CN112241922A - Power grid asset comprehensive value evaluation method based on improved naive Bayes classification - Google Patents
Power grid asset comprehensive value evaluation method based on improved naive Bayes classification Download PDFInfo
- Publication number
- CN112241922A CN112241922A CN202010927208.3A CN202010927208A CN112241922A CN 112241922 A CN112241922 A CN 112241922A CN 202010927208 A CN202010927208 A CN 202010927208A CN 112241922 A CN112241922 A CN 112241922A
- Authority
- CN
- China
- Prior art keywords
- power grid
- asset
- grid asset
- follows
- data
- 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.)
- Granted
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 32
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000000605 extraction Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 44
- 239000000243 solution Substances 0.000 claims description 26
- 239000011259 mixed solution Substances 0.000 claims description 12
- 238000000034 method Methods 0.000 abstract description 9
- 238000011161 development Methods 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 3
- 238000003745 diagnosis Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Primary Health Care (AREA)
- Probability & Statistics with Applications (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a power grid asset comprehensive value evaluation method based on improved naive Bayes classification. The problem that common power grid assets are difficult to evaluate is solved. The method comprises the following steps: s1: extracting independent components of the power grid asset attributes from the power grid asset attribute characteristic data; s2: carrying out primary processing on the independent components of the asset attributes of the power grid to obtain an algorithm model; s3: and evaluating the value according to the algorithm model. The beneficial effects are as follows: key attribute characteristics of the power grid assets are extracted through the ICA and the LDA, independent component extraction and dimension reduction processing are carried out, the extracted key attribute characteristics are used as input variables of the WNB model, a power grid asset assessment prediction model is established, power grid asset management can dynamically track the power grid asset attribute characteristics and asset value changes caused by the changes of the power grid asset attribute characteristics, comprehensive, real-time, efficient, convenient and fast multi-scale value assessment of the system is achieved, and great convenience is provided for clear development requirements, optimization directions and the like.
Description
Technical Field
The invention relates to the field of power grid asset evaluation, in particular to a power grid asset comprehensive value evaluation method based on improved naive Bayesian classification.
Background
A multi-scale assessment technology for overall process management of power grid assets is based on lean management of a whole life cycle, and research on power grid asset value measurement and diagnosis assessment is innovative research work which is developed for scientifically assessing comprehensive value capability of power grid assets and meeting reasonable value level of the power grid assets under the constraint of development targets of elements such as safety, reliability, electric quantity, electricity price and the like through system diagnosis. The method is beneficial to determining the asset value capability of the power grid and reasonably determining the development target and the investment requirement. In view of the above, key attribute features of the power grid assets are extracted through ICA and LDA, independent component extraction and dimension reduction processing are carried out, the extracted features are used as input variables of a WNB model, a power grid asset evaluation prediction model is established, a power grid asset value multi-scale evaluation technology is systematically constructed, and technical support is provided for scientific diagnosis and evaluation of power grid asset value problems and clear development requirements and optimization directions.
Disclosure of Invention
The invention solves the problem that the common power grid assets are difficult to evaluate, and provides a power grid asset comprehensive value evaluation method based on improved naive Bayes classification.
In order to solve the technical problems, the technical scheme of the invention is as follows: the power grid asset comprehensive value evaluation method based on the improved naive Bayes classification comprises the following steps of:
s1: extracting independent components of the power grid asset attributes from the power grid asset attribute characteristic data;
s2: carrying out primary processing on the independent components of the asset attributes of the power grid to obtain an algorithm model;
s3: and evaluating the value according to the algorithm model.
As a preferable scheme of the above scheme, the extracting in step S1 is to extract the independent component of the grid asset attribute through an ICA algorithm.
As a preferable scheme of the above scheme, the ICA algorithm adopts negative entropy maximization as an independent criterion of each component, and comprises the following steps:
s21: obtaining a mixed solution matrix according to the iteration formula;
s22: and obtaining the independent component of the property of the power grid asset according to the mixed solution matrix and the independent component solution formula.
As a preferable solution of the above solution, the iterative formula in step S21 is as follows:
wherein W is a mixed solution matrix;
a is a constant between intervals (1, 2); beta ═ E [ W ]TXG(WTX)]β is a constant value; x is the property characteristic sample data of the power grid assets, and X is { X ═ X1，x2，...，xNWhere N is the total number of samples, xiA column vector of attribute values for each sample, and
the independent component solution in step S22 is as follows:
S＝WTX
wherein S is an independent component of the asset attribute of the power grid, and W is a mixed solution matrix; x is the property characteristic sample data of the power grid assets, and X is { X ═ X1，x2，...，xNWhere N is the total number of samples, xiA column vector of attribute values for each sample, and
as a preferable mode of the above, the one-time processing in step S2 includes the steps of:
s31: performing dimensionality reduction processing on the independent component of the power grid asset attribute by using an LDA algorithm to obtain dimensionality reduction data;
s32: and constructing a WNB algorithm model according to the dimension reduction data.
The LDA method in step S31 is a supervised learning dimension reduction technique in which the intra-class variance and the inter-class variance after projection are the minimum and the maximum, and the data set samples are output by class.
As a preferable solution of the above solution, the dimension reduction processing in step S31 includes the following steps:
s41: obtaining a covariance matrix of a class c sample according to the property characteristic sample data of the power grid asset;
s42: obtaining an intra-class divergence matrix according to the covariance matrix of the class c sample;
s43: obtaining a projection matrix W according to the first d characteristic values of the intra-class divergence matrix;
s44: and obtaining dimension reduction data Y according to the projection matrix W and the independent component S of the power grid asset attribute.
In step S43, the value d is preset, and the projection matrix W is composed of eigenvectors corresponding to the first d eigenvalues of the intra-class divergence matrix.
As a preferable solution of the above solution, the covariance matrix expression in step S41 is as follows:
wherein, C0For the total number of asset classes, NcFor the amount of the class c sample,
is a class c sample mean vector, X(c)jIs the jth sample in class c;
the intra-class divergence matrix expression in step S42 is as follows:
wherein N is the total number of samples;
the solving formula of the dimension reduction data Y in step S44 is as follows:
Y＝WTS，Y∈Rd×N
wherein W is a projection matrix, S is an independent component of the asset attribute of the power grid, and N is the total number of samples.
As a preferable scheme of the above scheme, the constructing the WNB algorithm model in step S32 includes the following steps:
s51: presetting class data corresponding to the sample as C;
s52: obtaining a correlation coefficient and mutual information according to the dimension reduction data Y and the category data C;
s53: normalizing the correlation coefficient and the mutual information to obtain a normalized correlation coefficient and normalized mutual information;
s54: obtaining a weight coefficient according to the normalized correlation coefficient and the normalized mutual information;
s55: and obtaining an algorithm model according to the dimension reduction data and the weight coefficient.
Wherein the correlation coefficient in step S52 is the evaluation index variable yiAnd C, mutual information is an evaluation index variable yiAnd C.
As a preferable solution of the above solution, the expression of the category data C in step S51 is as follows:
C＝{c1；c2；...；cN}
the expression of the dimension reduction data in step S52 is as follows:
Y＝{y1；y2；...；yd}，yi＝{yi1；yi2；...；yiN}
the correlation coefficient solution in step S52 is as follows:
the mutual information solution in step S52 is as follows:
I(C，yi)＝H(C)-H(C|yi)
wherein H (·) is entropy;
the solving equation of the weight coefficient in step S54 is as follows:
ωi＝λωCi+(1-λ)ωIi
wherein, ω isCiFor normalized correlation coefficient, ωIiThe lambda value is preset and is in the interval (0, 1) for normalized mutual information;
wherein, the algorithm model in the step S55 is a weighted naive bayes model, and the expression thereof is as follows:
wherein, ω isiIs the weighting coefficient of the ith attribute, and d is the number of eigenvalues.
As a preferable mode of the above, the value evaluation in step S3 includes the steps of:
s61: substituting the data to be evaluated into the algorithm model to obtain the posterior probability;
s62: and selecting the asset value category c corresponding to the maximum posterior probability as a value evaluation result.
Compared with the prior art, the invention has the beneficial effects that:
key attribute characteristics of the power grid assets are extracted through the ICA and the LDA, independent component extraction and dimension reduction processing are carried out, the extracted key attribute characteristics are used as input variables of the WNB model, a power grid asset assessment prediction model is established, power grid asset management can dynamically track the power grid asset attribute characteristics and asset value changes caused by the changes of the power grid asset attribute characteristics, comprehensive, real-time, efficient, convenient and fast multi-scale value assessment of the system is achieved, and great convenience is provided for clear development requirements, optimization directions and the like.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution of the present invention is further described below by way of examples with reference to the accompanying drawings.
Example (b): in this embodiment, as shown in fig. 1, the method for evaluating the comprehensive value of a power grid asset based on the improved naive bayes classification includes the following steps:
s1: extracting independent components of the power grid asset attributes from the power grid asset attribute characteristic data;
s2: carrying out primary processing on the independent components of the asset attributes of the power grid to obtain an algorithm model;
s3: and evaluating the value according to the algorithm model.
The extraction in the step S1 is to extract the independent components of the attributes of the power grid by using the negative entropy maximization as the independent criterion of each component through the ICA algorithm, and the method includes the following steps:
s21: obtaining a mixed solution matrix according to the iteration formula;
s22: and obtaining the independent component of the property of the power grid asset according to the mixed solution matrix and the independent component solution formula.
The iterative equation in step S21 is as follows:
wherein W is a mixed solution matrix;
a is a constant between intervals (1, 2); beta ═ E [ W ]TXG(WTX)]，βIs a constant value; x is the property characteristic sample data of the power grid assets, and X is { X ═ X1，x2，...，xNWhere N is the total number of samples, xiA column vector of attribute values for each sample, and
the independent component solution in step S22 is as follows:
S＝WTX
wherein S is an independent component of the asset attribute of the power grid, and W is a mixed solution matrix; x is the property characteristic sample data of the power grid assets, and X is { X ═ X1，x2，...，xNWhere N is the total number of samples, xiA column vector of attribute values for each sample, and
wherein, the one-time processing in step S2 includes the following steps:
s31: performing dimensionality reduction processing on the independent component of the power grid asset attribute by using an LDA algorithm to obtain dimensionality reduction data;
s32: and constructing a WNB algorithm model according to the dimension reduction data.
The LDA method in step S31 is a supervised learning dimension reduction technique in which the intra-class variance and the inter-class variance after projection are the minimum and the maximum, and the data set samples are output by class.
The dimension reduction processing in step S31 includes the following steps:
s41: obtaining a covariance matrix of a class c sample according to the property characteristic sample data of the power grid asset;
s42: obtaining an intra-class divergence matrix according to the covariance matrix of the class c sample;
s43: obtaining a projection matrix W according to the first d characteristic values of the intra-class divergence matrix;
s44: and obtaining dimension reduction data Y according to the projection matrix W and the independent component S of the power grid asset attribute.
In step S43, the value d is preset, and the projection matrix W is composed of eigenvectors corresponding to the first d eigenvalues of the intra-class divergence matrix.
Wherein, the covariance matrix expression in step S41 is as follows:
wherein, C0For the total number of asset classes, NcFor the amount of the class c sample,
is a class c sample mean vector, X(o)jIs the jth sample in class c;
the intra-class divergence matrix expression in step S42 is as follows:
wherein N is the total number of samples;
the solving formula of the dimension reduction data Y in step S44 is as follows:
Y＝WTS，Y∈Rd×N
wherein W is a projection matrix, S is an independent component of the asset attribute of the power grid, and N is the total number of samples.
The step S32 of constructing the WNB algorithm model comprises the following steps:
s51: presetting class data corresponding to the sample as C;
s52: obtaining a correlation coefficient and mutual information according to the dimension reduction data Y and the category data C;
s53: normalizing the correlation coefficient and the mutual information to obtain a normalized correlation coefficient and normalized mutual information;
s54: obtaining a weight coefficient according to the normalized correlation coefficient and the normalized mutual information;
s55: and obtaining an algorithm model according to the dimension reduction data and the weight coefficient.
Wherein the correlation coefficient in step S52 is the evaluation index variable yiAnd the coefficient of correlation between C and C,mutual information as evaluation index variable yiAnd C.
In step S51, the expression of the category data C is as follows:
C＝{c1；c2；...；cN}
the expression of the dimension reduction data in step S52 is as follows:
Y＝{y1；y2；...；yd}，yi＝{yi1；yi2；...；yiN}
the correlation coefficient solution in step S52 is as follows:
the mutual information solution in step S52 is as follows:
I(C，yi)＝H(C)-H(C|yi)
wherein H (·) is entropy;
the solving equation of the weight coefficient in step S54 is as follows:
ωi＝λωCi+(1-λ)ωIi
wherein, ω isCiFor normalized correlation coefficient, ωIiThe lambda value is preset and is in the interval (0, 1) for normalized mutual information;
wherein, the algorithm model in the step S55 is a weighted naive bayes model, and the expression thereof is as follows:
wherein, ω isiIs the weighting coefficient of the ith attribute, and d is the number of eigenvalues.
Wherein, the value evaluation in step S3 includes the following steps:
s61: substituting the data to be evaluated into the algorithm model to obtain the posterior probability;
s62: and selecting the asset value category c corresponding to the maximum posterior probability as a value evaluation result.
In step S61, the data to be evaluated is a sample asset x (x ═ x)1，x2，...，xd}). And classifying according to the calculated posterior probability, wherein the asset value class c corresponding to the maximum value of the posterior probability is the matrix evaluation classification, namely the evaluation result, of the sample asset x.
Furthermore, it is to be emphasized that C0For the total number of grid asset classes, C is class data, and C ═ C1；c2；...；cNAnd c represents a certain category, X is the attribute feature sample data of the power grid asset, and X is the sample asset to be evaluated.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The power grid asset comprehensive value evaluation method based on the improved naive Bayes classification is characterized by comprising the following steps of:
s1: extracting independent components of the power grid asset attributes from the power grid asset attribute characteristic data;
s2: carrying out primary processing on the independent components of the asset attributes of the power grid to obtain an algorithm model;
s3: and evaluating the value according to the algorithm model.
2. The improved naive Bayes classification-based power grid asset comprehensive value evaluation method of claim 1, wherein the extraction in the step S1 is to extract the independent component of the power grid asset attribute through ICA algorithm.
3. The improved naive Bayes classification based power grid asset comprehensive value evaluation method according to claim 2, wherein the ICA algorithm adopts negative entropy maximization as an independent criterion of each component, comprising the following steps:
s21: obtaining a mixed solution matrix according to the iteration formula;
s22: and obtaining the independent component of the property of the power grid asset according to the mixed solution matrix and the independent component solution formula.
4. The improved naive Bayes classification-based power grid asset comprehensive value evaluation method of claim 3, wherein the iterative formula in the step S21 is as follows:
wherein W is a mixed solution matrix;
a is a constant between intervals (1, 2); beta ═ E [ W ]TXG(WTX)]β is a constant value; x is the property characteristic sample data of the power grid assets, and X is { X ═ X1，x2，...，xNWhere N is the total number of samples, xiA column vector of attribute values for each sample, and
the independent component solution in step S22 is as follows:
S＝WTX
wherein s is an independent component of the asset attribute of the power grid, and W is a mixed solution matrix; x is the property characteristic sample data of the power grid assets, and X is { X ═ X1，x2，...，xNWhere N is the total number of samples, xiA column vector of attribute values for each sample, and
5. the improved naive Bayes classification-based power grid asset comprehensive value evaluation method according to claim 1, wherein the primary processing in the step S2 comprises the following steps:
s31: performing dimensionality reduction processing on the independent component of the power grid asset attribute by using an LDA algorithm to obtain dimensionality reduction data;
s32: and constructing a WNB algorithm model according to the dimension reduction data.
6. The improved naive Bayes classification-based power grid asset comprehensive value evaluation method of claim 5, wherein the dimension reduction processing in the step S31 comprises the following steps:
s41: obtaining a covariance matrix of a class c sample according to the property characteristic sample data of the power grid asset;
s42: obtaining an intra-class divergence matrix according to the covariance matrix of the class c sample;
s43: obtaining a projection matrix W according to the first d characteristic values of the intra-class divergence matrix;
s44: and obtaining dimension reduction data Y according to the projection matrix W and the independent component S of the power grid asset attribute.
7. The improved naive Bayes classification-based power grid asset comprehensive value evaluation method of claim 6, wherein the covariance matrix expression in the step S41 is as follows:
wherein, CoFor the total number of asset classes, NcFor the amount of the class c sample,
is a class c sample mean vector, X(c)jIs the jth sample in class c;
the intra-class divergence matrix expression in step S42 is as follows:
wherein N is the total number of samples;
the solving formula of the dimension reduction data Y in step S44 is as follows:
Y＝WTS，Y∈Rd×N
wherein W is a projection matrix, S is an independent component of the asset attribute of the power grid, and N is the total number of samples.
8. The improved naive Bayes classification-based power grid asset comprehensive value evaluation method according to claim 5, wherein the step S32 of constructing a WNB algorithm model comprises the following steps:
s51: presetting class data corresponding to the sample as C;
s52: obtaining a correlation coefficient and mutual information according to the dimension reduction data Y and the category data C;
s53: normalizing the correlation coefficient and the mutual information to obtain a normalized correlation coefficient and normalized mutual information;
s54: obtaining a weight coefficient according to the normalized correlation coefficient and the normalized mutual information;
s55: and obtaining an algorithm model according to the dimension reduction data and the weight coefficient.
9. The improved naive bayes classification based power grid asset comprehensive value evaluation method according to claim 8, wherein the expression of class data C in the step S51 is as follows:
C＝{c1；c2；...；cN}
the expression of the dimension reduction data in step S52 is as follows:
Y＝{y1；y2；...；yd}，yi＝{yi1；yi2；...；yiN}
the correlation coefficient solution in step S52 is as follows:
the mutual information solution in step S52 is as follows:
I(C，yi)＝H(C)-H(C|yi)
wherein H (·) is entropy;
the solving equation of the weight coefficient in step S54 is as follows:
ωi＝λωCi+(1-λ)ωIi
wherein, ω isiIs a weight coefficient, ω, of the ith attributeCiFor normalized correlation coefficient, ωIiThe lambda value is preset and is in the interval (0, 1) for normalized mutual information;
wherein, the algorithm model in the step S55 is a weighted naive bayes model, and the expression thereof is as follows:
wherein, ω isiAnd d is the weight coefficient of the ith attribute, d is the number of the characteristic values, and C is the total number of the asset classes of the power grid.
10. The improved naive Bayes classification based power grid asset comprehensive value evaluation method as claimed in claim 1, wherein the value evaluation in the step S3 comprises the following steps:
s61: substituting the data to be evaluated into the algorithm model to obtain the posterior probability;
s62: and selecting the asset value category c corresponding to the maximum posterior probability as a value evaluation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010927208.3A CN112241922B (en) | 2020-09-07 | 2020-09-07 | Power grid asset comprehensive value assessment method based on improved naive Bayesian classification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010927208.3A CN112241922B (en) | 2020-09-07 | 2020-09-07 | Power grid asset comprehensive value assessment method based on improved naive Bayesian classification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112241922A true CN112241922A (en) | 2021-01-19 |
| CN112241922B CN112241922B (en) | 2024-03-05 |
Family
ID=74171090
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010927208.3A Active CN112241922B (en) | 2020-09-07 | 2020-09-07 | Power grid asset comprehensive value assessment method based on improved naive Bayesian classification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112241922B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115689596A (en) * | 2022-08-27 | 2023-02-03 | 北京华宜信科技有限公司 | Non-customized data asset valuation method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105913066A (en) * | 2016-04-13 | 2016-08-31 | 刘国栋 | Digital lung sound characteristic dimension reducing method based on relevance vector machine |
| CN105932774A (en) * | 2016-05-11 | 2016-09-07 | 国网冀北电力有限公司张家口供电公司 | Device state early warning method in smart transformer substation based on ICA algorithm |
| CN108876146A (en) * | 2018-06-14 | 2018-11-23 | 国网山东省电力公司聊城供电公司 | A kind of electric network data class assets management method and system |
| CN110222744A (en) * | 2019-05-23 | 2019-09-10 | 成都信息工程大学 | A kind of Naive Bayes Classification Model improved method based on attribute weight |
| CN110278198A (en) * | 2019-06-04 | 2019-09-24 | 西安邮电大学 | The safety risk estimating method of assets in network based on game theory |
| CN110400231A (en) * | 2019-06-06 | 2019-11-01 | 湖南大学 | A kind of electric energy measuring equipment crash rate predictor method weighting Nonlinear Bayesian |
| US20190355025A1 (en) * | 2018-05-17 | 2019-11-21 | Alibaba Group Holding Limited | Blockchain-based resource value evaluation methods and apparatus |
| CN110993004A (en) * | 2019-11-12 | 2020-04-10 | 华中科技大学 | Naive Bayes classification method, engine and system based on resistive random access memory array |
| US20200151628A1 (en) * | 2008-02-29 | 2020-05-14 | Fico | Adaptive Fraud Detection |
-
2020
- 2020-09-07 CN CN202010927208.3A patent/CN112241922B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200151628A1 (en) * | 2008-02-29 | 2020-05-14 | Fico | Adaptive Fraud Detection |
| CN105913066A (en) * | 2016-04-13 | 2016-08-31 | 刘国栋 | Digital lung sound characteristic dimension reducing method based on relevance vector machine |
| CN105932774A (en) * | 2016-05-11 | 2016-09-07 | 国网冀北电力有限公司张家口供电公司 | Device state early warning method in smart transformer substation based on ICA algorithm |
| US20190355025A1 (en) * | 2018-05-17 | 2019-11-21 | Alibaba Group Holding Limited | Blockchain-based resource value evaluation methods and apparatus |
| CN108876146A (en) * | 2018-06-14 | 2018-11-23 | 国网山东省电力公司聊城供电公司 | A kind of electric network data class assets management method and system |
| CN110222744A (en) * | 2019-05-23 | 2019-09-10 | 成都信息工程大学 | A kind of Naive Bayes Classification Model improved method based on attribute weight |
| CN110278198A (en) * | 2019-06-04 | 2019-09-24 | 西安邮电大学 | The safety risk estimating method of assets in network based on game theory |
| CN110400231A (en) * | 2019-06-06 | 2019-11-01 | 湖南大学 | A kind of electric energy measuring equipment crash rate predictor method weighting Nonlinear Bayesian |
| CN110993004A (en) * | 2019-11-12 | 2020-04-10 | 华中科技大学 | Naive Bayes classification method, engine and system based on resistive random access memory array |
Non-Patent Citations (1)
| Title |
|---|
| 郭群: "电网有效资产风险管理研究", 《中国博士学位论文全文数据库 工程科技II辑》, no. 3, pages 042 - 1 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115689596A (en) * | 2022-08-27 | 2023-02-03 | 北京华宜信科技有限公司 | Non-customized data asset valuation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112241922B (en) | 2024-03-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zekić-Sušac et al. | Predicting energy cost of public buildings by artificial neural networks, CART, and random forest | |
| US20220391767A1 (en) | System and method for relational time series learning with the aid of a digital computer | |
| US8346691B1 (en) | Computer-implemented semi-supervised learning systems and methods | |
| Sheng et al. | Multi-view anomaly detection: Neighborhood in locality matters | |
| CN110674636B (en) | Power consumption behavior analysis method | |
| Tu et al. | Optimisation‐based training of evolutionary convolution neural network for visual classification applications | |
| CN110781970A (en) | Method, device and equipment for generating classifier and storage medium | |
| Wang et al. | Label propagation with structured graph learning for semi-supervised dimension reduction | |
| Zhou et al. | Credit card fraud identification based on principal component analysis and improved AdaBoost algorithm | |
| CN112241922B (en) | Power grid asset comprehensive value assessment method based on improved naive Bayesian classification | |
| Karami | Taming wild high dimensional text data with a fuzzy lash | |
| CN111090679B (en) | Time sequence data representation learning method based on time sequence influence and graph embedding | |
| Puspita | Time series forecasting for electricity consumption using kernel principal component analysis (kPCA) and support vector machine (SVM) | |
| CN109615018B (en) | User personalized behavior evaluation method and device, computer equipment and storage medium | |
| Long et al. | Power quality disturbance identification and optimization based on machine learning | |
| Yang et al. | Credit risk assessment in commercial banks based on SVM using PCA | |
| Zhang et al. | Multi-strategy active learning for power quality disturbance identification | |
| CN112085414A (en) | Harmonic pollution degree evaluation method, terminal equipment and storage medium | |
| Wei et al. | Classification Method of Customer Based on Load Curve Image Information | |
| Mokriš | The independent component analysis with the linear regression–predicting the energy costs of the public sector buildings in Croatia | |
| Marzbani et al. | A Hybrid Multi-model Ensemble Feature Selection and SVR Prediction Approach for Accurate Electric Vehicle Demand Prediction: A US Case Study | |
| Wiktorski et al. | Approximate approach to finding generic utility of sequential patterns | |
| CN113723835B (en) | Water consumption evaluation method and terminal equipment for thermal power plant | |
| Butunoi et al. | Shapelet based classification of customer consumption patterns | |
| US20230316210A1 (en) | Policy decision support apparatus and policy decision support method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |