CN107025274A - Equipment health status intelligent perception system and method based on Hadoop - Google Patents
Equipment health status intelligent perception system and method based on Hadoop Download PDFInfo
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Abstract
The invention belongs to industrial big data processing correlative technology field, it discloses a kind of equipment health status intelligent perception system based on Hadoop and method, the equipment health status intelligent perception system includes multiple sensors, Hadoop platform, data management module, data processing module, state monitoring module, life-span monitoring modular and data visualization module；Multiple sensors are used for the status data of detection device；The Hadoop platform is used to carry out distributed treatment to the status data；The data management module is used for importing and management to the status data；The data processing module is handled the status data and the status data after processing is carried out into classification storage；The state monitoring module is used to the state of equipment is identified and predicted；The life prediction module is used for the life-span of pre- measurement equipment；The data visualization module is shown for extracting status data with carrying out visualization.
Description
Technical field
The invention belongs to industrial big data processing correlative technology field, more particularly, to a kind of setting based on Hadoop
Standby health status intelligent perception system and method.
Background technology
Under the promotion of the emerging technologies such as cloud computing, big data and Internet of Things, the whole world has been started with manufacturing industry transition and upgrade
For the new round industrial change of top priority.In this wheel industrial change, networking, digitlization, intelligent turn into manufacture dress
The emphasis direction of standby industry development, and timely and accurate diagnosis and prediction how is carried out to manufacturing equipment health status turns into it
In one of key point.The research of traditional diagnosis and prediction for manufacturing equipment health status is to be based on being equipped in fortune mostly
The Condition Monitoring Data collected during dynamic.However, renewal and the recursion of monitoring time with detection means, retrievable
Data class is more and more, and data volume is increasing, and data value density is more and more lower, and data processing speed will seek quickness, but
Existing method can not meet the demand of processing speed.
Industrial big data analytical technology has obtained the attention of more and more units as nearest study hotspot both at home and abroad, and
The Condition Monitoring Data of assembling magnanimity cannot be handled timely and effectively, cause less efficient, reliability is relatively low, be unfavorable for pair setting
The progress of standby health status is accurately detected.Correspondingly, this area has development one kind and quickly equipment health status can be supervised
Survey equipment health status intelligent perception system and method that data are handled.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, the invention provides a kind of equipment health based on Hadoop
Condition intelligent sensory perceptual system and method, the equipment health status intelligent perception system are based on Hadoop platform, use
MapReduce distributed process engines, transfer different R linguistic algorithms by rJava interfaces and status data are handled, or
Person uses the algorithm in the MXnet deep learning frameworks in Spark engines, realizes and the Distributed Calculation of data is handled, can
The processing speed of magnanimity status data is significantly increased, the real-time perception to equipment health status is realized.In addition, the equipment is strong
Health condition intelligent sensory perceptual system takes data classification to store, and the magnanimity status data that sensor is collected is in HDFS databases
Storage, the unstructured data after processing is stored with HBase databases, and structural data is deposited with MySQL database
Storage, is conducive to the raising for the treatment of effeciency.
To achieve the above object, according to one aspect of the present invention, there is provided a kind of healthy shape of equipment based on Hadoop
State intelligent perception system, it is characterised in that：
The equipment health status intelligent perception system includes multiple sensors being embedded on smart machine, Hadoop
Platform, data management module, data processing module, state monitoring module, life-span monitoring modular, data visualization module and system
Management module；
Multiple sensors are used for the status data of detection device；The Hadoop platform is used for the status data
Carry out distributed treatment；The data management module is used for importing and management to the status data；The data processing mould
Block is handled the status data and the status data after processing is carried out into classification storage；The state monitoring module
Current state, the working condition of pre- measurement equipment next stage and current state possible duration for identification equipment, and
By state recognition and predict the outcome storage into MySQL database；The life prediction module is used for according to the status data
Judge whether equipment has reached the life deterioration stage and estimated the remaining life of equipment；The data visualization module is used for
Extract the initial data, the processing data of the data processing module, the state detection module of the data management module
Grouped data and the prediction data of the life-span monitoring modular are shown with carrying out visualization；The system management module is used to manage
User right.
Further, the status data is distributed using MapReduce distributed process engines and Spark engines
Formula processing.
Further, the structural data in the status data after being handled through the data processing module is saved to
In the MySQL database, unstructured data is saved in Hbase numbers storehouse.
Further, the status data that the data management module is imported is stored in HDFS databases.
Further, the life prediction module be provided with one-parameter prediction submodule and multi-parameter prediction submodule for
Family is selected, and two prediction submodules correspond respectively to parameter Bayesian Estimation method and Monte Carlo method of estimation.
Further, the state monitoring module includes state recognition submodule and status predication submodule, the state
Identification submodule is used for the model selected according to user by the data input disaggregated model after data processing module processing
To carry out the identification that equipment fills state, and then perceive the health status residing for current device；The status predication submodule is used for
It is possible according to the working condition and current state of the result of the status data of current device pre- measurement equipment next stage
Duration.
It is another aspect of this invention to provide that there is provided a kind of equipment health status Intellisense method based on Hadoop,
It comprises the following steps：
(1) the equipment health status intelligent perception system as described above based on Hadoop is provided, it is strong using the equipment
The sensor of health condition intelligent sensory perceptual system is gathered in real time to the operational factor of equipment, with different frequency and different pieces of information lattice
The status data of formula collecting device；
(2) status data collected is transmitted and stored in HDFS databases by radio sensing network；
(3) Hadoop platform is called, and starts MapReduce distributed process engines and Spark engines, to HDFS
Status data in database carries out distributed treatment；
(4) obtained result data progress classification storage will be handled, wherein the result data of structuring is saved in into MySQL
In database, non-structured result data is saved in Hbase numbers storehouse；
(5) data in the data visualization module called data storehouse are simultaneously shown with scheming tableau format.
In general, by the contemplated above technical scheme of the present invention compared with prior art, the base that the present invention is provided
Mainly had the advantages that in Hadoop equipment health status intelligent perception system and method：
(1) Hadoop platform is called, and starts MapReduce distributed process engines and Spark engines to HDFS data
Status data in storehouse carries out distributed treatment, realizes the quick processing to mass data；
(2) classification storage is carried out to data, HDFS databases, HBase databases and MySQL database has been respectively adopted,
Different intelligent part is entered with the signal data after the mass data of the sensor raw signals of intelligent type and processing with realizing
Row classification is preserved；
(3) the equipment health status intelligent perception system has good stability and portability, is suitable for intelligence
The severe condition of equipment working environment, and can realize and take algorithms of different model for different intelligent position different sensors signal
The function of being calculated and predicted, algorithms of different model obtains Different Results, and then it is relatively reliable to assess any model,
Improve the accuracy of sensing results；
(4) data are graphically shown using data visualization module, it is simple and clear, it is easy to user to look into
See；The equipment health status intelligent perception system can also individually carry out data analysis to the different parts of equipment, judge intelligence
The health status and residual life of part, are conducive to equipment personnel to prepare corresponding maintenance policy.
Brief description of the drawings
Fig. 1 is the knot for the equipment health status intelligent perception system based on Hadoop that better embodiment of the present invention is provided
Structure frame diagram.
Fig. 2 is the function signal of the software section of the equipment health status intelligent perception system based on Hadoop in Fig. 1
Figure.
Fig. 3 is the stream for the equipment health status Intellisense method based on Hadoop that better embodiment of the present invention is provided
Cheng Tu.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below
Not constituting conflict each other can just be mutually combined.
Fig. 1 and Fig. 2 is referred to, the equipment health status based on Hadoop that better embodiment of the present invention is provided intelligently is felt
Know system, the equipment health status intelligent perception system by the way of soft or hard combination, hardware components be responsible for hind computation and
Data Collection and storage, software section are used to provide the user guidance and selection function, and result is shown.
The equipment health status intelligent perception system includes multiple sensors being embedded on smart machine, Hadoop
Platform, data management module, data processing module, state monitoring module, life-span monitoring modular, data visualization module and system
Management module.Multiple sensors are used for the shapes such as temperature, frequency, speed, acceleration, voltage, electric current, the signal of detection device
State data.In present embodiment, the Hadoop platform is used to carry out distributed treatment to status data.
The data management module is used for the importing and management of the status data, and it provides corresponding data importing and connect
Mouthful, with the signal data of the different part different times of type difference according to sensor.The data management module also includes number
Submodule is deleted according to collection addition submodule, data set and information is changed and data selection submodule, the data addition submodule,
The data set deletes submodule and described information modification and data selection submodule is respectively used to realize the addition of data set, deleted
Remove and change remark information.The data management module is additionally operable to individually carry out data analysis to the different parts of smart machine.
After user's selection pending data, it is pending that status data will enter the data processing module etc..
The data processing module includes pretreatment submodule, feature extraction submodule, correlation analysis submodule, cluster
Submodule and Fusion Features submodule are analyzed, each submodule of the above can carry out the selection of algorithm, to realize to having selected number
Analyzed and compared according to using a kind of algorithm or many algorithms.Result data after data processing module processing according to
Structuring and unstructured it is respectively stored in MySQL database and HBase databases.The pretreatment submodule can be taken
Filtering, go it is empty, except methods such as singular value, FFT, wavelet analysises；The correlation analysis submodule is used
Autocorrelation analysis and cross-correlation analysis method；The feature extraction submodule is used to carry out feature in time domain, frequency domain, time-frequency domain
The extraction of value；The clustering submodule carries out clustering using subtractive clustering, k- averages, k- central points scheduling algorithm；Institute
State Fusion Features submodule be used for realize the characteristic value progress abatement fusion that obtains algorithms of different, to obtain carrying than single algorithm
Take characteristic value more superior characteristic value.
The state monitoring module includes state recognition submodule and status predication submodule, the state recognition submodule
For the model selected according to user, by the data input disaggregated model after data processing module processing to carry out equipment
The identification of state, and then perceive which kind of health status current device is in.The status predication submodule is used for basis and currently set
The working condition of the result pre- measurement equipment next stage of standby status data and current state possible duration.Shape
State is recognized and the result of status predication is stored in MySQL database.State recognition submodule and the status predication
The moulder that module is used has ANFIS models, deep learning model, supporting vector machine model, HMM.
The life prediction module is provided with one-parameter prediction submodule and multi-parameter prediction submodule is selected for user, and two
Individual prediction submodule corresponds respectively to parameter Bayesian Estimation method and Monte Carlo method of estimation.Treat that user chooses parameter pre-
Survey after submodule, the life prediction module is used for the data after being handled according to the data processing module and the status monitoring
Module, to judge whether current device has reached the life deterioration stage, is estimated the classification results of equipment present case simultaneously
The remaining life of equipment, and life prediction result is stored in MySQL database.
The data visualization module is used to extract the initial data of the data management module, the data processing module
Processing data, the prediction data of the grouped data of the state detection module and the life-span monitoring modular to be to be visualized
It has been shown that, realizes the function to equipment condition monitoring, real-time display device variation tendency, so as to the maintenance feelings of user in time to equipment
Condition makes decisions.In present embodiment, the data visualization module is respectively from the HDFS databases, the MySQL data
Corresponding data is extracted in storehouse and the HBase databases, and utilizes radar map, cake chart, scatter diagram, block diagram, scatter diagram, song
The forms such as line chart, bar chart and form are shown.
The system management module is used for management platform user right to realize increasing, delete, change user function, and it is additionally operable to show
Show parameter setting and system login, exit record historical data reservation.
The framework of the equipment health status intelligent perception system includes four layers, respectively data gathering layer, data storage
Layer, data analysis layer and data application layer.The data analysis layer introduces Hadoop platform, and data can be achieved rapidly and efficiently
Processing.The data storage layer employs tri- kinds of databases of HDFS, HBase and MySQL, realizes the classification storage of data.
The data gathering layer is entered using the various sensors being embedded on smart machine to the running state parameter of equipment
Row collection, to obtain status data.Various sensors are adopted by different acquisition systems with different frequency and different data format
Collect the state parameter of equipment, and status data timing after simple noise reduction process and compression is sent by radio sensing network
Stored into specified service end, complete the collection and preliminary storage work of reset condition data.
The reset condition data that the data gathering layer is collected into by the data storage layer carry out classification preservation, relationship type
Data and non-relational data are preserved respectively, and using the extensive file data of HDFS database purchases, HBase databases enter
The efficient real-time storage of the non-structured data of row, MySQL database structured data.User passes through data management mould
Block, can be achieved the editor to data message and selection.
The data analysis layer calls Hadoop platform, and starts MapReduce distributed process engines and Spark draws
Hold up, the different algorithms integrated by calling are made a concrete analysis of to Various types of data.The algorithm called includes machine learning algorithm
Storehouse, statistical analysis algorithms storehouse, the integrated storehouse of model algorithm and deep learning algorithm integration storehouse.The data analysis layer can realize number
According to functions such as processing, status monitoring and life predictions, and result is preserved.User passes through the data processing mould
Block, the state monitoring module and the life prediction module can realize the selection to algorithm.
Selection of the data application layer according to user in the visualization model, obtains the result of analyzing and processing, leads to
Cross visualization means, with scheme or form in the form of show, making the state, health status and residue of each part of equipment makes
Clear with the life-span, auxiliary user makes maintenance decision.
The present invention also provides a kind of equipment health status Intellisense method based on Hadoop, and it mainly includes following step
Suddenly：
Step one is set there is provided the equipment health status intelligent perception system based on Hadoop as described above using described
The various sensors of standby health status intelligent perception system are gathered in real time to the operational factor of equipment, with different frequency and not
With the status data of data format collecting device.
Step 2, the status data collected is transmitted and stored in HDFS databases by radio sensing network.
Step 3, calls Hadoop platform, and starts MapReduce distributed process engines and Spark engines, to HDFS
Status data in database carries out distributed treatment.Specifically, the sensor detection is transferred from the HDFS databases
The status data arrived, and status data is pre-processed, then, data are distributed by rJava interface interchange algorithms
Formula processing, or distributed treatment is carried out to data using the deep learning algorithm in MXnet deep learning frameworks.The HDFS
The input burst of database is distributed to multiple Map and handled simultaneously, exports after shuffle, multiple reduce are distributed to again
Task, realizes distributed treatment.
Step 4, the result data that processing is obtained carries out classification storage, wherein the result data of structuring is saved in
In MySQL database, non-structured result data is saved in Hbase numbers storehouse.
Data in step 5, the data visualization module called data storehouse are simultaneously shown with scheming tableau format.
Equipment health status intelligent perception system based on Hadoop and method that the present invention is provided, it is flat based on Hadoop
Platform, with MapReduce distributed process engines, transfers different R linguistic algorithms by rJava interfaces and status data is carried out
Processing, or with the algorithm in the MXnet deep learning frameworks in Spark engines, realize to the Distributed Calculation of data
Reason, can be significantly increased the processing speed of magnanimity status data, realize the real-time perception to equipment health status.In addition, institute
Stating equipment health status intelligent perception system takes data classification to store, and the magnanimity status data that sensor is collected is in HDFS
Stored in database, the unstructured data after processing is stored with HBase, and structural data is stored with MySQL, is had
Beneficial to the raising for the treatment of effeciency.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, it is not used to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention etc., it all should include
Within protection scope of the present invention.
Claims (7)
1. a kind of equipment health status intelligent perception system based on Hadoop, it is characterised in that：
The equipment health status intelligent perception system, which includes multiple sensors being embedded on smart machine, Hadoop, puts down
Platform, data management module, data processing module, state monitoring module, life-span monitoring modular, data visualization module and system pipes
Manage module；
Multiple sensors are used for the status data of detection device；The Hadoop platform is used to carry out the status data
Distributed treatment；The data management module is used for importing and management to the status data；The data processing module pair
The status data is handled and the status data after processing is carried out into classification storage；The state monitoring module is used for
The current state of identification equipment, the working condition of pre- measurement equipment next stage and current state possible duration, and by shape
State recognizes and predicted the outcome storage into MySQL database；The life prediction module is used to judge according to the status data
Whether equipment has reached the life deterioration stage and has estimated the remaining life of equipment；The data visualization module is used to extract
The initial data of the data management module, the processing data of the data processing module, the classification of the state detection module
Data and the prediction data of the life-span monitoring modular are shown with carrying out visualization；The system management module is used to manage user
Authority.
2. the equipment health status intelligent perception system as claimed in claim 1 based on Hadoop, it is characterised in that：The shape
State data carry out distributed treatment using MapReduce distributed process engines and Spark engines.
3. the equipment health status intelligent perception system based on Hadoop as described in claim any one of 1-2, its feature exists
In：The structural data in the status data after being handled through the data processing module is saved to the MySQL data
In storehouse, unstructured data is saved in Hbase numbers storehouse.
4. the equipment health status intelligent perception system based on Hadoop as described in claim any one of 1-2, its feature exists
In：The status data that the data management module is imported is stored in HDFS databases.
5. the equipment health status intelligent perception system based on Hadoop as described in claim any one of 1-2, its feature exists
In：The life prediction module is provided with one-parameter prediction submodule and multi-parameter prediction submodule is selected for user, and two pre-
Survey submodule and correspond respectively to parameter Bayesian Estimation method and Monte Carlo method of estimation.
6. the equipment health status intelligent perception system based on Hadoop as described in claim any one of 1-2, its feature exists
In：The state monitoring module includes state recognition submodule and status predication submodule, and the state recognition submodule is used for
The model selected according to user will fill shape in the data input disaggregated model after data processing module processing to carry out equipment
The identification of state, and then perceive the health status residing for current device；The status predication submodule is used for according to current device
The working condition of the result pre- measurement equipment next stage of status data and current state possible duration.
7. a kind of equipment health status Intellisense method based on Hadoop, it comprises the following steps：
(1) the equipment health status intelligent perception system based on Hadoop as described in claim any one of 1-6 is provided, utilized
The sensor of the equipment health status intelligent perception system is gathered in real time to the operational factor of equipment, with different frequency and
The status data of different data format collecting device；
(2) status data collected is transmitted and stored in HDFS databases by radio sensing network；
(3) Hadoop platform is called, and starts MapReduce distributed process engines and Spark engines, to HDFS data
Status data in storehouse carries out distributed treatment；
(4) obtained result data progress classification storage will be handled, wherein the result data of structuring is saved in into MySQL data
In storehouse, non-structured result data is saved in Hbase numbers storehouse；
(5) data in the data visualization module called data storehouse are simultaneously shown with scheming tableau format.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388605A (en) * | 2018-02-06 | 2018-08-10 | 广东暨通信息发展有限公司 | Big data analysis platform based on Internet of Things |
| CN108563781A (en) * | 2018-04-25 | 2018-09-21 | 广州绿源信息科技有限公司 | Internet of Things big data processing method based on Hadoop and system |
| CN109063093A (en) * | 2018-07-27 | 2018-12-21 | 重庆工商职业学院 | A kind of internet of things data preprocess method and system |
| CN109086704A (en) * | 2018-07-23 | 2018-12-25 | 杭州电子科技大学 | A kind of physical activity recognition methods classified based on rarefaction representation and Softmax |
| CN109411094A (en) * | 2018-10-23 | 2019-03-01 | 上海市疾病预防控制中心 | Health life expectancy in life expectancy application support information system and method based on hygiene medical treatment big data |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101192997A (en) * | 2006-11-24 | 2008-06-04 | 中国科学院沈阳自动化研究所 | Remote status monitoring and fault diagnosis system for distributed devices |
| CN102923538A (en) * | 2012-07-06 | 2013-02-13 | 天津大学 | Elevator health management and maintenance system based on Internet of things and collection and assessment method |
| US20140188825A1 (en) * | 2012-12-31 | 2014-07-03 | Kannan Muthukkaruppan | Placement policy |
| CN104281130A (en) * | 2014-09-22 | 2015-01-14 | 国家电网公司 | Hydroelectric equipment monitoring and fault diagnosis system based on big data technology |
| CN105678398A (en) * | 2015-12-24 | 2016-06-15 | 国家电网公司 | Power load forecasting method based on big data technology, and research and application system based on method |
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106406296A (en) * | 2016-12-14 | 2017-02-15 | 东北大学 | Train fault diagnosis system and method based on vehicle and cloud |
-
2017
- 2017-03-21 CN CN201710167930.XA patent/CN107025274A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101192997A (en) * | 2006-11-24 | 2008-06-04 | 中国科学院沈阳自动化研究所 | Remote status monitoring and fault diagnosis system for distributed devices |
| CN102923538A (en) * | 2012-07-06 | 2013-02-13 | 天津大学 | Elevator health management and maintenance system based on Internet of things and collection and assessment method |
| US20140188825A1 (en) * | 2012-12-31 | 2014-07-03 | Kannan Muthukkaruppan | Placement policy |
| CN104281130A (en) * | 2014-09-22 | 2015-01-14 | 国家电网公司 | Hydroelectric equipment monitoring and fault diagnosis system based on big data technology |
| CN105678398A (en) * | 2015-12-24 | 2016-06-15 | 国家电网公司 | Power load forecasting method based on big data technology, and research and application system based on method |
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106406296A (en) * | 2016-12-14 | 2017-02-15 | 东北大学 | Train fault diagnosis system and method based on vehicle and cloud |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019140734A1 (en) * | 2018-01-17 | 2019-07-25 | 平安科技（深圳）有限公司 | Fund transaction clearing method, apparatus and device, and computer-readable storage medium |
| CN108388605A (en) * | 2018-02-06 | 2018-08-10 | 广东暨通信息发展有限公司 | Big data analysis platform based on Internet of Things |
| CN108563781A (en) * | 2018-04-25 | 2018-09-21 | 广州绿源信息科技有限公司 | Internet of Things big data processing method based on Hadoop and system |
| CN109086704A (en) * | 2018-07-23 | 2018-12-25 | 杭州电子科技大学 | A kind of physical activity recognition methods classified based on rarefaction representation and Softmax |
| CN109063093A (en) * | 2018-07-27 | 2018-12-21 | 重庆工商职业学院 | A kind of internet of things data preprocess method and system |
| CN109446278A (en) * | 2018-09-21 | 2019-03-08 | 贵州途遇旅行网科技有限公司 | A kind of big data management platform system based on block chain |
| CN109411094A (en) * | 2018-10-23 | 2019-03-01 | 上海市疾病预防控制中心 | Health life expectancy in life expectancy application support information system and method based on hygiene medical treatment big data |
| CN109740941A (en) * | 2019-01-04 | 2019-05-10 | 北京环境特性研究所 | Military products data processing system |
| CN110131593A (en) * | 2019-05-18 | 2019-08-16 | 北京化工大学 | Pipe leakage assisted detection system based on big data platform |
| CN110555008A (en) * | 2019-08-06 | 2019-12-10 | 国网河北省电力有限公司电力科学研究院 | Generator comprehensive diagnosis and state evaluation cloud platform |
| CN110555008B (en) * | 2019-08-06 | 2022-05-24 | 国网河北省电力有限公司电力科学研究院 | Generator comprehensive diagnosis and state evaluation cloud platform |
| CN111538762A (en) * | 2020-04-22 | 2020-08-14 | 深圳市欣横纵技术股份有限公司 | Information management analysis method based on data mining technology |
| CN111600890A (en) * | 2020-05-18 | 2020-08-28 | 广东电网有限责任公司惠州供电局 | Network security perception system based on big data |
| CN111600890B (en) * | 2020-05-18 | 2022-10-18 | 广东电网有限责任公司惠州供电局 | Network security perception system based on big data |
| CN112650654A (en) * | 2020-12-11 | 2021-04-13 | 西安诺瓦星云科技股份有限公司 | Early warning method and device for display control visualization system, storage medium and processor |
| CN114785821A (en) * | 2022-03-16 | 2022-07-22 | 陇东学院 | Urban Internet of things data processing system and method based on Hadoop |
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