CN103678402B - A kind of method of data real-time statistics under mass data - Google Patents
A kind of method of data real-time statistics under mass data Download PDFInfo
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- CN103678402B CN103678402B CN201210355631.6A CN201210355631A CN103678402B CN 103678402 B CN103678402 B CN 103678402B CN 201210355631 A CN201210355631 A CN 201210355631A CN 103678402 B CN103678402 B CN 103678402B
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The present invention relates to a kind of methods of data real-time statistics under mass data, and steps are as follows：1）The data received from terminal are transmitted to data processing server processing by data collection server；2）Data processing server carries out database purchase to original signal；3）Data processing server carries out signal statistics and analysis；The step 3）It is specific as follows：3.1）The data type of distinguishing signal, is identified as value type and non-numeric type；3.2）For the signal of value type, the maximum value, minimum value of each signal of value type, average value in fixed duration are counted, and store into the database table of value type；For the signal of non-numeric type, the number of each state appearance of each signal of non-numeric type in fixed duration, the frequency of variation are counted, and store into the database table of non-numeric type.Using the present invention, original data volume to be checked is greatly reduced, largely improves the speed and man-machine interaction experience of report query.
Description
Technical field
The present invention relates to a kind of statistics of data acquisition methods, real-time more specifically to data under a kind of mass data
The method of statistics.
Background technology
In signal acquiring system, the semaphore frequency that vehicle termination reports is very high, is reported with the frequency of Millisecond, single
Terminal, which reports, can reach ten thousand/second, and with the increase of vehicle number, the data amount of reporting is even more huge.In face of so big data
Amount, search request higher occasion when some such as reports show logarithm factually, traditional data query mode, which certainly exists, to be looked into
The problems such as inquiry speed is slow, poor user experience.
Chinese invention patent application 200610027566.9 discloses a kind of statistical method of high magnitude of data, and this method has
Tri- database tables of MT, MC, MO, and establish five data table spaces respectively in three tables.Three tables are respectively classified into 91 points
Area, for preserving continuous 90 days data.The inquiry and statistical analysis of data can calculate the partition number to be inquired, root according to the date
The data of specified partition are searched according to partition number.Since the data volume of each subregion is only equivalent to 1st/90th of total data, because
This inquiry and the speed for preserving data are improved.Multilist association is changed to single table inquiry and improves inquiry velocity.Query result
Difference set and intersection technology make the data volume that need to be handled reduce, to improve statistic property.
Technical solution described in above-mentioned patent even if database is divided into multiple database tables, and carries out database table
Subregion, but the preservation of each subregion is still initial data, if the data volume in table is huge, effect still with do not divide table,
There is no the prior art of subregion the same, inquiry velocity is slow, and query time is long.And by the way of the quantity of limitation database table
The promotion for carrying out inquiry velocity, is only applicable to the field to that need not save historical data, the scope of application is small, limited by practical.
Chinese invention patent ZL200910081509.2 discloses a kind of mass data inquiry method：A）To mass data reality
Body surface is spaced according to set time carries out subregion；B）The Two-dimensional Statistical table for establishing Property Name in the entity table, wherein one-dimensional
Indicate each time interval in the time interval of setting, the attribute data in the attribute column of another one-dimensional representation Property Name, system
There are the titles of the entity table of some attribute data in some time interval for content representation in meter table；C）When inquiry, if looking into
Inquiry condition includes the Property Name through statistics, then according to the time zone set in the statistical form of the Property Name and querying condition
Between obtain the set of entity table subregion in the time interval；D）Mass data query context is reduced according to the set to carry out again
Inquiry.
Technical solution described in above-mentioned patent, though the entity table of database is carried out subregion, but each subregion preserve according to
So initial data, if the data volume in subregion is huge, effect still with the prior art one of not dividing table, not no subregion
Sample, inquiry velocity is slow, and query time is long.Using the means of subregion, query context is reduced, is suitable for the metastable neck of data volume
Domain, the scope of application are small.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of realities that data can be carried out under mass data
When collect statistics so that front end report form showing can obtain the side of data real-time statistics under the mass data of most fast reaction speed
Method.
Technical scheme is as follows：
A kind of method of data real-time statistics under mass data, steps are as follows：
1）The data received from terminal are transmitted to data processing server processing by data collection server；
2）Data processing server carries out database purchase to original signal；
3）Data processing server carries out signal statistics and analysis；
The step 3）It is specific as follows：
3.1）The data type of distinguishing signal, is identified as value type and non-numeric type；
3.2）For the signal of value type, count the maximum value, minimum value of each signal of value type in fixed duration,
Average value, and store into the database table of value type；
For the signal of non-numeric type, time that each state of each signal of non-numeric type in fixed duration occurs is counted
Number, the frequency changed, and store into the database table of non-numeric type.
Preferably, being counted step by step to the database table according to different fixation durations, and store at new
Database table.
Preferably, the step 3.2）Specially：
For the signal of value type, the maximum value, minimum value, average value of each signal of each number of seconds Value Types are counted,
And it stores into the database table of second series Value Types；
For the signal of non-numeric type, the number of each state appearance of each signal of each second non-numeric type of statistics,
The frequency of variation, and store into the database table of second grade non-numeric type.
Preferably, the step 3）Further comprise：
3.3）Database background application is per minute to step 3.2 by clocked flip mechanism）The number of the second series Value Types of storage
It is counted, and is stored to minute grade according to the maximum value, minimum value, average value of each signal of the data type of each second in the table of library
In the database table of value type；
It is per minute to step 3.2）The non-data type of each second in the database table of the second grade non-numeric type of storage
The frequency of number, variation that each each state of signal occurs is counted, and stores the database to minute grade non-numeric type
In table.
Preferably, the step 3）Further comprise：
3.4）Per hour to step 3.3）The data class of each minute in the database table of the minute series Value Types of storage
The maximum value of each signal of type, minimum value, average value are counted, and are stored into the database table of hour series Value Types；
Per hour to step 3.3）The non-data class of each minute in the database table of the minute grade non-numeric type of storage
The frequency of number, variation that each state of each signal of type occurs is counted, and stores the number to hour grade non-numeric type
According in the table of library.
Preferably, the database carries out a point library using at least one divided data library using library framework is divided to data
Storage.
Preferably, the physical table of the database, which uses, divides dial framework structure, using at least one point of physical table to each number
A point table is carried out according to library table by the fixed cycle to store.
Preferably, described divide physical table to use subregion framework, using at least one divided data area to divide physical table by
Fixed time period carries out partitioned storage.
Preferably, the physical table of the database, which uses, divides dial framework structure, second level data library table, minute level data library
The form that table, hour level data library table are all made of a month table is stored.
Preferably, each menology of each database table carries out partitioned storage with day unit, daily data are one point
Area.
Preferably, the temporal characteristics for defining the data to be inquired answer corresponding database table, query steps as follows：
A）Judge the signal characteristic of the data to be inquired, determines the type of the database table of inquiry；
B）The temporal characteristics for judging the data to be inquired, according to pre-defined correspondence, data that determination should be inquired
Library table；
C）It is inquired in data processing server to corresponding database table, inquires all data for meeting condition.
Beneficial effects of the present invention are as follows：
Using data statistical approach of the present invention, original data volume to be checked is greatly reduced, largely
Improve the speed and man-machine interaction experience of report query.Simultaneously as using rational data summarization statistical method, statistics knot
Fruit can reach the accurate impression that directly inquiry initial data is the same.
Due to Fen Ku, dividing table, subregion incalculable limitation, according to the number of data volume, can voluntarily expand and step by step
Statistics, inquiry velocity are hardly influenced by increased amount of, are suitable for various fields, have huge practicability.
Specific implementation mode
The present invention is further described in detail with reference to embodiments.
A kind of method of data real-time statistics under mass data, steps are as follows：
1）The data received from terminal are transmitted to data processing server processing by data collection server；
2）Data processing server carries out database purchase to original signal；
3）Data processing server carries out signal statistics and analysis：
3.1）The data type of distinguishing signal, is identified as value type and non-numeric type；
3.2）For the signal of value type, count the maximum value, minimum value of each signal of value type in fixed duration,
Average value, and store into the database table of value type；For the signal of non-numeric type, count nonumeric in fixed duration
The frequency of number, variation that each state of each signal of type occurs, and store into the database table of non-numeric type.
The step 3.2）Specially：
For the signal of value type, the maximum value, minimum value, average value of each signal of each number of seconds Value Types are counted,
And it stores into the database table of second series Value Types；
For the signal of non-numeric type, the number of each state appearance of each signal of each second non-numeric type of statistics,
The frequency of variation, and store into the database table of second grade non-numeric type.
According to report show data characteristics, in entire business procession, method of the present invention to data into
Capable collect statistics several times.
Method of the present invention counts the database table according to different fixation durations step by step, and stores
The database table of Cheng Xin.
Therefore, the step 3）It may further include：
3.3）Database background application is per minute to step 3.2 by clocked flip mechanism）The number of the second series Value Types of storage
It is counted, and is stored to minute grade according to the maximum value, minimum value, average value of each signal of the data type of each second in the table of library
In the database table of value type；
It is per minute to step 3.2）The non-data type of each second in the database table of the second grade non-numeric type of storage
The frequency of number, variation that each each state of signal occurs is counted, and stores the database to minute grade non-numeric type
In table.
It is furthermore preferred that the step 3）It can further include：
3.4）Per hour to step 3.3）The data class of each minute in the database table of the minute series Value Types of storage
The maximum value of each signal of type, minimum value, average value are counted, and are stored into the database table of hour series Value Types；
Per hour to step 3.3）The non-data class of each minute in the database table of the minute grade non-numeric type of storage
The frequency of number, variation that each state of each signal of type occurs is counted, and stores the number to hour grade non-numeric type
According in the table of library.
In center server, including data collection server, data processing server and database.Data receiver takes
The data received from terminal are transmitted to data processing server processing by business device.Data processing server carries out original signal
While database purchase, signal statistics are carried out on one side, the original millisecond signal of same vehicle in the same second are passed through certain
Method carries out reductive analysis.
Divide a signal into value type and non-numeric type：The signal of value type refers to that signal value is continuous type,
With the real number representation of mixed decimal position, such as speed, temperature；The signal of non-numeric type refers to the signal of Status Type, signal
Value is discrete type, with integer representation, the status signal switched such as lock.
For the signal of value type, the maximum, minimum and average value of the signal in this second are counted：
Smax=Max{S1,S2……Sn},
Smax=Min{S1,S2……Sn},
Wherein, S1,S2……Sn∈ Second (S) indicate the data value that a signal uploads in one second.After having counted,
It will be in the storage to the database table of second series Value Types of these three values.
And for the signal of non-numeric type, we count the frequency n that interior each each state of signal per second occursS, with
And the frequency f of variationS, we store the two values into the database table of second grade non-numeric type.
It is stored in this way, millisecond signal is just aggregated into second grade signal.Meanwhile database background application passes through clocked flip machine
System, it is per minute that primary analysis statistics is carried out by vehicle and signal grouping to second grade signal.
For the signal of value type, from the central database in get numerical value all in the signal one minute, then
Find out maximum, minimum and average value：
Mmax=Max{M1,M2……Mn},
Mmax=Min{M1,M2……Mn},
Wherein, M1,M2……Mn∈ Minute (M) indicate that the signal is stored in the data of lane database in one minute
Value.
And for the signal of non-numeric type, obtain the number and frequency that the signal is all in one minute from lane database
It is secondary, then count the total degree n of signal appearance in this minuteM, and variation total frequency fM：
nM=∑(nS1, nS2……nS60),
fM=∑(fS1,fS2……fS60)。
In this way, second grade information is just aggregated into the information of minute grade, store into the database table of minute grade.Same side
Method, database can periodically per hour be analyzed and counted minute grade signal by vehicle and signal grouping again, for value type
Signal, from the central database in get numerical value all in the signal one hour, then find out maximum, minimum and average
Value obtains the number and the frequency that the signal is all in one hour from lane database, counts this to the signal of non-numeric type
The total frequency for the total degree and variation that this hour of signal occurs, after having counted in storage to the database table of hour grade.
In this way, original signal dumps to second grade, the data of minute grade, hour grade respectively by data analysis statistical several times
In the table of library, data volume is successively decreased successively.
In order to further reach faster inquiry velocity, in method of the present invention, the design of database using
Fen Ku, the mode for dividing table and subregion to be combined.
The database carries out a point library storage using at least one divided data library using library framework is divided to data.It is described
Database physical table using dividing dial framework structure, each database table is divided by the fixed cycle using at least one point of physical table
Table stores.Described divides physical table to use subregion framework, using at least one divided data area to dividing physical table by fixed time period
Carry out partitioned storage.
Firstly, since the data volume of terminal real-time report is huge, in order to reduce the storage pressure of separate unit database, database
Using library framework is divided, i.e., data are stored with multiple libraries, the design of point library can meet database purchase performance requirement and
The needs of database horizontal extension.
Secondly, the physical table in each library is using dividing table to design, second level data library table, minute level data library table, hour
Level data library table all uses the form of a month table, can reduce the data volume of every table in this way, improves search efficiency.
Finally, each menology of each database table carries out partitioned storage with day unit, and daily data are a subregion.Often
Zhang Yuebiao, and multidomain treat-ment is daily carried out according to the period, it is a subregion daily.Using table zoning design, fully take into account
Access performance improves search efficiency to greatest extent in the case where not influencing storage performance.
Point day that physical table of the present invention can be used further, point moon, divide year to design.The establishment of table is using automatic wound
Mechanism is built, all collect statistics processing of data is automatically performed by database server, during which need not artificially be controlled.
Database made of being counted for method of the present invention before being inquired, first defines the number to be inquired
According to temporal characteristics answer corresponding database table.
Specific query steps are as follows：
A）Judge the signal characteristic of the data to be inquired, determines the type of the database table of inquiry；
B）The temporal characteristics for judging the data to be inquired, according to pre-defined correspondence, data that determination should be inquired
Library table；
C）It is inquired in data processing server to corresponding database table, inquires all data for meeting condition.
Some times inquired by user when signal report form showing and signal characteristic, using different inquiry mechanisms.Number
According to acquisition realized by data processing server.
When the time span of user's inquiry is small less than 1, is inquired, inquired in data processing server to second grade table
All data for meeting condition.Since the signal in second grade table is the statistics to original signal and summarizes, retaining original signal
Under the premise of essential information, and the data volume of inquiry can be made to greatly reduce, to greatly reduce signal inquiry and processing time.
Same reason, when queried between span between 1 hour and 7 days when, data processing server switchs to phase
It goes to obtain information in the minute grade table answered.
When the time span of inquiry is more than 7 days, data processing server switchs to grade table of corresponding hour go to obtain to believe
Breath.In this way, after obtaining these data informations, filtering and operation method of the data access layer Jing Guo some data finally obtain
Report shows result.By this data statistical approach, original data volume to be checked is greatly reduced, is largely carried
The speed and man-machine interaction experience of report query are risen.Simultaneously as using rational data summarization statistical method, statistical result
The accurate impression that directly inquiry initial data is the same can be reached.
Above-described embodiment is intended merely to illustrate the present invention, and is not used as limitation of the invention.As long as according to this hair
Bright technical spirit is changed above-described embodiment, modification etc. will all be fallen in the scope of the claims of the present invention.
Claims (10)
1. a kind of method of data real-time statistics under mass data, steps are as follows：
1) data received from terminal are transmitted to data processing server processing by data collection server；
2) data processing server carries out database purchase to original signal；
3) data processing server carries out signal statistics and analysis；
It is characterized in that, the step 3) is specific as follows：
3.1) data type of distinguishing signal, is identified as value type and non-numeric type；
3.2) database table is counted step by step according to different fixation durations, and is stored into new database table：
For the signal of value type, the maximum value, minimum value of each signal of value type, average value in fixed duration are counted,
And it stores into the database table of value type；
For the signal of non-numeric type, count number that each state of each signal of non-numeric type in fixed duration occurs,
The frequency of variation, and store into the database table of non-numeric type.
2. the method for data real-time statistics under mass data according to claim 1, which is characterized in that the step
3.2) it is specially：
For the signal of value type, the maximum value, minimum value, average value of each signal of each number of seconds Value Types are counted, and is deposited
It stores up into the database table of second series Value Types；
For the signal of non-numeric type, number, the variation that each state of each signal of each second non-numeric type occurs are counted
The frequency, and store to the second grade non-numeric type database table in.
3. the method for data real-time statistics under mass data according to claim 2, which is characterized in that the step 3)
Further comprise：
3.3) database background application passes through clocked flip mechanism, the database of the second series Value Types per minute to step 3.2) storage
The maximum value of each signal of the data type of each second in table, minimum value, average value are counted, and are stored to minute value of series
In the database table of type；
Each letter of the non-data type of each second in database table per minute to the second grade non-numeric type of step 3.2) storage
The frequency of number, variation that number each state occurs is counted, and is stored into the database table of minute grade non-numeric type.
4. the method for data real-time statistics under mass data according to claim 3, which is characterized in that the step 3)
Further comprise：
3.4) per hour to the data type of each minute in the database table of the minute series Value Types of step 3.3) storage
The maximum value of each signal, minimum value, average value are counted, and are stored into the database table of hour series Value Types；
Per hour to the non-data type of each minute in the database table of the minute grade non-numeric type of step 3.3) storage
The frequency of number, variation that each each state of signal occurs is counted, and stores the database to hour grade non-numeric type
In table.
5. the method for data real-time statistics under mass data according to claim 1, which is characterized in that the database
Using library framework is divided, a point library storage is carried out to data using at least one divided data library.
6. the method for data real-time statistics under mass data according to claim 5, which is characterized in that the database
Physical table using dividing dial framework structure, carrying out a point table by the fixed cycle to each database table using at least one point of physical table stores.
7. the method for data real-time statistics under mass data according to claim 6, which is characterized in that described divides physics
Table uses subregion framework, using at least one divided data area to dividing physical table to carry out partitioned storage by fixed time period.
8. the method for data real-time statistics under mass data according to claim 4, which is characterized in that the database
Physical table using dividing dial framework structure, second level data library table, minute level data library table, hour level data library table to be all made of one month one
The form for opening table is stored.
9. the method for data real-time statistics under mass data according to claim 8, which is characterized in that each database table
Each menology carries out partitioned storage with day unit, and daily data are a subregion.
10. the method for data real-time statistics under mass data according to claim 1, which is characterized in that definition is intended to inquire
The temporal characteristics of data answer corresponding database table, query steps as follows：
A the signal characteristic for) judging the data to be inquired, determines the type of the database table of inquiry；
B the temporal characteristics for) judging the data to be inquired, according to pre-defined correspondence, database table that determination should be inquired；
C it) is inquired in data processing server to corresponding database table, inquires all data for meeting condition.
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Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104199945A (en) * | 2014-09-10 | 2014-12-10 | 北京国双科技有限公司 | Data storing method and device |
| TWI607331B (en) * | 2015-09-23 | 2017-12-01 | 財團法人工業技術研究院 | Method and device for analyzing data |
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| CN106250493A (en) * | 2016-07-29 | 2016-12-21 | 无锡天脉聚源传媒科技有限公司 | A kind of data processing method and device |
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| CN110427362B (en) * | 2018-04-27 | 2022-03-08 | 杭州海康威视数字技术股份有限公司 | Method and device for acquiring database types |
| CN109189655A (en) * | 2018-08-08 | 2019-01-11 | 郑州云海信息技术有限公司 | A method of storage device history performance data is counted based on mean value optimization algorithm |
| CN111061758B (en) * | 2018-10-16 | 2023-10-20 | 杭州海康威视数字技术股份有限公司 | Data storage method, device and storage medium |
| CN109710616B (en) * | 2018-12-29 | 2021-10-22 | 郑州春泉节能股份有限公司 | Data storage method |
| CN110276359A (en) * | 2019-05-24 | 2019-09-24 | 天津亿玛科技有限公司 | A kind of feature extracting method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1747398A (en) * | 2004-09-08 | 2006-03-15 | 大唐移动通信设备有限公司 | Mass performance data statistical method in network element management system |
| CN101087203A (en) * | 2006-06-11 | 2007-12-12 | 上海全成通信技术有限公司 | A statistical method of high magnitude of data |
| CN101206644A (en) * | 2006-12-18 | 2008-06-25 | 厦门雅迅网络股份有限公司 | Method for processing vehicle running track data |
| CN101533406A (en) * | 2009-04-10 | 2009-09-16 | 北京锐安科技有限公司 | Mass data querying method |
| CN102508925A (en) * | 2011-11-25 | 2012-06-20 | 上海锅炉厂有限公司 | Method for implement of utility boiler operational performance analyzing system |
| CN102622398A (en) * | 2011-12-31 | 2012-08-01 | 武汉理工大学 | City tunnel monitoring data storage method and system based on XML (Extensive Makeup Language) |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008034213A1 (en) * | 2006-09-18 | 2008-03-27 | Infobright Inc. | A method and system for data compression in a relational database |
-
2012
- 2012-09-21 CN CN201210355631.6A patent/CN103678402B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1747398A (en) * | 2004-09-08 | 2006-03-15 | 大唐移动通信设备有限公司 | Mass performance data statistical method in network element management system |
| CN101087203A (en) * | 2006-06-11 | 2007-12-12 | 上海全成通信技术有限公司 | A statistical method of high magnitude of data |
| CN101206644A (en) * | 2006-12-18 | 2008-06-25 | 厦门雅迅网络股份有限公司 | Method for processing vehicle running track data |
| CN101533406A (en) * | 2009-04-10 | 2009-09-16 | 北京锐安科技有限公司 | Mass data querying method |
| CN102508925A (en) * | 2011-11-25 | 2012-06-20 | 上海锅炉厂有限公司 | Method for implement of utility boiler operational performance analyzing system |
| CN102622398A (en) * | 2011-12-31 | 2012-08-01 | 武汉理工大学 | City tunnel monitoring data storage method and system based on XML (Extensive Makeup Language) |
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