CN115273456B - Method, system and storage medium for judging illegal running of two-wheeled electric vehicle - Google Patents
Method, system and storage medium for judging illegal running of two-wheeled electric vehicle Download PDFInfo
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Abstract
The invention relates to a method, a system and a storage medium for judging illegal running of a two-wheeled electric vehicle, which comprise the following steps: training the originally generated countermeasure network model based on the collected historical driving data of the plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model; substituting current driving data of the two-wheeled electric vehicle to be tested into a target to generate an countermeasure network model to obtain a current violation index of the two-wheeled electric vehicle to be tested; judging whether the current violation index is larger than a preset violation index, if so, judging that the two-wheel electric vehicle to be tested has violation running. According to the invention, the characteristics that the countermeasures network model is generated without constructing a labeled training set and modeling is performed on complex high-dimensional data are utilized, and according to the characteristic that the proportion of the illegal driving data of the two-wheel electric vehicle in the original training data set is small, the process of manually screening the combined characteristics is omitted, and meanwhile, the accuracy of the recognition result of the illegal driving behavior of the two-wheel electric vehicle is improved.
Description
Technical Field
The invention relates to the technical field of vehicle supervision, in particular to a method, a system and a storage medium for judging illegal running of a two-wheeled electric vehicle.
Background
Currently, in order to solve various potential safety hazards existing in actual use of the two-wheeled electric vehicle, related departments go out of a plurality of specifications to restrict the behavior of the two-wheeled electric vehicle, and in order to ensure effective execution of the specifications, a supervision method needs to be introduced, and when the two-wheeled electric vehicle enters an illegal state, the illegal behavior is timely identified and corresponding prompt and record are made. The traditional method is to arrange manual work to identify the illegal action, but the method has higher labor cost. The other is based on the traditional supervised or unsupervised machine learning method, but the supervised machine learning method needs to manually mark a large amount of two-wheel vehicle operation history data according to the driving specification, has high cost and limited coverage rate, and is often insufficient to meet the training requirement of a supervised machine learning model; although the unsupervised machine learning method can avoid the construction of the annotation set, the existing unsupervised method still has the problems of poor modeling performance of complex high-dimensional data and the like; meanwhile, the ratio of illegal behaviors in a sample set is very small, and the traditional machine learning method usually requires equivalent positive and negative sample volumes to achieve an ideal effect.
Therefore, a technical solution is needed to solve the existing problems.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a system and a storage medium for judging illegal running of a two-wheeled electric vehicle.
The technical scheme of the method for judging the illegal running of the two-wheeled electric vehicle is as follows:
training the originally generated countermeasure network model based on the collected historical driving data of the plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model;
acquiring current driving data of the two-wheeled electric vehicle to be tested, substituting the current driving data into the target to generate an countermeasure network model, and obtaining a current violation index of the two-wheeled electric vehicle to be tested;
judging whether the current violation index is larger than a preset violation index or not, obtaining a judging result, and if the judging result is yes, judging that the two-wheel electric vehicle to be tested has violation running.
The method for judging the illegal running of the two-wheeled electric vehicle has the following beneficial effects:
the method utilizes the characteristics that the generation of the countermeasure network model does not need to construct a labeling training set and models on complex high-dimensional data, and according to the characteristic that the proportion of the illegal driving data of the two-wheel electric vehicle in the original training data set is small, the method omits the process of manually screening the combined characteristics and improves the accuracy of the recognition result of the illegal driving behavior of the two-wheel electric vehicle.
On the basis of the scheme, the method for judging the illegal running of the two-wheeled electric vehicle can be improved as follows.
Further, the original generator of the original generation countermeasure network model adopts a stacked stride convolution layer, and the original arbiter of the original generation countermeasure network model adopts a standard CNN network structure.
Further, training to obtain a target generation countermeasure network model according to all the historical driving data and the original generation countermeasure network model, including:
training the original generator and the original discriminator based on a training optimization function and all historical driving data to obtain a target generator and a target discriminator;
obtaining the target generation countermeasure network model according to the target generator and the target discriminator; wherein the training optimization function is:
for the training optimization function, x is a feature vector formed by any historical driving data, D 1 G as the original discriminator 1 For the original generator, z is the original generating input variable of the countermeasure network, p data (x) For the distribution obeyed by x, D 1 (x) For x is the output of said original arbiter,/i>Indicating that when x obeys p data (x) D at the time of distribution 1 (x) Expected value of logarithmic value of p z (z) is a uniform distribution obeyed by z,indicating that when z obeys the distribution p z (z) 1-D 1 (G 1 (z)) is determined.
Further, substituting the current driving data into the target to generate an countermeasure network model to obtain a current violation index of the two-wheeled electric vehicle to be detected, including:
substituting the current driving data into an objective function corresponding to the objective generation countermeasure network model for iterative computation until the objective function value is minimum, and obtaining the current violation index of the two-wheeled electric vehicle to be detected; wherein the objective function is: l (z) = (1- λ) Σ|x 1 - G 2 (z)|+λ∑|f(x 1 )-f(G 2 (z))|；x 1 G, for the current driving data 2 G for the target generator 2 (z) is the data generated by the input variable z after the target generator acts, and f is the target discriminator D 2 L (z) is the objective function for measuring G 2 (z) and x 1 Similarity of (2); lambda is a preset weight, L (z γ ) Z is the current violation index γ The value of z corresponding to the time when L (z) converges to the minimum.
Further, the historical driving data of any two-wheeled electric vehicle comprises: driving speed, driving longitude, driving latitude, course angle, driving gear, car light state, longitudinal acceleration, transverse acceleration, motor output speed, motor torque, yaw rate, front wheel speed, rear wheel speed, battery residual capacity and road direction.
Further, the method further comprises the following steps: and when the judgment result is yes, sending illegal prompt information to the two-wheel electric vehicle to be tested.
The technical scheme of the system for judging the illegal running of the two-wheeled electric vehicle is as follows:
comprising the following steps: the device comprises a construction module, an operation module and a judgment module;
the construction module is used for: training the originally generated countermeasure network model based on the collected historical driving data of the plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model;
the operation module is used for: acquiring current driving data of the two-wheeled electric vehicle to be tested, substituting the current driving data into the target to generate an countermeasure network model, and obtaining a current violation index of the two-wheeled electric vehicle to be tested;
the judging module is used for: judging whether the current violation index is larger than a preset violation index or not, obtaining a judging result, and if the judging result is yes, judging that the two-wheel electric vehicle to be tested has violation running.
The system for judging the illegal running of the two-wheeled electric vehicle has the following beneficial effects:
the system utilizes the characteristics that the generation of the countermeasure network model does not need to construct a labeling training set and models on complex high-dimensional data, and according to the characteristic that the proportion of the illegal driving data of the two-wheel electric vehicle in the original training data set is small, the system omits the process of manually screening the combined characteristics and improves the accuracy of the recognition result of the illegal driving behavior of the two-wheel electric vehicle.
On the basis of the scheme, the system for judging the illegal running of the two-wheeled electric vehicle can be improved as follows.
Further, the original generator of the original generation countermeasure network model adopts a stacked stride convolution layer, and the original arbiter of the original generation countermeasure network model adopts a standard CNN network structure.
Further, the construction module is specifically configured to:
training the original generator and the original discriminator based on a training optimization function and all historical driving data to obtain a target generator and a target discriminator;
obtaining the target generation countermeasure network model according to the target generator and the target discriminator; wherein the training optimization function is:
for the training optimization function, x is a feature vector formed by any historical driving data, D 1 G as the original discriminator 1 For the original generator, z is the original generating input variable of the countermeasure network, p data (x) For the distribution obeyed by x, D 1 (x) For x is the output of said original arbiter,/i>Indicating that when x obeys p data (x) D at the time of distribution 1 (x) Expected value of logarithmic value of p z (z) is a uniform distribution obeyed by z,indicating that when z obeys the distribution p z (z) 1-D 1 (G 1 (z)) is determined.
The technical scheme of the storage medium is as follows:
the storage medium stores instructions that, when read by a computer, cause the computer to execute steps of a method for determining the offensive running of the two-wheeled electric vehicle according to the present invention.
Drawings
Fig. 1 is a flow chart of a method for determining illegal running of a two-wheeled electric vehicle according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of a system for determining illegal running of a two-wheeled electric vehicle according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the method for determining the illegal running of the two-wheeled electric vehicle according to the embodiment of the invention comprises the following steps:
s1, training an originally generated countermeasure network model based on collected historical driving data of a plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model.
Wherein, the historical driving data of any two-wheeled electric motor car all includes: speed X of travelling crane 0 Longitude X 1 Latitude X 2 Heading angle X 3 Gear X 4 Vehicle lamp state X 5 Longitudinal acceleration X 6 Lateral acceleration X 7 Motor output rotation speed X 8 Motor torque X 9 Yaw rate X 10 Front wheel speed X 11 Rear wheel speed X 12 Battery remaining capacity X 13 Road direction X 14 。
Wherein the initially generated antagonism network model generates a antagonism network (GAN).
Generating an original input feature vector X of the countermeasure network through any historical driving data structure; x= [ X ] 0 ，X 1 ，X 2 ，X 3 ，X 4 ，X 5 ，X 6 ，X 7 ，X 8 ，X 9 ，X 10 ，X 11 ，X 12 ，X 13 ， X 14 ]And each X of the constructs is combined into a training dataset that originally generated the countermeasure network model.
The target generation countermeasure network model is as follows: and training the original generated countermeasure network model through the training data set to obtain the generated countermeasure network model.
S2, acquiring current driving data of the two-wheeled electric vehicle to be tested, substituting the current driving data into the target to generate an countermeasure network model, and obtaining the current violation index of the two-wheeled electric vehicle to be tested.
The two-wheel electric vehicle to be detected is the two-wheel electric vehicle to be judged, and the current driving data and the data type selected by the historical driving data are the same, and all the two-wheel electric vehicle to be detected comprises the 14 index features.
Wherein, the current violation index is: and generating a value corresponding to the nearest point in the distribution of the characteristic vectors X and G of the two-wheel electric vehicle to be detected, which is obtained by performing reverse iteration on the objective function corresponding to the countermeasure network model according to the objective.
And S3, judging whether the current violation index is larger than a preset violation index or not, obtaining a judgment result, and if the judgment result is yes, judging that the two-wheeled electric vehicle to be tested has violation running.
The preset violation index is a value preset by a user, and can be adjusted according to the severity of the judgment of the violation in actual use.
Specifically, if the current violation index is larger than the preset violation index, judging that the two-wheel electric vehicle is in violation; otherwise, judging that the two-wheel electric vehicle is in normal running.
Preferably, the original generator of the original generation countermeasure network model adopts a stacked stride convolution layer, and the original arbiter of the original generation countermeasure network model adopts a standard CNN network structure.
The original generation countermeasure network model includes a generator (corresponding to the original generator in the present embodiment) and a discriminator (corresponding to the original discriminator in the present embodiment).
Specifically, the original generator G 1 For learning the distribution of random variables z through x in the training data set from a potential uniform distribution, a convolutional decoder network constructed from a stack of stride convolutional layers is selected as the original generator in this embodiment; wherein the stacked representation is a way of constructing a decoder network as an original generator; the stride convolution layer represents that when the convolution layer performs mapping operation on input data, the step length of each movement of the convolution kernel is greater than 1 (namely, the convolution kernel movement has the characteristic of stride movement). Original discriminator D 1 For outputting representative of a given inputThe feature vector X of (e.g., the feature vector X corresponding to the current driving data of the two-wheeled electric vehicle to be tested in the embodiment) is derived from the training data in the training data set or from the probability estimation generated by G (z), and in the embodiment, the standard CNN network structure is selected as the original discriminator. The standard CNN network belongs to the category of neural networks, and particularly forms a network structure by stacking a group of convolution layers, so that mapping from an input space to an output space is realized.
Preferably, the training to obtain the target generated countermeasure network model according to all the historical driving data and the original generated countermeasure network model includes:
and training the original generator and the original discriminator based on the training optimization function and all the historical driving data to obtain a target generator and a target discriminator.
And obtaining the target generation countermeasure network model according to the target generator and the target discriminator. Wherein the training optimization function is:
for the training optimization function, x is a feature vector formed by any historical driving data, D 1 G as the original discriminator 1 For the original generator, z is the original generating input variable of the countermeasure network, p data (x) For the distribution obeyed by x, D 1 (x) For x is the output of said original arbiter,/i>Indicating that when x obeys p data (x) D at the time of distribution 1 (x) Expected value of logarithmic value of p z (z) is a uniform distribution obeyed by z,indicating that when z obeys the distribution p z (z) 1-D 1 (G 1 (z)) is determined.
Wherein, training the optimization function pair G 1 And D 1 Training parameters of (1) to obtain generator G meeting user requirements 2 And discriminator D 2 。
It should be noted that, in this embodiment, the training process for the original generator and the original arbiter is the prior art. Specifically, as can be seen from the training optimization function described above, the training process includes the following three steps:
(1) the training process of the original discriminator is as follows: fixed original generator G 1 Iteratively adjusting the parameters of the original arbiter D 1 Up to V (D) 1 ，G 1 ) The value converges to a maximum value;
(2) the training process of the original generator is as follows: fixed original discriminant D 1 Parameter, iteratively adjust original generator G 1 Up to V (D) 1 ，G 1 ) The value converges to a minimum value;
(3) the process of steps 1 and 2 is circularly performed until G 1 And V (D) 1 ，G 1 ) Until the parameters of (a) are stably converged.
Preferably, substituting the current driving data into the target generation countermeasure network model to obtain the current violation index of the two-wheeled electric vehicle to be detected includes:
substituting the current driving data into an objective function corresponding to the objective generation countermeasure network model for iterative computation until the objective function value is minimum, and obtaining the current violation index of the two-wheeled electric vehicle to be detected; wherein the objective function is: l (z) = (1- λ) Σ|x 1 - G 2 (z)|+λ∑|f(x 1 )-f(G 2 (z))|；x 1 G, for the current driving data 2 G for the target generator 2 (z) is the data generated by the input variable z after the target generator acts, and f is the target discriminator D 2 L (z) is the objective function for measuring G 2 (z) and x 1 Similarity of (2)The method comprises the steps of carrying out a first treatment on the surface of the Lambda is a preset weight, L (z γ ) Z is the current violation index γ The value of z corresponding to the time when L (z) converges to the minimum.
Wherein L (z) is used for measuring the data G generated by any z after the action of the target generator 2 (z) and x 1 Is indirectly reflected by x 1 The degree of the normal driving data, so that the larger L (z), the x 1 The greater the likelihood of being a driving violation.
The objective function in this embodiment is obtained by weighted averaging of the two indices. The first part is: sigma|x 1 -G 2 (z) is used for the two-wheeled electric vehicle x to be tested 1 And z via target generator G 2 The result of the action is expressed as x 1 The distance in the feature space is the similarity of the scale measurement, and the similarity describes the similarity under the view angle of the target generator; the second part is Σ|f (x 1 )- f(G 2 (z))ifor measuring the two-wheeled electric vehicle x to be measured 1 With target generator G 2 The similarity of the generated result in the space after the mapping of the intermediate layer f of the target discriminator, wherein the similarity describes the similarity under the view angle of the target discriminator; the fused objective function L (z) is obtained by weighted averaging the two indices by the parameter λ.
In this embodiment, the trained target generation countermeasure network model is integrated into the road side equipment, and when the road side equipment receives current driving data broadcast by the peripheral two-wheeled electric vehicles through the V2X BSM message in real time through the PC5 interface, the road side equipment can monitor the illegal driving behaviors of the two-wheeled electric vehicles on both sides of the road.
Preferably, the historical driving data of any two-wheeled electric vehicle comprises: driving speed, driving longitude, driving latitude, course angle, driving gear, car light state, longitudinal acceleration, transverse acceleration, motor output speed, motor torque, yaw rate, front wheel speed, rear wheel speed, battery residual capacity and road direction.
Preferably, the method further comprises: and when the judgment result is yes, sending illegal prompt information to the two-wheel electric vehicle to be tested.
Specifically, when determining that the two-wheeled electric vehicle to be detected is driving against rules, sending rule-breaking prompt information to the two-wheeled electric vehicle to be detected, wherein the prompt information can be: notifying the driver of safety, etc. For example, when the road test device determines that a certain two-wheeled electric vehicle is driving in a violation, the road test device sends a prompt message back to the two-wheeled electric vehicle in the violation through the PC5 interface and informs a driver of safety through the human-computer interaction interface HMI.
Besides sending the illegal prompt information to the two-wheeled electric vehicle to be tested, the method can also register the illegal vehicle or further confirm whether the illegal vehicle runs or not through manual verification.
According to the technical scheme, the characteristics that the countermeasures network model is generated without constructing a labeling training set and modeling is performed on complex high-dimensional data are utilized, and according to the characteristic that the proportion of the illegal driving data of the two-wheel electric vehicle in the original training data set is small, the process of manually screening the combined characteristics is omitted, and meanwhile, the accuracy of the recognition result of the illegal driving behavior of the two-wheel electric vehicle is improved.
Fig. 2 is a system 200 for determining illegal driving of a two-wheeled electric vehicle according to an embodiment of the present invention, including: a construction module 210, a running module 220, and a decision module 230;
the construction module 210 is configured to: training the originally generated countermeasure network model based on the collected historical driving data of the plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model;
the operation module 220 is configured to: acquiring current driving data of the two-wheeled electric vehicle to be tested, substituting the current driving data into the target to generate an countermeasure network model, and obtaining a current violation index of the two-wheeled electric vehicle to be tested;
the determining module 230 is configured to: judging whether the current violation index is larger than a preset violation index or not, obtaining a judging result, and if the judging result is yes, judging that the two-wheel electric vehicle to be tested has violation running.
Preferably, the original generator of the original generation countermeasure network model adopts a stacked stride convolution layer, and the original arbiter of the original generation countermeasure network model adopts a standard CNN network structure.
Preferably, the construction module is specifically configured to:
training the original generator and the original discriminator based on a training optimization function and all historical driving data to obtain a target generator and a target discriminator;
obtaining the target generation countermeasure network model according to the target generator and the target discriminator; wherein the training optimization function is:
for the training optimization function, x is a feature vector formed by any historical driving data, D 1 G as the original discriminator 1 For the original generator, z is the original generating input variable of the countermeasure network, p data (x) For the distribution obeyed by x, D 1 (x) For x is the output of said original arbiter,/i>Indicating that when x obeys p data (x) D at the time of distribution 1 (x) Expected value of logarithmic value of p z (z) is a uniform distribution obeyed by z,indicating that when z obeys the distribution p z (z) 1-D 1 (G 1 (z)) is determined.
According to the technical scheme, the characteristics that the countermeasures network model is generated without constructing a labeling training set and modeling is performed on complex high-dimensional data are utilized, and according to the characteristic that the proportion of the illegal driving data of the two-wheel electric vehicle in the original training data set is small, the process of manually screening the combined characteristics is omitted, and meanwhile, the accuracy of the recognition result of the illegal driving behavior of the two-wheel electric vehicle is improved.
The steps for implementing the corresponding functions of the parameters and the modules in the system 200 for determining the illegal running of the two-wheeled electric vehicle according to the present invention may refer to the parameters and the steps in the embodiments of the method for determining the illegal running of the two-wheeled electric vehicle, which are not described herein.
The storage medium provided by the embodiment of the invention comprises: the storage medium stores instructions, and when the instructions are read by the computer, the computer executes the steps of the method for determining the illegal running of the two-wheeled electric vehicle, and the specific reference may be made to the parameters and the steps in the embodiment of the method for determining the illegal running of the two-wheeled electric vehicle, which are not described herein.
Computer storage media such as: flash disk, mobile hard disk, etc.
Those skilled in the art will appreciate that the present invention may be implemented as a method, system, and storage medium.
Thus, the invention may be embodied in the form of: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code. Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (5)
1. The method for judging the illegal running of the two-wheeled electric vehicle is characterized by comprising the following steps of:
training the originally generated countermeasure network model based on the collected historical driving data of the plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model;
acquiring current driving data of the two-wheeled electric vehicle to be tested, substituting the current driving data into the target to generate an countermeasure network model, and obtaining a current violation index of the two-wheeled electric vehicle to be tested;
judging whether the current violation index is larger than a preset violation index or not to obtain a judging result, and if the judging result is yes, judging that the two-wheel electric vehicle to be tested has violation running;
the original generator of the original generation countermeasure network model adopts a laminated stride convolution layer, and the original discriminator of the original generation countermeasure network model adopts a standard CNN network structure;
training to obtain a target generation countermeasure network model according to all the historical driving data and the original generation countermeasure network model, wherein the training comprises the following steps:
training the original generator and the original discriminator based on a training optimization function and all historical driving data to obtain a target generator and a target discriminator;
obtaining the target generation countermeasure network model according to the target generator and the target discriminator; wherein the training optimization function is:
for the training optimization function, x is a feature vector formed by any historical driving data, D 1 G as the original discriminator 1 For the original generator, z is the original generating input variable of the countermeasure network, p data (x) For the distribution obeyed by x, D 1 (x) For x is the output of said original arbiter,/i>Indicating that when x obeys p data (x) D at the time of distribution 1 (x) Expected value of logarithmic value of p z (z) is a uniform distribution obeyed by z,indicating that when z obeys the distribution p z (z) 1-D 1 (G 1 (z)) expected values of logarithmic values;
substituting the current driving data into the target to generate an countermeasure network model to obtain the current violation index of the two-wheeled electric vehicle to be detected, wherein the method comprises the following steps:
substituting the current driving data into an objective function corresponding to the objective generation countermeasure network model for iterative computation until the objective function value is minimum, and obtaining the current violation index of the two-wheeled electric vehicle to be detected; wherein the objective function is: l (z) = (1- λ) Σ|x 1 -G 2 (z)|+λ∑|f(x 1 )-f(G 2 (z))|；x 1 G, for the current driving data 2 G for the target generator 2 (z) is the data generated by the input variable z after the action of the target generator, and f is the target discriminator D 2 L (z) is the objective function for measuring G 2 (z) and x 1 Similarity of (2); lambda is a preset weight, L (z γ ) Z is the current violation index γ The value of z corresponding to the time when L (z) converges to the minimum.
2. The method for determining the illegal driving of the two-wheeled electric vehicle according to claim 1, wherein the historical driving data of any two-wheeled electric vehicle comprises: driving speed, driving longitude, driving latitude, course angle, driving gear, car light state, longitudinal acceleration, transverse acceleration, motor output speed, motor torque, yaw rate, front wheel speed, rear wheel speed, battery residual capacity and road direction.
3. The two-wheeled electric vehicle offence running determination method according to claim 1 or 2, characterized by further comprising: and when the judgment result is yes, sending illegal prompt information to the two-wheel electric vehicle to be tested.
4. The utility model provides a two-wheeled electric motor car rule-breaking travel decision system which characterized in that includes: the device comprises a construction module, an operation module and a judgment module;
the construction module is used for: training the originally generated countermeasure network model based on the collected historical driving data of the plurality of two-wheeled electric vehicles to obtain a target generated countermeasure network model;
the operation module is used for: acquiring current driving data of the two-wheeled electric vehicle to be tested, substituting the current driving data into the target to generate an countermeasure network model, and obtaining a current violation index of the two-wheeled electric vehicle to be tested;
the judging module is used for: judging whether the current violation index is larger than a preset violation index or not to obtain a judging result, and if the judging result is yes, judging that the two-wheel electric vehicle to be tested has violation running;
the original generator of the original generation countermeasure network model adopts a laminated stride convolution layer, and the original discriminator of the original generation countermeasure network model adopts a standard CNN network structure;
the construction module is specifically used for:
training the original generator and the original discriminator based on a training optimization function and all historical driving data to obtain a target generator and a target discriminator;
obtaining the target generation countermeasure network model according to the target generator and the target discriminator; wherein the training optimization function is:
for the training optimization function, x is a feature vector formed by any historical driving data, D 1 G as the original discriminator 1 For the original generator, z is the original generating input variable of the countermeasure network, p data (x) For the distribution obeyed by x, D 1 (x) For x is the output of said original arbiter,/i>Indicating that when x obeys p data (x) D at the time of distribution 1 (x) Expected value of logarithmic value of p z (z) is a uniform distribution obeyed by z,indicating that when z obeys the distribution p z (z) 1-D 1 (G 1 (z)) is described asExpected values of logarithmic values;
the operation module is specifically used for:
substituting the current driving data into an objective function corresponding to the objective generation countermeasure network model for iterative computation until the objective function value is minimum, and obtaining the current violation index of the two-wheeled electric vehicle to be detected; wherein the objective function is: l (z) = (1- λ) Σ|x 1 -G 2 (z)|+λ∑|f(x 1 )-f(G 2 (z))|；x 1 G, for the current driving data 2 G for the target generator 2 (z) is the data generated by the input variable z after the action of the target generator, and f is the target discriminator D 2 L (z) is the objective function for measuring G 2 (z) and x 1 Similarity of (2); lambda is a preset weight, L (z γ ) Z is the current violation index γ The value of z corresponding to the time when L (z) converges to the minimum.
5. A storage medium having stored therein instructions which, when read by a computer, cause the computer to execute a two-wheeled electric vehicle offence running determination method as claimed in any one of claims 1 to 3.
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