CN110489671A - A kind of road charging pile recommended method based on encoder-decoder model - Google Patents
A kind of road charging pile recommended method based on encoder-decoder model Download PDFInfo
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Abstract
The present invention relates to a kind of road charging pile recommended methods using encoder-decoder model, belong to depth learning technology field, using encoder-decoder model, the model uses the Move Mode analyzed in electric car historical track based on the encoder module of shot and long term memory network, and Future Trajectory sequence is generated using the decoder module of LSTM, probability of the output by softmax layers of generation Future Trajectory sequence, before then being exported using Beam Search algorithmKThe Future Trajectory sequence conduct of maximum probabilityKPath candidate, finally according to the most short principle pair of additional moving distanceKPath candidate determinesKA charging pile, what is obtainedKA charging pile and its priority recommend electric car user.
Description
Technical field
The present invention relates to a kind of road charging pile, specifically a kind of road charging pile recommended method belongs to depth
Practise technical field.
Background technique
In traditional neural network model, input layer between hidden layer node and hidden layer to output layer it
Between node all connect entirely, the node between every layer be it is connectionless, this performance in processing sequence problem is bad.
Recognition with Recurrent Neural Network (Recurrent Neural Networks, RNNs) remembers the information of front, and is applied to current
In the calculating of output, i.e., the node between hidden layer has connection, and the input of hidden layer is not only defeated including input layer
It out, further include the output of last moment hidden layer.
RNNs can there are problems that relying on (Long-Term Dependencies) for a long time, i.e., most start to be input to the interior of RNNs
Holding the influence exported to current time can be smaller and smaller, proposes LSTM (Long Short Term Memory) mechanism thus,
LSTM is a kind of improvement to RNNs unit, it is increased multiple " doors " by the unit of hidden layer in reinforcement original RNNs, real
Now to the memory of content, forgetting, so as to improve the long-term Dependence Problem of RNNs.
In encoder-decoder model, encoder module is responsible for successively reading in each unit of list entries, will
It is encoded into a context vector, and decoder module is responsible for predicting output sequence in the case where given context vector.
In trajectory predictions processing, encoder and decoder module has selected LSTM to realize.
Summary of the invention
The road charging pile recommended method based on encoder-decoder model that the object of the present invention is to provide a kind of,
Encoder module obtains the status information about electric car, and decoder module is used to generate Future Trajectory, then utilizes softmax
Layer obtains the probability of every prediction Future Trajectory, recycles Beam Search algorithm to generate K optimal candidate track sets, most
K charging pile is determined according to the smallest principle of total path afterwards, K charging pile and its corresponding probability are recommended into user, so that
Charging pile is recommended more accurate.
The object of the present invention is achieved like this: a kind of road charging pile recommendation based on encoder-decoder model
Method, comprising the following steps:
Step 1) is at full charge to each electric taxi, and a low battery threshold value δ is arranged；
For step 2) when the electricity of some electric taxi is lower than threshold value δ, which issues the pre- of not enough power supply
Alert signal starts to recommend road charging pile for the electric taxi；
80% historical track of electric taxi is input in encoder-decoder model and instructs by step 3)
Practice；
The historical track of the residue 20% of electric taxi is input to encoder-decoder model and surveyed by step 4)
Examination；
Step 5) is using the output result of encoder-decoder model as softmax layers of input, the softmax
Probability of the layer to predict every Future Trajectory subsequence, the probability that output Future Trajectory sequence occurs, the future of every prediction
The new probability formula of track subsequence are as follows:
Wherein, i represents the number of this track subsequence appearance, and j represents the number that all track subsequences occur；
Step 6) is using softmax layers of output result as the input of Beam Search algorithm, by BeamSearch algorithm
The path candidate of K maximum probability before exporting；The condition probability formula of the Future Trajectory point sequence of every prediction are as follows:
Step 7) selects charging pile: to each path candidate, selecting the charging pile for meeting the following conditions: 1) electronic
The dump energy of taxi is enough to maintain to be moved to the charging pile from current location；2) charging pile it is all it is eligible 1)
Additional moving distance is minimum in charging pile, that is, the driving path for deviateing the path candidate is most short；The charging pile selected is as this time
The corresponding charging pile of routing diameter selects the probability of the charging pile to be equal to the probability of this path candidate；
Step 8) repeats step 7, until K path candidate all selects corresponding charging pile and probability；
K charging pile and corresponding probability are recommended user by step 9).
Compared with prior art, the beneficial effects of the present invention are recommend present invention can ensure that electric taxi reaches
Charging pile before will not cast anchor, and the extra path from the current location of electric car to destination is most short.The present invention can
For in electric vehicle.
It is further limited as of the invention, training in step 3) method particularly includes: by the longitude and latitude of tracing point
The location mode at T moment is input in decoder module, decoder module by sequence as input by encoder module
The output in each stage not as next stage input, but directly using the track point sequence after the T moment as input, with
This analogizes, and finally obtains a trained encoder-decoder model.The training method, which mainly passes through, to be forgotten to carry out correlation
Important information preservation is got off in the update of inessential information, can thus remember key message constantly.
It is further limited as of the invention, place of the encoder module in step 4) test phase to track point sequence
Reason process is as the encoder module in the training stage, and in decoder module, the prediction result in each stage will be inputted
Into next stage；Finally obtain the Future Trajectory point sequence predicted by decoder module；In each LSTM structure
Calculating process includes:
Forget door:
Input gate:
Candidate door:
Memory unit state vector: Ct=ft×Ct-1+it×C′t
Out gate:
Output quantity: ht=Ot×tanh(Ct)
Wherein, σ (x): sigmoid function, ft, it, Ot, C 't: gate function,Weight square
Battle array, bf, bi, bc, bo: variable deviation vector, ut: electric car historical track sequence, Ct: memory unit state vector, ht: state output
Vector.Each step of the test method exports all as the input information of next step, and the output result of last moment will affect down
The prediction result at one moment, obtained prediction result can be more accurate.
Detailed description of the invention
Fig. 1 is the road scene figure that electric taxi selects charging pile.
Fig. 2 is flow chart of the invention.
Fig. 3 is the schematic diagram of encoder-decoder model；
It illustrates:
u1,u2,…uT: the historical track sequence of electric car；
C1,C2,…CT: the memory unit state in encoder module each stage；
h1,h2,…hT-1: the state output in encoder module each stage；
C’0,C’1,C’2,…C’T’-1: the memory unit state in deconder module each stage；
h’1,h’2,…h’T’-1: the state output in decoder module each stage；
S1,S2,…ST’: the prediction Future Trajectory sequence of electric car.
Fig. 4 is the schematic diagram of LSTM structure.
It illustrates:
ht-1: the state output of last moment；
Ct-1: the memory unit state of last moment；
ut: current time input；
ht: the state output at current time；
Ct: the memory unit state at current time.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
A kind of road charging pile recommended method based on encoder-decoder model as shown in Figure 2, including following step
Suddenly.
Step 1, at full charge to each electric taxi, and a low battery threshold value δ is set.
Step 2, when the electricity of some electric taxi is lower than threshold value 6, the state of electric taxi is as shown in figure 1 at this time
Shown in automobile 1, which issues the pre-warning signal of not enough power supply, starts to recommend road charging for the electric taxi
Stake.
Step 3,80% historical track of electric taxi is input in encoder-decoder model and is instructed
Practice, using the longitude of tracing point and latitude sequence as the input (u in Fig. 3t), by encoder module by the cell-like at T moment
State (the C in Fig. 3T) be input in decoder module, the output (S in Fig. 3 in decoder module each staget) not as under
The input in one stage, but directly using the track point sequence after the T moment as input, and so on, finally obtain an instruction
The encoder-decoder model perfected.
Step 4, the historical track of the residue 20% of electric taxi is tested, wherein in test phase
Encoder module to the treatment process of track point sequence as the encoder module in the training stage, in decoder module
In, the prediction result s in each stagetIt is input in next stage.Finally obtain the track predicted by decoder module
Sequence；
Calculating process in each LSTM structure (such as Fig. 4) includes:
Forget door:
Input gate:
Candidate door:
Memory unit state vector: Ct=ft×Ct-1+it×C′t
Out gate:
Output quantity: ht=Ot×tanh(Ct)
Wherein, σ (x): sigmoid function
ft, it, Ot, C 't: gate function
Weight matrix
bf, bi, bc, bo: variable deviation vector
ut: electric car historical track sequence
Ct: memory unit state vector
ht: state output vector.
Step 5, using the output result of encoder-decoder model as softmax layers of input, Future Trajectory is exported
The probability that sequence occurs.The new probability formula of the Future Trajectory subsequence of every prediction are as follows:
Wherein i represents the number of this track subsequence appearance, and j represents the number that all track subsequences occur.
Step 6, using softmax layers of output result as the input of Beam Search algorithm, by BeamSearch algorithm
The path candidate of K maximum probability before exporting；The condition probability formula of the Future Trajectory point sequence of every prediction are as follows:
Step 7, it selects charging pile: to each path candidate, selecting the charging pile for meeting the following conditions: (1) is electric
The dump energy of dynamic taxi is enough to maintain to be moved to the charging pile from current location；(2) charging pile is all eligible
(1) additional moving distance is minimum in charging pile, that is, the driving path for deviateing the path candidate is most short.The charging pile conduct selected
The corresponding charging pile of this path candidate selects the probability of the charging pile to be equal to the probability of this path candidate.
Step 8, step 7 is repeated, until K path candidate all selects corresponding charging pile and probability.
Step 9, K charging pile and corresponding probability are recommended into user.
The above, the only specific embodiment in the present invention, but scope of protection of the present invention is not limited thereto, appoints
What is familiar with the people of the technology within the technical scope disclosed by the invention, it will be appreciated that expects transforms or replaces, and should all cover
Within scope of the invention, therefore, the scope of protection of the invention shall be subject to the scope of protection specified in the patent claim.
Claims (3)
1. a kind of road charging pile recommended method based on encoder-decoder model, which is characterized in that including following step
It is rapid:
Step 1) is at full charge to each electric taxi, and a low battery threshold value δ is arranged；
For step 2) when the electricity of some electric taxi is lower than threshold value δ, which issues the early warning letter of not enough power supply
Number, start to recommend road charging pile for the electric taxi；
80% historical track of electric taxi is input in encoder-decoder model and is trained by step 3)；
The historical track of the residue 20% of electric taxi is input to encoder-decoder model and tested by step 4)；
Using the output result of encoder-decoder model as softmax layers of input, described softmax layers is used step 5)
To predict the probability of every Future Trajectory subsequence, the probability that output Future Trajectory sequence occurs, the Future Trajectory of every prediction
The new probability formula of subsequence are as follows:
Wherein, i represents the number of this track subsequence appearance, and j represents the number that all track subsequences occur；
Step 6) is defeated by Beam Search algorithm using softmax layers of output result as the input of Beam Search algorithm
The path candidate of preceding K maximum probability out；The condition probability formula of the Future Trajectory point sequence of every prediction are as follows:
Step 7) selects charging pile: to each path candidate, selecting charging pile for meeting the following conditions: 1) electronic taxi
The dump energy of vehicle is enough to maintain to be moved to the charging pile from current location；2) charging pile is in all eligible chargings 1)
Additional moving distance is minimum in stake, that is, the driving path for deviateing the path candidate is most short；The charging pile selected is as this candidate road
The corresponding charging pile of diameter selects the probability of the charging pile to be equal to the probability of this path candidate；
Step 8) repeats step 7, until K path candidate all selects corresponding charging pile and probability；
K charging pile and corresponding probability are recommended user by step 9).
2. a kind of road charging pile recommended method based on encoder-decoder model according to claim 1, special
Sign is, trains in step 3) method particularly includes: using the longitude of tracing point and latitude sequence as input, by encoder
The location mode at T moment is input in decoder module by module, and the output in decoder module each stage is not as next
The input in stage, but directly using the track point sequence after the T moment as input, and so on, finally obtain a training
Good encoder-decoder model.
3. a kind of road charging pile recommended method based on encoder-decoder model according to claim 2, special
Sign is that the encoder module in step 4) test phase is in the treatment process and training stage of track point sequence
Encoder module is the same, and in decoder module, the prediction result in each stage will be input in next stage；Finally
To the Future Trajectory point sequence predicted by decoder module；Calculating process in each LSTM structure includes:
Forget door:
Input gate:
Candidate door:
Memory unit state vector: Ct=ft×Ct-1+it×C′t
Out gate:
Output quantity: ht=Ot×tanh(Ct)
Wherein, σ (x): sigmoid function, ft, it, Ot, C 't: gate function,Weight matrix, bf,
bi, bc, bo: variable deviation vector, ut: electric car historical track sequence, Ct: memory unit state vector, ht: state output vector.
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| CN111313449A (en) * | 2020-03-02 | 2020-06-19 | 华北电力大学 | Cluster electric vehicle power optimization management method based on machine learning |
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