CN113657651A - Diesel vehicle emission prediction method, medium and equipment based on deep migration learning - Google Patents
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Abstract
The invention relates to a diesel vehicle emission prediction method, medium and equipment based on deep migration learning, which comprises the steps of constructing a diesel vehicle emission prediction model by utilizing computer equipment, and then predicting NOx emission of a target domain vehicle by using the model; the construction steps of the prediction model are as follows: s10: acquiring source domain vehicle related data and target domain vehicle driving related data and preprocessing the acquired data; s20: the pre-training feature projection module is used for projecting the source domain vehicle features and the target domain vehicle features into a public subspace; s30: the pre-training tail gas prediction module is used for building a full-connection neural network prediction model with double hidden layers by using the reconstructed source domain data characteristics and labels; s40: and combining the pre-trained projection module and the prediction module to construct a migration model, considering the data distribution difference between the source domain vehicle and the target domain vehicle, adding KL divergence between the source domain and the target domain into the loss function part, enabling the target domain data distribution to be close to the source domain data distribution, and finely adjusting the whole model.
Description
Technical Field
The invention relates to the technical field of diesel vehicle exhaust prediction in the field of environmental monitoring, in particular to a diesel vehicle emission prediction method, medium and equipment based on deep migration learning.
Background
With the development of human society, the usage amount of motor vehicles is continuously increased, and the exhaust emission of the motor vehicles brings great harm to the environment. Therefore, it is necessary to strictly control the exhaust emission to realize effective treatment of the air environment. The exhaust emission of diesel vehicles in the exhaust pollution accounts for a considerable proportion, so that the monitoring of the exhaust emission of the diesel vehicles is particularly important.
Most of traditional diesel vehicle exhaust prediction schemes are based on deep learning or physical modeling, and exhaust prediction models are constructed by using relevant data of diesel vehicles during running. These methods often require a large amount of high-quality data to construct an accurate model, however, in actual monitoring, it is difficult to acquire a large amount of high-quality data. Therefore, most of data given to common predicted vehicle models is sparse. Aiming at sparse data, a reliable prediction model is established by utilizing an exhaust gas prediction model of an existing vehicle model and existing sparse data, so that a scheme for reducing time and economic cost in data monitoring is very necessary.
Disclosure of Invention
The diesel vehicle emission prediction method, medium and equipment based on deep migration learning can solve the problem of poor diesel vehicle exhaust prediction accuracy under the condition of insufficient training data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a diesel vehicle emission prediction method based on deep migration learning comprises the following steps:
the method comprises the steps of constructing a diesel vehicle emission prediction model by utilizing computer equipment, and then predicting NOx emission of a target domain vehicle by using the model;
wherein, the construction of the diesel vehicle emission prediction model by utilizing the computer equipment comprises the following steps,
s10: acquiring source domain vehicle related data and target domain vehicle driving related data and preprocessing the acquired data;
s20: the pre-training feature projection module is used for projecting the source domain vehicle features and the target domain vehicle features into a public subspace;
s30: the pre-training tail gas prediction module is used for building a full-connection neural network prediction model with double hidden layers by using the reconstructed source domain data characteristics and labels;
s40: and combining the pre-trained projection module and the prediction module to construct a migration model, considering the data distribution difference between the source domain vehicle and the target domain vehicle, adding KL divergence between the source domain and the target domain to the loss function part to enable the target domain data distribution to be close to the source domain data distribution, and finely adjusting the whole model to obtain a stable diesel vehicle emission prediction model.
In another aspect, the present invention also discloses a computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method as described above.
In a third aspect, the present invention also discloses a computer device comprising a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method as described above.
According to the technical scheme, the diesel vehicle emission prediction method based on deep migration learning is a field-adaptive method, the concentration of NOx is estimated under the condition that the related data of the NOx of the diesel vehicle is sparse, the accuracy is high, the method migrates the high-quality NOx data of other diesel vehicles, does not need to collect the sparse data, can greatly reduce the cost, is good in real-time performance and high in practicability, and is beneficial to popularization.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a network model of the method of the present invention;
FIG. 3 is a line graph illustrating accuracy verification of reconstructed features according to an embodiment of the present invention;
FIG. 4 is a predicted line graph according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
As shown in fig. 1, the method for predicting the emission of a diesel vehicle based on deep migration learning according to the embodiment includes:
s10: acquiring source domain vehicle related data with sufficient training data and target domain vehicle related data with sparse training data during driving, and preprocessing the acquired data;
s20: the pre-training feature projection module is used for projecting the source domain vehicle features and the target domain vehicle features into a public subspace;
s30: the pre-training tail gas prediction module is used for building a full-connection neural network prediction model with double hidden layers by using the reconstructed source domain data characteristics and labels;
s40: and combining the pre-trained projection module and the prediction module to construct a migration model, considering the data distribution difference between the source domain vehicle and the target domain vehicle, adding KL divergence between the source domain and the target domain into the loss function part, enabling the target domain data distribution to be close to the source domain data distribution, and finely adjusting the whole model.
The following is a detailed description:
the S10 obtains source domain vehicle-related data with sufficient training data and target domain vehicle-related data with sparse training data during driving, and preprocesses the collected data, specifically including the following steps:
s11: various data of the source domain diesel vehicle during running are obtained through vehicle-mounted exhaust gas detection equipment (PEMS), and a small amount of data of the target domain diesel vehicle during running are collected.
The data collected from PEMS includes license plate, terminal number, engine speed, actual output torque percentage, engine water temperature, engine fuel temperature, engine oil temperature, aftertreatment downstream NOx value, aftertreatment downstream oxygen percentage, barometric pressure, ambient temperature, aftertreatment exhaust gas mass flow, urea tank liquid level percentage, urea tank temperature, vehicle speed, accelerator pedal opening, single trip mileage, total mileage, engine instantaneous fuel injection, engine instantaneous fuel consumption, engine average fuel consumption, engine accumulated fuel consumption, battery voltage, fuel tank liquid level, engine accumulated run time, longitude, latitude, SCR upstream temperature, SCR downstream temperature;
s12: and carrying out abnormal value processing, missing value processing, non-correlation data deletion and normalization operation on the data.
The S20 pre-training feature projection module projects the source domain vehicle features and the target domain vehicle features into a common subspace, and specifically includes:
s21: training a sparse self-encoder, taking the feature data of a source domain vehicle and a target domain vehicle as input and output actual values, setting the number of neurons of a hidden layer to be three times of the input, and setting a loss function as follows:
wherein
In order to be the initial source-domain feature,
to reconstruct the source domain features, DKLIn order to solve the KL divergence function, rho is the preset neuron activation probability,
w is the weight, and beta and lambda are empirical parameters;
s22: minimizing the loss function in S21 by gradient descent, reversely propagating and updating the weight, stopping iteration after network convergence, and retaining the weight from the input layer to the hidden layer, which is marked as W1Output the hidden layer h1Retention, h1The calculation formula is as follows:
h1＝fSAE_1(W1,Xs)＝W1·Xs，
s23: outputting S22 hidden layer h1Training as new sparse self-encoder input according to S22, after network convergence, keeping weight input layer to hidden layer weight, and recording as W2Output the hidden layer h2Retention, h2The calculation formula is as follows:
h2＝fSAE_2(W2,h1)＝W2·h1
s24: and then taking the output of the S23 sparse self-coding hidden layer as the input of a new sparse self-coder, training according to S23, after network convergence, keeping the weight from the input layer to the hidden layer, and recording as W3(ii) a Outputting the hidden layer h3Retention, h3The calculation formula is as follows:
h3＝fSAE_3(W3,h2)＝W3·h2
s25: stacking sparse autoencoders, with W1,W2,W3Adding an output layer (weight random initialization) as an initial weight, constructing a multi-layer sparse self-encoder, taking a loss function as a first item in S21, and after reverse iteration integral optimization, keeping an updated weight W1',W2',W3'
S26: verifying the reliability of the reconstruction characteristics, respectively establishing a source domain prediction model by using the source domain original characteristics and the reconstruction characteristics, selecting 100 samples as a test set, and enabling the rest samples to be 8: 2 into training and validation sets, the prediction results over 100 samples are shown in fig. 3, and the NOx prediction information using the original and reconstructed features as inputs on the source domain data, respectively, and the true values of the selected 100 source domain sample points are given in fig. 3. As can be seen from FIG. 3, the accuracy of the reconstruction characteristics is effectively verified by the high coincidence of the respective predicted values and the true values of the two input training networks;
the S30 pre-training exhaust gas prediction module builds a double-hidden-layer fully-connected neural network prediction model by using the reconstructed source domain data characteristics and labels, and comprises the following specific steps:
s31: based on S24, using reserved third-layer hidden layer output data as input of a prediction model, using a downstream NOx value as output, constructing a double-hidden-layer fully-connected neural network, selecting Relu as an activation function, selecting Adam as an optimizer, setting the iteration number to 15000 times, and stopping iteration when the loss value is not reduced for more than 10 continuous times;
s32: minimizing the Mean Square Error (MSE) of the real NOx value and the predicted value, and updating hidden layer weights W4, W5 and W6 through back propagation until convergence;
the S40 combines the pre-trained projection module and the prediction module to construct a migration model, considers the data distribution difference between the source domain vehicle and the target domain vehicle, adds the KL divergence between the source domain and the target domain in the loss function part to enable the target domain data distribution to be close to the source domain, and finely adjusts the whole model by the following specific steps:
s41: based on the weight W of steps S20, S301',W2',W3',W4,W5,W6Multiplexing the weights, building a 6-layer migration neural network, inputting various characteristics of a source domain vehicle and a target domain vehicle, and predicting an emission value with a label of NOx;
s42: by adding KL divergence in the loss function and minimizing the loss function, the distribution difference of the source domain vehicle and the target domain vehicle in the subspace is reduced, the similarity of the source domain vehicle and the target domain vehicle is improved, and therefore knowledge transfer from the source domain vehicle to the target domain vehicle is achieved, and the loss function is as follows:
wherein
NO for i-th sample of source domain vehiclexThe value of the one or more of the one,
is the ith vehicle NO of the source area vehiclexBeta is an empirical parameter, DKLTo solve the KL divergence function, psFor the feature distribution of the source domain features in the subspace, ρtIs the characteristic distribution of the target domain vehicle among the subspaces.
S43: the whole migration prediction network is finely adjusted through gradient descent back propagation, and after the network is converged, the model is used for predicting NO of the target domain vehiclexEmission, NOxThe emission formula is as follows:
preNOx＝freg_3(W6,freg_2(W5,freg_1(W4,fSAE_3(W3',fSAE_2(W2',fSAE_1(W1',XS))))))
wherein f isreg_x/SAE_x(W,X)＝W·X。
Fig. 4 gives an exemplary graph of the results obtained on the basis of the present invention. As shown in fig. 4, the predicted values of the 100 sample points in the deep migration learning and the deep learning, and the true values of the 100 sample points are given in the final result diagram. From the information in the figure, the method based on the patent has better prediction performance and is beneficial to popularization. From the information in fig. 4, the method provided by the present invention can realize effective prediction by relying on high-quality NOx data of other diesel vehicles under the condition that NOx data of the diesel vehicle is sparse, and the method does not need to use other expensive equipment, so that the cost can be greatly reduced.
In another aspect, the present invention also discloses a computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method as described above.
In a third aspect, the present invention also discloses a computer device comprising a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method as described above.
It is understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, the example and the beneficial effects of the related contents can refer to the corresponding parts in the method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A diesel vehicle emission prediction method based on deep migration learning is characterized by comprising the following steps: includes constructing a diesel vehicle emission prediction model using a computer device and then using the model to predict NO for a target domain vehiclexDischarging;
wherein, the construction of the diesel vehicle emission prediction model by utilizing the computer equipment comprises the following steps,
s10: acquiring source domain vehicle related data and target domain vehicle driving related data and preprocessing the acquired data;
s20: the pre-training feature projection module is used for projecting the source domain vehicle features and the target domain vehicle features into a public subspace;
s30: the pre-training tail gas prediction module is used for building a full-connection neural network prediction model with double hidden layers by using the reconstructed source domain data characteristics and labels;
s40: and combining the pre-trained projection module and the prediction module to construct a migration model, considering the data distribution difference between the source domain vehicle and the target domain vehicle, adding KL divergence between the source domain and the target domain to the loss function part to enable the target domain data distribution to be close to the source domain data distribution, and finely adjusting the whole model to obtain a stable diesel vehicle emission prediction model.
2. The diesel vehicle emission prediction method based on deep migration learning of claim 1, wherein: the S10: the method comprises the steps of obtaining source domain vehicle related data and target domain vehicle related data during running and preprocessing the collected data, and specifically comprises the following steps:
s11: acquiring various data of a source domain diesel vehicle during running through vehicle-mounted exhaust gas detection equipment (PEMS), and collecting a set amount of data of a target domain diesel vehicle during running;
s12: and carrying out abnormal value processing, missing value processing, non-correlation data deletion and normalization operation on the data.
3. The diesel vehicle emission prediction method based on deep migration learning of claim 2, wherein: the data collected from the vehicle-mounted tail gas detection device in the step S11 includes a license plate, a terminal number, an engine speed, an actual output torque percentage, an engine water temperature, an engine fuel temperature, an engine oil temperature, an aftertreatment downstream NOx value, an aftertreatment downstream oxygen percentage, an atmospheric pressure, an ambient temperature, an aftertreatment exhaust gas mass flow, a urea tank liquid level percentage, a urea tank temperature, a vehicle speed, an accelerator pedal opening, a single-trip mileage, a total mileage, an engine instantaneous fuel injection quantity, an engine instantaneous fuel consumption rate, an engine average fuel consumption rate, an engine accumulated fuel consumption, a battery voltage, a fuel tank liquid level, an engine accumulated operation time, a longitude, a latitude, an SCR upstream temperature, and an SCR downstream temperature.
4. The diesel vehicle emission prediction method based on deep migration learning of claim 3, wherein: the S20 pre-training feature projection module projects the source domain vehicle features and the target domain vehicle features into a common subspace, and specifically includes:
s21: training a sparse self-encoder, taking the feature data of a source domain vehicle and a target domain vehicle as input and output actual values, setting the number of neurons of a hidden layer to be three times of the input, and setting a loss function as follows:
wherein
In order to be the initial source-domain feature,
to reconstruct the source domain features, DKLIn order to solve the KL divergence function, rho is the preset neuron activation probability,
w is the weight, and beta and lambda are empirical parameters;
s22: minimizing the loss function in S21 by gradient descent, reversely propagating and updating the weight, stopping iteration after network convergence, and retaining the weight from the input layer to the hidden layer, which is marked as W1Output the hidden layer h1Retention, h1The calculation formula is as follows:
h1＝fSAE_1(W1,Xs)＝W1·Xs，
s23: outputting S22 hidden layer h1Training as new sparse self-encoder input according to S22, after network convergence, keeping weight input layer to hidden layer weight, and recording as W2Output the hidden layer h2Retention, h2The calculation formula is as follows:
h2＝fSAE_2(W2,h1)＝W2·h1
s24: and then taking the output of the S23 sparse self-coding hidden layer as the input of a new sparse self-coder, training according to S23, after network convergence, keeping the weight from the input layer to the hidden layer, and recording as W3(ii) a Outputting the hidden layer h3Retention, h3The calculation formula is as follows:
h3＝fSAE_3(W3,h2)＝W3·h2；
s25: stacking sparse autoencoders, with W1,W2,W3Adding an output layer as an initial weight, building a multi-layer sparse self-encoder, wherein a loss function is a first item in S21, and after reverse iteration integral optimization, keeping an updated weight W1',W2',W3'；；
S26: verifying the authenticity of the reconstructed features.
5. The diesel vehicle emission prediction method based on deep migration learning of claim 4, wherein: the verifying the reliability of the reconstruction features of S26 includes respectively establishing a source domain prediction model using the source domain original features and the reconstruction features, selecting 100 samples as a test set, and performing, on the remaining samples, a test of 8: 2 into a training set and a validation set.
6. The diesel vehicle emission prediction method based on deep migration learning of claim 4, wherein: the S30: the pre-training exhaust gas prediction module builds a full-connection neural network prediction model with double hidden layers by using the reconstructed source domain data characteristics and labels, and specifically comprises the following steps:
s31: based on the step S24, using the retained output data of the third hidden layer as the input of a prediction model, post-processing a downstream NOx value as the output, constructing a double-hidden-layer fully-connected neural network, selecting Relu as an activation function, selecting Adam as an optimizer, setting the iteration number to 15000 times, and stopping iteration when the loss value is not reduced for more than 10 continuous times;
s32: and minimizing the Mean Square Error (MSE) of the real NOx value and the predicted value, and updating hidden layer weights W4, W5 and W6 in a back propagation mode until convergence.
7. The diesel vehicle emission prediction method based on deep migration learning of claim 6, wherein: s40: combining a pre-trained projection module and a prediction module to construct a migration model, considering the data distribution difference between a source domain vehicle and a target domain vehicle, adding KL divergence between the source domain and the target domain in a loss function part, enabling the target domain data distribution to be close to the source domain data distribution, and finely adjusting the whole model to obtain a stable diesel vehicle emission prediction model, which specifically comprises the following steps:
s41: based on the weight W of steps S20, S301',W2',W3',W4,W5,W6Multiplexing the weights, building a 6-layer migration neural network, inputting various characteristics of a source domain vehicle and a target domain vehicle, and predicting an emission value with a label of NOx;
s42: by adding KL divergence in the loss function and minimizing the loss function, the distribution difference of the source domain vehicle and the target domain vehicle in the subspace is reduced, the similarity of the source domain vehicle and the target domain vehicle is improved, and therefore knowledge transfer from the source domain vehicle to the target domain vehicle is achieved, and the loss function is as follows:
wherein
NO for i-th sample of source domain vehiclexThe value of the one or more of the one,
is the ith vehicle NO of the source area vehiclexBeta is an empirical parameter, DKLTo solve the KL divergence function, psFor the feature distribution of the source domain features in the subspace, ρtCharacteristic distribution of the target domain vehicle in the subspace;
s43: the whole migration prediction network is finely adjusted through gradient descent back propagation, and after the network is converged, the model is used for predicting NO of the target domain vehiclexEmission, NOxThe emission formula is as follows:
wherein f isreg_x/SAE_x(W,X)＝W·X。
8. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 7.
9. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 7.
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