CN110798525A - Industrial robot multisource data cloud storage system - Google Patents

Industrial robot multisource data cloud storage system Download PDF

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Publication number
CN110798525A
CN110798525A CN201911060449.6A CN201911060449A CN110798525A CN 110798525 A CN110798525 A CN 110798525A CN 201911060449 A CN201911060449 A CN 201911060449A CN 110798525 A CN110798525 A CN 110798525A
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China
Prior art keywords
database
layer
storage
data
management
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Pending
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CN201911060449.6A
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Chinese (zh)
Inventor
徐国
熊忠元
于振中
李文兴
江瀚澄
徐磊
李阳阳
曹振武
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HRG International Institute for Research and Innovation
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HRG International Institute for Research and Innovation
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Priority to CN201911060449.6A priority Critical patent/CN110798525A/en
Publication of CN110798525A publication Critical patent/CN110798525A/en
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
    • H04L67/1097Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10File systems; File servers
    • G06F16/18File system types
    • G06F16/182Distributed file systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22Indexing; Data structures therefor; Storage structures
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28Databases characterised by their database models, e.g. relational or object models
    • G06F16/284Relational databases

Abstract

The invention provides an industrial robot multi-source data cloud storage system and method, wherein the system comprises: the system comprises an application layer, an interface layer, a management layer, a storage layer and an equipment layer, wherein the application layer is provided with a plurality of software applications for reading different types of technical data of the industrial robot; the interface layer is used for realizing network access, user authentication and authority management of the system and providing a public API interface, an application software interface and a web service interface; the management layer is used for providing online storage management service and offline storage management service; the storage layer is provided with a plurality of databases aiming at different types of data, and the equipment layer comprises: IP-SAN storage devices and FC-SAN storage devices. By applying the embodiment of the invention, the technical problem of data disorder can be avoided.

Description

Industrial robot multisource data cloud storage system
Technical Field
The invention relates to a cloud storage system, in particular to an industrial robot multi-source data cloud storage system.
Background
Industrial robots have become an important marker for measuring the state of manufacturing and technology as an irreplaceable and important equipment and means in advanced manufacturing. At present, in the important period of accelerating transformation and upgrading in China, the equipment number of industrial robots is continuously increased, and correspondingly, the technical data of the industrial robots are also increased explosively in a large amount, including angles, positions, speeds, currents, voltages, system logs, fault data, video picture data and the like of the robots. Therefore, how to store massive technical data is an urgent technical problem to be solved.
The invention patent application with the application number of CN201910350049.2 discloses a distributed real-time processing method for rail transit multi-source stream data, which comprises two parts of merging of the multi-source stream data and distributed processing of the merged stream data; merging multi-source flow data, namely performing dimensional merging on real-time data of the same vehicle on the same track line, and performing extensive merging on new flows obtained after the dimensional merging; the distributed processing of the merged stream data is realized on a distributed system, and the distributed system has two types of managers which are JobManager and TaskManager respectively; setting a plurality of JobManagers; the invention has certain flexibility, and the flexibility of the whole framework can not reduce or increase the whole flow processing calculated amount; the invention has the characteristic of high performance; when the distributed processing is carried out, the invention adopts a mode of synchronizing the distributed multi-JobManager states to realize complete distributed processing.
However, the inventor finds that although the storage of multi-source data is realized in the prior art, data of the same vehicle on the same track is merged and then distributed, the same vehicle can contain multiple types of technical data, and if different types of technical data are stored together, a technical problem of data disorder can be caused.
Disclosure of Invention
The invention aims to provide an industrial robot multi-source data cloud storage system and method, and aims to solve the technical problem of data disorder in the prior art.
The invention solves the technical problems through the following technical means:
the embodiment of the invention provides an industrial robot multi-source data cloud storage system, which comprises: an application layer, an interface layer, a management layer, a storage layer, and a device layer, wherein,
the application layer is provided with a plurality of software applications for reading different types of technical data of the industrial robot;
the interface layer is used for realizing network access, user authentication and authority management of the system and providing a public API interface, an application software interface and a web service interface;
the management layer is used for providing online storage management service and offline storage management service;
the storage layer is provided with a plurality of databases aiming at different types of data, wherein the databases comprise: relational databases, non-relational databases, distributed file storage databases, time series databases, Key-Value databases,
the device layer includes: IP-SAN storage devices and FC-SAN storage devices.
By applying the embodiment of the invention, the database suitable for storing the type of data can be used for storing the corresponding data by deploying the plurality of databases aiming at the different types of data in the storage layer, and compared with the prior art, the technical problem of data disorder can be avoided by combining the different types of data and then storing the data in the same database.
Optionally, the relational database; one of an Oracle database and a Mysql database;
the non-relational database comprising: a MongoDB database;
the distributed database comprises: one of Hbase database, Elasticsearch database;
a distributed file storage database comprising: one of a dfs database and a Fastdfs database;
a time series database comprising: one of an InfluxDB database and an OpenTSDB database;
a Key-Value database comprising: redis database.
Optionally, the online storage management includes:
index management, log management, scheduling management, resource management, cluster management and equipment management;
the offline storage management comprises: data backup and data disaster recovery.
The embodiment of the invention also provides an industrial robot multi-source data cloud storage method based on any one of the storage systems, and the method comprises the following steps:
acquiring different types of technical data of the industrial robot by using software application deployed by an application layer based on an interface provided by an interface layer;
processing the collected technical data based on an interface provided by an interface layer, wherein the processing comprises: one or a combination of online storage management and offline storage management;
and storing the processed technical data in a corresponding database in a storage layer deployed in the equipment layer.
Optionally, the management layer is further configured to:
when the residual space of the current database in the storage layer is lower than a preset threshold value, processing the technical data to be stored into a target format, and storing the technical data to be stored in the target format into the database corresponding to the target format.
Optionally, the management layer is further configured to:
and sending the data sent by the application layer to a kafka message queue, and then pulling the data from the kafka by a Flink event router for data distribution.
Optionally, the management layer is further configured to:
performing real-time analysis processing on the technical parameters by using a Spark cluster, and storing the analysis result into an Elasticissearch memory database and/or an Hbase distributed database in real time;
and a timing task is established to perform offline analysis on the Elasticisch and Hbase database data, a structured database is used for durably analyzing the result, and the durably analyzed data is placed into a Redis cache to be applied to an industrial robot cloud platform.
Optionally, the management layer is further configured to, when the database of the storage layer is deployed, reserve a storage space with a set size on the storage device and divide the storage space into a plurality of spare storage spaces, and release one of the plurality of spare storage spaces to the current database when a remaining space of the current database of the storage layer is lower than a preset threshold.
Optionally, when the number of the spare storage spaces released by the current database reaches the set number, extracting the key information in the technical data with the earliest writing time in the current database for storage, and deleting the technical data with the earliest writing time.
The invention has the advantages that:
by applying the embodiment of the invention, the database suitable for storing the type of data can be used for storing the corresponding data by deploying the plurality of databases aiming at the different types of data in the storage layer, and compared with the prior art, the technical problem of data disorder can be avoided by combining the different types of data and then storing the data in the same database.
Drawings
Fig. 1 is a schematic structural diagram of an industrial robot multi-source data cloud storage system provided in embodiment 1 of the present invention;
fig. 2 is a schematic flow chart of an industrial robot multi-source data cloud storage method provided in embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Fig. 1 is a schematic structural diagram of an industrial robot multi-source data cloud storage system provided in embodiment 1 of the present invention, and as shown in fig. 1, the system includes: an application layer, an interface layer, a management layer, a storage layer, and a device layer, wherein,
the application layer is provided with a plurality of software applications for reading different types of technical data of the industrial robot;
the interface layer is used for realizing network access, user authentication and authority management of the system and providing a public API interface, an application software interface and a web service interface;
the management layer is used for providing online storage management service and offline storage management service;
the storage layer is provided with a plurality of databases aiming at different types of data, wherein the databases comprise: relational databases, non-relational databases, distributed file storage databases, time series databases, Key-Value databases,
the device layer includes: IP-SAN (IInternet Protocol Address-Storage Area Network) Storage devices and FC-San (fiber Channel-Storage Area Network) Storage devices.
For example, taking a voltage reading application deployed in an application layer as an example, the voltage reading application obtains voltage data of an industrial robot, and sends the voltage data to a management layer through an 80 port, because the management layer receives a large amount of voltage data and other data, and sources of the technical data belong to different industrial robots, the management layer adds the technical data to different message queues according to types or sources of the technical data, and then scheduling management software deployed on the management layer judges the voltage data in each message queue, such as the voltage data in the voltage data queue, one by one, for example, the voltage data is Key-Value (Key Value pair) data, so that the voltage data can be stored by using a Key-Value database, and the scheduling management software sends the voltage data to the Key-Value database for storage.
Of course, the software applications deployed by the application layer include, but are not limited to, voltage, current, coordinate information of the industrial robot, current images of the robot, temperature of various locations on the robot, and other technical data.
Further, the relational database; one of an Oracle database and a Mysql database is used for storing working time length data of the robot and the like;
the non-relational database comprising: the MongoDB database can be used for storing log files, XML documents, JSON documents, Email and the like of the industrial robot;
the distributed database comprises: one of Hbase database, Elasticsearch database, can be used to store structured data, such as robotic data;
a distributed file storage database comprising: one of a dfs database and a Fastdfs database can be used for storing video, audio and picture data of the robot, and a data index is established by using an elastic search full-text search engine, so that massive unstructured data can be quickly inquired;
a time series database comprising: one of an InfluxDB database and an OpenTSDB database can be used for storing control instruction data of robot data;
a Key-Value database comprising: redis database, which may be used to store voltage data, current data, pressure data, etc.
Further, the online storage management includes:
index management, log management, scheduling management, resource management, cluster management and equipment management;
the offline storage management comprises: data backup and data disaster recovery.
The application of the embodiment of the invention can have the following technical effects:
(1) the storage layer is provided with the plurality of databases aiming at different types of data, the database suitable for storing the type of data can be used for storing the corresponding data, and compared with the prior art, the technical problem of data disorder can be avoided by combining the different types of data and then storing the combined data in the same database. And the data security is improved compared with the data stored together.
(2) The embodiment of the invention can also realize the high-efficiency and quick convergence and access of mass data. The cloud storage framework realizes efficient convergence storage of multi-source data. The problems of low storage efficiency and lack of data aggregation management in the mass data environment of the Internet of things are effectively solved.
(3) The embodiment of the invention is deployed at the cloud end, so that the storage resources can be virtualized and integrated, and the user management efficiency is improved; meanwhile, the online expansion of storage resources can be supported, and the linear increase of capacity and performance is realized; thereby flexibly adjusting the size of the virtual space.
(4) According to the embodiment of the invention, a distributed full-text search engine Elasticissearch is adopted, inverted indexes are added to important query parameters such as equipment numbers, timestamps and the like in the structured robot data, and second-level retrieval of PB-level data can be realized;
(5) a full-clustering design can be adopted to improve the reliability of the system, so that uninterrupted, efficient and sustainable data service is provided for 7 x 24 hours, and the safety and reliability of data are fully protected;
(6) the embodiment of the invention can deploy different application software in the application and is directly called by the upper-layer service platform; and may support access to standard storage devices.
Example 2
The embodiment of the invention also provides an industrial robot multi-source data cloud storage method based on the storage system in the embodiment 1.
Fig. 2 is a schematic flow chart of an industrial robot multi-source data cloud storage method provided in embodiment 2 of the present invention, and as shown in fig. 2, the method includes:
s201: and acquiring different types of technical data of the industrial robot by using the software application deployed by the application layer based on the interface provided by the interface layer.
After the software application collects the technical data, the data type of the data is written into a data packet for transmitting the technical data, wherein the data type is predetermined by a user.
S202: processing the collected technical data based on an interface provided by an interface layer, wherein the processing comprises: online storage management and offline storage management.
Illustratively, taking the voltage data collected by the voltage collection software as an example, the type information of the voltage data is read, the read type information is judged to be a key-value pair data type, a database storing the key-value pair data type, such as a Redis database, is found, and then the voltage data is stored in the Redis database.
It should be noted that, the data type stored in each database is pre-calibrated S203: and storing the processed technical data in a corresponding database in a storage layer deployed in the equipment layer.
By applying the embodiment of the invention, the database suitable for storing the type of data can be used for storing the corresponding data by deploying the plurality of databases aiming at the different types of data in the storage layer, and compared with the prior art, the technical problem of data disorder can be avoided by combining the different types of data and then storing the data in the same database.
In a specific implementation manner of the embodiment of the present invention, the management layer is further configured to:
when the residual space of the current database in the storage layer is lower than a preset threshold value, processing the technical data to be stored into a target format, and storing the technical data to be stored in the target format into the database corresponding to the target format.
In a specific implementation manner of the embodiment of the present invention, the management layer is further configured to:
and sending the data sent by the application layer to a kafka message queue, and then pulling the data from the kafka by a Flink event router for data distribution.
In a specific implementation manner of the embodiment of the present invention, the management layer is further configured to:
performing real-time analysis processing on the technical parameters by using a Spark cluster, and storing the analysis result into an Elasticissearch memory database and/or an Hbase distributed database in real time;
and a timing task is established to perform offline analysis on the Elasticisch and Hbase database data, a structured database is used for durably analyzing the result, and the durably analyzed data is placed into a Redis cache to be applied to an industrial robot cloud platform.
In a specific implementation manner of the embodiment of the present invention, the management layer is further configured to reserve a storage space with a set size on the storage device to be divided into a plurality of spare storage spaces when the database of the storage layer is deployed, and release one of the plurality of spare storage spaces to the current database when a remaining space of the current database of the storage layer is lower than a preset threshold.
Illustratively, the cloud storage platform has 1000G of storage space, 100G of storage space is allocated to each database, there are 8 databases in total, and then the remaining 200G is divided into 5 spare storage spaces of 40G each.
And when the residual storage space of the Redis database is lower than 10% of the total amount of the self space, or lower than 10G, releasing one spare storage space to the Redis database for use by the Redis database.
By applying the embodiment of the invention, the storage space of the database with insufficient residual storage space can be dynamically increased, and the use efficiency of the cloud storage space is improved.
In a specific implementation manner of the embodiment of the present invention, when the number of the spare storage spaces that have been released by the current database reaches the set number, the key information in the technical data with the earliest write time in the current database is extracted and stored, and the technical data with the earliest write time is deleted.
Illustratively, when the remaining storage space of the Redis database is lower than 10% of the total amount of the self space, or lower than 10G, one of the spare storage spaces is released to the Redis database and used by the Redis database. In such a circulation manner, after the storage space of the Redis database is combined with the three standby storage spaces, the existing keyword extraction algorithm extracts the key information written in the technical data with the earliest writing time in the Redis database and stores the key information so as to save the storage space in the Redis database.
Furthermore, the data in the Redis database, the time length of which is more than the set time length from the last update time, can be processed to save the storage space of the database.
In practical application, for video, audio or image data in a dfs database and a Fastdfs database, algorithms such as a neural network can be used for extracting image key information so as to save the storage space of the database.
By applying the embodiment of the invention, the storage space of the database with insufficient residual storage space can be dynamically increased, and the use efficiency of the cloud storage space is improved.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (9)

1. An industrial robot multi-source data cloud storage system, characterized in that the system comprises: an application layer, an interface layer, a management layer, a storage layer, and a device layer, wherein,
the application layer is provided with a plurality of software applications for reading different types of technical data of the industrial robot;
the interface layer is used for realizing network access, user authentication and authority management of the system and providing a public API interface, an application software interface and a web service interface;
the management layer is used for providing online storage management service and offline storage management service;
the storage layer is provided with a plurality of databases aiming at different types of data, wherein the databases comprise: relational databases, non-relational databases, distributed file storage databases, time series databases, Key-Value databases,
the device layer includes: IP-SAN storage devices and FC-SAN storage devices.
2. An industrial robot multi-source data cloud storage system according to claim 1, wherein the relational database; one of an Oracle database and a Mysql database;
the non-relational database comprising: a MongoDB database;
the distributed database comprises: one of Hbase database, Elasticsearch database;
a distributed file storage database comprising: one of a dfs database and a Fastdfs database;
a time series database comprising: one of an InfluxDB database and an OpenTSDB database;
a Key-Value database comprising: redis database.
3. An industrial robot multi-source data cloud storage system according to claim 1, wherein the online storage management comprises:
index management, log management, scheduling management, resource management, cluster management and equipment management;
the offline storage management comprises: data backup and data disaster recovery.
4. An industrial robot multi-source data cloud storage method based on the storage system of any one of claims 1-3, wherein the method comprises the following steps:
acquiring different types of technical data of the industrial robot by using software application deployed by an application layer based on an interface provided by an interface layer;
processing the collected technical data based on an interface provided by an interface layer, wherein the processing comprises: one or a combination of online storage management and offline storage management;
and storing the processed technical data in a corresponding database in a storage layer deployed in the equipment layer.
5. The industrial robot multi-source data cloud storage method according to claim 4, wherein the management layer is further configured to:
when the residual space of the current database in the storage layer is lower than a preset threshold value, processing the technical data to be stored into a target format, and storing the technical data to be stored in the target format into the database corresponding to the target format.
6. The industrial robot multi-source data cloud storage method according to claim 4, wherein the management layer is further configured to:
and sending the data sent by the application layer to a kafka message queue, and then pulling the data from the kafka by a Flink event router for data distribution.
7. The industrial robot multi-source data cloud storage method according to claim 4, wherein the management layer is further configured to:
performing real-time analysis processing on the technical parameters by using a Spark cluster, and storing the analysis result into an Elasticissearch memory database and/or an Hbase distributed database in real time;
and a timing task is established to perform offline analysis on the Elasticisch and Hbase database data, a structured database is used for durably analyzing the result, and the durably analyzed data is placed into a Redis cache to be applied to an industrial robot cloud platform.
8. The multi-source data cloud storage method for the industrial robot as claimed in claim 4, wherein the management layer is further configured to reserve a storage space with a set size on the storage device to be divided into a plurality of spare storage spaces when the database of the storage layer is deployed, and release one of the plurality of spare storage spaces to the current database of the storage layer when the remaining space of the current database is lower than a preset threshold.
9. The multi-source data cloud storage method for the industrial robot according to claim 8, wherein when the number of the spare storage spaces of the current database which are already released reaches a set number, key information in the technical data with the earliest writing time in the current database is extracted and stored, and the technical data with the earliest writing time is deleted.
CN201911060449.6A 2019-11-01 2019-11-01 Industrial robot multisource data cloud storage system Pending CN110798525A (en)

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