CN109830001A - A kind of Data Quality Assessment Methodology and device - Google Patents
A kind of Data Quality Assessment Methodology and device Download PDFInfo
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- CN109830001A CN109830001A CN201910063005.1A CN201910063005A CN109830001A CN 109830001 A CN109830001 A CN 109830001A CN 201910063005 A CN201910063005 A CN 201910063005A CN 109830001 A CN109830001 A CN 109830001A
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Abstract
The embodiment of the invention provides a kind of Data Quality Assessment Methodology and devices, it is related to field of computer technology, the complexity to data quality accessment can be reduced, embodiments herein includes: the flying quality for obtaining aircraft, wherein, flying quality is used to indicate the state of flight of aircraft.Then the corresponding qualitative attribute parameter of each qualitative attribute and the corresponding weight coefficient of each qualitative attribute of flying quality are determined.Further according to the corresponding weight coefficient of each qualitative attribute, summation is weighted to each qualitative attribute parameter, determines the assessment result to flying quality.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of Data Quality Assessment Methodology and device.
Background technique
Wide area aerial surveillance systems are the systems for monitoring flight status.Wide area aerial surveillance systems are received by radar
Then the flight information of aircraft positions the position of aircraft, according to the flight information of aircraft to ensure flight safety.
It can be seen that the height of the flight information quality of aircraft has important shadow to the processing and analysis of monitoring system
It rings.The data of high quality enable to the processing result of system with a high credibility.And low-quality data not only allow system processing to be tied
Fruit is with a low credibility, it is also possible to influence flight safety.And in the prior art, it generally can use machine learning techniques combination nerve net
Network assesses the quality of data, however needs to pre-select training sample in this way, with training sample training neural network,
Trained neural network is determined as neural network model, neural network model just can be used later, the quality of data is carried out
Assessment realizes that process is complex.
Summary of the invention
The embodiment of the present invention is designed to provide a kind of Data Quality Assessment Methodology and device, is reduced with realizing to data
The complexity of quality evaluation.Specific technical solution is as follows:
In a first aspect, providing a kind of Data Quality Assessment Methodology, which comprises
Obtain the flying quality of aircraft；The flying quality is used to indicate the state of flight of aircraft；
Determine the corresponding qualitative attribute parameter of each qualitative attribute of the flying quality；
Determine the corresponding weight coefficient of each qualitative attribute；
According to the corresponding weight coefficient of each qualitative attribute, summation is weighted to each qualitative attribute parameter, is determined to described
The assessment result of flying quality.
Optionally, the flying quality for obtaining aircraft, comprising:
It obtains in preset time period, the flying quality for the aircraft that radar scanning is arrived；The flying quality includes: aircraft mark
Knowledge, the horizontal position of aircraft, aircraft height and position and timestamp.
Optionally, the corresponding qualitative attribute parameter of each qualitative attribute of the determination flying quality, comprising:
Determine the corresponding integrity parameters of the flying quality, repeated parameter, rough error parameter, horizontal position turnover rate,
One of data item update accuracy, aircraft identity accuracy, height accuracy and position Loss Rate or multiple combinations；
The integrity parameters are as follows: meet the data bulk and the flying quality of integrity demands in the flying quality
The ratio of total amount；
The repeatability parameter are as follows: the ratio of duplicate data bulk and the flying quality total amount in the flying quality
Value；
The rough error parameter are as follows: include that the data bulk beyond preset range meets completely with described in the flying quality
Property require data bulk ratio；
The horizontal position turnover rate are as follows: the timeslice quantity and timeslice in the flying quality including horizontal position are total
The ratio of amount；The timeslice is to scan in preset time period for indicating specified time length, the timeslice total amount
The sum of timeslice quantity locating for the flying quality of each aircraft；
The data item update accuracy are as follows: include the timeslice quantity of at least one correct data in the flying quality
With the ratio of the timeslice quantity including horizontal position；
The aircraft identity accuracy are as follows: in the flying quality including correct aircraft identification data bulk with include fly
The ratio of the data bulk of machine mark；
The height accuracy are as follows: data bulk in the flying quality including correct height item with include height item
The ratio of data bulk；
The position Loss Rate are as follows: within a preset time interval do not include the timeslice quantity of any parameter in flying quality
With the ratio of the timeslice total amount.
Optionally, the corresponding weight coefficient of each qualitative attribute of the determination, comprising:
Determine that the corresponding attribute ratings of each qualitative attribute, the attribute ratings are used to reflect the significance level of qualitative attribute；
According to the corresponding attribute ratings of each qualitative attribute, the corresponding weight coefficient of each qualitative attribute is calculated separately；Quality
The weight of attribute attribute ratings corresponding with qualitative attribute are negatively correlated.
Optionally, the corresponding attribute ratings of each qualitative attribute of the determination, comprising:
According to the corresponding relationship of preset qualitative attribute corresponding qualitative attribute parameter and attribute ratings, each quality category is determined
The corresponding attribute ratings of property；Alternatively,
According to the corresponding relationship of preset qualitative attribute and attribute ratings, the corresponding attribute ratings of each qualitative attribute are determined.
Second aspect, provides a kind of data quality accessment device, and described device includes:
Module is obtained, for obtaining the flying quality of aircraft；The flying quality is used to indicate the state of flight of aircraft；
Determining module, the corresponding quality of each qualitative attribute of the flying quality for determining the acquisition module acquisition
Property parameters；Determine the corresponding weight coefficient of each qualitative attribute；According to the corresponding weight coefficient of each qualitative attribute, to each quality category
Property parameter be weighted summation, determine the assessment result to the flying quality.
Optionally, the acquisition module, is specifically used for:
It obtains in preset time period, the flying quality for the aircraft that radar scanning is arrived；The flying quality includes: aircraft mark
Knowledge, the horizontal position of aircraft, aircraft height and position and timestamp.
Optionally, the determining module, is specifically used for:
Determine the corresponding integrity parameters of the flying quality, repeated parameter, rough error parameter, horizontal position turnover rate,
One of data item update accuracy, aircraft identity accuracy, height accuracy and position Loss Rate or multiple combinations；
The integrity parameters are as follows: meet the data bulk and the flying quality of integrity demands in the flying quality
The ratio of total amount；
The repeatability parameter are as follows: the ratio of duplicate data bulk and the flying quality total amount in the flying quality
Value；
The rough error parameter are as follows: include that the data bulk beyond preset range meets completely with described in the flying quality
Property require data bulk ratio；
The horizontal position turnover rate are as follows: the timeslice quantity and timeslice in the flying quality including horizontal position are total
The ratio of amount；The timeslice is to scan in preset time period for indicating specified time length, the timeslice total amount
The sum of timeslice quantity locating for the flying quality of each aircraft；
The data item update accuracy are as follows: include the timeslice quantity of at least one correct data in the flying quality
With the ratio of the timeslice quantity including horizontal position；
The aircraft identity accuracy are as follows: in the flying quality including correct aircraft identification data bulk with include fly
The ratio of the data bulk of machine mark；
The height accuracy are as follows: data bulk in the flying quality including correct height item with include height item
The ratio of data bulk；
The position Loss Rate are as follows: within a preset time interval do not include the timeslice quantity of any parameter in flying quality
With the ratio of the timeslice total amount.
Optionally, the determining module, is specifically used for:
Determine that the corresponding attribute ratings of each qualitative attribute, the attribute ratings are used to reflect the significance level of qualitative attribute；
According to the corresponding attribute ratings of each qualitative attribute, the corresponding weight coefficient of each qualitative attribute is calculated separately；The power of qualitative attribute
Weight attribute ratings corresponding with qualitative attribute are negatively correlated.
Optionally, the determining module, is specifically used for:
According to the corresponding relationship of preset qualitative attribute corresponding qualitative attribute parameter and attribute ratings, each quality category is determined
The corresponding attribute ratings of property；Alternatively, determining each qualitative attribute pair according to the corresponding relationship of preset qualitative attribute and attribute ratings
The attribute ratings answered.
The third aspect, provides a kind of electronic equipment, the electronic equipment include processor, communication interface, memory and
Communication bus, wherein processor, communication interface, memory complete mutual communication by communication bus；
Memory, for storing computer program；
Processor when for executing the program stored on memory, realizes any of the above-described data quality accessment
Method and step.
Fourth aspect, it is described computer-readable to deposit the embodiment of the invention also provides a kind of computer readable storage medium
Computer program is stored in storage media, the computer program realizes any of the above-described data matter when being executed by processor
Measure appraisal procedure step.
5th aspect, the embodiment of the invention also provides a kind of computer program products comprising instruction, when it is being calculated
When being run on machine, so that computer executes any of the above-described Data Quality Assessment Methodology step.
A kind of Data Quality Assessment Methodology and device provided in an embodiment of the present invention, can according to the flying quality of aircraft,
It determines the corresponding qualitative attribute parameter of each qualitative attribute, then determines the corresponding weight coefficient of each qualitative attribute.Further according to each matter
The corresponding weight coefficient of attribute is measured, summation is weighted to each qualitative attribute parameter, determines the assessment result of flying quality.Due to
The embodiment of the present invention can directly determine the flying quality quality of aircraft, not need to train neural network in advance.So of the invention
Embodiment can reduce the complexity to data quality accessment.
Certainly, implement any of the products of the present invention or method it is not absolutely required at the same reach all the above excellent
Point.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of Data Quality Assessment Methodology flow chart provided in an embodiment of the present invention；
Fig. 2 is a kind of timeslice illustrative diagram provided in an embodiment of the present invention；
Fig. 3 is a kind of exemplary pie chart of data total evaluation result provided in an embodiment of the present invention；
Fig. 4 is a kind of exemplary column diagram of data assessment result provided in an embodiment of the present invention；
Fig. 5 is a kind of exemplary pie chart of data assessment result provided in an embodiment of the present invention；
Fig. 6 is the exemplary pie chart of another data assessment result provided in an embodiment of the present invention；
Fig. 7 is a kind of data quality accessment apparatus structure schematic diagram provided in an embodiment of the present invention；
Fig. 8 is the structural schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, Fig. 1 is a kind of Data Quality Assessment Methodology flow chart provided in an embodiment of the present invention, is applied to electronics
Equipment.Wherein, the electronic equipment in the embodiment of the present invention can be the electronic equipments such as mobile phone, tablet computer or computer.The party
Method includes the following steps:
Step 101, the flying quality of aircraft is obtained.
Wherein, flying quality is used to indicate the state of flight of aircraft.
In a kind of embodiment, obtain in preset time period, the flying quality for the aircraft that radar scanning is arrived.Flying quality packet
Include: aircraft identification, the horizontal position of aircraft, aircraft height and position and timestamp.Wherein, timestamp can get for radar
At the time of flying quality.
Step 102, the corresponding qualitative attribute parameter of each qualitative attribute of flying quality is determined.
In a kind of embodiment, the corresponding integrity parameters of flying quality, repeatability ginseng can be determined according to actual needs
Number, rough error parameter, horizontal position turnover rate, data item update accuracy, aircraft identity accuracy, height accuracy and position are lost
One of mistake rate or multiple combinations.
Wherein, integrity parameters are as follows: meet the data bulk and flying quality total amount of integrity demands in flying quality
Ratio.
Incomplete property parameter=1- integrity parameters.
In embodiments of the present invention, the flying quality for meeting integrity demands refers to that each data item does not lack, and every number
All meet the data of parameter format standard according to parameter.
Repeated parameter are as follows: the ratio of duplicate data bulk and flying quality total amount in flying quality.
Non-repeatability parameter=1- repeatability parameter.
Such as: radar scanning to flying quality include aircraft identification, the horizontal position of aircraft, aircraft height and position and
Timestamp if scanning is to 10 datas altogether, and has the aircraft identification of 2 datas, the horizontal position of aircraft in this 10 data
Set, the height and position of aircraft it is identical with timestamp, then using wherein 1 data in this two identical datas as duplicate
Data, the repeated parameter of flying quality are as follows: 0.1.
Rough error parameter are as follows: include the data bulk for exceeding preset range and the data for meeting integrity demands in flying quality
The ratio of quantity.
Non- rough error parameter=1- rough error parameter.
Such as: the scanning range of radar is 300 kms to 370 kms, if the horizontal position of the aircraft of the radar scanning is super
Cross this range, then it represents that the data exceed preset range.
Horizontal position turnover rate are as follows: the ratio of timeslice quantity and timeslice total amount in flying quality including horizontal position
Value；Timeslice is the flight number of each aircraft scanned in preset time period for indicating specified time length, timeslice total amount
According to locating the sum of timeslice quantity.
As shown in Fig. 2, radar gets the data of aircraft A from 000 moment for the first time, got for the first time at 002 moment
The data of aircraft B, a determining timeslice every 4 seconds.If preset time period be 000 moment to 016 moment, aircraft A altogether pair
4 timeslices are answered, aircraft B corresponds to 4 timeslices altogether.If only the data of this two airplane, timeslice are arrived in scanning in this 16 seconds
Total amount is 8.If preset time period is 000 moment to 013 moment, aircraft A corresponds to 4 timeslices altogether, and aircraft B corresponds to 3 altogether
A timeslice.If only the data of this two airplane are arrived in scanning in this 13 seconds, timeslice total amount is 7.
The non-turnover rate in the horizontal position=horizontal position 1- turnover rate.
It should be noted that in the ideal case, scanning and arriving within a scan period including radar in each timeslice
An airplane a data, timeslice total amount=flying quality total amount at this time.But when it is present between include radar in piece
When a plurality of data of the airplane scanned within a scan period (such as: it include two identical numbers in timeslice
According to), timeslice total amount < flying quality total amount.When not including data in piece between when it is present, timeslice total amount > flying quality is total
Amount.
Height accuracy are as follows: the data bulk in flying quality including correct height item and the data bulk including height item
Ratio.
Height error rate=1- height accuracy.
Illustratively, judge whether the height item of the data in flying quality is correct, it can be in the following manner: if
It include: A (aircraft identification), [116 ° of E, 40 ° of N] (horizontal position of aircraft), 5600 (height of aircraft in the data being currently judged
Spend position) and 0060 (timestamp).At the time of available timestamp indicates before 0060, calculating includes A in flying quality
Data aircraft height and position average value, according to the height of the height and position of the aircraft for the data being currently judged and aircraft
The difference of position mean judges whether the height item of the aircraft for the data being currently judged is correct.
Such as: by height and position from 600 kms to 8400 kms, one height layer of every 500 meters of divisions.Assuming that current
The height and position of the aircraft for the data being judged is 5600 kms, at the time of current time stamp indicates before data in aircraft
Height and position average value is 5500 kms, belongs to height layer range are as follows: 5400 kms to 5900 kms, then 5600 kms belong to this
Within height layer range, so the height item of the aircraft for the data being currently judged is correct.
Data item update accuracy are as follows: in flying quality include at least one of correct data timeslice quantity with include water
The ratio for the timeslice quantity that prosposition is set.
Data item update error rate=1- data item update accuracy.
Illustratively, correct data can be correct altitude information.
Aircraft identity accuracy are as follows: data bulk in flying quality including correct aircraft identification with include aircraft identification
The ratio of data bulk.
Aircraft identity error rate=1- aircraft identity accuracy.
In embodiments of the present invention, if in flying quality in the corresponding two adjacent datas of same aircraft identification, the time
It stabs difference and is greater than preset threshold, then it represents that the aircraft identification of the later flying quality of timestamp is incorrect.
Illustratively, in the timeslice of the A aircraft of Fig. 2, each timeslice includes 4 seconds, and 000 receives A aircraft for the 1st time
The timestamp of data, 007 for receive for the 2nd time A aircraft data timestamp, 013 for receive for the 3rd time A aircraft data when
Between stab.If preset threshold is 6 seconds, differ 7 seconds, is greater than preset threshold 6 seconds, so 007 corresponding aircraft between 000 and 007
It identifies incorrect.And 6 seconds are differed between 007 and 013, it is not more than preset threshold 6 seconds, so 013 corresponding aircraft identification is correct.
If radar only scans the data for having arrived A aircraft at 000 moment to 0016 moment, aircraft identity accuracy is 1/3.
Position Loss Rate are as follows: within a preset time interval do not include the timeslice quantity and timeslice total amount of any parameter
Ratio.The non-Loss Rate in the position=position 1- Loss Rate.
It is understood that being directed to same aircraft identification, within a preset time interval, it is corresponding that the aircraft identification is not scanned
Aircraft horizontal position and aircraft height and position, then it represents that lose the corresponding aircraft position of the aircraft identification.Wherein, it presets
Time interval can be 1,2 or 3.1 radar scanning period.
The timestamp of the data of A aircraft is received for the 1st time as shown in Fig. 2, in the timeslice of the A aircraft of Fig. 2,000,007
The timestamp of the data of A aircraft is received for the 2nd time, 013 receives the timestamp of the data of A aircraft for the 3rd time.Assuming that preset time
Between be divided into 1 radar scanning period (i.e. prefixed time interval be 4 seconds), although between 000 and 007 difference be greater than within 7 seconds it is default
Time interval 4 seconds, but in this 7 seconds do not include the timeslice for not including any parameter.And difference is 6 seconds big between 007 and 013
It in prefixed time interval 4 seconds, and include 1 (timeslice of A aircraft in Fig. 2 of the timeslice comprising any parameter in this 6 seconds
3).If radar only scans the data for having arrived A aircraft at 000 moment to 0016 moment, position Loss Rate is 0.25.
Step 103, the corresponding weight coefficient of each qualitative attribute is determined.
In a kind of embodiment, each quality can be determined according to the corresponding relationship of preset qualitative attribute and weight coefficient
The corresponding weight coefficient of attribute.
Step 104, according to the corresponding weight coefficient of each qualitative attribute, summation is weighted to each qualitative attribute parameter, really
The fixed assessment result to flying quality.
Illustratively, the corresponding qualitative attribute of flying quality and the corresponding qualitative attribute parameter of qualitative attribute are as follows: quality category
Property A=2, qualitative attribute B=5 and qualitative attribute C=3.The corresponding weight parameter of qualitative attribute A is 0.5, and qualitative attribute B is corresponding
Weight parameter be 0.2, the corresponding weight parameter of qualitative attribute C be 0.3.These three qualitative attribute parameters are weighted and are asked
With obtain: 2 × 0.5+5 × 0.2+3 × 0.3=2.9.
A kind of Data Quality Assessment Methodology provided in an embodiment of the present invention can determine each according to the flying quality of aircraft
The corresponding qualitative attribute parameter of qualitative attribute, then determines the corresponding weight coefficient of each qualitative attribute.Further according to each qualitative attribute
Corresponding weight coefficient is weighted summation to each qualitative attribute parameter, determines the assessment result of flying quality.Due to the present invention
Embodiment can directly determine the flying quality quality of aircraft, not need to train neural network in advance.So the embodiment of the present invention
It can reduce the complexity to data quality accessment.
Optionally, the method that above-mentioned steps 103 determine the corresponding weight coefficient of each qualitative attribute, may include following two
Step:
Step 1: the corresponding attribute ratings of each qualitative attribute are determined.Wherein, attribute ratings are used to reflect the weight of qualitative attribute
Want degree.Qualitative attribute higher grade, indicates that the qualitative attribute is more important for assessment flying quality.
It, can be according to pair of preset qualitative attribute corresponding qualitative attribute parameter and attribute ratings in a kind of embodiment
It should be related to, determine the corresponding attribute ratings of each qualitative attribute.
Optionally, ranking can be carried out to each qualitative attribute, will be arranged according to each qualitative attribute corresponding qualitative attribute parameter
Name result is determined as the corresponding attribute ratings of each qualitative attribute.
Such as: qualitative attribute A=2, qualitative attribute B=5 and the qualitative attribute C=3 of flying quality, then to these three quality
Attribute is ranked up according to the sequence of qualitative attribute parameter from small to large, is respectively as follows: qualitative attribute A, qualitative attribute C, quality category
Property B.So determine that the corresponding attribute ratings of qualitative attribute A are 1, the corresponding attribute ratings of qualitative attribute B are 3, C pairs of qualitative attribute
The attribute ratings answered are 2.
In embodiments of the present invention, each qualitative attribute is ranked up according to the sequence of qualitative attribute parameter from small to large,
It is that the influence of qualitative attribute in order to make qualitative attribute parameter smaller to assessment flying quality quality is bigger；Allow qualitative attribute parameter
Influence of the bigger qualitative attribute to assessment flying quality quality is smaller.
Optionally, from preset qualitative attribute parameter and can be belonged to according to the corresponding qualitative attribute parameter of each qualitative attribute
In the table of comparisons of property grade, the corresponding attribute ratings of each qualitative attribute are determined.
Such as: the table of comparisons of preset qualitative attribute parameter and attribute ratings is as shown in Table 1:
Table one
| Qualitative attribute parameter x | Attribute ratings |
| x≤3 | 1 |
| x>3 | 2 |
Assuming that the qualitative attribute A=2 of flying quality, qualitative attribute B=5 and qualitative attribute C=3, then qualitative attribute A is corresponding
Attribute ratings be 1, the corresponding attribute ratings of qualitative attribute B be 2, the corresponding attribute ratings of qualitative attribute C be 1.
In another embodiment, each matter can be determined according to the corresponding relationship of preset qualitative attribute and attribute ratings
Measure the corresponding attribute ratings of attribute.
Step 2: according to the corresponding attribute ratings of each qualitative attribute, the corresponding weight system of each qualitative attribute is calculated separately
Number.Wherein, the weight of qualitative attribute attribute ratings corresponding with qualitative attribute are negatively correlated.
In a kind of embodiment, the corresponding weight coefficient of each qualitative attribute can be calculated according to formula (1):
Wherein, WkFor the corresponding weight coefficient of k-th of qualitative attribute, S is attribute ratings total quantity, and N is to participate in assessment number
According to the qualitative attribute quantity of quality, SkFor the corresponding attribute ratings of k-th of qualitative attribute.
Illustratively, it is assumed that flying quality corresponding qualitative attribute A=0.9, qualitative attribute B=0.8, qualitative attribute C=
0.7, this three qualitative attributes are ranked up according to the sequence of qualitative attribute parameter from small to large, and ranking results are determined
For the corresponding attribute ratings of these three qualitative attributes, then the corresponding attribute ratings of qualitative attribute A are 3, the corresponding category of qualitative attribute B
Property grade be 2, the corresponding attribute ratings of qualitative attribute C be 1.According to formula (1), it is corresponding to calculate separately these three qualitative attributes
Weight coefficient:
In embodiments of the present invention, if determining the corresponding weight coefficient of each qualitative attribute, each quality category using formula (1)
Property grade need meet since grade 1, continuously and do not repeat.
As it can be seen that in embodiments of the present invention, can determine each quality category for assessing the quality of data according to actual needs
Property, and the corresponding attribute ratings of each qualitative attribute can also determine according to actual needs.So the embodiment of the present invention have compared with
Strong scalability.
In addition, Data Quality Assessment Methodology provided in an embodiment of the present invention can carry out off-line calculation, reality can also be carried out
When calculate, applied widely, calculating speed is fast.In real-time calculate, it is only necessary to limit preset time period, determine preset time period
The quality of data of interior flying quality.When getting the flying quality of radar scanning again, redefine in preset time period
Including flying quality determine the quality of the flying quality of the radar scanning using the flying quality redefined.As it can be seen that this
The Data Quality Assessment Methodology timeliness that inventive embodiments provide is strong.
It in embodiments of the present invention, can will be in the flying quality when getting the flying quality of radar scanning again
Timestamp be determined as finish time of preset time period and redefine preset time then according to the duration of preset time period
The flying quality for including in section.
Illustratively, it is obtained the embodiment of the invention also provides one group using data assessment method provided in an embodiment of the present invention
The assessment result of the corresponding flying quality of each radar obtained, assessment result are as shown in Table 2:
Table two
The 2nd row to the 30th row of table two respectively indicates each qualitative attribute parameter sum number of the flying quality of 29 radar scanning
According to quality, the flying quality of the data entirety of table two is that the flying quality of this 29 radar scannings is assumed to be to a radar scanning
Flying quality obtained from data, the corresponding qualitative attribute parameter of each qualitative attribute of data entirety be according to hypothesis this
What the flying quality of one radar scanning calculated.The quality of data of data entirety is joined according to each qualitative attribute of data entirety
Number determination.Each qualitative attribute parameter of data entirety is as shown in Figure 3.
In embodiments of the present invention, bad data parameter is the sum of the ratio data for not meeting 8 qualitative attributes in table two.
Such as: the sum of 8 qualitative attribute parameters of data entirety are 7.78877, then the bad data parameter of data entirety are as follows: 8-
7.78877=0.21123.
The flying quality quality that integrally can be seen that radar scanning from data is good, still, can not find out each radar difference
How is the flying quality quality of scanning.But it in embodiments of the present invention, can be commented for the flying quality of each radar scanning
Estimate the quality of data.
Figure 4, it is seen that No. 6 radars, No. 9 radars and No. 13 radars are not in use, No. 8 radars and No. 23 radars
The flying quality of scanning is second-rate, and the flying quality quality of remaining radar scanning is preferable.
Further, in 29 radars, the flying quality quality of No. 1 radar is 0.98350084, is highest scoring.1
Each qualitative attribute parameter of number radar is as shown in Figure 5.As can be seen from Figure 5 each quality of the flying quality of No. 1 radar scanning
Attribute is good, and bad data parameter is lower.
In 26 radars in use, it is lowest score that the flying quality quality of No. 8 radars, which is 0.30833364,.No. 8
Each qualitative attribute parameter of radar is as shown in Figure 6.From fig. 6 it can be seen that due to No. 8 radar scanning flying quality it is complete
Property is poor, causes other qualitative attributes poor, so that the flying quality quality of No. 8 radar scannings is lower.
Data Quality Assessment Methodology provided in an embodiment of the present invention can not only carry out rough comment to flying quality quality
Estimate (such as: utilize integrity parameters, rough error parameter, the quality of height accuracy and repeated parameter evaluation flying quality), also
Flying quality quality can further be assessed according to the semanteme between data (such as: it is updated using horizontal position
Rate, data item update accuracy, the quality of aircraft identity accuracy and position Loss Rate assessment flying quality).As it can be seen that compared to
The method of the coarse evaluation quality of data, the assessment result obtained using Data Quality Assessment Methodology provided in an embodiment of the present invention
Confidence level is higher.
Corresponding to above method embodiment, as shown in fig. 7, the embodiment of the invention provides a kind of data quality accessment dresses
It sets, is applied to electronic equipment, which includes: to obtain module 701 and determining module 702.
Module 701 is obtained, for obtaining the flying quality of aircraft；Flying quality is used to indicate the state of flight of aircraft；
Determining module 702, for determining the corresponding qualitative attribute of each qualitative attribute for obtaining the flying quality that module obtains
Parameter；Determine the corresponding weight coefficient of each qualitative attribute；According to the corresponding weight coefficient of each qualitative attribute, each qualitative attribute is joined
Number is weighted summation, determines the assessment result to flying quality.
Optionally, module 701 is obtained, can be specifically used for:
It obtains in preset time period, the flying quality for the aircraft that radar scanning is arrived；Flying quality includes: aircraft identification, flies
The horizontal position of machine, the height and position of aircraft and timestamp.
Optionally, determining module 702 can be specifically used for:
Determine the corresponding integrity parameters of flying quality, repeated parameter, rough error parameter, horizontal position turnover rate, data
Item updates one of accuracy, aircraft identity accuracy, height accuracy and position Loss Rate or multiple combinations；
Integrity parameters are as follows: meet the data bulk of integrity demands and the ratio of flying quality total amount in flying quality；
Repeated parameter are as follows: the ratio of duplicate data bulk and flying quality total amount in flying quality；
Rough error parameter are as follows: include the data bulk for exceeding preset range and the data for meeting integrity demands in flying quality
The ratio of quantity；
Horizontal position turnover rate are as follows: the ratio of timeslice quantity and timeslice total amount in flying quality including horizontal position
Value；Timeslice is the flight number of each aircraft scanned in preset time period for indicating specified time length, timeslice total amount
According to locating the sum of timeslice quantity；
Data item update accuracy are as follows: in flying quality include at least one of correct data timeslice quantity with include water
The ratio for the timeslice quantity that prosposition is set；
Aircraft identity accuracy are as follows: data bulk in flying quality including correct aircraft identification with include aircraft identification
The ratio of data bulk；
Height accuracy are as follows: the data bulk in flying quality including correct height item and the data bulk including height item
Ratio；
Position Loss Rate are as follows: in flying quality within a preset time interval comprising any parameter timeslice quantity and when
Between piece total amount ratio.
Optionally, determining module 702 can be specifically used for:
Determine that the corresponding attribute ratings of each qualitative attribute, attribute ratings are used to reflect the significance level of qualitative attribute；According to
The corresponding attribute ratings of each qualitative attribute, calculate separately the corresponding weight coefficient of each qualitative attribute；The weight of qualitative attribute with
The corresponding attribute ratings of qualitative attribute are negatively correlated.
Optionally, determining module 702 can be specifically used for:
According to the corresponding relationship of preset qualitative attribute corresponding qualitative attribute parameter and attribute ratings, each quality category is determined
The corresponding attribute ratings of property；Alternatively, determining each qualitative attribute pair according to the corresponding relationship of preset qualitative attribute and attribute ratings
The attribute ratings answered.
A kind of data quality accessment device provided in an embodiment of the present invention can determine each according to the flying quality of aircraft
The corresponding qualitative attribute parameter of qualitative attribute, then determines the corresponding weight coefficient of each qualitative attribute.Further according to each qualitative attribute
Corresponding weight coefficient is weighted summation to each qualitative attribute parameter, determines the assessment result of flying quality.Due to the present invention
Embodiment can directly determine the flying quality quality of aircraft, not need to train neural network in advance.So the embodiment of the present invention
It can reduce the complexity to data quality accessment.
The embodiment of the invention also provides a kind of electronic equipment, as shown in figure 8, include processor 801, communication interface 802,
Memory 803 and communication bus 804, wherein processor 801, communication interface 802, memory 803 are complete by communication bus 804
At mutual communication,
Memory 803, for storing computer program；
Processor 801 when for executing the program stored on memory 803, is realized in above method embodiment by electricity
The step of sub- equipment executes.
The communication bus that above-mentioned electronic equipment is mentioned can be Peripheral Component Interconnect standard (Peripheral Component
Interconnect, PCI) bus or expanding the industrial standard structure (Extended Industry Standard
Architecture, EISA) bus etc..The communication bus can be divided into address bus, data/address bus, control bus etc..For just
It is only indicated with a thick line in expression, figure, it is not intended that an only bus or a type of bus.
Communication interface is for the communication between above-mentioned electronic equipment and other equipment.
Memory may include random access memory (Random Access Memory, RAM), also may include non-easy
The property lost memory (Non-Volatile Memory, NVM), for example, at least a magnetic disk storage.Optionally, memory may be used also
To be storage device that at least one is located remotely from aforementioned processor.
Above-mentioned processor can be general processor, including central processing unit (Central Processing Unit,
CPU), network processing unit (Network Processor, NP) etc.；It can also be digital signal processor (Digital Signal
Processing, DSP), it is specific integrated circuit (Application Specific Integrated Circuit, ASIC), existing
It is field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete
Door or transistor logic, discrete hardware components.
In another embodiment provided by the invention, a kind of computer readable storage medium is additionally provided, which can
It reads to be stored with computer program in storage medium, the computer program realizes any of the above-described quality of data when being executed by processor
The step of appraisal procedure.
In another embodiment provided by the invention, a kind of computer program product comprising instruction is additionally provided, when it
When running on computers, so that computer executes any data method for evaluating quality in above-described embodiment.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When implemented in software, it can entirely or partly realize in the form of a computer program product.The computer program
Product includes one or more computer instructions.When loading on computers and executing the computer program instructions, all or
It partly generates according to process or function described in the embodiment of the present invention.The computer can be general purpose computer, dedicated meter
Calculation machine, computer network or other programmable devices.The computer instruction can store in computer readable storage medium
In, or from a computer readable storage medium to the transmission of another computer readable storage medium, for example, the computer
Instruction can pass through wired (such as coaxial cable, optical fiber, number from a web-site, computer, server or data center
User's line (DSL)) or wireless (such as infrared, wireless, microwave etc.) mode to another web-site, computer, server or
Data center is transmitted.The computer readable storage medium can be any usable medium that computer can access or
It is comprising data storage devices such as one or more usable mediums integrated server, data centers.The usable medium can be with
It is magnetic medium, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid state hard disk
Solid State Disk (SSD)) etc..
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
Each embodiment in this specification is all made of relevant mode and describes, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for device reality
For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method
Part explanation.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all
Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention
It is interior.
Claims (10)
1. a kind of Data Quality Assessment Methodology, which is characterized in that the described method includes:
Obtain the flying quality of aircraft；The flying quality is used to indicate the state of flight of aircraft；
Determine the corresponding qualitative attribute parameter of each qualitative attribute of the flying quality；
Determine the corresponding weight coefficient of each qualitative attribute；
According to the corresponding weight coefficient of each qualitative attribute, summation is weighted to each qualitative attribute parameter, is determined to the flight
The assessment result of data.
2. the method according to claim 1, wherein the flying quality for obtaining aircraft, comprising:
It obtains in preset time period, the flying quality for the aircraft that radar scanning is arrived；The flying quality includes: aircraft identification, flies
The horizontal position of machine, the height and position of aircraft and timestamp.
3. according to the method described in claim 2, it is characterized in that, each qualitative attribute of the determination flying quality is corresponding
Qualitative attribute parameter, comprising:
Determine the corresponding integrity parameters of the flying quality, repeated parameter, rough error parameter, horizontal position turnover rate, data
Item updates one of accuracy, aircraft identity accuracy, height accuracy and position Loss Rate or multiple combinations；
The integrity parameters are as follows: meet in the flying quality integrity demands data bulk and the flying quality total amount
Ratio；
The repeatability parameter are as follows: the ratio of duplicate data bulk and the flying quality total amount in the flying quality；
The rough error parameter are as follows: include that the data bulk beyond preset range is wanted with the integrality that meets in the flying quality
The ratio for the data bulk asked；
The horizontal position turnover rate are as follows: timeslice quantity and timeslice total amount in the flying quality including horizontal position
Ratio；The timeslice is each winged for what is scanned in preset time period for indicating specified time length, the timeslice total amount
The sum of timeslice quantity locating for the flying quality of machine；
The data item update accuracy are as follows: include the timeslice quantity and institute of at least one correct data in the flying quality
State the ratio of the timeslice quantity including horizontal position；
The aircraft identity accuracy are as follows: in the flying quality including correct aircraft identification data bulk with include aircraft mark
The ratio of the data bulk of knowledge；
The height accuracy are as follows: the data bulk in the flying quality including correct height item and the data including height item
The ratio of quantity；
The position Loss Rate are as follows: within a preset time interval do not include timeslice quantity and the institute of any parameter in flying quality
State the ratio of timeslice total amount.
4. according to the method described in claim 3, it is characterized in that, the corresponding weight coefficient of each qualitative attribute of the determination, packet
It includes:
Determine that the corresponding attribute ratings of each qualitative attribute, the attribute ratings are used to reflect the significance level of qualitative attribute；
According to the corresponding attribute ratings of each qualitative attribute, the corresponding weight coefficient of each qualitative attribute is calculated separately；Qualitative attribute
Corresponding with the qualitative attribute attribute ratings of weight it is negatively correlated.
5. according to the method described in claim 4, it is characterized in that, the corresponding attribute ratings of each qualitative attribute of the determination, packet
It includes:
According to the corresponding relationship of preset qualitative attribute corresponding qualitative attribute parameter and attribute ratings, each qualitative attribute pair is determined
The attribute ratings answered；Alternatively,
According to the corresponding relationship of preset qualitative attribute and attribute ratings, the corresponding attribute ratings of each qualitative attribute are determined.
6. a kind of data quality accessment device, which is characterized in that described device includes:
Module is obtained, for obtaining the flying quality of aircraft；The flying quality is used to indicate the state of flight of aircraft；
Determining module, the corresponding qualitative attribute of each qualitative attribute of the flying quality for determining the acquisition module acquisition
Parameter；Determine the corresponding weight coefficient of each qualitative attribute；According to the corresponding weight coefficient of each qualitative attribute, each qualitative attribute is joined
Number is weighted summation, determines the assessment result to the flying quality.
7. device according to claim 6, which is characterized in that the acquisition module is specifically used for:
It obtains in preset time period, the flying quality for the aircraft that radar scanning is arrived；The flying quality includes: aircraft identification, flies
The horizontal position of machine, the height and position of aircraft and timestamp.
8. device according to claim 7, which is characterized in that the determining module is specifically used for:
Determine the corresponding integrity parameters of the flying quality, repeated parameter, rough error parameter, horizontal position turnover rate, data
Item updates one of accuracy, aircraft identity accuracy, height accuracy and position Loss Rate or multiple combinations；
The integrity parameters are as follows: meet in the flying quality integrity demands data bulk and the flying quality total amount
Ratio；
The repeatability parameter are as follows: the ratio of duplicate data bulk and the flying quality total amount in the flying quality；
The rough error parameter are as follows: include that the data bulk beyond preset range is wanted with the integrality that meets in the flying quality
The ratio for the data bulk asked；
The horizontal position turnover rate are as follows: timeslice quantity and timeslice total amount in the flying quality including horizontal position
Ratio；The timeslice is each winged for what is scanned in preset time period for indicating specified time length, the timeslice total amount
The sum of timeslice quantity locating for the flying quality of machine；
The data item update accuracy are as follows: include the timeslice quantity and institute of at least one correct data in the flying quality
State the ratio of the timeslice quantity including horizontal position；
The aircraft identity accuracy are as follows: in the flying quality including correct aircraft identification data bulk with include aircraft mark
The ratio of the data bulk of knowledge；
The height accuracy are as follows: the data bulk in the flying quality including correct height item and the data including height item
The ratio of quantity；
The position Loss Rate are as follows: within a preset time interval do not include timeslice quantity and the institute of any parameter in flying quality
State the ratio of timeslice total amount.
9. device according to claim 8, which is characterized in that the determining module is specifically used for:
Determine that the corresponding attribute ratings of each qualitative attribute, the attribute ratings are used to reflect the significance level of qualitative attribute；According to
The corresponding attribute ratings of each qualitative attribute, calculate separately the corresponding weight coefficient of each qualitative attribute；The weight of qualitative attribute with
The corresponding attribute ratings of qualitative attribute are negatively correlated.
10. device according to claim 9, which is characterized in that the determining module is specifically used for:
According to the corresponding relationship of preset qualitative attribute corresponding qualitative attribute parameter and attribute ratings, each qualitative attribute pair is determined
The attribute ratings answered；Alternatively, determining that each qualitative attribute is corresponding according to the corresponding relationship of preset qualitative attribute and attribute ratings
Attribute ratings.
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