CN110008286A - A kind of injection molding equipment big data acquisition and storage system and method - Google Patents
A kind of injection molding equipment big data acquisition and storage system and method Download PDFInfo
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- 238000001746 injection moulding Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000003860 storage Methods 0.000 title claims abstract description 24
- 238000005516 engineering process Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000007726 management method Methods 0.000 claims description 9
- 238000004519 manufacturing process Methods 0.000 claims description 8
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- 239000000155 melt Substances 0.000 claims description 3
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- 238000011958 production data acquisition Methods 0.000 description 1
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
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Abstract
The invention discloses a kind of injection moldings to equip big data acquisition and storage system and method.In system and method for the invention, Kubernetes cluster passes through the management of docker container virtualization technology and deployment Nifi data flow cluster, Kafka cluster, Kafka consumer's cluster and distributed data base, Nifi data flow cluster is from data sources and handles data, it is write data into the corresponding Topic subregion of Kafka cluster after data processing according to data type, Kafka consumer's cluster subscribes to the Topic in Kafka cluster, writes data into distributed data base and stores.The present invention realizes the acquisition and storage of magnanimity creation data using Nifi, Kafka and distributed data base technique, solves the problems, such as big data set expandability, stability and poor reliability using Kubernetes container orchestration technology and container virtualization technology.
Description
Technical field
The present invention relates to big data, cloud computing and injection molding fields, and in particular to being molded into based on Kubernetes
Type equips big data acquisition and storage system and method.
Background technique
The development of big data technology has pushed the informationization of traditional manufacture, accelerates manufacturing industry intelligence transition process,
Manufacturing industry primarily solves the problems, such as it is mass data collection and storage problem to intelligence transition, in injection molding field, injection molding
The conventional method for equipping the Data acquisition and storage of creation data is using industrial stokehold standard OPC technology in production scene
It is then stored into local data base, opc server field equipment connecting after collecting data, user writes OPC visitor according to OPC agreement
Family end connects opc server, and obtains required data with standard OPC read-write mode access opc server interface, in order to realize life
The big data acquisition and storage for producing data needs on the basis of traditional data acquires using big data acquisition technique to getting
Data carry out secondary acquisition, and store data into distributed data base, such as Chinese patent application (application number
CN201711387375 a kind of injection molding equipment big data acquisition system and method) are disclosed, passes through OPC communication acquisition to giving birth to
Kafka distributed post subscription message system is recycled to write data into distributed data base after producing data, this mode
Although solving the problems, such as big data acquisition and storage, due to big, the single distributed message of production data acquisition changes in demand
Queue is difficult to be made adjustment in time according to data acquisition demand, and Data Stream Processing expansibility is poor, cannot to a variety of data sources
Effectively integration is handled, simultaneously because big data application environment deployment configuration is complicated, needs specially to construct the O&M of efficient stable
System usually requires manual investigation problem after server failure and repairs, and the portability and poor reliability for causing system are asked
Topic.
Ubernetes is based on container technique, realizes the automation of resource management, it has complete cluster management ability
Including security protection and mechanism of permitting the entrance, multi-tenant support, service registration and service discovery mechanisms, built-in load balanced device, failure
It was found that and repairing, servicing rolling upgrade and on-line rapid estimation ability, expansible resource Automatic dispatching mechanism.Meanwhile
Kubernetes provides perfect management tool, these tools are covered each including developing, disposing test, O&M monitoring
Link.Kubernetes is not only a distributed structure/architecture solution and a complete distribution based on container technique
Formula system development and support platform.It, can be from the bottom code and function unrelated with business using Kubernetes designing system
It frees in module, service improvement, monitoring, troubleshooting and the load balancing problem of system is solved, to improve system
Expansibility, portability and reliability, as Chinese patent (CN201810856195) discloses one kind based on Kubernetes
The implementation method for setting up Hadoop cluster, realizes the Dynamic Maintenance to Hadoop cluster, the height for improving Hadoop cluster can
With property and high scalability.
Summary of the invention
The present invention in view of the deficiencies of the prior art and defect, provides a kind of injection molding equipment big data acquisition and storage system
System and method, to solve the problems, such as current techniques expansibility, portability and poor reliability.
To achieve the above object, the present invention one of at least adopts the following technical scheme that.
A kind of injection molding equipment big data acquisition and storage system and method comprising:
Kubernetes cluster passes through docker container virtualization technology management and deployment Nifi data flow cluster, Kafka collection
Group, Kafka consumer's cluster and distributed data base；
Nifi data flow cluster is used for from various data sources and processing injection molding machine data；
Distributed Message Queue Kafka cluster for being the corresponding Topic of data creation, and configures subregion and data backup, with
Receive the mass data that Nifi data flow cluster generates；
Kafka consumer's cluster obtains data for subscribing to each Topic in Kafka cluster and stores data into distribution
In formula database；
Distributed data base is used for distributed storage magnanimity injection molding machine data；
The Kubernetes cluster realizes Kubernetes component High Availabitity using keepalived and haproxy, uses
The two-way authentication of x509 certificate and RBAC authorization carry out security control, and installation DNS plug-in unit coredns, dashboard login authentication is inserted
Part, heapster monitoring plug-in unit, docker-registry mirror image manage plug-in unit to manage deployment cluster；
A kind of injection molding equipment big data acquisition and storage method based on Kubernetes, comprising the following steps:
Nifi data flow cluster is run with Zero-Master normal form, constructs multiple data flows on each node in cluster to connect
Receive and handle the data of multiple data sources；
Use PutKafka Processor according to Kafka Message message format according to data after data receiver processing
Type is written to Kafka cluster and corresponds in the subregion of Topic；
Kafka consumer's cluster connects zookeeper, subscribes to the Topic in Kafka cluster, consumes from Kafka cluster
Data are stored to distributed data base；
The stepData flow includes machine parameter data stream, production alert data stream, technological parameter data flow and product matter
Measure data flow；
The stepThe data that four class data flows specifically include are as follows:
(1) machine parameter data stream, including the critical machine that quality of item has a major impact is arranged in each link of injection molding
Parameter；
(2) alert data stream is produced, including the abnormal alarm log generated in injection molding process；
(3) technological parameter data flow, the melt state parameter including directly determining quality of item in each link of injection molding；
(4) quality of item data flow, including quality of item judgement；
The stepKafka Message message format, to produce sequence id for Key, to use Avro to serialize four respectively
Byte sequence after class data is Value;
The stepKafka cluster is that corresponding Topic is respectively created in four kinds of data types, totally 4 Topic, and is each
Topic configures subregion and backup；
The beneficial effects of the present invention are be based on Nifi data flow cluster, acquire data from multiple data sources and sort data into
Kafka distributed message distribution subscription system is written, solves the problems, such as that Data Stream Processing ability expansion is poor, passes through
Kubernetes cluster management and deployment Nifi data flow cluster, Kafka cluster, Kafka consumer's cluster and distributed data
Library makes each cluster and distributed number using the dynamic capacity-expanding of Kubernetes, fault detection and reparation and load-balancing mechanism
The Service micro services that a High Availabitity is stood alone as according to library, improve the reliability of data system, pass through container virtualization technology
Docker encapsulates each cluster and distributed data base runtime environment to construct docker mirror image, and is carried out with docker mirror image
Deployment, by the application virtualization mode of docker, can quickly set up a set of reusable application runtime environment, pass through offer
Using the mode of mirror image, it can will achieve the purpose that fast construction, deployment using application, solve using all users are distributed to
The problem of traditional big data system portability difference.
Detailed description of the invention
Fig. 1 is the injection molding equipment big data acquisition and storage system construction drawing based on Kubernetes.
Fig. 2 is the injection molding equipment big data acquisition and storage method flow diagram based on Kubernetes.
Specific embodiment
Implementation of the invention is described in further detail with reference to the accompanying drawings and detailed description, but reality of the invention
Apply and protect it is without being limited thereto, if it is noted that below have not especially detailed description process or symbol, be art technology
Personnel can refer to the prior art understand or realize.
Such as Fig. 1, a kind of injection molding equipment big data acquisition conjunction storage system based on Kubernetes comprising:
Kubernetes cluster passes through docker container virtualization technology management and deployment Nifi data flow cluster, Kafka collection
Group, Kafka consumer's cluster and distributed data base, the present embodiment use three node deployment Kubernetes clusters,
Each core component deployment strategy is as follows in Kubernetes cluster:
Core component kube-APIserver realizes three node High Availabitities using keepalived and haproxy, closes non-peace
Full port 8080 and anonymous access receive https request in secure port 6443, are authenticated and awarded using x509, token and RBAC
Power strategy accesses kubelet and etcd using https coded communication.
Core component kube-controller-manger realizes that three node height can using keepalived and haproxy
With closing non-security port, receive https request in secure port 10252, use kubeconfig access APIserver
Secure port.
Core component kube-scheduler realizes three node High Availabitities using keepalived and haproxy, uses
The secure port of kubeconfig access APIserver.
Core component kubelet is closed in the JSON file configuration major parameter of KubeletConfiguration type
A read port is closed, https request is received in secure port 10250, request is authenticated and is authorized, anonymous access and non-is refused
Authorization access uses the secure port of kubeconfig access APIserver.
Core component kube-proxy accesses the secure port of APIserver using kubeconfig,
The JSON file configuration major parameter of KubeProxyConfigurationl type, uses ipvs proxy mode.
DNS plug-in unit coredns, dashboard login authentication plug-in unit is installed, heapster monitors plug-in unit, docker-
Registry mirror image manages plug-in management Kubernetes cluster.
Nifi data flow cluster is with the operation of Zero-Master normal form, and cluster is with the use of docker mirror image forms
Kubernetes StatefulSet deployment, constructs multiple data flows in the cluster to receive and handle multiple numbers on each node
According to the data in source, quality of item data flow data source is database, data flow component include ExecuteSQL,
ConvertAvroToJSON and PutKafka, ExecuteSQL extract data from database, and ConvertAvroToJSON will
JSON data are written in Kafka cluster at JSON format, PutKafka for the data conversion being drawn into, machine parameter data stream,
Producing alert data stream and technological parameter data flow data source is network, data flow component include ListenHttp and
PutKafka monitors corresponding network port by ListenHttp component and passes through http network communication protocol to obtain injection molding factory
The data sended over reuse PutKafka and write data into Kafka cluster.
Kafka cluster, including at least one broker server, broker server number pass through depending on communication requirement
Zookeeper dynamic adjusts, and Kafka cluster is stored in state in zookeeper, therefore also needs to construct zookeeper.This
Embodiment builds zookeeper and Kafka cluster using three machines, and Kafka and zookeeper are stateful cluster services,
Need storage dish to preserve status information, thus using in kubernetes StatefulSet deployment and management Kafka and
zookeeper.StatefulSet initializes cluster by Init Container, is maintained to collect with Headless Service
The stable relation of group members provides network storage with Persistent Volume and Persistent Volume Claim to hold
Longization data service the rear end Persistent Volume using NFS, and NFS service disposes conduct using containerization
A part of Kubernetes cluster-based storage resource.Kubernetes is described and is configured application using yaml file,
Relevant configuration is written in the yaml file of zookeeper, uses " kubectl create-f yaml filename " order creation
Zookeeper service writes kafka yaml file after creating zookeeper service, same to use " kubectl
Create-f yaml filename " order creation kafka cluster.
Kafka consumer's cluster obtains data for subscribing to each Topic in Kafka cluster and stores data into
In distributed data base, there are four class data flows in Kafka cluster, every class data flow at least corresponds to a Kafka consumer,
Kafka consumer writes realization according to Kafka API with Java language, first setting connection Kafka cluster attribute file, including
Kafka cluster link address, consumer identification (it is identical to be set as all consumer identifications), if automatically confirm that offset, from
Dynamic confirmation offset time interval, the serializing class of key, the serializing class of value, the offline time-out time of heartbeat, subscription
Topic, creation consumer's object etc. is to be polled after attribute is set, and after being polled to the Topic of subscription, reads data, in order to
It writes data into Hbase database, needs the Java API using Hbase, according to API, configuration data connection first belongs to
Property, the HTable object of specified Hbase table is created, the Put class creation Put object reused in API writes data into database
In, Kafka consumer's cluster is operated in docker container with jar packet form, and cluster is with the use of docker mirror image forms
Kubernetes StatefulSet deployment.
Distributed data base, for storing a large amount of injection molding machine creation datas, it is preferred to employ Hbase storing data,
Hbase use table, row and column storing data, for four kinds of data stream types build respectively four Table I njectionSettings,
ProcessData, AlarmData and QualityData.Deployment Hbase database needs to rely on HDFS cluster and zookeeper
Cluster, HDFS are made of namenode and datanode, and the suitable mirror image of pull, writes HDFS first from DockerHub
After the yaml file of cluster, the Service of HDFS cluster is created by " kubectl create-f yaml filename ", is passed through
Kubectl get services/rc/pods can check corresponding Service and Pod.
Such as Fig. 2, a kind of injection molding equipment big data acquisition and storage method based on Kubernetes, feature exists
In including the following steps;
Nifi data flow cluster is run with Zero-Master normal form, constructs multiple data flows on each node in cluster to connect
Receive and handle the data of multiple data sources；
Use PutKafka Processor according to Kafka Message message format according to data after data receiver processing
Type is written to Kafka cluster and corresponds in the subregion of Topic；
Kafka consumer's cluster connects zookeeper, subscribes to the Topic in Kafka cluster, consumes from Kafka cluster
Data are stored to distributed data base；
Specifically, stepThe data flow of Nifi data flow cluster is divided into four classes:
(1) machine parameter data stream, including the critical machine that quality of item has a major impact is arranged in each link of injection molding
Parameter；
(2) alert data stream is produced, including the abnormal alarm log generated in injection molding process；
(3) technological parameter data flow, the melt state parameter including directly determining quality of item in each link of injection molding；
(4) quality of item data flow, including quality of item judgement；
Quality of item data flow data source be database, data flow component include ExecuteSQL, ConvertAvroToJSON and
PutKafka, ExecuteSQL extract data from database, ConvertAvroToJSON by the data conversion being drawn at
JSON data are written in Kafka cluster by JSON format, PutKafka, machine parameter data stream, production alert data stream and work
Skill parameter data stream data source is network, and data flow component includes ListenHttp and PutKafka, passes through ListenHttp group
Part monitors corresponding network port to obtain the data that injection molding factory is sended over by http network communication protocol, reuses
PutKafka writes data into Kafka cluster.
StepKafka Message message format serializes four class numbers with Avro to produce sequence id as Key respectively
Byte sequence after is Value, sets 4 Topic in Kafka cluster and stores corresponding data flow data respectively, and is each
Topic creation backup and subregion.
StepKafka consumer's cluster, including four class consumers, every at least one server of class consumer, Kafka
Consumer is write using Java language, and respective attributes are arranged first according to Kafka API, then is created consumer's object and subscribed to accordingly
Theme is simultaneously polled, and is subscribed to after Topic to write data into database using distributed data base API and is stored.
Although the embodiments of the invention are described in conjunction with the attached drawings, but those skilled in the art should understand that, this
On the basis of inventive technique scheme, those skilled in the art, which are not required to make the creative labor, can make various modifications and variations, this
The modifications and variations of sample are still in the scope of the present invention.
Claims (8)
1. big data acquisition and storage system is equipped in a kind of injection molding, characterized in that include:
Kubernetes cluster passes through docker container virtualization technology management and deployment Nifi cluster, Distributed Message Queue
Kafka cluster, Kafka consumer's cluster and distributed data base；
Nifi cluster is used for from data sources and processing injection molding machine data；
Distributed Message Queue Kafka cluster for being the corresponding Topic of data creation, and configures subregion and data backup, with
Receive the mass data that Nifi data flow cluster generates；
Kafka consumer's cluster obtains data for subscribing to each Topic in Kafka cluster and stores data into distribution
In formula database；
Distributed data base is used for distributed storage magnanimity injection molding machine data.
2. big data acquisition and storage system is equipped in a kind of injection molding as described in claim 1, characterized in that the injection molding
Machine data include machine parameter data, production alert data, technological parameter data, quality of item data.
3. big data acquisition and storage system is equipped in a kind of injection molding as described in claim 1, characterized in that described
Kubernetes cluster realizes Kubernetes component High Availabitity using keepalived and haproxy, double using x509 certificate
It is authorized to certification and RBAC and carries out security control, installation DNS plug-in unit coredns, dashboard login authentication plug-in unit,
Heapster monitoring plug-in unit, docker-registry mirror image manage plug-in unit to manage and dispose cluster.
4. a kind of equip big data acquisition and storage using the injection molding based on Kubernetes of system described in claim
Method, characterized in that the following steps are included:
1. Nifi cluster is run with Zero-Master normal form, multiple data flows are constructed to receive on each node in Nifi cluster
With the data for handling multiple data sources；
2. using PutKafka Processor according to Kafka Message message format according to data class after data receiver processing
Type is written to Kafka cluster and corresponds in the subregion of Topic；
3. Kafka consumer's cluster connects zookeeper, the Topic in Kafka cluster is subscribed to, consumes number from Kafka cluster
It is stored according to distributed data base.
5. method as claimed in claim 4, characterized in that 1. the data flow includes machine parameter data stream, production to step
Alert data stream, technological parameter data flow and quality of item data flow.
6. method as claimed in claim 5, characterized in that step 1. in the data that specifically include of four class data flows are as follows:
(1) machine parameter data stream, including the critical machine that quality of item has a major impact is arranged in each link of injection molding
Parameter；
(2) alert data stream is produced, including the abnormal alarm log generated in injection molding process；
(3) technological parameter data flow, the melt state parameter including directly determining quality of item in each link of injection molding；
(4) quality of item data flow, including quality of item judgement.
7. method as claimed in claim 4, characterized in that the step 2. Kafka Message message format, to produce sequence
Column id is Key, with the byte sequence after using Avro to serialize four class data respectively for Value.
8. method as claimed in claim 4, characterized in that 3. the Kafka cluster is that four kinds of data types are created respectively to step
Corresponding Topic is built, totally 4 Topic, and configures subregion and backup for each Topic.
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| CN113157658B (en) * | 2021-05-13 | 2021-11-09 | 心动互动娱乐有限公司 | Client log collecting and distributing method and device and computer equipment |
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