CN113379187B - Traffic meteorological disaster assessment method and device and computer readable storage medium - Google Patents
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Abstract
The invention relates to a traffic meteorological disaster assessment method, a device and a computer readable storage medium, wherein the method comprises the following steps: acquiring historical meteorological information and historical traffic accident data information; establishing a traffic meteorological disaster prediction model according to the historical meteorological information and the historical traffic accident data information, and training the traffic meteorological disaster prediction model to obtain a trained traffic meteorological disaster prediction model; and re-acquiring historical meteorological information and historical traffic accident data information, inputting the historical meteorological information and the historical traffic accident data information into a trained traffic meteorological disaster prediction model to obtain a traffic accident state vector, and evaluating the traffic meteorological disasters according to the traffic accident state vector. The traffic meteorological disaster assessment method provided by the invention realizes the assessment of the traffic meteorological disaster.
Description
Technical Field
The invention relates to the technical field of meteorological disasters, in particular to a traffic meteorological disaster assessment method and device and a computer readable storage medium.
Background
At present, the early warning problem of meteorological disasters and emergencies is one of the important subjects in the traffic safety field, and attracts people's attention all the time. Due to the variable external environment of traffic, the current communication control means is difficult to accurately grasp the sudden change of weather and a series of emergency events in the traffic environment. Therefore, indexes which cannot be measured originally are predicted through reinforcement learning, and more comprehensive current state analysis and future trend prediction are achieved. The assessment of the meteorological disasters of the road traffic has great significance and value for reducing the traffic accident rate caused by the meteorological disasters. How to avoid traffic accidents caused by severe weather and make accurate and effective assessment and reasonable and safe planning of vehicle speed and lanes in advance is an important challenge in the field of road traffic early warning. The prior art can not evaluate the traffic meteorological disasters.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus and a computer readable storage medium for evaluating a traffic meteorological disaster, which are used to solve the problem that the prior art cannot evaluate the traffic meteorological disaster.
The invention provides a traffic meteorological disaster assessment method, which comprises the following steps:
acquiring historical meteorological information and historical traffic accident data information;
establishing a traffic meteorological disaster prediction model according to the historical meteorological information and the historical traffic accident data information, and training the traffic meteorological disaster prediction model to obtain a trained traffic meteorological disaster prediction model;
and re-acquiring historical meteorological information and historical traffic accident data information, inputting the historical meteorological information and the historical traffic accident data information into a trained traffic meteorological disaster prediction model to obtain a traffic accident state vector, and evaluating the traffic meteorological disasters according to the traffic accident state vector.
Further, according to the historical weather information and the historical traffic accident data information, a prediction model of the traffic weather disaster is established, and the method specifically comprises the following steps:
and establishing a traffic accident state vector according to the historical meteorological information and the historical traffic accident data information, establishing a recurrent neural network, taking the traffic accident state vector at the previous moment and the meteorological information at the current moment as the input of the recurrent neural network, and taking the current traffic accident state vector as the output of the recurrent neural network to establish a prediction model of the traffic meteorological disasters.
Further, the method for establishing the prediction model of the traffic meteorological disaster by using the traffic accident state vector at the previous moment and the meteorological information at the current moment as the input of the recurrent neural network and using the current traffic accident state vector as the output of the recurrent neural network specifically comprises the following steps:
and respectively determining the output information of a forgetting gate, the output information of an input gate and the output information of an output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment and the meteorological information at the current moment, and determining the current traffic accident state vector according to the output information of the forgetting gate, the output information of the input gate and the output information of the output gate so as to establish a traffic meteorological disaster prediction model.
Further, determining the output information of the forgotten door in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time, specifically comprising:
determining the output information of a forgetting gate in an LSTM memory unit structure according to the traffic accident state vector at the previous moment, the weather information at the current moment and an output expression of the forgetting gate, wherein the output expression of the forgetting gate is
Wherein, f t For output information of a forgetting gate, σ is an activation function, W f Weight matrix for forgetting gate, b f In order to forget the biased item of the door,
is the traffic accident state vector at the previous moment, Y t And the weather information is the weather information at the current moment.
Further, determining the output information of the input gate in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time, specifically comprising:
determining the output information of an input gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment, the meteorological information at the current moment and an output expression of the input gate, wherein the output expression of the output gate is
Wherein, the i t For inputting output information of the gate, W i As a weight matrix of the input gates, b i To enter the paranoia of them.
Further, determining the output information of the output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time, specifically comprising:
according to the above oneDetermining the output information of an output gate in an LSTM memory unit structure by using the traffic accident state vector of the moment, the meteorological information of the current moment and an output gate output expression, wherein the output gate output expression is
Wherein o is t For outputting the output information of the gate, W o Weight matrix for output gates, b o Is the biased item of the output gate.
Further, determining the current traffic accident state vector according to the output information of the forgotten gate, the output information of the input gate and the output information of the output gate, specifically comprising:
and determining the memory state of the current moment according to the output information of the forgetting gate, the output information of the input gate and the output information of the output gate, acquiring a state unit of the current moment according to the memory state of the current moment, and acquiring a current traffic accident state vector according to the state unit of the current moment and the output information of the output gate.
Further, acquiring a state unit of the current moment according to the memory state of the current moment, and acquiring a current traffic accident state vector according to the state unit of the current moment and the output information of the output door, specifically comprising:
acquiring a state unit at the current moment according to the memory state and the state unit expression at the current moment, and acquiring a current traffic accident state vector according to the state unit at the current moment, the output information of an output gate and the state vector expression; the state unit expression is
The state vector expression is
Wherein the content of the first and second substances,
for the current memory state, C t As a status unit of the current time, f t For output information of forgetting gate, i t For inputting output information of the gate, o t Is the output information of the output gate.
The invention also provides a traffic weather disaster assessment device, which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the traffic weather disaster assessment method of any technical scheme is realized.
The invention also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the traffic weather hazard assessment method according to any one of the above technical solutions.
Compared with the prior art, the invention has the beneficial effects that: historical meteorological information and historical traffic accident data information are obtained; establishing a traffic meteorological disaster prediction model according to the historical meteorological information and the historical traffic accident data information, and training the traffic meteorological disaster prediction model to obtain a trained traffic meteorological disaster prediction model; re-acquiring historical meteorological information and historical traffic accident data information, inputting the historical meteorological information and the historical traffic accident data information into a trained traffic meteorological disaster prediction model to obtain a traffic accident state vector, and evaluating the traffic meteorological disaster according to the traffic accident state vector; the assessment of the traffic meteorological disasters is realized.
Drawings
FIG. 1 is a schematic flow chart illustrating an embodiment of a traffic weather disaster assessment method according to the present invention;
FIG. 2 shows the structure of the memory cell of the LSTM provided by the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The invention provides a traffic meteorological disaster assessment method, wherein the flow schematic diagram of one embodiment is shown in figure 1, and the method comprises the following steps:
s1, acquiring historical meteorological information and historical traffic accident data information;
s2, establishing a traffic meteorological disaster prediction model according to the historical meteorological information and the historical traffic accident data information, and training the traffic meteorological disaster prediction model to obtain a trained traffic meteorological disaster prediction model;
and S3, re-acquiring historical meteorological information and historical traffic accident data information, inputting the historical meteorological information and the historical traffic accident data information into a trained traffic meteorological disaster prediction model to obtain a traffic accident state vector, and evaluating the traffic meteorological disasters according to the traffic accident state vector.
In a specific embodiment, historical weather information and historical traffic accident data information are obtained, a data twin is constructed by using a data twin technology, and a virtual environment which is mapped to a physical world is constructed.
As a preferred embodiment, establishing a prediction model of the traffic weather disaster according to the historical weather information and the historical traffic accident data information specifically includes:
and establishing a traffic accident state vector according to the historical meteorological information and the historical traffic accident data information, establishing a recurrent neural network, taking the traffic accident state vector at the previous moment and the meteorological information at the current moment as the input of the recurrent neural network, and taking the current traffic accident state vector as the output of the recurrent neural network to establish a prediction model of the traffic meteorological disasters.
In one embodiment, historical weather information of a past period of time is input into a prediction model of the traffic weather disaster, and the historical weather information comprises T continuous time instants (1,2.. The., T) in the history and a weather observation value corresponding to the T continuous time instants (1,2.. The., T) as Y t ＝(y 1 ,y 2 ,...,y T ) Wherein, the weather information data corresponding to a certain time t includes k weather index values, which can be expressed as
Then, a history of a past period of time is inputtedTraffic accident data information, S = (S) 1 ,s 2 ,...,s T ) (ii) a Establishing the influence relationship between the meteorological traffic conditions and the traffic accidents,
wherein the content of the first and second substances,
the traffic accident state vector is obtained after the influence of meteorological conditions on traffic safety is considered.
As a preferred embodiment, the method for establishing a prediction model of a traffic meteorological disaster by using a traffic accident state vector at a previous moment and meteorological information at a current moment as inputs of a recurrent neural network and using a current traffic accident state vector as an output of the recurrent neural network specifically includes:
and respectively determining the output information of a forgetting gate, the output information of an input gate and the output information of an output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment and the meteorological information at the current moment, and determining the current traffic accident state vector according to the output information of the forgetting gate, the output information of the input gate and the output information of the output gate so as to establish a traffic meteorological disaster prediction model.
In one embodiment, an LSTM algorithm in a recurrent neural network is used for establishing a prediction model of the traffic meteorological disaster; in a recurrent neural network, the input to the neural network in the recurrent body has two parts, one being the state vector at the upper time instant under consideration of the influence of meteorological conditions on the traffic accident
The other part is the input meteorological sample Y at the current moment t (ii) a After splicing the two, outputting a new state vector through tanh operation
As a preferred embodiment, determining the output information of the forgotten gate in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time specifically includes:
determining the output information of a forgetting gate in an LSTM memory unit structure according to the traffic accident state vector at the previous moment, the weather information at the current moment and an output expression of the forgetting gate, wherein the output expression of the forgetting gate is
Wherein f is t For output information of a forgetting gate, σ is an activation function, W f Weight matrix for forgetting gate, b f In order to forget the biased item of the door,
is the traffic accident state vector at the previous moment, Y t And the weather information is the weather information at the current moment.
In one embodiment, the memory cell structure of LSTM, as shown in FIG. 2, the forgetting gate output expression is
f t A status unit C for the output information of the forgetting gate, the forgetting gate determining the last time t-1 How many state cells can be left to current, the activation function, typically a sigmoid function,
means to concatenate two vectors into one longer vector;
as a preferred embodiment, determining the output information of the input gate in the LSTM memory unit structure according to the traffic accident status vector at the previous time and the weather information at the current time specifically includes:
determining the output information of an input gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment, the meteorological information at the current moment and an output expression of the input gate, wherein the output expression of the output gate is
Wherein, the i t For inputting output information of the gate, W i As a weight matrix of the input gates, b i To input their paranoia.
The output information of the gate is input, and the gate determines Y of the network at the present time t How much to save to cell state C t It is possible to prevent the currently insignificant contents from entering the memory.
As a preferred embodiment, determining the output information of the output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time specifically includes:
determining the output information of an output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment, the meteorological information at the current moment and an output gate output expression, wherein the output gate output expression is
Wherein o is t For outputting the output information of the gate, W o Weight matrix for output gates, b o Is the biased item of the output gate.
It should be noted that the output information of the output gate indicates how much of the state of the output gate control unit is output to the current output value of the LSTM, that is, the influence of the control long-term memory on the current output.
As a preferred embodiment, determining the current traffic accident state vector according to the output information of the forgotten gate, the output information of the input gate, and the output information of the output gate specifically includes:
and determining the memory state of the current moment according to the output information of the forgetting gate, the output information of the input gate and the output information of the output gate, acquiring the state unit of the current moment according to the memory state of the current moment, and acquiring the state vector of the current traffic accident according to the state unit of the current moment and the output information of the output gate.
As a preferred embodiment, the method for acquiring a state unit at the current time according to a memory state at the current time and acquiring a current traffic accident state vector according to the state unit at the current time and output information of an output gate includes:
according toAcquiring a state unit at the current moment according to the memory state and the state unit expression at the current moment, and acquiring a current traffic accident state vector according to the state unit at the current moment, the output information of an output door and the state vector expression; the state unit expression is
The state vector expression is
Wherein the content of the first and second substances,
for the current memory state, C t As a status unit of the current time, f t To forget the output information of the door, i t For inputting output information of the gate, o t Is the output information of the output gate.
In one embodiment, the current memory state
Obtaining the current memory state through state adjustment
The final output part transforms the memory value into a value between-1 and +1 by using an activation function tanh (), and a positive interval represents reasonable output. After the traffic accident state vector is obtained, the traffic meteorological disasters are evaluated, the larger the numerical value is, the larger the traffic meteorological disasters are, the real-time state monitoring and the dynamic state evaluation of the target object can be realized by utilizing the VR presenting technology, and a feedback and early warning report is sent to a physical entity.
The invention also provides a traffic meteorological disaster assessment device, which comprises a processor and a memory, wherein the memory is stored with a computer program, and the computer program is executed by the processor to realize all the above mentioned traffic meteorological disaster assessment methods. It can be understood that, in the present embodiment, the traffic weather disaster assessment apparatus can achieve the same functions as the above method, and has the same technical effects, which are not described herein again.
Embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements all the traffic weather hazard assessment methods mentioned above. It can be understood that, in the present embodiment, the traffic weather disaster assessment apparatus can achieve the same functions as the above method, and has the same technical effects, which are not described herein again.
In summary, the invention discloses a traffic weather disaster assessment method, a device and a computer readable storage medium, which are used for obtaining historical weather information and historical traffic accident data information; establishing a traffic meteorological disaster prediction model according to the historical meteorological information and the historical traffic accident data information, and training the traffic meteorological disaster prediction model to obtain a trained traffic meteorological disaster prediction model; re-acquiring historical meteorological information and historical traffic accident data information, inputting the historical meteorological information and the historical traffic accident data information into a trained traffic meteorological disaster prediction model to obtain a traffic accident state vector, and evaluating the traffic meteorological disaster according to the traffic accident state vector; the assessment of the traffic meteorological disasters is realized. The technical scheme of the invention combines a prediction model with a digital twin, simulates a prediction result in a virtual model, realizes real-time state monitoring and dynamic state evaluation of a target object by utilizing a VR presentation technology, and feeds back and early warning reports to a physical entity.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (9)
1. A traffic meteorological disaster assessment method is characterized by comprising the following steps:
acquiring historical meteorological information and historical traffic accident data information;
establishing a traffic meteorological disaster prediction model according to the historical meteorological information, the historical traffic accident data information and the meteorological information at the current moment, and training the traffic meteorological disaster prediction model to obtain a trained traffic meteorological disaster prediction model;
re-acquiring historical meteorological information and historical traffic accident data information, inputting the historical meteorological information, the historical traffic accident data information and the meteorological information at the current moment into a trained traffic meteorological disaster prediction model to obtain a current traffic accident state vector, and evaluating the traffic meteorological disaster according to the current traffic accident state vector;
according to the historical meteorological information, the historical traffic accident data information and the meteorological information at the current moment, a prediction model of the traffic meteorological disaster is established, and the method specifically comprises the following steps:
and establishing a traffic accident state vector according to the historical meteorological information and the historical traffic accident data information, establishing a recurrent neural network, taking the traffic accident state vector at the previous moment and the meteorological information at the current moment as the input of the recurrent neural network, and taking the current traffic accident state vector as the output of the recurrent neural network to establish a prediction model of the traffic meteorological disasters.
2. The method for assessing traffic meteorological disasters according to claim 1, wherein the method for establishing a predictive model of traffic meteorological disasters by using the traffic accident state vector at the previous moment and the meteorological information at the current moment as inputs of a recurrent neural network and using the current traffic accident state vector as an output of the recurrent neural network comprises:
and respectively determining the output information of a forgetting gate, the output information of an input gate and the output information of an output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment and the meteorological information at the current moment, and determining the current traffic accident state vector according to the output information of the forgetting gate, the output information of the input gate and the output information of the output gate so as to establish a traffic meteorological disaster prediction model.
3. The method for assessing traffic weather disasters according to claim 2, wherein determining the output information of a forgotten door in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time specifically comprises:
determining the output information of a forgetting gate in an LSTM memory unit structure according to the traffic accident state vector at the previous moment, the weather information at the current moment and an output expression of the forgetting gate, wherein the output expression of the forgetting gate is
Wherein f is t For output information of a forgetting gate, σ is an activation function, W f Weight matrix for forgetting gate, b f In order to forget the biased term of the door,
is the traffic accident state vector at the previous moment, Y t And the weather information is the weather information at the current moment.
4. The method for assessing traffic weather disasters according to claim 3, wherein determining the output information of the input gates in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time comprises:
determining the output information of an input gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment, the meteorological information at the current moment and an output expression of the input gate, wherein the output expression of the output gate is
Wherein, the i t For inputting output information of the gate, W i As a weight matrix of the input gates, b i Is the offset term of the input gate.
5. The method for assessing traffic weather disasters according to claim 4, wherein determining the output information of the output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous time and the weather information at the current time comprises:
determining the output information of an output gate in the LSTM memory unit structure according to the traffic accident state vector at the previous moment, the meteorological information at the current moment and an output gate output expression, wherein the output gate output expression is
Wherein o is t For outputting the output information of the gate, W o As a weight matrix of output gates, b o Is the bias term of the output gate.
6. The method for assessing traffic weather disasters according to claim 2, wherein determining a current traffic accident state vector according to the output information of the forgotten gate, the output information of the input gate, and the output information of the output gate specifically comprises:
and determining the memory state of the current moment according to the output information of the forgetting gate, the output information of the input gate and the output information of the output gate, acquiring the state unit of the current moment according to the memory state of the current moment, and acquiring the state vector of the current traffic accident according to the state unit of the current moment and the output information of the output gate.
7. The method for assessing traffic meteorological disasters according to claim 6, wherein the obtaining the state unit of the current time according to the memory state of the current time, and the obtaining the state vector of the current traffic accident according to the state unit of the current time and the output information of the output gate specifically comprise:
acquiring a state unit at the current moment according to the memory state and the state unit expression at the current moment, and acquiring a current traffic accident state vector according to the state unit at the current moment, the output information of an output gate and the state vector expression at the current moment; the state unit expression is
The state vector expression is
Wherein the content of the first and second substances,
for the current memory state, C t As a status unit of the current time, f t For output information of forgetting gate, i t For inputting output information of the gate, o t Is the output information of the output gate.
8. A traffic weather hazard assessment apparatus comprising a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, carries out the traffic weather hazard assessment method according to any one of claims 1 to 7.
9. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a traffic weather hazard assessment method according to any one of claims 1 to 7.
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