WO2016071083A1 - Procédé de mémorisation et de sélection de donnees - Google Patents

Procédé de mémorisation et de sélection de donnees Download PDF

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Publication number
WO2016071083A1
WO2016071083A1 PCT/EP2015/073643 EP2015073643W WO2016071083A1 WO 2016071083 A1 WO2016071083 A1 WO 2016071083A1 EP 2015073643 W EP2015073643 W EP 2015073643W WO 2016071083 A1 WO2016071083 A1 WO 2016071083A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
iib
stored
line sections
selection
Prior art date
Application number
PCT/EP2015/073643
Other languages
German (de)
English (en)
Inventor
Ralf MOSSHAMMER
Yaroslav BARSUKOV
Tobias GAWRON-DEUTSCH
Original Assignee
Siemens Ag Österreich
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Ag Österreich filed Critical Siemens Ag Österreich
Publication of WO2016071083A1 publication Critical patent/WO2016071083A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2474—Sequence data queries, e.g. querying versioned data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand

Definitions

  • the present invention relates to a method for
  • Data memory in which data from multiple data sources in a decentralized system architecture, for example in a low-voltage network, are present, which are stored at least in a data set according to a time sequence and are queried by means of a user request.
  • Such data sources may be: power generators (e.g., photovoltaic systems, small wind turbines) or their
  • Actuators e.g., transducers
  • flexible consumers e.g., flexible consumers
  • Transformers measuring devices such as smart meters and in particular so-called intelligent electronic devices (intelligent electronic devices), such as processor-controlled regulators of
  • Circuit breakers or transformers are Circuit breakers or transformers.
  • Data within the meaning of the invention may be: measured values for electrical current, measured values for electrical voltage, measured values for electrical power, time of the measurement, as well as metadata relating to the transmission, eg number of nodes passed, latency (delay time).
  • Historical data can be used in a central database not only for current and voltage regulation, but also for short-term correction in case of data loss, and facilitate various planning.
  • Such a central database is using a
  • the data from a low level of the system architecture e.g., intelligent electronic equipment (IED)
  • IED intelligent electronic equipment
  • High-speed connections for example in the form of broadband connections, must be used.
  • these high-speed connections can only be done with a small number of node connections, such as For example, in an intelligent building services
  • DLC Distribution Line Communication
  • the data center or the central control and control logic represents a danger point for potential errors, the default probability and the
  • the actuators in low-voltage networks are usually arranged in the network itself, that is, either on the secondary side, for example in the form of a
  • a volatile memory such as a RAM memory
  • a RAM memory to secure measurement data in a device has the advantage that very fast access times compared to a permanent memory are possible, but the risk of data loss is enormous. It is known that a planned or unscheduled restart of the device can lead to a complete loss of data,
  • the maximum possible data volume is one
  • Volatile memory is usually much lower than that of a so-called read-only memory (ROM memory, i.
  • Hard drives, flash drives, EEPROMs, etc. due to the high cost of these preferably embedded or
  • Lines is statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be statically formed. Of course they can be
  • Rows and columns are also designed to be dynamic and static in the opposite way. In general, however, this division into a dynamic and a static area presents a significant space problem since databases are best suited for storing dense data and a two-dimensional table for large volumes of data
  • Line segments that cover predetermined time segments are combined into data blocks, wherein the method for selecting data in a user request comprises the following steps:
  • An inventive data set thus consists of several lines and contains for each data source at least one line.
  • all data are entered after the time of their measurement, whereby the position of the respective data in the line corresponds to a certain point in time.
  • the line could be subdivided into many time units, in particular the same length, the data being allocated to a specific time unit. Time units of a line where there is no data of the corresponding data source remain free.
  • the lines are now subdivided into line sections that correspond to a given period of time (that is, contain a certain number of time units), which is the same for all lines. All line sections of a given period of time result in a data block, also called
  • time strip Contains a data block So the data of all data sources in a given
  • Data sources the data for a desired period of time
  • Time strips are selected to cover the desired period of time, then it is analyzed which line sections are data, and then these line sections
  • the data structure consists of rows and columns, the columns correspond to specific time periods,
  • network nodes correspond.
  • the length of the lines will increase with time and is not limited to the
  • the user queries a particular area within the temporal sequence, with those
  • Detected line segments analyzed in more detail, which is determined which line sections actually contain data. Thereafter, the data information of the data-containing line segments are provided to the user.
  • This row and column structure is present both in the lower level of the data memory or in a RAM where the most recent data is stored, as well as in the persistent memory or upper level of the data memory.
  • the present application discloses a two-stage data memory for a decentralized
  • Low-voltage network for the selection of at least arranged in a record data.
  • Data records in rows which are each associated with a data source, stored according to the time sequence and successive line segments covering predetermined periods, combined into data blocks, wherein the data structure is formed at least two stages by a first stage as volatile memory contains younger data, while a second stage as permanent storage contains older data than the first stage.
  • volatile memory eg RAM memory etc.
  • RAM memory e.g RAM memory etc.
  • Permanent memory eg ROM, HDD, flash memory, etc.
  • network nodes with local memories which are, for example, local computer architectures with
  • the second layer of the memory architecture represents a project-specific data specialization, which
  • Step whereby a rapid query according to the above-described method can be performed.
  • Line data existing data carried out. This essentially serves to quickly respond to a user request
  • the download of data is selected from the selected ones
  • Line sections limited to a maximum number. This is to prevent user requests that cause excessive data traffic and other important processes in the data store or in the communication network decentralized system architecture, such as collecting the data in the data store.
  • the method may also be designed to analyze latencies (in the transmission of the data to the data memory) or even data failures based on statistics when querying the line segments, thereby
  • the described architecture is thus suitable for storing measured values in a time sequence, which also makes the method flexible with respect to an actual data implementation.
  • the two-stage concept is thus suitable for storing measured values in a time sequence, which also makes the method flexible with respect to an actual data implementation.
  • a frequency distribution and / or an arrival time of the data record is stored for each of the data blocks. That is, it is determined in which temporal
  • Fig. 1 is a schematic representation of the basic
  • Fig. 1 shows a schematic representation of a
  • time units from 0 to 6 are summarized at a predetermined period corresponding to the data block 6, the time units from 7 to 12 are combined at a predetermined period corresponding to the data block 7, and the time units from 13 to 20 are combined at a predetermined time that the
  • Data block 8 corresponds. The periods, which the
  • Data blocks 6-8 are equal in length.
  • exemplary data sources (data points) A, Bx, By, Bz, C are given in the form of lines 2,9,10,11,12 with each other.
  • the time sequence 3 is a kind
  • Breakdown is divided into data blocks 6,7,8, which is concerned with the lines 2,9,10,11,12 in the field of
  • "B *" includes all "B” data points, ie Bx, By and Bz.
  • Frequency distribution of the requests and / or an associated time of arrival are stored, whereby also the duration of the data transmission is recorded, which serves two purposes:
  • the frequency measurements can be exported to provide useful statistics about the
  • the frequencies of the requested data blocks 7,8 can be compared with respect to the affected data 5 and its lines 2,9,11,12 and
  • Line sections 2a, 2b, 2c, 9a, 9b, 9c, 10a, 1 la, 1 lb, 12b are used to rapidly generate an estimate of the data sets. This could, for example, also serve to suppress very large inquiries by which the

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Computing Systems (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

L'invention concerne un procédé de mémorisation et de sélection de données (5) qui proviennent d'une mémoire de données, dans laquelle se trouvent des données (5) provenant d'une pluralité de sources de données dans une architecture de système décentralisée, par exemple dans un réseau à basse tension, et qui sont mémorisées au moins dans un ensemble de données (1) selon un ordre temporel (3) et récupérées par une requête d'un utilisateur. Pour garantir un accès rapide et fiable à la base de données, les ensembles de données (1) sont mémorisées suivant des lignes (2, 9, 10, 11, 12), associées chacune à une source de données, selon un ordre temporel (3) et des parties de lignes successives (2a, 2b, 2c, 9a, 9b, 9c, 10a, 11a, 11b, 12b), qui couvrent des intervalles de temps prédéterminés, sont rassemblées en blocs de données (6, 7, 8). Le procédé de sélection de données (5) comporte lors d'une requête d'un utilisateur les étapes suivantes consistant à : - sélectionner les blocs de données (7, 8) qui couvrent la zone (4) recherchée ; - analyser des données (5) qui se trouvent dans les parties de lignes (2b, 2c, 9b, 9c, 11b, 12b) situées dans les blocs de données (7, 8) ; et - sélectionner les parties de lignes (2b, 2c, 9b, 9c, 12b, 11b) qui contiennent des données (5).
PCT/EP2015/073643 2014-11-06 2015-10-13 Procédé de mémorisation et de sélection de donnees WO2016071083A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ATA50812/2014 2014-11-06
ATA50812/2014A AT516425A2 (de) 2014-11-06 2014-11-06 Verfahren zum Abspeichern und zur Auswahl von Daten

Publications (1)

Publication Number Publication Date
WO2016071083A1 true WO2016071083A1 (fr) 2016-05-12

Family

ID=54292812

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2015/073643 WO2016071083A1 (fr) 2014-11-06 2015-10-13 Procédé de mémorisation et de sélection de donnees

Country Status (2)

Country Link
AT (1) AT516425A2 (fr)
WO (1) WO2016071083A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110543496B (zh) * 2019-09-06 2022-04-15 中国联合网络通信集团有限公司 用于时序数据库集群的数据处理方法和装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
65.209.36.34: "Time series database", 29 September 2014 (2014-09-29), pages 1 - 3, XP055231597, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Time_series_database&oldid=627601912> [retrieved on 20151126] *

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Publication number Publication date
AT516425A2 (de) 2016-05-15

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