CN109657862A - A kind of multi- source Remote Sensing Data data production workflow self-organizing method - Google Patents
A kind of multi- source Remote Sensing Data data production workflow self-organizing method Download PDFInfo
- Publication number
- CN109657862A CN109657862A CN201811563359.4A CN201811563359A CN109657862A CN 109657862 A CN109657862 A CN 109657862A CN 201811563359 A CN201811563359 A CN 201811563359A CN 109657862 A CN109657862 A CN 109657862A
- Authority
- CN
- China
- Prior art keywords
- remotely
- product
- sensed data
- data
- production
- 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
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of multi- source Remote Sensing Data data production workflow self-organizing method, the following steps are included: S1 parsing remotely-sensed data production order obtains the remotely-sensed data name of product of pre-manufactured, and judge according to remotely-sensed data name of product to produce the feasibility of the remotely-sensed data product；S2 is by obtaining the hierarchical relationship up and down of the remotely-sensed data product in level relational knowledge base above and below remotely-sensed data product；S3 produces the required lower level product and creation data of the remotely-sensed data product by obtaining in remotely-sensed data product dependence knowledge base；S4 filters out Optimal Production data according to the difference analysis of satellite sensor characteristic parameter and the remotely-sensed data product；S5 uses the Optimal Production data of each lower level product of Kepler scientific workflow engine tissue, forms Physics Work stream.Beneficial effects of the present invention: selection best source improves automation, the intelligent level of remotely-sensed data production to improve the precision level of remotely-sensed data production.
Description
Technical field
The present invention relates to remotely-sensed data product technical field more particularly to a kind of multi- source Remote Sensing Data data production workflows
Self-organizing method.
Background technique
The development of remote sensing technology causes the explosive growth of the data scale of construction, and product category and quantity are continuously increased, remote sensing
Application field is constantly widened.However, existing remotely-sensed data product production system, product manufacturing process tissue are mostly artificial with expert
Based on mode, process fixes and needs to prepare in advance, can not be according to users ' individualized requirement tissue products production procedure, Wu Faman
Sufficient different user, complicated and changeable, personalized product production requirement.In addition, on the one hand magnanimity, the remotely-sensed data of multi-source cause
Select permeability is preferentially recommended in a variety of approximate data sources in process of producing product, on the other hand for long-term sequence, big region
There may be specific data missings, the poor not available problem of the quality of data again for the synthesis earth observation production of range.
Therefore, multi- source Remote Sensing Data data production needs to solve the problems, such as that best source is recommended, sub-optimal data source is substituted.
Summary of the invention
In view of this, the embodiment provides a kind of multi- source Remote Sensing Data data production workflow self-organizing sides
Method.
The embodiment of the present invention provides a kind of multi- source Remote Sensing Data data production workflow self-organizing method, including following step
It is rapid:
S1 parses the remotely-sensed data name of product that remotely-sensed data production order obtains pre-manufactured, and according to remotely-sensed data
Name of product is judged to produce the feasibility of the remotely-sensed data product, if feasible, receive the remotely-sensed data production order and
Step S2 is executed, the remotely-sensed data production order is otherwise refused；
S2 is by obtaining the hierarchical relationship up and down of the remotely-sensed data product in level relational knowledge base above and below remotely-sensed data product；
S3 produces the required lower level of the remotely-sensed data product by obtaining in remotely-sensed data product dependence knowledge base
The creation data of product and these lower level products；
S4 is according to the characteristic parameter of the satellite sensor in the lower level product and the remotely-sensed data product of pre-manufactured
Difference analysis filters out the Optimal Production data of these lower level products；
S5 uses the Optimal Production data of each lower level product of Kepler scientific workflow engine tissue, and forming production, this is distant
Feel the Physics Work stream of data product.
Further, judged in the step S1 and according to remotely-sensed data name of product to produce the remotely-sensed data product
Feasibility method particularly includes: product and production procedure needed for producing the remotely-sensed data product are judged according to name of product,
If having product and production procedure needed for producing the remotely-sensed data product, receive the remotely-sensed data production order and execution
Otherwise step S2 refuses the remotely-sensed data production order.
Further, level relational knowledge base produces remotely-sensed data product described in the step S2 according to remotely-sensed data up and down
Product grade scale is established.
Further, level relational knowledge base includes four big levels to the remotely-sensed data product up and down, is divided from top to bottom
Not Wei initial data product, precision processing remotely-sensed data product, inverting index products and thematic product, and each big level includes more
A child level, the hierarchical relationship binary coding representation up and down for the child level that each big level includes with it.
Further, remotely-sensed data product dependence knowledge base includes all remotely-sensed data products in the step S3
Upper and lower hierarchical relationship.
Further, the step S4 specifically: pass through the spy to the different satellite sensors in the lower level product
The difference analysis for levying parameter and remotely-sensed data product, establishes the remotely-sensed data production data source based on spectral simulation and pushes away
Model is recommended, is evaluated by the suitability for producing remotely-sensed data product to each satellite sensor, is determined and is given birth to according to evaluation result
The Optimal Production data of the remotely-sensed data product suitable for producing of production.
Further, the workflow in the step S5 includes that workflow is split, subtask scheduling, secondary are sub between node
Mission Operations scheduling and workflow fault-tolerant management.
The technical solution that the embodiment of the present invention provides has the benefit that a kind of multi- source Remote Sensing Data data of the present invention produces
Product production workflow self-organizing method, by the combing for remotely-sensed data product, establishing remotely-sensed data product, level is closed up and down
It is knowledge base, remotely-sensed data product dependence knowledge base and inference rule, can establish the language of each rank remote sensing information product
Justice association, realizes the automatic semantic selection of remotely-sensed data production data source；Establish remotely-sensed data production best source
Recommendation rules, can from numerous alternative multi- source Remote Sensing Data datas the optimal data source of intelligent selection, to improve remote sensing number
According to the precision level of production；Can be turned to the circulation of the logic working of self-organizing based on scientific workflow engine Kepler can
With the Physics Work stream actually executed, automation, the intelligent level of remotely-sensed data production are improved.
Detailed description of the invention
Fig. 1 is a kind of flow chart of multi- source Remote Sensing Data data production workflow self-organizing method of the present invention；
Fig. 2 is Remote Sensing Products hierarchical relationship figure；
Fig. 3 is the flow chart of the Optimal Production data of lower level product；
Fig. 4 is remotely-sensed data production Workflow Management figure.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is further described.
Referring to FIG. 1, the embodiment provides a kind of multi- source Remote Sensing Data data production workflow self-organizing sides
Method, comprising the following steps:
S1 parses the remotely-sensed data name of product that remotely-sensed data production order obtains pre-manufactured, is sentenced according to name of product
It is disconnected go out produce the remotely-sensed data product needed for product and production procedure, here by remotely-sensed data product dependence knowledge
It carries out retrieval in library to be judged, if product and production procedure needed for having the production remotely-sensed data product, show have life
The ability for producing the remotely-sensed data product receives the remotely-sensed data production order and executes step S2, otherwise refuses the remote sensing
Data product produces order.
S2 is by obtaining the hierarchical relationship up and down of the remotely-sensed data product in level relational knowledge base above and below remotely-sensed data product；
Referring to FIG. 2, level relational knowledge base is according to remotely-sensed data grading standard above and below the remotely-sensed data product
It establishes, level relational knowledge base includes four big levels to the remotely-sensed data product up and down, from top to bottom respectively initial data
Product, precision processing remotely-sensed data product, inverting index products and thematic product, and each big level includes multiple child levels, this
The hierarchical relationship binary coding representation up and down for the child level that each big level includes with it in embodiment, specific coding result
It is as follows:
1 remotely-sensed data grading coding schedule of table
Remotely-sensed data product coding at different levels represents the hierarchical relationship up and down of Remote Sensing Products in upper table, and level is higher, secondly
Scale coding value is bigger.The big level of Remote Sensing Products is produced from bottom initial data product to top thematic product
Product coding front two is increased step by step from " 00 " to " 10 "；For lower level product required for producing major level product not, compile
Code is then increased from " 000 " to " 111 " step by step.If precision processing Remote Sensing Products are encoded to " 01000xxxx ", thematic product coding is
" 11000xxxx ", preceding 5 encoded radios significantly identify the hierarchical relationship up and down of encoded radio size and product.Based on productions at different levels
This hierarchical relationship up and down of product, during remotely-sensed data production workflow organization, it can to the production work of tissue
Make stream and carry out Semantic judgement, retrieval obtains the hierarchical relationship up and down of the remotely-sensed data product, prevents illogical remote sensing
Production workflow.
S3 produces the required lower level of the remotely-sensed data product by obtaining in remotely-sensed data product dependence knowledge base
The creation data of product and these lower level products；
Remotely-sensed data product dependence knowledge base and inference rule establishment process: above and below each remotely-sensed data product
Hierarchical relationship, it can parse the next stage or stage further product that each remotely-sensed data production may need, really with this
Determine remotely-sensed data product dependence knowledge base.This remote sensing is retrieved in the remotely-sensed data product dependence knowledge base
Data source in data product production process.
S4 is according to the characteristic parameter of the satellite sensor in the lower level product and the difference for the remotely-sensed data product being somebody's turn to do
Property analysis, filter out the Optimal Production data of these lower level products；
Remotely-sensed data production Optimal Production data source recommendation rules are established: passing through the spy for different satellite sensors
The difference analysis for levying parameter and remotely-sensed data product establishes the Remote Sensing Products creation data source based on spectral simulation and recommends mould
Type is evaluated by the suitability for producing remotely-sensed data product to each satellite data source, recommends each remote sensing according to evaluation result
Optimized remote sensing data source or suboptimum substitute the remotely-sensed data source of data product suitable for producing, available remotely-sensed data product
The logic working stream of production.
The remotely-sensed data selection and scheduling recommended based on Remote Sensing Products production best source: by being passed for different satellites
The characteristic parameter of sensor and the difference analysis of remotely-sensed data product, referring to FIG. 3, assumed in the present embodiment one it is ideal
Remote sensor all has ideal spectral response ability for all atural objects, i.e., is for the reflectivity of all atural objects
100%.Then, using the spectral response of the ideal transducer as all remotely-sensed data source spectral response merit ratings to be recommended
Benchmark.Simultaneously, it is contemplated that the spectral response of remote sensor record is the spectral response of reflectance spectrum and sensor by atural object
The coefficient result of function.Thus, it is supposed that the standard spectral curves of atural object are the reflectance spectrum of atural object, the standard spectrum is bent
Line can be derived from the study plots object light spectral curve such as USGS, JPL library, can also be derived from situ measurements of hyperspectral reflectance curve library etc..Based on
Upper two o'clock assumes the evaluation criteria established and MODTRAN spectral response simulations, we can obtain ideal transducer and reality
Border sensor and can calculate related coefficient between the two for the spectral response of specific atural object.The related coefficient represents
Sensor is named as production suitability comprehensive evaluation index CPSI for the synthesis responding ability of specific atural object herein.And
For specific remotely-sensed data product, it would be desirable to the spectral response of sensor and real sensor substitutes into production calculation formula,
The relative coefficient of calculated results is the specific evaluation number SPSI of production suitability.Finally, being calculated based on above
Final production suitability evaluation index PSI can be obtained in the CPSI and SPSI obtained, and it is distant which can be used as multi-source
Feel the foundation that data source is recommended in process of producing product.
S5 uses the Optimal Production data of each lower level product of Kepler scientific workflow engine tissue, and forming production, this is distant
Feel the complete job stream of data product.
Referring to FIG. 4, using Kepler scientific workflow engine can will be each needed for remotely-sensed data production under
The Optimal Production data of level product, conversion logic workflow are the Physics Work stream that can actually execute, physics work here
Make the Kepler workflow that stream is actual motion, mainly includes that workflow is split, subtask scheduling, secondary subtask are made between node
The links such as industry scheduling, workflow fault-tolerant management, it can realize the self-organizing of Remote Sensing Products production logic flow.
Herein, the nouns of locality such as related front, rear, top, and bottom are to be located in figure with components in attached drawing and zero
Part mutual position defines, only for the purpose of expressing the technical solution clearly and conveniently.It should be appreciated that the noun of locality
Use should not limit the claimed range of the application.
In the absence of conflict, the feature in embodiment and embodiment herein-above set forth can be combined with each other.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of multi- source Remote Sensing Data data production workflow self-organizing method, which comprises the following steps:
S1 parses the remotely-sensed data name of product that remotely-sensed data production order obtains pre-manufactured, and according to remotely-sensed data product
Title is judged to produce the feasibility of the remotely-sensed data product, if feasible, receives the remotely-sensed data production order and execution
Otherwise step S2 refuses the remotely-sensed data production order；
S2 is by obtaining the hierarchical relationship up and down of the remotely-sensed data product in level relational knowledge base above and below remotely-sensed data product；
S3 produces the required lower level product of the remotely-sensed data product by obtaining in remotely-sensed data product dependence knowledge base,
And the creation data of these lower level products；
S4 according to the characteristic parameter of the satellite sensor in the lower level product and the difference analysis of the remotely-sensed data product,
Filter out the Optimal Production data of these lower level products；
S5 uses the Optimal Production data of each lower level product of Kepler scientific workflow engine tissue, is formed and produces the remote sensing number
According to the Physics Work stream of product.
2. a kind of multi- source Remote Sensing Data data production workflow self-organizing method as described in claim 1, which is characterized in that institute
State in step S1 and judge to produce according to remotely-sensed data name of product the feasibility of the remotely-sensed data product method particularly includes:
Product and production procedure needed for producing the remotely-sensed data product are judged according to name of product, produce the remotely-sensed data if having
Product needed for product and production procedure receive the remotely-sensed data production order and execute step S2, it is distant otherwise to refuse this
Feel data product and produces order.
3. a kind of multi- source Remote Sensing Data data production workflow self-organizing method as described in claim 1, it is characterised in that: institute
Stating remotely-sensed data product described in step S2, level relational knowledge base is established according to remotely-sensed data grading standard up and down.
4. a kind of multi- source Remote Sensing Data data production workflow self-organizing method as claimed in claim 3, it is characterised in that: institute
Stating remotely-sensed data product, level relational knowledge base includes four big levels up and down, from top to bottom respectively initial data product, essence
Remotely-sensed data product, inverting index products and thematic product are handled, and each big level includes multiple child levels, each big level
The hierarchical relationship binary coding representation up and down for the child level for including with it.
5. a kind of multi- source Remote Sensing Data data production workflow self-organizing method as described in claim 1, it is characterised in that: institute
State the hierarchical relationship up and down that remotely-sensed data product dependence knowledge base in step S3 includes all remotely-sensed data products.
6. a kind of multi- source Remote Sensing Data data production workflow self-organizing method as described in claim 1, which is characterized in that institute
State step S4 specifically: pass through the characteristic parameter and remotely-sensed data product to the different satellite sensors in the lower level product
Difference analysis, the remotely-sensed data production data source recommended models based on spectral simulation are established, by each satellite
The suitability of sensor production remotely-sensed data product is evaluated, and the remotely-sensed data product for determining production according to evaluation result is suitable for
The Optimal Production data of production.
7. a kind of multi- source Remote Sensing Data data production workflow self-organizing method as described in claim 1, it is characterised in that: institute
Stating the workflow in step S5 includes subtask scheduling between workflow fractionation, node, secondary subtask job scheduling and work
Flow fault-tolerant management.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811563359.4A CN109657862A (en) | 2018-12-20 | 2018-12-20 | A kind of multi- source Remote Sensing Data data production workflow self-organizing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811563359.4A CN109657862A (en) | 2018-12-20 | 2018-12-20 | A kind of multi- source Remote Sensing Data data production workflow self-organizing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109657862A true CN109657862A (en) | 2019-04-19 |
Family
ID=66115415
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811563359.4A Pending CN109657862A (en) | 2018-12-20 | 2018-12-20 | A kind of multi- source Remote Sensing Data data production workflow self-organizing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109657862A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111737335A (en) * | 2020-07-29 | 2020-10-02 | 太平金融科技服务（上海）有限公司 | Product information integration processing method and device, computer equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985709A (en) * | 2018-06-26 | 2018-12-11 | 中国科学院遥感与数字地球研究所 | Workflow management method towards more satellite data centers collaboration Remote Sensing Products production |
-
2018
- 2018-12-20 CN CN201811563359.4A patent/CN109657862A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985709A (en) * | 2018-06-26 | 2018-12-11 | 中国科学院遥感与数字地球研究所 | Workflow management method towards more satellite data centers collaboration Remote Sensing Products production |
Non-Patent Citations (2)
| Title |
|---|
| YAN, JINING ETAL.: "A cloud-based remote sensing data production system", 《FUTURE GENERATION COMPUTER SYSTEMS》 * |
| 阎继宁: "多数据中心架构下遥感云数据管理及产品生产关键技术研究", 《中国优秀博硕士学位论文全文数据库(博士)信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111737335A (en) * | 2020-07-29 | 2020-10-02 | 太平金融科技服务（上海）有限公司 | Product information integration processing method and device, computer equipment and storage medium |
| CN111737335B (en) * | 2020-07-29 | 2020-11-24 | 太平金融科技服务（上海）有限公司 | Product information integration processing method and device, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Boisseau et al. | Open-design: A state of the art review | |
| CN102947842B (en) | For analyzing and synthesize the system and method that complicated knowledge represents | |
| CN109670267A (en) | A kind of data processing method and device | |
| Nowok et al. | Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R 1 | |
| CN102016887A (en) | Method, system, and computer program for user-driven dynamic generation of semantic networks and media synthesis | |
| CN101556574A (en) | Computer intelligent naming based on client wish | |
| CN109933834A (en) | A kind of model creation method and device of time series data prediction | |
| CN106354799B (en) | Subject data set multilayer facet filtering method and system based on data quality | |
| Gu et al. | Shape grammars: A key generative design algorithm | |
| Hlava | The taxobook: Principles and practices of building taxonomies, part 2 of a 3-part series | |
| Pal et al. | Rapid tooling route selection for metal casting using QFD–ANP methodology | |
| CN109657862A (en) | A kind of multi- source Remote Sensing Data data production workflow self-organizing method | |
| CN108153754B (en) | Data processing method and device | |
| Adami et al. | HBIM for planned conservation: A new approach to information management | |
| CN111768037A (en) | LS-SVMR-based movie box-office prediction method and system | |
| Kamali | The interaction of history and modern thought in the creation of Iran's architecture by investigating the approaches of past-oriented architecture | |
| Golpayegani et al. | Towards process lines for agent-oriented requirements engineering | |
| Masin et al. | Pluggable analysis viewpoints for design space exploration | |
| Napps et al. | Consideration of detailing in the graph-based retrieval of design variants | |
| Kucherov et al. | The active data warehouse of a configurable information system | |
| Yang et al. | Knowledge graph representation method for semantic 3D modeling of Chinese grottoes | |
| Kazemi et al. | Algorithmic Approach Functions in Digital Architecture and its Effect on Architectural Design Process | |
| Qabshoqa | Conceptual Operational Model of Architecture: An Approach for Capturing Values in Architectural Practices Based on Big Data Capabilities | |
| Huber et al. | The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level | |
| Zamanian et al. | Evaluation of palm groves technical efficiency using bootstrap data envelopment analysis: A case study of Roodkhanehbar Area, Iran |
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: 20190419 |