CN110097266A - A kind of process industry equipment military service safety risk estimating method based on cloud model - Google Patents
A kind of process industry equipment military service safety risk estimating method based on cloud model Download PDFInfo
- Publication number
- CN110097266A CN110097266A CN201910318253.6A CN201910318253A CN110097266A CN 110097266 A CN110097266 A CN 110097266A CN 201910318253 A CN201910318253 A CN 201910318253A CN 110097266 A CN110097266 A CN 110097266A
- Authority
- CN
- China
- Prior art keywords
- risk
- cloud
- cloud model
- result
- 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 65
- 238000012502 risk assessment Methods 0.000 claims abstract description 71
- 238000011156 evaluation Methods 0.000 claims abstract description 70
- 238000004364 calculation method Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000005484 gravity Effects 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 235000013399 edible fruits Nutrition 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000007812 deficiency Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 239000003513 alkali Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 239000013587 production medium Substances 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001988 toxicity Effects 0.000 description 1
- 231100000419 toxicity Toxicity 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of, and the process industry based on cloud model equips military service safety risk estimating method, by the possibility occurrence cloud model, sequence severity cloud model and overall merit cloud model of establishing complex equipment risk factors, risk evaluation result is quantified using cloud model in evaluation method, the uncertainty of evaluation procedure is featured in terms of the ambiguity of evaluation language and randomness two, so that risk evaluation result has more objectivity and accuracy；On the basis of evaluation information, the weight of uncertainty and sequence severity in risk factors is determined using entropy assessment, each risk evaluation result shared weight in risk assessment is determined using neighbour's propagation clustering method, generate the risk factors evaluation cloud model of each risk factors, group effectiveness is maximized when analyzing ultimate risk assessment result, individual is minimized simultaneously, so that the analysis result obtained is eliminated subjectivity and uncertainty in risk assessment, keeps risk evaluation result more accurate.
Description
Technical field
The invention belongs to process industries to equip military service security risk assessment field, and in particular to a kind of stream based on cloud model
Journey industrial equipment military service safety risk estimating method.
Background technique
Modern process industry system, which is one, has the characteristics that the distributed complex Mechatronic Systems of device cluster, production system are related
Connection relationship is complicated, the high feature of the degree of coupling.Since the production process of process industry is complicated and mostly high temperature and pressure or strong acid is strong
The severe production status such as alkali, production medium majority have toxicity and flammable and explosive, once equipment of being on active service breaks down, will bring
Very big property loss and casualties.A possibility that equipment of being on active service accident can occur for risk assessment is assessed, and is
Enterprise reduces production risk and provides the method for a set of science, provides foundation for policymaker, and risk is carried out early stage accident occurs
Control, has very important significance to the safe and stable operation of process industry.But in current method only by expert
Risk estimation information is simply weighted and averaged, and does not fully consider the ambiguity and randomness of information given by expert,
It is difficult to make accurate judgement to these evaluation informations, so that final risk evaluation result has stronger subjectivity.
Summary of the invention
The purpose of the present invention is to provide a kind of, and the process industry based on cloud model equips military service safety risk estimating method,
With overcome the deficiencies in the prior art.
In order to achieve the above objectives, the present invention adopts the following technical scheme:
A kind of process industry equipment military service safety risk estimating method based on cloud model, comprising the following steps:
Step 1), using cloud model as theoretical basis, standard cloud is mutually generated with Fibonacci method in conjunction with cloud model generation method；
Step 2) carries out M risk assessment to risk factors present in equipment of being on active service using semantic variant, obtains M
Risk evaluation result, each risk evaluation result include possibility occurrence risk evaluation result and sequence severity risk assessment knot
Fruit；The standard cloud obtained according to step 1) will carry out all risk evaluation results that risk assessment obtains using semantic variant and convert
For the M risk assessment value indicated by cloud model parameter；
Step 3), according to entropy assessment calculation risk assessment result risk factor possibility occurrence weight and sequence severity
Weight, and according to possibility occurrence weight and sequence severity weight to step 2) obtained in risk assessment value assemble
Cloud model is evaluated to a risk factors；
Step 4) clusters the risk factors evaluation cloud model in step 3) using neighbour's propagation clustering algorithm, according to
Each risk evaluation result weight shared in all risk evaluation results is calculated according to the number of members of each class cluster, and according to each wind
Dangerous assessment result weight shared in all risk evaluation results is merged to obtain risk factors comprehensive to risk evaluation result
Close evaluation cloud；
Step 5), the similarity for calculating each risk factors overall merit cloud and standard cloud, can be completed each risk factors
Risk assessment.
Further, each risk assessment value includes possibility occurrence risk assessment value and sequence severity risk assessment
Value.
Further, Natural language evaluation is divided into 5 grades, it is corresponding to generate each grade using Fibonacci method
Standard cloud.
Further, it is first determined the standard cloud number of required generation, then according to previous standard cloud and latter standard
Expectation, entropy and the super entropy of cloud complete the generation of standard cloud parameter at the principle of golden section.
Further, in step 2), the semantic variant includes very low, low, general, high and very high.
Further, standard cloud parameter includes desired Ex, entropy En and super entropy He.
Further, which is characterized in that correspond five standard clouds and five semantic variants to M risk assessment knot
Fruit is converted to the risk assessment value indicated by expectation Ex, entropy En and the super entropy He of cloud model.
Further, in step 3), wind is measured by comparing the diversity factor between risk indicator and each risk assessment value
The different degree of dangerous index comes the weight of two risks and assumptions results of calculation risk factor, the i.e. possibility occurrence of risk factors
With the weight of sequence severity:
Wherein: fijIt is the feature specific gravity of i-th risk assessment under j-th of risks and assumptions result；vijIt is that i-th risk is commented
Estimate the decision value of j-th of risks and assumptions result in result,It is the sum of all decision values under j-th of risks and assumptions result,
hjIt is the entropy of j-th of risks and assumptions result, wherein 0≤hj≤ 1,0≤wj≤ 1, and
Further, for n cloud model C1(E1,E1,H1),C2(E2,E2,H2),…,Cn(En,En,Hn) weight them
Collection becomes a comprehensive cloud: C (Ex, En, He) then:
Wherein: wiIt is the weight of each cloud model.
Further, it is iterated calculating using the similarity matrix of data set and degree of membership matrix in step 5), until
Cluster result, which is no longer changed or calculates the number of iterations and reaches upper limit value, obtains the cluster result of object data set.
Compared with prior art, the invention has the following beneficial technical effects:
A kind of process industry based on cloud model of the present invention equips military service safety risk estimating method, by using cloud mould
Type, entropy assessment, neighbour's propagation clustering method carry out risk assessment to being on active service to equip；First using cloud model to each risk factors expert
The possibility occurrence that obtains is assessed to be converted with sequence severity, used during fusion entropy assessment determine each risk because
The possibility occurrence and sequence severity proportion of element, determine weight shared by each risk assessment with neighbour's propagation clustering method,
The semantic assessment result of expert is quantified using cloud model in evaluation method, from the ambiguity and randomness of evaluation language
Two aspects feature the uncertainty of evaluation procedure, so that risk evaluation result has more objectivity and accuracy；It is evaluating
On the basis of information, the weight of uncertainty and sequence severity in each risk factors is determined using entropy assessment, is passed using neighbour
It broadcasts clustering procedure and determines each expert's assessment result shared weight in risk assessment, generate expert's overall merit of each risk factors
Cloud maximizes group effectiveness when analyzing ultimate risk assessment result, while minimizing individual and regretting, and makes the analysis result obtained
The subjectivity and uncertainty in risk assessment information are eliminated, risk evaluation result is more accurate.The present invention can be from randomness
The uncertainty of risk assessment is comprehensively considered with two aspects of ambiguity, while any priori knowledge is not depended on when determining weight
With supervisor's assignment, it is completely dependent on the iotave evaluation information that expert provides, the as far as possible subjectivity in elimination risk assessment information
With uncertainty, make time of day of the risk evaluation result more close to equipment of being on active service.
Detailed description of the invention
Fig. 1 is specific embodiment of the invention flow chart；
Fig. 2 is that carbon washes tower structure schematic diagram；
Fig. 3 is risk factors cloud charts.
Specific embodiment
The invention will be described in further detail with reference to the accompanying drawing:
A kind of process industry equipment military service safety risk estimating method based on cloud model, comprising the following steps:
1), using cloud model as theoretical basis, standard cloud, standard are mutually generated with Fibonacci method in conjunction with cloud model generation method
Cloud parameter includes desired Ex, entropy En and super entropy He；
2) M risk assessment, is carried out to risk factors present in equipment of being on active service using semantic variant, obtains M risk
Assessment result, each risk evaluation result include two risks and assumptions as a result, i.e. possibility occurrence (L) risk evaluation result is with after
Fruit severity (S) risk evaluation result；The standard cloud obtained according to step 1) will carry out risk assessment using semantic variant and obtain
All risk evaluation results be converted to the M risk assessment value indicated by expectation Ex, entropy En and the super entropy He of cloud model；
Each risk assessment value includes possibility occurrence risk assessment value and sequence severity risk assessment value；
3) it, is weighed according to entropy assessment calculation risk assessment result risk factor possibility occurrence weight and sequence severity
Weight, and according to possibility occurrence weight with sequence severity weight to step 2) obtained in risk assessment value assembled to obtain
One risk factors evaluates cloud model；
4), the risk factors evaluation cloud model in step 3) is clustered using neighbour's propagation clustering algorithm, according to every
The number of members of a class cluster calculates each risk evaluation result weight shared in all risk evaluation results, and is commented according to each risk
Estimate result weight shared in all risk evaluation results risk evaluation result is merged to obtain risk factors synthesis and comments
Valence cloud；
5) similarity for, calculating each risk factors overall merit cloud and standard cloud, can be completed the risk of each risk factors
Assessment.
In step 1), it is first determined the standard cloud number of required generation, then according to previous standard cloud and latter mark
Expectation, entropy and the super entropy of quasi- cloud complete the generation of standard cloud parameter at the principle of golden section；Natural language evaluation is divided into
5 grades generate the corresponding standard cloud of each grade using Fibonacci method；
In step 2), M risk assessment is carried out to risk factors present in equipment of being on active service using semantic variant, is obtained
The risk evaluation result of risk factors, the semantic variant are (H) and very high including very low (VL), low (L), general (F), height
(VH)；Five standard clouds and five semantic variant one-to-one correspondence are converted to by cloud model M risk evaluation result
Expectation Ex, the risk assessment value that indicates of entropy En and super entropy He.
In step 3), risk indicator is measured by comparing the diversity factor between risk indicator and each risk assessment value
Different degree, carrys out the weight of two risks and assumptions results of calculation risk factor, i.e. the possibility occurrence of risk factors and consequence is tight
The weight of severe:
Wherein: fijIt is the feature specific gravity of i-th risk assessment under j-th of risks and assumptions result；vijIt is that i-th risk is commented
Estimate the decision value of j-th of risks and assumptions result in result,It is the sum of all decision values under j-th of risks and assumptions result,
hjIt is the entropy of j-th of risks and assumptions result, wherein 0≤hj≤ 1,0≤wj≤ 1, and
For n cloud model C1(E1,E1,H1),C2(E2,E2,H2),…,Cn(En,En,Hn) their weight-sets are become one
Piece comprehensive cloud: C (Ex, En, He) then:
Wherein: wiIt is the weight of each cloud model.
It is iterated calculating using the similarity matrix of data set and degree of membership matrix in step 5), until cluster result
It is no longer changed or calculates the number of iterations and reach upper limit value and obtain the cluster result of object data set.
A kind of process industry based on cloud model of the present invention equips military service safety risk estimating method, adopts in evaluation method
Risk evaluation result is quantified with cloud model, features and evaluated in terms of the ambiguity of evaluation language and randomness two
The uncertainty of journey, so that risk evaluation result has more objectivity and accuracy；On the basis of evaluation information, entropy weight is utilized
Method determines the weight of uncertainty and sequence severity in risk factors, determines each risk assessment knot using neighbour's propagation clustering method
Fruit shared weight in risk assessment generates the risk factors evaluation cloud model of each risk factors, in analysis ultimate risk assessment
Maximize group effectiveness when as a result, while minimizing individual, make the analysis result obtained eliminate risk assessment in subjectivity and
Uncertainty keeps risk evaluation result more accurate.
Referring to attached drawing 1, the present invention specifically includes the following steps:
1) risk assessment team is established, and determines the risk factors being primarily present in equipment, is washed with the carbon of certain chemical company
For tower, specific such as table 1, carbon wash the structural schematic diagram such as attached drawing 2 of tower.
Table 1
2) by 10 risk assessment expert (S1,S2,…S9,S10) composition expert group using semantic evaluation variable it is { very low
(VL), low (L), general (F), height (H), very high (VH) } risk assessment is carried out respectively to each Risk mode, it obtains 10 risks and comments
Estimate as a result, every risk assessment expert's risk assessment factor includes possibility occurrence (L) and consequence seriousness (S) occurs, such as table
2, table 3:
2 possibility occurrence expert opinion result of table
3 sequence severity expert opinion result of table
3) expert's semantic evaluation result converts fusion process
3.1) effective domain U=[X is givenmin,Xmax]=[0,100], He0=0.1, generate 5 cloudlets and PASCAL evaluation PASCAL collection pair
It answers, numerical characteristic such as the following table 4:
4 standard cloud model feature of table
3.2) cloud is carried out to the semantic evaluation result that expert provides according to the transformational relation of semantic evaluation variable and cloud model
Risk assessment expert is carried out the wind that risk assessment obtains to risk factors present in equipment of being on active service using semantic variant by quantization
Dangerous assessment result is converted into the risk assessment value indicated by three parameter of cloud model；
3.3) possibility occurrence and sequence severity weight of each risk factors are calculated according to entropy assessment:
Wherein: fijIt is the feature specific gravity of i-th risk assessment under j-th of risks and assumptions result；vijIt is that i-th risk is commented
Estimate the decision value of j-th of risks and assumptions result in result,It is the sum of all decision values under j-th of risks and assumptions result,
hjIt is the entropy of j-th of risks and assumptions result, wjFor the weight of j-th of risks and assumptions result；
It is as shown in table 5 below to obtain the weight of possibility occurrence and sequence severity in each risk factors:
Table 5 risk factors risks and assumptions (L, S) weight table
According to possibility occurrence and sequence severity weight shared in each risk factors, risk assessment value is carried out
Assembly obtains risk factors evaluation cloud model；
The risk evaluation result of each risk factors is merged, fused cloud model such as the following table 6, each risk factors
Synthesis cloud atlas such as attached drawing 3
6 risk factors of table integrate cloud model evaluation table
3.4) according to clustering rule, setting expert and clustering number is 3, evaluates cloud to risk factors are obtained using AP clustering procedure
Model executes cluster, and according to the number of members of each class cluster, the weight of all decision members is calculated, as shown in table 7 below:
7 expert AP of table clusters weight table
4) similarity for calculating each risk factors overall merit cloud and standard cloud, obtains the risk class of each risk factors,
Such as table 8:
The similarity of table 8 each risk factors and standard cloud
It obtains carbon and washes the risk class of tower outlet relief valve inlet and outlet blocking to be 2 grades；The risk class of tower tray deformation is 3
Grade；Demister deformation, installing risk class not in place is 2 grades；Barrel distortion has crack, there is the risk class obviously damaged
It is 2 grades；Lower cone washes away, crannied risk class is 2 grades；Risk class of the big elbow thickness in top less than 10mm is 1 grade.
For deficiency existing for current risk assessment, the invention proposes a kind of improved methods of risk assessment, by adopting
Risk assessment is carried out to equipment of being on active service with cloud model, entropy assessment, neighbour's propagation clustering method；First using cloud model to each risk because
The possibility occurrence that plain risk assessment obtains is converted with sequence severity, is determined respectively during fusion using entropy assessment
The possibility occurrence and sequence severity proportion of risk factors, are determined shared by each expert opinion with neighbour's propagation clustering method
Weight, the present invention can comprehensively consider the uncertainty of risk assessment in terms of randomness and ambiguity two, while in determination
Any priori knowledge and supervisor's assignment are not depended on when weight, are completely dependent on the iotave evaluation information that expert provides, are made finally to obtain
Analysis result can maximize group effectiveness, while also energy minimization individual is sorry, as far as possible elimination risk assessment information
In subjectivity and uncertain, make time of day of the risk evaluation result more close to equipment of being on active service.
Claims (10)
1. a kind of process industry based on cloud model equips military service safety risk estimating method, which is characterized in that including following step
It is rapid:
Step 1), using cloud model as theoretical basis, standard cloud is mutually generated with Fibonacci method in conjunction with cloud model generation method；
Step 2) carries out M risk assessment to risk factors present in equipment of being on active service using semantic variant, obtains M risk
Assessment result, each risk evaluation result include possibility occurrence risk evaluation result and sequence severity risk evaluation result；
The standard cloud obtained according to step 1) will carry out all risk evaluation results that risk assessment obtains using semantic variant and be converted to
The M risk assessment value indicated by cloud model parameter；
Step 3) is weighed according to entropy assessment calculation risk assessment result risk factor possibility occurrence weight and sequence severity
Weight, and according to possibility occurrence weight with sequence severity weight to step 2) obtained in risk assessment value assembled to obtain
One risk factors evaluates cloud model；
Step 4) clusters the risk factors evaluation cloud model in step 3) using neighbour's propagation clustering algorithm, according to every
The number of members of a class cluster calculates each risk evaluation result weight shared in all risk evaluation results, and is commented according to each risk
Estimate result weight shared in all risk evaluation results risk evaluation result is merged to obtain risk factors synthesis and comments
Valence cloud；
Step 5), the similarity for calculating each risk factors overall merit cloud and standard cloud, can be completed the risk of each risk factors
Assessment.
2. a kind of process industry based on cloud model according to claim 1 equips military service safety risk estimating method,
It is characterized in that, each risk assessment value includes possibility occurrence risk assessment value and sequence severity risk assessment value.
3. a kind of process industry based on cloud model according to claim 1 equips military service safety risk estimating method,
It is characterized in that, Natural language evaluation is divided into 5 grades, generate the corresponding standard of each grade using Fibonacci method
Cloud.
4. a kind of process industry based on cloud model according to claim 3 equips military service safety risk estimating method,
Be characterized in that, it is first determined the standard cloud number of required generation, then according to previous standard cloud and latter standard cloud expectation,
Entropy and super entropy complete the generation of standard cloud parameter at the principle of golden section.
5. a kind of process industry based on cloud model according to claim 1 equips military service safety risk estimating method,
It is characterized in that, in step 2), the semantic variant includes very low, low, general, high and very high.
6. a kind of process industry based on cloud model according to claim 5 equips military service safety risk estimating method,
It is characterized in that, standard cloud parameter includes desired Ex, entropy En and super entropy He.
7. a kind of process industry based on cloud model according to claim 6 equips military service safety risk estimating method,
It is characterized in that, five standard clouds and five semantic variant one-to-one correspondence is converted to by cloud M risk evaluation result
The risk assessment value that expectation Ex, entropy En and the super entropy He of model are indicated.
8. a kind of process industry based on cloud model according to claim 1 equips military service safety risk estimating method,
It is characterized in that, in step 3), measures risk indicator by comparing the diversity factor between risk indicator and each risk assessment value
Different degree, carrys out the weight of two risks and assumptions results of calculation risk factor, i.e. the possibility occurrence of risk factors and consequence is tight
The weight of severe:
Wherein: fijIt is the feature specific gravity of i-th risk assessment under j-th of risks and assumptions result；vijIt is i-th risk evaluation result
In j-th of risks and assumptions result decision value,It is the sum of all decision values, h under j-th of risks and assumptions resultjIt is jth
The entropy of a risks and assumptions result, wherein 0≤hj≤ 1,0≤wj≤ 1, and
9. a kind of process industry based on cloud model according to claim 8 equips military service safety risk estimating method,
It is characterized in that, for n cloud model C1(E1,E1,H1),C2(E2,E2,H2),…,Cn(En,En,Hn) their weight-sets are become one
Piece comprehensive cloud: C (Ex, En, He) then:
Wherein: wiIt is the weight of each cloud model.
10. a kind of process industry based on cloud model according to claim 1 equips military service safety risk estimating method,
It is characterized in that, calculating is iterated using the similarity matrix of data set and degree of membership matrix in step 5), until cluster result
It is no longer changed or calculates the number of iterations and reach upper limit value and obtain the cluster result of object data set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910318253.6A CN110097266A (en) | 2019-04-19 | 2019-04-19 | A kind of process industry equipment military service safety risk estimating method based on cloud model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910318253.6A CN110097266A (en) | 2019-04-19 | 2019-04-19 | A kind of process industry equipment military service safety risk estimating method based on cloud model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110097266A true CN110097266A (en) | 2019-08-06 |
Family
ID=67445351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910318253.6A Pending CN110097266A (en) | 2019-04-19 | 2019-04-19 | A kind of process industry equipment military service safety risk estimating method based on cloud model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110097266A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110659814A (en) * | 2019-09-12 | 2020-01-07 | 国网山东省电力公司寿光市供电公司 | Power grid operation risk evaluation method and system based on entropy weight method |
| CN111476454A (en) * | 2020-03-03 | 2020-07-31 | 浙江海洋大学 | Method for evaluating danger degree of complex water traffic situation by using cloud model |
| CN112508622A (en) * | 2020-12-15 | 2021-03-16 | 河南科技大学 | Taxi service level evaluation system and method based on improved cloud model |
| CN112967153A (en) * | 2021-04-06 | 2021-06-15 | 国网安徽省电力有限公司滁州供电公司 | Energy transformation state assessment method |
| CN113269330A (en) * | 2021-04-07 | 2021-08-17 | 广东省安全生产科学技术研究院 | Chemical equipment leakage probability correction method and system based on cloud model |
| CN114254935A (en) * | 2021-12-22 | 2022-03-29 | 武汉理工大学 | Method and system for estimating risk of cruise ship based on cloud model and entropy weight method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106446540A (en) * | 2016-09-20 | 2017-02-22 | 华北电力大学（保定） | Real-time evaluation method for health state of wind turbine unit |
| US20170147952A1 (en) * | 2015-11-24 | 2017-05-25 | International Business Machines Corporation | Collaborative workplace accident avoidance |
| CN107122616A (en) * | 2017-05-09 | 2017-09-01 | 山东大学 | A kind of multi objective Normal Cloud integrated evaluating method of quality of groundwater |
-
2019
- 2019-04-19 CN CN201910318253.6A patent/CN110097266A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170147952A1 (en) * | 2015-11-24 | 2017-05-25 | International Business Machines Corporation | Collaborative workplace accident avoidance |
| CN106446540A (en) * | 2016-09-20 | 2017-02-22 | 华北电力大学（保定） | Real-time evaluation method for health state of wind turbine unit |
| CN107122616A (en) * | 2017-05-09 | 2017-09-01 | 山东大学 | A kind of multi objective Normal Cloud integrated evaluating method of quality of groundwater |
Non-Patent Citations (3)
| Title |
|---|
| 安相华 等: "基于云模型与协同决策的FMEA耦合评估方法", 《计算机集成制造系统》 * |
| 张伟: "山岭公路隧道施工安全风险管理研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
| 赵坤 等: "基于前景理论及云模型风险型多准则决策方法", 《控制与决策》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110659814A (en) * | 2019-09-12 | 2020-01-07 | 国网山东省电力公司寿光市供电公司 | Power grid operation risk evaluation method and system based on entropy weight method |
| CN111476454A (en) * | 2020-03-03 | 2020-07-31 | 浙江海洋大学 | Method for evaluating danger degree of complex water traffic situation by using cloud model |
| CN112508622A (en) * | 2020-12-15 | 2021-03-16 | 河南科技大学 | Taxi service level evaluation system and method based on improved cloud model |
| CN112967153A (en) * | 2021-04-06 | 2021-06-15 | 国网安徽省电力有限公司滁州供电公司 | Energy transformation state assessment method |
| CN113269330A (en) * | 2021-04-07 | 2021-08-17 | 广东省安全生产科学技术研究院 | Chemical equipment leakage probability correction method and system based on cloud model |
| CN114254935A (en) * | 2021-12-22 | 2022-03-29 | 武汉理工大学 | Method and system for estimating risk of cruise ship based on cloud model and entropy weight method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110097266A (en) | A kind of process industry equipment military service safety risk estimating method based on cloud model | |
| CN108536971B (en) | Bayesian model-based structural damage identification method | |
| WO2021120788A1 (en) | Machine learning-based hydrologic forecasting precision evaluation method and system | |
| CN103793854B (en) | The overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized | |
| CN109583520B (en) | State evaluation method of cloud model and genetic algorithm optimization support vector machine | |
| CN107146009B (en) | Water supply pipe network operation state evaluation method | |
| CN107292502B (en) | Power distribution network reliability assessment method | |
| CN110111024A (en) | Scientific and technological achievement market valuation method based on AHP model of fuzzy synthetic evaluation | |
| CN102496069A (en) | Cable multimode safe operation evaluation method based on fuzzy analytic hierarchy process (FAHP) | |
| CN105303331A (en) | Transformer repair risk decision-making method | |
| CN110378549B (en) | Transmission tower bird damage grade assessment method based on FAHP-entropy weight method | |
| CN107967571A (en) | Index system generation method based on entropy assessment | |
| CN104835073A (en) | Unmanned aerial vehicle control system operation performance evaluating method based on intuitionistic fuzzy entropy weight | |
| CN115018384A (en) | Building site security risk assessment method and system | |
| CN112418682A (en) | Security assessment method fusing multi-source information | |
| CN108615098A (en) | Water supply network pipeline burst Risk Forecast Method based on Bayesian survival analysis | |
| CN112862266B (en) | Coal mine accident emergency plan evaluation model construction method based on fuzzy decision | |
| CN114862267A (en) | Evaluation method and system of oil and gas pipeline alarm management system | |
| CN105741184B (en) | Transformer state evaluation method and device | |
| CN104616209B (en) | A kind of power cable connector information fusion evaluation method based on on-line monitoring | |
| CN111932081B (en) | Method and system for evaluating running state of power information system | |
| CN112001600B (en) | Water leakage risk monitoring method based on SVM and DS theory | |
| CN105976099A (en) | Fuzzy information-based aerospace model scientific research and production management level evaluation method | |
| CN105160448A (en) | Electricity transaction monitoring risk index evaluation method based on unascertained rational numbers | |
| CN106709522B (en) | High-voltage cable construction defect classification method based on improved fuzzy trigonometric number |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190806 |