CN109548128A - Building Integrate Natural Disasters Prevention data processing method and system, computer based on big data - Google Patents
Building Integrate Natural Disasters Prevention data processing method and system, computer based on big data Download PDFInfo
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- CN109548128A CN109548128A CN201811330860.6A CN201811330860A CN109548128A CN 109548128 A CN109548128 A CN 109548128A CN 201811330860 A CN201811330860 A CN 201811330860A CN 109548128 A CN109548128 A CN 109548128A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
- H04W52/244—Interferences in heterogeneous networks, e.g. among macro and femto or pico cells or other sector / system interference [OSI]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
Abstract
The invention belongs to build Integrate Natural Disasters Prevention field, a kind of building Integrate Natural Disasters Prevention data processing method and system, computer based on big data is disclosed, loader is connect by conducting wire with processor；Calculator is connected with processor by conducting wire；Cloud server is connected with processor；Processor is connected by conducting wire with converter；Converter is connected by conducting wire with amplifier.The present invention provides base platform by cloud computing, by structure design model, design moment, shearing, axle power, wind load, geological process, material type, intensity operates on this platform, the kinds of platform of the different data in different location is connected with communication network by programs such as distributed processing system(DPS)s, comprehensive satewe analysis finite element analysis, specification information handling task in phase, it obtains by comparing and meets designed building disaster prevention treating method, the link that transfinites is analyzed according to obtained data, it is subject to reinforcement again, it is with a kind of reliable, efficiently, telescopic treating method.
Description
Technical field
The invention belongs to build at Integrate Natural Disasters Prevention field more particularly to a kind of building Integrate Natural Disasters Prevention data based on big data
Manage method and system, computer.
Background technique
Currently, the prior art commonly used in the trade is such that
The development of current city is faced with problems, the Rapid Expansion of population, leads to the anxiety and environment of resource
Deteriorate, population pressure becomes larger, and land resource is increasingly in short supply, and numerous high buildings are rised sheer from level ground, and consequent is earthquake, fire etc.
Various disasters, in order to preferably safeguard the reliability and durability of building property, the structure design model for needing to build, design
Moment of flexure, shearing, axle power, wind load, geological process, material type, strength information technological incorporation together, build smart city, benefit
Technical support is provided with cloud computing, big data for building Integrate Natural Disasters Prevention.Big data analysis compared to traditional data warehouse applications,
Have the characteristics that data volume is big, query analysis is complicated.However in the prior art, " big data " research institution is given in data processing
Such definition is gone out." big data " is to need new tupe that could have stronger decision edge, see clearly discovery power and process
Magnanimity, high growth rate and the diversified information assets of optimization ability.
In conclusion problem of the existing technology is:
Covering surface is narrow, and comprehensive performance is not strong, lacks certain novelty, cannot be synchronous with the development of society.
There are corresponding laws and regulations in developed country in this respect at present, right both for the conventional detection of main structure
Some special and building Integrate Natural Disasters Prevention demand seldom notices excessively unilateral in the scheme of some analyses, shortage timeliness, right
It resists disaster and lacks certain innovation validity.And China also lacks corresponding system and application environment in this respect, only pursues
Surface cannot be combined with design specification, national standard, need further to improve relevant Industrial Construction.
In the prior art, building disaster prevention data are calculated, for the signal of communication that interference-limited calculator is led directly to, are needed
Consideration business reach at random and time varying channel conditions under, need professional to theorize analysis model, design power distribution is calculated
Method.To quantitatively disclose the authenticity of building disaster prevention data, the criterion hardly possible that theorizes in prior art engineering design is caused, no
It can predict punctual data.
Summary of the invention
In view of the problems of the existing technology, at the building Integrate Natural Disasters Prevention data based on big data that the present invention provides a kind of
Manage method and system, computer.
The invention is realized in this way a kind of building Integrate Natural Disasters Prevention data processing method based on big data, comprising:
By loader by the structure design model of building, design moment, shearing, axle power, wind load, geological process, material
Expect type, intensity input, computations is then sent to calculator by processor, calculator is comprehensive with multivariate statistical method
Satewe analysis, finite element analysis calculate data, obtain building disaster prevention data model；Multivariate statistical method to data into
During row calculates,
For the communication system of the interference-limited calculator of the big data network coverage, draw enrolled concept, it is assumed that communication
System is there are R orthogonal frequency ranges, and all users that would operate in the same orthogonal frequency range are defined as a group, i.e. system exists
R group, therefore the interference between user exists only in group, there is no interference between group and group；It provides in group r and works in honeycomb mould
Reception Signal to Interference plus Noise Ratio of the user of formula in base stationWith work calculator direct mode operation user in calculator
The reception Signal to Interference plus Noise Ratio of straight-through receiving endDefined formula；It is provided in group r and is worked in bee by shannon formula
The transmission rate of the user of snap formulaWith the transmission rate of the user of calculator direct mode operationDefined formula；It gives
Transmission rate R of the system work in the user of honeycomb mode outm(t), it works in the transmission speed of the user of calculator direct mode operation
Rate Rn(t) and the overall transmission rate R of the user of all workingtot(t) defined formula；Provide the instantaneous power consumption P of single userk(t)、
Long-term average power consumptionWith the instantaneous total power consumption P of systemtot(t) defined formula:
Wherein, ξkThe effect of for power amplifier coefficient,For an indicative parameter, if user k is the user to work in group r,
Then value is 1；Otherwise value is 0,Transmission power for the user k to work in group,For the fixation circuit power consumption of equipment:Wherein, PkIt (t) is the instantaneous power consumption of single user, T is timeslot number；Wherein, ξkThe effect of for power amplifier coefficient,Transmission power for the user k to work in group r,For the fixation circuit power consumption of equipment；In order to it is quantitative portray efficiency with
Trade-off relation between time delay gives real data queue Qk(t) more new formula and efficiency ηEEDefined formula；Specifically:
Qk(t+1)=max [Qk(t)-Rk(t),0]+Ak(t)；Wherein, max [Qk(t)-Rk(t), it 0] is Qk(t)-Rk(t) maximum with 0
Value, Rk(t) rate, A are left for the business of time slot tk(t) the business arrival rate for being time slot t；
Network energy efficiency ηEEIt is defined as long-term network total power consumption and the ratio of corresponding total transmitted data amount, unit is
Joule/bit/Hz can describe the influence of time varying channel conditions and random traffic arrival to delay performance, defined formula:Wherein,It is average for a long time for system
Total power consumption,For system for a long time be averaged overall transmission rate；Building disaster prevention data model is established to disclose based on the big data network coverage
The straight-through communication system of interference-limited calculator efficiency and time delay trade-off relation:
s.t.C1:C2: queue queue Qk(t) Mean Speed is stablized,C3:C4:C5:Wherein,For
The average power consumption thresholding of each time slot of user,For organize in all working calculator direct mode operation user to work in bee
The interference threshold of the user of snap formula,It is straight-through in calculator to work in the user of honeycomb mode to organize interior all working
The interference threshold of the user of mode；C1 is used to guarantee the life cycle of mobile device；C2 is string stability constraint, for guaranteeing
There are the data of arrival to leave network within the limited time；User pair of all working in calculator direct mode operation in C3 limitation group
It works in the interference of the user of honeycomb mode；All working is straight in calculator to working in the user of honeycomb mode in C4 limitation group
The interference of the user of logical mode；C5 is a non-negative transimission power constraint；
For the restrictive condition C1 for handling building disaster prevention data model, introduces and provide virtual power queue Vk(t) concept
And defined formula, wherein Vk(0)=0；If power distribution algorithm makes all virtual power string stabilities, meet average function
Rate limits C1:
Vk(t+1)=max [Vk(t)+yk(t),0]
Wherein, max [Vk(t)+yk(t), it 0] is Vk(t)+yk(t) maximum value with 0, PkIt (t) is the instantaneous function of single user
Consumption,For the average power consumption thresholding of each time slot of user；Using non-linear fractional programming, optimization problem is transformed into as follows
Optimization problem:
S.t.C1, C2, C3, C4, C5；
Wherein,
Wherein,For system for a long time be averaged total power consumption,For system for a long time be averaged overall transmission rate,
Ptot(P (τ), G (τ)) is the instantaneous total power consumption of system, Rtot(P (τ), G (τ)) is the instantaneous overall transmission rate of system；
Statistical disposition is carried out with Set Valued Statistics method again, show that building transfinites data；Set Valued Statistics method is counted
In processing, the Gaussian filter matrix model of cum rights is established:
In formula: Q is electric-wave filter matrix, and Q is the matrix of 1*n；N is matrix size threshold values；
I is the relative coordinate values of distance center coordinate points, i.e. is that the coordinate points are poor with respect to the weight of central point obtained by Q [i]；
Calculate Gaussian smoothing central point with respect to left and right threshold values difference with；
In formula: put centered on S [k] opposite left and right threshold values difference and；
The sample measurement put centered on buf [k]；
N is electric-wave filter matrix size；
Sample value after calculating Gaussian smoothing:
Obtaining building transfinites data；In formula: centered on buf ' [k] point treated value；The sample put centered on buf [k]
Measured value；
N is electric-wave filter matrix size；
Then cloud server is sent instructions to by processor；The big data in cloud is transferred, amplifier is passed through
With the integrated treatment of converter, the data transmitted are sent in comparator, by the big data comparison with cloud, obtains and builds
Build correlation data；
Then perfect information is sent in adjuster, overall cost data, structured data, facility data obtain
It takes precautions against natural calamities data processing model, then by showing that equipment is intuitively shown.
It further,, need to also be into after optimization problem is transformed into following optimization problem using non-linear fractional programming
Row: utilizing Leah Pu Luofu migration technology, designs the efficiency-time delay compromise method for solving transition problem；Solve transition problem
Efficiency-time delay compromise method specific implementation include:
The first step observes current real data queue Q in each time slot tk(t) and virtual power queue Vk(t) and believe
Road condition G (t) solves following optimization problem and obtains the power distribution of this time slot；
S.t.C3, C4, C5
Second step, according to the real data queue Q of current time slots tk(t), virtual power queue Vk(t) and efficiency ηEE(t)
More new formula updates real data queue Q when next time slot startsk(t+1), virtual power queue Vk(t+1) and efficiency ηEE(t
+1)；It is calculated as follows:
Qk(t+1)=max [Qk(t)-Rk(t),0]+Ak(t)
Wherein, max [Qk(t)-Rk(t), it 0] is Qk(t)-Rk(t) maximum value with 0, Rk(t) it is left for the business of time slot t
Rate, Ak(t) the business arrival rate for being time slot t；
Vk(t+1)=max [Vk(t)+yk(t),0]
Wherein, max [Vk(t)+yk(t), it 0] is Vk(t)+yk(t) maximum value with 0, PkIt (t) is the instantaneous function of single user
Consumption,For the average power consumption thresholding of each time slot of user；
Wherein, Ptot(P (τ), G (τ)) is the instantaneous total power consumption of system, Rtot(P (τ), G (τ)) is system overall transmission rate.
Further, the optimization problem in efficiency-time delay compromise method first step is decomposed by R son according to the concept of group
Problem proposes the iterative power allocation algorithm IPAA for being directed to subproblem:
S.t.C3, C4, C5；
It is implemented as follows:
The first step initializes initial value power Pr,(0)(t), active user k=0 and maximum tolerance δ > 0 calculates Ir,(0)=f
(Pr,(0)(t))-h(Pr,(0)(t))；
Wherein:
Second step solves following optimization problem and obtains optimal solution Pr,*(t)；
S.t.C3, C4, C5；
The power k=k+1, P of next user is arranged in the optimal solution of second step by third stepr,(k)(t)=Pr,*(t)；
4th step calculates Ir,(k)=f (Pr,(k)(t))-h(Pr,(k)(t))；
5th step, judges inequality | Ir,(k)-Ir,(k-1)|≤δ, if inequality is set up, which terminates output and optimizes
Power；Otherwise second step is returned；
Further,
By Leah Pu Luofu migration technology, the quantitative efficiency-time delay trade-off relation that analyzes is [O (1/V), O (V)]；
Wherein,For the optimal solution of step 6 optimization problem, B is positive real number, RminFor all working user it is total
The boundary minimum value of transmission rate, V are control parameter；
Wherein, B is positive real number, and V is control parameter,For the optimal solution of optimization problem, RmaxFor the use of all working
The boundary maximum value of the overall transmission rate at family, PmaxFor the boundary maximum value of the instantaneous total power consumption of system, ε is that each customer service arrives
Minimum range up to rate away from network capacity domain boundary.
Further, the factor weighs the algorithm steps of model surely and includes:
Factor molecule is obtained into factor degree of membership in conjunction with fuzzy mathematics degree of membership, following formula:
X0 representative building data target is previous in formula builds the data level that transfinites；
X1 represents building data target the latter and builds data level；
X represents current building data samples values；
According to upper formula to building data individual event metrics evaluation；
W is building data indices sample set, and L is that building data indices build the data class set that transfinites,
Establish following formula:
In formula: A is sample values；
N is index number；
M is to build the data number of levels evidence that transfinites；
The factor degree of membership of single index is calculated by lower formula, corresponding n building data target obtains the matrix of m*n
R；
Calculate comprehensive weight；
Building the data that transfinite is as caused by multiple indexs, and difference building data should have an impact to Comprehensive Assessment weight,
The following formula of weight calculation of single index:
I.e.
In formula: Ai represents current criteria numerical value；
Lk represents index grade；
Using the weight calculation that unitizes in fuzzy model, following formula is obtained:
In formula: Wk represents single index weights；
Have n building data target to get to building aggregation of data weight matrix B, following formula:
B=[W1, W2 ..., Wn]；
By matrix R and matrix composite computing, building, which is calculated, to transfinite data status.
Another object of the present invention is to provide the building Integrate Natural Disasters Prevention data processings described in a kind of realize based on big data
The computer program of method.
Another object of the present invention is to provide the building Integrate Natural Disasters Prevention data processings described in a kind of realize based on big data
The computer of method.
Another object of the present invention is to provide a kind of computer readable storage mediums, including instruction, when it is in computer
When upper operation, so that computer executes the building Integrate Natural Disasters Prevention data processing method based on big data.
Another object of the present invention is to provide the building Integrate Natural Disasters Prevention data processings described in a kind of realize based on big data
The building Integrate Natural Disasters Prevention data processing system based on big data of method, at the building Integrate Natural Disasters Prevention data based on big data
Reason system is provided with loader；
Loader is connect by conducting wire with processor；Calculator is connected with processor by conducting wire；Cloud server and place
Device is managed to be connected；Processor is connected by conducting wire with converter；Converter is connected by conducting wire with amplifier；Amplifier and comparator
It is connected with conducting wire；
Comparator is connected by conducting wire with adjuster；Comparator and processor are connected with conducting wire；Show equipment connection everywhere
Manage device；Printing device is connected with display equipment with conducting wire；
Storage platform is connected by data/address bus with distributed processing platform, and the data of storage are used for transmission；At distribution
Platform conducting wire is connected with function converter, for changing data format；Function converter and data source pass through bus
It is connected；Data source is for transmitting wirelessly data to application platform.
Another object of the present invention is to provide the building Integrate Natural Disasters Prevention data processings described in a kind of carrying based on big data
The urban architecture Integrate Natural Disasters Prevention data processing equipment of system.
In conclusion advantages of the present invention and good effect are as follows:
The present invention provides base platform by cloud computing, and the big data application on cloud server is operated in this
On platform, in different location and the kinds of platform communication network of different data will be possessed by programs such as distributed processing system(DPS)s
It connects, under the unified management control of processor, completes information handling task in phase, obtained set by meeting by comparing
The building disaster prevention treating method of meter analyzes weak link according to obtained data, then is subject to reinforcement, is more widely applied,
It is a kind of reliable, efficient, telescopic treating method.
The present invention calculates in building disaster prevention data, for the signal of communication that interference-limited calculator is led directly to, does not need
Consideration business is reached at random under time varying channel conditions, and can theorize analysis model, design power allocation algorithm.Depending on
Amount discloses the authenticity of building disaster prevention data, and to theorize in Building Engineering Design, criterion provides foundation, and it is accurate to predict
Data.
For the present invention by drawing enrolled concept, all users that would operate in the same orthogonal frequency range are defined as a group,
Therefore the interference between user exists only in group, there is no interference between group and group, organizes the introducing of concept for the optimal of the algorithm
Change PROBLEM DECOMPOSITION at a series of subproblem, so that the complexity of calculating greatly reduce.The present invention is quantitative to be disclosed
The trade-off relation of time delay and efficiency in the communication system, this quantitative relationship be in the design of building disaster prevention data engineering control and
Tradeoff time delay and performance efficiency provide important theoretical criterion.Algorithm proposed by the present invention requires no knowledge about any related business
The priori knowledge of arrival rate and the statistical distribution of channel condition has the advantages that signal overhead is small, so as to be easily applied to
It is practical.
The present invention carries out statistical disposition with Set Valued Statistics method, show that building transfinites data；Set Valued Statistics method carries out
In statistical disposition, the Gaussian filter matrix model of cum rights is established:
In formula: Q is electric-wave filter matrix, and Q is the matrix of 1*n；N is matrix size threshold values；
I is the relative coordinate values of distance center coordinate points, i.e. is that the coordinate points are poor with respect to the weight of central point obtained by Q [i]；
Calculate Gaussian smoothing central point with respect to left and right threshold values difference with；
In formula: put centered on S [k] opposite left and right threshold values difference and；
The sample measurement put centered on buf [k]；
N is electric-wave filter matrix size；
Sample value after calculating Gaussian smoothing:
Obtaining building transfinites data；And data accuracy improves nearly 9 percentage points compared with the prior art, reaches
98.82% or so.
Detailed description of the invention
Fig. 1 is the building Integrate Natural Disasters Prevention data processing method structural representation provided in an embodiment of the present invention based on big data
Figure；
Fig. 2 is cloud server structure chart provided in an embodiment of the present invention；
In figure: 1, loader；2, calculator；3, cloud server；4, processor；5, converter；6, amplifier；7, compare
Device；8, adjuster；9, equipment is shown；10, printing device；11, storage platform；12, distributed processing platform；13, function is converted
Device；14, data source.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
As shown in Figure 1 and Figure 2, the building Integrate Natural Disasters Prevention data processing system provided in an embodiment of the present invention based on big data,
It include: loader 1, calculator 2, cloud server 3, processor 4, converter 5, amplifier 6, comparator 7, adjuster 8, display
Equipment 9, printing device 10, storage platform 11, distributed processing platform 12, function converter 13, data source 14.
The loader 1 is connect by conducting wire with processor 4；The calculator 2 is connected with processor 4 by conducting wire；Institute
Cloud server 3 is stated to be connected with processor 4；The processor 4 is connected by conducting wire with converter 5；The converter 5 is by leading
Line is connected with amplifier 6；The amplifier 6 is connected with comparator 7 with conducting wire；
The comparator 7 is connected by conducting wire with adjuster 8；The comparator 8 is connect with processor 4 with conducting wire；It is described
Display equipment 9 is connected to processor 4；The printing device 10 is connected with display equipment 9 with conducting wire.
The storage platform 11 is connected by data/address bus with distributed processing platform 12, and the data of storage are used for transmission；
The distributed processing platform 12 conducting wire is connected with function converter 13, for changing data format, to carry out in next step
Transmission；The function converter 13 is connected with data source 14 by bus；The data source 14 is for transmitting wirelessly
Data are to application platform.
Distributed proccessing application Hadoop mode in the cloud server.The storage rank of the storage platform
It can reach EP rank.The calculator uses multivariate statistical method.The printing device position latest version iX6880.The converter
Model AD13280AF.
The present invention is inputted building disaster prevention data by loader, and computations are then sent to calculating by processor
Device, calculator calculate data with multivariate statistical method, obtain building disaster prevention data model；
Statistical disposition is carried out with Set Valued Statistics method again, show that building transfinites data；
Then cloud server is sent instructions to by processor；The big data in cloud is transferred, amplifier is passed through
With the integrated treatment of converter, the data transmitted are sent in comparator, by the big data comparison with cloud, obtains and builds
Build correlation data；
Then perfect information is sent in adjuster, overall cost data, structured data, facility data obtain
It takes precautions against natural calamities data processing model, then by showing that equipment is intuitively shown.It can also be presented on paper by printing device.
The invention will be further described combined with specific embodiments below.
Building Integrate Natural Disasters Prevention data processing method provided in an embodiment of the present invention based on big data, comprising:
Building disaster prevention data are inputted by loader, computations are then sent to calculator by processor, are calculated
Device calculates data with multivariate statistical method, obtains building disaster prevention data model；Multivariate statistical method carries out data
In calculating,
For the communication system of the interference-limited calculator of the big data network coverage, draw enrolled concept, it is assumed that communication
System is there are R orthogonal frequency ranges, and all users that would operate in the same orthogonal frequency range are defined as a group, i.e. system exists
R group, therefore the interference between user exists only in group, there is no interference between group and group；It provides in group r and works in honeycomb mould
Reception Signal to Interference plus Noise Ratio of the user of formula in base stationWith work calculator direct mode operation user in calculator
The reception Signal to Interference plus Noise Ratio of straight-through receiving endDefined formula；It is provided in group r and is worked in bee by shannon formula
The transmission rate of the user of snap formulaWith the transmission rate of the user of calculator direct mode operationDefined formula；It gives
Transmission rate R of the system work in the user of honeycomb mode outm(t), it works in the transmission speed of the user of calculator direct mode operation
Rate Rn(t) and the overall transmission rate R of the user of all workingtot(t) defined formula；Provide the instantaneous power consumption P of single userk(t)、
Long-term average power consumptionWith the instantaneous total power consumption P of systemtot(t) defined formula:
Wherein, ξkThe effect of for power amplifier coefficient,For an indicative parameter, if user k is the user to work in group r,
Then value is 1；Otherwise value is 0,Transmission power for the user k to work in group,For the fixation circuit power consumption of equipment:Wherein, PkIt (t) is the instantaneous power consumption of single user, T is timeslot number；Wherein, ξkThe effect of for power amplifier coefficient,Transmission power for the user k to work in group r,For the fixation circuit power consumption of equipment；In order to it is quantitative portray efficiency with
Trade-off relation between time delay gives real data queue Qk(t) more new formula and efficiency ηEEDefined formula；Specifically:
Qk(t+1)=max [Qk(t)-Rk(t),0]+Ak(t)；Wherein, max [Qk(t)-Rk(t), it 0] is Qk(t)-Rk(t) maximum with 0
Value, Rk(t) rate, A are left for the business of time slot tk(t) the business arrival rate for being time slot t；
Network energy efficiency ηEEIt is defined as long-term network total power consumption and the ratio of corresponding total transmitted data amount, unit is
Joule/bit/Hz can describe the influence of time varying channel conditions and random traffic arrival to delay performance, defined formula:Wherein,It is average for a long time for system
Total power consumption,For system for a long time be averaged overall transmission rate；Building disaster prevention data model is established to disclose based on the big data network coverage
The straight-through communication system of interference-limited calculator efficiency and time delay trade-off relation:
s.t.C1:C2: queue queue Qk(t) Mean Speed is stablized,C3:C4:C5:Wherein,For
The average power consumption thresholding of each time slot of user,For organize in all working calculator direct mode operation user to work in bee
The interference threshold of the user of snap formula,It is straight-through in calculator to work in the user of honeycomb mode to organize interior all working
The interference threshold of the user of mode；C1 is used to guarantee the life cycle of mobile device；C2 is string stability constraint, for guaranteeing
There are the data of arrival to leave network within the limited time；User pair of all working in calculator direct mode operation in C3 limitation group
It works in the interference of the user of honeycomb mode；All working is straight in calculator to working in the user of honeycomb mode in C4 limitation group
The interference of the user of logical mode；C5 is a non-negative transimission power constraint；
For the restrictive condition C1 for handling building disaster prevention data model, introduces and provide virtual power queue Vk(t) concept
And defined formula, wherein Vk(0)=0；If power distribution algorithm makes all virtual power string stabilities, meet average function
Rate limits C1:
Vk(t+1)=max [Vk(t)+yk(t),0]
Wherein, max [Vk(t)+yk(t), it 0] is Vk(t)+yk(t) maximum value with 0, PkIt (t) is the instantaneous function of single user
Consumption,For the average power consumption thresholding of each time slot of user；Using non-linear fractional programming, by optimization problem transform into as
Lower optimization problem:
S.t.C1, C2, C3, C4, C5；
Wherein,
Wherein,For system for a long time be averaged total power consumption,For system for a long time be averaged overall transmission rate,
Ptot(P (τ), G (τ)) is the instantaneous total power consumption of system, Rtot(P (τ), G (τ)) is the instantaneous overall transmission rate of system；
Statistical disposition is carried out with Set Valued Statistics method again, show that building transfinites data；Set Valued Statistics method is counted
In processing, the Gaussian filter matrix model of cum rights is established:
In formula: Q is electric-wave filter matrix, and Q is the matrix of 1*n；N is matrix size threshold values；
I is the relative coordinate values of distance center coordinate points, i.e. is that the coordinate points are poor with respect to the weight of central point obtained by Q [i]；
Calculate Gaussian smoothing central point with respect to left and right threshold values difference with；
In formula: put centered on S [k] opposite left and right threshold values difference and；
The sample measurement put centered on buf [k]；
N is electric-wave filter matrix size；
Sample value after calculating Gaussian smoothing:
Obtaining building transfinites data；In formula: centered on buf ' [k] point treated value；The sample put centered on buf [k]
Measured value；
N is electric-wave filter matrix size；
Then cloud server is sent instructions to by processor；The big data in cloud is transferred, amplifier is passed through
With the integrated treatment of converter, the data transmitted are sent in comparator, by the big data comparison with cloud, obtains and builds
Build correlation data；
Then perfect information is sent in adjuster, overall cost data, structured data, facility data obtain
It takes precautions against natural calamities data processing model, then by showing that equipment is intuitively shown.
It further,, need to also be into after optimization problem is transformed into following optimization problem using non-linear fractional programming
Row: utilizing Leah Pu Luofu migration technology, designs the efficiency-time delay compromise method for solving transition problem；Solve transition problem
Efficiency-time delay compromise method specific implementation include:
The first step observes current real data queue Q in each time slot tk(t) and virtual power queue Vk(t) and believe
Road condition G (t) solves following optimization problem and obtains the power distribution of this time slot；
S.t.C3, C4, C5
Second step, according to the real data queue Q of current time slots tk(t), virtual power queue Vk(t) and efficiency ηEE(t)
More new formula updates real data queue Q when next time slot startsk(t+1), virtual power queue Vk(t+1) and efficiency ηEE(t
+1)；It is calculated as follows:
Qk(t+1)=max [Qk(t)-Rk(t),0]+Ak(t)
Wherein, max [Qk(t)-Rk(t), it 0] is Qk(t)-Rk(t) maximum value with 0, Rk(t) it is left for the business of time slot t
Rate, Ak(t) the business arrival rate for being time slot t；
Vk(t+1)=max [Vk(t)+yk(t),0]
Wherein, max [Vk(t)+yk(t), it 0] is Vk(t)+yk(t) maximum value with 0, PkIt (t) is the instantaneous function of single user
Consumption,For the average power consumption thresholding of each time slot of user；
Wherein, Ptot(P (τ), G (τ)) is the instantaneous total power consumption of system, Rtot(P (τ), G (τ)) is system overall transmission rate.
The optimization problem in efficiency-time delay compromise method first step is decomposed into R subproblem according to the concept of group, is mentioned
It is directed to the iterative power allocation algorithm IPAA of subproblem out:
S.t.C3, C4, C5；
It is implemented as follows:
The first step initializes initial value power Pr,(0)(t), active user k=0 and maximum tolerance δ > 0 calculates Ir,(0)=f
(Pr,(0)(t))-h(Pr,(0)(t))；
Wherein:
Second step solves following optimization problem and obtains optimal solution Pr,*(t)；
S.t.C3, C4, C5；
The power k=k+1, P of next user is arranged in the optimal solution of second step by third stepr,(k)(t)=Pr,*(t)；
4th step calculates Ir,(k)=f (Pr,(k)(t))-h(Pr,(k)(t))；
5th step, judges inequality | Ir,(k)-Ir,(k-1)|≤δ, if inequality is set up, which terminates output and optimizes
Power；Otherwise second step is returned；
By Leah Pu Luofu migration technology, the quantitative efficiency-time delay trade-off relation that analyzes is [O (1/V), O (V)]；
Wherein,For the optimal solution of step 6 optimization problem, B is positive real number, RminFor all working user it is total
The boundary minimum value of transmission rate, V are control parameter；
Wherein, B is positive real number, and V is control parameter,For the optimal solution of optimization problem, RmaxFor the use of all working
The boundary maximum value of the overall transmission rate at family, PmaxFor the boundary maximum value of the instantaneous total power consumption of system, ε is that each customer service arrives
Minimum range up to rate away from network capacity domain boundary.
The algorithm steps that the factor weighs model surely include:
Factor molecule is obtained into factor degree of membership in conjunction with fuzzy mathematics degree of membership, following formula:
X0 representative building data target is previous in formula builds the data level that transfinites；
X1 represents building data target the latter and builds data level；
X represents current building data samples values；
According to upper formula to building data individual event metrics evaluation；
W is building data indices sample set, and L is that building data indices build the data class set that transfinites,
Establish following formula:
In formula: A is sample values；
N is index number；
M is to build the data number of levels evidence that transfinites；
The factor degree of membership of single index is calculated by lower formula, corresponding n building data target obtains the matrix of m*n
R；
Calculate comprehensive weight；
Building the data that transfinite is as caused by multiple indexs, and difference building data should have an impact to Comprehensive Assessment weight,
The following formula of weight calculation of single index:
I.e.
In formula: Ai represents current criteria numerical value；
LK represents index grade；
Using the weight calculation that unitizes in fuzzy model, following formula is obtained:
In formula: Wk represents single index weights；
Have n building data target to get to building aggregation of data weight matrix B, following formula:
B=[W1, W2 ..., Wn]；
By matrix R and matrix composite computing, building, which is calculated, to transfinite data status.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When using entirely or partly realizing in the form of a computer program product, the computer program product include one or
Multiple computer instructions.When loading on computers or executing the computer program instructions, entirely or partly generate according to
Process described in the embodiment of the present invention or function.The computer can be general purpose computer, special purpose computer, computer network
Network or other programmable devices.The computer instruction may be stored in a computer readable storage medium, or from one
Computer readable storage medium is transmitted to another computer readable storage medium, for example, the computer instruction can be from one
A web-site, computer, server or data center pass through wired (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)
Or wireless (such as infrared, wireless, microwave etc.) mode is carried out to another web-site, computer, server or data center
Transmission).The computer-readable storage medium can be any usable medium or include one that computer can access
The data storage devices such as a or multiple usable mediums integrated server, data center.The usable medium can be magnetic Jie
Matter, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid state hard disk Solid
State Disk (SSD)) etc..
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (10)
1. a kind of building Integrate Natural Disasters Prevention data processing method based on big data, which is characterized in that the building based on big data
Building Integrate Natural Disasters Prevention data processing method includes:
By loader by Architectural Structure Design model, design moment, shearing, axle power, wind load, seismic force, material type, strong
Computations, are then sent to calculator by processor, calculator counts data with multivariate statistical method by degree input
It calculates, obtains building disaster prevention data model；During multivariate statistical method calculates data,
For the communication system of the interference-limited calculator of the big data network coverage, draw enrolled concept, it is assumed that communication system
There are R orthogonal frequency ranges, all users that would operate in the same orthogonal frequency range are defined as a group, i.e., there are R for system
Group, therefore the interference between user exists only in group, there is no interference between group and group；It provides in group r and works in honeycomb mode
User base station reception Signal to Interference plus Noise RatioIt is straight in calculator in the user of calculator direct mode operation with work
Connect the reception Signal to Interference plus Noise Ratio of receiving endDefined formula；It is provided in group r and is worked in honeycomb by shannon formula
The transmission rate of the user of modeWith the transmission rate of the user of calculator direct mode operationDefined formula；It provides
System works in the transmission rate R of the user of honeycomb modem(t), it works in the transmission rate R of the user of calculator direct mode operationn
(t) and the overall transmission rate R of the user of all workingtot(t) defined formula；Provide the instantaneous power consumption P of single userk(t), long
The average power consumption of phaseWith the instantaneous total power consumption P of systemtot(t) defined formula:Its
In, ξkThe effect of for power amplifier coefficient,For an indicative parameter, if user k is the user to work in group r,
Value is 1；Otherwise value is 0,Transmission power for the user k to work in group,For the fixation circuit power consumption of equipment:Wherein, PkIt (t) is the instantaneous power consumption of single user, T is timeslot number；Wherein, ξkThe effect of for power amplifier coefficient,Transmission power for the user k to work in group r,For the fixation circuit power consumption of equipment；In order to it is quantitative portray efficiency with
Trade-off relation between time delay gives real data queue Qk(t) more new formula and efficiency ηEEDefined formula；Specifically:
Qk(t+1)=max [Qk(t)-Rk(t),0]+Ak(t)；Wherein, max [Qk(t)-Rk(t), it 0] is Qk(t)-Rk(t) maximum with 0
Value, Rk(t) rate, A are left for the business of time slot tk(t) the business arrival rate for being time slot t；
Network energy efficiency ηEEIt is defined as long-term network total power consumption and the ratio of corresponding total transmitted data amount, unit Joule/
Bit/Hz can describe the influence of time varying channel conditions and random traffic arrival to delay performance, defined formula:Wherein,It is average for a long time for system
Total power consumption,For system for a long time be averaged overall transmission rate；Building disaster prevention data model is established to disclose based on the big data network coverage
The straight-through communication system of interference-limited calculator efficiency and time delay trade-off relation:
s.t.C1:C2: queue queue Qk(t) Mean Speed is stablized,C3:C4:C5:Wherein,For
The average power consumption thresholding of each time slot of user,For organize in all working calculator direct mode operation user to work in bee
The interference threshold of the user of snap formula,It is straight-through in calculator to work in the user of honeycomb mode to organize interior all working
The interference threshold of the user of mode；C1 is used to guarantee the life cycle of mobile device；C2 is string stability constraint, for guaranteeing
There are the data of arrival to leave network within the limited time；User pair of all working in calculator direct mode operation in C3 limitation group
It works in the interference of the user of honeycomb mode；All working is straight in calculator to working in the user of honeycomb mode in C4 limitation group
The interference of the user of logical mode；C5 is a non-negative transimission power constraint；
For the restrictive condition C1 for handling building disaster prevention data model, introduces and provide virtual power queue Vk(t) concepts and definitions
Formula, wherein Vk(0)=0；If power distribution algorithm makes all virtual power string stabilities, meet average power limit
C1:
Vk(t+1)=max [Vk(t)+yk(t),0]
Wherein, max [Vk(t)+yk(t), it 0] is Vk(t)+yk(t) maximum value with 0, PkIt (t) is the instantaneous power consumption of single user,
For the average power consumption thresholding of each time slot of user；Using non-linear fractional programming, optimization problem is transformed into following optimal
Change problem:
S.t.C1, C2, C3, C4, C5；
Wherein,
Wherein,For system for a long time be averaged total power consumption,For system for a long time be averaged overall transmission rate, Ptot(P
(τ), G (τ)) it is the instantaneous total power consumption of system, Rtot(P (τ), G (τ)) is the instantaneous overall transmission rate of system；
Statistical disposition is carried out with Set Valued Statistics method again, show that building transfinites data；Set Valued Statistics method carries out statistical disposition
In, establish the Gaussian filter matrix model of cum rights:
In formula: Q is electric-wave filter matrix, and Q is the matrix of 1*n；N is matrix size threshold values；
I is the relative coordinate values of distance center coordinate points, i.e. is that the coordinate points are poor with respect to the weight of central point obtained by Q [i]；
Calculate Gaussian smoothing central point with respect to left and right threshold values difference with；
In formula: put centered on S [k] opposite left and right threshold values difference and；
The sample measurement put centered on buf [k]；
N is electric-wave filter matrix size；
Sample value after calculating Gaussian smoothing:
Obtaining building transfinites data；In formula: centered on buf ' [k] point treated value；The sample measurement put centered on buf [k]
Value；
N is electric-wave filter matrix size；
Then cloud server is sent instructions to by processor；The big data in cloud is transferred, by amplifier and is turned
The data transmitted are sent in comparator by the integrated treatment of parallel operation, by the big data comparison with cloud, obtain building pair
Compare data；
Then perfect information is sent in adjuster, overall cost data, structured data, facility data are obtained and taken precautions against natural calamities
Data processing model, then by showing that equipment is intuitively shown.
2. the building Integrate Natural Disasters Prevention data processing method based on big data as described in claim 1, which is characterized in that utilize non-
Linear fractional programming after optimization problem is transformed into following optimization problem, also needs to carry out: being deviated using Leah Pu Luofu
Technology designs the efficiency-time delay compromise method for solving transition problem；Solve efficiency-time delay compromise method tool of transition problem
Body is realized
The first step observes current real data queue Q in each time slot tk(t) and virtual power queue Vk(t) and channel item
Part G (t) solves following optimization problem and obtains the power distribution of this time slot；
S.t.C3, C4, C5
Second step, according to the real data queue Q of current time slots tk(t), virtual power queue Vk(t) and efficiency ηEE(t) it updates public
Formula updates real data queue Q when next time slot startsk(t+1), virtual power queue Vk(t+1) and efficiency ηEE(t+1)；It presses
Following formula calculates:
Qk(t+1)=max [Qk(t)-Rk(t),0]+Ak(t)
Wherein, max [Qk(t)-Rk(t), it 0] is Qk(t)-Rk(t) maximum value with 0, Rk(t) rate is left for the business of time slot t,
Ak(t) the business arrival rate for being time slot t；
Vk(t+1)=max [Vk(t)+yk(t),0]
Wherein, max [Vk(t)+yk(t), it 0] is Vk(t)+yk(t) maximum value with 0, PkIt (t) is the instantaneous power consumption of single user,
For the average power consumption thresholding of each time slot of user；
Wherein, Ptot(P (τ), G (τ)) is the instantaneous total power consumption of system, Rtot(P (τ), G (τ)) is system overall transmission rate.
3. the building Integrate Natural Disasters Prevention data processing method based on big data as claimed in claim 2, which is characterized in that according to group
Concept the optimization problem in efficiency-time delay compromise method first step is decomposed into R subproblem, propose for subproblem
Iterative power allocation algorithm IPAA:
S.t.C3, C4, C5；
It is implemented as follows:
The first step initializes initial value power Pr,(0)(t), active user k=0 and maximum tolerance δ > 0 calculates Ir,(0)=f (Pr,(0)
(t))-h(Pr,(0)(t))；
Wherein:
Second step solves following optimization problem and obtains optimal solution Pr,*(t)；
max f(Pr(t))-[h(Pr,(k)(t))+▽hT(Pr,(k)(t))(Pr(t)-Pr,(k)(t))]；
S.t.C3, C4, C5；
The power k=k+1, P of next user is arranged in the optimal solution of second step by third stepr,(k)(t)=Pr,*(t)；
4th step calculates Ir,(k)=f (Pr,(k)(t))-h(Pr,(k)(t))；
5th step, judges inequality | Ir,(k)-Ir,(k-1)|≤δ, if inequality is set up, which terminates output and optimizes function
Rate；Otherwise second step is returned.
4. the building Integrate Natural Disasters Prevention data processing method based on big data as claimed in claim 2, which is characterized in that
By Leah Pu Luofu migration technology, the quantitative efficiency-time delay trade-off relation that analyzes is [O (1/V), O (V)]；
Wherein,For the optimal solution of step 6 optimization problem, B is positive real number, RminFor total transmission of the user of all working
The boundary minimum value of rate, V are control parameter；
Wherein, B is positive real number, and V is control parameter,For the optimal solution of optimization problem, RmaxFor the user of all working
The boundary maximum value of overall transmission rate, PmaxFor the boundary maximum value of the instantaneous total power consumption of system, ε is each customer service arrival rate
Minimum range away from network capacity domain boundary.
5. the building Integrate Natural Disasters Prevention data processing method based on big data as described in claim 1, which is characterized in that
The algorithm steps that the factor weighs model surely include:
Factor molecule is obtained into factor degree of membership in conjunction with fuzzy mathematics degree of membership, following formula:
X0 representative building data target is previous in formula builds the data level that transfinites；
X1 represents building data target the latter and builds data level；
X represents current building data samples values；
According to upper formula to building data individual event metrics evaluation；
W is building data indices sample set, and L is that building data indices build the data class set that transfinites, and is established
Following formula:
In formula: A is sample values；
N is index number；
M is to build the data number of levels evidence that transfinites；
The factor degree of membership of single index is calculated by lower formula, corresponding n building data target obtains the matrix R of m*n；
Calculate comprehensive weight；
Building the data that transfinite is as caused by multiple indexs, and difference building data should have an impact to Comprehensive Assessment weight, individually
The following formula of the weight calculation of index:
I.e.
In formula: Ai represents current criteria numerical value；
Lk represents index grade；
Using the weight calculation that unitizes in fuzzy model, following formula is obtained:
In formula: Wk represents single index weights；
Have n building data target to get to building aggregation of data weight matrix B, following formula:
B=[W1, W2 ..., Wn]；
By matrix R and matrix composite computing, building, which is calculated, to transfinite data status.
6. a kind of building Integrate Natural Disasters Prevention data processing method realized described in Claims 1 to 5 any one based on big data
Computer program.
7. a kind of building Integrate Natural Disasters Prevention data processing method realized described in Claims 1 to 5 any one based on big data
Computer.
8. a kind of computer readable storage medium, including instruction, when run on a computer, so that computer is executed as weighed
Benefit requires the building Integrate Natural Disasters Prevention data processing method described in 1-5 any one based on big data.
9. a kind of building Integrate Natural Disasters Prevention data processing method realized described in claim 1 based on big data based on big data
Build Integrate Natural Disasters Prevention data processing system, which is characterized in that the building Integrate Natural Disasters Prevention data processing system based on big data
It is provided with loader；
Loader is connect by conducting wire with processor；Calculator is connected with processor by conducting wire；Cloud server and processor
It is connected；Processor is connected by conducting wire with converter；Converter is connected by conducting wire with amplifier；Amplifier and comparator are with leading
Line is connected；
Comparator is connected by conducting wire with adjuster；Comparator and processor are connected with conducting wire；Display equipment is connected to processor；
Printing device is connected with display equipment with conducting wire；
Storage platform is connected by data/address bus with distributed processing platform, and the data of storage are used for transmission；Distributed treatment is flat
Platform conducting wire is connected with function converter, for changing data format；Function converter is connected with data source by bus；
Data source is for transmitting wirelessly data to application platform.
10. a kind of urban architecture for carrying the building Integrate Natural Disasters Prevention data processing system described in claim 1 based on big data is comprehensive
Conjunction is taken precautions against natural calamities data processing equipment.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102110250A (en) * | 2009-12-29 | 2011-06-29 | 中国科学院合肥物质科学研究院 | Method for optimizing and determining disaster-reduction decision scheme |
| CN103634850A (en) * | 2013-12-03 | 2014-03-12 | 西安电子科技大学 | Cellular network coverage based device-to-device communication system energy efficiency and time delay tradeoff method |
| CN104133804A (en) * | 2013-05-03 | 2014-11-05 | 中国建筑科学研究院 | Building integrated disaster prevention data processing method and building integrated disaster prevention data processing device |
| CN105913411A (en) * | 2016-05-10 | 2016-08-31 | 云南大学 | Lake water quality evaluation prediction system and method based on factor weighting model |
| CN107332889A (en) * | 2017-06-20 | 2017-11-07 | 湖南工学院 | A kind of high in the clouds information management control system and control method based on cloud computing |
| CN107564231A (en) * | 2017-09-15 | 2018-01-09 | 山东建筑大学 | Building fire early warning and fire disaster situation assessment system and method based on Internet of Things |
| CN108540581A (en) * | 2018-07-11 | 2018-09-14 | 广东水利电力职业技术学院（广东省水利电力技工学校） | Service system and method for servicing based on more web servers, storage medium |
| CN108747063A (en) * | 2018-05-25 | 2018-11-06 | 广东水利电力职业技术学院（广东省水利电力技工学校） | A kind of cooling device and control method of laser engraving machine |
-
2018
- 2018-11-09 CN CN201811330860.6A patent/CN109548128A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102110250A (en) * | 2009-12-29 | 2011-06-29 | 中国科学院合肥物质科学研究院 | Method for optimizing and determining disaster-reduction decision scheme |
| CN104133804A (en) * | 2013-05-03 | 2014-11-05 | 中国建筑科学研究院 | Building integrated disaster prevention data processing method and building integrated disaster prevention data processing device |
| CN103634850A (en) * | 2013-12-03 | 2014-03-12 | 西安电子科技大学 | Cellular network coverage based device-to-device communication system energy efficiency and time delay tradeoff method |
| CN105913411A (en) * | 2016-05-10 | 2016-08-31 | 云南大学 | Lake water quality evaluation prediction system and method based on factor weighting model |
| CN107332889A (en) * | 2017-06-20 | 2017-11-07 | 湖南工学院 | A kind of high in the clouds information management control system and control method based on cloud computing |
| CN107564231A (en) * | 2017-09-15 | 2018-01-09 | 山东建筑大学 | Building fire early warning and fire disaster situation assessment system and method based on Internet of Things |
| CN108747063A (en) * | 2018-05-25 | 2018-11-06 | 广东水利电力职业技术学院（广东省水利电力技工学校） | A kind of cooling device and control method of laser engraving machine |
| CN108540581A (en) * | 2018-07-11 | 2018-09-14 | 广东水利电力职业技术学院（广东省水利电力技工学校） | Service system and method for servicing based on more web servers, storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 曹湛等: "基于智慧技术的城市综合防灾体系及构建方法", 《建筑学报》 * |
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