CN106339829A - Big data, Cloud, IoT and mobile internet technologies based active maintenance panorama monitoring system of power distribution network - Google Patents
Big data, Cloud, IoT and mobile internet technologies based active maintenance panorama monitoring system of power distribution network Download PDFInfo
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- CN106339829A CN106339829A CN201610992500.7A CN201610992500A CN106339829A CN 106339829 A CN106339829 A CN 106339829A CN 201610992500 A CN201610992500 A CN 201610992500A CN 106339829 A CN106339829 A CN 106339829A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention provides a Big data, Cloud, IoT and mobile internet technologies based active maintenance panorama monitoring system of power distribution network. The system comprises a monitoring center, personal handheld terminal and vehicle-mounted terminals of maintenance vehicles; the personal handheld terminal and the vehicle-mounted terminals of the maintenance vehicles realize data interaction with the control center via a wireless network, and upload position information and work order processing information. The monitoring center comprises a monitoring display screen, a real-time monitoring system, an early warning system, a work order distributing system, an information issuing system and databases, wherein the monitoring display screen displays image and character information; the real-time monitoring system generates real-time map information; the early warning system is used for calculation and generates early warning information; the information issuing system generates the character information; the work order distributing system generates a work order distributing scheme; and the databases store data information. The active maintenance panorama monitoring system can monitor all links in the whole maintenance process in real time, assign staff in real time, and realize early warning analysis on a power fault so that the fault can be eliminated in the very beginning.
Description
Technical field
The invention belongs to power grid maintenance, specifically a kind of overall view monitoring is actively rushed to repair based on the power distribution network that great Yun thing moves technology
System.
Background technology
" shifting of great Yun thing " is the abbreviation of emerging technology means such as " big data, cloud computing, Internet of Things, mobile Internets ", its
Essence is big data collection, transmission, the extension storing, utilizing technology, and basic and core is the efficient digging utilization of data assets.
In recent years, big data becomes a kind of emerging productivity, " shifting of great Yun thing " technology fast development.Electrical network is the thing of bulky complex the most
Networked system, flow of power is energy stream, Business Stream, is also information flow, data flow.With strong intelligent grid and global energy
The continuous upgrading of Internet Construction, electric power has more depth and range with the fusion of informationization technology, and electrical network big data magnanimity increases.
Compare other industry, electrical network big data scale is huger, information is more rich, management also faces bigger challenge.In particular with joining
Electric automation and power information acquisition system cover application comprehensively, and the information of customer side assumes exponential form blast and increases, and makes electrical network
Low-pressure side, user side also possess traditional mesohigh electrical network considerable, can survey and controllability.How to improve power distribution network and user side
Big data controling power, be that network distributing failure emergency repair services, be the key subjects that power supply enterprise faces.
For current electrical network O＆M and monitoring, there is the defect of following several respects:
1st, informatization platform and means build imperfection it is difficult to support distribution repairing efficiently to work in coordination with.Distribution repairing is related to
Marketing, produce, multiple professional systems such as scheduling, but conventional informatization is unordered, polyisomenism is serious, between each operation system
Mutually not insertion is supported, information on services cannot coordination sharing, form information " isolated island "；Even same index is in different business systems
Data is inconsistent, the quality of data not high it is difficult to supporting work in coordination with needs.
2nd, service end support means fall behind, and client's demand is difficult to short circuit and links up, and live task is unable to remote collaborative.At present
Repairing class work on the spot relies primarily on telephonic communication, and information-based means fall behind, and one is to be connected to after 95598 clients report for repairment to need and visitor
Family phone confirmation address and report content for repairment repeatedly, affects CSAT；Two is that field data can not be fully shared, remote technology
Support and mission bit stream updates more delayed, impact service collaboration efficiency.
3rd, monitoring means lack it is impossible to realize the collaborative early warning of operation monitoring.Monitoring side to distribution repairing work at present
Formula still result formula afterwards, cannot realize early warning in advance, process monitoring to service whole process；In addition data analysis capabilities are not high,
Can not abundant, accurate early warning.Particularly the breakdown repair business time limit has high demands, and lacks problem early warning and real-time deviation correcting means, robs
Repair real-time situation can not grasp it is difficult to support repairing business from passive to the transformation of active.
Content of the invention
In order to solve the above problems, the present invention is based on big data analytical technology, with Internet of Things and mobile interchange for should
With front end, with cloud computing platform for supporting, develop and overall view monitoring system actively rushed to repair based on the power distribution network of great Yun thing shifting technology,
One is the grading forewarning system realizing troublshooting by big data analysis means such as cluster, prediction, associations, supports and actively rushes to repair；Two
It is to realize work order optimum by optimized algorithm to distribute, improve work order and distribute efficiency and accuracy；Three is by data visualization skill
Art realizes breakdown repair from service handling to the tracking of fault recovery full-service track, realizes process monitoring and real-time deviation correcting；Four
By development of Mobile Internet technology and Internet of Things, realize device data real-time monitoring and repairing business information timely on descend
Send out；Four is by cloud computing and big data analytical technology, history trouble ticket and business information to be analyzed, excavate report for repairment event with
Incidence relation between weather, equipment health status, power distribution network investment situation, traffic and inherent law, realize repairing effect
Rate and the quantitative analysis management and control afterwards of work quality, provide data supporting for improving operating efficiency further.
The present invention employs the following technical solutions: overall view monitoring system is actively rushed to repair based on the power distribution network of great Yun thing shifting technology,
It is characterized in that, including Surveillance center, personal hand-held terminal and maintenance vehicle car-mounted terminal, personal hand-held terminal and maintenance vehicle
Car-mounted terminal realizes data interaction, upload location information and the worksheet information with control centre by wireless network respectively；
Described Surveillance center includes monitoring display screen, real-time monitoring system, early warning system, work order distribute system, information issuing system
Database, monitoring display screen is used for display image and Word message；Real-time monitoring system is used for generating real-time cartographic information；
Early warning system is used for calculating and generating early warning information；Information issuing system is used for generating Word message；Work order distribute system for
Generate work order and distribute scheme；Database is used for data storage information.
Further, described database includes
Public database, the data not high for storing the renewal frequencies such as map, environment；
Activity database, stores the data in the current or certain short time, reports for repairment including meteorological, client, main offer
Inquiry service, after certain time, the data in activity database moves into historical data base；
Historical data base, the data before storage a period of time；
Knowledge base, stores up pertinent arts, and knowledge section derives from expert, is derived partly from machine learning, provides analysis
The service of model；
Customer data base, for storing the essential information of user, and the user characteristics obtaining after data mining.
Further, database also includes prediction scheme database, and prediction scheme database purchase is directed to the conventional prediction scheme of power failure
Script.
Further, described early warning system includes acquisition module and analysis module, acquisition module and each data system
Docking, for gathering client's real-time electricity consumption associated data；Analysis module is passed through to compare historical data and real time data, analyzes user
Warning level and early warning reason.
Further, described analysis module includes pretreatment module, coding module, code storage module and judges mould
Block, pretreatment module is used for the filtering screening data planning of power information data, and coding module is used for the coding of data, coding
Memory module is used for storing coding information, and judge module is used for the comparison of coding information.
Further, described coding module is the Hash coding module using hash algorithm.
Further, described work order distributes system and includes hadoop system, mahout system, and it is right that mahout system is used for
The excavation of work order data and scoring, hadoop system includes hdfs and mapreduce, in the calculating and calculating process of data
The storage of data.
Further, work order distributes system and also includes impala inquiry system, dedicated for the extraction inquiry of data, lifting
The speed of data query, meets the requirement of work order analysis in real time.
Further, described real-time monitoring system includes electronic chart and public notification of information plate, and electronic chart combines geographical
Position display real-time situation and information；Public notification of information plate passes through word and form shows real time information.
Further, described information issuing system includes information extraction modules, ATL, message processing module, information
Cache module, information issuing module, information extraction modules distribute system, extract data letter database from early warning system, work order
Breath；The related information such as ATL memory storage early warning, work order issues script；Message processing module is used for the data extracted and pin
This combination generates file publishing；Information cache module can utilize cache chip, realizes in file publishing transmitting procedure
Buffering and storage；Information issuing module is used for for file publishing being associated with real-time monitoring system.
The invention has the beneficial effects as follows:
1st, whole system can realize the judgement of power failure warning level and type, and fault message is shown in large-size screen monitors
On, and the geographical position of trouble point is labeled on map, realize comprehensive management and control of overall grid early warning information；Work order monitoring system
System can generate the real time information of worksheet, and is shown on monitoring large-size screen monitors or electronic chart by written form, meanwhile, knot
Close the worksheet procedure links setting realizing dividing, the position of link residing for each work order can be specified.
2nd, early warning system realizes fault anticipation automated intelligent.Fault anticipation link before work order distributes, its precision and
Validity directly influences accuracy and the first-aid repair efficiency of distribute leaflets, in the past by commanding's anticipation by rule of thumb, will 3 minute time limit
Under the examination asked limits, anticipation result accuracy is very poor.By automatically transferring, comparing associated services system information, in the short time
Inside judging that user reports for repairment is that arrearage power failure, customer equipment fault cause, or section has a power failure, line outage causes, thus smart
Accurate, targetedly take different Rush Repair Schemes, greatly improve breakdown repair efficiency and speed.
3rd, work order distributes system and realizes work order and distributes flexible optimization.Reported work order for repairment is to distribute by region fixation in the past, does not have
In view of real-time road, repairing resource, the repairing point impact to first-aid repair efficiency for the workload.By real-time positioning recovery vehicle, rob
Repair the mobile phone app of personnel, show its working condition, association reports address, real-time traffic condition and each repairing point task amount for repairment to group
Singly it is optimized, is automatically the optimal selection that repairing commanding provides distribute leaflets, commanding can select from reporting address for repairment
Near idle repair personnel distributes work order, mutually working in coordination between repairing point also be ensure that from mechanism in advance simultaneously it is achieved that
Flexible distribute leaflets, significantly improve first-aid repair efficiency.
4th, database adopts the database of multiple independent separations, by the classification storage of data, data can be avoided to mix,
Reduce the process obtaining target data, realize the efficient of data management.And, the data storage in database is by based on work
The tables of data of task, improves the specific aim of data, by the association of task, can be found with prestissimo and using having
Effect data.
5th, it is provided with prediction scheme database, carries out and report active forewarning for repairment, immediate repair plans standardize.Troublshooting is carried out in innovation
Primary, intermediate, serious three-level early warning, the associated data based on trans-sectoral business, cross-system for the early warning at different levels, using big data digging technology
Realize.Early warning information refine in units of power supply area, and it is pre- that every class early warning all makes specific aim, standardized repairing in advance
Case, early warning information utilizes the informationization technology means such as data visualization, mobile Internet after producing, before repairing service handling
It is pushed to commanding and repairing operation maintenance personnel, pre-cooling immediate repair plans in time, realize rushing to repair master from passive order arrangement
Dynamic early warning arrangement repairing.
6th, because client's amount is huge, the data faced by us has very high dimension；Meanwhile, different types of data determines
We need to extract different types of feature and attribute.We select using Hash coding module, using multi views Hash side
Method carrys out processing data, it is possible to increase search out the speed of similar state client, improves computational efficiency.
7th, utilize als-wr algorithm process complex information, reach the time a little of reporting for repairment using maintenance personal or vehicle as final
Appraisal standards, in conjunction with condition of road surface, fault severity level, complain situation, work order quantity, maintenance personal or vehicle operation ability etc.
Relevance between work order and maintenance personal or vehicle can be given a mark by related information, obtains optimum recommendation.
8th, work order distributes system and is provided with impala inquiry system, thus having liberated mahout system and hadoop system,
Need the data extracted from database during mahout system and hadoop system-computed, can directly will require to be sent to
Impala inquiry system, on the one hand makes mahout system and hadoop system only undertake computing function, realizes function treatment
Unicity, on the other hand by impala inquiry system quickly inquiry velocity itself, improve the process work order of whole system
Efficiency, meet the real-time that whole electrical network work order distributes.
Brief description
Fig. 1 is the overall structured flowchart of present system；
Fig. 2 is the structure chart of database of the present invention；
Fig. 3 is the structure chart of analysis module in early warning system of the present invention；
Fig. 4 is the principle assumption diagram that work order of the present invention distributes system；
Fig. 5 is the principle assumption diagram of information issuing system of the present invention.
Specific embodiment
As shown in Figure 1 actively rushes to repair overall view monitoring system based on the power distribution network that great Yun thing moves technology, including Surveillance center,
Personal hand-held terminal and maintenance vehicle car-mounted terminal, personal hand-held terminal and maintenance vehicle car-mounted terminal pass through wireless network respectively
Realize data interaction, upload location information and the worksheet information with control centre；It is aobvious that described Surveillance center includes monitoring
Display screen, real-time monitoring system, early warning system, work order distribute system, information issuing system database, and monitoring display screen is used for showing
Diagram picture and Word message；Real-time monitoring system is used for generating real-time cartographic information；Early warning system is used for calculating and generating pre-
Alarming information；Information issuing system is used for generating Word message；Work order distributes system and distributes scheme for generating work order；Database is used
Carry out data storage information.
As shown in Fig. 2 described database includes public database, not high for storing the renewal frequencies such as map, environment
Data；Activity database, stores the data in the current or certain short time, reports for repairment including meteorological, client, and main offer is looked into
Ask service, after certain time, the data in activity database moves into historical data base；Historical data base, storage a period of time
Front data；Knowledge base, stores up pertinent arts, and knowledge section derives from expert, is derived partly from machine learning, provides analysis
The service of model；Customer data base, for storing the essential information of user, and the user being obtained after data mining is special
Levy；Prediction scheme database, storage is for the conventional prediction scheme script of power failure.
Early warning system includes acquisition module and analysis module, and acquisition module is docked with each data system, for gathering visitor
The real-time electricity consumption in family associated data, the data being related to include trans-departmental trans-sectoral business 95598, marketing, with adopting, pms, oms, ems, join
The electric power datas such as automation and weather, client's Internet of Things monitoring data；Analysis module is passed through to compare historical data and is counted in real time
According to analysis user's warning level and early warning reason.Shown in personnel Fig. 3, described analysis module includes pretreatment module, coding mould
Block, code storage module and judge module, pretreatment module is used for the filtering screening data planning of power information data, coding
Module is used for the coding of data, and code storage module is used for storing coding information, and judge module is used for the comparison of coding information.In advance
Processing module includes some storm modules and hbase module, and storm module is used for filtering screening data and plans, hbase mould
Block is used for the storage of storm module process data.Described coding module is the Hash coding module using hash algorithm.
The flow process that early warning system realizes function is:
Before system operation, historical data base obtains and store historical data in advance, and analysis module is passed through pretreatment and compiled
Code module obtains baseline encoded after all historical datas are encoded, and is stored in code storage module.
Preprocessing process is particularly as follows: this real-time processing of increasing income carries out filtering screening, data with computing technique using storm
Planning, to pretreated data, is stored using hbase.
The detailed process of coding is:
Input following parameter: hashcode digit k, number of views m, client number n, client's similarityClient characteristics vector
Combination algorithm hashingcodelearning (k, m, n,), export following parameter: the overall Hash of client is compiled
Code u, each view weight α, each view hash function
Initialization
Build connection matrix
Build Laplacian Matrix (dp)-1/2lp(dp)-1/2, p=1,2 ..., m, judge whether to restrain, if not converged, follow
The following calculating process of ring:
It is calculated
It is calculated
It is calculated matrix
It is calculated the minimum characteristic vector of the k character pair value of matrix h (α)；
Hash encoder matrix u is generated according to characteristic vector；
It is calculated
Obtain α using Novel Algorithm；
Return u,α.
After obtaining serialization Hash coding, binaryzation is carried out to it, obtain the Hash that each value is -1 or 1 and compile
Code.
After system commencement of commercial operation, process is as follows:
Acquisition module obtains real-time one-level warning data, generates one-level early warning coding, judges one-level early warning coding and benchmark
Whether coding is similar, if so, sends one-level early warning.One-level warning data at least includes data below in real time: client reports tendency for repairment
Data, history transship data, historical failure data, history power failure data, historical weather data again.
Acquisition module obtains real-time one-level warning data and real-time secondary warning data, common two grades of early warning codings of generation.
Real-time secondary warning data at least includes data below: data is failed to report in overload data, power failure issue again in real time.
Acquisition module obtains real-time one-level warning data, real-time secondary warning data, real-time three-level warning data, generates three
Level early warning coding.Three-level warning data at least includes data below: oms fault outage data, in real time power failure data, electric current in real time
Accidental data, forecasting weather data, client's Internet of Things monitoring data.
The sequencing of above-mentioned three-level early warning can have following several modes:
The first pattern: acquisition module first gathers one-level warning data, determines whether one-level early warning, then here basis
Two grades of warning data of upper increase, determine whether two grades of early warning, then increase three-level warning data again, determine whether three-level
Early warning, once middle a certain process interrupt or be negative evaluation, is stopped analysis and is simultaneously defined by advanced warning grade now.
Second pattern: acquisition module directly starts to judge from three-level early warning, if judged result is three-level early warning, directly eventually
Only analyze, if it is not, be reduced to two grades of early warning judging, by that analogy, until judging to complete.
The third pattern: acquisition module random acquisition simultaneously judges, after a samsara terminates, is defined by highest advanced warning grade.
As shown in figure 4, work order distributes system includes hadoop system, mahout system, impala inquiry system, mahout
System is used for the excavation of work order data and scoring, and hadoop system includes hdfs and mapreduce, the calculating for data and
The storage of data in calculating process, wherein, hdfs is that the data of magnanimity provides storage, and mapreduce is that the data of magnanimity carries
Supply to calculate.Impala inquiry system is inquired about dedicated for the extraction of data, the speed of lifting data query, meets work order real-time
The requirement of analysis.
Mahout system adopts als-wr algorithm, and concrete principle is as follows:
Als is the abbreviation of alternating least squares, means alternating least-squares；And als-wr is
The abbreviation of alternating-least-squares with weighted- λ-regularization, means weighting regularization
Alternating least-squares.The method is usually used in based in the commending system of matrix decomposition.For example: by user (user) to commodity
(item) rating matrix is decomposed into two matrixes: one is the preference matrix to commodity hidden feature for the user, and another is business
The matrix of the hidden feature that product are comprised.During this matrix decomposition, scoring disappearance item filled that is to say, that
The scoring that we can be filled based on this is come to user's commercial product recommending.
Due to there being substantial amounts of disappearance item in score data, traditional matrix decomposition svd (singular value decomposition) is inconvenient to process
This problem, and als can be good at solving this problem.For the matrix of r (m × n), als is intended to find two low-dimensional squares
Battle array x (m × k) and matrix y (n × k), carry out close approximation r (m × n) it may be assumed thatWherein r (m
× n) represent the rating matrix to commodity for the user, x (m × k) represents the preference matrix to hidden feature for the user, and y (n × k) represents
The matrix of the comprised hidden feature of commodity, the transposition of t representing matrix y.
In practice, typically take k < < min (m, n), that is, be equivalent to dimensionality reduction.Here low-dimensional matrix, some places
It is low-rank matrix.
Make low-rank matrix x and y approach r as much as possible to find, need to minimize following square error loss function:Wherein xu (1 × k) shows the implicit spy of the preference of user u
Levy vector, yi (1 × k) represents the hidden feature vector that commodity i comprises, rui represents the scoring to commodity i for the user u, vector x u and
Inner product xutyi of yi be user u commodity i is scored approximate.
The problems such as loss function generally needed to be added regularization term to avoid over-fitting, we use l2 regularization, so
Above formula transform as:
Wherein λ is the coefficient of regularization term.
Arrive here, collaborative filtering has become an optimization problem with regard to successful conversion.Because variable xu and yi is coupled together, this
Individual problem bad solution, thus we introduce als that is to say, that we can first fix y (such as random initializtion x),
Then first solve x using formula (2), then fix x, then solve y, so alternately reciprocal until convergence, that is, so-called alternately
Little square law solving method.
Concrete method for solving is described as follows:
First fix y, loss function l (x, y) sought local derviation to xu, and makes derivative=0, obtain:
xu=(yty+λi)-1ytru......(3)
Fix x in the same manner, can obtain:
yi=(xtx+λi)-1xtri... (4) wherein ru (1 × n) is the u row of r, and ri (1 × m) is i-th row of r, i
It is the unit matrix of k × k.
Iterative step: random initializtion y first, updated using formula (3) and obtain x, then utilize formula (4) to update y, directly
Become rmseization very little to root-mean-square error or reach maximum iteration time.
Model mentioned above is applied to the application scenarios solving there is clear and definite rating matrix, but in many cases, user
Clearly do not feed back the preference to commodity, that is, directly do not give a mark, we can only be inferred by some behaviors of user
His preference to commodity.Such as, in the problem of television program recommendations, the number of times to TV program watching or duration, at this moment
We can speculate that number of times is more, see the time is longer, the preference of user is higher, but for the program do not watched,
It is likely due to user and does not know there is this program, or do not have approach to obtain this program, we are unascertainable to speculate user not
Like this program.Als-wr solves these problems by confidence weight: is assigned to larger for the item more be sure oing user preference
Weight, for do not have feed back item, be assigned to less weight.The formal specification of als-wr model is as follows:
The object function of als-wr:
cui=1+ α rui
Wherein α is confidence level coefficient.
Solution mode or least square method:
xu=(ytcuy+λi)-1ytcuru......(6)
yi=(xtcix+λi)-1xtciri......(7)
Wherein cu is the diagonal matrix of n × n, and ci is the diagonal matrix of m × m；Cuii=cui, ciii=cii.
Described real-time monitoring system includes electronic chart and public notification of information plate, and the display of electronic chart combining geographic location is real
When situation and information, electronic chart includes road granularity map and piece chorography, road granularity map be main assume pattern,
The position of maintenance personal and state can be shown in real time, report for repairment a little or the position of early-warning point and fault message, supplemented by piece chorography
Help display pattern, the major failure information of each section and state are shown, mainly for being easy to manage and planning；Information
Bulletin board passes through word and form shows real time information.
As shown in figure 5, information issuing system includes information extraction modules, ATL, message processing module, information cache mould
Block, information issuing module, information extraction modules distribute system, extract data message database from early warning system, work order；Template
The related information such as storehouse memory storage early warning, work order issues script；Message processing module is used for being combined the data extracted with script
Generate file publishing；Information cache module can utilize cache chip, realize buffering in file publishing transmitting procedure and
Storage；Information issuing module is used for for file publishing being associated with real-time monitoring system.
The file publishing of early warning information at least include warning level, platform area numbering, platform area title, early warning reason it is also possible to
Process prediction scheme including the power failure obtaining from prediction scheme database.The file publishing of repairing information at least includes platform area numbering, platform
Area's title is it is also possible to increase brief fault state description.The file publishing of work order information include maintenance personal's name, numbering,
The information such as time of received orders, time of reaching the spot, deadline, handling duration.
In conjunction with whole system, it is as follows that it realizes the idiographic flow of function:
1st, report early warning for repairment: by fault pre-alarming or the repairing information that accepts client, start the flow process of repairing.
2nd, work order is set up: according to the early warning information of reporting for repairment receiving, sets up repairing work order.
3rd, work order distributes: work order distributes system to be realized work order and distribute, and specifically comprises the following steps that
1) data collecting system obtains real-time work order and distributes associated data information, and work order distributes associated data information at least
Including data below: work order quantity, work order calling information, report a position and fault message, recovery vehicle and personnel positions letter for repairment
Breath, real-time road condition information, processed work order complete duration information.
2) data of mahout system combination data collecting system database, calculates each work order and each maintenance personal
Or degree of association scoring between vehicle, the final core that it is passed judgment on is maintenance personal or vehicle reaches the time reported for repairment a little, is related to
Related information include condition of road surface, fault severity level, complaint situation, work order quantity, maintenance personal or vehicle operation ability
Deng, such as condition of road surface is to reach, according to maintenance personal or vehicle, the path reported for repairment a little to determine the time used on road, therefore
The barrier order of severity and complaint situation are all to determine that the ability to work of the priority that work order distributes, maintenance personal or vehicle is in statistics
The average time of various faults is solved, the maintenance personal in calculating Under Repair or vehicle complete before maintenance personal or vehicle
Connect the time of work order.
3) hadoop system is checked and is scored and generate recommendation list, and general recommendation list provides the scoring ranking dimension of first three
Repair personnel or vehicle；
4) select maintenance personal or vehicle from recommendation list and send work order, whole process both can be that system is sent out automatically
Send or manually send, when system sends automatically, acquiescence issues the maintenance personal ranking the first or vehicle.
4th, work order accepts: after maintenance personal receives work order, by handheld terminal or car-mounted terminal, the information of acceptance is sent to
Work order monitoring system.
5th, reach the spot: maintenance personal arrives at after reporting for repairment a little, upload field failure photo by taking pictures, on the one hand confirm dimension
The personnel that repair are actual to show up, and on the other hand with the maintenance personal of control centre, fault handling method can be discussed.
6th, failture evacuation: after the completion of troubleshooting, maintenance personal uploads troubleshooting information, and control centre restores electricity,
Examine fault to exclude.
7th, work order confirms: maintenance personal uploads work order and completes information form, and control centre examines work order and completes situation.
8th, work order is paid a return visit: pays a return visit personnel's phone confirmation customer trouble disposition, and information record is filed.
The information processing situation of each of the above step, after processing via information issuing system, all can be sent to monitor in real time
System, is shown on monitoring display screen.
It should be pointed out that the above specific embodiment can make those skilled in the art that the present invention is more fully understood
Concrete structure, but never in any form limit the invention.Therefore, although specification and drawings and Examples are to the present invention
Creation has been carried out describing in detail, it will be understood by those skilled in the art, however, that still can repair to the invention
Change or equivalent；And the technical scheme of all spirit and scope without departing from the invention and its improvement, it is all covered
In the middle of the protection domain of the invention patent.
Claims (10)
1. overall view monitoring system is actively rushed to repair based on the power distribution network that great Yun thing moves technology it is characterised in that inclusion Surveillance center, individual
Human hand held terminal and maintenance vehicle car-mounted terminal, personal hand-held terminal and maintenance vehicle car-mounted terminal are real by wireless network respectively
The now data interaction with control centre, upload location information and worksheet information；Described Surveillance center includes monitoring display
Screen, real-time monitoring system, early warning system, work order distribute system, information issuing system database, and monitoring display screen is used for showing
Image and Word message；Real-time monitoring system is used for generating real-time cartographic information；Early warning system is used for calculating and generating early warning
Information；Information issuing system is used for generating Word message；Work order distributes system and distributes scheme for generating work order；Database is used for
Data storage information.
2. according to claim 1 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described database includes
Public database, the data not high for storing renewal frequency；
Activity database, stores the data in the current or certain short time, provides inquiry service, after certain time, activity data
Data in storehouse moves into historical data base；
Historical data base, the data before storage a period of time；
Knowledge base, stores up pertinent arts, and knowledge section derives from expert, is derived partly from machine learning, provides analysis model
Service；
Customer data base, for storing the essential information of user, and the user characteristics obtaining after data mining.
3. according to claim 2 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, database also includes prediction scheme data storage storehouse, prediction scheme data storage library storage is directed to the conventional prediction scheme script of power failure.
4. according to claim 1 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described early warning system includes acquisition module and analysis module, and acquisition module is docked with each data system, for gathering
Client's real-time electricity consumption associated data；Analysis module passes through to compare historical data and real time data, analysis user's warning level and pre-
Alert reason.
5. according to claim 4 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described analysis module includes pretreatment module, coding module, code storage module and judge module, pretreatment module
For the filtering screening data planning of power information data, coding module is used for the coding of data, and code storage module is used for
Storage coding information, judge module is used for the comparison of coding information.
6. according to claim 5 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described coding module is the Hash coding module using hash algorithm.
7. according to claim 1 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described work order distributes system and includes hadoop system, mahout system, mahout system is used for work order data is dug
Pick and scoring, hadoop system includes hdfs and mapreduce, the storage of data in the calculating for data and calculating process.
8. according to claim 7 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, work order distributes system and also includes impala inquiry system, for the extraction inquiry of data.
9. according to claim 1 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described real-time monitoring system includes electronic chart and public notification of information plate, electronic chart combining geographic location shows in real time
Situation and information；Public notification of information plate passes through word and form shows real time information.
10. according to claim 1 based on great Yun thing move technology power distribution network actively rush to repair overall view monitoring system, its feature
It is, described information issuing system includes information extraction modules, ATL, message processing module, information cache module, information
Release module, information extraction modules distribute system, extract data message database from early warning system, work order；ATL internal memory
The related information such as storage early warning, work order issue script；Message processing module is used for for the data extracted being combined generation with script
Cloth file；Information cache module can utilize cache chip, realizes the buffering in file publishing transmitting procedure and storage；Letter
Breath release module is used for for file publishing being associated with real-time monitoring system.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102169505A (en) * | 2011-05-16 | 2011-08-31 | 苏州两江科技有限公司 | Recommendation system building method based on cloud computing |
| CN105741043A (en) * | 2016-02-03 | 2016-07-06 | 国家电网公司 | Distribution network first-aid system and method capable of combining with power network graph |
-
2016
- 2016-11-10 CN CN201610992500.7A patent/CN106339829B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102169505A (en) * | 2011-05-16 | 2011-08-31 | 苏州两江科技有限公司 | Recommendation system building method based on cloud computing |
| CN105741043A (en) * | 2016-02-03 | 2016-07-06 | 国家电网公司 | Distribution network first-aid system and method capable of combining with power network graph |
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