CN110336703A - Industrial big data based on edge calculations monitors system - Google Patents
Industrial big data based on edge calculations monitors system Download PDFInfo
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- CN110336703A CN110336703A CN201910628162.2A CN201910628162A CN110336703A CN 110336703 A CN110336703 A CN 110336703A CN 201910628162 A CN201910628162 A CN 201910628162A CN 110336703 A CN110336703 A CN 110336703A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0823—Errors, e.g. transmission errors
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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Abstract
The present invention discloses a kind of industrial big data monitoring system based on edge calculations.The system comprises: data active layer, marginal layer, podium level and application layer.The data active layer is acquired heterogeneous data information by various technology of Internet of things, and is sent data on Edge Server using short-range communication technique.The real time data that data active layer acquires specifically is analyzed and is handled in edge side by the marginal layer, and the data for extracting high value are transferred to podium level.The podium level, it is main that the functions such as industrial data analysis and data storage are realized to the data that marginal layer transmits using cloud computing platform.The application layer provides relevant software service based on podium level, information is showed user by subscriber terminal equipment.The problems such as present invention mainly solves tradition based on the data monitoring system of cloud computing platform in the case where industrial data explodes environment, and platform load is big, data-handling efficiency is low, network delay is high low with safety.
Description
Technical field
The present invention relates to internet of things field, and in particular to a kind of industrial big data monitoring system based on edge calculations
System.
Background technique
With informationization technology and industrialized depth integration, data caused by mechanical equipment, information system are increasingly
It is abundant.Industrial big data has the feature that the scale of construction is big, type is more, value density is low and processing speed is fast, carries out to data real-time
Effective monitoring, diagnosing, may be implemented the predictive maintenance of equipment, and discovery production manages, the decision problem in scheduling flow.So
And traditional data monitoring system is unable to satisfy the service such as the analysis to industrial big data, processing and storage.Therefore, to industry
The intelligent Application of big data has become the core ring that enterprise reduces production cost, improves the market competitiveness and production efficiency
Section.
In recent years, the quantity of smart machine is continuously increased in industrial Internet of Things, and the 5th third-generation mobile communication (5G) technology goes out
Industrial data explosive growth will be now brought with the access of bulk device, a large amount of monitoring data uploads cause cloud data center to handle
Pressure is excessive, is no longer satisfied low time delay, high efficiency, the work of high reliability by the big data processing technique of core of cloud computing
Sparetime university's data application demand, and edge data transmits data to Cloud Server can consume a large amount of electric energy.Close to data source
The edge side of head provides the platform of converged network, calculating and storage capacity, provides nearest intelligent Service for industry spot, meets number
According to the functions such as cleaning, fusion, detection and storage filter out high quality using marginal layer by edge calculations in conjunction with cloud computing
It is transferred to cloud platform with the data of high value, low time delay, high efficiency, the application demand of high reliability is not only met, reduces
Power consumption, and protect the privacy of data.
It is contemplated that edge calculations are applied in the processing of industrial big data, cooperate with cloud computing, can be industry
The construction of Internet of Things brings tremendous influence.Therefore, it for the construction of industrial Internet of Things, solves industrial big data analysis processing and asks
Topic, the present invention is quasi- to propose a kind of industrial big data monitoring system based on edge calculations.
Summary of the invention
Regarding the issue above, the present invention provides a kind of industrial big data based on edge calculations monitors system,
To realize that industrial production is information-based and intelligent.
In order to achieve the above object, the present invention adopts the following technical scheme:
A kind of industrial big data monitoring system based on edge calculations, system includes data active layer, marginal layer, podium level and application
Layer, in which:
(1) data active layer is acquired more by the big data sampling instrument of various Internet of Things cognition technologies and information system
Source, heterogeneous data information, and sent data on live gateway or distributed server using short-range communication technique；
(2) real time data that data active layer acquires specifically is analyzed, handled and is stored in edge side by the marginal layer,
It extracts specific worth of data and is transferred to podium level；
(3) podium level is built industrial big data cloud platform using cloud computing and big data technology, is transmitted to marginal layer
Data realize the function of industrial data analysis, industrial micro services and data storage；
(4) application layer forms the industrial service software for meeting different scenes based on podium level, will by subscriber terminal equipment
Information shows user.
Above-mentioned data active layer includes awareness apparatus and information system；
The awareness apparatus acquires work in conjunction with the means of RFID, video monitoring and production monitoring equipment by various kinds of sensors
The Typical physical amount data and industrial environment data of industry equipment state utilize honeycomb network technology or local network transport to edge
Layer；
The information system acquires production and operation associated traffic data, utilizes local by big data acquisition technique and tool
Net is transferred to marginal layer.
Above-mentioned marginal layer includes intelligent gateway and distributed server；
The intelligent gateway and distributed server realize network, calculating and storage resource by Docker virtualization technology
Unified management；
The intelligent gateway converges the data of data active layer, carries out protocol analysis, data cleansing and data fusion to isomeric data
After be transmitted to distributed server；
The distributed server, as the main calculate node of marginal layer, to real time data of the intelligent gateway after processed into
Abnormal data is real-time transmitted to podium level and is further processed by row data normalization, feature extraction and abnormality detection, and will
Normal data is pre-stored on the server, uploads to data base of the podium level as industrial big data analysis to the periodic maintenance period
Plinth.
Abnormality detection the following steps are included:
(1) historical data is randomly selected as sample, and several random binary trees are constructed based on isolated forest algorithm；
(2) several random binary trees are combined and constitutes abnormality detection model；
(3) real time data is subjected to anomaly evaluation by abnormality detection model；
(4) show that evaluation score judges whether data are abnormal according to abnormality detection model；
(5) it extracts real time data and forms new sample data update abnormal detection model.
The podium level is made of Cloud Server；The Cloud Server and marginal layer distributed server cooperate, right
The real time data that marginal layer transmits carries out anomaly analysis and data storage, carries out data mining, statistical to historical data
Analysis, autoknowledge decision.
Anomaly analysis the following steps are included:
(1) data mining is carried out to historical data and builds abnormity diagnosis knowledge base；
(2) abnormal data is analyzed according to abnormity diagnosis knowledge base, judges whether there is failure；
(3) identification is out of order, and obtains fault solution using expert system, can not be identified and then be carried out data to real time data
It excavates, more new knowledge base.
The application layer is made of user terminal；The user terminal includes hand-held intelligent electronic equipment, and user passes through spy
Determine the function of software realization abnormity diagnosis, Optimizing manufacture, Analysis of Policy Making, cooperating manufacture.
Compared with prior art, advantage of the present invention is as follows:
First is that data-handling capacity is stronger, by handling data in marginal layer, only by a small amount of high quality and high value
Data be transferred to platform layer analysis, effectively reduce network transfer delay, alleviate the calculating pressure of cloud platform, improve number
According to treatment effeciency.
Second is to reduce production cost, by the real-time monitoring to industrial production data and based on industrial big data analysis
Optimizing manufacture reduces production risk and energy consumption, improves industrial production efficiency.Meanwhile the marginalisation of data processing is big
Reduce network transmission cost greatly.
Third is to improve safety, by will have the data of higher privacy requirements to handle in marginal layer, is eliminated
The risk for being monitored or stealing in network transmission, effectively increases the safety of data.
Detailed description of the invention
Fig. 1 is system architecture diagram of the invention；
Fig. 2 is abnormality detection analysis flow chart diagram of the invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing, to of the invention
Specific embodiment is described in more details.It should be appreciated that specific embodiment described herein is only to explain this hair
It is bright, it is not intended to limit the present invention.
As shown in Figure 1, a kind of industrial big data monitoring system based on edge calculations mainly includes data active layer, edge
Layer, podium level and application layer.
Data active layer, by the big data sampling instrument of various Internet of Things cognition technologies and information system acquire multi-source,
Heterogeneous data information, and sent data on live gateway or distributed server using short-range communication technique.
The real time data that data active layer acquires specifically is analyzed, handled and is stored in edge side by marginal layer, is extracted
Specific worth of data is transferred to podium level.
Application layer forms the industrial service software for meeting different scenes based on podium level, will certainly by subscriber terminal equipment
The valuable information such as plan scheme, diagnosis shows user.
Podium level is mainly built industrial big data cloud platform using cloud computing and big data technology, is transmitted across to marginal layer
The data come realize the functions such as industrial data analysis, industrial micro services and data storage.
The data active layer includes awareness apparatus and information system.
The awareness apparatus is adopted by various kinds of sensors in conjunction with means such as RFID, video monitoring and production monitoring equipment
The Typical physical amount data and industrial environment data for collecting industrial equipment state, are arrived using honeycomb network technology or local network transport
Marginal layer.In view of WiFi can not carry out reliable guarantee in terms of anti-interference, safety certification, service quality and business continuance,
It is difficult to meet requirement of the industrial scene to wireless transmission quality, therefore honeycomb network technology is selected to carry out short range wireless transmission.
The information system, including Enterprise Resources Plan (ERP), manufacturing execution system (MES), product life cycle pipe
Business management softwares such as (PLM) are managed, the big datas such as Flume, Logstash, Scribe acquisition technique and tool, acquisition life are passed through
Industry seeks associated traffic data, utilizes local network transport to marginal layer.
The marginal layer includes intelligent gateway and distributed server, realizes network, meter by Docker virtualization technology
It calculates and the unified management of storage resource, specific workflow is as shown in Figure 2.
The intelligent gateway converges the data of data active layer, since the mode that industrial field data is acquired and transmitted is a variety of
Multiplicity, and accessed by different Data Transport Protocols, intelligent gateway must have the adaptation ability for accessing various interface protocols,
And different data format, data rate etc. are converted into unified Data Transport Protocol.Due to the limitation or work of equipment itself
The interference of industry site environment factor, it may appear that the problems such as data redundancy, shortage of data, error in data, be unfavorable for the subsequent of data
It utilizes.Data are cleaned and are merged, redundancy, the data of mistake can be effectively removed, and fill up to missing data.This
Outside, probabilistic complex relationship can also be converted into intelligible accurate expression by data fusion, with the perception of multiple low precision
Data replace high-precision perception data to reduce the cost of system to improve the quality of data.
The distributed server, the reality as the main calculate node of marginal layer, first to intelligent gateway after processed
When data carry out data normalization and feature extraction, form characteristic data set and carry out abnormality detection.Then, abnormal data is real-time
It is transferred to podium level to be further processed, normal data is pre-stored on the server, uploads to platform to the periodic maintenance period
Data basis of the layer as industrial big data analysis.
The method for detecting abnormality is as follows:
(1) historical data is randomly selected as sample, and several random binary trees are constructed based on IForest algorithm；
(2) several random binary trees are combined and constitutes abnormality detection model；
(3) real time data is subjected to anomaly evaluation by abnormality detection model；
(4) show that evaluation score judges whether data are abnormal according to abnormality detection model；
(5) it extracts real time data and forms new sample data update abnormal detection model.
The podium level is made of Cloud Server.The Cloud Server is cooperateed with the formation of marginal layer distributed server, right
The real time data that marginal layer transmits carries out anomaly analysis and data storage, carries out big data processing to historical data, specifically
Scheme is as follows:
(1) data mining, based on technologies such as artificial intelligence, machine learning, statistical methods, in large data repository, automatically
The reasoning Deductive method of inductive is made on ground to data, and exploration is hidden in value information therein.
(2) it statisticallys analyze, with statistical method and knowledge related with analysis object, by quantitative analysis and qualitative analysis phase
In conjunction with reaching to the more deep understanding of analysis object, be suitable for discovery business administration and plan problem and weakness in execution
Link.
(3) autoknowledge decision, Integrated Knowledge Representation, knowledge acquisition, knowledge connection, Knowledge Reorganizing and knowledge reasoning etc.
A series of more educated automatic algorithms models improve whole decision mechanism, are suitable for discovery and solve production, management, scheduling flow
In key decision point decision problem.
As shown in Fig. 2, the anomaly analysis process is as follows:
(1) data mining is carried out to historical data and builds abnormity diagnosis knowledge base.
(2) abnormal data is analyzed according to abnormity diagnosis knowledge base, judges whether there is failure.
(3) identification is out of order, and obtains fault solution using expert system, can not be identified and then be carried out to real time data
Data mining, more new knowledge base.
The application layer is made of user terminal.The user terminal includes the hand-held intelligents such as mobile phone, plate and computer electricity
Sub- equipment, user can realize that the intelligent Application to industrial big data, concrete application are as follows by specific software or APP:
(1) abnormity diagnosis analyzes abnormal data based on abnormity diagnosis model by carrying out abnormality detection to real time data
Processing, diagnosis Exception Type, abnormal position and reason carry out predictive maintenance and early warning to product facility, to avoid occurring
Fault in production realizes control of product quality and production risk control.
(2) Optimizing manufacture, the real time data based on industry spot carry out big data analysis and excavation, find the singular point of efficiency
With deficiency, optimized production process.Production plan and scheduling are predicted by the statistical analysis to historical data, in conjunction with intelligence
Optimization algorithm obtains reasonable effective production plan, and Supervisory Surveillance Program is in actual deviation.
(3) Analysis of Policy Making is analyzed by the data to industrial big data, more effectively from a large amount of rambling data
Obtain useful information, in conjunction with autoknowledge decision, find and solve the decision problem in corporate operation, improve the efficiency of decision-making and
Decision Quality.
(4) cooperating manufacture, by by the Deep integrating of production process and information system, by the supply of material, producing and selling and object
Multiple links such as stream are cascaded, so that it is guaranteed that enterprise meets the differentiation and individual demand of mass market.
It is a specific embodiment of the invention described in above instructions, various illustrations are not to reality of the invention
Matter Composition of contents limitation, for making other various forms of variations or variation on the basis of above description, still in the present invention
Protection scope among.
Claims (7)
1. a kind of industrial big data based on edge calculations monitors system, it is characterised in that: system includes data active layer, edge
Layer, podium level and application layer, in which:
(1) data active layer is acquired more by the big data sampling instrument of various Internet of Things cognition technologies and information system
Source, heterogeneous data information, and sent data on live gateway or distributed server using short-range communication technique；
(2) real time data that data active layer acquires specifically is analyzed, handled and is stored in edge side by the marginal layer,
It extracts specific worth of data and is transferred to podium level；
(3) podium level is built industrial big data cloud platform using cloud computing and big data technology, is transmitted to marginal layer
Data realize the function of industrial data analysis, industrial micro services and data storage；
(4) application layer forms the industrial service software for meeting different scenes based on podium level, will by subscriber terminal equipment
Information shows user.
2. the industrial big data according to claim 1 based on edge calculations monitors system, which is characterized in that the data
Active layer includes awareness apparatus and information system；
The awareness apparatus acquires work in conjunction with the means of RFID, video monitoring and production monitoring equipment by various kinds of sensors
The Typical physical amount data and industrial environment data of industry equipment state utilize honeycomb network technology or local network transport to edge
Layer；
The information system acquires production and operation associated traffic data, utilizes local by big data acquisition technique and tool
Net is transferred to marginal layer.
3. the industrial big data according to claim 1 based on edge calculations monitors system, which is characterized in that the edge
Layer includes intelligent gateway and distributed server；
The intelligent gateway and distributed server realize network, calculating and storage resource by Docker virtualization technology
Unified management；
The intelligent gateway converges the data of data active layer, carries out protocol analysis, data cleansing and data fusion to isomeric data
After be transmitted to distributed server；
The distributed server, as the main calculate node of marginal layer, to real time data of the intelligent gateway after processed into
Abnormal data is real-time transmitted to podium level and is further processed by row data normalization, feature extraction and abnormality detection, and will
Normal data is pre-stored on the server, uploads to data base of the podium level as industrial big data analysis to the periodic maintenance period
Plinth.
4. the industrial big data according to claim 1 based on edge calculations monitors system, which is characterized in that the platform
Layer is made of Cloud Server；The Cloud Server and marginal layer distributed server cooperate, and transmit to marginal layer
Real time data carries out anomaly analysis and data storage, determines to historical data progress data mining, statistical analysis, autoknowledge
Plan.
5. the industrial big data according to claim 1 based on edge calculations monitors system, which is characterized in that the application
Layer is made of user terminal；The user terminal includes hand-held intelligent electronic equipment, and user realizes abnormal examine by specific software
Disconnected, Optimizing manufacture, Analysis of Policy Making, cooperating manufacture function.
6. the industrial big data according to claim 3 based on edge calculations monitors system, which is characterized in that the exception
Detection the following steps are included:
(6-1) randomly selects historical data as sample, constructs several random binary trees based on isolated forest algorithm；
Several random binary trees are combined and constitute abnormality detection model by (6-2)；
Real time data is carried out anomaly evaluation by abnormality detection model by (6-3)；
(6-4) show that evaluation score judges whether data are abnormal according to abnormality detection model；
(6-5) extracts real time data and forms new sample data update abnormal detection model.
7. the industrial big data according to claim 4 based on edge calculations monitors system, which is characterized in that the exception
Analysis the following steps are included:
(7-1) carries out data mining to historical data and builds abnormity diagnosis knowledge base；
(7-2) analyzes abnormal data according to abnormity diagnosis knowledge base, judges whether there is failure；
(7-3) identification is out of order, and is obtained fault solution using expert system, can not be identified, count to real time data
According to excavation, more new knowledge base.
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