CN106485327A - Under a kind of hazardous condition, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution - Google Patents
Under a kind of hazardous condition, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution Download PDFInfo
- Publication number
- CN106485327A CN106485327A CN201610815386.0A CN201610815386A CN106485327A CN 106485327 A CN106485327 A CN 106485327A CN 201610815386 A CN201610815386 A CN 201610815386A CN 106485327 A CN106485327 A CN 106485327A
- Authority
- CN
- China
- Prior art keywords
- individual
- knowledge
- crowd
- tramples
- trampling
- 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
- 238000000034 method Methods 0.000 title claims abstract description 28
- 231100001261 hazardous Toxicity 0.000 title claims abstract description 15
- 230000009467 reduction Effects 0.000 claims abstract description 29
- 230000007246 mechanism Effects 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims description 32
- 239000008186 active pharmaceutical agent Substances 0.000 claims description 8
- 230000003993 interaction Effects 0.000 claims description 8
- 238000012502 risk assessment Methods 0.000 claims description 8
- 230000003542 behavioural effect Effects 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 6
- 230000037396 body weight Effects 0.000 claims description 5
- 230000004069 differentiation Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000000903 blocking effect Effects 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 238000002408 directed self-assembly Methods 0.000 claims description 2
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 230000000694 effects Effects 0.000 claims 1
- 238000011160 research Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 206010034912 Phobia Diseases 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 208000019899 phobic disease Diseases 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000002341 toxic gas Substances 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 101000642661 Homo sapiens Serine-threonine kinase receptor-associated protein Proteins 0.000 description 1
- 241000406668 Loxodonta cyclotis Species 0.000 description 1
- 102100035934 Serine-threonine kinase receptor-associated protein Human genes 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004138 cluster model Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000875 corresponding 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
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011438 discrete method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000505 pernicious effect Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000000192 social effect Effects 0.000 description 1
- 238000000547 structure data Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
-
- 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
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to crowd tramples the Methods of Knowledge Discovering Based of behavior evolution under a kind of hazardous condition, comprise the following steps:1) extraction crowd tramples scenario factors feature, is emulated in colony intelligence crowd evacuation model, tramples evolved behavior feature during being evacuated, and builds the domain object set of domain space layer;2) trampling scenario factors feature to the crowd and trampling evolved behavior feature carries out rough set attribute sliding-model control;3) that sets up " conditional decision " two-dimensional signal pattern tramples evolution mechanism knowledge expression, and carries out Reduction of Knowledge to the evolution mechanism knowledge expression of trampling;4) meta-rule storehouse is generated according to the evolved behavior feature of trampling;5) load the meta-rule storehouse and example is evacuated, generate the abstraction rule storehouse for trampling evolution.Compared with prior art, by classical rough set theory, the crowd of realization tramples the automatic Knowledge Discovery of behavior evolution mechanism to the present invention, and the crowd of solution tramples the lack of knowledge of evolution mechanism.
Description
Technical field
The present invention relates to crowd tramples precautionary technology field, the crowd's behavior of trampling under a kind of hazardous condition that especially relates to is drilled
The Methods of Knowledge Discovering Based of change.
Background technology
The analysis of accident is trampled currently for crowd and prevent also in the starting stage.Occur during crowd evacuation
Trample what possibly " rumour-fear " pattern individually occurred, it is also possible to which (fire, earthquake, blast, poison gas are let out used as burst fire-disaster
Leakage etc. accident) secondary disaster, with " disaster-fear " Mode Coupling generation.
Mostly existing crowd evacuation model is to focus on the research in terms of crowd movement is learned with dynamics, and main description crowd dredges
Scattered behavior, and when analyzing building evacuation key element (stairs port and passageway for fire apparatus minimum widith etc.) and crowd's traffic capacity and evacuating
Between relation, provide building some contingency management strategies for trampling of prevention crowd and suggestion.Most existing models not yet system
Condition and the evolution mechanism of generation are trampled in research, are only examined as the typical unstable phenomenon in existing evacuation model using trampling
Consider.
Existing crowd evacuation research conclusion is analyzed it is found that the elementary step is in for the research for trampling evolution mechanism,
The problems such as still suffering from knowledge experience dependence and lack of knowledge.For Precautions are trampled, rest on the prediction scheme pipe based on experience more
In the reason stage, for the real-time control problem that tramples, depend on the tissue experience of emergency evacuation floor manager personnel and crowd more
Degree of cooperation.In the urgent need to adopting scientific method, analysis crowd tramples origin mechanism, and announcement crowd tramples Evolution.
Content of the invention
The purpose of the present invention is exactly to provide a kind of hazardous condition servant to overcome the defect of above-mentioned prior art presence
Group tramples the Methods of Knowledge Discovering Based of behavior evolution, only relies only on actual domain record data itself, tight using rough set theory
Combing logic, calculating and discovery crowd trample the knowledge of behavior evolution automatically.
The purpose of the present invention can be achieved through the following technical solutions:
Under a kind of hazardous condition, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, comprises the following steps:
1) extraction crowd tramples scenario factors feature, is emulated, obtain evacuating in colony intelligence crowd evacuation model
Evolved behavior feature being trampled in journey, building the domain object set of domain space layer, the evolved behavior feature of trampling includes to step on
Step on probability;
2) trampling scenario factors feature to the crowd and trampling evolved behavior feature is carried out at rough set attribute discretization
Reason;
3) scenario factors feature is trampled as the conditional attribute of domain object using the crowd after the sliding-model control, with phase
The evolved behavior feature of trampling that answers sets up " conditional decision " two-dimensional signal pattern as the decision attribute of domain object
Evolution mechanism knowledge expression is trampled, and Reduction of Knowledge is carried out to the evolution mechanism knowledge expression of trampling;
4) meta-rule storehouse is generated according to the evolved behavior feature of trampling;
5) load the meta-rule storehouse and example is evacuated, generate the abstraction rule storehouse for trampling evolution.
The crowd tramples scenario factors feature to be included to evacuate individual physiologic factor, social factor, behavioural characteristic and environment
Feature, the evacuation individuality physiologic factor include age, sex, disability degree, agility and body weight, and the social factor includes
Strange degree, the behavioural characteristic include panic degree, and the environmental characteristic includes disaster factors and space constraint.
The fear degree is mapped as evacuating the individual of individuality according to Helbing, D. fear " Psychology and behavior " volatility model
Body diameter, the solution procedure of the individuality diameter are specially:
fiw={ Aiexp[(ri-diw)/Bi]+kg(ri-diw)}niw-γg(ri-diw)vitiw(3)
In formula, miIt is the individual quality of i-th evacuation,It is the individual ideal velocity of i-th evacuation,It is i-th evacuation
Individual direction initialization, viIt is the individual actual speed of i-th evacuation, τiIt it is i-th evacuation individual characteristic time, when t is
Between, fijIt is to evacuate the interaction force between individuality i and evacuation individuality j, fiwIt is to evacuate phase interaction of the individuality between i and border
Firmly, Ai、BiFor constant, dcijIt is the individual mass centre's distance of two evacuations, dijDistance between individuality, n are evacuated for twoijBe by
Evacuate the standard vector that individuality j points to i, tijIt is nijTangential direction,It is the phasor difference of t speed, kg
(dij-dcij) represent mass force,Represent t force of sliding friction, k and γ is for determining to evacuate individuality i and j
Between interaction blocking effect parameter, diwIt is to evacuate the distance between individuality i and border, niwRefer to vertical direction,
tiwRefer to tangential direction, riIt is the individual individual diameter of i-th evacuation, viIndividual individual speed is evacuated for i-th, g (x) is
One function, if evacuate individuality collided, g (x)=0, otherwise g (x)=x.
The disaster factors are mapped as the individual individual speed of evacuating after panic propagation and are expressed, specially:
Defining rule θ is:
In formula, μDAFor the membership function of casualty loss degree DA,DAmaxFor maximum casualty loss degree,
For risk assessment intensity I0Membership function,ImaxIntensity is assessed for greateset risk, subscript t is hdisSequence
Number, s is the sequence number of i value, and i is risk assessment intensity I0On coordinate value, n for i maximum, rstIt is in fuzzy relation matrix
Element, using inference formula:
DA=I0θR (5)
By risk assessment intensity I0It is assigned on control point in the method for information distribution, finally obtains calamity source index
hdisValue, R be fuzzy relation matrix;
Calculate each individual speed for individuality being evacuated after panic propagation:
hdis=f (ρ) (6)
In formula, ρ is density of stream of people, and f () represents calamity source index hdisWith density of stream of people ρ institute linear relationship function, DL
=NAP/WALA=ρ AP, DLIt is the evacuation individual amount of unit area in horizontal plane, N is the total number of persons that walks in the stream of people, AP
For the horizontal projected area of single people, WAFor the width of the stream of people, LAFor the length of the stream of people, viIndividual individuality speed is evacuated for i-th
Degree.
The crowd tramples scenario factors feature and is divided into qualitative features and quantitative characteristic, execution step 2) when, for qualitative
Feature, is directly mapped as rough set Category Attributes;For quantitative characteristic, rough set connection attribute is first mapped as, so
Adopt heuristic SOM self-organizing clustering model afterwards, automatic sliding-model control is carried out to rough set connection attribute, rough set is continuous
Property value is converted into the distinguishable mathematic sign of rough set matrix.
The Reduction of Knowledge includes the domain space dimensionality reduction yojan carried out using full distance dimensionality reduction model and with coarse
Attribute reduction and Value reduction that the theoretical yojan of collection and core computation model are carried out.
Reduction of Knowledge is carried out with rough set theory to be specially:
For knowledge system S=(U, A), U is domain object set, U={ x1,x2,…,xn, element x thereiniIt is opinion
Object in domain, n are object sum, and A is the community set of non-NULL, and A=C ∪ D, C are conditional attribute set, and D is decision attribute
Collection, and C ∩ D=φ, calculate the differentiation matrix M of the knowledge systemDS(C):
Element m in discrimination matrixijIt is to discriminate between object xiAnd xjAll conditions attribute set;
Define Boolean function fDSAs follows:
Wherein, Boolean variableCorresponding to m conditional attributeSymbol ∨
Represent computing of extracting, symbol ∧ represents conjunction computing;
The decision matrix M of calculation knowledge system SDS(C):
Element in decision matrixIt is to discriminate between object xiAnd xjAll conditions attribute set;
Define decision function fDRDSAs follows:
Here
Compared with prior art, the present invention has advantages below:
Structure crowd tramples the rough set Knowledge Discovery Model of evolution mechanism, and crowd is trampled the Knowledge Discovery of evolution
It is converted into rough set decision rule and automatically generates problem.
Crowd is trampled to the feature extraction of scenario factors and the research of discretization, based on Self-organizing Maps (SOM) network
Heuristic automatic cluster model, extraction crowd tramples key scene key element and evolved behavior feature, and discrete turns to rough set
The conditional attribute of domain object and decision attribute, build and contain the two-dimensional signal table that crowd tramples evolution knowledge, with succinct and complete
Standby mode proposes the knowledge-representation system that crowd tramples evolution mechanism.Known by rough set matrix computations auto-building model
Know expression meta-rule, load crowd evacuation during trample situational examples, reduction tight for meta-rule can be become tool
There is the abstraction rule of realistic background meaning, form Explicit Knowledge.The uniformity and completeness of above-mentioned each method, be system-computed simultaneously
It was found that crowd tramples the valuable knowledge contained by evolved behavior, and scientific basis is provided, become the innovative point of the present invention.
Description of the drawings
Fig. 1 is the principle schematic of the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention be not limited to
Following embodiments.
As shown in figure 1, the present embodiment provides the Methods of Knowledge Discovering Based that crowd under a kind of hazardous condition tramples behavior evolution, bag
Include following steps:
Step one, extraction crowd trample scenario factors feature, emulated, dredged in colony intelligence crowd evacuation model
Evolved behavior feature being trampled during dissipating, building the domain object set of domain space layer, trampling evolved behavior feature includes to step on
Step on probability.
The crowd tramples scenario factors feature to be included to evacuate individual physiologic factor, social factor, behavioural characteristic and environment
Feature, the evacuation individuality physiologic factor include age, sex, disability degree, agility and body weight etc., the social factor bag
Strange degree is included, the behavioural characteristic includes panic degree, the environmental characteristic includes disaster factors and space constraint.
In the present invention, the fear degree is mapped as evacuating according to Helbing, D. fear " Psychology and behavior " volatility model
Individual individual diameter, measures its panic degree from the individual tensity (Psychological phobia) of evacuation and velocity variations, by panic journey
Degree is mapped as evacuating individual diameter, sets up panic quantitative model.
The solution procedure of the individuality diameter is specially:
fiw={ Aiexp[(ri-diw)/Bi]+kg(ri-diw)}niw-γg(ri-diw)vitiw(3)
In formula, miIt is the individual quality of i-th evacuation,It is the individual ideal velocity of i-th evacuation,It is i-th evacuation
Individual direction initialization, viIt is the individual actual speed of i-th evacuation, τiIt it is i-th evacuation individual characteristic time, when t is
Between, fijIt is to evacuate the interaction force between individuality i and evacuation individuality j, fiwIt is to evacuate phase interaction of the individuality between i and border
Firmly, Ai、BiFor constant, dcijIt is the individual mass centre's distance of two evacuations, dijDistance between individuality, n are evacuated for twoijBe by
Evacuate the standard vector that individuality j points to i, tijIt is nijTangential direction,It is the phasor difference of t speed, kg
(dij-dcij) represent mass force,Represent t force of sliding friction, k and γ is for determining to evacuate individuality i and j
Between interaction blocking effect parameter, diwIt is to evacuate the distance between individuality i and border, niwRefer to vertical direction,
tiwRefer to tangential direction, riIt is the individual individual diameter of i-th evacuation, viIndividual individual speed is evacuated for i-th, g (x) is
One function, if evacuate individuality collided, g (x)=0, otherwise g (x)=x.
The model data of the typical disaster such as fire and toxic gas leakage is derived from hazard model storehouse, extracts disaster to evacuation
Key influence factor;Derive from building model storehouse and related building space structure data is evacuated, extract space about
Key influence factor of the bundle (such as exit width and wall locations etc.) to evacuation.Will be special as environment to disaster factors and space constraint
Levy, and classified according to qualitative and quantitative.Disaster is the trigger condition that crowd panic is propagated, and disaster factors are mapped to fear
In propagation model, individual speed is evacuated in disaster factors impact.
In the present invention, the disaster factors are mapped as the individual individual speed of evacuating after panic propagation and are expressed,
Specially:
Defining rule θ is:
In formula, μDAFor the membership function of casualty loss degree DA,DAmaxFor maximum casualty loss degree,
For risk assessment intensity I0Membership function,ImaxIntensity is assessed for greateset risk, subscript t is hdisSequence
Number, s is the sequence number of i value, and i is risk assessment intensity I0On coordinate value, n for i maximum, rstIt is in fuzzy relation matrix
Element, using inference formula:
DA=I0θR (5)
By risk assessment intensity I0It is assigned on control point in the method for information distribution, finally obtains calamity source index
hdisValue, R be fuzzy relation matrix;
Calculate each individual speed for individuality being evacuated after panic propagation:
hdis=f (ρ) (6)
In formula, ρ is density of stream of people, and f () represents calamity source index hdisWith density of stream of people ρ institute linear relationship function, DL
=NAP/WALA=ρ AP, DLIt is the evacuation individual amount of unit area in horizontal plane, N is the total number of persons that walks in the stream of people, AP
For the horizontal projected area of single people, WAFor the width of the stream of people, LAFor the length of the stream of people, viIndividual individuality speed is evacuated for i-th
Degree.
Step 2, scenario factors feature is trampled to crowd and tramples evolved behavior feature and carry out at rough set attribute discretization
Reason, carries out discretization using the characteristic attribute discretization algorithm based on SOM self-organized mapping network to the evacuation feature for extracting, and
Carry out quantifying and dependent linearity mapping.
The crowd tramples scenario factors feature and is divided into qualitative features and quantitative characteristic, execution step 2) when, for qualitative
Feature (as sex), is directly mapped as rough set Category Attributes (such as 1 and 0 etc.);For quantitative characteristic (as age, fear
Spend and trample probability etc.), rough set connection attribute is first mapped as, heuristic SOM self-organizing clustering model is then adopted, right
Rough set connection attribute carries out automatic sliding-model control, and rough set continuous property is converted into the distinguishable number of rough set matrix
Symbol (such as 1,2 ... etc.).
Step 3, trampled using the crowd after the sliding-model control scenario factors feature as domain object condition belong to
Property, using the corresponding evolved behavior feature of trampling as the decision attribute of domain object, set up " conditional decision " two dimension letter
Breath pattern trample evolution mechanism knowledge expression, and Reduction of Knowledge is carried out to the evolution mechanism knowledge expression of trampling.
In terms of evolution knowledge representation and reduction method research is trampled, the attribute discretization of scenario factors is trampled based on crowd
Expression, using rough set theory Knowledge Representation Model (domain, attribute, property value, information function), analyzes scenario factors (as people
Group's quantity, translational speed and panic degree etc.) relation between attribute and variation characteristic, formed knowledge-representation system attribute and
Property value.
U={ x1,x2,x3···xn} (8)
Wherein, U is domain set, each element x in set, and x represents that crowd evacuation result (is dredged containing crowd in this patent
Dissipate each characteristic value) wall scroll record.
With the physiology in scenario factors, society, psychology and environmental characteristic as the conditional attribute a of rough set domain objecti, with
The decision attribute D that probability is rough set domain object of trampling in colony intelligence evacuation model simulation result, " condition is determined for proposition
The information function expression formula of plan " two-dimensional signal table schema, forms the knowledge representation for trampling evolution mechanism, can be expressed as:
C={ a1,a2,a3···an} (9)
Wherein, C is conditional attribute, can arrange a1For age, a2For sex, a3For disability degree, a4For agility, a5For body
Weight, a6It is the individual diameter mapped by panic degree, a7It is the individual speed mapped by disaster factors.D is decision kind set.
In terms of Reduction of Knowledge, under conditions of keeping domain object set classification capacity constant, using full distance dimensionality reduction mould
Type merges domain object, carries out dimensionality reduction yojan to domain space, simplifies follow-up domain space and calculates.With rough set theory yojan
(Reduct) redundant attributes of domain object and core (Core) computation model, are deleted, attribute reduction and Value reduction is carried out, with
Simplify and trample evolution knowledge.If Q is independent, and Q ∈ C, if having
IND (Q)=IND (C) (10)
Then Q is a yojan of equivalence relation family C, and in C, all collection that can not save relation are combined into the core of equivalence relation family C,
Note Core (C).There are multiple yojan, represent the set of all yojan of C with Red (C).
In the present invention, Reduction of Knowledge includes the domain space dimensionality reduction yojan carried out using full distance dimensionality reduction model and utilization
Attribute reduction and Value reduction that rough set theory yojan and core computation model are carried out.
Step 4, trample the rough set Knowledge Discovery of evolution mechanism.Unit's rule are generated according to the evolved behavior feature of trampling
Then storehouse, loads the meta-rule storehouse and evacuates example, generate the abstraction rule storehouse for trampling evolution.
Using classical Skowron matrix computational approach, rough set matrix computations model is built;For knowledge system S=(U,
A, V, f), U is domain, xiFor the object in domain, U={ x1,x2,…,xn}.A is the community set of non-NULL, and A=C ∪ D, C are
Conditional attribute, D are decision attributes, and C ∩ D=φ.V represents property value, and f is information function.For system S, condition is only considered
Attribute, forms the information system with regard to conditional attribute C.Number of objects in the differentiation matrix DM of the system, its exponent number and domain
Relevant, as n × n rank, is designated as MDS(C):
Element m in discrimination matrixijIt is to discriminate between object xiAnd xjAll conditions attribute set.For variable j, i
Scope is defined as 1≤j≤i≤n, obtains lower triangular matrix, but as variable j=i, i.e. MDS(C), diagonal entry is obtained,
And object oneself result of the comparison is empty set φ certainly.So comparing to reduce n time, the scope to j, i is defined as here:1
≤ j < i≤n, reduces the computation complexity of original definition.
Introduce a Boolean function fDS, referred to as resolution function, as follows:
Wherein, Boolean variableCorresponding to m conditional attributeSymbol ∨
Represent computing of extracting, symbol ∧ represents conjunction computing;
The decision matrix of calculation knowledge system S, its exponent number are relevant with the number of objects in domain, and as n × n rank, is designated as
MDS(C):
With finding to the calculating of above-mentioned matrix, this definition only reflect decision attribute values identical beyond object compare
Situation, without the description for conflict object (inconsistent).Conditional attribute value is identical and conflict that decision attribute values are different is right
The matrix value of elephant remains as empty set, and this cannot just reflect the presence of conflict phenomenon, cannot more reflect the degree problem of conflict.For
This, has carried out improving definition to the matrix, has eliminated limitation.
Element in decision matrixIt is to discriminate between object xiAnd xjAll conditions attribute set.
Define decision function fDRDSAs follows:
Here
The purpose of rough set theory research is to simplify calculating, reduces computation complexity, optimized algorithm structure, more effectively
Solve engineering problem.Summary achievement in research, in data preprocessing phase, carries out MAWD dimensionality reduction meter to domain space
Calculate;For connection attribute, cluster calculation is carried out using the SOM network method based on modified IMDV;For quantitative attributes or
The qualitative attribute of change little (i.e. value is relatively fixed), adopts direct-discrete method here.
So, the optimization achievement of links above is integrated into organic whole, just obtains the MS- of overall performance improvement
VPRS Knowledge Discovery Model.Chapters and sections afterwards, if how research is applied to IMSRS model in engineering and medical domain
In dry typical diagnostic case, it is enterprise customer and medical researchers' solving practical problems, also from Knowledge Discovery and decision support
Aspect, the validity to the model and versatility carry out engineering verification.
Rough set matrix computations model can generate the meta-rule of attribute nucleus collection, and be input to meta-rule storehouse;Meta-rule can table
It is shown as:
Rule 1:
if a3=0and a4=0and a7=1.then d=0
Rule 2:
if a3=1and a4=0and a7=1.then d=1
Rule 3:
if a3=0and a4=1and a7=0.then d=1
Rule 4:
if a3=1and a4=0and a7=1.then d=2
Meta-rule is loaded, using the reversible process analytic method of attribute discretization, by domain object properties with trample feelings
Scape key element forms invertible mapping;Domain characteristics of objects value is reloaded, the mathematic sign (if gender attribute value is 0) of discretization is also
Original becomes evacuation scenario factors (if sex is man);The regular generating portion of evolution is being trampled, and example is being evacuated by loading, will be coarse
Collection meta-rule is extensive to be become towards the concrete abstraction rule for evacuating example, forms the abstraction rule storehouse with practical guided significance,
Form Explicit Knowledge.As rule 1, under disaster serious conditions, non-disability, overweight women old man, when not producing Psychological phobia
The probability that tramples is 0.
A case verification said method with Hongqiao in Shanghai transport hub.
The first step, the extraction Shanghai Hongqiao Integrative Transport Hub construction example data that pass through, analysis and extraction environmental characteristic, mould
The real conditions at the scene of drawing up, are emulated.(calculating according to State Statistics Bureau's population ratio data can for total number of persons N=2109 people
Obtain 723 people of man, 686 people of woman, 348 people of children, 221 people of old man and 131 people of physical disabilities).
Second step, 4 record composition domain set U, and U={ x is extracted from whole emulation1,x2,x3,x4}.a1For year
Age, a2For sex, a3For disability degree, a4For agility, a5For body weight, a6It is the individual diameter mapped by panic degree, a7For
The individual speed mapped by disaster factors, C={ a1,a2,a3,a4,a5,a6,a7}.Wherein, old man is 2, and children are 1, in youth
Year is 0;Man is 1, and female is 0;Disability is 1, and non-disability is 0;Agile is 2, and action is generally 1, and handicapped is 0;Overweight
For 2, it is 1 less than normal type, normal type is 0;Panic is 2, and nervous is 1, is normally 0;Disaster scenarios it is seriously 1, disaster
Situation is not seriously 0, as shown in table 1.
Table 1
U | a1 | a2 | a3 | a4 | a5 | a6 | a7 | D |
x1 | 2 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
x2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
x3 | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 1 |
x4 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 2 |
3rd step, obtain differentiation matrix, resolution function and decision function.
According to formula (11), differentiation matrix is obtained:
Resolution function is:
fDS=(a1∨a2∨a3∨a5∨a6)(a1∨a2∨a4∨a5∨a6∨a7)
(a2∨a3∨a4∨a6∨a7)(a1∨a2∨a3∨a5∨a6)
(a1∨a2∨a5∨a6)(a1∨a3∨a4∨a5∨a7)
=a1a2a4a5a7
According to formula (14), discrimination matrix is obtained:
Obtain decision function:
fDRDS=(a1∨a2∨a3∨a5∨a6)(a1∨a2∨a4∨a5∨a6∨a7)
(a2∨a3∨a4∨a6∨a7)(a1∨a2∨a5∨a6)
(a1∨a3∨a4∨a5∨a7)
=a3a4a7
After yojan as shown in table 2.
Table 2
U | a3 | a4 | a7 | D |
x1 | 0 | 0 | 1 | 0 |
x2 | 1 | 0 | 1 | 1 |
x3 | 0 | 1 | 0 | 1 |
x4 | 1 | 0 | 1 | 2 |
According to meta-rule, the element value in decision kind set D (D is the probability that tramples, in the range from 0-1) is obtained
For;Pass through abstraction rule again, decision attribute is translated as Explicit Knowledge, d1=0 is 0, d for the probability trampled2=1 for sending out
The probability that life is trampled is 0.1, d3=1 is 0.1, d for the probability trampled4=2 is 0.2 for the probability trampled.
The present invention proposes the Methods of Knowledge Discovering Based that crowd tramples evolution mechanism, evacuates scenario factors and people under hazardous condition
Group tramples extraction correlated characteristic key element in evolved behavior, is calculated with existing rule base, obtains decision attribute values, prevents
Pernicious accident of trampling provides scientific basis and theories integration, with important theory value and social effect.
Claims (7)
1. under a kind of hazardous condition, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that comprise the following steps:
1) extraction crowd tramples scenario factors feature, is emulated, during being evacuated in colony intelligence crowd evacuation model
Evolved behavior feature is trampled, the domain object set of domain space layer is built, the evolved behavior feature of trampling includes to trample generally
Rate;
2) trampling scenario factors feature to the crowd and trampling evolved behavior feature carries out rough set attribute sliding-model control;
3) scenario factors feature is trampled as the conditional attribute of domain object using the crowd after the sliding-model control, with corresponding
The evolved behavior feature of trampling sets up trampling for " conditional decision " two-dimensional signal pattern as the decision attribute of domain object
Evolution mechanism knowledge expression, and Reduction of Knowledge is carried out to the evolution mechanism knowledge expression of trampling;
4) meta-rule storehouse is generated according to the evolved behavior feature of trampling;
5) load the meta-rule storehouse and example is evacuated, generate the abstraction rule storehouse for trampling evolution.
2. under hazardous condition according to claim 1, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that
The crowd tramples scenario factors feature to be included to evacuate individual physiologic factor, social factor, behavioural characteristic and environmental characteristic, described
Evacuating individual physiologic factor includes age, sex, disability degree, agility and body weight, and the social factor includes strange degree,
The behavioural characteristic includes panic degree, and the environmental characteristic includes disaster factors and space constraint.
3. under hazardous condition according to claim 2, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that
The fear degree is mapped as evacuating individual individual diameter, institute according to Helbing, D. fear " Psychology and behavior " volatility model
The solution procedure for stating individual diameter is specially:
fiw={ Aiexp[(ri-diw)/Bi]+kg(ri-diw)}niw-γg(ri-diw)vitiw(3)
In formula, miIt is the individual quality of i-th evacuation,It is the individual ideal velocity of i-th evacuation,It is that i-th evacuation is individual
Direction initialization, viIt is the individual actual speed of i-th evacuation, τiIt it is i-th evacuation individual characteristic time, t is the time, fij
It is to evacuate the interaction force between individuality i and evacuation individuality j, fiwIt is to evacuate interaction force of the individuality between i and border,
Ai、BiFor constant, dcijIt is the individual mass centre's distance of two evacuations, dijDistance between individuality, n are evacuated for twoijIt is individual by evacuating
Body j points to the standard vector of i, tijIt is nijTangential direction,It is the phasor difference of t speed, kg (dij-dcij)
Represent mass force,Represent t force of sliding friction, k and γ is mutual between decision evacuation individuality i and j
The parameter of the blocking effect of effect, diwIt is to evacuate the distance between individuality i and border, niwRefer to vertical direction, tiwRefer to tangential
Direction, riIt is the individual individual diameter of i-th evacuation, viIndividual individual speed is evacuated for i-th, g (x) is a function, such as
Fruit is evacuated individuality and collides, g (x)=0, otherwise g (x)=x.
4. under hazardous condition according to claim 2, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that
The disaster factors are mapped as the individual individual speed of evacuating after panic propagation and are expressed, specially:
Defining rule θ is:
In formula, μDAFor the membership function of casualty loss degree DA,DAmaxFor maximum casualty loss degree,For wind
Danger assessment intensity I0Membership function,ImaxIntensity is assessed for greateset risk, subscript t is hdisSequence number, s
It is the sequence number of i value, i is risk assessment intensity I0On coordinate value, n for i maximum, rstIt is the unit in fuzzy relation matrix
Element, using inference formula:
DA=I0θR (5)
By risk assessment intensity I0It is assigned on control point in the method for information distribution, finally obtains calamity source index hdis's
Value, R are fuzzy relation matrix;
Calculate each individual speed for individuality being evacuated after panic propagation:
hdis=f (ρ) (6)
In formula, ρ is density of stream of people, and f () represents calamity source index hdisWith density of stream of people ρ institute linear relationship function, DL=
NAP/WALA=ρ AP, DLIt is the evacuation individual amount of unit area in horizontal plane, N is the total number of persons that walks in the stream of people, APFor
The horizontal projected area of single people, WAFor the width of the stream of people, LAFor the length of the stream of people, viIndividual individuality speed is evacuated for i-th
Degree.
5. under hazardous condition according to claim 1, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that
The crowd tramples scenario factors feature and is divided into qualitative features and quantitative characteristic, execution step 2) when, for qualitative features, directly
It is mapped as rough set Category Attributes;For quantitative characteristic, rough set connection attribute is first mapped as, then using inspiration
Formula SOM self-organizing clustering model, carries out automatic sliding-model control to rough set connection attribute, and rough set continuous property is converted
For the distinguishable mathematic sign of rough set matrix.
6. under hazardous condition according to claim 1, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that
The Reduction of Knowledge is included the domain space dimensionality reduction yojan carried out using full distance dimensionality reduction model and uses rough set theory about
Attribute reduction and Value reduction that letter and core computation model are carried out.
7. under hazardous condition according to claim 6, crowd tramples the Methods of Knowledge Discovering Based of behavior evolution, it is characterised in that
Reduction of Knowledge is carried out with rough set theory to be specially:
For knowledge system S=(U, A), U is domain object set, U={ x1,x2,…,xn, element x thereiniFor in domain
Object, n be object sum, A for non-NULL community set, A=C ∪ D, C are conditional attribute set, and D is decision kind set, and
C ∩ D=φ, calculates the differentiation matrix M of the knowledge systemDS(C):
Element m in discrimination matrixijIt is to discriminate between object xiAnd xjAll conditions attribute set;
Define Boolean function fDSAs follows:
Wherein, Boolean variableCorresponding to m conditional attribute a1,...,am,Symbol ∨ represents
Extract computing, symbol ∧ represents conjunction computing;
The decision matrix M of calculation knowledge system SDS(C):
Element in decision matrixIt is to discriminate between object xiAnd xjAll conditions attribute set;
Define decision function fDRDSAs follows:
Here
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610815386.0A CN106485327B (en) | 2016-09-08 | 2016-09-08 | Crowd tramples the Methods of Knowledge Discovering Based of behavior evolution under a kind of hazardous condition |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610815386.0A CN106485327B (en) | 2016-09-08 | 2016-09-08 | Crowd tramples the Methods of Knowledge Discovering Based of behavior evolution under a kind of hazardous condition |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106485327A true CN106485327A (en) | 2017-03-08 |
CN106485327B CN106485327B (en) | 2019-04-02 |
Family
ID=58273723
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610815386.0A Active CN106485327B (en) | 2016-09-08 | 2016-09-08 | Crowd tramples the Methods of Knowledge Discovering Based of behavior evolution under a kind of hazardous condition |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106485327B (en) |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107330147A (en) * | 2017-05-26 | 2017-11-07 | 北京交通大学 | A kind of subway station personnel emergency evacuation emulation mode and system |
CN107665282A (en) * | 2017-10-11 | 2018-02-06 | 山东师范大学 | Crowd evacuation emulation method and system based on isomery emotional appeal model |
CN108256152A (en) * | 2017-12-19 | 2018-07-06 | 同济大学 | T-shaped road junction evacuation emulation method based on crowd evacuation macromodel |
CN109542949A (en) * | 2018-11-07 | 2019-03-29 | 太原理工大学 | A kind of decision information system knowledge acquisition method based on type vector |
CN109871582A (en) * | 2019-01-11 | 2019-06-11 | 山东师范大学 | Knowledge based engineering crowd evacuation emulation method, system and medium in foreign environment |
CN110555216A (en) * | 2018-05-30 | 2019-12-10 | 郑州大学 | Crowd crowding scene simulation method and system |
CN111080080A (en) * | 2019-11-25 | 2020-04-28 | 桂林理工大学南宁分校 | Method and system for estimating risk of geological disaster of villages and small towns |
CN108256155B (en) * | 2017-12-20 | 2021-03-26 | 同济大学 | Passenger getting-off point selection method for T-junction passenger car |
CN116415047A (en) * | 2023-06-09 | 2023-07-11 | 湖南师范大学 | Resource screening method and system based on national image resource recommendation |
CN118230260A (en) * | 2024-05-27 | 2024-06-21 | 山东科技大学 | Method for quantifying and early warning analysis of trampling risk of high-density crowd at subway platform |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100004975A1 (en) * | 2008-07-03 | 2010-01-07 | Scott White | System and method for leveraging proximity data in a web-based socially-enabled knowledge networking environment |
CN105760484A (en) * | 2016-02-17 | 2016-07-13 | 中国科学院上海高等研究院 | Crowd treading pre-warning method and system and server with system |
-
2016
- 2016-09-08 CN CN201610815386.0A patent/CN106485327B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100004975A1 (en) * | 2008-07-03 | 2010-01-07 | Scott White | System and method for leveraging proximity data in a web-based socially-enabled knowledge networking environment |
CN105760484A (en) * | 2016-02-17 | 2016-07-13 | 中国科学院上海高等研究院 | Crowd treading pre-warning method and system and server with system |
Non-Patent Citations (1)
Title |
---|
杨立兵: "建筑火灾人员疏散行为及优化研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107330147A (en) * | 2017-05-26 | 2017-11-07 | 北京交通大学 | A kind of subway station personnel emergency evacuation emulation mode and system |
CN107330147B (en) * | 2017-05-26 | 2020-04-24 | 北京交通大学 | Simulation method and system for emergency evacuation of personnel in subway station |
CN107665282A (en) * | 2017-10-11 | 2018-02-06 | 山东师范大学 | Crowd evacuation emulation method and system based on isomery emotional appeal model |
CN107665282B (en) * | 2017-10-11 | 2021-06-25 | 山东师范大学 | Crowd evacuation simulation method and system based on heterogeneous emotional infection model |
CN108256152A (en) * | 2017-12-19 | 2018-07-06 | 同济大学 | T-shaped road junction evacuation emulation method based on crowd evacuation macromodel |
CN108256152B (en) * | 2017-12-19 | 2021-05-11 | 同济大学 | T-shaped intersection evacuation simulation method based on crowd evacuation macro model |
CN108256155B (en) * | 2017-12-20 | 2021-03-26 | 同济大学 | Passenger getting-off point selection method for T-junction passenger car |
CN110555216B (en) * | 2018-05-30 | 2022-12-13 | 郑州大学 | Crowd crowding scene simulation method and system |
CN110555216A (en) * | 2018-05-30 | 2019-12-10 | 郑州大学 | Crowd crowding scene simulation method and system |
CN109542949B (en) * | 2018-11-07 | 2022-04-12 | 太原理工大学 | Formal vector-based decision information system knowledge acquisition method |
CN109542949A (en) * | 2018-11-07 | 2019-03-29 | 太原理工大学 | A kind of decision information system knowledge acquisition method based on type vector |
CN109871582A (en) * | 2019-01-11 | 2019-06-11 | 山东师范大学 | Knowledge based engineering crowd evacuation emulation method, system and medium in foreign environment |
CN111080080A (en) * | 2019-11-25 | 2020-04-28 | 桂林理工大学南宁分校 | Method and system for estimating risk of geological disaster of villages and small towns |
CN111080080B (en) * | 2019-11-25 | 2023-05-26 | 桂林理工大学南宁分校 | Village geological disaster risk prediction method and system |
CN116415047A (en) * | 2023-06-09 | 2023-07-11 | 湖南师范大学 | Resource screening method and system based on national image resource recommendation |
CN116415047B (en) * | 2023-06-09 | 2023-08-18 | 湖南师范大学 | Resource screening method and system based on national image resource recommendation |
CN118230260A (en) * | 2024-05-27 | 2024-06-21 | 山东科技大学 | Method for quantifying and early warning analysis of trampling risk of high-density crowd at subway platform |
Also Published As
Publication number | Publication date |
---|---|
CN106485327B (en) | 2019-04-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106485327B (en) | Crowd tramples the Methods of Knowledge Discovering Based of behavior evolution under a kind of hazardous condition | |
Ashraf et al. | Emergency decision support modeling for COVID‐19 based on spherical fuzzy information | |
Li et al. | Extended TODIM method for multi-attribute risk decision making problems in emergency response | |
Yu et al. | A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support | |
Fei et al. | An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory | |
Du et al. | New failure mode and effects analysis: an evidential downscaling method | |
An et al. | A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system | |
Said et al. | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series | |
Wu et al. | Modeling crowd evacuation via behavioral heterogeneity-based social force model | |
CN107103100A (en) | A kind of fault-tolerant intelligent semantic searching method based on data collection of illustrative plates, Information Atlas and knowledge mapping framework for putting into driving | |
CN104346379A (en) | Method for identifying data elements on basis of logic and statistic technologies | |
Guerrero et al. | Decision support system in health care building design based on case-based reasoning and reinforcement learning | |
CN109800881A (en) | A kind of causality determination method based on Bayesian network | |
Borah et al. | Examining the correlation between the weather conditions and COVID-19 pandemic in India: a mathematical evidence | |
Zhu et al. | Behavioral, data-driven, agent-based evacuation simulation for building safety design using machine learning and discrete choice models | |
Shi et al. | Determination of effective management strategies for scenic area emergencies using association rule mining | |
Liu et al. | A new evacuation accessibility analysis approach based on spatial information | |
CN113672599A (en) | Visual aid decision-making method for realizing government affair informatization project construction management by creating domain knowledge graph | |
CN110472225A (en) | The railway accident analysis of causes method of word-based extension LDA | |
Boufares et al. | Heterogeneous data-integration and data quality: Overview of conflicts | |
Ye et al. | Virtual linguistic trust degree-based evidential reasoning approach and its application to emergency response assessment of railway station | |
Wang et al. | Identifying critical factors in systems with interrelated components: A method considering heterogeneous influence and strength attenuation | |
US20230085575A1 (en) | Latent feature based model bias mitigation in artificial intelligence systems | |
Zhang et al. | Community centered public safety resilience under public emergencies: A case study of COVID‐19 | |
Zhao et al. | Dynamic crowd accident-risk assessment based on internal energy and information entropy for large-scale crowd flow considering covid-19 epidemic |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | 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 |