CN108846568A - The comprehensive performance trend analysis of Aviation engine - Google Patents
The comprehensive performance trend analysis of Aviation engine Download PDFInfo
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Abstract
The present invention provides a kind of comprehensive performance trend analysis of Aviation engine, which solve the technical problems of existing aeroengine Performance Monitoring difficulty, it includes the typing of basic data in relevant database, design distributed file system model, acquire aero-engine monitoring data, and carry out pretreatment processing, key and value will be formed after operable data processing after parsing, bind key and value, it stores to the corresponding file directory of engine, designer trends analysis rule, carry out simple trend analysis, carry out integrative trend analysis, it invention can be widely used in aero-engine data management field.
Description
Technical field
The present invention relates to aero-engine technical field of data administration, more particularly, to a kind of the comprehensive of Aviation engine
Close performance trend analysis method.
Background technique
Aircraft provides highly efficient and efficiently trip mode as a kind of convenient vehicles, for people, however,
Flight safety is an eternal topic, and for aircraft, engine is important just as heart, how to ensure that engine can
Work operation in normal state, is a most important problem for airline；In the daily of aero-engine
In operation, a large amount of monitoring data can be generated, for the different engine performance monitoring data in various sources, how to each source
Data carry out unified supervision and analysis, to realize the trend analysis monitored to engine performance, it has also become urgent need.
Summary of the invention
The present invention provides a kind of aero-engine aiming at the technical problem of existing aeroengine Performance Monitoring difficulty
Performance monitoring is accurate, the comprehensive performance trend analysis of stable Aviation engine.
For this purpose, the present invention provides a kind of comprehensive performance trend analysis of Aviation engine, including following step
Suddenly：
Step 1：The typing of basic data in relevant database；Basic data includes type of airplane, aircraft type, aircraft
Registration, engine type, engine model, engine registration, aircraft flight data, data source, mission phase, monitoring belong to
Property, original message parsing template, manufacturer data parsing template, decoding data parse template；
Step 2：Design distributed file system model；Engine performance monitoring data is realized using distributed file system
Storage, performance monitoring data includes original message, manufacturer data and decoding data, and the engine performance after parsing monitors number
According to according to the daily performance trend analysis of engine rule, using monitoring engine object and data source as distributed document
The file name of system, using monitoring attributes and monitoring period as the key value of column, by data source, mission phase and monitoring value
Value value as column is stored；
Step 3：Aero-engine monitoring data is acquired, and carries out pretreatment processing；According to different data source, by flying
Machine engine information determines parsing template, parses effective performance monitoring data from file according to the concrete configuration of template；
Step 4：By after parsing operable data processing after formed key and value, bind key and value, store to
The corresponding file directory of engine；
Step 5：Designer trends analysis rule；Monitoring attributes can be observed from different perspectives by different icon modes
Long-term change trend realizes multi-level simulation tool to monitoring attributes；
Step 6：Carry out simple trend analysis；, parameter packet mode difference different according to engine packet mode, will be final
Result set be combined into iconic model according to regular group that trend analysis is set, and be shown；
Step 7：Carry out integrative trend analysis；By the aircraft flight data of increment management in system, can by message data,
Manufacturer data and decoding data are associated extraction by flight time, Flight Information and airplane information.
Preferably, step 1 is main is realized by following steps：
Type of airplane model is as follows：
ACTYPE={ ID, actype }
Wherein ID is the unique identification of type of airplane, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Actype is the title of type of airplane, this model is used to the airplane type information of nonproductive poll aircraft；
Aircraft type model is as follows：
ADMODEL={ ID, acmodel }
Wherein ID is the unique identification of aircraft type, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Acmodel is the title of aircraft type, this model is used to the aircraft type information of nonproductive poll aircraft；
It is as follows that aircraft registers model：
Airplane={ ID, basicInfo }
Wherein ID is the globally unique identifier of aircraft, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
BasicInfo is aircraft essential information, an aircraft at least two engines, and engine can constantly change, this model
For the aircraft engine machine information of nonproductive poll different periods；
Engine type model is as follows：
ENTYPE={ ID, entype }
Wherein ID is the unique identification of engine type, and length must be 36 bit digitals, upper and lower case letter and strigula group
It closes；Entype is the title of engine type, this model is used to the engine type information of nonproductive poll engine；
Engine model model is as follows：
ENMODEL={ ID, enmodel }
Wherein ID is the unique identification of engine model, and length must be 36 bit digitals, upper and lower case letter and strigula group
It closes, enmodel is the title of engine model, this model is used to the engine model information of nonproductive poll engine；
It is as follows that engine registers model：
ENGINE={ esn, pos, ac, basicInfo }
Wherein esn is the globally unique identifier of engine, as the subdirectory unique identification of distributed file system, pos
As the hair position of engine, ac is the aircraft where engine, and basicInfo is other essential informations of engine, this model is used
Carry out nonproductive poll engine information；
Aircraft flight data model is as follows：
ACHIS={ ac, esn1, esn2, from, to, starttime, endtime, flightnum, basicinfo }
The motor number of position is sent out in the motor number that wherein ac is aircraft number, esn1 is 1 hair position, the position esn2 2, and from is this time
The origin of flight, to are the end place of the secondary flight, and at the beginning of starttime is this time flight, endtime is
The end time of the secondary flight, flightnum are the flight number of the secondary flight, and basicinfo is basic for other of the secondary flight
Information, this model are used to the flying quality of nonproductive poll aircraft, are whereby associated the different data in source；
Data source model is as follows：
DATAFROM={ ID, name }
Wherein ID is the unique identification of data source, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
As a part of distributed storage file system table title, name is data source title, currently used value be DFD,
EHM,QAR；
Mission phase model is as follows：
FLIGHTPHASE={ ID, name }
Wherein ID is the unique identification of mission phase, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
As a part of distributed storage file system value, name is mission phase title；
Monitoring attributes model is as follows：
STANDARDPARAM={ name }
Name is the unique identification of monitoring attributes, monitoring attributes title is indicated, as distributed storage file system key's
A part；
It is as follows that original message parses template model：
DFDMODEL=ID, actype, flightphase, entype, dateformat, timeformat,
xmlModel,paramlist}
Wherein ID is the unique identification that original message parses template, and length must be 36 bit digitals, upper and lower case letter and short
Horizontal line combination, actype are applicable type of airplane, and flightphase is applicable mission phase, and entype is applicable hair
Motivation type, dateformat are the date format in message, and timeformat is the time format in message, and xmlModel is
Template content, format are xml format；Paramlist is the monitoring attributes information of message, mark the titles of monitoring attributes, length,
It sends out position, conversion coefficient, whether be monitoring parameter；
It is as follows that manufacturer data parses template model：
EHMMODEL=ID, flightphase, entype, datetimeformat, headerline, dataline,
esncolumn,datetimecolumn,accolumn,paramlist}
Wherein ID is the unique identification that manufacturer data parses template, and length must be 36 bit digitals, upper and lower case letter and short
Horizontal line combination, flightphase are applicable mission phase, and entype is applicable engine type, datetimeformat
For the time format in file, headerline is expert at by the header line in file, and dataline is that the data in file are opened
It beginning, esncolumn is motor number column in file, and datetimecolumn is time column in file,
Acccolumn is aircraft number column in file, and paramlist is the monitoring attributes information in file, marks monitoring attributes
Title, column name；
It is as follows that decoding data parses template model：
QARMODEL=ID, actype, entype, dateformat, timeformat, headerline,
dataline,esn1column,esn2column,datecolumn,timecolumn,accolumn,paramlist}
Wherein ID is the unique identification that decoding data parses template, and length must be 36 bit digitals, upper and lower case letter and short
Horizontal line combination, actype are applicable type of airplane, and entype is applicable engine type, and dateformat is in data
Date format, timeformat are the time format in data, and headerline is expert at by the header line in data,
Dataline is the data starting row in data, and esn1column is 1 hair position motor number column, esn2column in data
For 2 hair position motor number column in data, datecolumn is date column, and timecolumn is time place in data
Column, acccolumn are aircraft number column in data, and paramlist is the monitoring attributes information in data, mark monitoring attributes
Title, column name, affiliated engine.
Preferably, step 2 is main is realized by following steps：
In distributed file system, storage model is as follows：
FileSystem={ ESNi+DATAFROMj| i=1,2 ... n, j=DFD, EHM, QAR }
ESN={ keyi, columnFamily | i=1,2 ... n }
ColumnFamily={ DataFrom, Flight_phase, Value }
Wherein ESN+DATAFROM is the unique identification of storage catalogue, that is, from the unique identification of engine, key
For rowKey, be made of monitoring attributes and acquisition time, columnFamily be main monitoring content, including data source, fly
Row order section and monitoring value.
Preferably, step 3 is main is realized by following steps：
The primary format for parsing obtained data is listings format：
ModelList={ modelList }
Model={ Airplane, Engine, Date, Flight_phase, datafrom, paramList }
ParamListModel={ StandardParam, value }
Wherein ModelList is the list of all data, includes multiple modelList, and a modelList includes multiple
Airplane is airplane information in Model, Model, and Engine is engine information, and Date is acquisition time, will be as distribution
A part of formula file system key is stored, and Flight_phase is mission phase information, and a model includes one
ParamList, paramList are monitoring information list, and the model of paramList is paramListModel,
ParamListModel includes details, and StandardParam is monitoring attributes, and value is monitoring value.
Preferably, step 4 is main is realized by following steps：
After key and value combination, obtained data model is：
ModelList={ modelList }
Model={ ESN+DATAFROM, key, paramList }
Key={ StandardParam+date }
ParamListModel={ Flight_phase, datafrom, value }
Wherein ModelList is the list of all data, includes multiple modelList, and a modelList includes multiple
ESN is engine unique identification information in Model, Model, and key is line unit, is made of monitoring attributes and time, and value is key
Value, is made of mission phase, data source and monitoring value.
It according to Engine, stores data into corresponding file, first detection catalogue whether there is first, if do not deposited
It is needing to create directory, then carry out data storage, if had existed, is directly carrying out data storage, process is as follows：
If(Exist(ESN+DATAFROM))
Then Save(Engine,ModelList)
Else create (ESN+DATAFROM), Save (Engine, ModelList)
Preferably, step 5 is main is realized by following steps：
Trend analysis rule model is as follows：
AnalyzeRule=ID, datafrom, esngroup, linestyle, paramgroup, datarange,
esnarray,flightarray,paramList}
Wherein ID is the unique identification of trend analysis rule, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination；
Datafrom is data source, and DFD, EHM, QAR may be selected at present, and esngroup is engine packet mode, can
It selects single-shot, double hairs and owns；
Linestyle is plotting mode, and scatter plot and line chart may be selected；
Paramgroup is parameter packet mode, may be selected to concentrate to draw and draw with independent；
Datarange is data area, and section, recent, self loader may be selected；
Esnarray is the engine list that carry out trend analysis；
ParamList is the parameter information of trend analysis, and title, the reference axis of flag parameters information, the title of parameter can
Think time and monitoring attributes.
Preferably, step 6 is main is realized by following steps：
Since X-coordinate axle can be the time, or monitoring parameter, therefore the mode for obtaining data is different, below according to
It is secondary to be illustrated.
X, X2 axis is the time, and Y, Y2 axis are monitoring parameter：The time started is obtained at the end of by datarange first
Between, the monitoring parameter information of circulation Y, Y2 axis obtains phase according to the unique identification of parameter information and time started and end time
The monitoring value of period is answered, method is as follows：
Resultscan0=scan (ESN, paramid0+starttime,paramid0+endtime)
...
Resultscann=scan (ESN, paramidn+starttime,paramidn+endtime)
Recycle resultscan0…resultscann, the value from result set handles result set again, final to carry out
The data model of trend analysis is identical, as follows：
Map<esn,Map<paramname,parambean>>
Parambean={ esn, datetime, value, sublabel, datafrom }
Wherein esn is motor number, and paramname is monitoring parameter title, and datetime is the time, and value is monitoring
Parameter value.Sublabel is mission phase, and datafrom is data source.
X, X2 axis is monitoring parameter, and Y, Y2 axis are monitoring parameter：Time started and end are obtained by datarange first
Time recycles the monitoring parameter information of X, X2, Y, Y2 axis, according to the unique identification of parameter information and time started at the end of
Between, the monitoring value of corresponding period is obtained, method is as follows：
Resultscan0=scan (ESN, paramid0+starttime,paramid0+endtime)
...
Resultscann=scan (ESN, paramidn+starttime,paramidn+endtime)
Recycle resultscan0…resultscann, taken from result set point X and Y at the same time have value as a result,
Result set is handled again, the final data model for carrying out trend analysis is identical, as follows：
Map<esn,Map<paramname,paramparambean>>
Paramparambean={ esn, datetime, xvalue, yvalue, sublabel, datafrom }
Wherein esn is motor number, and paramname is monitoring parameter title, and datetime is the time, and xvalue is X-axis
Monitoring parameter value, yvalue are Y-axis monitoring parameter value, and Sublabel is mission phase, and datafrom is data source.
Preferably, step 7 is main is realized by following steps：
Trend analysis rule, trend analysis are established according to ACARS flight data, time of flight data and airplane information first
Rule model is as follows：
AnalyzeRule=ID, DFD, esngroup, linestyle, paramgroup, datarange,
esnarray,flightarray,paramList}
Wherein ID is the unique identification of trend analysis rule, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination, DFD are defaulted as Datafrom, and esngroup is engine packet mode, and linestyle is plotting mode,
Paramgroup is parameter packet mode, and datarange is data area, and esnarray is the engine that carry out trend analysis
List, flightarray be flight list, paramList be trend analysis parameter information, the title of flag parameters information,
Reference axis.
The present invention utilizes unified supervision, united analysis method, realizes the integrative trend analysis of engine performance monitoring；It will not
Engine performance monitoring data with source connects, and can both observe the rough Long-term change trend of different mission phases, can also
To observe accurate Long-term change trend；A kind of integrative trend analysis method is provided for the engine performance monitoring data of separate sources,
Based on two-tiered structure storage mode, realize that the loose coupling management of the efficient management and basic data of magnanimity monitoring data, promotion become
The high efficiency of potential analysis management and comprehensive.
Detailed description of the invention
Fig. 1 is integrated stand composition of the invention；
Fig. 2 is the storage model block diagram of relevant database；
Fig. 3 is the storage model block diagram of distributed file system.
Specific embodiment
The specific embodiment that the present invention will be described in detail with reference to the accompanying drawings.
As shown in Figure 1, Figure 2 and Figure 3, the present invention provides a kind of comprehensive performance trend analysis sides of Aviation engine
Method includes the following steps：
Step 1：The typing of basic data in relevant database；Basic data includes type of airplane, aircraft type, aircraft
Registration, engine type, engine model, engine registration, aircraft flight data, data source, mission phase, monitoring belong to
Property, original message parsing template, manufacturer data parsing template, decoding data parse template；
Upper layer utilizes the management of relevant database optimized integration data, and basic data mainly stores the base of aero-engine
Plinth data provide the query service of basic data for distributed file system and basic data increment service, step 1 mainly pass through
Following steps are realized：
Type of airplane model is as follows：
ACTYPE={ ID, actype }
Wherein ID is the unique identification of type of airplane, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Actype is the title of type of airplane, this model is used to the airplane type information of nonproductive poll aircraft；
Aircraft type model is as follows：
ADMODEL={ ID, acmodel }
Wherein ID is the unique identification of aircraft type, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Acmodel is the title of aircraft type, this model is used to the aircraft type information of nonproductive poll aircraft；
It is as follows that aircraft registers model：
Airplane={ ID, basicInfo }
Wherein ID is the globally unique identifier of aircraft, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
BasicInfo is aircraft essential information, an aircraft at least two engines, and engine can constantly change, this model
For the aircraft engine machine information of nonproductive poll different periods；
Engine type model is as follows：
ENTYPE={ ID, entype }
Wherein ID is the unique identification of engine type, and length must be 36 bit digitals, upper and lower case letter and strigula group
It closes；Entype is the title of engine type, this model is used to the engine type information of nonproductive poll engine；
Engine model model is as follows：
ENMODEL={ ID, enmodel }
Wherein ID is the unique identification of engine model, and length must be 36 bit digitals, upper and lower case letter and strigula group
It closes, enmodel is the title of engine model, this model is used to the engine model information of nonproductive poll engine；
It is as follows that engine registers model：
ENGINE={ esn, pos, ac, basicInfo }
Wherein esn is the globally unique identifier of engine, as the subdirectory unique identification of distributed file system, pos
As the hair position of engine, ac is the aircraft where engine, and basicInfo is other essential informations of engine, this model is used
Carry out nonproductive poll engine information；
Aircraft flight data model is as follows：
ACHIS={ ac, esn1, esn2, from, to, starttime, endtime, flightnum, basicinfo }
The motor number of position is sent out in the motor number that wherein ac is aircraft number, esn1 is 1 hair position, the position esn2 2, and from is this time
The origin of flight, to are the end place of the secondary flight, and at the beginning of starttime is this time flight, endtime is
The end time of the secondary flight, flightnum are the flight number of the secondary flight, and basicinfo is basic for other of the secondary flight
Information, this model are used to the flying quality of nonproductive poll aircraft, are whereby associated the different data in source；
Data source model is as follows：
DATAFROM={ ID, name }
Wherein ID is the unique identification of data source, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
As a part of distributed storage file system table title, name is data source title, currently used value be DFD,
EHM,QAR；
Mission phase model is as follows：
FLIGHTPHASE={ ID, name }
Wherein ID is the unique identification of mission phase, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
As a part of distributed storage file system value, name is mission phase title；
Monitoring attributes model is as follows：
STANDARDPARAM={ name }
Name is the unique identification of monitoring attributes, monitoring attributes title is indicated, as distributed storage file system key's
A part；
It is as follows that original message parses template model：
DFDMODEL=ID, actype, flightphase, entype, dateformat, timeformat,
xmlModel,paramlist}
Wherein ID is the unique identification that original message parses template, and length must be 36 bit digitals, upper and lower case letter and short
Horizontal line combination, actype are applicable type of airplane, and flightphase is applicable mission phase, and entype is applicable hair
Motivation type, dateformat are the date format in message, and timeformat is the time format in message, and xmlModel is
Template content, format are xml format；Paramlist is the monitoring attributes information of message, mark the titles of monitoring attributes, length,
It sends out position, conversion coefficient, whether be monitoring parameter；
It is as follows that manufacturer data parses template model：
EHMMODEL=ID, flightphase, entype, datetimeformat, headerline, dataline,
esncolumn,datetimecolumn,accolumn,paramlist}
Wherein ID is the unique identification that manufacturer data parses template, and length must be 36 bit digitals, upper and lower case letter and short
Horizontal line combination, flightphase are applicable mission phase, and entype is applicable engine type, datetimeformat
For the time format in file, headerline is expert at by the header line in file, and dataline is that the data in file are opened
It beginning, esncolumn is motor number column in file, and datetimecolumn is time column in file,
Acccolumn is aircraft number column in file, and paramlist is the monitoring attributes information in file, marks monitoring attributes
Title, column name；
It is as follows that decoding data parses template model：
QARMODEL=ID, actype, entype, dateformat, timeformat, headerline,
dataline,esn1column,esn2column,datecolumn,timecolumn,accolumn,paramlist}
Wherein ID is the unique identification that decoding data parses template, and length must be 36 bit digitals, upper and lower case letter and short
Horizontal line combination, actype are applicable type of airplane, and entype is applicable engine type, and dateformat is in data
Date format, timeformat are the time format in data, and headerline is expert at by the header line in data,
Dataline is the data starting row in data, and esn1column is 1 hair position motor number column, esn2column in data
For 2 hair position motor number column in data, datecolumn is date column, and timecolumn is time place in data
Column, acccolumn are aircraft number column in data, and paramlist is the monitoring attributes information in data, mark monitoring attributes
Title, column name, affiliated engine.
Step 2：Design distributed file system model；Engine performance monitoring data is realized using distributed file system
Storage, performance monitoring data includes original message, manufacturer data and decoding data, and the engine performance after parsing monitors number
According to according to the daily performance trend analysis of engine rule, using monitoring engine object and data source as distributed document
The file name of system, using monitoring attributes and monitoring period as the key value of column, by data source, mission phase and monitoring value
Value value as column is stored；
Bottom realizes the storage of engine magnanimity performance monitoring data, in storing process, root using distributed file system
The characteristics of according to daily monitoring, keyword is added in unique identification, and records each attribute of daily key monitoring, it is this
Storage method can greatly improve the locating speed and retrieval rate of performance monitoring data, guarantee comprehensive performance trend analysis
High efficiency and uniformity.
Trend analysis is using the monitoring data of aero-engine as operation basis, so basic difference is aeroplane engine
Machine, and according to data source difference, optional parameters with can drawing image mode it is different, therefore, sent out in file system with aviation
Unique identification of the motivation+data source as catalogue.Mainly the monitoring attributes in the period are detected in system, therefore
By monitoring attributes, ageing is used as mark to key with treated.It is related to the flight of data when retrieving data in system
Stage and source, therefore in addition to monitoring value in value, it is also necessary to store mission phase and data origin information.
In distributed file system, storage model is as follows：
FileSystem={ ESNi+DATAFROMj| i=1,2 ... n, j=DFD, EHM, QAR }
ESN={ keyi, columnFamily | i=1,2 ... n }
ColumnFamily={ DataFrom, Flight_phase, Value }
Wherein ESN+DATAFROM is the unique identification of storage catalogue, that is, from the unique identification of engine, key
For rowKey, be made of monitoring attributes and acquisition time, columnFamily be main monitoring content, including data source, fly
Row order section and monitoring value.
Step 3：Aero-engine monitoring data is acquired, and carries out pretreatment processing；According to different data source, by flying
Machine engine information determines parsing template, parses effective performance monitoring data from file according to the concrete configuration of template.
The monitoring data source multiplicity of aero-engine, format is different, needs uniformly to be pre-processed before storage, generates just
Really effective uniform format can storing data.Need to obtain effective aircraft, engine, flight rank from collected data
Section, monitoring attributes and corresponding monitoring value, the three of mainstream kind file format is original message, manufacturer data and decoding number at present
According to.
Step 3 is main to be realized by following steps：
The primary format for parsing obtained data is listings format：
ModelList={ modelList }
Model={ Airplane, Engine, Date, Flight_phase, datafrom, paramList }
ParamListModel={ StandardParam, value }
Wherein ModelList is the list of all data, includes multiple modelList, and a modelList includes multiple
Airplane is airplane information in Model, Model, and Engine is engine information, and Date is acquisition time, will be as distribution
A part of formula file system key is stored, and Flight_phase is mission phase information, and a model includes one
ParamList, paramList are monitoring information list, and the model of paramList is paramListModel,
ParamListModel includes details, and StandardParam is monitoring attributes, and value is monitoring value.
Step 4：By after parsing operable data processing after formed key and value, bind key and value, store to
The corresponding file directory of engine；
Because mainly being detected to the monitoring attributes in the period in system, must include in the information of key
Monitoring attributes and temporal information, because of an engine, a time point, it is primary to collect monitoring attributes, also energy
Enough ensure the uniqueness of key；The unique identification name length of StandardParam be it is indefinite, the more accurate acquisition time the better, because
This is accurate to the second, and being converted to convenient for the time format of operation is 14, such as 2018-04-2708:08:08 is converted to and can operate
Time is 20180427080808, and length is 14.
It is related to mission phase and the source of data when retrieving data in system, therefore in addition to monitoring value in value,
Also need to fly brief and source information.Because mission phase and source are used merely as the data retrieved to be checked,
It is not intended as search condition, therefore is only used as value and is stored, mission phase and data source uniformly store respective unique
Identify ID.
After key and value combination, obtained data model is：
ModelList={ modelList }
Model={ ESN+DATAFROM, key, paramList }
Key={ StandardParam+date }
ParamListModel={ Flight_phase, datafrom, value }
Wherein ModelList is the list of all data, includes multiple modelList, and a modelList includes multiple
ESN is engine unique identification information in Model, Model, and key is line unit, is made of monitoring attributes and time, and value is key
Value, is made of mission phase, data source and monitoring value.
It according to Engine, stores data into corresponding file, first detection catalogue whether there is first, if do not deposited
It is needing to create directory, then carry out data storage, if had existed, is directly carrying out data storage, process is as follows：
If(Exist(ESN+DATAFROM))
Then Save(Engine,ModelList)
Else create (ESN+DATAFROM), Save (Engine, ModelList)
Step 5：Designer trends analysis rule；Monitoring attributes can be observed from different perspectives by different icon modes
Long-term change trend realizes multi-level simulation tool to monitoring attributes；
Trend analysis rule model is as follows：
AnalyzeRule=ID, datafrom, esngroup, linestyle, paramgroup, datarange,
esnarray,flightarray,paramList}
Wherein ID is the unique identification of trend analysis rule, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination；
Datafrom is data source, and DFD, EHM, QAR may be selected at present, and esngroup is engine packet mode, can
It selects single-shot, double hairs and owns；If selecting single-shot, the Long-term change trend of single engine is drawn together.If selection
Double hairs are then to draw the Long-term change trend of two engines on an airplane together.It is will own if selection is all
The Long-term change trend of engine is drawn together.
Linestyle is plotting mode, and scatter plot and line chart may be selected；
Paramgroup is parameter packet mode, may be selected to concentrate to draw and draw with independent；If drawn in choice set,
The Long-term change trend of all monitoring parameters is drawn together.It is drawn if selection is independent, by the Long-term change trend list of monitoring parameter
Solely draw.
Datarange is data area, and section, recent, self loader may be selected；If selecting section, needs to be arranged and open
Begin time and end time.If selection is in the recent period, nearest number of days is set.If selection self loader, system default be from
Engine installation starts to be analyzed.
Esnarray is the engine list that carry out trend analysis；
ParamList is the parameter information of trend analysis, and title, the reference axis of flag parameters information, the title of parameter can
Think time and monitoring attributes, it can carry out the multiple combinations trend analysis of parameter VS time and parameter VS parameter.
Step 6：Carry out simple trend analysis；, parameter packet mode difference different according to engine packet mode, will be final
Result set be combined into iconic model according to regular group that trend analysis is set, and be shown；
Since X-coordinate axle can be the time, or monitoring parameter, therefore the mode for obtaining data is different, below according to
It is secondary to be illustrated.
X, X2 axis is the time, and Y, Y2 axis are monitoring parameter：The time started is obtained at the end of by datarange first
Between, the monitoring parameter information of circulation Y, Y2 axis obtains phase according to the unique identification of parameter information and time started and end time
The monitoring value of period is answered, method is as follows：
Resultscan0=scan (ESN, paramid0+starttime,paramid0+endtime)
...
Resultscann=scan (ESN, paramidn+starttime,paramidn+endtime)
Recycle resultscan0…resultscann, the value from result set handles result set again, final to carry out
The data model of trend analysis is identical, as follows：
Map<esn,Map<paramname,parambean>>
Parambean={ esn, datetime, value, sublabel, datafrom }
Wherein esn is motor number, and paramname is monitoring parameter title, and datetime is the time, and value is monitoring
Parameter value.Sublabel is mission phase, and datafrom is data source.
X, X2 axis is monitoring parameter, and Y, Y2 axis are monitoring parameter：Time started and end are obtained by datarange first
Time recycles the monitoring parameter information of X, X2, Y, Y2 axis, according to the unique identification of parameter information and time started at the end of
Between, the monitoring value of corresponding period is obtained, method is as follows：
Resultscan0=scan (ESN, paramid0+starttime,paramid0+endtime)
...
Resultscann=scan (ESN, paramidn+starttime,paramidn+endtime)
Recycle resultscan0…resultscann, point X and Y at the same time is taken to have the result of value from result set.
Result set is handled again, the final data model for carrying out trend analysis is identical, as follows：
Map<esn,Map<paramname,paramparambean>>
Paramparambean={ esn, datetime, xvalue, yvalue, sublabel, datafrom }
Wherein esn is motor number, and paramname is monitoring parameter title, and datetime is the time, and xvalue is X-axis
Monitoring parameter value, yvalue are Y-axis monitoring parameter value.Sublabel is mission phase, and datafrom is data source.
According to data source difference, the method for trend analysis is different, the data one of data and producer from original message
As according to single engine short-term or long-term trend variation analyzed, the general basis of the monitoring data from decoding data
Flight or aircraft are analyzed according to the time or with the short-term trend variation of flight, pass through the time range and prison of setting
Control object retrieves corresponding performance monitoring data from distributed file system, after being analyzed and being combined, the mode of an icon
Carry out trend displaying, the intuitive Long-term change trend for embodying monitored object.
Step 7：Carry out integrative trend analysis；By the aircraft flight data of increment management in system, can by message data,
Manufacturer data and decoding data are associated extraction by flight time, Flight Information and airplane information；
By the aircraft flight data of increment management in system, message data, manufacturer data and decoding data can be passed through
Flight time, Flight Information and airplane information are associated extraction.
First according to ACARS (aircraft communication addressing and reporting system) flight data, time of flight data and airplane information
Trend analysis rule is established, trend analysis rule model is as follows：
AnalyzeRule=ID, DFD, esngroup, linestyle, paramgroup, datarange,
esnarray,flightarray,paramList}
Wherein ID is the unique identification of trend analysis rule, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination；
DFD is defaulted as Datafrom；
Esngroup is engine packet mode, and single-shot, double hairs may be selected and own, will be single if selection single-shot
The Long-term change trend of engine is drawn together, if the double hairs of selection, are to become the trend of two engines on an airplane
Change and draw together, is to draw the Long-term change trend of all engines together if selection is all；
Linestyle is plotting mode, and scatter plot and line chart may be selected；
Paramgroup is parameter packet mode, may be selected to concentrate to draw and draw with independent, if drawn in choice set,
The Long-term change trend of all monitoring parameters is drawn together, is drawn if selection is independent, by the Long-term change trend list of monitoring parameter
Solely draw；
Datarange is data area, and section, recent, self loader may be selected, if selection section, needs to be arranged and open
Begin time and end time, if selection is in the recent period, nearest number of days be set, if selection self loader, system default be from
Engine installation starts to be analyzed；
Esnarray is the engine list that carry out trend analysis；
Flightarray is flight list；
ParamList is the parameter information of trend analysis, and title, the reference axis of flag parameters information, the title of parameter can
Think time and monitoring attributes, it can carry out the multiple combinations trend analysis of parameter VS time and parameter VS parameter；
Then the data source for directly modifying ACARS trend-analyzing model is EHM, then carries out the trend point of relevant parameter
Analysis；The data source that finally can directly modify trend-analyzing model is QAR, carries out the trend analysis of QAR data；It establishes primary
ACARS data trend analysis model behind switch data source, carries out the hair of the engine trend data analysis and QAR data of EHM data
Motivation trend analysis, the long-time variation of observable single-point variation tendency, treated data variation trend and single-point original point
Trend.
When the engine trend data analysis variation by a kind of data source observes abnormal, it can directly pass through time, boat
Class information association to the engine other two kinds of data sources Long-term change trend, message data general record initial data, and
And data volume is small, the data of the data and cruising phase of general only record takeoff phase, if observed by message data
It is abnormal, the decoding data of the flight can be inquired by the Flight Information of message accounting, decoding data records flight in detail
Various parameters, frequency are generally 1 second one group of data, and all data accuracies and short time variation are more clear, convenient for full side
The Long-term change trend of position observation engine.
Only as described above, only specific embodiments of the present invention, when the model that cannot be limited the present invention with this and implement
It encloses, therefore the displacement of its equivalent assemblies, or according to equivalent changes and modifications made by the invention patent protection scope, should still belong to this hair
The scope that bright claims are covered.
Claims (8)
1. a kind of comprehensive performance trend analysis of Aviation engine, which is characterized in that include the following steps：
Step 1：The typing of basic data in relevant database；Basic data includes type of airplane, aircraft type, aircraft note
Volume, engine type, engine model, engine registration, aircraft flight data, data source, mission phase, monitoring attributes,
Original message parses template, manufacturer data parsing template, decoding data and parses template；
Step 2：Design distributed file system model；Depositing for engine performance monitoring data is realized using distributed file system
Storage, performance monitoring data includes original message, manufacturer data and decoding data, the engine performance monitoring data after parsing, root
According to the daily performance trend analysis rule of engine, using monitoring engine object and data source as distributed file system
File name, using monitoring attributes and monitoring period as the key value of column, using data source, mission phase and monitoring value as column
Value value stored；
Step 3：Aero-engine monitoring data is acquired, and carries out pretreatment processing；According to different data source, sent out by aircraft
Motivation information determines parsing template, parses effective performance monitoring data from file according to the concrete configuration of template；
Step 4：Key and value will be formed after operable data processing after parsing, binds key and value, store to starting
The corresponding file directory of machine；
Step 5：Designer trends analysis rule；The trend of monitoring attributes can be observed from different perspectives by different icon modes
Variation realizes multi-level simulation tool to monitoring attributes；
Step 6：Carry out simple trend analysis；, parameter packet mode difference different according to engine packet mode, by final knot
Fruit collection is combined into iconic model according to regular group that trend analysis is set, and is shown；
Step 7：Carry out integrative trend analysis；It, can be by message data, producer by the aircraft flight data of increment management in system
Data and decoding data are associated extraction by flight time, Flight Information and airplane information.
2. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 1 is main to be realized by following steps：
Type of airplane model is as follows：
ACTYPE={ ID, actype }
Wherein ID is the unique identification of type of airplane, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Actype is the title of type of airplane, this model is used to the airplane type information of nonproductive poll aircraft；
Aircraft type model is as follows：
ADMODEL={ ID, acmodel }
Wherein ID is the unique identification of aircraft type, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Acmodel is the title of aircraft type, this model is used to the aircraft type information of nonproductive poll aircraft；
It is as follows that aircraft registers model：
Airplane={ ID, basicInfo }
Wherein ID is the globally unique identifier of aircraft, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
BasicInfo is aircraft essential information, an aircraft at least two engines, and engine can constantly change, this model
For the aircraft engine machine information of nonproductive poll different periods；
Engine type model is as follows：
ENTYPE={ ID, entype }
Wherein ID is the unique identification of engine type, and length must be 36 bit digitals, upper and lower case letter and strigula combination；
Entype is the title of engine type, this model is used to the engine type information of nonproductive poll engine；
Engine model model is as follows：
ENMODEL={ ID, enmodel }
Wherein ID is the unique identification of engine model, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
Enmodel is the title of engine model, this model is used to the engine model information of nonproductive poll engine；
It is as follows that engine registers model：
ENGINE={ esn, pos, ac, basicInfo }
Wherein esn is the globally unique identifier of engine, as the subdirectory unique identification of distributed file system, pos conduct
The hair position of engine, ac are the aircraft where engine, and basicInfo is other essential informations of engine, this model is used to auxiliary
Help inquiry engine information；
Aircraft flight data model is as follows：
ACHIS={ ac, esn1, esn2, from, to, starttime, endtime, flightnum, basicinfo }
The motor number of position is sent out in the motor number that wherein ac is aircraft number, esn1 is 1 hair position, the position esn2 2, and from is the secondary flight
Origin, to be the secondary flight end place, starttime be this time flight at the beginning of, endtime be this time
The end time of flight, flightnum are the flight number of the secondary flight, and basicinfo is other essential informations of the secondary flight,
This model is used to the flying quality of nonproductive poll aircraft, is whereby associated the different data in source；
Data source model is as follows：
DATAFROM={ ID, name }
Wherein ID is the unique identification of data source, and length must be that 36 bit digitals, upper and lower case letter and strigula combine, as
A part of distributed storage file system table title, name be data source title, it is currently used value be DFD, EHM,
QAR；
Mission phase model is as follows：
FLIGHTPHASE={ ID, name }
Wherein ID is the unique identification of mission phase, and length must be that 36 bit digitals, upper and lower case letter and strigula combine, as
A part of distributed storage file system value, name are mission phase title；
Monitoring attributes model is as follows：
STANDARDPARAM={ name }
Unique identification of the name for monitoring attributes, expression monitoring attributes title, one as distributed storage file system key
Point；
It is as follows that original message parses template model：
DFDMODEL=ID, actype, flightphase, entype, dateformat, timeformat, xmlModel,
paramlist}
Wherein ID is the unique identification that original message parses template, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination, actype are applicable type of airplane, and flightphase is applicable mission phase, and entype is applicable engine
Type, dateformat are the date format in message, and timeformat is the time format in message, and xmlModel is template
Content, format are xml format；Paramlist is the monitoring attributes information of message, marks the title, length, hair of monitoring attributes
Whether position conversion coefficient, is monitoring parameter；
It is as follows that manufacturer data parses template model：
EHMMODEL=ID, flightphase, entype, datetimeformat, headerline, dataline,
esncolumn,datetimecolumn,accolumn,paramlist}
Wherein ID is the unique identification that manufacturer data parses template, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination, flightphase are applicable mission phase, and entype is applicable engine type, and datetimeformat is text
Time format in part, headerline are expert at by the header line in file, and dataline is the data starting row in file,
Esncolumn is motor number column in file, and datetimecolumn is time column in file, and acccolumn is
Aircraft number column in file, paramlist are the monitoring attributes information in file, mark title, the column name of monitoring attributes；
It is as follows that decoding data parses template model：
QARMODEL=ID, actype, entype, dateformat, timeformat, headerline, dataline,
esn1column,esn2column,datecolumn,timecolumn,accolumn,paramlist}
Wherein ID is the unique identification that decoding data parses template, and length must be 36 bit digitals, upper and lower case letter and strigula
Combination, actype are applicable type of airplane, and entype is applicable engine type, and dateformat is the date in data
Format, timeformat are the time format in data, and headerline is expert at by the header line in data, and dataline is
Data starting row in data, esn1column are 1 hair position motor number column in data, and esn2column is 2 in data
Position motor number column is sent out, datecolumn is date column, and timecolumn is time column in data,
Acccolumn is aircraft number column in data, and paramlist is the monitoring attributes information in data, marks monitoring attributes
Title, column name, affiliated engine.
3. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 2 is main to be realized by following steps：
In distributed file system, storage model is as follows：
FileSystem={ ESNi+DATAFROMj| i=1,2 ... n, j=DFD, EHM, QAR }
ESN={ keyi, columnFamily | i=1,2 ... n }
ColumnFamily={ DataFrom, Flight_phase, Value }
Wherein ESN+DATAFROM is the unique identification of storage catalogue, that is, from the unique identification of engine, key is
RowKey is made of monitoring attributes and acquisition time, and columnFamily is main monitoring content, including data source, flight
Stage and monitoring value.
4. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 3 is main to be realized by following steps：
The primary format for parsing obtained data is listings format：
ModelList={ modelList }
Model={ Airplane, Engine, Date, Flight_phase, datafrom, paramList }
ParamListModel={ StandardParam, value }
Wherein ModelList is the list of all data, includes multiple modelList, and a modelList includes multiple
Airplane is airplane information in Model, Model, and Engine is engine information, and Date is acquisition time, will be as distribution
A part of formula file system key is stored, and Flight_phase is mission phase information, and a model includes one
ParamList, paramList are monitoring information list, and the model of paramList is paramListModel,
ParamListModel includes details, and StandardParam is monitoring attributes, and value is monitoring value.
5. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 4 is main to be realized by following steps：
After key and value combination, obtained data model is：
ModelList={ modelList }
Model={ ESN+DATAFROM, key, paramList }
Key={ StandardParam+date }
ParamListModel={ Flight_phase, datafrom, value }
Wherein ModelList is the list of all data, includes multiple modelList, and a modelList includes multiple
ESN is engine unique identification information in Model, Model, and key is line unit, is made of monitoring attributes and time, and value is key
Value, is made of mission phase, data source and monitoring value；
It according to Engine, stores data into corresponding file, first detection catalogue whether there is first, if it does not, needing
It creaties directory, then carries out data storage, if had existed, directly carry out data storage, process is as follows：
If(Exist(ESN+DATAFROM))
Then Save(Engine,ModelList)
Else create (ESN+DATAFROM), Save (Engine, ModelList).
6. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 5 is main to be realized by following steps：
Trend analysis rule model is as follows：
AnalyzeRule=ID, datafrom, esngroup, linestyle, paramgroup, datarange,
esnarray,flightarray,paramList}
Wherein ID is the unique identification of trend analysis rule, and length must be 36 bit digitals, upper and lower case letter and strigula combination；
Datafrom is data source, and DFD, EHM, QAR may be selected at present, and esngroup is engine packet mode, be may be selected
Single-shot, double hairs and all；
Linestyle is plotting mode, and scatter plot and line chart may be selected；
Paramgroup is parameter packet mode, may be selected to concentrate to draw and draw with independent；
Datarange is data area, and section, recent, self loader may be selected；
Esnarray is the engine list that carry out trend analysis；
ParamList is the parameter information of trend analysis, and the title of title, the reference axis of flag parameters information, parameter can be
Time and monitoring attributes.
7. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 6 is main to be realized by following steps：
Since X-coordinate axle can be the time, or monitoring parameter, therefore the mode for obtaining data is different, next coming in order into
Row explanation；
X, X2 axis is the time, and Y, Y2 axis are monitoring parameter：Starting and end time is obtained by datarange first, is followed
The monitoring parameter information of ring Y, Y2 axis, according to the unique identification of parameter information and time started and end time, when obtaining corresponding
Between section monitoring value, method is as follows：
Resultscan0=scan (ESN, paramid0+starttime,paramid0+endtime)
...
Resultscann=scan (ESN, paramidn+starttime,paramidn+endtime)
Recycle resultscan0…resultscann, result set handles again, finally carries out trend by the value from result set
The data model of analysis is identical, as follows：
Map<esn,Map<paramname,parambean>>
Parambean={ esn, datetime, value, sublabel, datafrom }
Wherein esn is motor number, and paramname is monitoring parameter title, and datetime is the time, and value is monitoring parameter
Value, Sublabel is mission phase, and datafrom is data source；
X, X2 axis is monitoring parameter, and Y, Y2 axis are monitoring parameter：The time started is obtained at the end of by datarange first
Between, the monitoring parameter information of X, X2, Y, Y2 axis is recycled, according to the unique identification of parameter information and time started and end time,
The monitoring value of corresponding period is obtained, method is as follows：
Resultscan0=scan (ESN, paramid0+starttime,paramid0+endtime)
...
Resultscann=scan (ESN, paramidn+starttime,paramidn+endtime)
Recycle resultscan0…resultscann, taken from result set point X and Y at the same time have value as a result, will knot
Fruit collection is handled again, and the final data model for carrying out trend analysis is identical, as follows：
Map<esn,Map<paramname,paramparambean>>
Paramparambean={ esn, datetime, xvalue, yvalue, sublabel, datafrom }
Wherein esn is motor number, and paramname is monitoring parameter title, and datetime is the time, and xvalue is X-axis monitoring
Parameter value, yvalue are Y-axis monitoring parameter value, and Sublabel is mission phase, and datafrom is data source.
8. the comprehensive performance trend analysis of Aviation engine according to claim 1, it is characterised in that described
Step 7 is main to be realized by following steps：
Trend analysis rule, trend analysis rule are established according to ACARS flight data, time of flight data and airplane information first
Model is as follows：
AnalyzeRule=ID, DFD, esngroup, linestyle, paramgroup, datarange, esnarray,
flightarray,paramList}
Wherein ID is the unique identification of trend analysis rule, and length must be that 36 bit digitals, upper and lower case letter and strigula combine,
DFD is defaulted as Datafrom, and esngroup is engine packet mode, and linestyle is plotting mode, and paramgroup is ginseng
Number packet mode, datarange is data area, and esnarray is the engine list that carry out trend analysis,
Flightarray is flight list, and paramList is the parameter information of trend analysis, the title of flag parameters information, coordinate
Axis.
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