CN107506863A - One kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology - Google Patents
One kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology Download PDFInfo
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Abstract
The invention discloses one kind to be based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology, and this method includes multi-source data acquisition platform, data warehouse, MapReduce model and data and visualizes platform.The power network physical assets O＆M cost of overhaul provided by the invention forecasting system application big data analytical technology, ETL technologies, data warehouse technology and data visualization technique, using assets life cycle theory as guiding theory, using the assets monomer information data in PMS systems, ERP system and the O＆M cost of overhaul as object, pass through the big data analysis means such as cluster analysis, classification analysis, association analysis, statistical relationship is established to magnanimity unstructured data, in being provided for the power network physical assets O＆M cost of overhaul with scale and development trend, Long-term analysis prediction.The present invention is realized in power network physical assets operation management to the efficiently integrated of multiple business system data, improves the accuracy that maintenance O＆M overhauls Cost Forecast.
Description
Technical field
The present invention relates to Forecasting Methodology, is more particularly to the power network physical assets O＆M maintenance based on big data analytical technology
The prediction of expense.
Technical background
Physical assets refers to that including fixed assets, low value durable goods, material easily-consumed products etc. has physical form, and possesses bright
The assets of value item can really be quantified.Power grid enterprises belong to asset intensive enterprise, physical assets proportion in its asset structure
Up to more than 80%, the tangible money such as primary equipment, secondary device, communication network, instrument and meter, building, the vehicles, consumptive material
Production belongs to physical assets, and physical assets scale and utilization ratio directly determine the production capacity of enterprise.With electric grid investment
Continue to increase, grid company physical assets scale and the O＆M cost of overhaul are by sustainable growth.
In order to prejudge the financial pressure and various risks that company will face in future in advance, proposed for investment, O＆M, disposal opinion
Scientific proposals, need badly and establish a set of scientific and effective physical assets O＆M cost of overhaul Forecasting Methodology and instrument.Existing O＆M
The cost of overhaul is built on principle of accounting with Forecasting Methodology, mainly finds power network physical assets scale using linear regression method
With the existing relation that influences each other of the O＆M cost of overhaul, target variable and related variable are determined, is then asked with homing method
The regression equation gone out between variable, used based on following O＆M cost of overhaul of historical data prediction.The major defect of the method is model
The definite state of power network physical assets is not accounted for, such as the utilization power of all kinds of physical assets, defect incidence, actual use longevity
The information such as life, thus short-term and rough analysis prediction can only be carried out, it is pre- to the different classes of physical assets O＆M cost of overhaul
Relatively large deviation be present in survey.Because the definite status data amount of physical assets is huge, traditional statistical prediction methods can not utilize this
A little data carry out correlation analysis.
The present invention with reference to domestic and international newest research results in the design process, wherein domestic and international Patents have 2, text
Offer 3：
Patent " a kind of information processing system and method for supporting 7 degree of freedom degree account equipment control ", inventor Lee has tomahawk etc.,
Application number CN201510494092.8 discloses a kind of information processing system and method for supporting 7 degree of freedom degree account equipment control, uses
It can not meet plant asset management requirement, the not high technical problem of assets service efficiency, the system in solving current asset management
System includes：Equipment control scheme determination unit, for carrying out feasibility analysis to asset of equipments plan, to determine equipment control side
Case；Corresponding relation establishes unit, for being encoded based on equipment control scheme, the unique identity card of generation equipment, and according to equipment
Managed Solution and equipment identities card coding establish equipment in asset management business system pass corresponding with financial management operation system
System；Equipment account administrative unit, in asset of equipments Life cycle, based on the corresponding relation, making equipment account, money
Production card and material object link, and realize orderly management of the equipment account on 7 degree of freedom degree.Realize equipment account, assets card
It is consistent between material object and information is accurate.
The patent proposes the management to physical assets account data to collect, the correlation technique guarantee of account card thing uniformity,
Full lifecycle theory method is refer to simultaneously, but analysis and utilization is not given to physical assets account data.
Patent " the asset management information processing method and processing device based on big data analysis ", as if inventor's Xu's appearance etc., application
Number：CN201510066350.2 is related to a kind of asset management information processing method and processing device based on big data analysis, including visitor
Family end, application server, database server, described application server are connected with client, database server respectively；Institute
The application server stated includes data integration module, assets As-Is analysis module, target strategy and formulates module, planning mould
Block, management system research and development module, support the key element research and development module, implementation process monitoring module and performance evaluation module.With existing skill
Art is compared, and systematic approach, all kinds of evaluation models and logic computing method are used in invention in different analysis modules, plans as a whole association
Adjust and management and control detection assets are in planning, design, buying, construction, O＆M, the achievement transformed, scrap the assets life cycle managements such as disposal
Information is imitated, there is the function to the job analysis of asset management loopful section, monitoring and prediction.
It this method propose the integration to physical assets data to utilize, monitor its key index, but do not consider to pass through foundation
Data model is predicted to the O＆M cost of overhaul.
Document " power network physical assets assessment indicator system ", Li Peidong etc., power construction, 2014.The document is based on power network
Physical assets management characteristic, with reference to assets whole-life cycle fee theory and assets wall model, for asset management policy center
The asset evaluation problem of the heart, from structure of size analysis, the general level of the health is analyzed, utilization ratio is analyzed, scraps retired this 4 dimensions of analysis
Degree structure power network physical assets assessment indicator system.The result of study can be persistently to deepen assets life-cycle management, predict O＆M
Technological transformation risk and realize electrical network economy, reliability service provide reference.
The correlation technique for establishing power network physical assets wall, and assets wall be this document propose in physical assets assay
In application, but its assets wall and O＆M maintenance do not account for the maintenance of other influences O＆M using simple positive incidence
The factor of expense.
Document " power network physical assets ' assets wall ' analysis method research ", Liu Yihe etc., east china electric power, 2014.The document
By taking the maintenance of Shanghai Electric Power Co Capital operation as an example, elaborate that assets wall is established, expected service life is analyzed, following technological transformation is pre-
Survey, reset scale forecast, equipment deficiency and time history analysis and the maintenance work prediction process of putting into operation.
The method that following O＆M maintenance scale is predicted using the translation of assets wall is this document propose, but is not accounted for current
Power grid construction is still within accelerated period, and newly-increased, dead assets is to there is the influence of certain waveform, thus the waveform integrally estimated has
Some changes.
Document " An Index Evaluation System for the Life Cycle of Assets
Management under the New Environment of Power Grid ", Jian Deng, Applied
Mechanics and Materials, 2013.It is scientific evaluation that the document, which discusses life cycle asset management assessment indicator system,
The basis of power grid asset managerial skills, it is the carrier of power grid quality operation.Paper is directed under new environment to the full life of asset management
The new demand in cycle, four layers of tree assessment indicator system are constructed, overall merit has been carried out to asset management level, has realized effect
Benefit maximizes.
The document explains the direct relation of physical assets index with tree, thus analyzes index and directly close
Connection, the influence coefficient for overhauling scale with O＆M for Judging index pair is had the certain significance, but correlative study method is not applied to
In the prediction of the O＆M cost of overhaul.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on big data point
The power network physical assets O＆M cost of overhaul forecasting system of analysis technology, system can gather the equipment in PMS systems, ERP system
Account information data and the O＆M cost of overhaul are used, using assets life cycle theory as guiding theory, by big data analysis means,
Statistical relationship is established to magnanimity unstructured data, realizes that power network physical assets O＆M cost of overhaul scale and development trend provide
In, Long-term analysis prediction.
In order to achieve the above object, the present invention adopts the following technical scheme that：
One kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology, and this method comprises the steps of:
A, the power network includes ERP System, engineering production management system；
B, physical assets information account and O＆M cost of overhaul account are obtained from ERP System, is produced from engineering
Management system obtains physical assets running status account, equipment deficiency record；
C, physical assets information account is by all devices Unified coding, corresponding one specific coding of each equipment, institute
State and N platform equipment is shared in power network, B is encoded to corresponding to i-th equipmenti, i=1,2 ..., N；
D, obtained from physical assets information account and O＆M cost of overhaul account and be encoded to BiPhysical assets initial value, net
Value and O＆M maintenance charge information, B is encoded to from physical assets running status account, equipment deficiency record acquisitioniUtilization rate
And ratio of defects, i=1,2 ..., N；
E, the data acquired in step d are handled
E1, calculate the total initial value Y of physical assets
Wherein YiIt is that physical assets is encoded to BiInitial asset value；
E2, calculate the total net value J of physical assets
Wherein JiIt is that physical assets is encoded to BiNet asset value；
Calculate the newness rate E in physical assets jth yearj
E3, calculate total O＆M cost of overhaul W
Wherein WiIt is that assets are encoded to BiThe O＆M cost of overhaul use；
E4, calculate average defect rate
Wherein FiIt is that assets are encoded to BiAssets ratio of defects；
E5, calculate average utilization
Wherein UiFor the assets utilization efficiency of such assets；
E6, influence curve p (E of the newness rate to ratio of defects is calculated using linear fit and " least square method "j)
M newness rate is collected in processing, and ratio of defects data are to gathering { (Ej,Fj) (j=1,2 ..., M), find a functionWherein e is the nature truth of a matter, makes the quadratic sum E of error2Minimum, wherein E2=∑ [p (Ej)-Fj]；
The fitting function p (x) of n ranks is obtained, wherein 1≤n≤5, obtain influenceing coefficient vector α；
E7, average utilization efficiency and average defect rate calculated using multiple linear regression analysis and " common least square method "
Influence coefficient to the O＆M cost of overhaul
M utilization rate U is collected in processingj, average defect rate FjWith O＆M cost of overhaul WjThree groups of corresponding data set { (Uj,
Fj,Wj) (j=1,2 ..., M), with Matlab mathematical tool polyfit program modules to function q (β, Uj,Fj) solved,
Wherein β is influence coefficient vector undetermined, makes the quadratic sum E of error2Minimum, wherein E2=∑ [q (Uj,Fj)-Wj]；
Fitting function q (β, the U of n ranks can be obtainedj,Fj), wherein 1≤n≤5, obtain selected influence coefficient vector β；
E8, calculating physical assets actual average scrap the age
Wherein SiIt is that assets are encoded to BiCorresponding assets actually scrap the age；
F, forecast model is established
F1, the retired asset size S scrapped for predicting jth year
C is invested assets total scale, P={ Pj, j=1 ..., M, wherein PjEquipment jth year scrapped for what statistics obtained
Asset size；The retired retirement curve function f (s, x) that assets are related to entering to use as a servant the time limit is asked, s is invested assets scale, and x is to scrap
The time limit, the method taken are fitting process, and function f (s, x) is solved using Matlab mathematical tool polyfit program modules
Solved；
Based on the function f (s, x) tried to achieve, the asset size that jth year is retired to scrap is：
Wherein siFor the asset size put into 1 year；
F2, the asset size R for predicting jth yearj
F3, the assets newness rate E for predicting jth yearj
Wherein, RnjFor the Net asset value scale in jth year, RojFor the initial asset value scale in jth year；
G, the O＆M cost of overhaul W in jth year is predictedj
Binary function q (β, the U obtained according to step e8j,Fj), wherein β obtains in step e7, and step e1-- steps
The acquired data of rapid e8 steps, the O＆M cost of overhaul for calculating jth year are used：
Wherein UjFor the utilization rate in jth year,GjFor the electricity sales amount in jth year, using electricity sales amount then as radix, sell
Electric annual growth is set as 5%.
Compared with prior art, the present invention possesses following advantage：
A kind of power network physical assets O＆M cost of overhaul forecasting system based on big data analytical technology, it is in kind by integrating
Assets value scale, quantity size, new assets scale, asset retirement are scrapped record, defect, the history maintenance O＆M cost of overhaul and used
Etc. mass data, the long-term analysis prediction in using of the O＆M cost of overhaul is realized, has and intuitively shows pattern.This method is linear with utilizing
The method of the regression forecasting future O＆M cost of overhaul is compared, and prediction process is clear, and prediction result is more accurate.
System can not only carry out overall prediction to the O＆M cost of overhaul of grid companies at different levels, moreover it is possible to realize local electricity
Net, single class assets, the analysis of univoltage grade independent prediction, strengthen the accuracy and reliability of data.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, the present invention is carried out below further
Describe in detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to limit this hair
It is bright.
One kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology, and this method comprises the steps of:
A, the power network includes ERP System, engineering production management system, and these systems are believed in modern power network
Breathization has generality and versatility in building, and have recorded the value and service condition of physical assets in detail；
B, physical assets information account and O＆M cost of overhaul account are obtained from ERP System, is produced from engineering
Management system obtains physical assets running status account, equipment deficiency record, and the present invention is carried out using ETL technologies to these data
Crawl and conversion, are stored in data warehouse；
C, physical assets information account is by all devices Unified coding, corresponding one specific coding of each equipment, institute
State and N platform equipment is shared in power network, B is encoded to corresponding to i-th equipmenti, i=1,2 ..., N, can be with by equipment Unified coding
The data that multiple systems are derived from data warehouse are associated；
D, obtained from physical assets information account and O＆M cost of overhaul account and be encoded to BiPhysical assets initial value, net
Value and O＆M maintenance charge information, B is encoded to from physical assets running status account, equipment deficiency record acquisitioniUtilization rate
And ratio of defects, i=1,2 ..., N, encoded and classified after the table of comparisons handled data using assets, uniformly deposit in number
According in the intermediate data table in warehouse, now data have been grouped as semi-structured data；
Intermediate data table is a kind of tables of data form for being used to deposit minimum data granularity in data warehouse, is remained original
All fields of data, the data from multiple systems are merged in intermediate data table；
E, the data acquired in step d are handled
E1, calculate the total initial value Y of physical assets
Wherein YiIt is that physical assets is encoded to BiInitial asset value；
Caused complete cost when initial asset value is progress capitalization in kind, mainly includes installation cost and installation fee；
E2, calculate the total net value J of physical assets
Wherein JiIt is that physical assets is encoded to BiNet asset value；
Net asset value is the type and age according to assets, the use value calculated according to certain allowance for depreciation；
Calculate the newness rate E in physical assets jth yearj
The computational methods of this newness rate consider whole samples in power network, the Cheng Xin that the conventional estimations that compare method obtains
Rate is more accurate, when particularly analyzing local assets, can take into full account the true composition of assets；
E3, calculate total O＆M cost of overhaul W
Wherein WiIt is that assets are encoded to BiThe O＆M cost of overhaul use；
E4, calculate average defect rate
Wherein FiIt is that assets are encoded to BiAssets ratio of defects；
Directly there is positive correlation in ratio of defects, ratio of defects is higher with the O＆M cost of overhaul, then the O＆M cost of overhaul is higher；
E5, calculate average utilization
Wherein UiFor the assets utilization efficiency of such assets；
There is positive correlation in average utilization, average utilization is higher with the O＆M cost of overhaul, then the O＆M cost of overhaul is used
It is higher；
E6, influence curve p (E of the newness rate to ratio of defects is calculated using linear fit and " least square method "j)
M newness rate is collected in processing, and ratio of defects data are to gathering { (Ej,Fj) (j=1,2 ..., M), find a functionWherein e is the nature truth of a matter, makes the quadratic sum E of error2Minimum, wherein E2=∑ [p (Ej)-Fj]；
The fitting function p (x) of n ranks is obtained, wherein 1≤n≤5, obtain influenceing coefficient vector α；
Assets newness rate is to influence the important indicator of assets ratio of defects, and the influence to ratio of defects shows as tub curve, is provided
Produce initial operation stage and retired ratio of defects early stage is occurred frequently, the low hair of ratio of defects in running；
Due to forming the physical assets overall age into more abundant by different level, the assets age between 1 to 30, therefore
Influence coefficient vector α and show as an irregular curve；
E7, average utilization efficiency and average defect rate calculated using multiple linear regression analysis and " common least square method "
Influence coefficient to the O＆M cost of overhaul
M utilization rate U is collected in processingj, average defect rate FjWith O＆M cost of overhaul WjThree groups of corresponding data set { (Uj,
Fj,Wj) (j=1,2 ..., M), with Matlab mathematical tool polyfit program modules to function q (β, Uj,Fj) solved,
Wherein β is influence coefficient vector undetermined, makes the quadratic sum E of error2Minimum, wherein E2=∑ [q (Uj,Fj)-Wj]；
Fitting function q (β, the U of n ranks can be obtainedj,Fj), wherein 1≤n≤5, obtain selected influence coefficient vector β；
Influence coefficient vector β and consider average defect rate and average utilization simultaneously, therefore show as a curved surface；
E8, calculating physical assets actual average scrap the age
Wherein SiIt is that assets are encoded to BiCorresponding assets actually scrap the age；
Because the physical assets data that the middle table of data warehouse is deposited are semi-structured data, traditional SQL query work
Tool can not carry out statistical analysis processing to data, therefore invention introduces with MapReduce big data analysis tools, the instrument
Statistical analysis can be carried out to magnanimity unstructured data；
Using MapReduce data are modeled and computing after, result of calculation storage to data warehouse result data
In table；
Result data table is the tables of data after dimension-reduction treatment in data warehouse, can be straight for depositing summary data
Connect and these data are read and showed；
F, forecast model is established
F1, the retired asset size S scrapped for predicting jth year
C is invested assets total scale, P={ Pj, j=1 ..., M, wherein PjEquipment jth year scrapped for what statistics obtained
Asset size；The retired retirement curve function f (s, x) that assets are related to entering to use as a servant the time limit is asked, s is invested assets scale, and x is to scrap
The time limit, the method taken are fitting process, and function f (s, x) is solved using Matlab mathematical tool polyfit program modules
Solved；
Based on the function f (s, x) tried to achieve, the asset size that jth year is retired to scrap is：
Wherein siFor the asset size put into 1 year；
F2, the asset size R for predicting jth yearj
F3, the assets newness rate E for predicting jth yearj
Wherein, RnjFor the Net asset value scale in jth year, RojFor the initial asset value scale in jth year；
Data are modeled using MapReduce big data analysis tools and computing, data are arrived into result of calculation storage
In the result data table in warehouse；
G, the O＆M cost of overhaul W in jth year is predictedj
Binary function q (β, the U obtained according to step e8j,Fj), wherein β obtains in step e7, and step e1-- steps
The acquired data of rapid e8 steps, the O＆M cost of overhaul for calculating jth year are used：
Wherein UjFor the utilization rate in jth year,GjFor the electricity sales amount in jth year, using electricity sales amount then as radix, sell
Electric annual growth is set as 5%；
Data are modeled using MapReduce big data analysis tools and computing, data are arrived into result of calculation storage
In the result data table in warehouse, final prediction result carries out visualization by data exhibiting platform and showed.
Claims (1)
1. one kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology, it is characterised in that this method includes down
Row step:
A, the power network includes ERP System, engineering production management system；
B, physical assets information account and O＆M cost of overhaul account are obtained from ERP System, from engineering production management
System obtains physical assets running status account, equipment deficiency record；
C, physical assets information account is by all devices Unified coding, corresponding one specific coding of each equipment, the electricity
N platform equipment is shared in net, B is encoded to corresponding to i-th equipmenti, i=1,2 ..., N；
D, obtained from physical assets information account and O＆M cost of overhaul account and be encoded to BiPhysical assets initial value, net value with
And O＆M maintenance charge information, it is encoded to B from physical assets running status account, equipment deficiency record acquisitioniUtilization rate and
Ratio of defects, i=1,2 ..., N；
E, the data acquired in step d are handled
E1, calculate the total initial value Y of physical assets
Wherein YiIt is that physical assets is encoded to BiInitial asset value；
E2, calculate the total net value J of physical assets
Wherein JiIt is that physical assets is encoded to BiNet asset value；
Calculate the newness rate E in physical assets jth yearj
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<msub>
<mi>E</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>J</mi>
<mi>j</mi>
</msub>
<msub>
<mi>Y</mi>
<mi>j</mi>
</msub>
</mfrac>
</mrow>
E3, calculate total O＆M cost of overhaul W
Wherein WiIt is that assets are encoded to BiThe O＆M cost of overhaul use；
E4, calculate average defect rate
Wherein FiIt is that assets are encoded to BiAssets ratio of defects；
E5, calculate average utilization
Wherein UiFor the assets utilization efficiency of such assets；
E6, influence curve p (E of the newness rate to ratio of defects is calculated using linear fit and " least square method "j)
M newness rate is collected in processing, and ratio of defects data are to gathering { (Ej,Fj) (j=1,2 ..., M), find a functionWherein e is the nature truth of a matter, makes the quadratic sum E of error2Minimum, wherein E2=∑ [p (Ej)-Fj]；
The fitting function p (x) of n ranks is obtained, wherein 1≤n≤5, obtain influenceing coefficient vector α；
E7, average utilization efficiency calculated with average defect rate to transporting using multiple linear regression analysis and " common least square method "
Tie up the influence coefficient of the cost of overhaul
M utilization rate U is collected in processingj, average defect rate FjWith O＆M cost of overhaul WjThree groups of corresponding data set { (Uj,Fj,
Wj) (j=1,2 ..., M), with Matlab mathematical tool polyfit program modules to function q (β, Uj,Fj) solved, its
Middle β is influence coefficient vector undetermined, makes the quadratic sum E of error2Minimum, wherein E2=∑ [q (Uj, Fj)-Wj]；
Fitting function q (β, the U of n ranks can be obtainedj,Fj), wherein 1≤n≤5, obtain selected influence coefficient vector β；
E8, calculating physical assets actual average scrap the age
Wherein SiIt is that assets are encoded to BiCorresponding assets actually scrap the age；
F, forecast model is established
F1, the retired asset size S scrapped for predicting jth year
C is invested assets total scale, P={ Pj, j=1 ..., M, wherein PjTo count the obtained assets scrapped in equipment jth year
Scale；The retired retirement curve function f (s, x) that assets are related to entering to use as a servant the time limit is asked, s is invested assets scale, and x is to scrap year
Limit, the method taken is fitting process, function f (s, x) solve using Matlab mathematical tool polyfit program modules into
Row solves；
Based on the function f (s, x) tried to achieve, the asset size that jth year is retired to scrap is：
<mrow>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>j</mi>
</munderover>
<mi>f</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
Wherein siFor the asset size put into 1 year；
F2, the asset size R for predicting jth yearj
<mrow>
<msub>
<mi>R</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>j</mi>
</munderover>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
<mo>+</mo>
<mi>J</mi>
</mrow>
F3, the assets newness rate E for predicting jth yearj
<mrow>
<msub>
<mi>E</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>R</mi>
<mrow>
<mi>n</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>R</mi>
<mrow>
<mi>o</mi>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mrow>
Wherein, RnjFor the Net asset value scale in jth year, RojFor the initial asset value scale in jth year；
G, the O＆M cost of overhaul W in jth year is predictedj
Binary function q (β, the U obtained according to step e8j,Fj), wherein β obtains in step e7, and step e1-- steps e8
The acquired data of step, the O＆M cost of overhaul for calculating jth year are used：
<mrow>
<msub>
<mi>W</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mi>q</mi>
<mrow>
<mo>(</mo>
<mi>&beta;</mi>
<mo>,</mo>
<msub>
<mi>U</mi>
<mi>j</mi>
</msub>
<mo>,</mo>
<msub>
<mi>F</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>q</mi>
<mrow>
<mo>(</mo>
<mi>&beta;</mi>
<mo>,</mo>
<msub>
<mi>U</mi>
<mi>j</mi>
</msub>
<mo>,</mo>
<mi>p</mi>
<mo>(</mo>
<msub>
<mi>E</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>q</mi>
<mrow>
<mo>(</mo>
<mi>&beta;</mi>
<mo>,</mo>
<msub>
<mi>U</mi>
<mi>j</mi>
</msub>
<mo>,</mo>
<mi>p</mi>
<mo>(</mo>
<mfrac>
<msub>
<mi>R</mi>
<mrow>
<mi>n</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>R</mi>
<mrow>
<mi>o</mi>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein UjFor the utilization rate in jth year,GjFor the electricity sales amount in jth year, using electricity sales amount then as radix, sale of electricity year
Growth rate is set as 5%.
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| CN109685261A (en) * | 2018-12-17 | 2019-04-26 | 国家电网有限公司 | A kind of grid equipment investment forecasting system and its prediction technique |
| CN110482815A (en) * | 2019-07-23 | 2019-11-22 | 湖南九层台环境科技有限公司 | A kind of rural area village septic tank handles O&M expense low level management system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685261A (en) * | 2018-12-17 | 2019-04-26 | 国家电网有限公司 | A kind of grid equipment investment forecasting system and its prediction technique |
| CN110482815A (en) * | 2019-07-23 | 2019-11-22 | 湖南九层台环境科技有限公司 | A kind of rural area village septic tank handles O&M expense low level management system |
| CN110482815B (en) * | 2019-07-23 | 2022-04-08 | 湖南九层台环境科技有限公司 | Rural village septic tank handles operation and maintenance cost extensive management system |
| CN110995465A (en) * | 2019-11-06 | 2020-04-10 | 国网浙江武义县供电有限公司 | Communication point panoramic view information operation and maintenance method and system |
| CN112668249A (en) * | 2020-07-17 | 2021-04-16 | 国网山东省电力公司电力科学研究院 | Online construction method and system for major repair technical modification scheme of primary equipment of power grid |
| CN112668249B (en) * | 2020-07-17 | 2023-05-02 | 国网山东省电力公司电力科学研究院 | Online construction method and system for power grid primary equipment overhaul technical modification scheme |
| CN112184072A (en) * | 2020-10-28 | 2021-01-05 | 中国联合网络通信集团有限公司 | Machine room equipment management method and device |
| CN112184072B (en) * | 2020-10-28 | 2023-07-25 | 中国联合网络通信集团有限公司 | Machine room equipment management method and device |
| CN112269779A (en) * | 2020-10-30 | 2021-01-26 | 国网上海市电力公司 | Big data analysis system and method for defects of power equipment |
| CN113570289A (en) * | 2021-09-26 | 2021-10-29 | 广东电网有限责任公司中山供电局 | Account card object consistency management method and system based on improved dragonfly optimization algorithm |
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