CN112132264A - Regional exhaust emission prediction method and system based on space-time residual perception network - Google Patents

Regional exhaust emission prediction method and system based on space-time residual perception network Download PDF

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CN112132264A
CN112132264A CN202010953126.6A CN202010953126A CN112132264A CN 112132264 A CN112132264 A CN 112132264A CN 202010953126 A CN202010953126 A CN 202010953126A CN 112132264 A CN112132264 A CN 112132264A
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许镇义
康宇
曹洋
刘斌琨
李泽瑞
吕文君
赵振怡
裴丽红
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Abstract

The regional exhaust emission prediction method and system based on the space-time residual error perception network can solve the technical problems that the existing methods are mostly based on test vehicle data, external influence factors are not enough to be considered, and relative errors are large. The method comprises the following steps: s100, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data; s200, constructing a time sequence division set according to the change characteristics of the tail gas; s300, based on a pre-constructed and trained exhaust pollution space-time prediction model, predicting exhaust emission at the future t + k moment by using external environment characteristic data of the current moment t and historical exhaust space-time sequence data before the t-1 moment. The invention considers that the exhaust emission has space-time heterogeneity and is influenced by various external complex environmental factors through the space-time residual sensing network, and can realize regional exhaust prediction with higher precision on real telemetering data.

Description

Regional exhaust emission prediction method and system based on space-time residual perception network
Technical Field
The invention relates to the technical field of environmental detection, in particular to a regional exhaust emission prediction method and system based on a space-time residual perception network.
Background
The northern city is influenced by large-scale weather, the air quality is obviously reduced, and the dust-haze area reaches 130 ten thousand square kilometers. The motor vehicle tail gas is one of PM2.5 sources, and the real-time acquisition of the time-space distribution information of the tail gas in urban areas is of great benefit to the prevention and control of motor vehicle pollution and the environmental protection. A monitoring and early warning system is necessary to be established, and urban area tail gas space-time distribution at any moment is acquired, so that urban area tail gas pollution early warning can be provided, and decision support is provided for urban traffic planning of traffic municipal departments.
The method is characterized in that the actual traffic conditions of all regions in a city are different and the influence of environmental factors can affect the driving conditions of vehicles, the influence of tail gas in space diffusion is considered, the tail gas distribution in a given region can be influenced by the adjacent region, and the tail gas shows similar change characteristics in time due to the periodicity and trend change of vehicle flow in the region on a time scale. Furthermore, exhaust emissions have spatiotemporal heterogeneity and are influenced by a variety of external complex environmental factors.
Disclosure of Invention
The invention provides a regional exhaust emission prediction method and system based on a space-time residual error perception network, which can solve the technical problems that the existing method is mostly based on test vehicle data, external influence factors are not considered enough, and relative errors are large.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method comprises the following steps:
s100, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data;
s200, constructing a time sequence division set according to the change characteristics of the tail gas;
s300, based on a pre-constructed and trained exhaust pollution space-time prediction model, predicting exhaust emission at the future t + k time by using external environment characteristic data at the current time t and historical exhaust space-time sequence data before the t-1 time.
Further, the construction steps of the tail gas pollution space-time prediction model in the step S300 are as follows:
s301, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data;
s302, constructing a time sequence division set according to the change characteristics of the tail gas;
s303, constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data;
s304, training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and external environment data to obtain a tail gas pollution space-time prediction model.
Further, the step S100 of acquiring historical exhaust gas space-time monitoring data and external environment data, and the data preprocessing performed on the acquired data specifically includes:
s101, acquiring historical tail gas space-time monitoring data of a vehicle and road network traffic, meteorological environment and urban interest point distribution external environment data by using non-contact measured tail gas remote sensing monitoring equipment;
and S102, performing missing value completion, abnormal value abandonment and data normalization processing on the obtained monitoring data.
Further, the step S200 of constructing a time-series sequence partition set according to the exhaust gas variation characteristic specifically includes:
s201, according to the sequence length l of the proximity time segmentcConstructing proximity time segments
Figure BDA0002677691300000021
Figure BDA0002677691300000022
S202, according to the length l of the periodic time segment sequencepConstructing periodic time slices
Figure BDA0002677691300000023
Figure BDA0002677691300000024
p is the time interval of the periodic time segment;
s203, according to the trend time segment sequence length lsConstructing trending time segments
Figure BDA0002677691300000031
Figure BDA0002677691300000032
s is the time interval of the trending temporal segment sequence.
Further, the step S303 of constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data includes:
s3031, extracting time-dependent characteristics by dividing a proximity time segment HcPeriodic time segment HpAnd trending time segment HsRespectively sending the shallow layer feature extraction products into the convolution layer units with the same structure to carry out shallow layer feature extraction;
the spatial and temporal distribution characteristics of the tail gas of the three time-division segments obtained by convolution operation are as follows,
Figure BDA0002677691300000033
Figure BDA0002677691300000034
Figure BDA0002677691300000035
where denotes the convolution operation, f denotes an activation function, in particular a linear rectifier unit ReLU, f (z) max (0, z); w(1)，b(1)Respectively obtaining a weight matrix to be learned and a bias vector parameter of the first layer convolution layer; hc (1)，Hp (1)，Hs (1)Respectively is a characteristic diagram of a first layer convolution layer proximity time segment, a periodic time segment and a trend time segment;
the layer output is then fed to a trending module, a periodicity module and a proximity module, respectively, to extract the time dependence of the exhaust gas distribution;
wherein the time-dependent extraction steps are as follows:
regarding the proximity time characteristic, considering that the tail gas changes in a short time are similar, the proximity characteristic diagram is kept as the original input;
for the periodic time characteristics, extracting periodic invariance characteristics on the time change of the tail gas by introducing a self-attention mechanism (self-attention);
for the trend time characteristics, averaging the trend time segment characteristic layers by introducing average pooling operation to obtain a trend characteristic subgraph;
wherein the content of the first and second substances,
the time-dependent extraction operation is as follows:
Figure BDA0002677691300000041
wherein
Figure BDA0002677691300000042
Representing residual join operations, g being a linear embedding function, Wθ，
Figure BDA0002677691300000043
Respectively, the embedding weight matrix to be learned, fAPIs an average pooling operation of the oil in the tank,
Figure BDA0002677691300000044
respectively carrying out time dependency processing on the proximity time segment, the periodic time segment and the trend time segment, and sending the feature graphs into a residual convolution unit for processing after front-end fusion;
s3032, fusing external environment characteristics;
mapping the external environment characteristic input x to an internal characteristic space representation z through an encoder, and then reconstructing z to an output y through a decoder;
the specific fusion steps include:
firstly, performing front-end fusion splicing on a proximity time segment, a periodic time segment and a trend time segment through a characteristic graph extracted by time dependence, and then sending the characteristic graphs to a stacked convolution residual unit for processing;
the front-end fusion operation performed on the feature map extracted by the time dependency is recorded as:
Figure BDA0002677691300000045
wherein
Figure BDA0002677691300000046
And b(2)Respectively, learning parameters to be optimized;
for the tail gas space-time residual error network part, the time correlation characteristics are processed by timeComponent front-end fusion output HstAnd designing a residual convolution unit to extract spatial dependency, wherein the output of a space-time residual network can be recorded as:
Figure BDA0002677691300000047
as for the external environmental factor, the external environmental factor E set at time ttIncluding road network structure information EroadWeather environmental factor EweatherTraffic flow factor EtrafficAnd point of interest information EPOIHaving different data dimension structures, learning deep layer characteristics of tail gas space-time distribution influenced by external environmental factors by stacking a plurality of self-encoders, and mapping hidden layer characteristics to network input layer X by utilizing full connection layertHigh-dimensional feature vectors of the same dimension;
the two parts are fused at the back end, and the final prediction result of the t moment is output by utilizing the tanh activation function and recorded as
Figure BDA0002677691300000051
Figure BDA0002677691300000052
Wherein XResIs a space-time residual network part output, XExtIs an external environmental factor feature extraction network output, WstAnd WExtRespectively corresponding weight parameter matrixes to be learned; the tanh activation function will eventually fuse the results
Figure BDA0002677691300000053
Mapping to [ -1, 1 [ ]]To (c) to (d);
by minimizing the predicted value
Figure BDA0002677691300000054
With the true value XtThe square error (MSE) between them is taken as the loss function of the space-time residual perception network model training and is recorded as:
Figure BDA0002677691300000055
where θ is all the parameters to be learned in the spatio-temporal residual perception network model.
Further, the encoder portion in S3032 is written as:
z＝σ(Wx+b)
w and b are weight and bias parameters of the encoder respectively, and sigma is a sigmoid activation function;
the corresponding decoder is written as:
y＝σ(W′z+b′)
where W and b are the weight and bias parameters of the decoder, respectively; the self-encoder replicates similar inputs of training data by minimizing the reconstruction error y-x.
Further, in S304, the training of the deep space-time residual perception network by using the preprocessed exhaust monitoring data and the external environment data to obtain the exhaust pollution space-time prediction model specifically includes:
initializing a training data set
Figure BDA0002677691300000056
Training samples ({ H) are created in sequence according to the available time stamp sequence t (1 ≦ t ≦ n-1)c，Hp，Hs，Et}，Xt) And is stored in a training set D, in which,
Figure BDA0002677691300000057
Figure BDA0002677691300000058
lc，lp， lsrespectively, a proximity time segment length, a periodic time segment length, and a trending time segment length, p, s are the corresponding time spans of the periodic time segment and the trending time segment, respectively, { X0，X1，…Xn-1And { E } and0，E1，…En-1respectively representing a preprocessed historical observation sequence and an external environment factor sequence;
initializing all parameters theta to be learned in a deep space-time residual perception network model to be trained;
randomly selecting a batch of training samples D from the training set DbatchUsing each training sample batch DbatchAnd training the model by a minimum loss function, and updating the learning parameter theta to reach a training termination condition to obtain a depth space-time residual perception network model after training.
Further, the S300, based on a pre-constructed and trained exhaust pollution space-time prediction model, specifically predicting exhaust emission at a future time t + k by using external environment feature data at the current time t and historical exhaust space-time sequence data before the time t-1, includes:
according to external characteristic data E of current time ttAnd a historical exhaust gas observation data set { H } before time t-1C，HP，HSAnd predicting the exhaust emission of the area at the time t.
On the other hand, the invention also discloses a regional tail gas emission prediction system based on the space-time residual error perception network, which comprises the following units:
the data acquisition unit is used for acquiring historical tail gas space-time monitoring data and external environment data and carrying out data preprocessing on the acquired data;
the time sequence division set construction unit is used for constructing a time sequence division set according to the change characteristic of the tail gas;
and the prediction unit is used for predicting the exhaust emission at the future t + k moment by utilizing the external environment characteristic data of the current moment t and the historical exhaust space-time sequence data before the t-1 moment based on a pre-constructed and trained exhaust pollution space-time prediction model.
Further, the following subunits are included:
the model construction unit is used for constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data;
and the model training unit is used for training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and the external environment data to obtain a tail gas pollution space-time prediction model.
According to the technical scheme, the regional exhaust emission prediction method and system based on the space-time residual sensing network are different from the traditional prediction method based on the standard grid space-time network model, the space-time residual sensing network considers that the exhaust emission has space-time heterogeneity and is influenced by various external complex environmental factors, and the regional exhaust prediction with higher precision can be realized on real telemetering data.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a front-end and back-end feature fusion module of the present invention;
FIG. 3 is a front-end and back-end fusion block diagram of spatio-temporal sequence prediction according to the present invention;
FIG. 4 is a 7 AM distribution plot of the spatiotemporal variation distribution of NOx pollution during early peak hours;
FIG. 5 is a graph of the 8 am distribution of the spatiotemporal variation distribution of NOx pollution during early peak hours;
fig. 6 is a NOx 24 hour change prediction curve and a truth curve.
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 regional exhaust emissions based on the space-time residual perception network according to this embodiment includes:
the method comprises the following steps:
s100, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data;
s200, constructing a time sequence division set according to the change characteristics of the tail gas;
s300, based on a pre-constructed and trained exhaust pollution space-time prediction model, predicting exhaust emission at the future t + k time by using external environment characteristic data at the current time t and historical exhaust space-time sequence data before the t-1 time.
The construction steps of the tail gas pollution space-time prediction model in the S300 are as follows:
s301, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data;
s302, constructing a time sequence division set according to the change characteristics of the tail gas;
s303, constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data;
s304, training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and external environment data to obtain a tail gas pollution space-time prediction model.
The following is a detailed description:
the step S101: acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data, wherein the method specifically comprises the following subdivision steps of S101 to S102:
s101, acquiring historical tail gas space-time monitoring data of a vehicle and external environment data such as road network traffic, meteorological environment, urban interest point distribution and the like by using non-contact measured tail gas remote sensing monitoring equipment;
and S102, performing missing value completion, abnormal value abandonment and data normalization processing on the obtained monitoring data.
The step S200: constructing a time sequence division set according to the variation characteristics of the tail gas, and specifically comprising the following subdivision steps S201 to S203:
s201, according to the sequence length l of the proximity time segmentcConstructing proximity time segments
Figure BDA0002677691300000081
Figure BDA0002677691300000082
S202, according to the length l of the periodic time segment sequencepConstructing periodic time slices
Figure BDA0002677691300000083
Figure BDA0002677691300000084
p is the time interval (one week) of the periodic time slices.
S203, according to the trend time segment sequence length lsConstructing trending time segments
Figure BDA0002677691300000085
Figure BDA0002677691300000086
s is the time interval (one month) of the trending temporal segment sequence.
The step S303: the regional exhaust pollution emission prediction model of the deep space-time residual perception network is constructed according to the exhaust division sequence data and the external environmental factor data, and the method specifically comprises the following subdivision steps S3031-S3032:
s3031, extracting time-dependent characteristics by dividing a proximity time segment HcPeriodic time segment HpAnd trending time segment HsAnd respectively sending the shallow layer feature extraction products into the convolution layer units with the same structure to carry out shallow layer feature extraction. The space distribution characteristics when the tail gas of three time-division segments is obtained through convolution operation are as follows,
Figure BDA0002677691300000091
Figure BDA0002677691300000092
Figure BDA0002677691300000093
where denotes the convolution operation and f denotes an activation function, such as the linear rectifier unit ReLU, f (z) max (0, z); w(1)，b(1)Respectively obtaining a weight matrix to be learned and a bias vector parameter of the first layer convolution layer; hc (1)，Hp (1)，Hs (1)Respectively, the first layer convolution layer proximity time segment, the periodicity time segment and the trend time segment. The layer output is then fed to a trending module, a periodicity module, and a proximity module, respectively, to extract the time dependence of the exhaust gas distribution, the time dependence extraction being shown in FIG. 2. The time-dependent extraction is detailed as follows, and regarding the proximity time characteristic, considering that the tail gas changes in a short time are similar, the proximity characteristic graph is kept as the original input; for the periodic time characteristics, extracting periodic invariance characteristics on the time change of the tail gas by introducing a self-attention mechanism (self-attention); and for the trend time characteristics, averaging the trend time segment characteristic layers by introducing an average pooling operation to obtain a trend characteristic subgraph. The time-dependent extraction operation is as follows:
Figure BDA0002677691300000094
wherein
Figure BDA0002677691300000095
Representing residual join operations, g being a linear embedding function, Wθ，
Figure BDA0002677691300000096
Respectively, the embedding weight matrix to be learned, fAPIs an average pooling operation of the oil in the tank,
Figure BDA0002677691300000097
where the time dependency is respectively the proximity time segment, the periodicity time segment and the trend time segmentAnd the processed characteristic diagram is fused at the front end and then sent to a residual convolution unit for processing.
As shown in fig. 2. FIG. 2 a temporal dependency extraction module.
And S3032, fusing external environment characteristics. The extrinsic ambient feature input x is mapped by the encoder to the intrinsic feature space representation z, which is then reconstructed by the decoder to the output y. The general encoder section can be written as:
z＝σ(Wx+b)
where W and b are the weight and bias parameters of the encoder, respectively, and σ is the sigmoid activation function. The corresponding decoder can be written as:
y＝σ(W′z+b′)
where W and b are the weight and bias parameters of the decoder, respectively. The self-encoder replicates similar inputs of training data by minimizing the reconstruction error y-x.
FIG. 3 shows a front-end fusion block diagram of spatio-temporal sequence prediction, in which the proximity time segments, the periodic time segments and the trend time segments are subjected to front-end fusion splicing by a time-dependent extracted feature map and then sent to a stacked convolution residual unit for processing. Front-end fusion is generally used for fusing data with similar structural features, and performing front-end fusion operation on a time-dependent extracted feature graph can be written as:
Figure BDA0002677691300000101
wherein
Figure BDA0002677691300000102
And b(2)Respectively, the learning parameters to be optimized.
For the tail gas space-time residual error network part, the time correlation characteristics are fused and output H through the front end of the time processing componentstAnd designing a residual convolution unit to extract spatial dependency, wherein the output of a space-time residual network can be recorded as:
Figure BDA0002677691300000103
regarding external environmental factors, considering that the spatial and temporal distribution of the tail gas in the area can be influenced by external complex environmental factors, such as road network traffic information, traffic flow information, urban functional interest points, meteorological environment and the like, and the external environmental factor E at the moment ttIncluding road network structure information EroadEnvironmental factors of qi-image EweatherTraffic flow factor EtrafficAnd point of interest information EPOIHaving different data dimension structures, learning deep layer characteristics of space distribution when external environmental factors influence tail gas by stacking a plurality of self-encoders, and mapping hidden layer characteristics to network input layer X by utilizing full connection layertHigh-dimensional feature vectors of the same dimension. The two parts, namely the external environmental factor characteristic and the tail gas space-time distribution characteristic, are fused at the rear end, and then the final prediction result of the t moment is output by utilizing the tanh activation function and recorded as
Figure BDA0002677691300000111
Figure BDA0002677691300000112
Wherein XResIs a space-time residual network part output, XExtIs an external environmental factor feature extraction network output, WstAnd WExtRespectively, the corresponding weight parameter matrix to be learned. the tanh activation function will eventually fuse the results
Figure BDA0002677691300000113
Mapping to [ -1, 1 [ ]]In the meantime. By minimizing the predicted value
Figure BDA0002677691300000114
With the true value XtThe square error (MSE) between the two is taken as a loss function of the space-time residual perception network model training and is recorded as:
Figure BDA0002677691300000115
where θ is all the parameters to be learned in the spatio-temporal residual perception network model.
The above step S304: and training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and external environment data to obtain a tail gas pollution space-time prediction model.
The method specifically comprises the following steps:
initializing a training data set
Figure BDA0002677691300000116
Training samples ({ H) are created in sequence according to the available time stamp sequence t (1 ≦ t ≦ n-1)c，Hp，Hs，Et}，Xt) And is stored in a training set D, in which,
Figure BDA0002677691300000117
Figure BDA0002677691300000118
lc，lp， lsrespectively, a proximity time segment length, a periodic time segment length, and a trending time segment length, p, s are the corresponding time spans of the periodic time segment and the trending time segment, respectively, { X0，X1，…Xn-1And { E } and0，E1，…En-1respectively representing a preprocessed historical observation sequence and an external environment factor sequence;
initializing all parameters theta to be learned in a deep space-time residual perception network model to be trained;
randomly selecting a batch of training samples D from the training set DbatchUsing each training sample batch DbatchTraining the model through a minimum loss function, and updating a learning parameter theta to reach a training termination condition to obtain a depth space-time residual perception network model after training is completed;
the step S300: based on a trained exhaust pollution space-time prediction model, the exhaust emission at the future t + k moment is predicted by using external environment characteristic data of the current moment t and historical exhaust space-time sequence data before the t-1 moment, and the method specifically comprises the following steps:
according to external characteristic data E of current time ttAnd a historical observation data set { H } of the exhaust gas before the time t-1C，HP，HSAnd predicting the zone exhaust emission at the time t, as shown in the figure. Specifically, as shown in fig. 4 and 5, fig. 4 is a 7 am distribution diagram of the spatio-temporal variation distribution of NOx pollution during early peak hours, and fig. 5 is an 8 am distribution diagram, it can be seen that the spatial distribution of exhaust gas in urban areas during early peak hours is significantly increased due to the increased commute volume in the area when people start to go home to work.
FIG. 6 is a NOx 24-hour change prediction curve and a true value curve, a solid line represents a predicted value of each pollutant, a dashed line represents a true measured value of each pollutant, the predicted value can be well fitted with the true value, and the effectiveness of the space-time residual error perception network designed by the embodiment for predicting the space-time distribution of the pollution of the area mobile source is proved.
As can be seen from the above, in the embodiment, the pollution time series data and the environmental data are rasterized into the similar multi-channel image sequence data, so that the exhaust emission is associated with the geographic environment information, and the deep space-time residual perception network model is adopted for processing, so that the high-precision exhaust prediction is realized on the real telemetering data.
On the other hand, the invention also discloses a regional tail gas emission prediction system based on the space-time residual error perception network, which comprises the following units:
the data acquisition unit is used for acquiring historical tail gas space-time monitoring data and external environment data and carrying out data preprocessing on the acquired data;
the time sequence division set construction unit is used for constructing a time sequence division set according to the change characteristic of the tail gas;
and the prediction unit is used for predicting the exhaust emission at the future t + k moment by utilizing the external environment characteristic data of the current moment t and the historical exhaust space-time sequence data before the t-1 moment based on a pre-constructed and trained exhaust pollution space-time prediction model.
Further, the following subunits are included:
the model construction unit is used for constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data;
and the model training unit is used for training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and the external environment data to obtain a tail gas pollution space-time prediction model.
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 (10)

1. A regional exhaust emission prediction method based on a space-time residual error perception network is characterized by comprising the following steps:
the method comprises the following steps:
s100, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data;
s200, constructing a time sequence division set according to the change characteristics of the tail gas;
s300, based on a pre-constructed and trained exhaust pollution space-time prediction model, predicting exhaust emission at the future t + k moment by using external environment characteristic data of the current moment t and historical exhaust space-time sequence data before the t-1 moment.
2. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 1, characterized in that: the construction steps of the tail gas pollution space-time prediction model in the S300 are as follows:
s301, acquiring historical tail gas space-time monitoring data and external environment data, and performing data preprocessing on the acquired data;
s302, constructing a time sequence division set according to the change characteristics of the tail gas;
s303, constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data;
s304, training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and external environment data to obtain a tail gas pollution space-time prediction model.
3. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 2, characterized in that: the S100, acquiring historical tail gas space-time monitoring data and external environment data, and preprocessing the acquired data specifically comprises the following steps:
s101, acquiring historical tail gas space-time monitoring data of a vehicle and road network traffic, meteorological environment and urban interest point distribution external environment data by using non-contact measured tail gas remote sensing monitoring equipment;
and S102, performing missing value completion, abnormal value abandonment and data normalization processing on the obtained monitoring data.
4. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 3, characterized in that: the S200 construction of the time sequence division set according to the tail gas variation characteristics specifically comprises:
s201, according to the sequence length l of the proximity time segmentcConstructing proximity time segments
Figure FDA0002677691290000011
Figure FDA0002677691290000012
S202, according to the length l of the periodic time segment sequencepConstructing periodic time slices
Figure FDA0002677691290000021
Figure FDA0002677691290000022
p is the time interval of the periodic time segment;
s203, according to the trend time segment sequence length lsConstructing trending time segments
Figure FDA0002677691290000023
Figure FDA0002677691290000024
s is the time interval of the trending time segment sequence.
5. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 4, characterized in that: the S303, according to the tail gas division sequence data and the external environmental factor data, constructing a regional tail gas pollution emission prediction model of the deep space-time residual perception network, comprises the following steps:
s3031, extracting time-dependent characteristics by dividing a proximity time segment HcPeriodic time segment HpAnd trending time segment HsRespectively sending the shallow layer feature extraction products into the convolution layer units with the same structure to carry out shallow layer feature extraction;
the spatial and temporal distribution characteristics of the tail gas of the three time-division segments obtained by convolution operation are as follows,
Figure FDA0002677691290000025
Figure FDA0002677691290000026
Figure FDA0002677691290000027
where denotes the convolution operation, f denotes an activation function, in particular a linear rectifier unit ReLU, f (z) max (0, z); w(1)，b(1)Respectively obtaining a weight matrix to be learned and a bias vector parameter of the first layer convolution layer; hc (1)，Hp (1)，Hs (1)Respectively is a characteristic diagram of a first layer convolution layer proximity time segment, a periodic time segment and a trend time segment;
the layer output is then fed to a trending module, a periodicity module, and a proximity module, respectively, to extract the time dependence of the exhaust gas distribution;
wherein the time-dependent extraction steps are as follows:
regarding the proximity time characteristic, considering that the tail gas changes in a short time are similar, the proximity characteristic diagram is kept as the original input;
for the periodic time characteristics, extracting periodic invariance characteristics on the time change of the tail gas by introducing a self-attention mechanism (self-attention);
for the trend time characteristics, averaging the trend time segment characteristic layers by introducing average pooling operation to obtain a trend characteristic subgraph;
wherein the content of the first and second substances,
the time-dependent extraction operation is as follows:
Figure FDA0002677691290000031
wherein
Figure FDA0002677691290000037
Representing the residual join operation, g is a linear embedding function, W,Wθ，
Figure FDA0002677691290000038
respectively, an embedded weight matrix to be learned, fAPIs an average pooling operation of the oil in the tank,
Figure FDA0002677691290000032
respectively carrying out time dependency processing on the proximity time segment, the periodic time segment and the trend time segment, and sending the feature graphs into a residual convolution unit for processing after front-end fusion;
s3032, fusing external environment characteristics;
mapping the external environment characteristic input x to an internal characteristic space representation z through an encoder, and then reconstructing z to an output y through a decoder;
the specific fusion steps include:
firstly, performing front-end fusion splicing on a proximity time segment, a periodic time segment and a trend time segment through a characteristic graph extracted by time dependence, and then sending the front-end fusion spliced characteristic graph to a stacked convolution residual error unit for processing;
the front-end fusion operation performed on the feature map extracted by the time dependency is recorded as:
Figure FDA0002677691290000033
wherein
Figure FDA0002677691290000034
And b(2)Respectively, learning parameters to be optimized;
for the tail gas space-time residual error network part, the time correlation characteristics are fused and output H through the front end of the time processing componentstAnd designing a residual convolution unit to extract spatial dependency, wherein the output of a space-time residual network can be recorded as:
Figure FDA0002677691290000035
as for the external environmental factor, the external environmental factor E set at time ttIncluding road network structure information EroadWeather environmental factor EweatherTraffic flow factor EtrafficAnd point of interest information EPOIHaving different data dimension structures, learning deep layer characteristics of tail gas space-time distribution influenced by external environmental factors by stacking a plurality of self-encoders, and mapping hidden layer characteristics to network input layer X by utilizing full connection layertHigh-dimensional feature vectors of the same dimension;
the two parts, namely the external environmental factor characteristic and the tail gas space-time distribution characteristic, are fused at the rear end, and then the final prediction result of the t moment is output by utilizing the tanh activation function and recorded as
Figure FDA0002677691290000036
Figure FDA0002677691290000041
Wherein XResIs a space-time residual network part output, XExtIs an external environmental factor feature extraction network output, WstAnd WExtRespectively corresponding weight parameter matrixes to be learned; the tanh activation function will eventually fuse the results
Figure FDA0002677691290000042
Mapping to [ -1, 1 [ ]]To (c) to (d);
by minimizing the predicted value
Figure FDA0002677691290000043
With the true value XtThe square error (MSE) between them is taken as the loss function of the space-time residual perception network model training and is recorded as:
Figure FDA0002677691290000044
where θ is all the parameters to be learned in the spatio-temporal residual perception network model.
6. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 5, characterized in that: the encoder part in S3032 is recorded as:
z＝σ(Wx+b)
w and b are weight and bias parameters of the encoder respectively, and sigma is a sigmoid activation function;
the corresponding decoder is written as:
y＝σ(W′z+b′)
where W and b are the weight and bias parameters of the decoder, respectively; the self-encoder replicates the similar input of training data by minimizing the reconstruction error y-x.
7. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 5, characterized in that: the S304 trains the deep space-time residual perception network by using the preprocessed exhaust monitoring data and external environment data, and the obtained exhaust pollution space-time prediction model specifically includes:
initializing a training data set
Figure FDA0002677691290000045
Training samples ({ H) are created in sequence according to the available time stamp sequence t (1 ≦ t ≦ n-1)c，Hp，Hs，Et}，Xt) And is stored in a training set D, in which,
Figure FDA0002677691290000046
Figure FDA0002677691290000047
lc，lp，lsrespectively, a proximity time segment length, a periodic time segment length, and a trend time segment length, p, s respectively being periodic time segmentsTime span corresponding to trending time slice, { X0，X1，…Xn-1And { E } and0，E1，…En-1respectively representing a preprocessed historical observation sequence and an external environment factor sequence;
initializing all parameters theta to be learned in a deep space-time residual perception network model to be trained;
randomly selecting a batch of training samples D from the training set DbatchUsing each training sample batch DbatchAnd training the model by a minimum loss function, and updating the learning parameter theta to reach a training termination condition to obtain a depth space-time residual perception network model after training.
8. The regional exhaust emission prediction method based on the space-time residual error perception network according to claim 7, characterized in that: the S300 is based on a pre-constructed and trained exhaust pollution space-time prediction model, and specifically comprises the following steps of predicting the exhaust emission at the future t + k moment by using external environment characteristic data of the current moment t and historical exhaust space-time sequence data before the t-1 moment:
according to external characteristic data E of current time ttAnd a historical observation data set { H } of the exhaust gas before the time t-1C，HP，HSAnd predicting the exhaust emission of the area at the time t.
9. A regional exhaust emission prediction system based on a space-time residual error perception network is characterized in that: the method comprises the following units:
the data acquisition unit is used for acquiring historical tail gas space-time monitoring data and external environment data and carrying out data preprocessing on the acquired data;
the time sequence division set construction unit is used for constructing a time sequence division set according to the tail gas change characteristics;
and the prediction unit is used for predicting the exhaust emission at the future t + k moment by utilizing the external environment characteristic data of the current moment t and the historical exhaust space-time sequence data before the t-1 moment based on a pre-constructed and trained exhaust pollution space-time prediction model.
10. The system according to claim 9, wherein the prediction system for regional exhaust emissions based on space-time residual error perception network,
the system also comprises the following subunits:
the model construction unit is used for constructing a regional exhaust pollution emission prediction model of the deep space-time residual perception network according to the exhaust division sequence data and the external environment factor data;
and the model training unit is used for training the deep space-time residual perception network by utilizing the preprocessed tail gas monitoring data and the external environment data to obtain a tail gas pollution space-time prediction model.
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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112819218A (en) * 2021-01-27 2021-05-18 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) High-resolution urban mobile source pollution space-time prediction method, system and storage medium
CN112991765A (en) * 2021-02-03 2021-06-18 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) Method, terminal and storage medium for updating road high-emission source recognition model
CN113128776A (en) * 2021-04-26 2021-07-16 中国科学技术大学先进技术研究院 Data self-migration multi-vehicle type diesel vehicle emission prediction method and system
CN113222236A (en) * 2021-04-30 2021-08-06 中国科学技术大学先进技术研究院 Data distribution self-adaptive cross-regional exhaust emission prediction method and system
CN113469405A (en) * 2021-05-18 2021-10-01 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) Cross-city tail gas migration prediction method, system and medium based on subgraph segmentation
CN113505160A (en) * 2021-07-21 2021-10-15 宁波工程学院 Motor vehicle exhaust emission estimation method based on convolution deep neural network
CN114741948A (en) * 2022-03-09 2022-07-12 西北工业大学 Aero-engine degradation trend prediction method based on residual stacked convolution network of sequence reconstruction
CN114743072A (en) * 2022-05-24 2022-07-12 中国科学院计算机网络信息中心 Training method of short-term time sequence prediction model
CN115964621A (en) * 2023-03-17 2023-04-14 中国科学技术大学 Regional road network exhaust emission data completion method
CN116577673A (en) * 2023-07-12 2023-08-11 深圳先进储能材料国家工程研究中心有限公司 Distributed neural network-based energy storage power station fault diagnosis method and system
CN116777120A (en) * 2023-08-16 2023-09-19 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) Urban road mobile source carbon emission calculation method based on road network OD pairs
CN117349650A (en) * 2023-09-20 2024-01-05 同济大学 Method for extracting foundation pit structural data multi-element space-time fusion and evolution features
CN117390938A (en) * 2023-12-13 2024-01-12 中交第四航务工程勘察设计院有限公司 High-pile wharf operation and maintenance period structure health monitoring method and system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106844857A (en) * 2016-12-21 2017-06-13 环境保护部信息中心 A kind of method and apparatus for simulating water pollution accident contaminated zone spatial and temporal distributions
CN108288109A (en) * 2018-01-11 2018-07-17 安徽优思天成智能科技有限公司 Motor-vehicle tail-gas concentration prediction method based on LSTM depth space-time residual error networks
CN109165690A (en) * 2018-09-03 2019-01-08 安徽优思天成智能科技有限公司 A kind of method and system of city road network tail gas pollution forecast of distribution
CN109214570A (en) * 2018-09-03 2019-01-15 安徽优思天成智能科技有限公司 A kind of urban area tail gas pollution prediction technique based on depth space-time correction model
US20190376890A1 (en) * 2018-06-08 2019-12-12 Ball Aerospace & Technologies Corp. Method and Systems for Remote Emission Detection and Rate Determination
CN111243269A (en) * 2019-12-10 2020-06-05 福州市联创智云信息科技有限公司 Traffic flow prediction method based on depth network integrating space-time characteristics

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106844857A (en) * 2016-12-21 2017-06-13 环境保护部信息中心 A kind of method and apparatus for simulating water pollution accident contaminated zone spatial and temporal distributions
CN108288109A (en) * 2018-01-11 2018-07-17 安徽优思天成智能科技有限公司 Motor-vehicle tail-gas concentration prediction method based on LSTM depth space-time residual error networks
US20190376890A1 (en) * 2018-06-08 2019-12-12 Ball Aerospace & Technologies Corp. Method and Systems for Remote Emission Detection and Rate Determination
CN109165690A (en) * 2018-09-03 2019-01-08 安徽优思天成智能科技有限公司 A kind of method and system of city road network tail gas pollution forecast of distribution
CN109214570A (en) * 2018-09-03 2019-01-15 安徽优思天成智能科技有限公司 A kind of urban area tail gas pollution prediction technique based on depth space-time correction model
CN111243269A (en) * 2019-12-10 2020-06-05 福州市联创智云信息科技有限公司 Traffic flow prediction method based on depth network integrating space-time characteristics

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ZHENYI XU 等: ""Deep spatiotemporal residual early-late fusion network for city region vehicle emission pollution prediction"", 《NEUROCOMPUTING》 *
王梦迪: ""城市公交尾气排放特性与预测模型研究"", 《中国优秀博硕士学位论文全文数据库(硕士)-工程科技Ⅰ辑》 *

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112819218B (en) * 2021-01-27 2022-05-06 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) High-resolution urban mobile source pollution space-time prediction method, system and storage medium
CN112819218A (en) * 2021-01-27 2021-05-18 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) High-resolution urban mobile source pollution space-time prediction method, system and storage medium
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CN113128776B (en) * 2021-04-26 2023-07-07 中国科学技术大学先进技术研究院 Multi-vehicle type diesel vehicle emission prediction method and system with data self-migration
CN113222236A (en) * 2021-04-30 2021-08-06 中国科学技术大学先进技术研究院 Data distribution self-adaptive cross-regional exhaust emission prediction method and system
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CN113469405B (en) * 2021-05-18 2024-04-02 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) Subimage segmentation-based cross-city tail gas migration prediction method, system and medium
CN113505160A (en) * 2021-07-21 2021-10-15 宁波工程学院 Motor vehicle exhaust emission estimation method based on convolution deep neural network
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CN115964621B (en) * 2023-03-17 2023-05-09 中国科学技术大学 Regional road network tail gas emission data complement method
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