CN107341576A - A kind of visual air pollution of big data is traced to the source and trend estimate method - Google Patents
A kind of visual air pollution of big data is traced to the source and trend estimate method Download PDFInfo
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Abstract
The invention provides a kind of visual air pollution of big data to trace to the source and trend estimate method, including：With reference to air diffusion conditions as the input factor, the multilayer feedforward neural network trained using error backpropagation algorithm, the feed forward models traced to the source for air pollution with trend estimate are established；Perform the learning process of feed forward models, the factor is wherein trained using input of the air pollution historical data as feed forward models, use the steepest descent method of error performance function, the weights and threshold value of network feed forward models are gradually adjusted by the backpropagation of error, so that the output error of network feed forward models constantly reduces.
Description
Technical field
The present invention relates to big data field, machine learning field, Computer Simulation field, field of environment protection, Yi Jiji
Calculation machine interactive system field, it is more particularly related to which a kind of visual air pollution of big data traces to the source and moved towards pre-
Survey method.
Background technology
In recent years, repeatedly outburst wide range of haze, haze physics are mainly PM2.5, PM10 etc. for China, chemical composition
Predominantly carbon, sulfate, lead, arsenic, cadmium, copper etc..For this haze, human respiratory system can not effectively defend, and seriously endanger
Health.
To the measure of pollutant, there are three kinds of methods in industry：
1st, gravimetric method：Principle is that constant speed extracts determined volume air, is trapped within the PM2.5 in surrounding air and PM10
On the filter membrane of known quality, according to quality, Volume Changes before and after filter membrane sampling, PM2.5 and PM10 concentration is calculated.
2nd, succusion：Principle is that the mass change of filter membrane is led when sampling air flow is deposited on filter membrane by filter membrane, particulate matter
The change of filter membrane frequency of oscillation is caused, the quality for being deposited on particulate matter on filter membrane is calculated by frequency of oscillation change.
3rd, ray method：Principle is that particulate matter is deposited in filter membrane when sampling air flow is deposited on filter membrane by filter membrane, particulate matter
On, when β rays pass through filter membrane, ray energy decay, by attenuation measure and calculation particle concentration.
China mainly uses gravimetric method to the measure of Atmospheric particulates at present, and sampling environment and sample frequency will be according to
HJ.T194 requirement performs.Disclosed official's air quality data is all from the environmental monitoring station of Chinese Ministry of Environmental Protection subordinate, and special messenger is regular
Sample, do composition of air examining report.Thus, the pre- of haze is carried out based on weather weather forecast, season, monitoring station historical data
Survey.
Environmental monitoring station's detection mode of existing Chinese Ministry of Environmental Protection subordinate, advantage be measure afterwards it is relatively accurate, but detect with
Based on Laboratory Instruments equipment, limited by cost and maintenance cost costliness, the human cost of special messenger's periodic sampling, official's prison
Survey station negligible amounts (year ends 2015, average each province was less than 10).Moreover, the air pollution index (API) using each city
Grading system, calculate evaluation of the urban air-quality number of days up to standard as the urban air-quality, have the measurement point time, region across
The characteristics of degree is big, more rough.
Due to these defects, tradition is caused to use static prediction method, based on weather weather forecast, season, traffic
Development degree, it is predicted with macro-indicators such as coal gas heating amount, this monitoring station historical datas, it is not fine accurate enough.
And for the real-time more accurate prediction for carrying out haze diffusion conditions, trend of microcosmic point, pollution source it is more accurate
Trace to the source and quickly handle, then need more dense real time data linkage analysis.
Official monitoring station quantity is few at present, density is low, practice property and the linkage level of informatization are relatively low, pollutes and supervises for government
Control is administered with hysteresis quality, lacks foresight, and real healthy path planning is carried out according to haze dispersal direction speed to the people
Guidance also lacks fine foundation.
The content of the invention
The technical problems to be solved by the invention are to be directed to have drawbacks described above in the prior art, there is provided a kind of big data can
Air pollution depending on change is traced to the source and trend estimate method.
According to the present invention, there is provided a kind of visual air pollution of big data is traced to the source and trend estimate method, including：
First step：With reference to air diffusion conditions as the input factor, the multilayer trained using error backpropagation algorithm
Feedforward neural network, establish the feed forward models traced to the source for air pollution with trend estimate；
Second step：The learning process of feed forward models is performed, wherein using air pollution historical data as feed forward models
Input training the factor, using the steepest descent method of error performance function, network is gradually adjusted by the backpropagation of error
The weights and threshold value of feed forward models, so that the output error of network feed forward models constantly reduces.
Preferably, the learning process of feed forward models includes following two stages：
The forward-propagating stage of the input training factor, wherein the input training factor enters network from input layer, through hidden layer
Afterwards, output layer is transmitted to, output signal is produced in output end, during this period, the weights and threshold values of each neuron of network keep constant,
Each layer of neuron only under the influence of one layer of neuron input and state, if not obtaining desired output valve in output end,
Network is the back-propagation phase for being transferred to error signal；
The back-propagation phase of error signal, wherein using the difference between the reality output of network and desired output as error
Signal, error signal successively return by output end, in this communication process, the weights and threshold values of each neuron of network according to
Error feedback is adjusted；
Wherein, the forward-propagating stage of the input training factor and the back-propagation phase alternate cycles of error signal are carried out,
In every completion one cycle pollution trend estimate is carried out using real-time indicators.
Preferably, the visual air pollution of the big data is traced to the source also includes third step with trend estimate method：It is logical
Cross the association correction that genetic algorithm carries out feed forward models.
Preferably, in third step, new sample is inputted every time, according to cross-validation method principle, calculates SVM classifier
Discrimination, Fitness analysis is carried out, do not set the stop value of genetic algorithm, end condition is used than supreme people's court, if wherein training
Discrimination higher than existing, be set to optimized parameter, otherwise perform selection, intersect and mutation operation is trained with further optimization and joined
Number.
Preferably, the visual air pollution of the big data is traced to the source also includes four steps with trend estimate method：It is logical
Prediction result is presented with crossing Modular Data interface various dimensions and result of tracing to the source.
Preferably, feed forward models include input layer, hidden layer and output layer.
Preferably, input the factor include wind speed, wind-force, wind direction, temperature, the temperature difference, air pressure, rainfall, one in cloud amount or
It is multiple.
Preferably, in the back-propagation phase of error signal, the activation primitive of counterpropagation network selects Sigmoid letters
Number
By the effect of activation primitive, the P training samples information of input travels on implicit layer unit first, passes through
F (u) effect obtains the output information of j-th of neuron of hidden layer
In formula：WijRepresent i-th of neuron of input layer to the weights of j-th of neuron of hidden layer, XPRepresent the P sample
In i-th of input value of input layer,Represent the threshold value of j-th of neuron of hidden layer.
Preferably, the desired value of pollutant is respectively yj=(y1j,y2j,y3j), corresponding weight coefficient vector is w=(w1,
w2,w3), wherein w is a decimal in the range of (- 1,1), and wherein first carries out subjective weights to w according to coefficient correlation, so
Adjustment is trained to w based on objective weighted model afterwards.
Preferably, capacitive equipments are introduced to weigh influence size of each factor to destination layer, judgment matrix defined in it
A：
If weight vectors are W, now W is 1 × (n+1) vectors, i.e.,：W=(w1,w2,w3L wn+1)。
Brief description of the drawings
With reference to accompanying drawing, and by reference to following detailed description, it will more easily have more complete understanding to the present invention
And be more easily understood its with the advantages of and feature, wherein：
Fig. 1 schematically shows the visual air pollution of big data according to the preferred embodiment of the invention and traces to the source and walk
To the flow chart of Forecasting Methodology.
Fig. 2 schematically shows the visual air pollution of big data according to the preferred embodiment of the invention and traces to the source and walk
The example of the feed forward models used to Forecasting Methodology.
Fig. 3 schematically shows the actual value of air pollution index API values and the predicted value of air pollution index API values
Between contrast.
It should be noted that accompanying drawing is used to illustrate the present invention, it is not intended to limit the present invention.Pay attention to, represent that the accompanying drawing of structure can
It can be not necessarily drawn to scale.Also, in accompanying drawing, same or similar element indicates same or similar label.
Embodiment
In order that present disclosure is more clear and understandable, with reference to specific embodiments and the drawings in the present invention
Appearance is described in detail.
Fig. 1 schematically shows the visual air pollution of big data according to the preferred embodiment of the invention and traces to the source and walk
To the flow chart of Forecasting Methodology.
As shown in figure 1, the visual air pollution of big data according to the preferred embodiment of the invention is traced to the source and trend estimate
Method includes：
First step S1：With reference to air diffusion conditions as the input factor, trained using error backpropagation algorithm more
Layer feedforward neural network, establishes the feed forward models traced to the source for air pollution with trend estimate；
For example, the input factor includes wind speed, wind-force, wind direction, temperature, the temperature difference, air pressure, rainfall, cloud amount etc..For example, wind speed
According to according to Pu Shi wind speed scale tables, division " 0 grade, 1 grade, 2 grades ..., 12 grades, more than 12 grades "；Wind direction is divided into " north wind, northeast
Wind, east wind, southeaster, south wind, southwester, west wind, northwester "；Rainfall has a discreteness characteristic, therefore using " no rain, small
Rain/shower, moderate rain, heavy rain, heavy rain, torrential rain, extra torrential rain " represents；Cloud amount is using " fine, partly cloudy, cloudy with some sunny periods, cloudy, cloudy
It asks cloudy, cloudy " represent.Other input parameters, as hidden neuron.
Moreover, specifically, feed forward models include input layer, hidden layer and output layer, such as shown in Figure 2.
Second step S2：The learning process of feed forward models is performed, wherein using air pollution historical data as feedforward mould
The input training factor of type, using the steepest descent method of error performance function, gradually net is adjusted by the backpropagation of error
The weights and threshold value of network feed forward models, so that the output error of network feed forward models constantly reduces；
Specifically, in this step, there is multifactor property, uncertainty, randomness, nonlinear air pollution history
Data train the factor as the input of feed forward models.
More specifically, the learning process of feed forward models includes following two stages：
1. the forward-propagating stage of the input training factor, wherein the input training factor enters network from input layer, through implicit
After layer, output layer is transmitted to, output signal is produced in output end, during this period, the weights and threshold values of each neuron of network are kept not
Become, each layer of neuron only under the influence of one layer of neuron input and state, if not obtaining desired output in output end
Value, network are the back-propagation phase for being transferred to error signal.
2. the difference between the reality output and desired output of the back-propagation phase of error signal, wherein network is error letter
Number, error signal successively returns by output end, and in this communication process, the weights and threshold values of each neuron of network are by error
Feedback is adjusted according to certain rule.
Two above stage alternate cycles are carried out, in every completion one cycle using real-time nearby observation station, wind-force
Pollution trend estimate is carried out with indexs such as wind directions.And it is possible to it is modified as described below using genetic algorithm.
Each each pollution factor of pollution factor data judging and the correlation of pollutant concentration can be used.Wherein, each pollution
The coefficient correlation of the factor and dependent variable pollutant concentration is not 0, i.e., each pollution factor and dependent variable various degrees
Related (positive correlation or negative correlation).Therefore, each pollution factor can all be calculated as the variable of linear regression model (LRM).
Preferably, as shown in figure 1, third step S3 can be performed：The association school of feed forward models is carried out by genetic algorithm
Just；
Specifically, new sample is inputted every time, according to cross-validation method principle, calculates SVM (support vector
Machine, SVMs) grader discrimination, Fitness analysis is carried out, does not set the stop value of genetic algorithm, terminates bar
Part is used than supreme people's court, if the discrimination of training higher than be set to optimized parameter, otherwise perform selection, intersection and make a variation etc. if existing
Operation further optimizes training parameter.
Lasting training subdivision, SVM classifier fitness function f (x are carried out with reference to big datai)=min (1-g (xi)), its
InAccuracy is divided to sample for SVM classifier.
Preferably, as shown in figure 1, four steps S4 can be performed：Presented by Modular Data interface multi-dimensional free
Finer prediction, result of tracing to the source.
Preferably, in the back-propagation phase of error signal, backpropagation (BackPropagation) BP networks swash
Function living selects Sigmoid functions
By the effect of activation primitive, the P training samples information of input can be traveled on implicit layer unit first,
The output information of j-th of neuron of hidden layer is obtained by f (u) effect
In formula：WijRepresent i-th of neuron of input layer to the weights of j-th of neuron of hidden layer, XPRepresent the P sample
In i-th of input value of input layer,Represent the threshold value of j-th of neuron of hidden layer.
The desired value of pollutant is respectively：
yj=(y1j,y2j,y3j)
Weight coefficient vector is accordingly：
W=(w1,w2,w3)
Wherein w is a decimal in the range of (- 1,1), wherein first carrying out subjective weights to w according to coefficient correlation, then
Enabling legislation (objective weighted model) based on " indicator difference " is trained adjustment to w.
To weigh influence size of the various factors to destination layer, capacitive equipments are introduced, define judgment matrix A
If weight vectors are W, now W is 1 × (n+1) vectors.
I.e.：W=(w1,w2,w3L wn+1)。
Illustrate referring to Fig. 3 curve comparison, by being trained to above-mentioned historical data, air is represented using curve 100
The actual value of pollution index API values, the predicted value of air pollution index API values is represented using curve 200, as seen from Figure 3, although
Also there is certain error in prediction, but very close.
It should be noted that unless otherwise indicated, otherwise the term in specification " first ", " second ", " the 3rd " etc. are retouched
The each component being used only in differentiation specification, element, step etc. are stated, without being intended to indicate that each component, element, step
Between logical relation or ordinal relation etc..
It is understood that although the present invention is disclosed as above with preferred embodiment, but above-described embodiment and it is not used to
Limit the present invention.For any those skilled in the art, without departing from the scope of the technical proposal of the invention,
Many possible changes and modifications are all made to technical solution of the present invention using the technology contents of the disclosure above, or are revised as
With the equivalent embodiment of change.Therefore, every content without departing from technical solution of the present invention, the technical spirit pair according to the present invention
Any simple modifications, equivalents, and modifications made for any of the above embodiments, still fall within the scope of technical solution of the present invention protection
It is interior.
Claims (10)
1. a kind of visual air pollution of big data is traced to the source and trend estimate method, it is characterised in that including：
First step：With reference to air diffusion conditions as the input factor, the multilayer feedforward trained using error backpropagation algorithm
Neutral net, establish the feed forward models traced to the source for air pollution with trend estimate；
Second step：The learning process of feed forward models is performed, wherein using air pollution historical data as the defeated of feed forward models
Enter to train the factor, using the steepest descent method of error performance function, feedovered by the backpropagation of error gradually to adjust network
The weights and threshold value of model, so that the output error of network feed forward models constantly reduces.
2. the visual air pollution of big data according to claim 1 is traced to the source and trend estimate method, it is characterised in that
The learning process of feed forward models includes following two stages：
In the forward-propagating stage of the input training factor, wherein the input training factor enters network from input layer, after hidden layer, pass
To output layer, output signal is produced in output end, during this period, the weights and threshold values of each neuron of network keep constant, each
Layer neuron only under the influence of one layer of neuron input and state, if not obtaining desired output valve, network in output end
It is transferred to the back-propagation phase of error signal；
The back-propagation phase of error signal, wherein believing the difference between the reality output of network and desired output as error
Number, error signal successively returns by output end, and in this communication process, the weights and threshold values of each neuron of network are according to by mistake
Difference feedback is adjusted；
Wherein, the forward-propagating stage of the input training factor and the back-propagation phase alternate cycles of error signal are carried out, every
When completing one cycle pollution trend estimate is carried out using real-time indicators.
Exist 3. the visual air pollution of big data according to claim 1 or 2 is traced to the source with trend estimate method, its feature
In also including third step：The association that feed forward models are carried out by genetic algorithm corrects.
4. the visual air pollution of big data according to claim 3 is traced to the source and trend estimate method, it is characterised in that
In third step, new sample is inputted every time, according to cross-validation method principle, is calculated SVM classifier discrimination, is adapted to
Degree is assessed, and does not set the stop value of genetic algorithm, and end condition is used than supreme people's court, if wherein the discrimination of training is higher than existing
Optimized parameter is then set to, otherwise performs selection, intersection and mutation operation further to optimize training parameter.
Exist 5. the visual air pollution of big data according to claim 1 or 2 is traced to the source with trend estimate method, its feature
In also including four steps：Prediction result is presented by Modular Data interface various dimensions and result of tracing to the source.
Exist 6. the visual air pollution of big data according to claim 1 or 2 is traced to the source with trend estimate method, its feature
In feed forward models include input layer, hidden layer and output layer.
Exist 7. the visual air pollution of big data according to claim 1 or 2 is traced to the source with trend estimate method, its feature
In the input factor includes one or more of wind speed, wind-force, wind direction, temperature, the temperature difference, air pressure, rainfall, cloud amount.
8. the visual air pollution of big data according to claim 2 is traced to the source and trend estimate method, it is characterised in that
In the back-propagation phase of error signal, the activation primitive of counterpropagation network selects Sigmoid functions
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In formula：WijRepresent i-th of neuron of input layer to the weights of j-th of neuron of hidden layer, XPRepresent the P sample defeated
Enter i-th of input value of layer,Represent the threshold value of j-th of neuron of hidden layer.
9. the visual air pollution of big data according to claim 8 is traced to the source and trend estimate method, it is characterised in that
The desired value of pollutant is respectively yj=(y1j,y2j,y3j), corresponding weight coefficient vector is w=(w1,w2,w3), wherein w is
A decimal in the range of (- 1,1), and subjective weights first wherein are carried out to w according to coefficient correlation, it is then based on Objective Weight
Method is trained adjustment to w.
Exist 10. the visual air pollution of big data according to claim 9 is traced to the source with trend estimate method, its feature
In introducing capacitive equipments and weigh influence size of each factor to destination layer, judgment matrix A defined in it：
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If weight vectors are W, now W is 1 × (n+1) vectors, i.e.,：W=(w1,w2,w3L wn+1)。
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| CN109115949A (en) * | 2018-07-26 | 2019-01-01 | 郑州轻工业学院 | Pollution source tracing method and computer-readable medium based on big data |
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