CN116821721A - Method, device, equipment and medium for identifying cross-city network about car - Google Patents
Method, device, equipment and medium for identifying cross-city network about car Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for identifying a cross-city network about car. Wherein the method comprises the following steps: acquiring target high-speed traffic data of a target vehicle, and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data; clustering is carried out on the high-speed passing characteristic information of the target, and a target cluster to which the high-speed passing characteristic information of the target belongs is determined; determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; and determining whether the target vehicle is a net-bound vehicle according to the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster. According to the technical scheme, the recognition model can be established in advance based on the high-speed traffic data of the vehicles with large volume, high coverage rate and high authority, whether the vehicles are network-bound vehicles or not is determined according to the recognition model, and accuracy and reliability of network-bound vehicle recognition are improved.
Description
Technical Field
The present invention relates to the field of vehicle identification technologies, and in particular, to a method, an apparatus, a device, and a medium for identifying a vehicle about crossing a metropolitan area network.
Background
Compared with the traditional taxis, the net appointment vehicles are more diversified in vehicle types and colors, and the recognition difficulty of the net appointment vehicles is increased due to the diversification. In the related art, sample data are clustered based on vehicle mobile phone signaling data, and whether the vehicle to be detected is a network vehicle or not is judged by calculating the clustering similarity between the vehicle to be detected and the known network vehicle. However, the characteristics of low data coverage rate, small data volume and poor data authority of the vehicle mobile phone signaling data easily cause deviation of vehicle clustering results, so that the judgment of the clustering similarity is inaccurate, and the accuracy of network vehicle recognition is affected.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for identifying a cross-city network vehicle, which can pre-establish an identification model based on high-speed traffic data of vehicles with large volume, high coverage rate and high authority, determine whether the vehicles are network vehicles according to the identification model, and are beneficial to improving the accuracy and the reliability of network vehicle identification.
According to an aspect of the present invention, there is provided a method for identifying a cross-metropolitan area network about vehicle, the method comprising:
acquiring target high-speed traffic data of a target vehicle, and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data; the target high-speed passing characteristic information comprises total passing times, total passing fees, secondary average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicle in a preset statistical period;
Clustering the target high-speed passing characteristic information to determine a target cluster to which the target high-speed passing characteristic information belongs; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information;
determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not;
and determining whether the target vehicle is a network vehicle or not according to the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster.
According to another aspect of the present invention, there is provided an identification apparatus for a cross-metropolitan area network about vehicle, including:
the system comprises a target traffic characteristic determining module, a target traffic characteristic determining module and a control module, wherein the target traffic characteristic determining module is used for acquiring target high-speed traffic data of a target vehicle and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data; the target high-speed passing characteristic information comprises total passing times, total passing fees, secondary average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicle in a preset statistical period;
The target cluster determining module is used for carrying out clustering processing on the target high-speed passing characteristic information and determining a target cluster to which the target high-speed passing characteristic information belongs; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information;
the target recognition model determining module is used for determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not;
and the network appointment vehicle identification module is used for determining whether the target vehicle is a network appointment vehicle according to the target high-speed passing characteristic information and a target identification model corresponding to the target cluster.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for identifying a cross-metropolitan area network about a vehicle according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for identifying a cross-metropolitan area network about vehicle according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the high-speed target passing data of the target vehicle are obtained, and the high-speed target passing characteristic information of the target vehicle is determined according to the high-speed target passing data; the target high-speed passing characteristic information comprises total passing times, total passing fees, time average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicles in a preset statistical period; clustering is carried out on the high-speed passing characteristic information of the target, and a target cluster to which the high-speed passing characteristic information of the target belongs is determined; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information; determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not; and determining whether the target vehicle is a net-bound vehicle according to the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster. According to the technical scheme, the recognition model can be built in advance based on the high-speed traffic data of the vehicles with large volume, high coverage rate and high authority, whether the vehicles are network-bound vehicles or not is determined according to the recognition model, and accuracy and reliability of network-bound vehicle recognition are improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for identifying a vehicle about crossing a metropolitan area network according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for identifying a vehicle about crossing a metropolitan area network according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an identifying device for a city-crossing vehicle according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing a method for identifying a vehicle about crossing a metropolitan area network according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," "target," and the like in the description and claims of the present invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for identifying a cross-metropolitan area network about car, which is provided in an embodiment of the present invention, and the method may be performed by an identifying device for a cross-metropolitan area network about car, where the identifying device for a cross-metropolitan area network about car may be implemented in a form of hardware and/or software, and the identifying device for a cross-metropolitan area network about car may be configured in an electronic device with data processing capability. As shown in fig. 1, the method includes:
S110, acquiring target high-speed traffic data of the target vehicle, and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data.
Wherein the target vehicle may refer to a vehicle waiting to be identified. The target high-speed traffic data may refer to historical traffic data recorded by the target vehicle during high-speed travel. By way of example, the target speed traffic data may include vehicle information (e.g., license plate number, vehicle color, vehicle load, etc.), high speed traffic information (e.g., entry and exit name, entry and exit time, and traffic amount, etc.). The target high-speed traffic characteristic information can be used for reflecting the high-speed traffic statistics characteristics of the target vehicle in a period of time, and specifically comprises the total traffic times, the total traffic fees, the sub-average traffic fees, the information entropy of the charging and discharging toll stations and the traffic time period of the target vehicle in a preset statistics period. The preset statistics period may be a preset information statistics period, which may be specifically set according to actual application requirements, and is not limited in this embodiment. For example, the preset statistical period may be set to 12 months. The information entropy of the charging and discharging station can be used for reflecting the change degree of vehicles entering and discharging the charging station for a plurality of times, and the smaller the information entropy is, the smaller the change degree is.
S120, clustering is carried out on the target high-speed traffic characteristic information, and a target cluster to which the target high-speed traffic characteristic information belongs is determined.
The target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information. For example, a K-means algorithm may be used to perform clustering processing on the target high-speed traffic feature information, and a target cluster to which the target high-speed traffic feature information belongs may be determined according to the clustering result. It should be noted that, each group of target high-speed traffic characteristic information corresponds to one target cluster.
S130, determining a target recognition model corresponding to the target cluster from at least two candidate recognition models.
The candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not. For example, the candidate recognition model may be a machine learning model. Optionally, the process of establishing the candidate recognition model includes: according to the candidate high-speed traffic data of the candidate vehicles, determining candidate high-speed traffic characteristic information of the candidate vehicles; wherein the candidate high-speed traffic data is authorized for use in establishing a candidate recognition model; clustering the candidate high-speed traffic characteristic information to obtain at least two candidate clustering clusters; the candidate cluster is used for representing the feature category corresponding to the candidate high-speed passing feature information; and performing supervised model training on the preset initial model according to the candidate high-speed traffic characteristic information to obtain a candidate recognition model corresponding to the candidate cluster.
The candidate vehicle may be a vehicle object used for training a candidate recognition model, and specifically, a passenger car of a type including a network about vehicle (a majority) and a non-network about vehicle (a minority) may be selected, that is, a positive sample and a negative sample may be used for model training. To ensure accuracy and practicality of model training, a large number of candidate vehicles are generally selected, and the specific number can be set according to actual requirements. The candidate high-speed traffic feature information may refer to high-speed traffic feature information corresponding to the candidate vehicle, and specifically may refer to a description related to the target high-speed traffic feature information.
The candidate cluster is used for representing feature categories corresponding to the candidate high-speed traffic feature information, and can be specifically set into which category according to actual requirements, which is not limited in this embodiment. Similarly, the candidate high-speed traffic feature information can be clustered by adopting a K-means algorithm. By way of example, with the candidate high-speed traffic characteristic information including the total number of passes, the total toll, and the sub-average toll, the candidate cluster may be determined as follows: 1. high total number of passes, high total toll and high-order average toll; 2. medium total number of passes, high total toll and high average toll; 3. high total number of passes, high total toll and medium average toll. The preset initial model may refer to a preset model training basic frame. For example, the preset initial model may select an XGBoost model, a LightGBM model, or an RF model. It should be noted that the number of the preset initial models may be one or more, specifically, may be set according to actual requirements, and each preset initial model may be correspondingly trained to obtain a candidate recognition model.
In this embodiment, when the candidate recognition model is built, first, candidate high-speed traffic data of the candidate vehicle is obtained, and candidate high-speed traffic feature information of the candidate vehicle is determined according to the candidate high-speed traffic data of the candidate vehicle. Wherein the candidate high-speed traffic data is authorized in advance for use in establishing the candidate recognition model. And then clustering the candidate high-speed traffic characteristic information to obtain at least two candidate clusters, wherein the at least two candidate clusters can be specifically set according to actual requirements. And further, for each candidate cluster, performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information included in the candidate cluster, and finally obtaining a candidate recognition model corresponding to the candidate cluster. In the model training process, the models with high accuracy and high stability can be screened based on a cross-validation mode, and for the models which do not meet the requirements of accuracy and stability, the requirements are met by adjusting and optimizing model parameters. For example, the GridSearch method may be used to perform model parameter tuning on the number of candidate clusters.
It should be noted that, because the candidate high-speed traffic characteristic information included in each candidate cluster is different, even if the same preset initial model is adopted for model training, the final candidate recognition model is different, that is, each candidate cluster corresponds to a different candidate recognition model. If a preset initial model is adopted, each candidate cluster corresponds to a candidate recognition model after model training; if a plurality of preset initial models are adopted, each candidate cluster corresponds to a plurality of candidate recognition models after model training.
And S140, determining whether the target vehicle is a net-bound vehicle according to the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster.
In this embodiment, the target high-speed traffic characteristic information needs to be input into the target recognition model corresponding to the target cluster, and whether the target vehicle is a network vehicle can be determined according to the output result of the target recognition model. The output of the target recognition model may be tag information representing whether the target vehicle is a network vehicle, for example, "yes" or "no", or probability information for recognizing that the target vehicle is a network vehicle, and may be specifically set according to actual requirements. If the output of the target recognition model is the label information, wherein 'yes' indicates that the target vehicle is a network vehicle, and 'no' indicates that the target vehicle is a non-network vehicle. If the output of the target recognition model is probability information, the output probability information is further compared with a preset probability threshold value, and whether the target vehicle is a network vehicle or not is determined according to the comparison result. The preset probability threshold may be a preset reference probability value for representing that the vehicle is a network vehicle, and may be specifically set according to actual requirements. Specifically, if the probability information output by the target recognition model is greater than a preset probability threshold, the target vehicle can be judged to be a network vehicle; otherwise, the target vehicle may be determined to be a non-net vehicle.
According to the technical scheme, the high-speed target passing data of the target vehicle are obtained, and the high-speed target passing characteristic information of the target vehicle is determined according to the high-speed target passing data; the target high-speed passing characteristic information comprises total passing times, total passing fees, time average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicles in a preset statistical period; clustering is carried out on the high-speed passing characteristic information of the target, and a target cluster to which the high-speed passing characteristic information of the target belongs is determined; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information; determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not; and determining whether the target vehicle is a net-bound vehicle according to the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster. According to the technical scheme, the recognition model can be built in advance based on the high-speed traffic data of the vehicles with large volume, high coverage rate and high authority, whether the vehicles are network-bound vehicles or not is determined according to the recognition model, and accuracy and reliability of network-bound vehicle recognition are improved.
In this embodiment, optionally, before the clustering processing is performed on the candidate high-speed traffic feature information, the method further includes: abnormal samples are removed from the candidate high-speed passing characteristic information, and first candidate characteristic information is obtained; performing data standardization processing on the first candidate feature information to obtain second candidate feature information; carrying out abnormal feature recognition on the second candidate feature information, and carrying out reassignment on the abnormal feature information in the recognized second candidate feature information to obtain third candidate feature information; correspondingly, clustering is carried out on the candidate high-speed traffic characteristic information to obtain at least two candidate clustering clusters, which comprises the following steps: and clustering the third candidate feature information to obtain at least two candidate clustering clusters.
The first candidate feature information may be information obtained by removing an abnormal sample from the candidate high-speed traffic feature information. The second candidate feature information may refer to information obtained by performing data normalization processing on the first candidate feature information. The third candidate feature information may be information obtained by reassigning the abnormal feature information in the second candidate feature information.
In this embodiment, in order to improve accuracy of model training, adverse effects of abnormal samples or abnormal features in model training parameters on model training are reduced, and abnormal sample rejection, data standardization processing and abnormal feature correction processing are added. Specifically, before the candidate high-speed traffic feature information is clustered, an abnormal sample of the candidate high-speed traffic feature information may be removed to obtain first candidate feature information. For example, an isolated forest approach may be employed for outlier sample rejection. And then, carrying out data standardization processing (such as Z-score standardization processing) on the first candidate feature information to obtain second candidate feature information so as to eliminate dimension differences among the features with different values and sizes. And further, carrying out abnormal feature recognition on the second candidate feature information, carrying out reassignment on the abnormal feature information in the second candidate feature information obtained through recognition to obtain third candidate feature information, and realizing abnormal feature correction on the basis of ensuring that feature dimensions are not lost. For example, the abnormal characteristic information can be identified and reassigned in a box graph mode. In addition, the reassigned abnormal characteristic information can be marked for representing that the abnormal characteristic information is reassigned. Correspondingly, at least two candidate clustering clusters can be obtained by clustering the third candidate feature information.
Through such setting, the abnormal sample rejection, the data standardization processing and the abnormal feature reassignment processing are carried out on the model training parameters, so that the adverse effect of the abnormal sample or the abnormal feature on the model training is reduced, and the accuracy of the model training is improved.
In this embodiment, optionally, after clustering the third candidate feature information to obtain at least two candidate clusters, the method further includes: carrying out feature engineering processing on the third candidate feature information of the candidate cluster to obtain fourth candidate feature information; the feature engineering processing comprises feature elimination, feature screening and unbalanced sample processing; correspondingly, performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information, including: and performing supervised model training on the preset initial model according to the fourth candidate feature information.
The fourth candidate feature information may be information obtained by performing feature engineering processing on the third candidate feature information. The feature engineering processing may include feature culling, feature screening, and unbalanced sample processing.
In this embodiment, after clustering the third candidate feature information to obtain at least two candidate clusters, feature engineering processing may be further performed on the third candidate feature information of each candidate cluster to obtain fourth candidate feature information. By way of example, the method can adopt a correlation method to perform feature elimination, adopts a recursion elimination method to perform feature screening, and can perform sample processing on unbalanced positive and negative samples, thereby being beneficial to improving the accuracy of model training. Correspondingly, performing supervised model training on the preset initial model according to the fourth candidate feature information to obtain candidate recognition models corresponding to the candidate cluster clusters.
Through the arrangement, the third candidate feature information is subjected to feature engineering processing, feature rejection, feature screening and unbalanced sample processing can be performed, and accuracy of model training is improved.
In this embodiment, optionally, the number of preset initial models is at least two; correspondingly, performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information to obtain a candidate recognition model corresponding to the candidate cluster, wherein the method comprises the following steps: respectively performing supervised model training on at least two preset initial models according to the candidate high-speed traffic characteristic information to obtain at least two candidate recognition models corresponding to the candidate cluster; the candidate recognition model is output as candidate probability information, and the candidate probability information is used for representing the probability of recognizing that the candidate vehicle is a network vehicle.
In this embodiment, there may be a problem that the reliability and practicality of the candidate recognition model are low, and the supervised model training may be performed on the multiple preset initial models according to the candidate high-speed traffic feature information, so that each candidate cluster corresponds to the multiple candidate recognition models, so that the network vehicle is subsequently recognized according to the comprehensive output result of the multiple candidate recognition models and the target high-speed traffic feature information belonging to the target cluster, thereby improving the accuracy of network vehicle recognition.
Example two
Fig. 2 is a flowchart of a method for identifying a vehicle about crossing a metropolitan area network according to a second embodiment of the present invention, and the present embodiment is optimized based on the foregoing embodiment. The concrete optimization is as follows: according to the target high-speed traffic characteristic information and a target identification model corresponding to the target cluster, determining whether the target vehicle is a net-bound vehicle or not comprises the following steps: inputting the target high-speed passing characteristic information into at least two target recognition models corresponding to the target cluster to obtain target probability information corresponding to the at least two target recognition models; carrying out average value processing on the target probability information to obtain integrated probability information; and determining whether the target vehicle is a network about vehicle or not based on the integration probability information and a preset integration probability threshold value.
As shown in fig. 2, the method of this embodiment specifically includes the following steps:
s210, acquiring target high-speed traffic data of the target vehicle, and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data.
The target high-speed passing characteristic information comprises total passing times, total passing fees, time average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicles in a preset statistical period.
S220, clustering is conducted on the target high-speed traffic characteristic information, and a target cluster to which the target high-speed traffic characteristic information belongs is determined.
The target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information.
S230, determining a target recognition model corresponding to the target cluster from at least two candidate recognition models.
The candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not. In addition, the target cluster corresponds to a plurality of target recognition models.
S240, the target high-speed passing characteristic information is respectively input into at least two target recognition models corresponding to the target cluster, and target probability information corresponding to the at least two target recognition models is obtained.
The target probability information may refer to probability information output by the target recognition model, and may be used to characterize a probability of recognizing the target vehicle as a network vehicle.
In this embodiment, the target cluster corresponds to a plurality of target recognition models, so that the target high-speed traffic characteristic information needs to be input into each target recognition model respectively, and the target probability information output by each target recognition model is correspondingly obtained. It will be appreciated that a target recognition model will output a corresponding target probability information.
S250, carrying out mean value processing on the target probability information to obtain integrated probability information.
In this embodiment, after obtaining target probability information corresponding to at least two target recognition models, average processing may be performed on each target probability information by using a soft voting manner to obtain integrated probability information, so as to obtain comprehensive probability information of each target probability information. The integrated probability information may refer to probability information obtained after average processing of target probability information.
S260, determining whether the target vehicle is a network about vehicle or not based on the integration probability information and a preset integration probability threshold value.
The preset integration probability threshold may refer to a preset integration probability reference value, which may be specifically set according to actual requirements. For example, the preset integration probability threshold may be set directly to a fixed value according to practical application experience, or may be determined based on a binning manner. For example, after target probability information corresponding to at least two target recognition models is obtained, each target probability information may be divided into 20 bins according to equidistant bin division, then a Brier score is calculated under the condition that positive and negative samples are determined by using different bin division values as thresholds, and a reasonable positive and negative sample division threshold (i.e. a preset integration probability threshold) is determined according to the Brier score.
In this embodiment, optionally, determining whether the target vehicle is a network-bound vehicle based on the integration probability information and a preset integration probability threshold includes: if the integration probability information is larger than a preset integration probability threshold value, determining that the target vehicle is a network contract vehicle; otherwise, the target vehicle is determined to be a non-network vehicle.
In this embodiment, after the integration probability information is obtained, the integration probability information may be compared with a preset integration probability threshold, and whether the target vehicle is a network vehicle may be determined according to the comparison result. Specifically, if the integration probability information is greater than a preset integration probability threshold, the probability that the target vehicle is a network vehicle is indicated to be greater, and the target vehicle can be determined to be the network vehicle at the moment; if the integration probability information is smaller than or equal to the preset integration probability threshold, the probability that the target vehicle is the network vehicle is smaller, and the target vehicle can be judged to be the non-network vehicle.
According to the scheme, through the arrangement, whether the target vehicle is the network vehicle or not can be rapidly and accurately determined based on the comparison result of the integration probability information and the preset integration probability threshold value.
According to the technical scheme, target high-speed passing characteristic information is input into at least two target recognition models corresponding to a target cluster, and target probability information corresponding to the at least two target recognition models is obtained; carrying out average value processing on the target probability information to obtain integrated probability information; and determining whether the target vehicle is a network about vehicle or not based on the integration probability information and a preset integration probability threshold value. According to the technical scheme, the recognition model can be built in advance based on the high-speed traffic data of the vehicles with large volume, high coverage rate and high authority, wherein one cluster is correspondingly built with a plurality of recognition models, and whether the vehicles are network-bound vehicles or not is determined according to comprehensive recognition results of the plurality of recognition models, so that the accuracy and the reliability of network-bound vehicle recognition are further improved.
Example III
Fig. 3 is a schematic structural diagram of a device for identifying a vehicle about crossing a metropolitan area network according to a third embodiment of the present invention, where the device may execute the method for identifying a vehicle about crossing a metropolitan area network according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 3, the apparatus includes:
the target traffic characteristic determining module 310 is configured to obtain target high-speed traffic data of a target vehicle, and determine target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data; the target high-speed passing characteristic information comprises total passing times, total passing fees, secondary average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicle in a preset statistical period;
the target cluster determining module 320 is configured to perform clustering processing on the target high-speed traffic feature information, and determine a target cluster to which the target high-speed traffic feature information belongs; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information;
a target recognition model determining module 330, configured to determine a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not;
The network about car recognition module 340 is configured to determine whether the target vehicle is a network about car according to the target high-speed traffic feature information and a target recognition model corresponding to the target cluster.
Optionally, the apparatus further includes:
the candidate passing feature determining module is used for determining candidate high-speed passing feature information of the candidate vehicle according to the candidate high-speed passing data of the candidate vehicle; wherein the candidate high-speed traffic data is authorized for use in establishing a candidate recognition model;
the candidate cluster determining module is used for carrying out clustering processing on the candidate high-speed traffic characteristic information to obtain at least two candidate clusters; the candidate cluster is used for representing the feature category corresponding to the candidate high-speed passing feature information;
and the candidate recognition model determining module is used for performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information to obtain a candidate recognition model corresponding to the candidate cluster.
Optionally, the apparatus further includes:
the first candidate feature information determining module is used for removing abnormal samples from the candidate high-speed passing feature information before clustering the candidate high-speed passing feature information to obtain first candidate feature information;
The second candidate feature information determining module is used for carrying out data standardization processing on the first candidate feature information to obtain second candidate feature information;
the third candidate feature information determining module is used for carrying out abnormal feature recognition on the second candidate feature information and carrying out reassignment on the abnormal feature information in the recognized second candidate feature information to obtain third candidate feature information;
correspondingly, the candidate cluster determining module is configured to:
and clustering the third candidate feature information to obtain at least two candidate clustering clusters.
Optionally, the apparatus further includes:
the fourth candidate feature information determining module is used for carrying out feature engineering processing on the third candidate feature information of the candidate cluster after carrying out cluster processing on the third candidate feature information to obtain at least two candidate cluster clusters to obtain fourth candidate feature information; the feature engineering processing comprises feature elimination, feature screening and unbalanced sample processing;
correspondingly, the candidate recognition model determining module is used for:
and performing supervised model training on a preset initial model according to the fourth candidate feature information.
Optionally, the number of the preset initial models is at least two;
correspondingly, the candidate recognition model determining module is further configured to:
respectively performing supervised model training on at least two preset initial models according to the candidate high-speed traffic characteristic information to obtain at least two candidate recognition models corresponding to the candidate cluster;
the candidate recognition model is output as candidate probability information, and the candidate probability information is used for representing the probability of recognizing that the candidate vehicle is a network about vehicle.
Optionally, the network vehicle identification module 340 includes:
the target probability information determining unit is used for respectively inputting the target high-speed passing characteristic information into at least two target recognition models corresponding to the target cluster to obtain target probability information corresponding to the at least two target recognition models;
the integrated probability information determining unit is used for carrying out average value processing on the target probability information to obtain integrated probability information;
and the network appointment vehicle identification unit is used for determining whether the target vehicle is a network appointment vehicle or not based on the integration probability information and a preset integration probability threshold value.
Optionally, the network about car identifying unit is used for:
If the integration probability information is larger than the preset integration probability threshold value, determining that the target vehicle is a network vehicle;
otherwise, determining that the target vehicle is a non-network vehicle.
The identifying device for the cross-city network about cars provided by the embodiment of the invention can execute the identifying method for the cross-city network about cars provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the identification of about cars across a metropolitan network.
In some embodiments, the method of identifying about cars across a metropolitan network may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method of identifying a cross-metropolitan area network about a vehicle described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method of identifying the vehicle about across the metropolitan area network by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for identifying a cross-metropolitan area network about vehicle, the method comprising:
acquiring target high-speed traffic data of a target vehicle, and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data; the target high-speed passing characteristic information comprises total passing times, total passing fees, secondary average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicle in a preset statistical period;
Clustering the target high-speed passing characteristic information to determine a target cluster to which the target high-speed passing characteristic information belongs; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information;
determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not;
and determining whether the target vehicle is a network vehicle or not according to the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster.
2. The method of claim 1, wherein the candidate recognition model creation process comprises:
according to the candidate high-speed traffic data of the candidate vehicle, determining candidate high-speed traffic characteristic information of the candidate vehicle; wherein the candidate high-speed traffic data is authorized for use in establishing a candidate recognition model;
clustering the candidate high-speed traffic characteristic information to obtain at least two candidate clusters; the candidate cluster is used for representing the feature category corresponding to the candidate high-speed passing feature information;
And performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information to obtain a candidate recognition model corresponding to the candidate cluster.
3. The method of claim 2, further comprising, prior to clustering the candidate high-speed traffic feature information:
abnormal sample rejection is carried out on the candidate high-speed passing characteristic information, and first candidate characteristic information is obtained;
performing data standardization processing on the first candidate feature information to obtain second candidate feature information;
carrying out abnormal feature recognition on the second candidate feature information, and carrying out reassignment on the abnormal feature information in the recognized second candidate feature information to obtain third candidate feature information;
correspondingly, clustering the candidate high-speed traffic characteristic information to obtain at least two candidate clusters, wherein the clustering comprises the following steps:
and clustering the third candidate feature information to obtain at least two candidate clustering clusters.
4. The method according to claim 3, further comprising, after clustering the third candidate feature information to obtain at least two candidate clusters:
Carrying out feature engineering processing on the third candidate feature information of the candidate cluster to obtain fourth candidate feature information; the feature engineering processing comprises feature elimination, feature screening and unbalanced sample processing;
correspondingly, performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information, including:
and performing supervised model training on a preset initial model according to the fourth candidate feature information.
5. The method according to claim 2, wherein the number of pre-set initial models is at least two;
correspondingly, performing supervised model training on a preset initial model according to the candidate high-speed traffic characteristic information to obtain a candidate recognition model corresponding to the candidate cluster, wherein the method comprises the following steps:
respectively performing supervised model training on at least two preset initial models according to the candidate high-speed traffic characteristic information to obtain at least two candidate recognition models corresponding to the candidate cluster;
the candidate recognition model is output as candidate probability information, and the candidate probability information is used for representing the probability of recognizing that the candidate vehicle is a network about vehicle.
6. The method of claim 5, wherein determining whether the target vehicle is a net-bound vehicle based on the target high-speed traffic characteristic information and a target recognition model corresponding to the target cluster comprises:
Respectively inputting the target high-speed passing characteristic information into at least two target recognition models corresponding to the target cluster to obtain target probability information corresponding to the at least two target recognition models;
carrying out average value processing on the target probability information to obtain integrated probability information;
and determining whether the target vehicle is a network about vehicle or not based on the integration probability information and a preset integration probability threshold value.
7. The method of claim 6, wherein determining whether the target vehicle is a network-bound vehicle based on the integration probability information and a preset integration probability threshold comprises:
if the integration probability information is larger than the preset integration probability threshold value, determining that the target vehicle is a network vehicle;
otherwise, determining that the target vehicle is a non-network vehicle.
8. An identification device for a cross-metropolitan area network about vehicle, the device comprising:
the system comprises a target traffic characteristic determining module, a target traffic characteristic determining module and a control module, wherein the target traffic characteristic determining module is used for acquiring target high-speed traffic data of a target vehicle and determining target high-speed traffic characteristic information of the target vehicle according to the target high-speed traffic data; the target high-speed passing characteristic information comprises total passing times, total passing fees, secondary average passing fees, information entropy of charging and discharging toll stations and passing time periods of the target vehicle in a preset statistical period;
The target cluster determining module is used for carrying out clustering processing on the target high-speed passing characteristic information and determining a target cluster to which the target high-speed passing characteristic information belongs; the target cluster is used for representing the characteristic category corresponding to the target high-speed passing characteristic information;
the target recognition model determining module is used for determining a target recognition model corresponding to the target cluster from at least two candidate recognition models; the candidate recognition model is pre-established based on the high-speed passing characteristic information of the vehicles in the candidate cluster and is used for recognizing whether the vehicles are network-bound vehicles or not;
and the network appointment vehicle identification module is used for determining whether the target vehicle is a network appointment vehicle according to the target high-speed passing characteristic information and a target identification model corresponding to the target cluster.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of identifying a cross-metropolitan area vehicle of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of identifying a cross-metropolitan area network about a vehicle as claimed in any one of claims 1-7.
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