CN107491381A - A kind of equipment condition monitoring quality of data evaluating system - Google Patents
A kind of equipment condition monitoring quality of data evaluating system Download PDFInfo
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- CN107491381A CN107491381A CN201710539091.XA CN201710539091A CN107491381A CN 107491381 A CN107491381 A CN 107491381A CN 201710539091 A CN201710539091 A CN 201710539091A CN 107491381 A CN107491381 A CN 107491381A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Abstract
The invention discloses a kind of equipment condition monitoring quality of data evaluating system, including analysis of Influential Factors module, evaluative dimension analysis module, verification rule structure module and evaluation model structure module.The principal element that the system of the present invention passes through the analyzing influence quality of data, the key characteristics such as the uniformity of data, the accuracy of data, the integrality of data, the promptness of data simultaneously establish quality testing index and data check rule, realize quality of data index calculate, statistical analysis and overall merit it is real-time, automatically process, meet system dynamic, carry out the requirement of quality of data quality Quantitative Diagnosis and evaluation in real time.
Description
Technical field
The present invention relates to technical field of data processing, more particularly to a kind of equipment condition monitoring quality of data evaluating system.
Background technology
Just produced and transported with unprecedented range, depth and power network in information technology of the information age using big data as representative
Row and management rapid fusion, play more and more important effect, turn into the boosting of new period power network production management lean transition
Device.The core of big data is caused mass data is excavated and analyzed, and so as to rapidly obtain valuable information, turns
Turn to knowledge and carry out decision-making and anticipation.Because creation data amount is very huge, and each information system is dispersed in, it is lonely each other
It is vertical, fail to be effectively integrated and share, the quality problems such as data integrity, accuracy, uniformity are still present.Data at present
The subject matter that increased quality faces has the following aspects：
（1）Data source is more：On the one hand the scattered construction due to information system, the source system of processing data quality are embodied in
It is more, the even same attribute of same class data, possible source and different systems are on the other hand embodied in, due to lacking blood relationship
Analysis, where its source, which is, even is not known to a part of data；
（2）It is of poor quality：Including due to being produced caused by each category dictionary item imperfection, substantial amounts of meaning is fuzzy or even insignificant number
According to the data of part of factory handing-over exist in paper form, and the structural data of old system without good maintenance because become
Insignificant structural data；
（3）Processing is slow：The production domain information system of current electric grid company is in from scattered independently build to integration in history and built
If transition stage, therefore system Construction promotes and historical data migration be in leading position, and quality of data control is in progress
Slightly the status in weak tendency improves without obvious.
Therefore, for business datum quantity is more, species is numerous and jumbled, across specialty it is more the features such as caused by the quality of data it is not high
Present situation, how using existing data, improve the quality of data, it is that one currently faced is important to meet the needs of different enterprises
Problem.
The content of the invention
To overcome the deficiencies in the prior art, the present invention proposes a kind of equipment condition monitoring quality of data evaluation and test system
System, the technical scheme of the system are：
A kind of equipment condition monitoring quality of data evaluating system, the system include analysis of Influential Factors module, evaluative dimension point
Analyse module, verification rule structure module and evaluation model structure module, the analysis of Influential Factors module and be used for analyzing influence number
Factor and existing data quality problem according to quality, the evaluative dimension analysis module are used to determine to be adapted to the data matter
The evaluative dimension of amount, the verification rule structure module are used for the verification rule for building the quality of data, the evaluation model
Structure module is used for the evaluation model for building the quality of data.
Preferably, the factor for influenceing the quality of data includes field, record, record type and data source.
Preferably, the evaluative dimension includes integrality, uniqueness, uniformity, legitimacy, accuracy and promptness.
Preferably, the integrality is the completeness dimension of characterize data, entity missing, field missing comprising data,
Record missing and field value missing；
The uniqueness is the uniqueness dimension of characterize data, and the major key comprising data is unique and Candidate Key is unique；
The uniformity is the dimension of characterize data incidence relation, includes data source, redundant storage and data bore；
The legitimacy be characterize data whether in scientific and reasonable scope, form, type, thresholding and business comprising data
The validity of rule；
The accuracy is the levels of precision of characterize data, includes the accuracy of data format, data bits and data structure；
The promptness is the promptness that characterize data is safeguarded, includes data access, data upload, data maintenance and data application
Promptness.
Preferably, the evaluative dimension analysis module determines different evaluative dimensions according to different data types, described
Data type includes basic data, offline operation/maintenance data, online monitoring data and achievement data.
Preferably, the basic data includes account data, rule base, computational methods and computation model, the offline fortune
Dimension data includes the defects of equipment record data, preventive trial record and test report data, the online monitoring data bag
The obtained data of monitoring real-time to equipment state are included, the achievement data includes technology index data and operational indicator data.
Preferably, the technology index data includes promptness rate, always success rate, the consumption sent in data syn-chronization or data
When, and the reliability of data transfer, economy, stability indicator, the operational indicator packet containing Unit account of plant, O＆M,
The data and the core index data of the service application of development that on-line monitoring, state evaluation, failure predication, technical supervision are related to.
Preferably, the evaluative dimension of the basic data includes integrality, uniqueness, uniformity and legitimacy, it is described from
The evaluative dimension of line operation/maintenance data includes integrality, uniqueness, accuracy and legitimacy, the evaluation dimension of the online monitoring data
Degree includes integrality, uniformity, accuracy and promptness, and the evaluative dimension of the achievement data includes uniformity and promptness.
Preferably, the verification rule structure module is to differentiate that rule establishes school based on electric physics law and artificial data
Rule is tested, its product process is as follows：
（1）Basic data rule：The feature of data is relatively simple, only simple to integrality, uniqueness, uniformity and legitimacy
Index is checked；
（2）Online monitoring data rule：Basic data rule is first associated with, is positioned by equipment, verifies the basic number of correlation
According to rule, then verify the rule of online monitoring data, including to data integrity, uniformity, accuracy and promptness
Index is verified；
（3）Offline operation/maintenance data rule：Basic data rule is first associated with, basic data is verified, further closed
Online monitoring data rule is linked to, the feature between online monitoring data inside is analyzed, finally analyses in depth offline operation/maintenance data
Between internal correlation degree, offline operation/maintenance data is verified, including to data integrity, uniqueness, accuracy and legal
The index of property is verified；
（4）Achievement data rule：Basic data rule is first associated with, verifies the basic data rule of correlation, then verification refers to
The rule of data is marked, including the index of data consistency and promptness is verified.
Preferably, the evaluation model structure module is used to build quality of data index definition model, data quality accessment
Algorithm or rule, draw quality of data diagnosis and evaluation result, and the quality of data index definition model is referred to using level evaluation
Mark tree design, realize to calculating and the analysis automatically of index weights, index score.
The present invention benefit be：Principal element of the invention by the analyzing influence quality of data, the uniformity of data,
The key characteristics such as the accuracy of data, the integrality of data, the promptness of data simultaneously establish quality testing index and data
Verification rule, to instruct and examine system data quality level under big data, realize quality of data index calculate, statistical analysis and
Overall merit it is real-time, automatically process, meet system dynamic, carry out quality of data quality Quantitative Diagnosis and evaluation in real time will
Ask.
Brief description of the drawings
Fig. 1 is the structural representation of the present invention.
Embodiment
A kind of equipment condition monitoring quality of data evaluating system, as shown in figure 1, the system includes analysis of Influential Factors mould
Block, evaluative dimension analysis module, verification rule structure module and evaluation model structure module.Wherein：
Analysis of Influential Factors module is used for the factor of the analyzing influence quality of data and existing data quality problem.Information system
Present in data quality problem have many kinds, include following four from the angle of existence range：
（1）Attribute（Field）Aspect, this kind of mistake are limited only to the value of single attribute.
（2）Record aspect, this kind of mistake show the inconsistency occurred between a record different attribute value.
（3）Record type, show as the inconsistency between record type different in same data source.
（4）Data source aspect, show as some property values or record and the correlation in other data sources in data source
Inconsistency.Quality of data main Types are as shown in table 1：
Table 1
Evaluative dimension analysis module is used to determine the evaluative dimension for being adapted to the quality of data.By the identification to the quality of data and
Processing, establishes the creation data using grid equipment multi-dimensional data as object and makes a report on specification, improve the quality of incremental data.
The evaluative dimension of the present embodiment includes integrality, uniqueness, uniformity, legitimacy, accuracy and promptness.Its
In, integrality is the completeness dimension of characterize data, and uniqueness is the uniqueness dimension of characterize data, and uniformity is characterize data
The dimension of incidence relation, legitimacy be characterize data whether in scientific and reasonable scope, accuracy is the accurate of characterize data
Degree, promptness are the promptnesses of characterize data access, data upload, data maintenance and data application.
The present embodiment splits data into basic data, offline operation/maintenance data, online monitoring data and achievement data.Wherein：
Basic data includes account data, rule base, computational methods and model；The defects of offline operation/maintenance data includes equipment records number
According to, preventive trial record and test report data；Online monitoring data includes the obtained number of monitoring real-time to equipment state
According to；Achievement data includes technology index data and operational indicator data, wherein, technology index data includes data syn-chronization or number
According to above send promptness rate, success rate, total time-consuming, and the reliability of data transfer, economy, stability indicator, operational indicator
The data and the industry of development that packet is related to containing Unit account of plant, O＆M, on-line monitoring, state evaluation, failure predication, technical supervision
The core index data of business application.
In specific implementation process, different evaluative dimensions, the evaluation of basic data are determined according to different data types
Dimension includes integrality, uniqueness, uniformity and legitimacy, the evaluative dimension of offline operation/maintenance data include integrality, uniqueness,
Accuracy and legitimacy, the evaluative dimension of online monitoring data include integrality, uniformity, accuracy and promptness, index number
According to evaluative dimension include uniformity and promptness.
Verification rule structure module is used for the verification rule for building the quality of data.The verification rule is based on electric
Physics law and artificial data differentiate what rule was established according to different data types.Specially：
1st, basic data
Basic data refers mainly to account data, all kinds of directive/guide storehouses, computational methods, the basic data of model, wherein account data with
Data volume is big, relation is complicated, data quality problem existing for wide material sources etc. is most, and problem is mainly manifested in integrality, unique
Property, four aspects such as uniformity and legitimacy.
（1）Integrality
Integrality includes entity missing, attribute missing, record missing and field value and lacks four aspects.Wherein:
Entity lacks：Basic data entity missing is mainly manifested in data and set by the synchronizing process presence of source system to goal systems
The defects of in terms of meter, implementation, cause tables of data without synchronous.It can be handled by changing Synchronization Design.
Attribute lacks：Attribute missing may due to source system design defect or be unsatisfactory for goal systems using need and produce
It is raw, such as there is no two parameters of longitude and dimension in substation information, then when system is shown using generalized information system, it can not realize
On map draw power transformation station location and remaining years attribute missing problem.
Record missing：The situation of record missing is relatively common, for example the transformer station's note newly gone into operation is not present in transformer station's table
Record, does not have newly-installed device class record etc. in device class table, problems more by business processing not in time or basic number
Caused not in time according to renewal, can be by being handled one by one after checking the quality of data.
Field value lacks：Field value defect is relatively common in basic data, shows as essential information, the skill of Unit account of plant
The data such as art parameter are not according to regulation typing.It is not strict that the reason for producing field value missing has system convention to check, rule lacks
Lose, data inputting sense of responsibility is not enough, data inputting lacks many-side, the problems such as effective review mechanism can be by setting word
Section checks rule, first checks for the quality of data, is handled afterwards according to actual conditions.
（2）Uniqueness
Uniqueness refers to major key uniquely and in terms of only one or two of Candidate Key.The main reason for the Uniqueness produces is that data area expands
After big, the major key of legacy data does not possess uniqueness, or the data of separate sources, and major key rule is different, can not be in new
Normal use in system.
（3）Uniformity
Uniformity refers to unified data source, redundant storage and unified data bore.Wherein, data source is inconsistent refers to this
System will obtain the data relevant with equipment state from different system, including management and running system, production management system and
All types of on-line monitoring main station systems, because of reasons such as history construction and compasss of competency, there is one to be arranged in above-mentioned each system
Standby machine account, therefore for the quality of data work of the system, there are multiple sources in account data.
（4）Legitimacy
Legitimacy mainly includes the validity of form, type, thresholding and business rule.
The legal sex chromosome mosaicism of account data has the forms such as the basic parameter of machine account information, technical parameter, data type, numerical value
Apparent error and structure, parameter value etc. do not meet business rule etc. Deng existing for.Such as in basic parameter or technical parameter
Be character type by numeric type mistake, input the half-angle full-shape mistake of character, main transformer capacity typing is 150000MVA（No
It is MVA to pay attention to unit, as KVA typings, differs 1000 times）；In terms of not meeting basic business rule, such as to oil immersed type transformation
Device, its " type " parameter is extended this as into SF6.Data validation problem is mainly because system convention monitoring in source is not enough improved and artificial record
Entering error in data causes, and can check rule, the identification of problem of implementation data by being set in being checked in the quality of data.
2nd, offline operation/maintenance data
The defects of offline operation/maintenance data of the system refers mainly to equipment record data, preventive trial record and test report number
According to the data source that is, from provincial production system, is synchronized to provincial main website in provincial production information system.By in the past right
From the point of view of the statistical analysis of such data, quality problems are mainly manifested in the following aspects：
（1）Integrality
Integrality includes entity missing, attribute missing, record missing and field value and lacks four aspects：
Entity lacks：Database table is mainly shown as during goal systems is synchronized to, there occurs the feelings of database table missing
Condition, such as in test report data during production system is synchronized to main station system, lack certain table, or because production system
Data structure change, cause it is new caused by test report database table be not synchronized in provincial main website, now regard
For there occurs the data quality problem that entity lacks.Entity lacks, and is mainly handled by hand inspection, is in design two first
During inter-system synchronization data, it is ensured that it is complete errorless, the database table of correlation is not omitted；Create a mechanism in addition, source system data
When structure changes, time update is synchronously set, and goal systems is immune.
Attribute lacks：The reason for attribute missing is possible is that part attribute is process by other attributes, and non-primary note
Record.Such as the three-phase imbalance rate in test report, by being calculated, it is not present in original test report.
Such data problem can solve by increasing Database field after data syn-chronization.
Record missing：Record missing is relatively common in offline operation/maintenance data, such as factor data structure change, original
Business datum does not complete data importing.In addition, because of other reasonses, data fail to be entered into information system in time.Record missing
Mainly solved by data amended record.
Field value lacks：Field missing may be caused by following reason：It is business datum first because failing to cover flow,
Lack part field；Next to that typing is imperfect；Finally being missing from verification rule causes determinant attribute to lack.Field value is because industry
It is normal business phenomenon that business data, which fail to complete flow and lack, it is not necessary to handle, and field caused by other situations lacks
Judge with regard to needing to be analyzed according to actual conditions, because for the data problem of field value missing, needed mainly in conjunction with business, it is right
Judgment rule is formulated per a kind of business datum, one by one discriminatory analysis.
（2）Uniqueness
Produce the Uniqueness the reason for be：Data are after larger range of data center is synchronized to, original major key（Whether
As new major key or Candidate Key）No longer there is uniqueness.Avoiding the occurrence of the major measure of the Uniqueness is, same in data
In new record caused by step, the major key new to data database design rather than the original major key of use.
（3）Accuracy
The accuracy problem of business datum is mainly manifested in manual entry or the content of selection is not detailed enough, such as defect
The defects of presentation, defect cause and defect processing measure key factor, because of typing, personnel lack a sense of responsibility, and java standard library support
The problems such as insufficient, often occur：Other, handle according to last time mode, solved etc. description, these data of directive/guide and carrying out
During analysis, through being abandoned frequently as junk data.Operation/maintenance data accuracy problem is solved, it is necessary to combine substantial amounts of other data, industry
Business experience, by means of other information, to identify the field without concrete meaning such as " other ", such as similar cases are found, to lacking
Presentation is fallen into, is matched etc. according to defect description field extraction keyword.
（4）Legitimacy
The legitimacy of off-line data is similar with basic data, is mainly asked by lack of standardization, information system verification scheme missing of typing etc.
Topic is caused, relatively common in test report data, such as when filling in test report, dielectric loss value has been filled out to the position of capacitance
Put, such legal sex chromosome mosaicism can be by data given threshold, being identified.
3rd, online monitoring data
（1）Integrality
Integrality includes entity missing, attribute missing, record missing and field value and lacks four aspects.
The integrity issue of on-line monitoring is mainly manifested in record missing, attribute and the aspect of field value missing two.Monitor number
According to record missing because device occurs abnormal, can not effectively realize monitoring function and produce；Attribute and field value missing because
The data item that the monitoring device of each producer specifically monitors is different, and the specification of unified content does not possess Compulsory Feature, so as to
The some fields for needing to obtain of directive/guide are not present in the monitoring of part producer.The solution of problems can be by supervising producer to enter
Luggage puts standardization transformation and two kinds of application function of adjustment.
（2）Uniformity
In terms of consistency problem mainly generates data application, for example, currently multi-purpose online monitoring data and off-line testing detect into
Row contrast, to find same type data, the different difference gathered under bore.This be data application consistency problem, one
As quality index not as online monitoring data in itself.
（3）Accuracy
The accuracy problem of online monitoring data is determined by the performance of producer's device more, seldom can in data transfer and gatherer process
The problem of causing accuracy to be unsatisfactory for requiring, therefore do not consider the accuracy problem of online monitoring data substantially in the application.
（4）Promptness
Promptness is the important indicator of Monitoring Data, not only including promptness caused by data, also including data transfer, at data
The promptness of reason, promptness chief reason：First, producer's device can not gather Monitoring Data in time because there is exception；Its
Secondary is that data syn-chronization has the problem of function or network, can not be synchronous in time.
4th, achievement data
Achievement data includes technology index data and operational indicator data.Technology index data refers to relevant data syn-chronization or data
On send promptness rate, success rate, total time-consuming etc., and the index such as the reliability of data transfer, economy, stability.Business refers to
Mark data cover what the system such as Unit account of plant, O＆M, on-line monitoring, state evaluation, failure predication and technical supervision were related to
The core index of data, the service application carried out, it is that may be present to technology index data and operational indicator data individually below
Data quality problem.The quality problems of achievement data are present in two aspects of uniformity and promptness.
（1）Uniformity
The uniformity of this method design, occurs mainly in following two situations：When in main website setting on business datum
Index there may be data difference with source system, cause inconsistence problems.Such as the system sets the defect elimination rate of defect
Or the online rate of on-Line Monitor Device, but because the reason for Statistical Criteria or data, it is possible to allow can with production system or
The index that person monitors main station system statistics on-line is inconsistent, and problems do not influence the system use typically, can Weakening treatment.Two
Be the system different business between statistical indicator have differences, such as the statistical indicator at monitoring center and technical supervision center
Inconsistent, problems mainly by the unified metric to different business modules, formulate identical statistical method, and ensure to count
It is consistent according to source.
（2）Promptness
Data interaction, including real-time and non-real-time data collection and processing, therefore for the timely of Various types of data acquisition interface
Property, it is proposed that higher requirement, data transfer promptness index will be set, and to online monitoring data, dispatch real time data
Upload in time and be monitored and examine；In the presence of the data interaction with production system, other monitoring main station systems etc., meeting data
On the basis of transmission, the data transfer pair with the said system time, the data of the system can be ensured by examining or check its promptness index
Integrality, when there are data transmission problems, it can find and handle in time.Timely sex chromosome mosaicism occurs mainly in data transfer link
On, such as preposition communication equipment failure, plant failure, communication network exception etc..Timely sex chromosome mosaicism mainly occur problem it
Afterwards, take measures, prevent the mode that problem occurs again from handling.
Evaluation model structure module is used for the evaluation model for building the quality of data, and the evaluation model includes data matter
Figureofmerit Definition Model, data quality accessment algorithm or rule, and derive quality of data diagnosis and evaluation result, the data
Quality index Definition Model is designed using level evaluation index tree, realizes and index weights, index score are calculated and analyzed automatically.
Claims (10)
1. a kind of equipment condition monitoring quality of data evaluating system, it is characterised in that the system includes analysis of Influential Factors mould
Block, evaluative dimension analysis module, verification rule structure module and evaluation model structure module, the analysis of Influential Factors module are used
Factor and existing data quality problem in the analyzing influence quality of data, the evaluative dimension analysis module are used to determine to fit
The evaluative dimension of the quality of data is closed, the verification rule structure module is used for the verification rule for building the quality of data,
The evaluation model structure module is used for the evaluation model for building the quality of data.
A kind of 2. equipment condition monitoring quality of data evaluating system according to claim 1, it is characterised in that the influence
The factor of the quality of data includes field, record, record type and data source.
A kind of 3. equipment condition monitoring quality of data evaluating system according to claim 1, it is characterised in that the evaluation
Dimension includes integrality, uniqueness, uniformity, legitimacy, accuracy and promptness.
A kind of 4. equipment condition monitoring quality of data evaluating system according to claim 3, it is characterised in that
The integrality is the completeness dimension of characterize data, entity missing, field missing, record missing and word comprising data
Segment value lacks；
The uniqueness is the uniqueness dimension of characterize data, and the major key comprising data is unique and Candidate Key is unique；
The uniformity is the dimension of characterize data incidence relation, includes data source, redundant storage and data bore；
The legitimacy be characterize data whether in scientific and reasonable scope, form, type, thresholding and business comprising data
The validity of rule；
The accuracy is the levels of precision of characterize data, includes the accuracy of data format, data bits and data structure；
The promptness is the promptness that characterize data is safeguarded, includes data access, data upload, data maintenance and data application
Promptness.
A kind of 5. equipment condition monitoring quality of data evaluating system according to claim 1, it is characterised in that the evaluation
Dimensional analysis module determines different evaluative dimensions according to different data types, the data type include basic data, from
Line operation/maintenance data, online monitoring data and achievement data.
A kind of 6. equipment condition monitoring quality of data evaluating system according to claim 5, it is characterised in that the basis
The defects of data include account data, rule base, computational methods and computation model, and the offline operation/maintenance data includes equipment records
Data, preventive trial record and test report data, the online monitoring data include monitoring real-time to equipment state and obtained
The data obtained, the achievement data include technology index data and operational indicator data.
A kind of 7. equipment condition monitoring quality of data evaluating system according to claim 6, it is characterised in that the technology
Promptness rate that achievement data includes sending in data syn-chronization or data, success rate, total time-consuming, and the reliability of data transfer,
Economy, stability indicator, the operational indicator packet are pre- containing Unit account of plant, O＆M, on-line monitoring, state evaluation, failure
The core index data of the service application of survey, the data that technical supervision is related to and development.
A kind of 8. equipment condition monitoring quality of data evaluating system according to claim 5, it is characterised in that the basis
The evaluative dimension of data includes integrality, uniqueness, uniformity and legitimacy, and the evaluative dimension of the offline operation/maintenance data includes
Integrality, uniqueness, accuracy and legitimacy, the evaluative dimension of the online monitoring data include integrality, uniformity, accurate
Property and promptness, the evaluative dimension of the achievement data include uniformity and promptness.
A kind of 9. equipment condition monitoring quality of data evaluating system according to claim 1, it is characterised in that the verification
Rule structure module is to differentiate that rule establishes verification rule based on electric physics law and artificial data, and its product process is as follows：
（1）Basic data rule：The feature of data is relatively simple, only simple to integrality, uniqueness, uniformity and legitimacy
Index is checked；
（2）Online monitoring data rule：Basic data rule is first associated with, is positioned by equipment, verifies the basic number of correlation
According to rule, then verify the rule of online monitoring data, including to data integrity, uniformity, accuracy and promptness
Index is verified；
（3）Offline operation/maintenance data rule：Basic data rule is first associated with, basic data is verified, further closed
Online monitoring data rule is linked to, the feature between online monitoring data inside is analyzed, finally analyses in depth offline operation/maintenance data
Between internal correlation degree, offline operation/maintenance data is verified, including to data integrity, uniqueness, accuracy and legal
The index of property is verified；
（4）Achievement data rule：Basic data rule is first associated with, verifies the basic data rule of correlation, then verification refers to
The rule of data is marked, including the index of data consistency and promptness is verified.
A kind of 10. equipment condition monitoring quality of data evaluating system according to claim 1, it is characterised in that institute's commentary
Valency model construction module is used to build quality of data index definition model, data quality accessment algorithm or rule, draws data matter
Diagnosis and evaluation result is measured, the quality of data index definition model is designed using level evaluation index tree, realizes and index is weighed
Weight, index score calculate automatically and analysis.
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