CN108205720A - A kind of power consumer credit estimation method and system based on index degree of variation - Google Patents
A kind of power consumer credit estimation method and system based on index degree of variation Download PDFInfo
- Publication number
- CN108205720A CN108205720A CN201611168777.4A CN201611168777A CN108205720A CN 108205720 A CN108205720 A CN 108205720A CN 201611168777 A CN201611168777 A CN 201611168777A CN 108205720 A CN108205720 A CN 108205720A
- Authority
- CN
- China
- Prior art keywords
- credit
- power consumer
- index
- assessed
- evaluation
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000011156 evaluation Methods 0.000 claims abstract description 322
- 239000011159 matrix material Substances 0.000 claims abstract description 129
- 238000013500 data storage Methods 0.000 claims abstract description 18
- 230000009466 transformation Effects 0.000 claims description 26
- 235000013350 formula milk Nutrition 0.000 claims description 23
- 238000007781 pre-processing Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 8
- 238000010606 normalization Methods 0.000 description 8
- 238000012854 evaluation process Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 230000005611 electricity Effects 0.000 description 5
- 238000003860 storage Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- FNMKZDDKPDBYJM-UHFFFAOYSA-N 3-(1,3-benzodioxol-5-yl)-7-(3-methylbut-2-enoxy)chromen-4-one Chemical compound C1=C2OCOC2=CC(C2=COC=3C(C2=O)=CC=C(C=3)OCC=C(C)C)=C1 FNMKZDDKPDBYJM-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- SBNFWQZLDJGRLK-UHFFFAOYSA-N phenothrin Chemical compound CC1(C)C(C=C(C)C)C1C(=O)OCC1=CC=CC(OC=2C=CC=CC=2)=C1 SBNFWQZLDJGRLK-UHFFFAOYSA-N 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0609—Buyer or seller confidence or verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of power consumer credit estimation method and system based on index degree of variation, wherein, this method includes：The actual assessment value of each credit evaluation index of power consumer to be assessed is chosen from data storage server, and then constructs the metrics evaluation matrix of power consumer to be assessed；According to metrics evaluation matrix and Boltzmann formula, the degree of variation of each credit evaluation index of power consumer to be assessed is calculated；Using the degree of variation of each credit evaluation index, the weight of each credit evaluation index is calculated；It adds up again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights, obtains the final credit evaluation value of each power consumer to be assessed；Wherein, final credit evaluation value is higher, then the credit rating of power consumer to be assessed is higher.
Description
Technical field
The invention belongs to power marketing field more particularly to a kind of power consumer credit evaluations based on index degree of variation
Method and system.
Background technology
In huge power consumer group, there are quite a few user's ability to ward off risks is weak, fund week is easily generated
Turn not smooth, can not pay the fees on time, so that the generation of the similar Credit Deficiency phenomenon such as stealing, arrearage.User credit missing is showed
As power supply enterprise has to pay a large amount of manpower and materials and financial resources are solved, this is to power supply enterprise in economy and resource side
Face brings serious waste.Therefore, it is whether normal, healthy, good to directly influence a power supply enterprise for the credit of power consumer
Good development, it has the normal business activities of power supply enterprise great and direct influence, in order to avoid power consumer credit
The loss brought to power supply enterprise is lacked, credit evaluation is carried out to power consumer, is taken measures in time for the user of credit difference,
It is that current power supply enterprise effectively avoids risk, improves the urgent problem to be solved that management level is faced.
During actual user's credit evaluation, first, the credit evaluation index of power consumer and corresponding assessment rule are determined
On the basis of then, the actual assessment value of each credit evaluation index of power consumer to be assessed is obtained；However based on electricity to be assessed
It is most of both at home and abroad at present to comment when the actual assessment value of each credit evaluation index of power user again assesses power consumer
Mechanism is estimated often using each credit evaluation of the appraisal procedure of the subjective judgement by appraiser to power consumer to be assessed
The actual assessment value of index is handled, such as expert assessment method, analytic hierarchy process (AHP).Such method rely primarily on appraiser with
Past experience and experience, evaluation decision also depend on the subjective judgement of evaluator, and this kind of appraisal procedure causes assessment result to have
Strong subjectivity and artificial property lack justice, it is difficult to ensure the accuracy of assessment result.
Invention content
In order to solve the disadvantage that the prior art, the first object of the present invention is to provide a kind of electricity based on index degree of variation
Power user credit appraisal procedure.
A kind of power consumer credit estimation method based on index degree of variation of the present invention, including：
Step 1：Actually commenting for each credit evaluation index of power consumer to be assessed is chosen from data storage server
Valuation, and then construct the metrics evaluation matrix of power consumer to be assessed；Every a line expression one of metrics evaluation matrix is to be evaluated
Estimate power consumer, and the actual assessment value for each credit evaluation index that the element per a line is corresponding power consumer to be assessed；
Step 2：According to metrics evaluation matrix and Boltzmann formula, each credit for calculating power consumer to be assessed is commented
Estimate the degree of variation of index；
Step 3：It is utilized respectively 1 and makees with the degree of variation of each credit evaluation index poor, obtain each credit evaluation index
The value information effectiveness of offer；It is utilized respectively value information effectiveness and all credit evaluations that each credit evaluation index provides again
The value information effectiveness that index provides adds up and makees quotient, calculates the weight of each credit evaluation index；
Step 4：It adds up again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights,
Obtain the final credit evaluation value of each power consumer to be assessed；Wherein, final credit evaluation value is higher, then electric power to be assessed is used
The credit rating at family is higher.
The present invention has abandoned traditional subjectivity assessment mode by the passing experience of appraiser and experience, according to index
The degree of variation of data in itself determines index weights, the finger that the power consumer credit estimation method based on index degree of variation calculates
Mark weight is consistent with convention, and with objective, fairness, is adapted to power consumer credit evaluation process, finally so that being somebody's turn to do
The more previous assessment result of method has more objectivity and fairness, effectively prevents the risk that error evaluation result is brought.
Further, this method before step 1, further includes：
It scores each power consumer, obtains each according to presetting credit evaluation index and its rule should be assessed
The actual assessment value of each credit evaluation index of power consumer is simultaneously stored to data storage server.
The present invention will integrate various objective condition, according to power consumer evaluation requirement, formulate power consumer credit evaluation and refer to
Mark system sets credit evaluation index and its corresponding assessment rule, the reality of each credit evaluation index of each power consumer
Border assessed value；To store in data storage server data basis is provided for the assessment of following needs user credit.
Further, the degree of variation of each credit evaluation index of power consumer to be assessed is calculated in the step 2
Before, it further includes：
Pretreatment is normalized to the element of metrics evaluation matrix.
The data of different dimensions and different number grade are transformed into the number being comparable by the present invention by data prediction
According to the data for preventing absolute value big flood the small data of absolute value.
Further, the element of metrics evaluation matrix is normalized using max min preprocess method pre-
Processing, detailed process are：
C1：Select the maxima and minima in each column element of metrics evaluation matrix；
C2：Calculate the difference of the maxima and minima in each column element；
C3：The difference of each element and minimum value in each row is calculated respectively；
C4：By the result of step C3 divided by step C2's as a result, obtaining normalizing pretreated metrics evaluation matrix.
Pretreatment is normalized to the element of metrics evaluation matrix, can be used at method for distinguishing such as mean-square value method
Reason, but the method that the element of metrics evaluation matrix is normalized in pretreatment using max min preprocess method,
Simplicity is calculated, it is efficient.
Further, it after pretreatment is normalized in the element to metrics evaluation matrix, calculates electric power to be assessed and uses
Before the degree of variation of each credit evaluation index at family, further include：It is carried out to normalizing pretreated metrics evaluation matrix
Matrixing, transformation for mula are：
OrWherein, y (i, j) represents that normalization is pretreated
The element value of the i-th row jth row of metrics evaluation matrix；F (i, j) represents the i-th row jth row of the metrics evaluation matrix after transformation
Element value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
The present invention carries out matrixing to normalizing pretreated metrics evaluation matrix, can intuitively show the electric power
The level that user's index comparison other users are in, the facility calculated using the transformation for back, and calculate easy.
The second object of the present invention is to provide a kind of power consumer credit evaluation system based on index degree of variation.
A kind of power consumer credit evaluation system based on index degree of variation of the present invention, including：
Metrics evaluation matrix construction module is used to choose each of power consumer to be assessed from data storage server
The actual assessment value of credit evaluation index, and then construct the metrics evaluation matrix of power consumer to be assessed；Metrics evaluation matrix
Every a line represent a power consumer to be assessed, and each credit that the element per a line is corresponding power consumer to be assessed is commented
Estimate the actual assessment value of index；
The degree of variation computing module of credit evaluation index is used for according to metrics evaluation matrix and Boltzmann formula,
Calculate the degree of variation of each credit evaluation index of power consumer to be assessed；
The weight computation module of credit evaluation index is used to be utilized respectively the 1 change off course with each credit evaluation index
It is poor that degree is made, and obtains the value information effectiveness that each credit evaluation index provides；Each credit evaluation index is utilized respectively again to provide
The value information effectiveness that provides of value information effectiveness and all credit evaluation indexs add up and make quotient, calculate each credit and comment
Estimate the weight of index；
Final credit evaluation value computing module is used for the actual assessment value of each credit evaluation index power corresponding to its
It adds up again be multiplied respectively again after, obtains the final credit evaluation value of each power consumer to be assessed；Wherein, final credit is commented
Valuation is higher, then the credit rating of power consumer to be assessed is higher.
The present invention has abandoned traditional subjectivity assessment mode by the passing experience of appraiser and experience, according to index
The degree of variation of data in itself determines index weights, the finger that the power consumer credit estimation method based on index degree of variation calculates
Mark weight is consistent with convention, and with objective, fairness, is adapted to power consumer credit evaluation process, finally so that originally
The more previous assessment result of invention has more objectivity and fairness, effectively prevents the risk that error evaluation result is brought.
Further, the system, further includes：
Power consumer grading module is used for basis and presets credit evaluation index and its should assess rule to each electricity
Power user scores, and obtains the actual assessment value of each credit evaluation index of each power consumer and store to data to store
In server.
The present invention will integrate various objective condition, according to power consumer evaluation requirement, formulate power consumer credit evaluation and refer to
Mark system sets credit evaluation index and its corresponding assessment rule, the reality of each credit evaluation index of each power consumer
Border assessed value；To store in data storage server data basis is provided for the assessment of following needs user credit.
Further, the system, further includes：
Preprocessing module is used to that pretreatment to be normalized to the element of metrics evaluation matrix.The present invention passes through data
The data of different dimensions and different number grade are transformed into the data being comparable by pretreatment, and the data for preventing absolute value big are flooded
Do not have the data that absolute value is small.
Further, the preprocessing module includes：
Most it is worth screening module, the maxima and minima being used in each column element for selecting metrics evaluation matrix；
First difference calculating module is used to calculate the difference of the maxima and minima in each column element；
Second difference calculating module is used to calculate the difference of each element and minimum value in each row respectively；
Make quotient module block, be used for it is by the result of the first difference calculating module divided by the second difference calculating module as a result,
To the pretreated metrics evaluation matrix of normalization.
Pretreatment is normalized to the element of metrics evaluation matrix, can be used at method for distinguishing such as mean-square value method
Reason, but the method that the element of metrics evaluation matrix is normalized in pretreatment using max min preprocess method,
Simplicity is calculated, it is efficient.
Further, the system further includes matrixing module, is used for normalizing pretreated metrics evaluation square
Battle array carries out matrixing, and transformation for mula is：
OrWherein, y (i, j) represents that normalization is pretreated
The element value of the i-th row jth row of metrics evaluation matrix；F (i, j) represents the i-th row jth row of the metrics evaluation matrix after transformation
Element value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
The present invention carries out matrixing to normalizing pretreated metrics evaluation matrix, can intuitively show the electric power
The level that user's index comparison other users are in, the facility calculated using the transformation for back, and calculate easy.
The present invention also provides power consumer credit evaluation system of the another kind based on index degree of variation, the system packets
It includes：
Data acquisition device is configured as choosing each credit of power consumer to be assessed from data storage server
The actual assessment value of evaluation index；
Credit evaluating service device, is configured as：
According to the actual assessment value of each credit evaluation index of power consumer to be assessed, power consumer to be assessed is constructed
Metrics evaluation matrix；Every a line of metrics evaluation matrix represents a power consumer to be assessed, and the element per a line is phase
Answer the actual assessment value of each credit evaluation index of power consumer to be assessed；
According to metrics evaluation matrix and Boltzmann formula, each credit evaluation index of power consumer to be assessed is calculated
Degree of variation；
It is utilized respectively 1 and makees with the degree of variation of each credit evaluation index poor, each credit evaluation index offer is provided
Value information effectiveness；The value information effectiveness that each credit evaluation index provides is utilized respectively again to carry with all credit evaluation indexs
The value information effectiveness of confession adds up and makees quotient, calculates the weight of each credit evaluation index；
It adds up, obtains every again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights
The final credit evaluation value of a power consumer to be assessed；Wherein, final credit evaluation value is higher, then the letter of power consumer to be assessed
Expenditure is higher.
The present invention has abandoned traditional subjectivity assessment mode by the passing experience of appraiser and experience, according to index
The degree of variation of data in itself determines index weights, the finger that the power consumer credit estimation method based on index degree of variation calculates
Mark weight is consistent with convention, and with objective, fairness, is adapted to power consumer credit evaluation process, finally so that originally
The more previous assessment result of invention has more objectivity and fairness, effectively prevents the risk that error evaluation result is brought.
Further, the credit evaluating service device, is additionally configured to：
Pretreatment is normalized to the element of metrics evaluation matrix.
The data of different dimensions and different number grade are transformed into the number being comparable by the present invention by data prediction
According to the data for preventing absolute value big flood the small data of absolute value.
Further, the credit evaluating service device, is configured as：
C1：Select the maxima and minima in each column element of metrics evaluation matrix；
C2：Calculate the difference of the maxima and minima in each column element；
C3：The difference of each element and minimum value in each row is calculated respectively；
C4：By the result of step C3 divided by step C2's as a result, obtaining normalizing pretreated metrics evaluation matrix.
Pretreatment is normalized to the element of metrics evaluation matrix, can be used at method for distinguishing such as mean-square value method
Reason, but the method that the element of metrics evaluation matrix is normalized in pretreatment using max min preprocess method,
Simplicity is calculated, it is efficient.
Further, the credit evaluating service device, is additionally configured to：
After pretreatment is normalized in the element to metrics evaluation matrix, each letter of power consumer to be assessed is calculated
Before the degree of variation of evaluation index, matrixing is carried out to normalizing pretreated metrics evaluation matrix, transformation is public
Formula is：
OrWherein, y (i, j) represents that normalization is pretreated
The element value of the i-th row jth row of metrics evaluation matrix；F (i, j) represents the i-th row jth row of the metrics evaluation matrix after transformation
Element value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
The present invention carries out matrixing to normalizing pretreated metrics evaluation matrix, can intuitively show the electric power
The level that user's index comparison other users are in, the facility calculated using the transformation for back, and calculate easy.
Beneficial effects of the present invention are：
The present invention has abandoned traditional subjectivity assessment mode by the passing experience of appraiser and experience, according to index
The degree of variation of data in itself determines index weights, the finger that the power consumer credit estimation method based on index degree of variation calculates
Mark weight is consistent with convention, and with objective, fairness, is adapted to power consumer credit evaluation process, finally so that originally
The more previous assessment result of invention has more objectivity and fairness, effectively prevents the risk that error evaluation result is brought.
Description of the drawings
Fig. 1 is a kind of flow chart of power consumer credit estimation method based on index degree of variation of the present invention.
Fig. 2 is the flow chart of the parameter degree of variation of the present invention.
A kind of one structure of power consumer credit evaluation system embodiment based on index degree of variation that Fig. 3 is the present invention is shown
It is intended to.
Fig. 4 is the preprocessing module structure diagram of the present invention.
A kind of two structure of power consumer credit evaluation system embodiment based on index degree of variation that Fig. 5 is the present invention is shown
It is intended to.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.
It is as shown in Figure 1 a kind of flow chart of the power consumer credit estimation method based on index degree of variation, this method
Realization step it is as follows：
Step 1：Actually commenting for each credit evaluation index of power consumer to be assessed is chosen from data storage server
Valuation, and then construct the metrics evaluation matrix of power consumer to be assessed；Every a line expression one of metrics evaluation matrix is to be evaluated
Estimate power consumer, and the actual assessment value for each credit evaluation index that the element per a line is corresponding power consumer to be assessed.
Before step 1, it further includes：
It scores each power consumer, obtains each according to presetting credit evaluation index and its rule should be assessed
The actual assessment value of each credit evaluation index of power consumer is simultaneously stored to data storage server.
Wherein, preset credit scoring model refers to including commercial credit, safety credit, law credit and cooperative credit
Mark, such as：
A：Comprehensive various objective condition, the present embodiment formulate power consumer credit evaluation index system, are set for each index
Identical full marks percentage standard is put, and formulates the corresponding assessment rule of each index.
The present embodiment will integrate various objective condition, according to power consumer evaluation requirement, formulate power consumer credit evaluation
Index system sets credit evaluation index and its corresponding assessment rule, each credit evaluation index of each power consumer
Actual assessment value；To store in data storage server data basis is provided for the assessment of following needs user credit.
Step 2：According to metrics evaluation matrix and Boltzmann formula, each credit for calculating power consumer to be assessed is commented
Estimate the degree of variation of index.
The actual assessment value for choosing each credit evaluation index of m power consumer below is sample data, it is carried out
Credit evaluation, for easier description evaluation process, by taking m takes 7 as an example：
Illustrate the process by carrying out assessment using the present invention to 7 users.Corresponding to the suitable of assessment level two-level index
Sequence, each user use Ti(i=1,2 ..., 7) is represented, 8 indexs of each user use T respectivelyj(j=1,2 ..., 8) is represented.
The single index evaluations matrix S of 7 users is constructed according to physical record7*8：
Before the degree of variation of each credit evaluation index for calculating power consumer to be assessed in step 2, further include：
Pretreatment is normalized to the element of metrics evaluation matrix, for not passing through data prediction by different dimensions and not
Data with the order of magnitude are transformed into the data that are comparable, and the data for preventing absolute value big flood the small data of absolute value.
Wherein, pretreatment is normalized to the element of metrics evaluation matrix, can be used method for distinguishing such as mean-square value method into
Row processing, but the element of metrics evaluation matrix is normalized using max min preprocess method the side of pretreatment
Method calculates simplicity, efficient.
It introduces in detail below and normalizing is carried out to the element of metrics evaluation matrix using max min preprocess method
Change the method for pretreatment, with to matrix S7*8For pretreatment is normalized using maximin method for pretreating, using such as figure
Index matrix data prediction flow shown in 2, step are as follows：
C1：Select the maxima and minima in each column element of metrics evaluation matrix；
C2：Calculate the difference of the maxima and minima in each column element；
C3：The difference of each element and minimum value in each row is calculated respectively；
C4：By the result of step C3 divided by step C2's as a result, obtaining normalizing pretreated metrics evaluation matrix
T(7*8)。
After pretreatment is normalized in the element to metrics evaluation matrix, each letter of power consumer to be assessed is calculated
Before the degree of variation of evaluation index, further include：Matrixing is carried out to normalizing pretreated metrics evaluation matrix,
Transformation for mula is：
OrWherein, y (i, j) represents that normalization is pretreated
The element value of the i-th row jth row of metrics evaluation matrix；F (i, j) represents the i-th row jth row of the metrics evaluation matrix after transformation
Element value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
Matrixing is carried out to normalizing pretreated metrics evaluation matrix, can intuitively show that the power consumer should
The level that index comparison other users are in, the facility calculated using the transformation for back, and calculate easy.
Below to be converted to achievement data matrix, using the transformation for mula of following form：
Obtain matrix F(i, j)。
Further according to Boltzmann formula, the degree of variation of each credit evaluation index of power consumer to be assessed is calculated：
The degree of variation for calculating each index is：
[H1=0.801 H2=0.607 H3=0.723 H4=0.870H5=0.921 H6=0.684 H7=0.913 H8
=0.976]
Step 3：It is utilized respectively 1 and makees with the degree of variation of each credit evaluation index poor, obtain each credit evaluation index
The value information effectiveness of offer；It is utilized respectively value information effectiveness and all credit evaluations that each credit evaluation index provides again
The value information effectiveness that index provides adds up and makees quotient, calculates the weight of each credit evaluation index.
Index degree of variation HjMaximum value for 1, represent that the index is capable of providing the effectiveness 0 of value information, then any finger
The effectiveness that mark provides value information is 1-Hj。
The weight of parameter is：
[W1=0.132 W2=0.261 W3=0.184 W4=0.086 W5=0.052 W6=0.209 W7=0.058
W9=0.016]
Step 4：It adds up again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights,
Obtain the final credit evaluation value of each power consumer to be assessed；Wherein, final credit evaluation value is higher, then electric power to be assessed is used
The credit rating at family is higher.
The practical index for choosing m power consumer is scored at sample data, carries out credit evaluation to it, is retouched in order to easier
Evaluation process is stated, m takes 7, by carrying out credit evaluation respectively to 7 users：
The final credit evaluation value of each power consumer is calculated according to the index weights that acquire, using first power consumer as
Example：
T1=90*0.132+70*0.261+80*0.184+100*0.086+100*0.052+70*0.209+ 84*0.058+
85*0.016=70.532.
This method of the present embodiment has abandoned traditional subjectivity assessment side by the passing experience of appraiser and experience
Formula determines index weights, the power consumer credit evaluation based on index degree of variation according to the degree of variation of achievement data in itself
The index weights that method calculates are consistent with convention, and with objective, fairness, are adapted to power consumer credit evaluation
Journey, the final assessment result for so that this method is more previous effectively prevent error evaluation result with more objectivity and fairness
The risk brought.
Fig. 3 is a kind of structure of power consumer credit evaluation system embodiment one based on index degree of variation of the present invention
Schematic diagram.Power consumer credit evaluation system based on index degree of variation as shown in Figure 3, including：Metrics evaluation matrix structure
Modeling block, the degree of variation computing module of credit evaluation index, the weight computation module of credit evaluation index and final credit are commented
Valuation computing module.
(1) metrics evaluation matrix construction module
Metrics evaluation matrix construction module is used to choose each of power consumer to be assessed from data storage server
The actual assessment value of credit evaluation index, and then construct the metrics evaluation matrix of power consumer to be assessed；Metrics evaluation matrix
Every a line represent a power consumer to be assessed, and each credit that the element per a line is corresponding power consumer to be assessed is commented
Estimate the actual assessment value of index.
(2) the degree of variation computing module of credit evaluation index
The degree of variation computing module of credit evaluation index is used for according to metrics evaluation matrix and Boltzmann formula,
Calculate the degree of variation of each credit evaluation index of power consumer to be assessed.
Further, the system, further includes：
Preprocessing module is used to that pretreatment to be normalized to the element of metrics evaluation matrix.The present invention passes through data
The data of different dimensions and different number grade are transformed into the data being comparable by pretreatment, and the data for preventing absolute value big are flooded
Do not have the data that absolute value is small.
Fig. 4 is the preprocessing module structure diagram of the present invention.Preprocessing module as shown in Figure 4 includes：
Most it is worth screening module, the maxima and minima being used in each column element for selecting metrics evaluation matrix；
First difference calculating module is used to calculate the difference of the maxima and minima in each column element；
Second difference calculating module is used to calculate the difference of each element and minimum value in each row respectively；
Make quotient module block, be used for it is by the result of the first difference calculating module divided by the second difference calculating module as a result,
To the pretreated metrics evaluation matrix of normalization.
Pretreatment is normalized to the element of metrics evaluation matrix, can be used at method for distinguishing such as mean-square value method
Reason, but the method that the element of metrics evaluation matrix is normalized in pretreatment using max min preprocess method,
Simplicity is calculated, it is efficient.
Further, the system further includes matrixing module, is used for normalizing pretreated metrics evaluation square
Battle array carries out matrixing, and transformation for mula is：
OrWherein, y (i, j) represents that normalization is pretreated
The element value of the i-th row jth row of metrics evaluation matrix；F (i, j) represents the i-th row jth row of the metrics evaluation matrix after transformation
Element value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
The present embodiment carries out matrixing to normalizing pretreated metrics evaluation matrix, can intuitively show the electricity
The level that power user index comparison other users are in, the facility calculated using the transformation for back, and calculate easy.
(3) weight computation module of credit evaluation index
The weight computation module of credit evaluation index is used to be utilized respectively the 1 change off course with each credit evaluation index
It is poor that degree is made, and obtains the value information effectiveness that each credit evaluation index provides；Each credit evaluation index is utilized respectively again to provide
The value information effectiveness that provides of value information effectiveness and all credit evaluation indexs add up and make quotient, calculate each credit and comment
Estimate the weight of index.
(4) final credit evaluation value computing module
Final credit evaluation value computing module is used for the actual assessment value of each credit evaluation index power corresponding to its
It adds up again be multiplied respectively again after, obtains the final credit evaluation value of each power consumer to be assessed；Wherein, final credit is commented
Valuation is higher, then the credit rating of power consumer to be assessed is higher.
The system of the present embodiment has abandoned traditional subjectivity assessment side by the passing experience of appraiser and experience
Formula determines index weights, the power consumer credit evaluation based on index degree of variation according to the degree of variation of achievement data in itself
The index weights that method calculates are consistent with convention, and with objective, fairness, are adapted to power consumer credit evaluation
Journey, it is final so that more previous assessment result of the invention effectively prevents error evaluation result with more objectivity and fairness
The risk brought.
Further, the system, further includes：
Power consumer grading module is used for basis and presets credit evaluation index and its should assess rule to each electricity
Power user scores, and obtains the actual assessment value of each credit evaluation index of each power consumer and store to data to store
In server.
The present embodiment will integrate various objective condition, according to power consumer evaluation requirement, formulate power consumer credit evaluation
Index system sets credit evaluation index and its corresponding assessment rule, each credit evaluation index of each power consumer
Actual assessment value；To store in data storage server data basis is provided for the assessment of following needs user credit.
A kind of two structure of power consumer credit evaluation system embodiment based on index degree of variation that Fig. 5 is the present invention is shown
It is intended to.A kind of power consumer credit evaluation system based on index degree of variation of the present invention shown in fig. 5, the system include：Number
According to harvester and credit evaluating service device.
(1) data acquisition device
Data acquisition device is configured as choosing each credit of power consumer to be assessed from data storage server
The actual assessment value of evaluation index.
Wherein, data acquisition device is existing structure, be can be realized as by the prior art.And the data in the present invention
Storage server, for storing the actual assessment value of each credit evaluation index of power consumer.
The source of the actual assessment value of each credit evaluation index of power consumer is：
First, it presets credit evaluation index and its rule should be assessed；Then, referred to according to preset credit evaluation
It marks and its rule should be assessed and score each power consumer；Finally, each credit evaluation for obtaining each power consumer refers to
Target actual assessment value.
(2) credit evaluating service device
Credit evaluating service device, is configured as：
According to the actual assessment value of each credit evaluation index of power consumer to be assessed, power consumer to be assessed is constructed
Metrics evaluation matrix；Every a line of metrics evaluation matrix represents a power consumer to be assessed, and the element per a line is phase
Answer the actual assessment value of each credit evaluation index of power consumer to be assessed；
According to metrics evaluation matrix and Boltzmann formula, each credit evaluation index of power consumer to be assessed is calculated
Degree of variation；
It is utilized respectively 1 and makees with the degree of variation of each credit evaluation index poor, each credit evaluation index offer is provided
Value information effectiveness；The value information effectiveness that each credit evaluation index provides is utilized respectively again to carry with all credit evaluation indexs
The value information effectiveness of confession adds up and makees quotient, calculates the weight of each credit evaluation index；
It adds up, obtains every again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights
The final credit evaluation value of a power consumer to be assessed；Wherein, final credit evaluation value is higher, then the letter of power consumer to be assessed
Expenditure is higher.
Further, the credit evaluating service device, is additionally configured to：
Pretreatment is normalized to the element of metrics evaluation matrix.
The data of different dimensions and different number grade are transformed into the number being comparable by the present invention by data prediction
According to the data for preventing absolute value big flood the small data of absolute value.
Further, the credit evaluating service device, is configured as：
C1：Select the maxima and minima in each column element of metrics evaluation matrix；
C2：Calculate the difference of the maxima and minima in each column element；
C3：The difference of each element and minimum value in each row is calculated respectively；
C4：By the result of step C3 divided by step C2's as a result, obtaining normalizing pretreated metrics evaluation matrix.
Pretreatment is normalized to the element of metrics evaluation matrix, can be used at method for distinguishing such as mean-square value method
Reason, but the method that the element of metrics evaluation matrix is normalized in pretreatment using max min preprocess method,
Simplicity is calculated, it is efficient.
Further, the credit evaluating service device, is additionally configured to：
After pretreatment is normalized in the element to metrics evaluation matrix, each letter of power consumer to be assessed is calculated
Before the degree of variation of evaluation index, matrixing is carried out to normalizing pretreated metrics evaluation matrix, transformation is public
Formula is：
OrWherein, y (i, j) represents that normalization is pretreated
The element value of the i-th row jth row of metrics evaluation matrix；F (i, j) represents the i-th row jth row of the metrics evaluation matrix after transformation
Element value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
The present invention carries out matrixing to normalizing pretreated metrics evaluation matrix, can intuitively show the electric power
The level that user's index comparison other users are in, the facility calculated using the transformation for back, and calculate easy.
The present invention has abandoned traditional subjectivity assessment mode by the passing experience of appraiser and experience, according to index
The degree of variation of data in itself determines index weights, the finger that the power consumer credit estimation method based on index degree of variation calculates
Mark weight is consistent with convention, and with objective, fairness, is adapted to power consumer credit evaluation process, finally so that originally
The more previous assessment result of invention has more objectivity and fairness, effectively prevents the risk that error evaluation result is brought.
It should be understood by those skilled in the art that, the embodiment of the present invention can be provided as method, system or computer program
Product.Therefore, the shape of the embodiment in terms of hardware embodiment, software implementation or combination software and hardware can be used in the present invention
Formula.Moreover, the present invention can be used can use storage in one or more computers for wherein including computer usable program code
The form of computer program product that medium is implemented on (including but not limited to magnetic disk storage and optical memory etc.).
The present invention be with reference to according to the method for the embodiment of the present invention, the flow of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that it can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
The processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that the instruction performed by computer or the processor of other programmable data processing devices is generated for real
The device of function specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction generation being stored in the computer-readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that counted
Series of operation steps are performed on calculation machine or other programmable devices to generate computer implemented processing, so as in computer or
The instruction offer performed on other programmable devices is used to implement in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer read/write memory medium
In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random
AccessMemory, RAM) etc..
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (10)
1. a kind of power consumer credit estimation method based on index degree of variation, which is characterized in that including：
Step 1：The actual assessment value of each credit evaluation index of power consumer to be assessed is chosen from data storage server,
And then construct the metrics evaluation matrix of power consumer to be assessed；Every a line of metrics evaluation matrix represents an electric power to be assessed
User, and the actual assessment value for each credit evaluation index that the element per a line is corresponding power consumer to be assessed；
Step 2：According to metrics evaluation matrix and Boltzmann formula, each credit evaluation for calculating power consumer to be assessed refers to
Target degree of variation；
Step 3：It is utilized respectively 1 and makees with the degree of variation of each credit evaluation index poor, each credit evaluation index offer is provided
Value information effectiveness；It is utilized respectively value information effectiveness and all credit evaluation indexs that each credit evaluation index provides again
The value information effectiveness of offer adds up and makees quotient, calculates the weight of each credit evaluation index；
Step 4：It adds up, obtains again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights
The final credit evaluation value of each power consumer to be assessed；Wherein, final credit evaluation value is higher, then power consumer to be assessed
Credit rating is higher.
2. a kind of power consumer credit estimation method based on index degree of variation as described in claim 1, which is characterized in that
This method before step 1, further includes：
It scores according to presetting credit evaluation index and its rule should be assessed each power consumer, obtains each electric power
The actual assessment value of each credit evaluation index of user is simultaneously stored to data storage server.
3. a kind of power consumer credit estimation method based on index degree of variation as described in claim 1, which is characterized in that
Before the degree of variation of each credit evaluation index that power consumer to be assessed is calculated in the step 2, further include：
Pretreatment is normalized to the element of metrics evaluation matrix.
4. a kind of power consumer credit estimation method based on index degree of variation as claimed in claim 3, which is characterized in that
Pretreatment is normalized to the element of metrics evaluation matrix using max min preprocess method, detailed process is：
C1：Select the maxima and minima in each column element of metrics evaluation matrix；
C2：Calculate the difference of the maxima and minima in each column element；
C3：The difference of each element and minimum value in each row is calculated respectively；
C4：By the result of step C3 divided by step C2's as a result, obtaining normalizing pretreated metrics evaluation matrix.
5. a kind of power consumer credit estimation method based on index degree of variation as claimed in claim 3, which is characterized in that
After pretreatment is normalized in the element to metrics evaluation matrix, each credit evaluation for calculating power consumer to be assessed refers to
Before target degree of variation, further include：Matrixing, transformation for mula are carried out to normalizing pretreated metrics evaluation matrix
For：
OrWherein, y (i, j) represents to normalize pretreated index
The element value of the i-th row jth row of evaluations matrix；F (i, j) represents the element of the i-th row jth row of the metrics evaluation matrix after transformation
Value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
6. a kind of power consumer credit evaluation system based on index degree of variation, which is characterized in that including：
Metrics evaluation matrix construction module is used to choose each credit of power consumer to be assessed from data storage server
The actual assessment value of evaluation index, and then construct the metrics evaluation matrix of power consumer to be assessed；Metrics evaluation matrix it is every
A line represents a power consumer to be assessed, and each credit evaluation that the element per a line is corresponding power consumer to be assessed refers to
Target actual assessment value；
The degree of variation computing module of credit evaluation index is used to, according to metrics evaluation matrix and Boltzmann formula, calculate
Go out the degree of variation of each credit evaluation index of power consumer to be assessed；
The weight computation module of credit evaluation index is used to be utilized respectively the 1 degree of variation work with each credit evaluation index
Difference obtains the value information effectiveness that each credit evaluation index provides；It is utilized respectively the valency that each credit evaluation index provides again
The value information effectiveness that value information effectiveness and all credit evaluation indexs provide adds up and makees quotient, calculates each credit evaluation and refers to
Target weight；
Final credit evaluation value computing module is used for the actual assessment value of each credit evaluation index and its respective weights point
Not Xiang Cheng after add up again, obtain the final credit evaluation value of each power consumer to be assessed；Wherein, final credit evaluation value
Higher, then the credit rating of power consumer to be assessed is higher.
7. a kind of power consumer credit evaluation system based on index degree of variation as claimed in claim 6, which is characterized in that
The system, further includes：
Power consumer grading module is used for basis and presets credit evaluation index and its should assess rule to each electric power use
Family is scored, and is obtained the actual assessment value of each credit evaluation index of each power consumer and is stored to data storage service
In device；
Or the system, it further includes：
Preprocessing module is used to that pretreatment to be normalized to the element of metrics evaluation matrix；
Or the system, matrixing module is further included, is used for normalizing pretreated metrics evaluation matrix into row matrix
Transformation, transformation for mula are：
OrWherein, y (i, j) represents to normalize pretreated index
The element value of the i-th row jth row of evaluations matrix；F (i, j) represents the element of the i-th row jth row of the metrics evaluation matrix after transformation
Value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
8. a kind of power consumer credit evaluation system based on index degree of variation as claimed in claim 7, which is characterized in that
The preprocessing module includes：
Most it is worth screening module, the maxima and minima being used in each column element for selecting metrics evaluation matrix；
First difference calculating module is used to calculate the difference of the maxima and minima in each column element；
Second difference calculating module is used to calculate the difference of each element and minimum value in each row respectively；
Make quotient module block, be used for the result of the first difference calculating module divided by the second difference calculating module as a result, being returned
One changes pretreated metrics evaluation matrix.
9. a kind of power consumer credit evaluation system based on index degree of variation, which is characterized in that including：
Data acquisition device is configured as choosing each credit evaluation of power consumer to be assessed from data storage server
The actual assessment value of index；
Credit evaluating service device, is configured as：
According to the actual assessment value of each credit evaluation index of power consumer to be assessed, the finger of power consumer to be assessed is constructed
Mark evaluations matrix；Every a line of metrics evaluation matrix represents a power consumer to be assessed, and the element per a line is accordingly treats
Assess the actual assessment value of each credit evaluation index of power consumer；
According to metrics evaluation matrix and Boltzmann formula, the change of each credit evaluation index of power consumer to be assessed is calculated
Off course degree；
It is utilized respectively 1 and makees with the degree of variation of each credit evaluation index poor, the value of each credit evaluation index offer is provided
Information utility；It is utilized respectively what the value information effectiveness that each credit evaluation index provides was provided with all credit evaluation indexs again
Value information effectiveness adds up and makees quotient, calculates the weight of each credit evaluation index；
It adds up again after the actual assessment value of each credit evaluation index is multiplied respectively with its respective weights, obtains each treat
Assess the final credit evaluation value of power consumer；Wherein, final credit evaluation value is higher, then the credit rating of power consumer to be assessed
It is higher.
10. a kind of power consumer credit evaluation system based on index degree of variation as claimed in claim 9, feature exist
In the credit evaluating service device is additionally configured to：Pretreatment is normalized to the element of metrics evaluation matrix, wherein, it is right
The process that pretreatment is normalized in the element of metrics evaluation matrix is：
C1：Select the maxima and minima in each column element of metrics evaluation matrix；
C2：Calculate the difference of the maxima and minima in each column element；
C3：The difference of each element and minimum value in each row is calculated respectively；
C4：By the result of step C3 divided by step C2's as a result, obtaining normalizing pretreated metrics evaluation matrix；
Or the credit evaluating service device, it is additionally configured to：
After pretreatment is normalized in the element to metrics evaluation matrix, each credit for calculating power consumer to be assessed is commented
Before the degree of variation for estimating index, matrixing is carried out to normalizing pretreated metrics evaluation matrix, transformation for mula is：
OrWherein, y (i, j) represents to normalize pretreated index
The element value of the i-th row jth row of evaluations matrix；F (i, j) represents the element of the i-th row jth row of the metrics evaluation matrix after transformation
Value；M represents the line number of metrics evaluation matrix, is also equal to the number of power consumer to be assessed；I, j, m are positive integer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611168777.4A CN108205720A (en) | 2016-12-16 | 2016-12-16 | A kind of power consumer credit estimation method and system based on index degree of variation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611168777.4A CN108205720A (en) | 2016-12-16 | 2016-12-16 | A kind of power consumer credit estimation method and system based on index degree of variation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108205720A true CN108205720A (en) | 2018-06-26 |
Family
ID=62602508
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611168777.4A Pending CN108205720A (en) | 2016-12-16 | 2016-12-16 | A kind of power consumer credit estimation method and system based on index degree of variation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108205720A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113240236A (en) * | 2021-04-07 | 2021-08-10 | 国网河北省电力有限公司衡水供电分公司 | User credit determination method, device and terminal |
-
2016
- 2016-12-16 CN CN201611168777.4A patent/CN108205720A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113240236A (en) * | 2021-04-07 | 2021-08-10 | 国网河北省电力有限公司衡水供电分公司 | User credit determination method, device and terminal |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zavadskas et al. | Selecting a contractor by using a novel method for multiple attribute analysis: Weighted Aggregated Sum Product Assessment with grey values (WASPAS-G) | |
| Song et al. | How China's current carbon trading policy affects carbon price? An investigation of the Shanghai Emission Trading Scheme pilot | |
| Kirikkaleli et al. | Political risk and environmental quality in Brazil: role of green finance and green innovation | |
| Qin et al. | Blockchain: a carbon-neutral facilitator or an environmental destroyer? | |
| Luthra et al. | Sustainable assessment in energy planning and management in Indian perspective | |
| Torkayesh et al. | A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications | |
| Young et al. | Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market | |
| CN110738405A (en) | Method and device for evaluating effectiveness of power market and storage medium | |
| CN109859052A (en) | A kind of intelligent recommendation method, apparatus, storage medium and the server of investment tactics | |
| Marshall et al. | Assessing wholesale competition in the Australian national electricity market | |
| Mnif et al. | Energy-conserving cryptocurrency response during the COVID-19 pandemic and amid the Russia–Ukraine conflict | |
| Wei et al. | A barrier evaluation framework for forest carbon sink project implementation in China using an integrated BWM-IT2F-PROMETHEE II method | |
| Jakšić et al. | Fuzzy approach in ranking of banks according to financial performances | |
| Xiao et al. | Impact of industry-university-research collaboration and convergence on economic development: Evidence from chengdu-chongqing economic circle in China | |
| Yürek et al. | Socio-political evaluation of renewable energy resources under uncertain environment | |
| CN108205720A (en) | A kind of power consumer credit estimation method and system based on index degree of variation | |
| Schwindenhammer | Patterns and Explanations of Corporate Voluntary Norm Compliance: Results from a Structured Focused Comparison of German G500 in the Global Reporting Initiative. | |
| Muhammad et al. | Information security investment prioritization using best-worst method for small and medium enterprises | |
| Li et al. | Assessment of Industrial Internet Platform Application in Manufacturing Enterprises: System Construction and Industrial Practice | |
| Razak et al. | Analytical Hierarchy Process for Automated Fertigation Blending System in Reducing Nutrient and Water Losses | |
| Jakus et al. | Algorithm for optimal wind power plant capacity allocation in areas with limited transmission capacity | |
| Avazkhodjaev et al. | The causal nexus between renewable energy, CO2 emissions, and economic growth: New evidence from CIS countries | |
| Ji et al. | Application of Set‐Valued Statistical Methods for Excellent Performance Evaluation | |
| Kumar et al. | AI-Empowered IoT for Sustainable Energy Technologies | |
| Ominyi et al. | Foreign direct investment and environmental sustainability in Nigeria |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180626 |
|
| RJ01 | Rejection of invention patent application after publication |