CN108647753A - A kind of construction site method for prewarning risk based on BIM and RFID technique - Google Patents
A kind of construction site method for prewarning risk based on BIM and RFID technique Download PDFInfo
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Abstract
The invention discloses a kind of construction site method for prewarning risk based on BIM and RFID technique, characterized in that include the following steps：1）RFID risk datas acquire；2）Risk data processing；3）BIM database sharings are stored with risk data；4）BIM model foundations are transferred with BIM database datas；5）Collision detection；6）Generate risk report；7）Send out Risk-warning alarm；8）Export BIM data historian data；9）The risk profile of PSO algorithm optimization LS SVM；10）Generate prediction result：To step 9）PSO algorithm optimization LS SVM generate risk profile result.This method high efficient and reliable, the accurate acquisition for ensuring construction site risk data, timely transmission, the effective storage of risk data of risk information, the dynamic early-warning control and ex ante forecasting for realizing work progress risk are evaded.
Description
Technical field
The present invention relates to engineering construction fields, and in particular to a kind of construction site risk based on BIM and RFID technique is pre-
Alarm method.
Background technology
Engineering construction has complexity and chronicity, along with the risk factors of construction site also have it is uncertain and with
Machine, the uncertainty and randomness of risk factors cause larger potential threat to the smooth implementation of project, seriously affect and apply
The social image of the life security of worker person, the construction speed of project and enterprise.
Information is transmitted not in time in construction management at present, and working method is uncoordinated between each work post of each department, does over again and delays work
Phenomenon is serious, and Building Information Model (Building Information Modeling, abbreviation BIM) is new as one kind of industry
Technology has many advantages, such as visualization, collaboration, simulation, information sharing, and the research boom of industry has just been started from introducing China,
Risk information can effectively be stored by carrying out construction site Risk-warning based on BIM technology, simulated risk accidents, visualized
Work progress dynamic monitor, realize construction site participant collaborative work, avoid the generation of risk accidents.
Mainly by Delphi method, questionnaire method, Risk-warning relies primarily on for the acquisition of construction site risk factor
Get sth into one's head by rule of thumb in construction personnel, therefore, risk data, which obtains, lacks reliability, and construction period is longer, with when
Between passage, risk factors also occur it is corresponding change, rely on initial judgement merely, lack data acquisition promptness and
Dynamic.Radio frequency identification (Radio Frequency Identification, abbreviation RFID) technology is convenient, flexible with its,
The features such as memory function is strong, penetrability is good, highly effective and safe is widely used in the necks such as vehicle toll collection, supply chain tracking, access control
Domain.Label is attached in material, machinery, equipment and other items in construction, identifier can track its position, the time, temperature, into
Degree penetrates coating, wall plastering, ice and snow into row label reading, and with uniqueness, construction material, equipment have any label in addition
Quality problems can be positioned quickly, effectively trace to the source, return producer, and the entirety due to quality less than position influence engineering is avoided to use
Service life.
Therefore effective pre-alarm and prevention is carried out to construction site risk factors before risk accidents occur, it is accurate to obtain construction
Risk is reduced in range as small as possible or controllable by risk factors in the process, is ensureing project schedule plan on schedule
Carry out, promote project delivery method level, reduces cost of investment variation etc. and have important practical significance.
Invention content
The purpose of the present invention is in view of the deficiencies of the prior art, and it is existing to provide a kind of construction based on BIM and RFID technique
Method for prewarning risk, this method high efficient and reliable, the accurate acquisition that can ensure that construction site risk data, risk information and
When transmit, effective storage of risk data, can realize that the dynamic early-warning control of work progress risk and ex ante forecasting are evaded.
Realizing the technical solution of the object of the invention is：
A kind of construction site method for prewarning risk based on BIM and RFID technique, unlike the prior art, including such as
Lower step：
1) RFID risk datas acquire：Using the RFID with electronic tag model acquire the temperature of construction site, wind speed,
Pressure that material is born, the position that material is placed, the electric current of mechanical equipment, voltage, the position of mechanical equipment, safety cap position
(judging the location of personnel with this) data information, the article surface that work progress is related to adhere to RFID tag, pass through
RFID emits radiofrequency signal, and the material, mechanical equipment, safety cap information for being attached with RFID tag enter the field regions of RFID work
In domain, RFID makes RFID generate induced current to obtain energy, RFID will by the radio wave being coupled to receive into magnetic field
Pressure that the temperature of construction site, wind speed, material are born, material placement location, the electric current of mechanical equipment, voltage, mechanical equipment
Position, safety cap position data be sent to RFID, RFID is read out and decodes；
2) risk data is handled：Using data warehouse technology (Extract-Transform-Load, abbreviation ETL) to step
1) position of collected temperature, wind speed, material are born in pressure, material placement, electric current, voltage, the machinery of mechanical equipment
The position of equipment, the position data of safety cap check the consistency of data using computer system automatically, handle invalid value
And missing values, it is grouped according to computer code, effective risk data is converted into IFC reference formats, and be loaded onto BIM
Database side；
3) BIM database sharings are stored with risk data：This engineering project is covered using existing BIM database models structure
The new BIM databases of all risk informations in life cycle management store the risk data of IFC reference formats in step 2) to new
In BIM databases；
4) BIM model foundations are transferred with BIM database datas：According to this construction project design drawing, modeled using BIM
Software Revit establishes the BIM models of this engineering project, and BIM models transfer the new BIM established in step 3) by interface in real time
The construction site risk data of databases storage；
5) collision detection：The step of is established by BIM models and is transferred for step 4) using collision detection tool Navisworks
3) risk data carry out automatic collision, pop up the associated components clashed and error message；
6) risk report is generated：Result according to step 5) collision detection automatically generates risk report；
7) Risk-warning alarm is sent out：Risk-warning alarm is sent out while generating risk report according to step 6), is carried out
Risk-warning；
8) BIM data historian data are exported：Deriving step 3) risk data that stores in the BIM databases established；
9) risk profile of PSO algorithm optimizations LS-SVM：The construction site stored in BIM databases is exported according to step 8)
The historical data of risk information, using least square method supporting vector machine (Least Squares Support Vector
Machine, abbreviation LS-SVM) construction site risk data is predicted, for decision LS-SVM learning abilities and extensive energy
The regularization parameter and kernel functional parameter of power pass through population (Particle Swarm Optimization, abbreviation PSO) algorithm
It optimizes；
10) prediction result is generated：Risk profile result is generated to step 9) PSO algorithm optimizations LS-SVM.
Step 9) includes：
Give one group of training sample set：S={ (xi, yi), i=1,2 ..., l, wherein xi∈RdNumber is inputted for LS-SVM
According to yi∈ R are output data, and l is training sample number, and the linear regression function in lower dimensional space is：
Y=ω x+b (1)
ω is weight vector in formula, and b is departure, and the regression function in high-dimensional feature space is：
In formulaIt is Nonlinear Mapping of the input space to high-dimensional feature space, the original according to structural risk minimization
Then, LS-SVM double optimizations object function is：
In formula, eiFor error variance, e ∈ Rl×1For error vector；C is regularization parameter, controls the punishment journey to error
Degree, in order to which solving-optimizing problem converts constrained optimization problem to unconstrained optimization problem by introducing Lagrange multipliers：
λ is Lagrange multipliers, λ ∈ R in formulal×1, obtaining optimal value according to KKT optimal conditions is：
Above formula eliminates ω and e, converts double optimization problem to solution system of linear equations, solution obtains：
In formula：Q=[1,1 ..., 1]T, I is unit matrix, y=[y1, y2..., yl]T, Ω
∈Rl×l, andK is the kernel function for meeting Mercer conditions, the main core with former space
Dot-product operation in function substitution high-dimensional feature space, the regression function expression formula for obtaining LS-SVM are：
Radial basis function has good because its form is simple, radial symmetric, slickness are good in terms of handling nonlinear data
Performance, the kernel function being selected as in regression model, expression formula is：
In formula：X is m dimensional input vectors, xiIt is the center of i-th of radial basis function, with x dimensions having the same；σ is core
Function parameter, determining function surround the width of central point；||x-xi| | it is vector x-xiNorm, indicate x and xiBetween away from
From, for improve LS-SVM learning ability and generalization ability, need to optimize two parameters of c and σ, particle cluster algorithm has
The simple in rule, superiority such as precision is high, convergence is fast, preferably solve the problems such as non-linear, multi-modal, are suitable for LS-SVM parameters
Optimization problem,
PSO algorithms are the evolution algorithms based on iteration optimization of rising in recent years, are initialized as the random particle of a group, are led to
Cross iterative search optimal solution, each time in iterative process, particle by track two extreme values with realize itself speed and under
The location updating of an iteration, one of extreme value are the optimal solutions that particle itself searches, referred to as individual extreme value pibest, another
A extreme value is the optimal solution that population searches, referred to as global extremum gbest,
Assuming that in the search space of d dimensions, a particle populations are formed by m particle, wherein i-th of particle is in d
Position in dimension search space is expressed as xi, speed be expressed as vi, the optimal location that searches be expressed as pi, definition vector xi
=(xi1, xi2..., xid)、vi=(vi1, vi2... vid)、pi={ pi1, pi2..., pid), i=1,2 ..., m, entire kind of group hunting
The optimal location arrived is pg=(pg1, pg2..., pgd), then the speed with location update formula of PSO algorithms particle are：
In formula：c1、c2For aceleration pulse, adjust to pibestAnd gbestThe maximum step-length of direction flight；ω is inertia weight system
Number, value size influence the optimizing ability of population, can balance ability of searching optimum, and particle is made to have the ability for exploring new region；
r1、r2It is the random function in [0,1]；K indicates optimization algebraically at this time；Indicate the search speed that particle i is tieed up in d when k；Indicate that particle i is in the spatial position that d tie up when k, the VC that SVM is built upon statistical learning is tieed up and grown up in theoretical foundation
A kind of machine learning method, the principle based on structural risk minimization can effectively solve the problem that small sample, non-linear, high-dimensional etc.
Problem.
LS-SVM is the extension of standard SVM, and parameter to be selected is relatively fewer, and the inequality in SVM is replaced with equality constraint
Constraint introduces least square linear and converts quadratic programming problem to Solving Linear, reduces the complicated journey that model calculates
Degree, improves convergent speed.
Generation risk profile described in step 10) includes：
(1) training sample the S={ (x of construction site risk data are inputtedi, yi), i=1,2 ..., l；
(2) optimal regularization parameter c and kernel functional parameter σ are obtained by particle group hunting；
(3) kernel function K (x, x appropriate are selectedi)；
(4) optimization problem is solved, optimal solution is obtained
(5) decision function is constructed
(6) model prediction is carried out with decision function.
Step (2) includes：
A. construction site risk data is pre-processed, irregular data is mainly converted to the number of rule by interpolation
According to processing, in order to the operation of computer；
B. the search range of setting population population m, c and σ, maximum iteration Tmax, the dimension n of particle individual, inertia
Weight coefficient ω, aceleration pulse c1And c2, average grain is away from threshold values α, fitness variance threshold β parameters；
C. PSO algorithm flows are run, population is initialized；
D. the adaptive value of each particle is calculated according to particle current locationAnd make comparisons, setting individual
Extreme value pibest, global extremum gbest；
E. according to particle rapidity and location update formula (10), (11), new population is generated；
F. the fitness f of each particle of new population is calculated again；
G. it respectively compared with population history optimal location and optimal velocity, is replaced if more excellent, it is otherwise constant；
H. it finally checks whether and meets the maximum times T that optimizing termination condition reaches iterationmax, t=is enabled if being unsatisfactory for
T+1 repeats step d, continues iteration optimizing；
I. optimal solution is obtained if meeting, the most optimized parameter is assigned to least square method supporting vector machine, terminates search.
This method high efficient and reliable, the accurate acquisition for ensuring construction site risk data, risk information it is timely transmit,
Effective storage of risk data, the dynamic early-warning control and ex ante forecasting for realizing work progress risk are evaded.
Description of the drawings
Fig. 1 is embodiment method flow schematic diagram；
Fig. 2 is the flow diagram that the optimization of PSO methods obtains SVM parameters c and σ in embodiment；
Fig. 3 is LS-SVM Risk Forecast Method flow diagrams in embodiment；
Specific implementation mode
The content of present invention is further elaborated with reference to the accompanying drawings and examples, but is not limitation of the invention.
Embodiment：
Referring to Fig.1, a kind of construction site method for prewarning risk based on BIM and RFID technique, includes the following steps：
1) RFID risk datas acquire：Using the RFID with electronic tag model acquire the temperature of construction site, wind speed,
Pressure that material is born, the position that material is placed, the electric current of mechanical equipment, voltage, the position of mechanical equipment, safety cap position
(judging the location of personnel with this) data information, the article surface that work progress is related to adhere to RFID tag, pass through
RFID emits radiofrequency signal, and the material, mechanical equipment, safety cap information for being attached with RFID tag enter the field regions of RFID work
In domain, RFID makes RFID generate induced current to obtain energy, RFID will by the radio wave being coupled to receive into magnetic field
Pressure that the temperature of construction site, wind speed, material are born, material placement location, the electric current of mechanical equipment, voltage, mechanical equipment
Position, safety cap position data be sent to RFID, RFID is read out and decodes；
2) risk data is handled：Using collected temperature, wind speed, material are born in data warehouse technology pressure, material
Expect that the position data utilization of the position placed, the electric current of mechanical equipment, voltage, the position of mechanical equipment, safety cap calculates
The consistency of machine automatic checking data handles invalid value and missing values, is grouped according to computer code, will be effective
Risk data is converted to IFC reference formats, and is loaded onto BIM database sides；
3) BIM database sharings are stored with risk data：This engineering project is covered using existing BIM database models structure
The new BIM databases of all risk informations in life cycle management store the risk data of IFC reference formats in step 2) to new
In BIM databases；
4) BIM model foundations are transferred with BIM database datas：According to this construction project design drawing, modeled using BIM
Software Revit establishes the BIM models of this engineering project, and BIM models transfer the new BIM established in step 3) by interface in real time
The construction site risk data of databases storage；
5) collision detection：The step of is established by BIM models and is transferred for step 4) using collision detection tool Navisworks
3) risk data carry out automatic collision, pop up the associated components clashed and error message；
6) risk report is generated：Result according to step 5) collision detection automatically generates risk report；
7) Risk-warning alarm is sent out：Risk-warning alarm is sent out while generating risk report according to step 6), is carried out
Risk-warning；
8) BIM data historian data are exported：Deriving step 3) risk data that stores in the BIM databases established；
9) risk profile of PSO algorithm optimizations LS-SVM：The construction site stored in BIM databases is exported according to step 8)
The historical data of risk information is predicted construction site risk data using least square method supporting vector machine, for decision
The regularization parameter and kernel functional parameter of LS-SVM learning abilities and generalization ability are optimized by particle cluster algorithm；
10) prediction result is generated：Risk profile result is generated to step 9) PSO algorithm optimizations LS-SVM.
Step 9) includes：
Give one group of training sample set：S={ (xi, yi), i=1,2 ..., l, wherein xi∈RdNumber is inputted for LS-SVM
According to yi∈ R are output data, and l is training sample number, and the linear regression function in lower dimensional space is：
Y=ω x+b (1)
ω is weight vector in formula, and b is departure, and the regression function in high-dimensional feature space is：
In formulaIt is Nonlinear Mapping of the input space to high-dimensional feature space, the original according to structural risk minimization
Then, LS-SVM double optimizations object function is：
In formula, eiFor error variance, e ∈ Rl×1For error vector；C is regularization parameter, controls the punishment journey to error
Degree, in order to which solving-optimizing problem converts constrained optimization problem to unconstrained optimization problem by introducing Lagrange multipliers：
λ is Lagrange multipliers, λ ∈ R in formulat×1, obtaining optimal value according to KKT optimal conditions is：
Above formula eliminates ω and e, converts double optimization problem to solution system of linear equations, solution obtains：
In formula：Q=[1,1 ..., 1]T, I is unit matrix, y=[y1, y2..., yl]T, Ω
∈Rl×l, andK is the kernel function for meeting Mercer conditions, the main core with former space
Dot-product operation in function substitution high-dimensional feature space, the regression function expression formula for obtaining LS-SVM are：
Radial basis function has good because its form is simple, radial symmetric, slickness are good in terms of handling nonlinear data
Performance, the kernel function being selected as in regression model, expression formula is：
In formula：X is m dimensional input vectors, xiIt is the center of i-th of radial basis function, with x dimensions having the same；σ is core
Function parameter, determining function surround the width of central point；||x-xi| | it is vector x-xiNorm, indicate x and xiBetween away from
From, for improve LS-SVM learning ability and generalization ability, need to optimize two parameters of c and σ, particle cluster algorithm has
The simple in rule, superiority such as precision is high, convergence is fast, preferably solve the problems such as non-linear, multi-modal, are suitable for LS-SVM parameters
Optimization problem,
PSO algorithms are the evolution algorithms based on iteration optimization of rising in recent years, are initialized as the random particle of a group, are led to
Cross iterative search optimal solution, each time in iterative process, particle by track two extreme values with realize itself speed and under
The location updating of an iteration, one of extreme value are the optimal solutions that particle itself searches, referred to as individual extreme value pibest, another
A extreme value is the optimal solution that population searches, referred to as global extremum gbest,
Assuming that in the search space of d dimensions, a particle populations are formed by m particle, wherein i-th of particle is in d
Position in dimension search space is expressed as xi, speed be expressed as vi, the optimal location that searches be expressed as pi, definition vector xi
=(xi1, xi2..., xid)、vi=(vi1, vi2..., vid)、pi=(pi1, pi2..., pid), i=1,2 ..., m, entire population are searched
The optimal location that rope arrives is pg=(pg1, pg2..., pgd), then the speed with location update formula of PSO algorithms particle are：
In formula：c1、c2For aceleration pulse, adjust to pibestAnd gbestThe maximum step-length of direction flight；ω is inertia weight system
Number, value size influence the optimizing ability of population, can balance ability of searching optimum, and particle is made to have the ability for exploring new region；
r1、r2It is the random function in [0,1]；K indicates optimization algebraically at this time；Indicate the search speed that particle i is tieed up in d when k；Indicate that particle i is in the spatial position that d tie up when k, the VC that SVM is built upon statistical learning is tieed up and grown up in theoretical foundation
A kind of machine learning method, the principle based on structural risk minimization can effectively solve the problem that small sample, non-linear, high-dimensional etc.
Problem.LS-SVM is the extension of standard SVM, and parameter to be selected is relatively fewer, and the inequality in SVM is replaced about with equality constraint
Beam introduces least square linear and converts quadratic programming problem to Solving Linear, reduces the complexity that model calculates,
Improve convergent speed.
As shown in figure 3, the generation risk profile described in step 10) includes：
(1) training sample the S={ (x of construction site risk data are inputtedi, yi), i=1,2 ..., l；
(2) optimal regularization parameter c and kernel functional parameter σ are obtained by particle group hunting；
(3) kernel function K (x, x appropriate are selectedi)；
(4) optimization problem is solved, optimal solution is obtained
(5) decision function is constructed
(6) model prediction is carried out with decision function.
As shown in Fig. 2, step (2) includes：
A. construction site risk data is pre-processed, irregular data is mainly converted to the number of rule by interpolation
According to processing, in order to the operation of computer；
B. the search range of setting population population m, c and σ, maximum iteration Tmax, the dimension n of particle individual, inertia
Weight coefficient ω, aceleration pulse c1And c2, average grain is away from threshold values α, fitness variance threshold β parameters；
C. PSO algorithm flows are run, population is initialized；
D. the adaptive value of each particle is calculated according to particle current locationAnd make comparisons, setting individual
Extreme value pibest, global extremum gbest；
E. according to particle rapidity and location update formula (10), (11), new population is generated；
F. the fitness f of each particle of new population is calculated again；
G. it respectively compared with population history optimal location and optimal velocity, is replaced if more excellent, it is otherwise constant；
H. it finally checks whether and meets the maximum times T that optimizing termination condition reaches iterationmax, t=is enabled if being unsatisfactory for
T+1 repeats d, continues iteration optimizing；
I. optimal solution is obtained if meeting, the most optimized parameter is assigned to least square method supporting vector machine, terminates search.
Claims (4)
1. a kind of construction site method for prewarning risk based on BIM and RFID technique, characterized in that include the following steps：
1) RFID risk datas acquire：Using the temperature of the RFID acquisitions construction site with electronic tag model, wind speed, material
The pressure born, the position that material is placed, the electric current of mechanical equipment, voltage, the position of mechanical equipment, safety cap position data
Information；
2) risk data is handled：Using data warehouse technology to collected temperature, wind speed, material are born in step 1) pressure
The position data utilization of power, the position that material is placed, the electric current of mechanical equipment, voltage, the position of mechanical equipment, safety cap
Computer system checks the consistency of data automatically, handles invalid value and missing values, is grouped, will have according to computer code
The risk data of effect is converted to IFC reference formats, and is loaded onto BIM database sides；
3) BIM database sharings are stored with risk data：This engineering project full longevity is covered using existing BIM database models structure
The new BIM databases for ordering all risk informations in the period, by the risk data storage of IFC reference formats in step 2) to new BIM
In database；
4) BIM model foundations are transferred with BIM database datas：According to this construction project design drawing, using BIM modeling softwares
Revit establishes the BIM models of this engineering project, and BIM models transfer the new BIM data established in step 3) by interface in real time
The construction site risk data stored in library；
5) collision detection：Step 4) is established in BIM models and the step 3) transferred using collision detection tool Navisworks
Risk data carries out automatic collision, pops up the associated components clashed and error message；
6) risk report is generated：Result according to step 5) collision detection automatically generates risk report；
7) Risk-warning alarm is sent out：Risk-warning alarm is sent out while generating risk report according to step 6), carries out risk
Early warning；
8) BIM data historian data are exported：Deriving step 3) risk data that stores in the BIM databases established；
9) risk profile of PSO algorithm optimizations LS-SVM：The construction site risk stored in BIM databases is exported according to step 8)
The historical data of information predicts construction site risk data using least square method supporting vector machine, for decision LS-
The regularization parameter and kernel functional parameter of SVM learning abilities and generalization ability are optimized by particle cluster algorithm；
10) prediction result is generated：Risk profile result is generated to step 9) PSO algorithm optimizations LS-SVM.
2. the construction site method for prewarning risk according to claim 1 based on BIM and RFID technique, characterized in that step
It is rapid 9) to include：
Give one group of training sample set：S={ (xi, yi), i=1,2 ..., l, wherein xi∈RdFor LS-SVM input datas, yi∈
R is output data, and l is training sample number, and the linear regression function in lower dimensional space is：
Y=ω x+b (1)
ω is weight vector in formula, and b is departure, and the regression function in high-dimensional feature space is：
In formulaIt is Nonlinear Mapping of the input space to high-dimensional feature space, according to the principle of structural risk minimization, LS-
SVM double optimization object functions are：
In formula, eiFor error variance, e ∈ Rl×1For error vector；C is regularization parameter, controls the punishment degree to error, draws
Enter Lagrange multipliers, converts constrained optimization problem to unconstrained optimization problem：
λ is Lagrange multipliers, λ ∈ R in formulal×1, obtaining optimal value according to KKT optimal conditions is：
Above formula eliminates ω and e, converts double optimization problem to solution system of linear equations, solution obtains：
In formula：Q=[1,1 ..., 1]T, I is unit matrix, y=[y1, y2..., yl]T, Ω ∈ Rl×l,
AndK is the kernel function for meeting Mercer conditions, and the main kernel function with former space takes
For the dot-product operation in high-dimensional feature space, the regression function expression formula for obtaining LS-SVM is：
Radial basis function has good property because its form is simple, radial symmetric, slickness are good in terms of handling nonlinear data
Energy, the kernel function being selected as in regression model, expression formula are：
In formula：X is m dimensional input vectors, xiIt is the center of i-th of radial basis function, with x dimensions having the same；σ is kernel function
Parameter, determining function surround the width of central point；||x-xi| | it is vector x-xiNorm, indicate x and xiThe distance between, it needs
Two parameters of c and σ are optimized,
Population is initialized as the random particle of a group, and by iterative search optimal solution, each time in iterative process, particle passes through
Tracking two extreme values with realize itself speed and next iteration location updating, one of extreme value is that particle itself is searched
The optimal solution that rope arrives, referred to as individual extreme value pibest, another extreme value is the optimal solution that population searches, referred to as global extremum
gbest,
Assuming that in the search space of d dimensions, a particle populations are formed by m particle, wherein i-th of particle is searched in d dimensions
Position in rope space is expressed as xi, speed be expressed as vi, the optimal location that searches be expressed as pi, definition vector xi=
(xi1, xi2..., xid)、vi=(vi1, vi2..., vid)、pi=(pi1, pi2..., pid), i=1,2 ..., m, entire kind of group hunting
The optimal location arrived is pg=(pg1, pg2..., pgd), then the speed with location update formula of PSO algorithms particle are：
In formula：c1、c2For aceleration pulse, adjust to PibestAnd gbestThe maximum step-length of direction flight；ω is inertia weight coefficient,
Its value size influences the optimizing ability of population, can balance ability of searching optimum, and particle is made to have the ability for exploring new region；r1、r2
It is the random function in [0,1]；K indicates optimization algebraically at this time；Indicate the search speed that particle i is tieed up in d when k；Table
Particle i is in the spatial position that d is tieed up when showing k.
3. the construction site method for prewarning risk according to claim 1 based on BIM and RFID technique, characterized in that step
It is rapid 10) described in generation risk profile include：
(1) training sample the S={ (x of construction site risk data are inputtedi, yi), i=1,2 ..., l；
(2) optimal regularization parameter c and kernel functional parameter σ are obtained by particle group hunting；
(3) kernel function K (x, x appropriate are selectedi)；
(4) optimization problem is solved, optimal solution is obtained
(5) decision function is constructed
(6) model prediction is carried out with decision function.
4. the construction site method for prewarning risk according to claim 3 based on BIM and RFID technique, characterized in that step
Suddenly (2) include：
A. construction site risk data is pre-processed, at the data that irregular data is mainly converted to rule by interpolation
Reason, in order to the operation of computer；
B. the search range of setting population population m, c and σ, maximum iteration Tmax, the dimension n of particle individual, inertia weight
Coefficient ω, aceleration pulse c1And c2, average grain is away from threshold values α, fitness variance threshold β parameters；
C. PSO algorithm flows are run, population is initialized；
D. the adaptive value of each particle is calculated according to particle current locationAnd make comparisons, individual extreme value is set
pibest, global extremum gbest；
E. according to particle rapidity and location update formula (10), (11), new population is generated；
F. the fitness f of each particle of new population is calculated again；
G. it respectively compared with population history optimal location and optimal velocity, is replaced if more excellent, it is otherwise constant；
H. it finally checks whether and meets the maximum times T that optimizing termination condition reaches iterationmax, t=t+1 is enabled if being unsatisfactory for,
Step d is repeated, iteration optimizing is continued；
I. optimal solution is obtained if meeting, the most optimized parameter is assigned to least square method supporting vector machine, terminates search.
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