess is successively read the corresponding spectral calibration data of each sounding id, location data and forecast number of fields
According to calling full physics Iterative inversion algorithm storehouse, complete gas concentration lwevel inverting；
(5) Redis memory databases are key-value storage systems, and read-write data are quick and precisely.CO2 retrieval products numbers
It is very big according to measuring, and meet key-value types, therefore select the read-write of redis memory databases progress inversion result；
(6) when subprocess completes the CO2 retrieving concentrations of single pixel, result is stored in Redis memory databases；
(7) when subprocess completes inverting task, exit functions is called to exit, parent process is by calling waitpid () function
Any one subprocess exited is withdrawn in time, and subprocess completes inverting task.
Step 5, the inversion result of each pixel in Redis memory databases, the inverting production of generation gas concentration lwevel are read
Product file and browse graph.
(1) the carbon dioxide inversion result of single pixel in Redis memory databases is read；
(2) according to each can inverting pixel sounding id read HSCO2L1B files location data, calibration data,
And generate carbon dioxide retrieval products file；
(3) the longitudes and latitudes projection pattern such as utilize, the CO2 concentration that inverting obtains is projected, and exports global titanium dioxide
Carbon is distributed browse graph；
(4) carbon dioxide inversion result in Redis memory databases is deleted.
Step 6, the Performance tuning of cross-node multi-process Parallel Computation realizes the maximum utilization of computer resource.
(1) operating status of top instruction analysis subprocess and computer hardware resource occupancy situation are passed through；
(2) according to the CPU core and computer hardware resource of each calculate node, optimal concurrent process number is started；
(3) using Intel's VTune Amplifier softwares analysis code performance, checking influences inversion speed in program
Key factor；
(4) in the case of hardware resource abundance, substantial amounts of data read-write operation is avoided, static data is all read in
In memory.
Correspondingly, gas concentration lwevel inverting optimization system of the invention includes：Data capture unit is obtained from greenhouse
Gas detecting instrument and cloud detection and the data of aerosol detection instrument；Pixel screening unit, using L2 grades of cloud detection products of CAPI and
Aerosol product, which is rejected in HS carbon dioxide L1B data, cloud pixel, filters out the pixel available for inverting gas concentration lwevel,
And different carbon moonscope patterns are directed to, filter out the pel data of different ground surface types；Inverting unit screens the pixel
The pixel of what unit filtered out can be used to inverting gas concentration lwevel is averagely allocated to multiple calculate nodes and carries out invertings, single meter
Operator node starts the inverting that several subprocess are carried out at the same time gas concentration lwevel using multi-process concurrent technique, and by inversion result
Write Redis memory databases；Data-reading unit reads the inversion result in the Redis memory databases, and generates two
Carbonoxide retrieval products file and browse graph.
Wherein, pixel screening unit includes：L2 grades of cloud detection of CAPI and aerosol product selecting unit parse COSS tune
Parameter is spent, the HS carbon dioxide L1B files are read, according to observation time Auto-matching CAPI corresponding with the observation time
L2 grades of cloud detection and aerosol product.
The pixel screening unit further includes：Observation mode determination unit parses the observation of the HS carbon dioxide L1B data
Pattern, and read the extra large land mask identifier of each pixel；Data culling unit under solar flare observation mode, rejects earth observation
Data under star under observation mode, target observation pattern, reject ocean overhead observation data.
Above-mentioned inverting unit includes：Pixel is read and computing unit, reads the total picture that can be used to inverting gas concentration lwevel
First number, each calculate node according to COSS scheduling parameters, calculate this node be assigned to can inverting pixel number and each may be used
Inverting pixel.
The inverting unit further includes：Multi-process parallel mode selecting unit, according to the test of a variety of parallel processing strategies
Multi-process parallel form is selected in screening；Subprocess creates allocation unit, creates several subprocess, each subprocess is distributed anti-
Pixel is drilled, each subprocess is directed to the inverting pixel being assigned to and carries out inverting；The subprocess includes：Data capture unit is read
Take corresponding spectral calibration data, location data and forecast field data；Subprocess inverting unit calls physics Iterative inversion algorithm
Gas concentration lwevel inverting is completed in storehouse.
The inversion result of above-mentioned data-reading unit single pixel in Redis memory databases are read generates titanium dioxide
After carbon retrieval products file and gas concentration lwevel distribution browse graph, the carbon dioxide inverting in Redis memory databases is deleted
As a result.
Above example is the specific example enumerated in order to illustrate the present invention, and protection scope of the present invention is with claim
Subject to secretary carries.Those skilled in the art know, in the range of without departing from present subject matter, can implement various replacements, change
More.
Claims (13)
1. a kind of gas concentration lwevel inverting optimization method, comprises the following steps：
Step 1：The data from greenhouse gases survey meter and cloud detection and aerosol detection instrument are obtained, and utilize L2 grades of CAPI
Cloud detection product and aerosol product, which are rejected in HS carbon dioxide L1B data, cloud pixel, filters out available for inverting titanium dioxide
The pixel of concentration of carbon；
Step 2：For different carbon moonscope patterns, the pel data for screening different ground surface types carries out inverting；
Step 3：The pixel that can be used to inverting gas concentration lwevel screened is averagely allocated to multiple calculate nodes to carry out
Inverting；
Step 4：Single calculate node starts several subprocess using multi-process concurrent technique and is carried out at the same time gas concentration lwevel
Inverting, and inversion result is write into Redis memory databases；
Step 5：Inversion result in Redis memory databases is read, generates carbon dioxide retrieval products file and browse graph.
2. gas concentration lwevel inverting optimization method according to claim 1, which is characterized in that
The step 1 includes：
Step 11：COSS scheduling parameters are parsed, read HS carbon dioxide L1B files, and according to observation time Auto-matching therewith
Corresponding CAPI L2 grades of cloud detection and aerosol product；With
Step 12：Using L2 grades of product screenings of CAPI go out in HS carbon dioxide L1B data can inverting gas concentration lwevel picture
Member.
3. gas concentration lwevel inverting optimization method according to claim 2, which is characterized in that
The step 2 includes：
Step 21：HS carbon dioxide L1B data observation patterns are parsed, and read the extra large land mask identifier of each pixel；
Step 22：Under solar flare observation mode, earth observation data are rejected；
Step 23：Under star under observation mode, target observation pattern, ocean overhead observation data are rejected.
4. gas concentration lwevel inverting optimization method according to claim 3, which is characterized in that
The step 3 includes：
Step 31：Read the total pixel number that can be used to inverting gas concentration lwevel；
Step 32：Each calculate node according to COSS scheduling parameters, calculate this node be assigned to can inverting pixel number and
It each can inverting pixel sounding id.
5. gas concentration lwevel inverting optimization method according to claim 4, which is characterized in that
The step 4 includes：
Step 41：By the test screen of a variety of parallel processing strategies, multi-process parallel form is selected；
Step 42：Parent process creates several subprocess；
Step 43：Each subprocess is directed to the inverting pixel sounding id being assigned to and carries out inverting, and subprocess reads corresponding
Spectral calibration data, location data and forecast field data, call physics Iterative inversion algorithm storehouse, it is anti-to complete gas concentration lwevel
It drills；
Step 44：The inversion result data volume of single pixel is very big, and meets key-value types, therefore selects redis memories
Database carries out the read-write of inversion result；
Step 45：When completing the carbon dioxide inverting of single pixel, result is stored in Redis memory databases；
Step 46：When subprocess completes gas concentration lwevel inverting, exit functions is called to exit.Parent process withdraws subprocess, son
Process completes inverting task.
6. gas concentration lwevel inverting optimization method according to claim 5, which is characterized in that
The step 5 includes：
Step 51：The inversion result of single pixel in Redis memory databases is read, generates carbon dioxide retrieval products file；
Step 52：Generate gas concentration lwevel distribution browse graph；
Step 53：Delete carbon dioxide inversion result in Redis memory databases.
7. gas concentration lwevel inverting optimization method according to claim 1, which is characterized in that further include：
Step 6, the Performance tuning of cross-node multi-process Parallel Computation,
The step 6 includes：
Step 61：Pass through the operating status of top instruction analysis subprocess and computer hardware resource occupancy situation；
Step 62：According to the CPU core number and hardware resource of each calculate node, start optimal concurrent process number；
Step 63：Performance tuning is carried out using Intel's VTune Amplifier softwares, checking influences inversion speed in program
Key factor；
Step 64：In the case of hardware resource abundance, static data is all read in memory.
8. a kind of gas concentration lwevel inverting optimization system, including：
Data capture unit obtains the data from greenhouse gases survey meter and cloud detection and aerosol detection instrument；
Pixel screening unit, being rejected using L2 grades of cloud detection products of CAPI and aerosol product in HS carbon dioxide L1B data is had
Cloud pixel filters out the pixel available for inverting gas concentration lwevel, and for different carbon moonscope patterns, filters out difference
The pel data of ground surface type；
The pixel that can be used to inverting gas concentration lwevel that the pixel screening unit filters out is averagely allocated to by inverting unit
Multiple calculate nodes carry out inverting, and single calculate node starts several subprocess using multi-process concurrent technique and is carried out at the same time dioxy
Change the inverting of concentration of carbon, and inversion result is write into Redis memory databases；
Data-reading unit reads the inversion result in the Redis memory databases, and generates carbon dioxide retrieval products text
Part and browse graph.
9. gas concentration lwevel inverting optimization system according to claim 8, which is characterized in that
The pixel screening unit includes：L2 grades of cloud detection of CAPI and aerosol product selecting unit, parsing COSS scheduling ginsengs
Number, reads the HS carbon dioxide L1B files, according to observation time Auto-matching CAPI L2 corresponding with the observation time
Grade cloud detection and aerosol product.
10. gas concentration lwevel inverting optimization system according to claim 9, which is characterized in that
The pixel screening unit further includes：
Observation mode determination unit, parses the observation mode of the HS carbon dioxide L1B data, and reads the Hai Lu of each pixel
Mask identifier；
Data culling unit under solar flare observation mode, rejects earth observation data, observation mode, target observation mould under star
Under formula, ocean overhead observation data are rejected.
11. gas concentration lwevel inverting optimization system according to claim 9, which is characterized in that
The inverting unit includes：Pixel is read and computing unit, reads the total pixel number that can be used to inverting gas concentration lwevel,
Each calculate node according to COSS scheduling parameters, calculate this node be assigned to can inverting pixel number and each can image by inversion
Member.
12. gas concentration lwevel inverting optimization system according to claim 11, which is characterized in that
The inverting unit includes：
Multi-process parallel mode selecting unit according to the test screen of a variety of parallel processing strategies, selects multi-process parallel
Mode；
Subprocess creates allocation unit, creates several subprocess, distributes each subprocess inverting pixel, and each subprocess, which is directed to, to be divided
The inverting pixel being fitted on carries out inverting；
The subprocess includes：Data capture unit reads corresponding spectral calibration data, location data and forecast field data；
Subprocess inverting unit calls physics Iterative inversion algorithm storehouse, completes gas concentration lwevel inverting.
13. gas concentration lwevel inverting optimization system according to claim 8, which is characterized in that
The inversion result of the data-reading unit single pixel in Redis memory databases are read, generation carbon dioxide are anti-
After drilling product documentation and gas concentration lwevel distribution browse graph, the carbon dioxide inversion result in Redis memory databases is deleted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711418084.0A CN108090031A (en) | 2017-12-25 | 2017-12-25 | Gas concentration lwevel inverting optimization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711418084.0A CN108090031A (en) | 2017-12-25 | 2017-12-25 | Gas concentration lwevel inverting optimization method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108090031A true CN108090031A (en) | 2018-05-29 |
Family
ID=62179069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711418084.0A Pending CN108090031A (en) | 2017-12-25 | 2017-12-25 | Gas concentration lwevel inverting optimization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108090031A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113239522A (en) * | 2021-04-20 | 2021-08-10 | 四川大学 | Atmospheric pollutant diffusion simulation method based on computer cluster |
| CN116297288A (en) * | 2023-05-24 | 2023-06-23 | 上海航天空间技术有限公司 | Rapid remote sensing inversion method and system for atmospheric methane dry air mixing ratio |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060251324A1 (en) * | 2004-09-20 | 2006-11-09 | Bachmann Charles M | Method for image data processing |
| CN101359333A (en) * | 2008-05-23 | 2009-02-04 | 中国科学院软件研究所 | Parallel data processing method based on latent dirichlet allocation model |
| CN103279974A (en) * | 2013-05-15 | 2013-09-04 | 中国科学院软件研究所 | High-accuracy high-resolution satellite imaging simulation engine and implementation method |
-
2017
- 2017-12-25 CN CN201711418084.0A patent/CN108090031A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060251324A1 (en) * | 2004-09-20 | 2006-11-09 | Bachmann Charles M | Method for image data processing |
| CN101359333A (en) * | 2008-05-23 | 2009-02-04 | 中国科学院软件研究所 | Parallel data processing method based on latent dirichlet allocation model |
| CN103279974A (en) * | 2013-05-15 | 2013-09-04 | 中国科学院软件研究所 | High-accuracy high-resolution satellite imaging simulation engine and implementation method |
Non-Patent Citations (2)
| Title |
|---|
| 司一丹: "卫星遥感影像反演PM2.5并行算法研究与应用", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》 * |
| 杨忠东 等: "即将入轨的我国首颗测量大气二氧化碳的专用高光谱卫星", 《国际太空》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113239522A (en) * | 2021-04-20 | 2021-08-10 | 四川大学 | Atmospheric pollutant diffusion simulation method based on computer cluster |
| CN113239522B (en) * | 2021-04-20 | 2022-06-28 | 四川大学 | Atmospheric pollutant diffusion simulation method based on computer cluster |
| CN116297288A (en) * | 2023-05-24 | 2023-06-23 | 上海航天空间技术有限公司 | Rapid remote sensing inversion method and system for atmospheric methane dry air mixing ratio |
| CN116297288B (en) * | 2023-05-24 | 2023-08-04 | 上海航天空间技术有限公司 | Rapid remote sensing inversion method and system for atmospheric methane dry air mixing ratio |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10360500B2 (en) | Two-phase distributed neural network training system | |
| CN111177095B (en) | Log analysis method, device, computer equipment and storage medium | |
| CN102915347B (en) | A kind of distributed traffic clustering method and system | |
| CN110929607A (en) | Remote sensing identification method and system for urban building construction progress | |
| CN110310474A (en) | A kind of vehicle flowrate prediction technique and device based on space-time residual error network | |
| CN113723715B (en) | Method, system, equipment and storage medium for automatically matching public transport network with road network | |
| CN103345566A (en) | Geochemical exploration anomaly recognizing and evaluating method based on contents contained in geology | |
| CN109113729B (en) | Lithology identification method and device based on well logging curve | |
| US10718188B2 (en) | Method for evaluation of fluid transport properties in heterogenous geological formation | |
| CN108090031A (en) | Gas concentration lwevel inverting optimization method and system | |
| CN111191696B (en) | Deep learning algorithm-based steel bar layering method and system | |
| Ke et al. | New framework for automatic identification and quantification of freeway bottlenecks based on wavelet analysis | |
| CN106570104A (en) | Multi-partition clustering preprocessing method of stream data | |
| US20130268242A1 (en) | Sparse Representation for Dynamic Sensor Networks | |
| CN115965102A (en) | Method and system for positioning and calculating carbon emission space-time big data | |
| CN110927793A (en) | Reservoir prediction method and system based on sequential random fuzzy simulation | |
| KR101703972B1 (en) | System and method for predicting groundwater potential area using spatial information | |
| CN105975973A (en) | Forest biomass-based remote sensing image feature selection method and apparatus | |
| Duan et al. | Comprehending and analyzing multiday trip-chaining patterns of freight vehicles using a multiscale method with prolonged trajectory data | |
| CN115599774B (en) | Spatiotemporal non-stationarity analysis method and system based on local spatiotemporal tree regression model | |
| CN112070129A (en) | Ground settlement risk identification method, device and system | |
| CN117093890A (en) | Comprehensive evaluation method for ecological environment of energy resource development area | |
| US20230065744A1 (en) | Graphical user interface for abating emissions of gaseous byproducts from hydrocarbon assets | |
| Liu et al. | A modified weights-of-evidence method for mineral potential prediction based on structural equation modeling | |
| CN115169578A (en) | AI model production method and system based on meta-space data markers |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180529 |