CN112527945A - Method and device for processing geographic space big data - Google Patents
Method and device for processing geographic space big data Download PDFInfo
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Abstract
The invention discloses a method and a device for processing large data of a geographic space. The method comprises the following steps: storing the geospatial data in a file system and a database according to types; establishing a real-time data warehouse; and associating the spatial data with the time data to establish a spatio-temporal data model. Therefore, the geospatial data are respectively stored in the file system and the database according to types; establishing a real-time data warehouse to realize real-time processing and analysis of the geospatial data; and establishing a space-time data model by correlating the spatial data and the time data, and realizing historical data analysis and data deduction prediction.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a device for processing geographic space big data, electronic equipment and a computer readable storage medium.
Background
At present, a geospatial data processing mode is mainly characterized in that a database for storing Geographic Information System (GIS) data is built, the GIS data is divided into three layers of hot data, warm data and cold data according to the number of times of data access, and the cold and hot balance of the GIS data is realized through a physical storage framework; data backup is completed in a manual timing physical backup mode, so that data loss is prevented; in addition, data analysis is mainly performed on the basis of professional GIS software, and analysis results are displayed on a GIS platform in a layer form.
At present, the spatial information data processing mode of the smart city and the city grade in the brain construction of the city is still in the original stage. The storage mode in the prior art has the disadvantages that the disadvantages are gradually highlighted in the big data background, and the following defects are mainly existed: 1. the traditional database has limited data volume; 2. the data maintenance mode is complex; 3. the real-time analysis capability is poor; 4. poor spatio-temporal analysis capability; 5. and the massive data retrieval capability is weak.
Disclosure of Invention
In view of the above, the present invention has been made to provide a geospatial big data processing method, apparatus, electronic device, computer readable storage medium that overcome or at least partially solve the above-mentioned problems.
One embodiment of the present invention provides a geospatial big data processing method, which includes:
storing the geospatial data in a file system and a database according to types;
establishing a real-time data warehouse;
and associating the spatial data with the time data to establish a spatio-temporal data model.
Optionally, the storing the geospatial data in the file system and the database respectively according to types includes:
storing the map, the satellite image, the terrain and the building model data in a file system;
and storing the data of the Internet of things, the public transportation records and the data of the E-commerce logistics data in a database.
Optionally, the establishing a real-time data warehouse includes:
connecting the database with an open source stream processing platform kafka through a distributed platform Debezium;
monitoring the change of the database through the distributed platform Debezium and synchronizing the change to a message queue of the open source stream processing platform kafka;
and pulling the data in the message queue of the open source stream processing platform kafka through a data engine Flink, dividing the data warehouse level, completing the data processing flow in an original data layer, a data detail layer, a data summary layer and an application layer respectively, and establishing a real-time data warehouse.
Optionally, the method further comprises:
and storing the index analysis result, the city Internet of things component data, the population data, the city POI data and the vector and the grid data in a search server Elasticissearch.
Optionally, the method further comprises:
and monitoring the quality of the data by adopting a component Griffin.
Another embodiment of the present invention provides a geospatial big data processing apparatus, including:
the data classification storage unit is used for respectively storing the geospatial data in a file system and a database according to types;
the data warehouse establishing unit is used for establishing a real-time data warehouse;
and the space-time data model establishing unit is used for associating the space data with the time data and establishing a space-time data model.
Optionally, the data classification storage unit is configured to:
storing the map, the satellite image, the terrain and the building model data in a file system;
storing the data of the Internet of things, the public transportation records and the data of the E-commerce logistics in a database;
the data warehouse establishing unit is used for:
connecting the database with an open source stream processing platform kafka through a distributed platform Debezium;
monitoring the change of the database through the distributed platform Debezium and synchronizing the change to a message queue of the open source stream processing platform kafka;
and pulling the data in the message queue of the open source stream processing platform kafka through a data engine Flink, dividing the data warehouse level, completing the data processing flow in an original data layer, a data detail layer, a data summary layer and an application layer respectively, and establishing a real-time data warehouse.
Optionally, the apparatus further comprises:
and the real-time searching unit is used for storing the index analysis result, the city Internet of things component data, the population data, the city POI data and vector and the grid data in a searching server Elasticissearch.
And the data quality monitoring unit is used for monitoring the quality of the data by adopting a component Griffin.
Another embodiment of the present invention provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the geospatial big data processing method described above.
Another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the geospatial big data processing method described above.
The method has the advantages that the geographic space data are respectively stored in the file system and the database according to types; establishing a real-time data warehouse to realize real-time processing and analysis of the geospatial data; and establishing a space-time data model by correlating the spatial data and the time data, and realizing historical data analysis and data deduction prediction.
The invention realizes real-time statistical query with large data volume (hundred million level) by searching the server elastic search.
The invention also introduces space data quality monitoring, and ensures the data quality by monitoring the integrity, accuracy, consistency and timeliness of the data.
Drawings
FIG. 1 is a schematic flow chart of a geospatial big data processing method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a geospatial big data processing method according to one embodiment of the invention;
FIG. 3 is a schematic structural diagram of a geospatial big data processing apparatus according to an embodiment of the present invention;
FIG. 4 shows a schematic structural diagram of an electronic device according to one embodiment of the invention;
fig. 5 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention combines the traditional GIS and the big data technology, can overcome the defects of limited storage of urban-level data quantity, real-time analysis and the like, and meets the requirement of urban brain construction.
Fig. 1 is a schematic flow chart of a geospatial big data processing method according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s11: storing the geospatial data in a file system and a database according to types;
different storage modes are designed according to different data types and different applications. The database implemented by the invention can be stored in a file system and establish a complete time sequence for spatial data such as maps, satellite images, terrains, building models and the like due to the relatively fixed update period and use scene; for other spatial data containing position information, such as data of the internet of things, public transportation records, e-commerce logistics and the like, a real-time data warehouse is constructed to process the data when the data are stored, so that the data utilization rate can be improved to the maximum extent.
S12: establishing a real-time data warehouse;
the spatial data stored in the real-time data warehouse comprise urban Internet of things data, public traffic records, E-commerce logistics, microblog WeChat footprint photos and the like, and the data have the following common points: timeliness, coordinate information and sensor information.
S13: and associating the spatial data with the time data to establish a spatio-temporal data model.
The real-time data warehouse can store all historical data, a space-time data structure is formed by combining coordinate position data (space data), time data and basic information data, a space-time data model is built, and the functions of historical query, process simulation and trend prediction of the data are achieved. The spatio-temporal data model has great effect in the fields of cadastral change, environmental monitoring, urban evolution and the like.
The space-time data model is a geoscience data model which effectively organizes and manages the real-time geoscience data, and has complete space, special subject and time semantics, and the establishment of a reasonable, perfect and efficient space-time data model is the basis and key for realizing the real-time GIS.
In the embodiment of the invention, two methods can be adopted to establish the model: a method of extending spatial dimensions over a temporal model and an object-oriented method build spatiotemporal data models, the former depicting object transitions and processes, and the latter, the temporal and spatial dimensions being perpendicular, representing changes in space and attributes of an entity. The established model is applied to the aspects of urban cadastre change and environmental monitoring, such as cadastre history tracing, environmental monitoring history replay, future prediction and the like.
The geospatial big data processing method of the embodiment of the invention respectively stores geospatial data in a file system and a database according to types; establishing a real-time data warehouse to realize real-time processing and analysis of the geospatial data; and establishing a space-time data model by correlating the spatial data and the time data, and realizing historical data analysis and data deduction prediction.
In an optional implementation manner of the embodiment of the present invention, as shown in fig. 2, the storing the geospatial data in the file system and the database according to types respectively includes:
storing the map, the satellite image, the terrain and the building model data in a file system;
and storing the data of the Internet of things, the public transportation records and the data of the E-commerce logistics data in a database.
In practical application, the first type of data is stored in an HDFS file system and a complete time sequence is established; the second kind of data needs to be respectively built and stored, such as a building internet database, a GPS database, an LBS database and the like, the building of the database is convenient for later-stage real-time acquisition and provides support, and not all data are stored.
Specifically, the establishing a real-time data warehouse, as shown in fig. 2, includes:
connecting the database with an open source stream processing platform kafka through a distributed platform Debezium;
monitoring the change of the database through the distributed platform Debezium and synchronizing the change to a message queue of the open source stream processing platform kafka;
and pulling the data in the message queue of the open source stream processing platform kafka through a data engine Flink, dividing the data warehouse level, completing the data processing flow in an original data layer, a data detail layer, a data summary layer and an application layer respectively, and establishing a real-time data warehouse.
The embodiment of the invention connects the database with the open source stream processing platform kafka through the distributed platform Debezium, monitors the change of the database, synchronizes the change to the kafka message queue, and uses the Flink data engine to pull the data in the message queue. After the data is pulled, the data is simply cleaned and classified to be stored in HBASE to serve as an ODS layer (original data layer), the original data is stored, and the backup function is achieved; carrying out further cleaning, desensitization and compression on ODS layer data through FlinkSQL, and carrying out dimension degradation to form a DWD layer (data detail layer) with a plurality of bins; forming a dimension width table such as an alarm information width table, a construction site monitoring width table and the like by analyzing dimension data, wherein the width table is stored as a DWS layer (data summary layer); and finally, developing various indexes according to specific requirements, wherein the index operation data is used as an APP layer (application layer).
Preferably, the method further comprises:
and storing the index analysis result, the city Internet of things component data, the population data, the city POI data and the vector and the grid data in a search server Elasticissearch.
The urban-level data volume is under great pressure on the traditional GIS retrieval means, and the method provided by the invention provides real-time statistical query of large data volume (hundred million level) based on the elastic search as a bottom-layer data framework, thereby fundamentally solving the problem.
According to the method, an ES (elastic search) is used as a bottom-layer data framework, index analysis results, retrievable data such as urban Internet of things component data, population data, urban POI data and the like, vector data and grid data are indexed and stored in the ES, and the second-level search of urban data is achieved by utilizing the full text search characteristic of distributed multi-user capability of the ES.
Preferably, the method further comprises:
and monitoring the quality of the data by adopting a component Griffin.
Data quality monitoring is essential when dealing with data at the city level. The embodiment of the invention focuses on the integrity, accuracy, consistency and timeliness of data, and is divided into three modules in design: data, rules, alarms, and runs through the entire data flow. The data quality is monitored by monitoring all different types of data, running through the whole data production flow, and establishing abnormal rules and alarm levels.
The embodiment of the invention uses the Griffin component to monitor the integrity, accuracy, consistency and timeliness of the data, and provides visual display of the monitoring result and alarm information notification.
The specific process is as follows:
(1) registering data, namely registering a data source of which the data quality is to be detected to the griffin;
(2) configuring a measurement model, wherein the model can be defined from the data quality dimension, and the accuracy, the integrity, the timeliness and the consistency are realized;
(3) configuring a timing task to submit a spark cluster, checking data regularly, and setting monitoring information such as mail alarm and the like according to indexes;
(4) and checking the indexes on a portal interface, and analyzing the data quality checking result.
Fig. 3 is a schematic structural diagram of a geospatial big data processing device according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the data classification storage unit 31 is used for storing the geospatial data in a file system and a database according to types;
a data warehouse establishing unit 32 for establishing a real-time data warehouse;
and a spatio-temporal data model establishing unit 33, configured to associate the spatial data with the temporal data to establish a spatio-temporal data model.
The geographic space big data processing device stores the geographic space data in a file system and a database according to types; establishing a real-time data warehouse to realize real-time processing and analysis of the geospatial data; and establishing a space-time data model by correlating the spatial data and the time data, and realizing historical data analysis and data deduction prediction.
Optionally, the data classification storage unit 31 is configured to:
storing the map, the satellite image, the terrain and the building model data in a file system;
storing the data of the Internet of things, the public transportation records and the data of the E-commerce logistics in a database;
the data warehouse establishing unit 32 is configured to:
connecting the database with an open source stream processing platform kafka through a distributed platform Debezium;
monitoring the change of the database through the distributed platform Debezium and synchronizing the change to a message queue of the open source stream processing platform kafka;
and pulling the data in the message queue of the open source stream processing platform kafka through a data engine Flink, dividing the data warehouse level, completing the data processing flow in an original data layer, a data detail layer, a data summary layer and an application layer respectively, and establishing a real-time data warehouse.
Optionally, the apparatus further comprises:
and the real-time searching unit is used for storing the index analysis result, the city Internet of things component data, the population data, the city POI data and vector and the grid data in a searching server Elasticissearch.
And the data quality monitoring unit is used for monitoring the quality of the data by adopting a component Griffin.
It should be noted that the geospatial big data processing apparatus in the foregoing embodiments can be respectively used for executing the methods in the foregoing embodiments, and therefore, detailed description thereof is omitted.
In summary, the geospatial data is respectively stored in the file system and the database according to the type; establishing a real-time data warehouse to realize real-time processing and analysis of the geospatial data; and establishing a space-time data model by correlating the spatial data and the time data, and realizing historical data analysis and data deduction prediction.
The invention realizes real-time statistical query with large data volume (hundred million level) by searching the server elastic search.
The invention also introduces space data quality monitoring, and ensures the data quality by monitoring the integrity, accuracy, consistency and timeliness of the data.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the apparatus for detecting a wearing state of an electronic device according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
For example, fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the invention. The electronic device conventionally comprises a processor 41 and a memory 42 arranged to store computer executable instructions (program code). The memory 42 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 42 has a storage space 43 for storing program code 44 for performing the method steps shown in fig. 1 and in any of the embodiments. For example, the storage space 43 for storing the program code may comprise respective program codes 44 for respectively implementing the various steps in the above method. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 5. The computer readable storage medium may have memory segments, memory spaces, etc. arranged similarly to the memory 42 in the electronic device of fig. 4. The program code may be compressed, for example, in a suitable form. In general, the memory space stores program code 51 for performing the steps of the method according to the invention, i.e. there may be program code, such as read by the processor 41, which, when run by the electronic device, causes the electronic device to perform the steps of the method described above.
While the foregoing is directed to embodiments of the present invention, other modifications and variations of the present invention may be devised by those skilled in the art in light of the above teachings. It should be understood by those skilled in the art that the foregoing detailed description is for the purpose of better explaining the present invention, and the scope of the present invention should be determined by the scope of the appended claims.
Claims (10)
1. A geospatial big data processing method, comprising:
storing the geospatial data in a file system and a database according to types;
establishing a real-time data warehouse;
and associating the spatial data with the time data to establish a spatio-temporal data model.
2. The method of claim 1, wherein storing geospatial data by type in a file system and a database, respectively, comprises:
storing the map, the satellite image, the terrain and the building model data in a file system;
and storing the data of the Internet of things, the public transportation records and the data of the E-commerce logistics data in a database.
3. The method of claim 2, wherein the establishing a real-time data warehouse comprises:
connecting the database with an open source stream processing platform kafka through a distributed platform Debezium;
monitoring the change of the database through the distributed platform Debezium and synchronizing the change to a message queue of the open source stream processing platform kafka;
and pulling the data in the message queue of the open source stream processing platform kafka through a data engine Flink, dividing the data warehouse level, completing the data processing flow in an original data layer, a data detail layer, a data summary layer and an application layer respectively, and establishing a real-time data warehouse.
4. The method of claim 1, further comprising:
and storing the index analysis result, the city Internet of things component data, the population data, the city POI data and the vector and the grid data in a search server Elasticissearch.
5. The method according to claim 1, characterized in that the method further comprises:
and monitoring the quality of the data by adopting a component Griffin.
6. A geospatial big data processing apparatus, comprising:
the data classification storage unit is used for respectively storing the geospatial data in a file system and a database according to types;
the data warehouse establishing unit is used for establishing a real-time data warehouse;
and the space-time data model establishing unit is used for associating the space data with the time data and establishing a space-time data model.
7. The apparatus of claim 6, wherein the data classification storage unit is configured to:
storing the map, the satellite image, the terrain and the building model data in a file system;
storing the data of the Internet of things, the public transportation records and the data of the E-commerce logistics in a database;
the data warehouse establishing unit is used for:
connecting the database with an open source stream processing platform kafka through a distributed platform Debezium;
monitoring the change of the database through the distributed platform Debezium and synchronizing the change to a message queue of the open source stream processing platform kafka;
and pulling the data in the message queue of the open source stream processing platform kafka through a data engine Flink, dividing the data warehouse level, completing the data processing flow in an original data layer, a data detail layer, a data summary layer and an application layer respectively, and establishing a real-time data warehouse.
8. The apparatus of claim 6, further comprising:
the real-time searching unit is used for storing the index analysis result, the urban Internet of things component data, the population data, the urban POI data and vector and the grid data in a searching server Elasticissearch;
and the data quality monitoring unit is used for monitoring the quality of the data by adopting a component Griffin.
9. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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| 毕思文: "《数字地球 地球系统数字学》", 31 October 2001 * |
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| CN113609238A (en) * | 2021-07-24 | 2021-11-05 | 全图通位置网络有限公司 | Hadoop platform-based geographic entity spatial data processing method and storage medium |
| CN114625794A (en) * | 2022-03-10 | 2022-06-14 | 北京国电高科科技有限公司 | Satellite Internet of things Spark data processing method, system, terminal and storage medium |
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