CN107274324A - A kind of method that accident risk assessment is carried out based on cloud service - Google Patents
A kind of method that accident risk assessment is carried out based on cloud service Download PDFInfo
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Abstract
The invention discloses a kind of method that accident risk assessment is carried out based on cloud service, comprise the steps of：With cloud service and big data technology, network risks model is built；The key risk analytical database is set up based on network risks model；Relevant expert is based on the key risk analytical database and carries out security risk assessment；The substantial amounts of expert's assessment result of iterative processing and by cloud service and big data technology, on the basis of logic-based, rule and semantic network, further gathered data source, and thus build security risk assessment model；Using security risk assessment model, selection pilot is applied forms final assessment result with feedback modifiers model.Risk evaluation model of the invention based on cloud service, expert knowledge library, logic, rule and semantic network, simulates thinking and the reasoning process of expert, and the dependence of reduction government and enterprises and institutions' risk assessment to expert is more intelligent, more professional.
Description
Technical field
The present invention relates to the public risk evaluation areas of accident, one kind carries out accident risk based on cloud service and commented
The method estimated.
Background technology
In recent years, all kinds of accidents such as Emergent Public Events, natural calamity, accident, social security events take place frequently,
Public safety is constituted a serious threat, the complexity and difficulties of contingency management and decision-making increasingly increase.Risk assessment is emergent pipe
The important step of reason, discovery early, the public risk for recognizing and assessing accident, to consequence of effectively controlling risk, reduces wind
Danger loss is significant.
In conventional risk assessment, the subject under discussion of thematic risk assessment is carried out needed for determining as needed, passes through organizes expert meeting
The methods such as business recognize that potential public health emergency or accident public health are threatened, and carry out risk analysis and evaluation,
And propose that risk management is advised；Because of the dissimilarity of region, expert needs unified time to go on business meeting, average risk assess the time compared with
Long, cost is high, program is cumbersome, calculating is complicated, and waste of human resource is more serious.
The content of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of new accident risk based on cloud service and big data
The method of assessment builds the risk evaluation model based on cloud service, expert knowledge library, logic, rule and semantic network, simulation
The thinking of expert and reasoning process, the dependence of reduction government and enterprises and institutions' risk assessment to expert are more intelligent, more professional.
The technical solution adopted in the present invention is：
A kind of method that accident risk assessment is carried out based on cloud service, is specifically comprised the steps of：
1) cloud service and big data technology are used, network risks model is built；
2) the key risk analytical database is set up based on network risks model；
3) relevant expert is based on the key risk analytical database and carries out security risk assessment；
4) the substantial amounts of expert's assessment result of iterative processing and by cloud service and big data technology, in logic-based, rule
On the basis of semantic network, further gathered data source, and thus build security risk assessment model；
5) security risk assessment model is utilized, selection pilot is applied forms final assessment result with feedback modifiers model.
Preferably, in step 1) described in network risks model include single network risk model and complex network risk
Model；
The single network risk model is the network risks structure chart built based on single risks and assumptions, when single risk
When the factor changes, the network risks structure chart of structure can also change；
The overall network that the complex network risk model is drawn by least two risks and assumptions of synthesis
Risk structure figure, when wherein a certain risks and assumptions change, the overall network risk structure figure of structure can also change.
Preferably, the risks and assumptions in the complex network risk model are set up not according to the risk class of risks and assumptions
Same risk model, after the risk class of risks and assumptions is determined, network is set up according to the rank of risk class from high to low
Risk model.
Preferably, the key risk analytical database includes static data storage and dynamic rules change two sides
Face；The static data storage includes the type to risks and assumptions and the description in assessment object essential sentence storehouse；The dynamic rule
Then change produces basic sentence storehouse by rule transformation, for automatic Evaluation, according to the correlation between different risks and assumptions, knot
Close word and close emergency preplan entry is associated with sentence, the complete dynamic rules that can substantially conform to logic according to word and sentence generation change
Database.
Preferably, the gathered data source includes data below：
A) internet public feelings data：Public sentiment data related on assessing the affiliated industry field of object on internet is gathered,
Set up basic dictionary；
B) object environment data are assessed：The basic datas such as Architectural Equipment, geographical environment and surrounding enviroment；
C) online questionnaire survey data：The online risk questionnaire survey data generated based on expertise library module；
D) Focus discussion data on line：Depth gathers typical problem data, point different role and different levels.
Preferably, the security risk assessment model includes static analysis data and dynamic analyze data, by static state point
Analysis data and dynamic analyze data are showed with diversified forms such as statistical chart, statistical form, tables of data and report files, multi-angle displaying
The hidden feature of data, makes risk assessment algorithm closer to human nature thinking.
The present invention has the advantages that compared with prior art：
1st, the present invention proposes the new risk assessment based on cloud service and big data on the basis of traditional methods of risk assessment
Method, builds the risk assessment expert model based on cloud service, expert knowledge library, logic, rule and semantic network, and simulation is special
The thinking of family and reasoning process, the dependence of reduction government and enterprises and institutions' risk assessment to expert are more intelligent, more professional.
2nd, risk assessment expert model of the present invention, researches and develops the risk assessment expert system based on cloud service, based on mutual
Space-time limitation is broken through in the use pattern of networking, simplifies many coordinations limitation of risk assessment, supports fragmentation to participate in, be conducive to
Normalization risk assessment work is carried out.
3rd, methods of risk assessment of the present invention substantially reduces the dependence that risk assessment works to human expert, using fixed
Amount and the qualitative analysis method being combined, support to assess high, medium and low grade inside and outside object tissue system and represent participation at many levels, replace
For external experts, different visual angles participate in model and ask tune, realize that maximization experience is extracted and risk assessment, effectively participation can be overcome to comment
The problems such as professional not enough, space-time of external experts estimated limits and floats on surface.
4th, the present invention combines internet public feelings big data, after the risk assessment knowledge base in a certain field of original upload, wind
Dangerous assessment experts system can capture assessment objects perimeter environmental data, and connecting inner assessment result automatically, be obtained from internet
Take more CROSS REFERENCE data, carry out intelligence learning, provide specific aim for risk assessment object, professional risk assessment.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the formation schematic diagram of single network risk model embodiment of the present invention；
Fig. 2 is the formation schematic diagram of complex network risk model embodiment of the present invention；
Fig. 3 is the formation schematic diagram of another embodiment of complex network risk model of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
The present invention investigates using theoretical research and on the spot the side being combined using accident risk assessment as research object
Logic-based, rule and semantic network are set up in method, discussion, can dynamic load different field expertise model library, and be based on
Intelligence learning that cloud service and internet big data are provided and break through space-time limitation accident risk assessment expert model and
Application system, is commented with being widely used in regional government, community, school, enterprises and institutions and public place accident risk
Estimate work.
The present invention is by each accident by being a network risks mould with cloud service and big data technical definition
Type, wherein, network risks model includes two aspects of single network risk model and complex network risk model.
Single network risk model is the network risks structure chart built based on single risks and assumptions, when single risks and assumptions
When changing, the network risks structure chart of structure can also change, because each accident may include a lot
Risks and assumptions, but on other occasions, not all risks and assumptions can all induce accident, for each time
Accident may simply wherein some risks and assumptions just result in the generation of accident, simultaneously as different risks
The same accident that the factor is induced, its event effect produced and processing mode and result afterwards are also different
's.So the network risks model that the accident that different single risks and assumptions induce is set up is also different.For not
The network risks model that same single risks and assumptions are set up is different.
Single network risk model is described further below, it is shown in Figure 1, it is assumed that for specific burst thing
Part have three risks and assumptions, i.e. risks and assumptions 1, risks and assumptions 2 and risks and assumptions 3 (actually induce the risk of accident because
Son has a lot), therefrom it will be seen that network wind produced by the identical accident that different risks and assumptions are induced
Dangerous model is different.Specifically as shown in FIG., by the accident that risks and assumptions 1 are induced obtains network risks mould
Type 1 (we in figure with hexagon come instead of network risks model 1), net obtained by the accident that risks and assumptions 2 are induced
Network risk model 2 (we in figure with ellipse come instead of network risks model 2), the burst thing induced by risks and assumptions 3
Part obtains network risks model 3 (we replace network risks model 3 with triangle in figure).Therefrom it will be seen that net
Network risk model 1, network risks model 2, network risks model 3 are that (i.e. structure is six to the different network risks model of structure
Side shape, ellipse and triangle).
The overall network risk that complex network risk model is drawn by least two risks and assumptions of synthesis
Structure chart, when a certain risks and assumptions therein change in composition, the overall network risk structure figure of structure can also occur
Change.
It is described further below for situation described above, it is shown in Figure 2, it is assumed that for occurring particular burst
The risk factors of event constitute (actual by wherein three in risks and assumptions 1, risks and assumptions 2, risks and assumptions 3 and risks and assumptions 4
The upper risks and assumptions induced in the accident risk factors might have multiple combinations, such as induce the hair of the accident
Life is made up of risks and assumptions 1, risks and assumptions 2, risks and assumptions 3 and risks and assumptions 4, or induces the generation of the accident by risk
The factor 1 and the composition of risks and assumptions 2 etc.), therefrom it will be seen that a certain risks and assumptions therein change in constituting
When, the overall network risk structure figure of structure can also change.Specifically, as shown in FIG., due to risks and assumptions 1, wind
The accident that the risk factors that the dangerous factor 2, risks and assumptions 3 are constituted are induced obtains network risks model a, and (we use six in figure
Side shape replaces network risks model a), by the accident that risks and assumptions 1, risks and assumptions 2, risks and assumptions 4 are induced is obtained
To network risks model b (in figure we with ellipse come instead of network risks model b), due to risks and assumptions 2, risks and assumptions 3,
The accident that risks and assumptions 4 are induced obtains network risks model c, and (we replace network risks model with triangle in figure
c).Therefrom it will be seen that network risks model a, network risks model b, network risks model c are the different net of structure
Network risk model (i.e. structure is hexagon, ellipse and triangle).
Further the present embodiment is optimized, we are the risks and assumptions in complex network risk model according to risk
The risk class of the factor sets up different risk models, when risks and assumptions risk class determine after, according to risk class by
High to Low rank sets up network risks model.
It is described further below for situation described above, it is shown in Figure 3, it is assumed that for occurring particular burst
The risk factors of event be made up of risks and assumptions 1, risks and assumptions 2 and risks and assumptions 3 (actually induce the accident risk because
Risks and assumptions in element might have multiple combinations), therefrom it will be seen that when a certain risks and assumptions therein in composition
When grade changes, the overall network risk structure figure of structure can also change.Specifically as shown in FIG., induction is worked as
The proportion of the risk factors risk factor 1 of accident is that the proportion of 30%, risks and assumptions 2 is 30%, risks and assumptions 3
When proportion is 40%, obtained network risks model A (we replace network risks model A with hexagon in figure), when
The proportion of the risk factors risk factor 1 for inducing accident is that the proportion of 20%, risks and assumptions 2 is 50%, risk
When the proportion of the factor 3 is 30%, (we replace network risks model to obtained network risks Model B with ellipse in figure
B), although therefrom it will be seen that inducing the risk factors of the accident by risks and assumptions 1, risks and assumptions 2, risk
The factor 3 is constituted, but (the accounting higher generation of risks and assumptions when the risk class of the risks and assumptions of each in risk factors is different
Table that risk class is higher, and the accounting of risks and assumptions is lower, and to represent risk class lower), the network risks model of formation is not yet
Equally (i.e. structure is hexagon, ellipse and triangle).
In the present embodiment, the different network risks models set up for different situations are based on cloud service and interconnection
Net big data is counted, so as to form the network risks model being directed to obtained by each situation in a computer.
The key risk analytical database is set up on the basis of based on network risks model, wherein, the key risk analyze data
Storehouse includes static data storage and dynamic rules change two aspects；The static data storage includes the type to risks and assumptions
And assess the description in object essential sentence storehouse；The dynamic rules change produces basic sentence storehouse by rule transformation, for automatic
Evaluate, according to the correlation between different risks and assumptions, bluebeard compound associates close emergency preplan entry with sentence, can be according to word
The complete dynamic rules delta data storehouse for substantially conforming to logic is generated with sentence.
Relevant expert is based on the key risk analytical database and carries out security risk assessment, and the substantial amounts of expert of iterative processing assesses
As a result and by cloud service and big data technology on the basis of logic-based, rule and semantic network, further gathered data
Source, and thus build security risk assessment model.
In the present embodiment, the process that relevant expert carries out security risk assessment is：The assessment result of expert is collected, and is calculated
Its characteristic value, specifically, based on each network risks model, collecting and record multiple experts, this is commented to network risks model
Estimate the recurrence forest (Regression Forest) that result is made up of a series of regression tree (Regression Trees) algorithm
Algorithm, iteratively handles substantial amounts of expert's assessment result.
On the basis of logic-based, rule and semantic network, further gathered data source, the gathered data source includes
Data below：The public sentiment data related on assessing the affiliated industry field of object, builds on internet public feelings data, collection internet
Vertical basis dictionary；Assess object environment data：The basic datas such as Architectural Equipment, geographical environment and surrounding enviroment；Online questionnaire is adjusted
Look into data：The online risk questionnaire survey data generated based on expertise library module；Focus discussion data on line：Depth is gathered
Typical problem data, point different role and different levels.
A large amount of expert's assessment results for iteratively handling and adopted on the basis of logic-based, rule and semantic network
The data source of collection, builds security risk assessment model, so, the risk evaluation model that methods of risk assessment is calculated, greatly
The dependence that big reduction risk assessment works to human expert, using the analysis method being qualitatively and quantitatively combined, supports assessment pair
As high, medium and low grade represents participation at many levels inside and outside organizational framework, different visual angles participate in model and ask tune, realize that maximization experience is taken out
Take and risk assessment, can effectively overcome professional not enough, the space-time limitation of the external experts for participating in assessing and float on surface etc. and ask
Topic.
In the present embodiment, security risk assessment model includes static analysis data and dynamic analyze data, by static state point
Analysis data and dynamic analyze data are showed with diversified forms such as statistical chart, statistical form, tables of data and report files, multi-angle displaying
The hidden feature of data, makes risk assessment algorithm closer to human nature thinking.Using security risk assessment model, pilot application is selected
Final assessment result is formed with feedback modifiers model.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
God is with principle, and any modification, equivalent substitution and improvements made etc. should be included in the scope of the protection.
Claims (6)
1. a kind of method that accident risk assessment is carried out based on cloud service, it is characterised in that comprise the steps of：
1) cloud service and big data technology are used, network risks model is built；
2) the key risk analytical database is set up based on network risks model；
3) relevant expert is based on the key risk analytical database and carries out security risk assessment；
4) the substantial amounts of expert's assessment result of iterative processing and by cloud service and big data technology, in logic-based, rule and language
On the basis of adopted network, further gathered data source, and thus build security risk assessment model；
5) security risk assessment model is utilized, selection pilot is applied forms final assessment result with feedback modifiers model.
2. the method according to claim 1 that accident risk assessment is carried out based on cloud service, it is characterised in that：Step
1) network risks model described in include single network risk model and complex network risk model；
The single network risk model is the network risks structure chart built based on single risks and assumptions, when single risks and assumptions
When changing, the network risks structure chart of structure can also change；
The overall network risk that the complex network risk model is drawn by least two risks and assumptions of synthesis
Structure chart, when wherein a certain risks and assumptions change, the overall network risk structure figure of structure can also change.
3. the method according to claim 2 that accident risk assessment is carried out based on cloud service, it is characterised in that：It is described
Risks and assumptions in complex network risk model set up different risk models according to the risk class of risks and assumptions, when risk because
After the risk class of son is determined, network risks model is set up according to the rank of risk class from high to low.
4. the method according to claim 1 that accident risk assessment is carried out based on cloud service, it is characterised in that：It is described
The key risk analytical database includes static data storage and dynamic rules change two aspects；
The static data storage includes the type to risks and assumptions and the description in assessment object essential sentence storehouse；
Dynamic rules change produces basis sentence storehouse by rule transformation, for automatic Evaluation, according to different risks and assumptions it
Between correlation, bluebeard compound associates close emergency preplan entry with sentence, can substantially conform to logic according to word and sentence generation
Complete dynamic rules delta data storehouse.
5. the method according to claim 1 that accident risk assessment is carried out based on cloud service, it is characterised in that collection
Data source includes data below：
A) internet public feelings data：Public sentiment data related on assessing the affiliated industry field of object on internet is gathered, is set up
Basic dictionary；
B) object environment data are assessed：The basic datas such as Architectural Equipment, geographical environment and surrounding enviroment；
C) online questionnaire survey data：The online risk questionnaire survey data generated based on expertise library module；
D) Focus discussion data on line：Depth gathers typical problem data, point different role and different levels.
6. the method according to claim 1 that accident risk assessment is carried out based on cloud service, it is characterised in that：It is described
Security risk assessment model includes static analysis data and dynamic analyze data, by static analysis data and dynamic analyze data with
The diversified forms such as statistical chart, statistical form, tables of data and report file show, the hidden feature of multi-angle display data, make risk
Assessment algorithm is closer to human nature thinking.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108876133A (en) * | 2018-06-07 | 2018-11-23 | 中国平安人寿保险股份有限公司 | Risk assessment processing method, device, server and medium based on business information |
| CN109005162A (en) * | 2018-07-18 | 2018-12-14 | 中国联合网络通信集团有限公司 | Industrial control system method for auditing safely and device |
| CN112989374A (en) * | 2021-03-09 | 2021-06-18 | 闪捷信息科技有限公司 | Data security risk identification method and device based on complex network analysis |
| CN114021928A (en) * | 2021-10-28 | 2022-02-08 | 中能电力科技开发有限公司 | Wind power enterprise network security dynamic risk assessment system based on ISMS |
| CN115271288A (en) * | 2021-10-29 | 2022-11-01 | 上海柠盟数据技术有限公司 | Quantitative evaluation system and method for cross-domain sharing utilization risk of data |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101894230A (en) * | 2010-07-14 | 2010-11-24 | 国网电力科学研究院 | Static and dynamic analysis technology-based host system security evaluation method |
| CN102801739A (en) * | 2012-08-25 | 2012-11-28 | 乐山师范学院 | Network risk determining and evidence obtaining method based on cloud computing environment |
| CN103544669A (en) * | 2013-10-22 | 2014-01-29 | 哈尔滨医科大学 | Portable public hygiene risk evaluating device and method for emergency |
| CN104820629A (en) * | 2015-05-14 | 2015-08-05 | 中国电子科技集团公司第五十四研究所 | Intelligent system and method for emergently processing public sentiment emergency |
| CN105939200A (en) * | 2015-07-08 | 2016-09-14 | 北京匡恩网络科技有限责任公司 | Method and system for performing network security risk evaluation by utilizing expert system |
| CN106056314A (en) * | 2016-06-29 | 2016-10-26 | 李明洋 | Risk assessment method for intelligent distribution network |
| CN106126694A (en) * | 2016-06-30 | 2016-11-16 | 中国科学院烟台海岸带研究所 | The support of a kind of impurely Rehabilitation decision and the construction method of online evaluation platform |
| CN106408193A (en) * | 2016-09-26 | 2017-02-15 | 贵州电网有限责任公司输电运行检修分公司 | Power transmission line gridding risk analysis and evaluation method |
-
2017
- 2017-06-06 CN CN201710418846.0A patent/CN107274324A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101894230A (en) * | 2010-07-14 | 2010-11-24 | 国网电力科学研究院 | Static and dynamic analysis technology-based host system security evaluation method |
| CN102801739A (en) * | 2012-08-25 | 2012-11-28 | 乐山师范学院 | Network risk determining and evidence obtaining method based on cloud computing environment |
| CN103544669A (en) * | 2013-10-22 | 2014-01-29 | 哈尔滨医科大学 | Portable public hygiene risk evaluating device and method for emergency |
| CN104820629A (en) * | 2015-05-14 | 2015-08-05 | 中国电子科技集团公司第五十四研究所 | Intelligent system and method for emergently processing public sentiment emergency |
| CN105939200A (en) * | 2015-07-08 | 2016-09-14 | 北京匡恩网络科技有限责任公司 | Method and system for performing network security risk evaluation by utilizing expert system |
| CN106056314A (en) * | 2016-06-29 | 2016-10-26 | 李明洋 | Risk assessment method for intelligent distribution network |
| CN106126694A (en) * | 2016-06-30 | 2016-11-16 | 中国科学院烟台海岸带研究所 | The support of a kind of impurely Rehabilitation decision and the construction method of online evaluation platform |
| CN106408193A (en) * | 2016-09-26 | 2017-02-15 | 贵州电网有限责任公司输电运行检修分公司 | Power transmission line gridding risk analysis and evaluation method |
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