CN109245310A - A kind of electric power monitoring system based on real-time data base - Google Patents
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Abstract
A kind of electric power monitoring system based on real-time data base, belongs to electric power monitoring system field, is divided into three modules, respectively human-computer interface module, real-time data base and communication module.Abnormal data is detected using least square method, and smoothing processing is done to abnormal data, swinging door compression algorithm is recycled to carry out compression processing to pretreated data；Electric power monitoring system realizes real-time data base using memory database, historical data base and disk storage three levels storage architecture jointly.This system is on Real-Time Databases System Technique, on the basis for guaranteeing historical data compression accuracy, abnormal data is detected using least square method, and the adverse effect that smoothing processing avoids abnormal data compressing data precision and compression efficiency is done to abnormal data, swinging door compression algorithm is recycled to carry out compression processing to pretreated data, it proposes to improve data compression rate, reduces the waste of memory space.
Description
Technical field
The invention belongs to electric power monitoring system fields, and in particular to a kind of electric power monitoring system based on real-time data base.
Background technique
With the development of information technology, computer technology, network technology and the communication technology are widely used in each neck
Domain, the key player that informationization plays in global economic development.Internet of Things is used as the world after computer, internet to believe
The third wave for ceasing industry development promotes information-based further fast development.Technology of Internet of things is a kind of fusion in fact
Technology, be related to sensing technology, radio-frequency technique, mechanics of communication, Internet technology, cloud computing, big data technology, automation skill
The various technologies such as art.The core of Internet of Things and basis are still internet, are the nets of extension based on the internet and extension
Network；Its user terminal extends and extends between any article and article, carries out information exchange and communication.Therefore, " Internet of Things is general
Read " it is that its user terminal is extended and is expanded between any article and article on the basis of " internet concept ", carry out information
A kind of network concept of exchange and communication.Internet of Things is widely used, spreads intelligent transportation, environmental protection, government work, public peace
Entirely, safety home, intelligent fire, industry monitoring, environmental monitoring, street lighting control, Landscape Lighting control, building lighting pipe
Control, plaza lighting control, old man's nursing, personal health, flower culture, water system monitoring, foodstuff traceability, enemy's situation investigation and information are searched
The multiple fields such as collection.
Data acquisition analysis system, i.e. SCADA system are the concrete applications of technology of Internet of things.SCADA system is extensive
Applied to electric power, the numerous areas such as waterpower, petroleum, chemical industry.In the power system, SCADA system is most widely used, technology
Comparative maturity.Electric power monitoring system is also known as Power SCADA system, is the information-based product combined with industrialization, and the first generation is
The SCADA system of special purpose computer and special purpose operating system.The second generation is computer based SCADA system, and the third generation is base
It can be realized the SCADA system networked on a large scale in distributed computing network and about database technology.At present both at home and abroad
Mainly have using more universal SCADA master system in the market: Australian CiTech, the U.S.
Fix, WinCC of Siemens Company of Interlution company etc., these systems more comprehensively solve traditional SCADA system
The main problem of system: data acquisition and monitoring information are sent；Alert process, historical trend show with record etc..
Important component of the real-time data base as electric power monitoring system.It is all kinds of with the high development of network technology
Enterprise realizes cross-platform, cross-region, real-time monitoring system spanning space-time using advanced means one after another.Real-time data base is to adopt
It is established with Real-time Data Model, for handling the fast-changing data constantly updated and the thing with time series characteristic
Business.Real-time data base is the product that real-time system and database technology shape combine, and solves real-time data base using database technology
In data management problem, while providing time driving and resource allocation algorithm using real-time technique for real-time data base.Tradition
Relevant database be suitable for processing stablize, permanent data, focus on the integrality and consistency of maintenance data, to real-time
It is required that relatively low.It is simple using relevant database and to be unsatisfactory for the high requirement of electric power monitoring system real-time property.Simultaneously
Electric power monitoring system generate historical data be for electric power enterprise it is of great value, enterprise pass through analysis of history data
To understand the equipment state of current and past to carry out the further perfect of the improvement of equipment and system.Real-time data base is adopted
With data compression technique, compression storage is carried out to historical data, can handle 100,000 points or more of data, historical data can protect
It deposits 3 years or more, and guarantees the accuracy and speed of data processing.The compression of historical data has been generallyd use in real-time data base
Compress technique is damaged, is broadly divided into: vector quantization method, signal converter technique, Piecewise.Side suitable for historical data compression
Method is Piecewise, and main includes tilting backwards method, matrix wave train method and revolving door algorithm.Domestic and international application is most widely
PI real-time data base, the compression method that this database uses is swinging door compression algorithm, and there is deficiencies for this algorithm: 1)
Compression parameters Δ E be have manually by experience set, Δ E be arranged the excessive or too small compression ratio that can all seriously affect historical data with
And compression accuracy.2) in the data acquisition of electric system, there can be the noise data as caused by error and due to setting
Abnormal data caused by standby failure, these data can equally reduce historical data compression effectiveness.So how to handle due to compression
The adverse effect of parameter setting error and abnormal data compressing data is present urgent problem to be solved.
Summary of the invention
This patent is on the basis of studying domestic and international Real-Time Databases System Technique, by being linked up with Management of Electrical Enterprise personnel,
The data characteristics acquired from electric power monitoring system, integrated use analytical database principle, in conjunction with the system engineering theory and
Software engineering thought, study and realize can efficient process real time data and historical data electric power monitoring system, from real-time
The allomeric function that system is improved in the level of database is saved with improved data compression technique compression histories data
The phenomenon that memory space of data, system Caton is even collapsed caused by avoiding because of memory space inadequate, the electric power prison of raising
The operational efficiency of control system guarantees the real-time and accuracy of its data.Meanwhile electricity is detected using the method for data statistic analysis
The abnormal data of power monitoring system in the process of running analyzes exceptional data point, and helping staff, i.e. discovery failure is set in time
It is standby, and maintenance measures are taken, the normal operation of safeguards system.
A kind of electric power monitoring system based on real-time data base, is divided into three modules, respectively human-computer interface module, in real time
Database and communication module, human-computer interface module be divided into user management module, engineering management module, assembly management module, with
And Gis map；Man-machine interface is serviced by restful and is interacted with real-time data base, and man-machine interface passes through interface from real-time
Data base querying historical data and project data obtain data source, graphical component and various static state under each engineering
Resource, and shown on interface；
It is characterized by:
The real-time push that message is realized using socket.io service will be collected by RabbitMQ message queue
Real time data is presented on human-computer interaction interface in time；Real time data library facility includes processing in real time, data analysis and data
Storage；Processing in real time includes real-time monitoring, fault detection, archives data；Real-time monitoring is the variation of monitoring data, by variation
Data-pushing achievees the effect that equipment state is synchronous with human-computer interaction interface to human-computer interaction interface；Fault detection is in data
After acquisition, abnormal data is detected using least square method, and smoothing processing is done to abnormal data, revolving door compression is recycled to calculate
Method carries out compression processing to pretreated data；
Electric power monitoring system is realized jointly using memory database, historical data base and disk storage three levels storage architecture
Real-time data base.
For the real-time for guaranteeing data, real time data can be first loaded into memory in each collection period, pass through message
Push realizes that field device is synchronous with the data of human-computer interaction interface；Memory database mainly store power scheduling real time data and
The higher curve data of enquiry frequency；Data in memory database are used for image display, alert process module and reality
When curve module；The data of collection in worksite can store the real-time operation for being convenient for data in core buffer first；
For the not high data of real-time, historical data can be become in next time cycle, historical data is pressed
It is saved in historical data base after contracting；The historical data of history data store compression, to going through when calling historical data curve module
Compressed data decompression in history database, transmits data in man-machine interface；
Disk storage is very frequent data, i.e., out-of-date process data for storing enquiry frequency not；Using database
Dynamic filing technology is transferred into disk by periodically identifying the inactive technology in historical data base.
This system includes the mobile devices such as hardware device, server end, database, computer or plate.It is whole to be based on B/S frame
Structure, human-computer interaction interface are opened using NodeJS technology, communication apparatus using computer is embedded using React frame, server-side
Plate is sent out, real-time data base is constituted using the wild fox of Rails and RabbitMQ technological development and database PostgreSQL.Computer
Or the mobile end equipment such as plate by Socket.io by being realized to the long-range control of hardware device and condition monitoring.This is
System mainly includes user management module, engineering management module, static resource management module, Gis mapping module.
Mobile terminal device facilitates staff to the control of each website power equipment operating status, while conveniently carry-on
It carries, as long as in the case where having network, either where all remotely controlling power equipment.Good human-computer interaction interface,
Structure is illustrated, and operation is simple, improves work efficiency.Human-computer interaction interface is realized and rear end by restful web
The remote control to power equipment is realized in the data interaction of server.Server is real-time by socket.io by the data of acquisition
It is pushed to terminal device, data is carried out and synchronizes, realize remote signalling and the telemetry function of electric power monitoring system.
Server-side will be stored by the data of period acquisition into the historical data base of real time data library module.Since data are adopted
The interval of events of collection is shorter, and the continuous acquisition constantly accumulation of data causes historical data to show the growth of exponential form, greatly
The historical data of amount has many redundant datas, this results in the wasting of resources of memory space.Therefore this system is in real-time data base
Technically, on the basis for guaranteeing historical data compression accuracy, abnormal data is detected using least square method, and do to abnormal data
Smoothing processing avoids the adverse effect of abnormal data compressing data precision and compression efficiency, recycles swinging door compression algorithm pair
Pretreated data carry out compression processing, propose to improve data compression rate, reduce the waste of memory space.
Detailed description of the invention
Fig. 1 is the structure principle chart of system.
Fig. 2 is the structure principle chart of real-time data base.
Fig. 3 is least square method curve matching figure.
Fig. 4 is a kind of scheme of historical data compression processing.
Specific embodiment
As shown in Figure 1, electric power monitoring system is divided into three modules, respectively human-computer interface module, real-time data base and
Communication module.
Human-computer interface module is the module that user and electric power facility carry out remote interaction, it be divided into again user management module,
Engineering management module, assembly management module and Gis map.User management, the staff of different role possess different
Permission, the operation according to the difference of permission, progress are also different.System manager has to engineering management, static resource management, sets
The permission of standby fault management.Plant maintenance personnel are due to the power checked to equipment running status, equipment obstacle management, list are filled in
Limit.General operation personnel have newly-increased to engineering and editor, equipment state management permission.
Engineering management is mainly the engineering different according to Website Building, and each engineering corresponds to the electric power facility of different websites.
Each engineering module includes view design function, data power management function, Code Design function, warning function, project plan again
With real time inspection function.Using the view design function under each engineering, scene figure and phase are built in conjunction with graphical component
The graphical equipment answered, and establish and be associated with corresponding power equipment.It simultaneously can be in Code Design view, by changing figure
Change the attribute of component to change each attribute and functional effect of component on scene figure.The real time data obtained from sensor network
Very more, they are organized into the data source of tree structure.Each data are a data points.Each hardware has the ID of oneself,
Each equipment corresponding data point.The device address that template and connection are selected when creating data source data point, is data source and data
Point forms mapping relations with hardware device.Alarm module is to find in time simultaneously in device fails convenient for staff
Take treatment measures.The type of alarm includes that switching value alarms, jumps alarm, lower limit under jump alarm, switching value in switching value
Alarm, upper limit alarm, lower deviation alarm and upper deviation alarm lamp, are reported by the purpose data point that monitoring data linkage generates
It is alert.After alarm occurs, staff confirms equipment fault, and alarm relevant information is saved in database, class of such as alarming
Not, time of fire alarming, device model, the information such as alarm description.Curve display module includes that historical data curve and real time data are bent
Two kinds of line, variation tendency is described the data by calling the data in database, and generate analysis report.
Project plan mainly includes local debugging and on-line debugging, and local debugging refers to that non-server-side establishes connection, works
Personnel can test whether various components are associated with data point foundation before project plan, and can confirmation carry out passing through dynamic
The corresponding value of change data point change the state of graphical component.On-line debugging, which refers to, establishes connection with back-end services end, leads to
The state change of graphical interface component to send telecommand to hardware device when crossing control operation, while can refresh in real time
Interface achievees the effect that data are synchronous when operation, i.e. acquisition telemetry, thus the state of refresh graphics component, such as temperature
The temperature display of component is counted, the direction etc. of pointer on instrument board.Real time inspection equipment running status and transmission are distant at runtime
Control instruction.
Assembly management module includes primitive control and static resource management.Wherein primitive control is mainly responsible for creation and editor
Graphical component builds scene figure by graphical component, the various electric power facilities of the website is arranged on each scene figure, such as electricity
The equipment such as power indicator light, switchgear, alarm, protractor, indicator.Each graphical component has each attribute, as width,
Highly, the attributes such as color, rotation.Graphical component is not in the case where changing original attribute, using the pattern of default.Graphically
Component is defined and is stored with JSON structure.Static resource management module includes the upload and downloading of picture, graphical component
Upload and downloading.Responsible graphical component can be built with basic graphical component, the picture of upload is used in figure
Change in the background setting of component.
Man-machine interface is serviced by restful and is interacted with real-time data base, and man-machine interface is counted by interface from real-time
According to library inquiry historical data and project data, data source, graphical component and the various static moneys under each engineering are obtained
Source, and shown on interface, realize good user experience.Data in order to realize live power equipment and man-machine interface are same
Step, is guaranteed the real-time of data, the real-time push of message is realized using socket.io service, pass through RabbitMQ message team
Column, collected real time data is presented on human-computer interaction interface in time.The major function of real-time data base includes locating in real time
Reason, data analysis and data storage.Processing in real time includes real-time monitoring, fault detection, archives data.Real-time monitoring is monitoring
The data-pushing of variation to human-computer interaction interface is reached the equipment state effect synchronous with human-computer interaction interface by the variation of data
Fruit.Fault detection be after data acquisition, to data carry out processing detection abnormal data, and to abnormal data carry out achieve or
Smoothing processing.
As shown in Fig. 2, electric power monitoring system uses memory database, PostgreSQL relational database and disk storage three
Grade memory module realizes real-time data base jointly.
Communication protocol is the core of real-time data base data acquisition.Communication protocol refer to both entities complete communication or
Service be must comply with rule and agreement.Modbus is a kind of serial communication protocol, and a Modbus order contains plan
The address Modbus of the equipment of execution, all devices can all receive order, but the equipment of only designated position can execute response and refer to
Enable, basic Modbus order can enable a RTU change it register some value, control or reading one port I/O,
And the data in commander's one or more its register of equipment loopback.When communicating on Modbus network, this agreement is determined
Their device address need be known by having determined each controller, identify the message sent by address, which kind of action decision will generate.
If necessary to respond, controller will generate feedback information and be issued with Modbus agreement.On other networks, Modbus is contained
The message of agreement is converted to the frame or pack arrangement used over the network.This conversion is also extended to be solved according to specific network
The method for location, routed path and the error detection of saving land.
For the real-time for guaranteeing data, real time data can be first loaded into memory in each collection period, pass through message
Push realizes that field device is synchronous with the data of human-computer interaction interface.Memory database mainly store power scheduling real time data and
The higher curve data of enquiry frequency.Data in memory database are commonly used in image display, alert process module with
And real-time curve module.The data of collection in worksite can store the real-time operation for being convenient for data in core buffer first.
For the not high data of real-time, historical data can be become in next time cycle, historical data is pressed
It is saved in historical data base after contracting.What historical data mainly stored is the historical data of compression, is set to grasp each electric power
Standby real-time running state, when carrying out data monitoring, sample frequency is relatively high, and the data volume acquired is huge.So using
Data compression technique first compresses data before data are stored in database, to achieve the purpose that save memory space.It adjusts
The compressed data in historical data base can be decompressed with the curve service of historical data base when historical data curve module, by data
It is sent in man-machine interface.
Disk storage is mainly used for storing enquiry frequency not being very frequent data, i.e., out-of-date process data.Mainly adopt
File technology with Database Dynamic, by periodically identifying the inactive technology in historical data base, by it according to certain strategy
It is transferred to other storage equipment.The wherein index of the storage organization and file of data, memory cache strategy, magnetic disc i/o etc.
It is related to the efficiency and performance of entire real-time data base.
As shown in figure 3, the compression processing of the historical data for magnanimity, traditional real-time data base is calculated using revolving door compression
Method is compressed to realize, but traditional revolving door compression carries out detection processing to abnormal data, and exceptional data point can be very
Reduce compression accuracy in big degree, adverse effect is generated to the compression of historical data.Electric power monitoring system needs to carry out failure
Detection and analysis, so the detection and independent processing to abnormal data are necessary.This is just needed before data compression
Data are pre-processed, abnormal data is detected.
It is carried out curve fitting using least square method to historical data variation tendency, detects exceptional data point, and process.
Least square method is a kind of statistical learning optimisation technique, it is by by the flat method of minimal error and as target, to find optimal
Model, this model can be fitted observation data.It is more steady based on observation data, utilize the more of least square method fundamental theorem
Item formula curve matching, if there is sample data T={ (x1,y1),(x2,y2),(x3,y3)...,(xn,yn), in function classIn look for a combination of function:
Keep error sum of squares minimum, i.e.,The problem of being fitted with least square method
Exactly f (x) is asked to make | | δ | |2It is minimum.It is converted to seek function of many variables S (a in this way0,a1,a2,...,ak) minimum the problem of,
The function of many variablesSolution obtains (a'0,a′1,a'2,...,a'k), from
And it obtains least square solution and isWith obtained least square
Solution predict that the data value at next time point is yt', actual observed value yt, calculate absolute error rt=| yt-y′t|, sample
The standard error of dataWherein Ei=yi-y′i, E is error.
The dispersion degree of data is generally indicated with standard deviationWherein u is sample data averages, yiFor data
Value, n is data count.If rt> k × σ ', then data point (t, yt) it is abnormal data, it is otherwise normal data points.Error coefficient k
It is determined by the data fluctuations variation of adjacent interval:Wherein σt,σt-1Respectively indicate the data of current period acquisition
The standard deviation of standard deviation and a upper cycle data.
As shown in figure 4, pre-processing first with least square method to data before historical data compression, minimum two is utilized
Carrying out curve fitting for multiplication detects abnormal data, and saves exceptional data point so that subsequent abnormal data is analyzed, and in original
Come on the basis of data abnormal data replacing with the data value come out by least square model, come by this method to exception
Data are smoothed, and reject the abnormal data on original data point set, to pretreated historical data with SDT algorithm into
Row compression.After the data point compression at the moment, whether detection subsequent time has new data, repeats if having new data
Compression process is stated, is terminated as compressed without if.
Claims (3)
1. a kind of electric power monitoring system based on real-time data base is divided into three modules, respectively human-computer interface module, in real time number
According to library and communication module, human-computer interface module be divided into user management module, engineering management module, assembly management module and
Gis map；Man-machine interface is serviced by restful and is interacted with real-time data base, and man-machine interface is counted by interface from real-time
According to library inquiry historical data and project data, data source, graphical component and the various static moneys under each engineering are obtained
Source, and shown on interface；
It is characterized by:
The real-time push that message is realized using socket.io service will be collected real-time by RabbitMQ message queue
Data are presented on human-computer interaction interface in time；Real time data library facility includes processing in real time, data analysis and data storage；
Processing in real time includes real-time monitoring, fault detection, archives data；Real-time monitoring is the variation of monitoring data, by the data of variation
It is pushed to human-computer interaction interface, achievees the effect that equipment state is synchronous with human-computer interaction interface；Fault detection is acquired in data
Later, abnormal data is detected using least square method, and smoothing processing is done to abnormal data, recycle swinging door compression algorithm pair
Pretreated data carry out compression processing；
Electric power monitoring system is realized in real time jointly using memory database, historical data base and disk storage three levels storage architecture
Database.
2. according to the method described in claim 1, it is characterized by:
For the real-time for guaranteeing data, real time data can be first loaded into memory in each collection period, be pushed by message
Realize that field device is synchronous with the data of human-computer interaction interface；Memory database mainly stores power scheduling real time data and inquiry
The higher curve data of frequency；Data in memory database are used for image display, alert process module and in real time song
Wire module；The data of collection in worksite can store the real-time operation for being convenient for data in core buffer first；
For the not high data of real-time, historical data can be become in next time cycle, after compressing to historical data
It is saved in historical data base；The historical data of history data store compression, to history number when calling historical data curve module
According to the compressed data decompression in library, transmit data in man-machine interface；
Disk storage is very frequent data, i.e., out-of-date process data for storing enquiry frequency not；Using Database Dynamic
Filing technology is transferred into disk by periodically identifying the inactive technology in historical data base.
3. according to the method described in claim 1, it is characterized by:
It is carried out curve fitting using least square method to historical data variation tendency, detects exceptional data point, and process；It utilizes
The polynomial curve fitting of least square method fundamental theorem, sample data T={ (x1,y1),(x2,y2),(x3,y3)...,(xn,
yn), in function classIn look for a combination of function:
Keep error sum of squares minimum, i.e.,The problem of being fitted with least square method is exactly to ask
F (x) makes | | δ | |2It is minimum；It is converted to seek function of many variables S (a in this way0,a1,a2,...,ak) minimum the problem of, polynary letter
NumberSolution obtains (a '0,a′1,a′2,...,a′k), to obtain
Least square solution isIt is predicted with the solution of obtained least square
The data value at next time point is y 't, actual observed value yt, calculate absolute error rt=| yt-y′t|, the mark of sample data
Quasi- errorWherein Ei=yi-y′i, E is error；Data from
Scattered degree is generally indicated with standard deviationWherein u is sample data averages, yiFor data value, n is number
According to sum；If rt> k × σ ', then data point (t, yt) it is abnormal data, it is otherwise normal data points；Error coefficient k is by adjacent region
Between data fluctuations change determine:Wherein σt,σt-1Respectively indicate current period acquisition data standard deviation and
The standard deviation of a upper cycle data.
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| CN110570255A (en) * | 2019-09-16 | 2019-12-13 | 山东浪潮通软信息科技有限公司 | DMP-based real-time analysis method and system for market demand data of multi-source system |
| CN112050871A (en) * | 2020-08-25 | 2020-12-08 | 长江武汉航道工程局 | Dredger pipeline transportation passive yield meter |
| CN112050869A (en) * | 2020-08-20 | 2020-12-08 | 长江武汉航道工程局 | Continuous alternating magnetic field measuring device and method thereof |
| CN112050870A (en) * | 2020-08-20 | 2020-12-08 | 长江武汉航道工程局 | Curve reduction anti-interference measuring device and method thereof |
| CN113157790A (en) * | 2021-04-20 | 2021-07-23 | 核工业西南物理研究院 | Nuclear fusion curve editing method, system, terminal and medium |
| CN114640355A (en) * | 2022-03-30 | 2022-06-17 | 北京诺司时空科技有限公司 | Lossy compression and decompression method, system, storage medium and equipment of time sequence database |
| CN116644864A (en) * | 2023-07-26 | 2023-08-25 | 国网山东省电力公司无棣县供电公司 | Method, system, terminal and medium for predicting electricity consumption of special date area |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080215641A1 (en) * | 2007-03-01 | 2008-09-04 | Mukhi Sultan Q | High speed data historian |
| CN103220285A (en) * | 2013-04-10 | 2013-07-24 | 中国科学技术大学苏州研究院 | Access system based on RESTful interface in ubiquitous service environment |
| CN103970903A (en) * | 2014-05-27 | 2014-08-06 | 重庆大学 | Large industrial system feedback data real-time processing method and system based on Web |
| CN106406836A (en) * | 2015-07-31 | 2017-02-15 | 中国电力科学研究院 | Power system analysis-oriented software platform and running and distributed development methods thereof |
| CN106649026A (en) * | 2016-09-26 | 2017-05-10 | 国家电网公司北京电力医院 | Monitoring data compression method applicable to operation and maintenance automation system |
| CN107122422A (en) * | 2017-04-06 | 2017-09-01 | 山东建筑大学 | Service-oriented wisdom settlement real-time dataBase system and its method of work |
-
2018
- 2018-11-13 CN CN201811343220.9A patent/CN109245310A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080215641A1 (en) * | 2007-03-01 | 2008-09-04 | Mukhi Sultan Q | High speed data historian |
| CN103220285A (en) * | 2013-04-10 | 2013-07-24 | 中国科学技术大学苏州研究院 | Access system based on RESTful interface in ubiquitous service environment |
| CN103970903A (en) * | 2014-05-27 | 2014-08-06 | 重庆大学 | Large industrial system feedback data real-time processing method and system based on Web |
| CN106406836A (en) * | 2015-07-31 | 2017-02-15 | 中国电力科学研究院 | Power system analysis-oriented software platform and running and distributed development methods thereof |
| CN106649026A (en) * | 2016-09-26 | 2017-05-10 | 国家电网公司北京电力医院 | Monitoring data compression method applicable to operation and maintenance automation system |
| CN107122422A (en) * | 2017-04-06 | 2017-09-01 | 山东建筑大学 | Service-oriented wisdom settlement real-time dataBase system and its method of work |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110209696A (en) * | 2019-04-25 | 2019-09-06 | 中国电力科学研究院有限公司 | A kind of blower unbalanced fault recovery process response curve library implementation method |
| CN110570255A (en) * | 2019-09-16 | 2019-12-13 | 山东浪潮通软信息科技有限公司 | DMP-based real-time analysis method and system for market demand data of multi-source system |
| CN112050869A (en) * | 2020-08-20 | 2020-12-08 | 长江武汉航道工程局 | Continuous alternating magnetic field measuring device and method thereof |
| CN112050870A (en) * | 2020-08-20 | 2020-12-08 | 长江武汉航道工程局 | Curve reduction anti-interference measuring device and method thereof |
| CN112050871A (en) * | 2020-08-25 | 2020-12-08 | 长江武汉航道工程局 | Dredger pipeline transportation passive yield meter |
| CN112050871B (en) * | 2020-08-25 | 2023-09-19 | 长江武汉航道工程局 | Pipeline conveying passive obstetric meter for dredger |
| CN113157790A (en) * | 2021-04-20 | 2021-07-23 | 核工业西南物理研究院 | Nuclear fusion curve editing method, system, terminal and medium |
| CN113157790B (en) * | 2021-04-20 | 2022-10-11 | 核工业西南物理研究院 | Nuclear fusion curve editing method, system, terminal and medium |
| CN114640355A (en) * | 2022-03-30 | 2022-06-17 | 北京诺司时空科技有限公司 | Lossy compression and decompression method, system, storage medium and equipment of time sequence database |
| CN116644864A (en) * | 2023-07-26 | 2023-08-25 | 国网山东省电力公司无棣县供电公司 | Method, system, terminal and medium for predicting electricity consumption of special date area |
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