CN107976934A - A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network - Google Patents
A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
Abstract
The invention discloses a kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network, realize and oil truck be detected and oil truck oil and gas leakage speed intelligent early-warning in way parameter, it is characterised in that：The intelligent early-warning system includes the collection of oil truck oil and gas leakage state parameter and intelligent early-warning platform, oil truck oil and gas leakage speed intelligent early-warning model；It is an object of the present invention to provide a kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network, the intelligent early-warning system monitors humiture of the oil truck in way is travelled, pressure and the security information such as whether leaks in real time, so as to avoid the generation problem of contingency.
Description
Technical field
The present invention relates to special transport vehicle testing techniques of equipment field, and in particular to one kind is based on wireless sensor network
Oil truck oil and gas leakage speed intelligent early-warning system.
Background technology
China oil field is more in remote districts, therefore petroleum transportation becomes an important step in petroleum industry.Oil is transported
Defeated to have long-distance and two kinds of short distance, long-distance transport relies primarily on oil pipeline, for short distance and it is some do not have along railwayed, stone
The outward transport work of oil relies primarily on oil truck.Oil truck accumulating because its it is motor-driven and flexible the features such as, become short distance oil product transport
Main carriers.Oil is flammability hazard article, and driver generally does not allow to stop privately during transport, unless running into
Situations such as roadlock plug, vehicle trouble, is forced to stop, and especially can not arbitrarily be stopped in city prosperity location, so as to avoid an accident thing
Therefore.In order to monitor humiture of the oil truck in way is travelled, pressure in real time and whether security information, the application such as oil leak devise
The wireless sensor network node of oil tank locomotive safety information is perceived, constructs a kind of oil truck oil based on wireless sensor network
Gas leakage speed intelligent early-warning system.The system is by the collection of oil truck oil and gas leakage state parameter and intelligent early-warning platform and oil tank
Car oil and gas leakage speed intelligent early-warning model two parts form, realize accurate monitoring to oil truck oil and gas leakage speed with it is pre-
It is alert, play good impetus to improving oil truck long-distance transport effect.
The content of the invention
It is an object of the present invention to provide a kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network
System, the intelligent early-warning system monitor humiture of the oil truck in way is travelled, pressure and the security information such as whether leak in real time, from
And avoid the generation problem of contingency.
The present invention is achieved through the following technical solutions：
A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network, realizes and exists to oil truck
Way parameter is detected and oil truck oil and gas leakage speed intelligent early-warning, it is characterised in that：The intelligent early-warning system includes oil
Tank car oil and gas leakage state parameter gathers and intelligent early-warning platform, oil truck oil and gas leakage speed intelligent early-warning model；
The oil truck oil and gas leakage state parameter collection is with intelligent early-warning platform by multiple detection nodes and on-site supervision
End composition, and oil truck is built into an ad-hoc fashion in the collection of way oil and gas leakage state parameter and intelligent early-warning platform；Detection
Node is made of sensor group module, microcontroller and wireless communication module NRF2401, the temperature on responsible detection oil truck surface,
The actual value of pressure and gas concentration and speed, on-site supervision end are realized and oil truck oil and gas leakage parameter are managed and to oil
Tank car carries out early warning in way Leakage Energy gas velocity degree；
The oil truck oil and gas leakage speed intelligent early-warning model include multiple GM (1,1) grey forecasting model, Fuzzy C-
Means clustering algorithm grader, multiple empirical mode decomposition models, multigroup DRNN Network Prediction Models, oil truck oil and gas leakage speed
Spend T-S network multiple spot Fusion Models and oil truck oil and gas leakage speed class grader；Each GM (1,1) grey forecasting model
The input as Fuzzy C-Means Clustering Algorithm grader is exported, Fuzzy C-Means Clustering Algorithm grader is to multiple GM (1,1)
Grey forecasting model output is classified, and each type of GM (1,1) grey forecasting model output is as each empirical modal point
Solve the input of model, the input of multiple outputs of each empirical mode decomposition model as every group of DRNN Network Prediction Model, often
The fusion forecasting value of group DRNN Network Prediction Models is as the defeated of oil truck oil and gas leakage speed T-S network multiple spot Fusion Models
Enter, oil truck oil and gas leakage speed T-S network multiple spot Fusion Models to the fusion forecasting values of multigroup DRNN Network Prediction Models into
Row fusion obtains oil truck oil and gas leakage speed nominal value, and oil truck oil and gas leakage speed class grader lets out oil truck oil gas
Leakage speed nominal value is classified.
Further Technological improvement plan is the present invention：
The input of the multiple GM (1,1) grey forecasting model is multiple test point gas concentration change rates, multiple GM (1,
1) grey forecasting model is predicted the oil and gas leakage speed of the multiple test points in oil truck surface, Fuzzy C-Means Clustering Algorithm
Grader classifies multiple GM (1,1) grey forecasting model output valve according to each test point oil and gas leakage velocity characteristic.
Further Technological improvement plan is the present invention：
Each type GM (1, the 1) grey forecasting model exports the input as each empirical mode decomposition model, respectively
The output per class GM (1,1) grey forecasting model is decomposed into low frequency trend part and multiple high frequency waves by a empirical mode decomposition model
Dynamic part, low frequency trend part and multiple high-frequency fluctuation parts respectively as every group of DRNN Network Prediction Model input, every group
The output equal weight phase of each DRNN Network Prediction Models and plus obtain the fusion forecasting value of every group of DRNN Network Prediction Model.
Further Technological improvement plan is the present invention：
The fusion forecasting value of multigroup DRNN Network Prediction Models is as oil truck oil and gas leakage speed T-S network multiple spots
The input of Fusion Model, oil truck oil and gas leakage speed T-S network multiple spot Fusion Models are to multigroup DRNN Network Prediction Models
Fusion forecasting value is merged to obtain oil truck oil and gas leakage speed nominal value, oil truck oil and gas leakage speed class grader pair
According to the size of oil truck oil and gas leakage speed nominal value be divided at high speed leakage, than fair speed leakage, medium speed leakage,
Low velocity leaks four alarm levels.
Compared with prior art, the present invention there is following obvious advantage：
First, the present invention predicts the time span of oil truck Leakage Energy gas velocity degree using multiple GM (1,1) grey forecasting model
It is long.The oil and gas leakage speed that can be monitored with GM (1,1) grey forecasting model model according to oil truck oil and gas leakage test point is joined
The multiple test point oil and gas leakage velocity amplitudes of numerical prediction future time instance oil truck, each monitoring point oil predicted in aforementioned manners
After gas leakage speed, multiple monitoring point oil and gas leakage velocity amplitudes are added again the original for being separately added into each monitoring point oil and gas leakage speed
In beginning ordered series of numbers, correspondingly remove a data modeling of ordered series of numbers beginning, then be predicted multiple monitoring point future oil truck oil gas
The prediction of leakage rate.The rest may be inferred, predicts oil truck oil and gas leakage velocity amplitude.This method is known as gray system mould
Type, it can realize the prediction of long period.Driver can more accurately grasp the variation tendency of oil truck oil and gas leakage speed,
Effectively to avoid oil and gas leakage from being ready.
2nd, the present invention builds fuzzy C-mean algorithm according to the characteristics of oil truck oil and gas leakage prediction of speed parameter differences between samples
Cluster (FCM) grader classify to oil truck oil and gas leakage speed multiple spot forecast sample parameter, design multiple EMD models and
Multigroup DRNN Network Prediction Models predict the sample parameter of oil truck oil and gas leakage prediction of speed again respectively, in oil truck
Among oil and gas leakage prediction of speed continuous and discrete process, take into full account oil truck oil and gas leakage speed when space characteristic,
Similar in the origin cause of formation, the data of relative homogeneous are extracted from the data of magnanimity level, and to establish, specific aim is stronger, can more react and appoint
Meaning time phase oil truck oil and gas leakage speed prediction model, improves precision of prediction.
3rd, original deformation GM (1,1) grey forecasting model is exported sequence by the present invention by empirical mode decomposition model (EMD)
Row are decomposed into the component of different frequency range, each component shows the different characteristic information lain in former sequence, to reduce
Sequence it is non-stationary.High frequency section data correlation is not strong, and frequency is higher, represents the ripple components of original series, has
Certain periodicity and randomness, this is consistent with the cyclically-varying of oil truck oil and gas leakage speed；Low-frequency component represents original
The variation tendency of sequence.It can be seen that EMD can decomposite the ripple components of oil truck oil and gas leakage speed, periodic component step by step and become
Gesture component, each component decomposited itself include identical deformation information, reduce different characteristic letter to a certain extent
Interfering between breath, and the original oil truck oil and gas leakage speed Deformation Series curve of each component variation curve ratio decomposited
It is smooth.It can be seen that EMD can effectively analyze the oil truck oil and gas leakage speed deformation data under multifactor collective effect, decompose
Each component is conducive to the foundation of DRNN Network Prediction Models and is better anticipated.It is pre- using DRNN networks are established respectively to each component
Model is surveyed, to avoid extreme learning machine from inputting random the problems such as being lost with component information that dimension is chosen, first to each heavy
Each component prediction result, is finally superimposed to obtain final fusion forecasting result by structure phase space.Case study shows, the fusion carried
Prediction result has higher precision of prediction.
4th, oil truck oil and gas leakage speed T-S network multiple spots Fusion Model of the present invention uses neutral net and fuzzy system
The connection mode of parallel connection type, they enjoy common input, and two parts of such network are not interfere with each other, can be with parallel processing number
According to improving the training speed of network, the advantages of which concentrates neutral net and fuzzy inference system at the same time, makes the Shandong of network
Rod and fault-tolerance are strengthened, and improve the approximation capability of T-S network models, and make the computing capability of system and extensive energy
Power greatly enhances, and realizes the fusion of the fusion forecasting value to multigroup DRNN Network Prediction Models, obtains whole oil truck oil gas and lets out
Leak the nominal value of monitoring point oil and gas leakage speed.
5th, the distribution space scope of present invention prediction oil truck oil and gas leakage speed is wide, including multiple monitoring nodes, can be with
Oil truck oil and gas leakage monitoring node is arranged in above oil truck compartment, following and trailing flank, can be to oil truck oil gas
The synchronization multiple spot monitoring of leakage rate and prediction, are realized to oil truck oil by oil truck oil and gas leakage speed Early-warning Model
The fusion of the multiple monitoring point oil and gas leakage speed of gas leakage, driver can predict the different prisons in synchronization oil truck compartment exactly
The exact value of the spatial distribution state of measuring point oil and gas leakage speed and whole oil truck oil and gas leakage speed, can be according to oil truck
The spatial distribution state of oil and gas leakage uses corresponding strategy.
6th, the present invention improves the science and reliability of oil truck oil and gas leakage velocity sorting, oil and gas leakage speed class
Grader is according to oil truck performance parameter, expertise and oil truck oil and gas leakage concerned countries standard, according to oil truck oil gas
The size of the nominal value of leakage rate carries out grade classification：At high speed leakage, than fair speed leakage, medium speed leakage and
Low velocity leaks four alarm levels, realizes the classification to oil truck oil and gas leakage speed state, improves oil truck oil and gas leakage
The science and reliability of speed early warning.
Brief description of the drawings
Fig. 1 is oil truck oil and gas leakage state parameter of the present invention collection and intelligent early-warning platform, wherein 1- detection nodes, 2-
On-site supervision end；
Fig. 2 is oil truck oil and gas leakage speed intelligent early-warning model of the present invention；
Fig. 3 is detection node functional diagram of the present invention；
Fig. 4 is this discovery field monitoring client software function diagram；
Fig. 5 is one group of DRNN Network Prediction Model of the invention；
Fig. 6 is oil truck oil and gas leakage speed T-S network multiple spot Fusion Models of the present invention；
Fig. 7 is oil truck oil and gas leakage state parameter of the present invention collection and intelligent early-warning platform plane layout drawing.
Embodiment
With reference to attached drawing 1-7, technical solution of the present invention is further described：
First, the design of system general function
The present invention devises a kind of oil truck oil and gas leakage speed intelligent early-warning system of wireless sensor network, realization pair
Oil truck compartment surface temperature, pressure and Leakage Energy gas speed parameter are detected intelligently pre- with oil truck oil and gas leakage speed
Alert, which is gathered intelligently pre- with intelligent early-warning platform and oil truck oil and gas leakage speed by oil truck oil and gas leakage state parameter
Alert model two parts composition.Oil truck oil and gas leakage state parameter is gathered includes detection node 1 and scene with intelligent early-warning platform
Monitoring client 2, they are built into wireless test and control network to realize between detection node 1 and on-site supervision end 2 in an ad-hoc fashion
Wireless communication；Detection node 1 by the oil truck of detection way state parameter be sent to on-site supervision end 2 and to sensing data into
Row preliminary treatment；On-site supervision end 2 shows oil truck oil and gas leakage speed early-warning parameters.Whole system structure is as shown in Figure 1.
2nd, the design of detection node
Using largely detection node 1 based on wireless sensor network as oil truck in way state parameter perception terminal,
Detection node 1 and on-site supervision end 2 are made up of the information interact system of wireless sensor network self-organizing.Detection node
1 includes collection oil truck surface temperature, pressure, the sensor of oil gas and corresponding signal conditioning circuit, MSP430 microprocessors
With NRF2401 wireless transport modules；The software of detection node mainly realizes wireless communication and oil truck in way conditions Ambient parameter
Collection and pretreatment.Software is designed using C programmer, and degree of compatibility is high, substantially increases the work of Software for Design exploitation
Efficiency, enhances the reliability, readability and portability of program code.Detection node structure is as shown in Figure 3.
3rd, on-site supervision end software
On-site supervision end 2 is an industrial control computer, and on-site supervision end 2, which is mainly realized, joins oil truck in way state
Number is acquired and oil truck oil and gas leakage speed intelligent early-warning model, realization and the information exchange of detection node 1, on-site supervision
2 major functions are held as messaging parameter setting, data analysis and data management and oil truck oil and gas leakage speed intelligent early-warning model.
The management software have selected Microsoft Visual++6.0 and come as developing instrument, the Mscomm communication controls of calling system
Communication program is designed, on-site supervision end software function is as shown in Figure 5.Oil truck oil and gas leakage speed intelligent early-warning modelling
Following steps：
1st, the design of multiple GM (1,1) grey forecasting models
The more traditional statistical prediction methods of gray prediction method have the advantages of more, it need not determine that predictive variable is
No Normal Distribution, it is not necessary to big sample statistic, it is not necessary to according to the change of oil truck oil and gas leakage speed input variable
Change and change prediction model at any time, by Accumulating generation technology, establish unified Differential Equation Model, cumulative oil truck oil gas is let out
Prediction result is drawn after the original value reduction of leakage speed, and Differential Equation Model has the precision of prediction of higher.Establish GM (1,1) grey
The essence of prediction model is to make one-accumulate generation to oil truck oil and gas leakage speed initial data, generation ordered series of numbers is presented certain
Rule, by establishing Differential Equation Model, tries to achieve matched curve, to be carried out to the multiple monitoring point oil and gas leakage speed of oil truck
Prediction.
2nd, cluster (FCM) grader of fuzzy C-mean algorithm
If finite aggregate X={ x1,x2,…xnIt is the set that n sample forms, they are oil truck oil and gas leakage speed respectively
The maximum value average value and minimum of multiple GM (1,1) grey forecasting model output are spent, C is predetermined classification number, mi(i=1,
2 ... c) be each cluster center, μj(xi) it is degree of membership of i-th of sample on jth class, clustering criteria function is by being subordinate to
Function is defined as:
In formula:||xi-mj| | it is xiTo mjBetween Euclidean distance；B is FUZZY WEIGHTED power exponent, is that can control cluster
As a result the parameter of fog-level；M is the Fuzzy C Matrix dividing of X, and V is the cluster centre set of X, and the result of FCM clustering algorithms is just
It is to obtain the M and V for criterion function is reached minimum.In fuzzy C-means clustering method, it is desired to person in servitude of the sample to each cluster
The sum of category degree is 1, i.e.,:
The minimum of formula (1) is sought under conditions of formula (2), makes J (M, V) to mjAnd μj(xi) partial derivative be 0, can obtain minimum
The necessary condition of value is:
FCM algorithms can be completed according to following iterative step:
A, clusters number c and parameter b is set, algorithm terminates threshold epsilon, iterations t=1, and permission greatest iteration number is
tmax；
B, each cluster centre m is initializedi；
C, membership function is calculated according to formula (4) with current cluster centre；
D, with current membership function all kinds of cluster centres are updated by formula (3)；
E, suitable matrix norm is chosen, if | | V (t+1)-V (t) | |≤ε or t >=tmax, stop computing；Otherwise, t
=t+1, return to step C.
When algorithmic statement, all kinds of cluster centres and each GM (1,1) grey forecasting model output valve sample pair are obtained
In all kinds of degrees of membership, fuzzy clustering division is completed.Fuzzy clustering result is finally subjected to de-fuzzy, fuzzy clustering is changed
Classify for certainty, realize that final GM (1,1) grey forecasting model output valve is classified.
3rd, empirical mode decomposition modelling
Empirical mode decomposition (EMD) is a kind of self-adapting signal screening technique, have calculate it is simple, directly perceived, based on experience
And the characteristics of adaptive.The trend step-sizing that it can will be present in different characteristic in signal comes out, and obtains multiple high-frequency fluctuations
Partly (IMF) and low frequency trend part.EMD decomposite come IMF components contain signal from high to low different frequency sections into
Point, the frequency resolution that each frequency band includes changes in itself with signal, has adaptive multiresolution analysis characteristic.Use
The purpose that EMD is decomposed is exactly to more accurately extract fault message.IMF components must simultaneously meet two conditions：1. treating
In decomposed signal, the number of extreme point is equal with the number of zero crossing, or at most difference one；2. on any time, by office
The envelope average that portion's maximum and local minimum define is zero.Empirical mode decomposition method is directed to GM (1,1) gray prediction mould
" screening " process steps of type output valve output value signal are as follows：
(1) all Local Extremums of GM (1,1) grey forecasting model output value signal are determined, then with cubic spline line
The Local modulus maxima of left and right is connected to form coenvelope line.
(2) local minizing point of GM (1,1) grey forecasting model output valve output valve is being connected with cubic spline line
Get up to be formed lower envelope line, upper and lower envelope should all data points of envelope.
(3) average value of upper and lower envelope is denoted as m1(t), obtain：
x(t)-m1(t)=h1(t) (5)
X (t) is NARX neural network prediction model output valve original signals, if h1(t) it is an IMF, then h1(t)
It is exactly first IMF component of x (t).Remember c1(t)=h1k(t), then c1(t) first for signal x (t) meets IMF conditions
Component.
(4) by c1(t) separate, obtain from x (t)：
r1(t)=x (t)-c1(t) (6)
By r1(t) initial data repeat step (1)-step (3) is used as, obtains the 2nd point for meeting IMF conditions of x (t)
Measure c2.Repetitive cycling n times, obtains the n components for meeting IMF conditions of signal x (t).So experience decomposition model just GM (1,
1) grey forecasting model output valve resolves into low frequency trend part and multiple high-frequency fluctuation parts.
4th, multigroup DRNN Network Prediction Models design
Each DRNN Network Prediction Models are a kind of Dynamic Recurrent Neural Networks with feedback and adaptation time-varying characteristics
Ability, the network more directly can vivo reflect oil truck tank leakage oil gas dynamics properties, can accurately predict oil
Tank car oil and gas leakage speed, the 3 layer network structures of each DRNN networks 3-7-1, its hidden layer are recurrence layer.Fig. 5 is oil truck oil
Tank Leakage Energy gas velocity degree DRNN Network Prediction Model structure charts.In this patent DRNN Network Prediction Models, if I=[I1(t),I2
(t),…,In(t)] it is network inputs vector, wherein Ii(t) it is i-th of nerve of oil and gas leakage prediction model DRNN network input layers
The input of first t moment, the output for returning j-th of neuron of layer is Xj(t), Sj(t) it is j-th of recurrent nerve member input summation, f
() is the function of S, then O (t) is the output of DRNN networks.Then the output layer of DRNN Network Prediction Models, which exports, is：
Each empirical mode decomposition model the output per class GM (1,1) grey forecasting model be decomposed into low frequency trend part and
Input of multiple high-frequency fluctuation parts as every group of DRNN Network Prediction Model, every group of DRNN Network Prediction Model are realized to every class
GM (1,1) grey forecasting model output valve predicts that every group of DRNN Network Prediction Models output equal weight adds up and be every group again
The fusion forecasting value of DRNN Network Prediction Models, structure chart is as shown in Figure 5.
5th, oil truck oil and gas leakage speed T-S networks multiple spot Fusion Model
(1), former piece network.1st layer is input layer, and the number of nodes of this layer is n.2nd layer is blurring layer, to input data
It is blurred, each neuron performs corresponding membership function3rd layer is fuzzy rule
Layer.4th layer of nodal point number is m, which realizes that normalization calculates.
(2), consequent network.1st layer is input layer, wherein the input value x of the 0th node0=1, its effect is to provide
The constant term of fuzzy rule consequent.2nd layer has m node, its effect is to calculate each consequent：
3rd layer of computing system output：
The central value c of the 2nd layer of membership function is adjusted by Learning AlgorithmsjWith width bjAnd consequent network
Connection weight pjk, for simplicity, by parameter pjkFixed, at this moment the consequent per rule becomes one layer in structure is simplified
Connection weight.The simplification structure and the fuzzy neural network of conventional model have identical structure, can apply mechanically conventional model
Result of calculation.Oil truck oil and gas leakage speed T-S network multiple spots Fusion Model is as shown in Figure 6.
6th, oil truck oil and gas leakage speed class grader
Oil truck oil and gas leakage speed class grader is let out according to oil truck performance parameter, expertise and oil truck oil gas
Leak concerned countries mark, the nominal value of oil truck oil and gas leakage speed is less than or equal to 0.2, less than or equal to 0.5, less than or equal to 0.7
It is worth with less than or equal to 1.0, the state for corresponding to oil and gas leakage speed respectively is：Low velocity leakage, medium speed's leakage, compare higher speed
Degree leakage and at high speed four kinds of alert status of leakage, realize the classification to oil truck oil and gas leakage speed state grade.
4th, the collection of oil truck oil and gas leakage state parameter and intelligent early-warning platform plane layout drawing
Detection node, prison are placed in the upper surface of oil truck oil tank, lower surface and front and back sides according to the structure of oil truck
Surveying oil truck, whether leakage situation occurs for oil tank temperature, pressure and gas in way is travelled.System arranges detection node 1 and shows
The horizontal layout installation diagram of monitoring client 2, wherein 1 equilibrium of detection node be arranged in detected oil truck oil tank upper and lower surface and
Front and back sides, whole system horizontal layout as shown in Figure 7, wherein upper and lower surface layout layout with front and back sides it is similar, in figure
Explained so that upper surface is layouted as an example, by collection of the system realization to oil truck oil tank oil and gas leakage parameter and in way oil
Gas leakage speed carries out early warning.
Technological means disclosed in the present invention program is not limited only to the technological means disclosed in the above embodiment, further includes
Formed technical solution is combined by above technical characteristic.It should be pointed out that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (4)
1. a kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network, is realized to oil truck on way
Parameter is detected and oil truck oil and gas leakage speed intelligent early-warning, it is characterised in that：The intelligent early-warning system includes oil tank
Car oil and gas leakage state parameter gathers and intelligent early-warning platform, oil truck oil and gas leakage speed intelligent early-warning model；
The oil truck oil and gas leakage state parameter collection is with intelligent early-warning platform by multiple detection nodes and on-site supervision end group
Into, and oil truck is built into an ad-hoc fashion in the collection of way oil and gas leakage state parameter and intelligent early-warning platform；Detection node
It is made of sensor group module, microcontroller and wireless communication module NRF2401, is responsible for temperature, the pressure on detection oil truck surface
With gas concentration and the actual value of speed, on-site supervision end is realized and oil truck oil and gas leakage parameter is managed and to oil truck
On way, Leakage Energy gas velocity degree carries out early warning；
The oil truck oil and gas leakage speed intelligent early-warning model includes multiple GM（1,1）Grey forecasting model, Fuzzy C-means
Clustering algorithm grader, multiple empirical mode decomposition models, multigroup DRNN Network Prediction Models, oil truck oil and gas leakage speed T-
S network multiple spot Fusion Models and oil truck oil and gas leakage speed class grader；Each GM（1,1）Grey forecasting model exports
As the input of Fuzzy C-Means Clustering Algorithm grader, Fuzzy C-Means Clustering Algorithm grader is to multiple GM（1,1）Grey
Prediction model output is classified, each type of GM（1,1）Grey forecasting model output is used as each empirical mode decomposition mould
The input of type, the input of multiple outputs of each empirical mode decomposition model as every group of DRNN Network Prediction Model, every group
Input of the fusion forecasting value of DRNN Network Prediction Models as oil truck oil and gas leakage speed T-S network multiple spot Fusion Models,
Oil truck oil and gas leakage speed T-S network multiple spot Fusion Models melt the fusion forecasting value of multigroup DRNN Network Prediction Models
Conjunction obtains oil truck oil and gas leakage speed nominal value, and oil truck oil and gas leakage speed class grader is to oil truck oil and gas leakage speed
Degree nominal value is classified.
A kind of 2. oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network according to claim 1
System, it is characterised in that：The multiple GM（1,1）The input of grey forecasting model is multiple test point gas concentration change rates, more
A GM（1,1）Grey forecasting model is predicted the oil and gas leakage speed of the multiple test points in oil truck surface, Fuzzy C-means
Clustering algorithm grader is according to each test point oil and gas leakage velocity characteristic to multiple GM（1,1）Grey forecasting model output valve into
Row classification.
3. a kind of oil truck oil and gas leakage speed based on wireless sensor network according to claim 1 or 2 is intelligently pre-
Alert system, it is characterised in that：The each type GM（1,1）Grey forecasting model output is used as each empirical mode decomposition model
Input, each empirical mode decomposition model is per class GM（1,1）Grey forecasting model output be decomposed into low frequency trend part and
Multiple high-frequency fluctuation parts, low frequency trend part and multiple high-frequency fluctuation parts are respectively as every group of DRNN Network Prediction Model
Input, the output equal weight phase of every group of each DRNN Network Prediction Model and add obtain the fusion of every group of DRNN Network Prediction Model
Predicted value.
4. a kind of oil truck oil and gas leakage speed based on wireless sensor network according to claim 1 or 2 is intelligently pre-
Alert system, it is characterised in that：The fusion forecasting value of multigroup DRNN Network Prediction Models is as oil truck oil and gas leakage speed
The input of T-S network multiple spot Fusion Models, oil truck oil and gas leakage speed T-S network multiple spot Fusion Models are to multigroup DRNN networks
The fusion forecasting value of prediction model is merged to obtain oil truck oil and gas leakage speed nominal value, oil truck oil and gas leakage speed etc.
Level grader to according to the size of oil truck oil and gas leakage speed nominal value be divided at high speed leakage, than fair speed leakage, in
Uniform velocity leakage, low velocity leak four alarm levels.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201711159727.4A CN107976934B (en) | 2017-11-20 | 2017-11-20 | A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711159727.4A CN107976934B (en) | 2017-11-20 | 2017-11-20 | A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network |
Publications (2)
| Publication Number | Publication Date |
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| CN110716012A (en) * | 2019-09-10 | 2020-01-21 | 淮阴工学院 | Oil gas concentration intelligent monitoring system based on field bus network |
| CN114399024A (en) * | 2021-12-20 | 2022-04-26 | 淮阴工学院 | Oil gas concentration big data intelligent detection system |
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