CN106529694A - Data optimization method and data optimization device - Google Patents
Data optimization method and data optimization device Download PDFInfo
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- CN106529694A CN106529694A CN201510588805.7A CN201510588805A CN106529694A CN 106529694 A CN106529694 A CN 106529694A CN 201510588805 A CN201510588805 A CN 201510588805A CN 106529694 A CN106529694 A CN 106529694A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
The invention discloses a data optimization method and a data optimization device. The data optimization method comprises the steps of acquiring to-be-optimized data of a node; determining the data type and time attribute of the to-be-optimized data of the to-be-optimized data; according to the data type and the time attribute, determining at least one first data of the node, wherein the first data comprise data which are sampled on the node in a time length range and are same with the time attribute and the data type; and determining at least one first data as second data of the optimized data through a performance data fitting algorithm. The data optimization method and the data optimization device realize optimization of incomplete data, thereby improving data utilization efficiency of a data processing system.
Description
Technical field
The present invention relates to intelligent grid monitoring technology, espespecially a kind of data optimization methods and device.
Background technology
In order to realize the economy and high efficiency of operation of power networks, people can be entered to electrical network by intelligent grid
Row is monitored in real time, is managed and Automatic Optimal.
Intelligent grid is a kind of full automatic electric power transmission network, i.e., by various sensors and remotely
The status data information of each grid nodes of terminal unit (RTU) Real-time Collection, then, by those states
Data information transfer enters line number to those status data information by data handling system to data handling system
According to analysis, the information and electricity from power plant to terminal use during whole power transmission and distribution between all nodes is realized
The two-way flow of energy.
However, in prior art, as the status data information transfer gathered from each grid nodes is to data
During processing system, link is more complicated, and the problems such as environment, it is not complete that status data information often occurs in Jing
Whole phenomenon, so as to affect use of the data handling system to status data information.
The content of the invention
In order to solve above-mentioned technical problem, the invention provides a kind of data optimization methods and device, to
Solve the problems, such as to affect data handling system to use status data information.
In order to reach the object of the invention, the invention provides a kind of data optimization methods, including：
Obtain the data to be optimized of a node；
According to the data to be optimized, the data type and time attribute of the data to be optimized are determined；
According to the data type and the time attribute, at least one first data of the node are determined,
First data are included in node up-sampling in long scope for the moment, with the data to be optimized
The data of the time attribute and the data type all same；
By described at least one first data by performance data fitting algorithm, optimization data are determined as
Second data.
Further, it is described that described at least one first data are passed through into performance data fitting algorithm, it is determined that
As optimization data the second data, including：
Described at least one first data are weighted into summation, the first data are obtained and is closed number；
First data conjunction number is carried out into average computation, second data are obtained；
According to second data, using second data as optimization data.
Further, it is described that described at least one first data are passed through into performance data fitting algorithm, it is determined that
Before the second data as optimization data, also include：
According to the data type, it is determined that the fitting algorithm for processing the data to be optimized is the performance number
According to fitting algorithm.
Further, it is described according to the data type and the time attribute, determine the node extremely
Before few first data, also include：
The data to be optimized are calculated by the first fitting algorithm, the first fitting data, institute is obtained
Stating the first fitting algorithm includes following any one or its combination:Null value fitting algorithm, maximum fitting are calculated
Method, minimum of a value fitting algorithm, mean value fitting algorithm；
If it is determined that first fitting data is unsatisfactory for data standard, then perform according to the data type and
The time attribute, determines at least one first data of the node.
Further, it is described according to the data to be optimized, determine the data type of the data to be optimized
Before time attribute, also include：
Configuration trigger condition, it is described according to the data to be optimized to trigger execution, determine described to be optimized
The data type and time attribute of data.
Present invention also offers a kind of data-optimized device, including：
Acquisition module, for obtaining the data to be optimized of a node；
First determining module, for according to the data to be optimized, determining the data of the data to be optimized
Type and time attribute；
Second determining module, for according to the data type and the time attribute, determining the node
At least one first data, first data include in node up-sampling in for the moment long scope,
With the time attribute and the data of the data type all same；
3rd determining module, for described at least one first data are passed through performance data fitting algorithm,
It is determined as the second data of optimization data.
Further, second determining module, specifically for described at least one first data are carried out
Weighted sum, obtains the first data and closes number；First data conjunction number is carried out into average computation, institute is obtained
State the second data；According to second data, using second data as optimization data.
Further, first determining module, is additionally operable to according to the data type, it is determined that processing institute
The fitting algorithm for stating data to be optimized is the performance data fitting algorithm.
Further, institute's first determining module, is additionally operable to intend the data to be optimized by first
Hop algorithm is calculated, and obtains the first fitting data, and first fitting algorithm includes following any one
Or its combination:Null value fitting algorithm, maximum fitting algorithm, minimum of a value fitting algorithm, mean value fitting are calculated
Method；If it is determined that first fitting data is unsatisfactory for data standard, then perform according to the data type and
The time attribute, determines at least one first data of the node.
Further, also include：Configuration module；
The configuration module, it is for configuring trigger condition, described according to the number to be optimized to trigger execution
According to determining the data type and time attribute of the data to be optimized.
In the present embodiment, the data to be optimized of a node are obtained；According to the data to be optimized, it is determined that
The data type and time attribute of the data to be optimized；According to the data type and the time attribute,
Determine at least one first data of the node, first data are included in long scope for the moment described
Node up-sampling, with the time attribute and the data of the data type all same；By described at least
One the first data is determined as the second data of optimization data by performance data fitting algorithm.Realize
Optimization to deficiency of data, so as to improve utilization rate of the data handling system to data.
Other features and advantages of the present invention will be illustrated in the following description, also, partly from froming the perspective of
Become apparent in bright book, or understood by implementing the present invention.The purpose of the present invention is excellent with other
Point can be realized and be obtained by specifically noted structure in specification, claims and accompanying drawing
.
Description of the drawings
Accompanying drawing is used for providing further understanding technical solution of the present invention, and constitutes one of specification
Point, together with embodiments herein it is used to explain technical scheme, does not constitute to the present invention
The restriction of technical scheme.
Fig. 1 is the schematic flow sheet of one embodiment of data optimization methods of the present invention；
Fig. 2 is the structural representation of one embodiment of data-optimized device of the invention；
Fig. 3 is the structural representation of two embodiment of data-optimized device of the invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention become more apparent, below in conjunction with accompanying drawing
Embodiments of the invention are described in detail.It should be noted that in the case where not conflicting, this Shen
Please in embodiment and the feature in embodiment mutually can be combined.
Data optimization methods provided in an embodiment of the present invention specifically can apply to carry out second-rate data
When data-optimized, for example, to when obtaining data from grid nodes in intelligent grid and carrying out data-optimized.This
The data optimization methods that embodiment is provided specifically can be performed by data-optimized device, and this is data-optimized
Device can be integrated in network node, or intelligent grid service end system, or be separately provided, the data
Optimizing device can be to be realized by the way of software and/or hardware.The data that below the present embodiment is provided
Optimization method and device is described in detail.
Fig. 1 is the schematic flow sheet of one embodiment of data optimization methods of the present invention.As shown in figure 1, this
The data optimization methods of bright offer, including：
Step 101, the data to be optimized for obtaining a node.
In the present embodiment, a node can be the node in intelligent grid, and the data to be optimized can be with
It is the second-rate data obtained from node sample, for example, the electricity consumption number of a node.Judge to obtain number
According to whether being data to be optimized, those skilled in the art's customary way, e.g., the data can be adopted
Whether field type meets data standard, or, obtain the number of data whether with corresponding equipment number
Matching, this is no longer going to repeat them.
Step 102, according to the data to be optimized, determine data type and the time of the data to be optimized
Attribute.
For example, the data type in the present embodiment can include power consumption, electrical network switch speed etc.,
Time attribute refers to the sampling time of the data to be optimized, and for example, Monday is adopting for the data to be optimized
The sample time, or, the sometime sampling time of the point for the data to be optimized of some day.
Step 103, according to the data type and the time attribute, determine at least one of the node
First data.
In the present embodiment, first data include what is up-sampled in the node in long scope for the moment,
With the data of the time attribute and the data type all same of the data to be optimized.
For example, the data type of the data to be optimized is the power consumption of 8 station terminal equipment, time category
Property be 9 points to 11 points of Monday morning, then can obtain in the range of one month, the morning on each Monday
The power consumption of upper 9 points to 11 points of 8 station terminal equipment.
Step 104, by described at least one first data by performance data fitting algorithm, be determined as excellent
Change the second data of data.
Performance data fitting algorithm in the present embodiment is referred to according at least one first in a periodic regime
Data, obtain the second data as optimization data by the algorithm of weighted sum.For example, by institute
State at least one first data and be weighted summation, obtain the first data and close number；First data are closed
Number carries out average computation, obtains second data；According to second data, by second data
As optimization data.
In the present embodiment, the data to be optimized of a node are obtained；According to the data to be optimized, it is determined that
The data type and time attribute of the data to be optimized；According to the data type and the time attribute,
Determine at least one first data of the node, first data are included in long scope for the moment described
Node up-sampling, with the time attribute and the data type all same of the data to be optimized
Data；By described at least one first data by performance data fitting algorithm, optimization data are determined as
The second data.The optimization to deficiency of data is realized, so that data handling system is improve to data
Utilization rate.
On the basis of above-described embodiment, it is determined that perform for described at least one first data to pass through performance number
According to fitting algorithm, the second data for being determined as optimization data can include two kinds of implementations：
Specifically, the first implementation：Described at least one first data are passed through into performance number described
According to fitting algorithm, before being determined as the second data of optimization data, can include：According to the data
Type, it is determined that the fitting algorithm for processing the data to be optimized is the performance data fitting algorithm.
Second implementation：It is described according to the data type and the time attribute, determine the section
Before at least one first data of point, can also include：By the data to be optimized by the first fitting
Algorithm is calculated, obtain the first fitting data, first fitting algorithm include following any one or
Its combination:Null value fitting algorithm, maximum fitting algorithm, minimum of a value fitting algorithm, mean value fitting are calculated
Method；If it is determined that first fitting data is unsatisfactory for data standard, then perform according to the data type and
The time attribute, determines at least one first data of the node.
Further, on the basis of above-described embodiment, according to the data to be optimized, it is determined that described treat
Before the data type and time attribute of optimization data, also include：
Configuration trigger condition, it is described according to the data to be optimized to trigger execution, determine described to be optimized
The data type and time attribute of data.
Optionally, on the configuration interface of intelligent grid service end system, the cycle of configuration data optimization matches somebody with somebody
Put.Or, the fitting algorithm of configuration data optimization.Or, configure the weight of history weekly data.
Fig. 2 is the structural representation of one embodiment of data-optimized device of the invention.As shown in Fig. 2 this
The data-optimized device of bright offer, including：Acquisition module 21, the first determining module 22, second determine mould
Block 23 and the 3rd determining module 24, wherein,
Acquisition module 21, for obtaining the data to be optimized of a node；
First determining module 22, for according to the data to be optimized, determining the number of the data to be optimized
According to type and time attribute；
Second determining module 23, for according to the data type and the time attribute, determining the section
At least one first data of point, first data include up-sampling in the node in long scope for the moment
, the data with the time attribute and the data type all same of the data to be optimized；
3rd determining module 24, for described at least one first data are passed through performance data fitting algorithm,
It is determined as the second data of optimization data.
In the present embodiment, the data to be optimized of a node are obtained；According to the data to be optimized, it is determined that
The data type and time attribute of the data to be optimized；According to the data type and the time attribute,
Determine at least one first data of the node, first data are included in long scope for the moment described
Node up-sampling, with the time attribute and the data type all same of the data to be optimized
Data；By described at least one first data by performance data fitting algorithm, optimization data are determined as
The second data.The optimization to deficiency of data is realized, so that data handling system is improve to data
Utilization rate.
On the basis of above-described embodiment, second determining module 23, specifically at least by described in
Individual first data are weighted summation, obtain the first data and close number；First data conjunction number is put down
Calculate, obtain second data；According to second data, using second data as optimization
Data.
Optionally, on the basis of above-described embodiment, first determining module 22 is additionally operable to according to institute
Data type is stated, it is determined that the fitting algorithm for processing the data to be optimized is the performance data fitting algorithm.
Optionally, on the basis of above-described embodiment, institute's first determining module 22 is additionally operable to institute
State data to be optimized to be calculated by the first fitting algorithm, obtain the first fitting data, described first intends
Hop algorithm includes following any one or its combination:Null value fitting algorithm, maximum fitting algorithm, minimum of a value
Fitting algorithm, mean value fitting algorithm；If it is determined that first fitting data is unsatisfactory for data standard, then
Perform according to the data type and the time attribute, determine at least one first data of the node.
Fig. 3 is the structural representation of two embodiment of data-optimized device of the invention.As shown in figure 3, this
The data-optimized device of bright offer, can also include：Configuration module 25；
The configuration module 25, it is for configuring trigger condition, described according to described to be optimized to trigger execution
Data, determine the data type and time attribute of the data to be optimized.
Although disclosed herein embodiment as above, described content is only to readily appreciate the present invention
And the embodiment for adopting, it is not limited to the present invention.Technology people in any art of the present invention
Member, without departing from disclosed herein spirit and scope on the premise of, can be in the form implemented and thin
Any modification and change, but the scope of patent protection of the present invention are carried out on section, still must be with appended right
The scope defined by claim is defined.
Claims (10)
1. a kind of data optimization methods, it is characterised in that include：
Obtain the data to be optimized of a node；
According to the data to be optimized, the data type and time attribute of the data to be optimized are determined；
According to the data type and the time attribute, at least one first data of the node are determined,
First data are included in node up-sampling in long scope for the moment, with the data to be optimized
The data of the time attribute and the data type all same；
By described at least one first data by performance data fitting algorithm, optimization data are determined as
Second data.
2. method according to claim 1, it is characterised in that described by described at least one first
Data are determined as the second data of optimization data by performance data fitting algorithm, including：
Described at least one first data are weighted into summation, the first data are obtained and is closed number；
First data conjunction number is carried out into average computation, second data are obtained；
According to second data, using second data as optimization data.
3. method according to claim 2, it is characterised in that described by described at least one first
Data, are also included before being determined as the second data of optimization data by performance data fitting algorithm：
According to the data type, it is determined that the fitting algorithm for processing the data to be optimized is the performance number
According to fitting algorithm.
4. method according to claim 2, it is characterised in that it is described according to the data type and
The time attribute, before determining at least one first data of the node, also includes：
The data to be optimized are calculated by the first fitting algorithm, the first fitting data, institute is obtained
Stating the first fitting algorithm includes following any one or its combination:Null value fitting algorithm, maximum fitting are calculated
Method, minimum of a value fitting algorithm, mean value fitting algorithm；
If it is determined that first fitting data is unsatisfactory for data standard, then perform according to the data type and
The time attribute, determines at least one first data of the node.
5. the method according to any one of claim 1-4, it is characterised in that treat described in the basis
Optimization data, before determining the data type and time attribute of the data to be optimized, also include：
Configuration trigger condition, it is described according to the data to be optimized to trigger execution, determine described to be optimized
The data type and time attribute of data.
6. a kind of data-optimized device, it is characterised in that include：
Acquisition module, for obtaining the data to be optimized of a node；
First determining module, for according to the data to be optimized, determining the data of the data to be optimized
Type and time attribute；
Second determining module, for according to the data type and the time attribute, determining the node
At least one first data, first data include in node up-sampling in for the moment long scope,
With the data of the time attribute and the data type all same of the data to be optimized；
3rd determining module, for described at least one first data are passed through performance data fitting algorithm,
It is determined as the second data of optimization data.
7. device according to claim 6, it is characterised in that second determining module, specifically
For described at least one first data are weighted summation, obtain the first data and close number；By described
One data are closed number and carry out average computation, obtain second data；According to second data, will be described
Second data are used as optimization data.
8. device according to claim 7, it is characterised in that first determining module, also uses
According to the data type, it is determined that the fitting algorithm for processing the data to be optimized is the performance data
Fitting algorithm.
9. device according to claim 7, it is characterised in that institute's first determining module, also
For the data to be optimized are calculated by the first fitting algorithm, the first fitting data, institute are obtained
Stating the first fitting algorithm includes following any one or its combination:Null value fitting algorithm, maximum fitting are calculated
Method, minimum of a value fitting algorithm, mean value fitting algorithm；If it is determined that first fitting data is unsatisfactory for number
According to standard, then perform according to the data type and the time attribute, determine at least the one of the node
Individual first data.
10. the device according to any one of claim 6-9, it is characterised in that also include：Configuration
Module；
The configuration module, it is for configuring trigger condition, described according to the number to be optimized to trigger execution
According to determining the data type and time attribute of the data to be optimized.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510588805.7A CN106529694A (en) | 2015-09-15 | 2015-09-15 | Data optimization method and data optimization device |
| PCT/CN2016/076736 WO2016177139A1 (en) | 2015-09-15 | 2016-03-18 | Data optimization method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510588805.7A CN106529694A (en) | 2015-09-15 | 2015-09-15 | Data optimization method and data optimization device |
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| Publication Number | Publication Date |
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| CN106529694A true CN106529694A (en) | 2017-03-22 |
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ID=57217329
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| Application Number | Title | Priority Date | Filing Date |
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| CN201510588805.7A Pending CN106529694A (en) | 2015-09-15 | 2015-09-15 | Data optimization method and data optimization device |
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| CN (1) | CN106529694A (en) |
| WO (1) | WO2016177139A1 (en) |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011027339A1 (en) * | 2009-09-03 | 2011-03-10 | Meishar Immediate Community (Mic) Ltd. | Methods and systems for managing electricity delivery and commerce |
| JP5664763B2 (en) * | 2011-03-18 | 2015-02-04 | 富士通株式会社 | Power leveling control device, power leveling power storage device, power leveling control method, and leveling program |
| CN103036231B (en) * | 2012-12-07 | 2014-10-29 | 国家电网公司 | Forecasting method, device, and upper computer of power load |
| CN103413253B (en) * | 2013-09-04 | 2016-05-18 | 国家电网公司 | A kind of classification of the annual peak load based on economy, meteorologic factor Forecasting Methodology |
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2015
- 2015-09-15 CN CN201510588805.7A patent/CN106529694A/en active Pending
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- 2016-03-18 WO PCT/CN2016/076736 patent/WO2016177139A1/en active Application Filing
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| WO2016177139A1 (en) | 2016-11-10 |
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Application publication date: 20170322 |