CN109379334A - The adaptive construction method of network security risk evaluation index weights and device - Google Patents
The adaptive construction method of network security risk evaluation index weights and device Download PDFInfo
- Publication number
- CN109379334A CN109379334A CN201811063741.9A CN201811063741A CN109379334A CN 109379334 A CN109379334 A CN 109379334A CN 201811063741 A CN201811063741 A CN 201811063741A CN 109379334 A CN109379334 A CN 109379334A
- Authority
- CN
- China
- Prior art keywords
- weight
- expert
- value
- assets
- index
- 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
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to field of information security technology, in particular to the adaptive construction method of a kind of network security risk evaluation index weights and device, this method include: obtaining the weight of information assets index evaluation value and original state in network security risk；The degrees of offset between assessed value and mean value is successively obtained according to assessed value and Weight Acquisition assets index evaluation mean value, and by Euclidean distance；The weights influence factor is obtained according to degrees of offset, and new weight is obtained by the weights influence factor；It is returned according to new weight and obtains assets index evaluation mean value, carry out the poll of a new round, determine the end of polling mechanism by the way that whether weight reaches stationary value, and output is taken turns with end and determines final information assets risk assessment index set and weight sets.The present invention, which can rationally reduce environmental factor and expert's subjectivity, influences assessed value, gets rid of and relies on historical data, has stronger practicability and operability, provides technical support to construct the assets assessment index set of high quality.
Description
Technical field
The invention belongs to field of information security technology, in particular to a kind of network security risk evaluation index weights are adaptive
Construction method and device.
Background technique
Risk assessment is one of the key element that information security is estimated, the security risk and information assets of information system by
Issuable loss is closely related after threat, and therefore, risk assessment must identify the relationship of assets relevant factor, and then accurate
Determine the size of assets value.Information assets, which refers to, has valuable information or resource to tissue, it includes physical asset, letter
Breath/data, software, the ability, personnel, the intangible asset that turn out a produce or service is provided etc..It will be to the assets of information system
It is assessed, currently used method is first to be analyzed the assets of information system, identified, then by expert from the guarantor of assets
Three close property, integrality and availability aspects carry out assets value assignment, and the synthesis of assets value is finally provided based on the method for weighting
Assessed value, therefore the determination Weight of Expert of science is assessed information assets, can be realized assets value from calmly
Property recognizes the transformation of quantitative assessment.2 kinds of methods are generallyd use in Weight of Expert determination at present: first is that Weight of Expert are waited, second is that
Determining weight is calculated based on the historical data of expert's assessment.Wherein, equal weight method there are environmental factor and expert it is subjective because
Element is influenced caused by assets assessment；And the method for weight is determined based on the historical data of expert there are the phases between historical data
Closing property and evaluation research correlation is inconsistent and the evaluation history data of expert have the problems such as acquisition hardly possible.
Summary of the invention
For this purpose, the present invention provides a kind of adaptive construction method of network security risk evaluation index weights and device, it can
Influence of the reasonable subjectivity for reducing environmental factor and expert to assessed value, has stronger practicability and operability.
According to design scheme provided by the present invention, a kind of adaptive side of building of network security risk evaluation index weights
Method includes following content:
A the weight of information assets index evaluation value and original state in network security risk) is obtained；
B) according to assessed value and Weight Acquisition assets index evaluation mean value, and by Euclidean distance successively obtain assessed value with
Degrees of offset between mean value；
C the weights influence factor) is obtained according to degrees of offset, and new weight is obtained by the weights influence factor；
D) B is returned according to new weight) poll that carries out a new round, poll is determined by the way that whether weight reaches stationary value
The end of mechanism, and output is taken turns with end and determines final information assets risk assessment index set and weight sets.
Above-mentioned, A) in, the Weight of Expert such as the weight combination expert contribution degree and empirical value of original state are determined, or used
Method determines.
Above-mentioned, B) specifically include following content:
B1) index set is evaluated respectively by m evaluation index compositions indicator collection, each expert in information assets,
Obtain assessment vector, wherein m is positive integer；
B2) according to the assessment vector of expert each in expert group, evaluating matrix is constructed；
B3) according to evaluating matrix and weight, the assessment mean value of each assets index is obtained；
B4 the assessment mean value two of each expert to the assessed value of each assets index and expert group to the assets index) is calculated
Euclidean distance between person；
B5) Euclidean distance is normalized, obtain assessed value and assesses the degrees of offset between mean value.
Above-mentioned, C) in, the degrees of offset in each assets index evaluation is obtained according to each expert of acquisition, by its with
The corresponding degrees of offset of other experts is compared, and determines influence of the expert to assets assessment, determines its weights influence factor；
According to the weights influence factor, weight is redistributed, obtains the weight in corresponding polling procedure assessment.
Above-mentioned, D) in, pass through the power for judging the weight obtained in current polling procedure and obtaining in previous polling procedure
Whether both weights are equal, to determine whether weight reaches stationary value.
A kind of adaptive construction device of network security risk evaluation index weights, include: collection module, is repaired computing module
Positive module, iteration module and output module, wherein
Collection module, for obtaining the weight of information assets index evaluation value and original state in network security risk；
Computing module, for foundation assessed value and Weight Acquisition assets index evaluation mean value, and successively by Euclidean distance
Calculate the degrees of offset between assessed value and mean value；
Correction module, the degrees of offset for obtaining according to computing module obtains the weights influence factor, and passes through weight shadow
The factor is rung to obtain new weight；
Iteration module, for obtaining the power of a new round by polling mechanism according to weight new obtained in correction module
Weight, determines the end of polling mechanism by the way that whether weight reaches stationary value；
Output module, for determined according to wheel output in end in iteration module final information assets risk assessment index set and
Weight sets simultaneously exports.
Beneficial effects of the present invention:
The present invention by taking turns advisory feedback, auxiliary building evaluation index more；Different expert opinions are calculated using Euclidean distance
The departure degree of data and average value；Index weight is obtained in conjunction with Weight of Expert；Metrics evaluation is put down after every wheel consulting
Mean value, evaluation data irrelevance and index weight feed back to expert, improve expert opinion using more wheel advisory feedback processes and anticipate
The quality seen reduces subjective and objective fault；The final different degree of expert opinion opinion parameter is taken turns using the end of high quality, as
According to being allocated to index weights, assets assessment index set and weight sets are formed.Compared to existing method, can reasonably reduce
The influence of environmental factor and the subjectivity of expert to assessed value, gets rid of the dependence to historical data, have stronger practicability and
Operability provides technical support to construct the assets assessment index set of high quality.
Detailed description of the invention:
Fig. 1 is the adaptive construction method flow diagram of weight in embodiment；
Fig. 2 is that degrees of offset obtains flow diagram in embodiment；
Fig. 3 is the adaptive construction device schematic diagram of weight in embodiment；
Fig. 4 is adaptive developing algorithm flow chart in embodiment.
Specific embodiment:
To make the object, technical solutions and advantages of the present invention clearer, understand, with reference to the accompanying drawing with technical solution pair
The present invention is described in further detail.The technical term being related in embodiment is as follows:
Weight usually passes through equal Weight of Expert or true based on assessment experts historical data in information security risk evaluation at present
The method for determining weight, because there are the subjective factors of environmental factor and expert influence caused by assets assessment, and historical data it
Between the correlation studied of correlation and evaluation it is inconsistent, the situations such as expert's evaluation history data acquisition difficulty.For this purpose, this
Inventive embodiments provide a kind of adaptive construction method of network security risk evaluation index weights, shown in Figure 1, include:
Obtain the weight of information assets index evaluation value and original state in network security risk；
Assessed value and is successively obtained according to assessed value and Weight Acquisition assets index evaluation mean value, and by Euclidean distance
Degrees of offset between value；
The weights influence factor is obtained according to degrees of offset, and new weight is obtained by the weights influence factor；
It is returned according to new weight, carries out the poll of a new round, determine poll machine by the way that whether weight reaches stationary value
The end of system, and output is taken turns with end and determines final information assets risk assessment index set and weight sets.
During obtaining the weight of information assets index evaluation value and original state, the weight of original state is in combination with specially
Family's contribution degree and empirical value determine, or using etc. Weight of Expert methods determine.
The degrees of offset between assessed value and mean value is calculated by Euclidean distance, another embodiment of the present invention, referring to fig. 2
Shown, calculating process specifically includes following content:
The index set is evaluated respectively, is obtained by m evaluation index compositions indicator collection, each expert in information assets
Assess vector, wherein m is positive integer；
According to the assessment vector of expert each in expert group, evaluating matrix is constructed；
According to evaluating matrix and weight, the assessment mean value of each assets index is obtained；
Calculate assessment mean value both of each expert to the assessed value of each assets index and expert group to the assets index
Between Euclidean distance；
Euclidean distance is normalized, assessed value is obtained and assesses the degrees of offset between mean value.
It has been generally acknowledged that the expert provides if the assessed value that an expert provides deviates the opinion that other experts provide
Opinion reference value with regard to lower, so that the weight of the expert should just reduce.According to the thought, γ is enabledi' expression expert's can
Reliability.γi' bigger higher the weight of confidence level for indicating the expert should be bigger, γiThe smaller weight of ' smaller expression expert reliability
It should be smaller.When assessing assets, the initial weight of expert group can be existed by evaluated tissue according to expert for expert group
The innovation achievement and the familiarity in terms of business studied in terms of information security provide, or use the Weight of Expert of equal weight
Determine that method determines.Assuming that expert group is made of n experts, if ωiIndicate the weight of i-th bit expert, thenEnable ω
Indicate expert group's weight vectors, then ω=(ω1,ω2,…,ωn).Identifying processing is carried out to assets assessment index, if assets are commented
Valence includes m evaluation index, and each expert evaluates index set, obtains the assessment vector of expert, be denoted asAccording to the assessed value that the expert of expert group provides, evaluating matrix is constructed
Wherein, aijIndicate the assessed value that i-th bit expert provides for j-th of evaluation index of assets.The row of matrix is expert to index
Collect the expert evaluated and assess vector, commenting for assets index is obtained according to the evaluating matrix that Weight of Expert and expert provide
Estimate mean value.In conjunction with ω and A, expert group is obtained to the assessment mean value of assets index, is enabledIndicate the assessment of j-th of index of assets
Mean value, then Obtain assessment mean vectorIt calculates
Euclidean distance between the assessed value of expert i and the assessed value of expert groupThen, to Euclidean distance into
Row normalized calculates the irrelevance r of Weight of Expert, indicates the deviation journey of the assessment mean value of expert's assessed value and expert group
Degree, calculation method are as follows:
①When 0:
②When 0:
Wherein, rjAnd rlIndicate the irrelevance of j-th and l expert, and ri,rl∈[0,1)
The theory significance of irrelevance: when expert assessed value deviate expert group assessment mean value it is remoter, obtained irrelevance
It is bigger, the corresponding weight that should just reduce the big expert of irrelevance.
The degrees of offset in each assets index evaluation is obtained according to each expert of acquisition, it is corresponding with other experts
Degrees of offset be compared, determine influence of the expert to assets assessment, determine its weights influence factor；According to weights influence
The factor redistributes weight, obtains the weight in corresponding polling procedure assessment.
The calculation method of the u of Weight of Expert impact factor is as follows, wherein
①When 0:
②When 0:
Above-mentioned formula guarantees the bigger expert of bias when being compared with other experts, and assessment weight becomes smaller,
That is the weights influence factor of the expert is smaller, to reduce influence of the expert to assets assessment.It can be reasonable according to the principle
The expert that reduces assess influence of the exceptional value to assessment result.In next round assessment, according to the power of weights influence factor pair expert
It redistributes, and is normalized again, obtain the weight of j-th of expert when the assessment of pth wheelIt is as follows:
Wherein ωi,ωjFor initial weight, p (p=1,2 ...) indicates the round of advisory feedback,Indicate the consulting of pth wheel
When assessment, the weight of j-th of expert is fed back by the expert consulting of more rounds, improves the quality of expert opinion opinion, reduces master
The adaptive adjustment of weight is realized in objective fault.Pass through the weight for judging to obtain in current polling procedure and previous polling procedure
Whether both weights of middle acquisition are equal, to determine whether weight reaches stationary value.
Based on above-mentioned method, the embodiment of the present invention also provides a kind of adaptive structure of network security risk evaluation index weights
Device is built, it is shown in Figure 3, include: collection module, computing module, correction module, iteration module and output module, wherein
Collection module, for obtaining the weight of information assets index evaluation value and original state in network security risk；
Computing module, for foundation assessed value and Weight Acquisition assets index evaluation mean value, and successively by Euclidean distance
Calculate the degrees of offset between assessed value and mean value；
Correction module, the degrees of offset for obtaining according to computing module obtains the weights influence factor, and passes through weight shadow
The factor is rung to obtain new weight；
Iteration module, for obtaining the power of a new round by polling mechanism according to weight new obtained in correction module
Weight, determines the end of polling mechanism by the way that whether weight reaches stationary value；
Output module, for determined according to wheel output in end in iteration module final information assets risk assessment index set and
Weight sets simultaneously exports.
In the present invention, Weight of Expert is successively obtained by polling mechanism, if obtaining after the completion of the assessment of certain round expert consulting
Mean value is assessed, if new variation has occurred in the irrelevance for calculating expert, in next round assessment, new assessment mean value is substituted into
In the calculating of expert's bias, the quality of expert opinion opinion is improved using more wheel advisory feedback processes, reduces subjective and objective prejudice,
Using the end wheel final different degree of expert opinion opinion parameter of high quality, index weights are allocated on this basis,
Form assets assessment index set and weight sets.The adaptive developing algorithm process of the evaluation index weight is as shown in Fig. 4, algorithm
Content can design as follows:
The adaptive developing algorithm of algorithm expert weight
Expert is inputted to the risk assessment matrix A and expert group initial weight vector ω of assets0
The Weight of Expert vector ω that output pth wheel is fed back(p)With asset estimated value vector M(p)
Information assets assessment mean value M is calculated according to Weight of Expert and expert's evaluating matrix in step 1；
Step 2 is according to assessment mean value M(p-1)Calculate the bias vector d of expert group(p)With expert group irrelevance vector R(p)；
The weights influence factor mu of step 3 calculating expert jj (p)With new Weight of Expert ωj (p), judge ωj (p)=ωj (p-1),
If set up, according to ωj (p)Calculate M(p), then go in next step；If invalid, p=p+1 is enabled, goes to step 2；
Step 4 exports ω, ω(2),L,ω(p)And M, M(2),L,M(p)；Algorithm terminates.
In step 3,For the decision condition that algorithm terminates, the knot when weight of expert reaches stationary value is indicated
Beam, the M of end wheel output(p)For information assets risk assessment value.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For device disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
The unit and method and step of each example described in conjunction with the examples disclosed in this document, can with electronic hardware,
The combination of computer software or the two is realized, in order to clearly illustrate the interchangeability of hardware and software, in above description
In generally describe each exemplary composition and step according to function.These functions are held with hardware or software mode
Row, specific application and design constraint depending on technical solution.Those of ordinary skill in the art can be to each specific
Using using different methods to achieve the described function, but this realization be not considered as it is beyond the scope of this invention.
Those of ordinary skill in the art will appreciate that all or part of the steps in the above method can be instructed by program
Related hardware is completed, and described program can store in computer readable storage medium, such as: read-only memory, disk or CD
Deng.Optionally, one or more integrated circuits also can be used to realize, accordingly in all or part of the steps of above-described embodiment
Ground, each module/unit in above-described embodiment can take the form of hardware realization, can also use the shape of software function module
Formula is realized.The present invention is not limited to the combinations of the hardware and software of any particular form.
The foregoing description of the disclosed embodiments makes professional and technical personnel in the field can be realized or use the application.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the application.Therefore, the application
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (6)
1. a kind of adaptive construction method of network security risk evaluation index weights, which is characterized in that include following content:
A the weight of information assets index evaluation value and original state in network security risk) is obtained；
B) according to assessed value and Weight Acquisition assets index evaluation mean value, and assessed value and mean value are successively obtained by Euclidean distance
Between degrees of offset；
C the weights influence factor) is obtained according to degrees of offset, and new weight is obtained by the weights influence factor；
D) B is returned according to new weight) poll that carries out a new round, polling mechanism is determined by the way that whether weight reaches stationary value
End, and output is taken turns with end and determines final information assets risk assessment index set and weight sets.
2. the adaptive construction method of network security risk evaluation index weights according to claim 1, which is characterized in that A)
In, the weight combination expert contribution degree and empirical value of original state determine, or using etc. Weight of Expert method determine.
3. the adaptive construction method of network security risk evaluation index weights according to claim 1 or 2, feature exist
In B) specifically comprising following content:
B1) index set is evaluated respectively, is obtained by m evaluation index compositions indicator collection, each expert in information assets
Assess vector, wherein m is positive integer；
B2) according to the assessment vector of expert each in expert group, evaluating matrix is constructed；
B3) according to evaluating matrix and weight, the assessment mean value of each assets index is obtained；
B4) calculate each expert to the assessed value of each assets index and expert group to both assessment mean values of the assets index it
Between Euclidean distance；
B5) Euclidean distance is normalized, obtain assessed value and assesses the degrees of offset between mean value.
4. the adaptive construction method of network security risk evaluation index weights according to claim 1, which is characterized in that C)
In, the degrees of offset in each assets index evaluation is obtained according to each expert of acquisition, it is corresponding with other experts partially
Shifting degree is compared, and determines influence of the expert to assets assessment, determines its weights influence factor；According to weights influence because
Son redistributes weight, obtains the weight in corresponding polling procedure assessment.
5. the adaptive construction method of network security risk evaluation index weights according to claim 1, which is characterized in that D)
In, by judging whether both the weight obtained in current polling procedure and the weight obtained in previous polling procedure are equal, come
Determine whether weight reaches stationary value.
6. a kind of adaptive construction device of network security risk evaluation index weights is, characterized by comprising: collection module, meter
Calculate module, correction module, iteration module and output module, wherein
Collection module, for obtaining the weight of information assets index evaluation value and original state in network security risk；
Computing module, for successively being calculated according to assessed value and Weight Acquisition assets index evaluation mean value, and by Euclidean distance
Degrees of offset between assessed value and mean value；
Correction module, the degrees of offset for obtaining according to computing module obtain the weights influence factor, and by weights influence because
Son obtains new weight；
Iteration module is led to for obtaining the weight of a new round by polling mechanism according to weight new obtained in correction module
Cross whether weight reaches stationary value to determine the end of polling mechanism；
Output module, for determining final information assets risk assessment index set and weight according to wheel output in end in iteration module
Collect and exports.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811063741.9A CN109379334B (en) | 2018-09-12 | 2018-09-12 | Network security risk assessment index weight self-adaptive construction method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811063741.9A CN109379334B (en) | 2018-09-12 | 2018-09-12 | Network security risk assessment index weight self-adaptive construction method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109379334A true CN109379334A (en) | 2019-02-22 |
| CN109379334B CN109379334B (en) | 2021-05-07 |
Family
ID=65405209
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811063741.9A Active CN109379334B (en) | 2018-09-12 | 2018-09-12 | Network security risk assessment index weight self-adaptive construction method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109379334B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110489974A (en) * | 2019-08-21 | 2019-11-22 | 中国电子信息产业集团有限公司第六研究所 | Attacking and defending tool performance appraisal procedure, device, electronic equipment and computer readable storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101334893A (en) * | 2008-08-01 | 2008-12-31 | 天津大学 | Fused image quality integrated evaluating method based on fuzzy neural network |
| CN102129627A (en) * | 2011-03-24 | 2011-07-20 | 江苏电力信息技术有限公司 | Performance appraisal method based on weight adjustment |
| CN102509240A (en) * | 2011-11-22 | 2012-06-20 | 天津市电力公司 | Grid investment benefit evaluation method based on multiple indexes and multiple levels |
| CN102694800A (en) * | 2012-05-18 | 2012-09-26 | 华北电力大学 | Gaussian process regression method for predicting network security situation |
| CN103617371A (en) * | 2013-12-10 | 2014-03-05 | 国家电网公司 | Method for comprehensively evaluating electric energy quality based on grey theory |
| CN103778574A (en) * | 2014-02-26 | 2014-05-07 | 广西电网公司 | Method for evaluating development coordination of island microgrid |
-
2018
- 2018-09-12 CN CN201811063741.9A patent/CN109379334B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101334893A (en) * | 2008-08-01 | 2008-12-31 | 天津大学 | Fused image quality integrated evaluating method based on fuzzy neural network |
| CN102129627A (en) * | 2011-03-24 | 2011-07-20 | 江苏电力信息技术有限公司 | Performance appraisal method based on weight adjustment |
| CN102509240A (en) * | 2011-11-22 | 2012-06-20 | 天津市电力公司 | Grid investment benefit evaluation method based on multiple indexes and multiple levels |
| CN102694800A (en) * | 2012-05-18 | 2012-09-26 | 华北电力大学 | Gaussian process regression method for predicting network security situation |
| CN103617371A (en) * | 2013-12-10 | 2014-03-05 | 国家电网公司 | Method for comprehensively evaluating electric energy quality based on grey theory |
| CN103778574A (en) * | 2014-02-26 | 2014-05-07 | 广西电网公司 | Method for evaluating development coordination of island microgrid |
Non-Patent Citations (3)
| Title |
|---|
| 刘业政等: ""多属性群决策中权重自适应调整的方法"", 《系统工程与电子技术》 * |
| 周宇峰等: ""一种综合评价中确定专家权重的方法"", 《工业工程》 * |
| 庞彦军等: ""综合评价系统客观性指标权重的确定方法"", 《系统工程理论与实践》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110489974A (en) * | 2019-08-21 | 2019-11-22 | 中国电子信息产业集团有限公司第六研究所 | Attacking and defending tool performance appraisal procedure, device, electronic equipment and computer readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109379334B (en) | 2021-05-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI690865B (en) | Transaction volume prediction method and device | |
| CN104809051B (en) | Method and apparatus for predicting exception and failure in computer application | |
| Costantini et al. | A hierarchical procedure for the combination of forecasts | |
| Morales et al. | A financial stability index for Colombia | |
| CN110288137B (en) | Road network level road infrastructure structure comprehensive maintenance decision method and system | |
| CN112668822B (en) | Scientific and technological achievement transformation platform sharing system, method, storage medium and mobile phone APP | |
| US20120016808A1 (en) | Business Review and Volume Optimizer (BRAVO) | |
| CN110837939A (en) | Power grid multi-target project screening method and system | |
| Garg et al. | Algorithms Based on COPRAS and Aggregation Operators with New Information Measures for Possibility Intuitionistic Fuzzy Soft Decision‐Making | |
| CN111144721A (en) | Power grid project demand evaluation model construction method and device and computing equipment | |
| CN114970667B (en) | Multi-source heterogeneous energy data fusion method | |
| Fu et al. | DEA cross‐efficiency aggregation based on preference structure and acceptability analysis | |
| Tang et al. | A multi-objective q-rung orthopair fuzzy programming approach to heterogeneous group decision making | |
| Akram et al. | Extended PROMETHEE approach with 2‐tuple linguistic m‐polar fuzzy sets for selection of elliptical cardio machine | |
| CN101645934B (en) | Web service evaluation method based on weight, Web service finding method and device thereof | |
| CN109379334A (en) | The adaptive construction method of network security risk evaluation index weights and device | |
| Chen et al. | An intuitionstic fuzzy factorial analysis model for multi-attribute decision-making under random environment | |
| CN112950048A (en) | National higher education system health evaluation based on fuzzy comprehensive evaluation | |
| CN110837962A (en) | Power customer viscosity calculation method | |
| Pavelková et al. | Passenger car sales projections: Measuring the accuracy of a sales forecasting model | |
| Zhang et al. | Evaluation and Selection of Manufacturing Suppliers in B2B E‐Commerce Environment | |
| Apergis | Forecasting energy prices: Quantile‐based risk models | |
| CN115409127A (en) | Training method and related device of contract default risk prediction model | |
| Zhang et al. | A novel approach for QoS prediction based on Bayesian combinational model | |
| TW202213239A (en) | A predicting the trend of stock prices and trade advising system based on neural networks of analyzing multiple technical analysis indicators |
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 |