CN116246361A

CN116246361A - Road vehicle charging system, method, device, equipment and storage medium

Info

Publication number: CN116246361A
Application number: CN202111465914.1A
Authority: CN
Inventors: 雷艺学; 胡鹏
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-12-03
Filing date: 2021-12-03
Publication date: 2023-06-09

Abstract

The application discloses a charging system, a charging method, a charging device, charging equipment and a charging storage medium for road vehicles, and belongs to the field of intelligent traffic. The system comprises: the cloud end equipment and the at least one edge equipment are connected with at least one road side acquisition equipment; the edge equipment is used for acquiring the vehicle information of the road vehicle acquired by the road side acquisition equipment; reporting the vehicle information of the road vehicle to the cloud device; the cloud device is used for receiving the vehicle information of the road vehicle reported by the edge device; determining a vehicle travel track of the road vehicle based on vehicle information of the road vehicle; and generating charging information of the road vehicle based on the vehicle running track.

Description

Road vehicle charging system, method, device, equipment and storage medium

Technical Field

The embodiment of the application relates to the field of traffic control, in particular to a charging system, a charging method, a charging device, charging equipment and a charging storage medium for road vehicles.

Background

An electronic toll collection (Electronic Toll Collection, ETC) system is an electronic system for automatic toll collection for highways or bridges. The ETC carries out special short-range communication between the vehicle-mounted electronic tag arranged on the vehicle windshield and the microwave antenna on the ETC lane of the toll station, so that the purpose that the vehicle can pay the toll of the expressway or the bridge without stopping the vehicle through the expressway or the bridge toll station is realized.

However, the ETC system has very limited computing power, so that not only is the billing information easy to be wrong, but also the updating is difficult.

Disclosure of Invention

The application provides a charging system, a method, a device, equipment and a storage medium for road vehicles, which adopt cloud equipment with stronger computing capability to improve the accuracy of charging information.

In one aspect, embodiments of the present application provide a charging system for a road vehicle, the system including: the cloud end equipment and the at least one edge equipment are connected with at least one road side acquisition equipment;

the edge equipment is used for acquiring the vehicle information of the road vehicle acquired by the road side acquisition equipment; reporting the vehicle information of the road vehicle to the cloud device;

the cloud device is used for receiving the vehicle information of the road vehicle reported by the edge device; determining a vehicle travel track of the road vehicle based on vehicle information of the road vehicle; and generating charging information of the road vehicle based on the vehicle running track.

On the other hand, an embodiment of the present application provides a charging method for a road vehicle, which is applied to a cloud device, and the method includes:

Receiving vehicle information of the road vehicle reported by edge equipment, wherein the edge equipment is connected with at least one road side acquisition equipment;

determining a vehicle travel track of the road vehicle based on vehicle information of the road vehicle;

and generating charging information of the road vehicle based on the vehicle running track.

On the other hand, an embodiment of the present application provides a charging method for a road vehicle, which is applied to an edge device, where the edge device is connected with at least one road side acquisition device, and the method includes:

acquiring vehicle information of the road vehicle acquired by the road side acquisition equipment;

reporting the vehicle information of the road vehicle to the cloud device so that the cloud device can determine the vehicle running track of the road vehicle based on the vehicle information of the road vehicle; and generating charging information of the road vehicle based on the vehicle travel track.

In another aspect, an embodiment of the present application provides a charging device for a road vehicle, including:

the receiving module is used for receiving vehicle information of the road vehicle reported by edge equipment, and the edge equipment is connected with at least one road side acquisition equipment;

A determining module for determining a vehicle travel track of the road vehicle based on vehicle information of the road vehicle;

and the generation module is used for generating the charging information of the road vehicle based on the vehicle running track.

the acquisition module is used for acquiring the vehicle information of the road vehicle acquired by the road side acquisition equipment;

the sending module is used for reporting the vehicle information of the road vehicle to the cloud device so that the cloud device can determine the vehicle running track of the road vehicle based on the vehicle information of the road vehicle; and generating charging information of the road vehicle based on the vehicle travel track.

In another aspect, embodiments of the present application provide a computer device, the computer device comprising: a processor and a memory storing a computer program that is executed by the processor to cause the computer device to implement the road vehicle charging method as described above.

In another aspect, embodiments of the present application provide a computer-readable storage medium comprising instructions that, when run on a computer, cause the computer to perform a method of charging a road vehicle as described above.

In another aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of charging a road vehicle as described above.

The beneficial effects that technical scheme that this application embodiment provided include at least:

through the cloud edge cooperation of the edge equipment and the cloud end equipment, the edge equipment is mainly responsible for information acquisition of road vehicles, the cloud end equipment is responsible for recognition, fusion and analysis of vehicle characteristic information of the road vehicles, and as the computing power of the cloud end equipment is higher, good vehicle recognition performance can be achieved, for example, accurate recognition of the road vehicles can be achieved from various aspects such as license plates, vehicle types, axles and vehicle bodies, and therefore recognition accuracy of vehicle driving tracks is improved, and accuracy of charging information is improved.

Drawings

Fig. 1 is a block diagram illustrating a charging system for a road vehicle according to an exemplary embodiment of the present application;

FIG. 2 illustrates a schematic diagram of a roadside acquisition device provided in an exemplary embodiment of the present application;

FIG. 3 illustrates a flow chart of a method for charging road vehicles provided in an exemplary embodiment of the present application;

fig. 4 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application;

Fig. 5 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application;

fig. 6 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application;

fig. 7 is a flowchart illustrating a method for charging a road vehicle according to an exemplary embodiment of the present application;

fig. 8 shows a block diagram of a charging device for a road vehicle according to an exemplary embodiment of the present application;

fig. 9 shows a block diagram of a charging device for a road vehicle according to an exemplary embodiment of the present application;

FIG. 10 illustrates a block diagram of a computer device provided in an exemplary embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Fig. 1 shows a block diagram of a road vehicle charging system 100 provided in an exemplary embodiment of the present application. The charging system 100 includes: an edge device 120 and a cloud device 140.

The edge devices 120 are one or more. Each edge device 120 is connected to at least one roadside acquisition device. The road side acquisition device includes: at least one of toll gate, road camera, toll station camera, roadside camera, geomagnetic coil, ground induction equipment, etc. In one example, one edge device 120 is connected to one roadside acquisition device; in another example, one edge device 120 is connected to multiple roadside acquisition devices. In the illustration of fig. 1, one edge device 120 is connected to the camera of the toll station S1, and to the camera and Road Side Unit (RSU) on the toll gate T1; the other edge device 120 is connected to cameras and RSUs on the toll gate T3, toll gate T4 and toll station S2. In some embodiments, edge device 120 is also known as an edge box.

Referring to fig. 2, rsu11 is a device for use with an On Board Unit (OBU) or a compound pass card (Composite Pass Card, CPC). The RSU11 is arranged on a portal 10 of the road, the OBU or CPC is arranged on the road vehicle, and the RSU11 writes the RSU identification to the OBU or CPC in the road vehicle when the road vehicle passes the portal. The road camera is a camera provided on the portal 10 of the road, the toll booth camera is a camera provided at the entrance and exit of the toll booth, and the roadside camera 12 is a camera provided on both right and left sides of the road.

Cloud device 140 is connected to edge device 120 via a wired network or a wireless network. In some embodiments, cloud device 140 includes edge cloud device 142, where edge cloud device 142 is configured by road segment, road, region, etc. level; in some embodiments, cloud device 140 includes a central cloud device 144, all edge devices 120 being connected to central cloud device 144; in some embodiments, cloud device 140 includes an edge cloud device 142 and a center cloud device 144, one or more edge cloud devices 142 connected to center cloud device 144, each edge cloud device 142 connected to at least one roadside collection device. The specific deployment manner of the cloud device 140 is limited by practical road conditions, administrative division conditions, original device conditions and other reasons, and the specific deployment manner of the cloud device 140 is not limited in the application.

The types of data collected by the plurality of road side collecting devices can be different, such as a camera collecting vehicle images, RSU marks of an OBU or CPC collecting way and the like.

In some embodiments, an onboard device on the vehicle or a mobile terminal of an onboard person is also connected to cloud device 140. For example, for some large passenger buses or dangerous goods operation vehicles, the vehicle-mounted device may report vehicle positioning information to the cloud device 140 at regular time; for another example, the mobile terminal of the driver starts a navigation program, and the navigation program reports the vehicle positioning information to the cloud device 140 in real time.

Fig. 3 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application. The method can be applied to the system, and the method can comprise the following steps:

302: the edge equipment acquires vehicle information of the road vehicle acquired by the road side acquisition equipment;

the edge equipment is connected with the road side acquisition equipment. The edge equipment receives the vehicle information of the road vehicle acquired by the road side acquisition equipment.

Under the condition that the road side acquisition equipment acquires the vehicle image of the road vehicle, the edge equipment acquires the vehicle image of the road vehicle; and under the condition that the road side acquisition equipment acquires the RSU identification of the road vehicle path, the edge equipment acquires the RSU identification of the road vehicle path and the vehicle attribute. The vehicle attributes include: at least one of license plate, weight and number of axles. Wherein the weight and number of axles may be obtained by a ground collection device.

304: the edge equipment reports vehicle information of the road vehicle to the cloud equipment;

the edge equipment is connected with the cloud equipment, and the edge equipment reports the acquired vehicle information of the road vehicle to the cloud equipment.

In some embodiments, the edge device directly reports the vehicle information of the road vehicle to the cloud device without processing the vehicle information; in other embodiments, when the vehicle information is a vehicle image, the edge device performs image recognition on the vehicle image to obtain an image recognition result, and reports the image recognition result to the cloud device.

Illustratively, the pattern recognition result includes at least one of license plate, vehicle model, axle, and body characteristics. Wherein, the automobile body characteristic includes: paint color, hub color, whether there is an externally hung spare tire, etc.

Correspondingly, the cloud device is used for receiving vehicle information of the road vehicle reported by the edge device;

306: the cloud end equipment determines a vehicle running track of the road vehicle based on vehicle information of the road vehicle;

illustratively, the vehicle information includes vehicle images acquired by a plurality of roadside acquisition devices, and the cloud device determines a vehicle travel track of the road vehicle based on the plurality of vehicle images. Alternatively, the vehicle information includes an RSU identification of the road vehicle pathway, and the cloud device determines a vehicle travel track of the road vehicle based on the RSU identification of the road vehicle pathway.

Because the data sources of the road side acquisition equipment may be multiple, the cloud end equipment performs fusion processing on the vehicle information of at least two data sources under the condition that the vehicle information of the road vehicle is at least two data sources, and obtains the vehicle running track of the road vehicle so as to more accurately determine the vehicle running track based on the data of the multiple data sources.

The vehicle travel track is used to indicate travel information of road vehicles on a toll road section. Alternatively, the vehicle running track is represented by a plurality of sequentially arranged anchor points, or the vehicle running track is represented by a plurality of sequentially arranged anchor points and line segments connecting adjacent anchor points.

In the case where a toll road section and a non-toll road section exist at the same time, the vehicle travel track may include only the travel track of the toll road section; the travel tracks of both the toll road section and the non-toll road section may be included. For example, the vehicle travel track includes a travel track between an entrance of a start toll station to an exit of an end toll station. In this case, it is possible that the vehicle running track is represented as a plurality of segmented tracks. Such as a charging section a, a non-charging section B, and a charging section C, the vehicle travel track may include only two segmented tracks of the charging section a and the charging section C. In the case of toll road sections throughout, the vehicle travel track may include a complete travel path of the road vehicle. The embodiment of the present application does not limit the manner of representing the vehicle travel track.

308: the cloud device generates charging information of the road vehicle based on the vehicle running track.

In summary, according to the method provided by the embodiment, through the cloud edge cooperation of the edge device and the cloud device, the edge device is mainly responsible for information acquisition of the road vehicle, and the cloud device is responsible for recognition, fusion and analysis of the vehicle characteristic information of the road vehicle.

In addition, as the identification program, the fusion program and the analysis program in the cloud device are built based on the cloud service architecture, the upgrading mode is simple and efficient. The method and the device do not need to upgrade the road side facilities, can be implemented by fusing the existing road side facilities, and can be compatible with the existing road side facilities.

Fig. 4 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application. The method is applied to the system, and the method can comprise the following steps:

402: the edge equipment acquires vehicle information of the road vehicle acquired by the road side acquisition equipment;

404: the edge equipment reports vehicle information of the road vehicle to the cloud equipment;

and the edge equipment reports the vehicle information of the road vehicle to the cloud equipment.

In some embodiments, the edge device directly reports the vehicle information of the road vehicle to the cloud device without processing the vehicle information; in other embodiments, the edge device processes the vehicle information of the road vehicle and reports the processed vehicle information to the cloud device.

In other embodiments, when the vehicle information is a vehicle image, the edge device performs image recognition on the vehicle image to obtain an image recognition result, and reports the image recognition result to the cloud device. Illustratively, an image recognition model based on machine learning is arranged in the edge equipment, and the edge equipment calls the image recognition model to recognize the vehicle image, so that an image recognition result is obtained. The road vehicles in each vehicle image are one or more.

Correspondingly, the cloud device receives vehicle information of the road vehicle reported by the edge device. The vehicle information carries vehicle characteristic information of the road vehicle.

In some embodiments, in order to protect personal privacy of the road vehicle, the edge device encrypts vehicle information of the road vehicle and reports the encrypted vehicle information to the cloud device, and the cloud device decrypts the vehicle information when the vehicle information is the encrypted information. Optionally, in the case where the cloud device includes an edge cloud device and a center cloud device, only the center cloud device has decryption authority, or only the edge cloud device is specified to have decryption authority. For example, the edge element device is divided into a city level edge cloud and a provincial level edge cloud, and only the provincial level edge cloud has decryption authority.

In the present embodiment, the vehicle image or the image recognition result, that is, the image-like vehicle information is included with the first type of vehicle information; the second type of vehicle information includes non-image-type vehicle information as an example.

406: the cloud end equipment obtains first vehicle characteristic information of the road vehicle based on the first type of vehicle information;

under the condition that the first type of vehicle information comprises an image recognition result, the cloud device performs data verification on the image recognition result to obtain first vehicle characteristic information of the road vehicle; the image recognition result is obtained by the image recognition of the vehicle image by the edge device.

Optionally, the cloud device stores first vehicle feature information of the road vehicle, such as license plate, vehicle type, axle and vehicle body color of the vehicle a, and then compares the image recognition result of the edge device with the pre-stored first vehicle feature information after receiving the image recognition result of the edge device. If the comparison is successful, the data verification is successful; if the comparison is unsuccessful, the identification of the image identification result of the edge equipment is considered to be inaccurate, or the road vehicle is considered to be an abnormal vehicle. For example, if the comparison is unsuccessful and the recognition probability of the image recognition result of the edge device is greater than a threshold value, the road vehicle is considered to be an abnormal vehicle; for example, if the comparison is unsuccessful and the recognition probability of the image recognition result of the edge device is not greater than a threshold, the recognition of the image recognition result is considered inaccurate, and the threshold is schematically 60%, 70%, 80%, or the like.

And under the condition that the first type of vehicle information comprises a vehicle image, the cloud device performs image recognition on the vehicle image to obtain first vehicle characteristic information of the road vehicle. The first vehicle characteristic information comprises at least one of license plates, vehicle types, axles and vehicle body characteristics.

Optionally, the cloud device stores the first vehicle feature information of the road vehicle in advance, and compares the image recognition result with the pre-stored first vehicle feature information after recognizing the image recognition result. If the comparison is successful, the data verification is successful; if the comparison is unsuccessful, the recognition of the image recognition result is considered to be inaccurate, or the road vehicle is considered to be an abnormal vehicle. For example, if the comparison is unsuccessful and the recognition probability of the image recognition result is greater than a threshold value, the road vehicle is considered to be an abnormal vehicle; for example, if the comparison is unsuccessful and the recognition probability of the image recognition result is not greater than a threshold value, the recognition of the image recognition result is considered inaccurate, and the threshold value is schematically 60%, 70%, 80%, or the like.

408: the cloud end equipment determines a first vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of first vehicle characteristic information;

For a single road vehicle, the cloud device determines a plurality of first vehicle characteristic information based on the first vehicle characteristic information having the road vehicle; and further determining deployment positions of the road side acquisition devices reporting the plurality of first vehicle characteristic information, and determining a first vehicle running track of the road vehicle based on the deployment positions of the road side acquisition devices.

Referring to fig. 1 in combination, assuming that a plurality of vehicle images having a vehicle a come from a toll station S1, a toll gate T2, a toll gate T3, a toll gate T4, and a toll station S2, respectively, a first vehicle travel trajectory of the vehicle a is represented by deployment positions of the toll gate S1, the toll gate T2, the toll gate T3, the toll gate T4, and the toll station S2.

410: the cloud end equipment obtains second vehicle characteristic information of the road vehicle based on the second type of vehicle information;

taking the second type of vehicle information including RSU identification and license plate information as an example, taking the license plate information as the identification of the road vehicle, and determining a plurality of RSU identifications of the road vehicle approach.

412: the cloud device determines a second vehicle running track of the road vehicle based on deployment positions of road side acquisition devices corresponding to the plurality of second vehicle characteristic information;

The cloud device determines a second vehicle running track of the road vehicle based on deployment positions of the road side acquisition devices corresponding to the plurality of RSU identifiers.

414: the cloud device fuses the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle;

since the roadside acquisition device that acquires the first vehicle characteristic information and the roadside acquisition device that acquires the second vehicle characteristic information are generally different, the first vehicle travel track and the second vehicle travel track may be different.

Referring to fig. 1, assuming that an overpass is provided between the toll gate T2 and the toll gate T3, a road vehicle makes an error in a route on the overpass, and detours to other routes and then detours back to the correct route. Since the cameras are provided on the other routes, there is a record of the road vehicle traveling to the wrong route on the first vehicle traveling track, and there is no record of the road vehicle traveling to the wrong route on the second vehicle traveling track.

The cloud device fuses the first vehicle running track and the second vehicle running track and determines the vehicle running track of the road vehicle. Illustratively, the cloud device determines overlapping road segments and non-overlapping road segments on the first vehicle travel track and the second vehicle travel track.

The cloud end equipment removes the weight of the overlapped road sections on the first vehicle running track and the second vehicle running track; and selecting the non-overlapping road section on one of the first vehicle running track and the second vehicle running track as a final road section according to the priority for the non-overlapping road section on the first vehicle running track and the second vehicle running track.

Illustratively, in the case where the priority of the first vehicle travel track is greater than the priority of the second vehicle travel track, selecting a non-overlapping road segment in the first vehicle travel track as the final road segment; in the case where the priority of the first vehicle travel track is smaller than the priority of the second vehicle travel track, a non-overlapping road section in the second vehicle travel track is selected as the final road section.

Optionally, in a case where the number of anchor points in the second vehicle running track is greater than the number of anchor points in the first vehicle running track, determining that the priority of the second vehicle running track is higher; in the case where the number of anchor points in the second vehicle travel track is smaller than the number of anchor points in the first vehicle travel track, it is determined that the priority of the second vehicle travel track is lower. Of course, the priority of the first/second vehicle running track may also be set in advance.

The locating points comprise deployment positions of road side acquisition equipment corresponding to the vehicle information. The priority may also be considered as a confidence level, which is used to indicate the degree of confidence or accuracy of the information. Taking a vehicle running track as an example, the higher the confidence coefficient is, the higher the accuracy of the vehicle running track is; the lower the confidence, the lower the accuracy of the vehicle travel track.

416: the cloud device generates charging information of the road vehicle based on the vehicle running track.

The cloud end equipment invokes a charging interface of the original charging system, and calculates the charging passing amount according to the running track of the vehicle; or the cloud end equipment reads the rate data and generates charging information of the road vehicle according to the rate data corresponding to the running track of the vehicle.

In some embodiments, the cloud device calculates first charging information of the road vehicle, obtains second charging information calculated by the original charging system for the road vehicle, compares and rectifies the first charging information and the second charging information, and generates more accurate charging information. Illustratively, the first charging information is taken as charging information with higher confidence, and deviation correction is performed on the second charging information.

In addition, as the identification program, the fusion program and the analysis program in the cloud device are built based on the cloud service architecture, the upgrading mode is simple and efficient. The method and the device do not need to upgrade the road side facilities, can be implemented by fusing the existing road side facilities, and can be almost compatible with all the existing road side facilities.

It should be noted that, because some road vehicles or mobile terminals have positioning capability, the road vehicles or mobile terminals have the capability of reporting license plates and vehicle positioning information to the cloud device. In the implementation scene, the cloud device acquires vehicle positioning information reported by a road vehicle; determining a third vehicle travel track based on vehicle positioning information of the road vehicle; and optimizing the vehicle running track through the third vehicle running track to obtain the optimized vehicle running track.

Illustratively, the cloud device determines overlapping road segments and non-overlapping road segments on the vehicle travel track and the third vehicle travel track.

The cloud end equipment removes the weight of the overlapped road sections on the vehicle running track and the third vehicle running track; and selecting the non-overlapping road section on one of the vehicle running track and the third vehicle running track as a final road section according to the priority for the non-overlapping road sections on the vehicle running track and the third vehicle running track.

Illustratively, in the case where the priority of the vehicle travel track is greater than the priority of the third vehicle travel track, a non-overlapping road section in the vehicle travel track is selected as the final road section; in the case where the priority of the vehicle travel locus is smaller than the priority of the third vehicle travel locus, a non-overlapping road section in the third vehicle travel locus is selected as the final road section.

Optionally, determining that the priority of the third vehicle running track is higher in the case that the number of positioning points in the third vehicle running track is greater than the number of positioning points in the vehicle running track; in the case where the number of anchor points in the third vehicle travel track is smaller than the number of anchor points in the vehicle travel track, it is determined that the priority of the third vehicle travel track is lower. Of course, the priority of the third vehicle travel track may be set in advance.

Another point to be described is that the cloud device includes an edge cloud device; or, a central cloud device; or edge cloud device + center cloud device. The steps can be independently executed by the edge cloud device, or independently executed by the center cloud, or cooperatively executed by the edge cloud device and the center cloud device.

In some scenarios, the primary task is undertaken by an edge cloud device, and the secondary task is undertaken by a central cloud device. For example, each provincial edge cloud device assumes a primary role and a regional or national central cloud device assumes a secondary role. In other scenarios, the secondary tasks are undertaken by edge cloud devices, and the primary tasks are undertaken by central cloud devices. For example, each provincial edge cloud device performs an image recognition task, and a central cloud device located at a regional level or a national level performs a related task of charging operation.

Fig. 5 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application. The method may include:

502: the edge equipment acquires vehicle information of the road vehicle acquired by the road side acquisition equipment;

504: the edge equipment reports vehicle information of the road vehicle to the edge cloud equipment;

and the edge equipment reports the vehicle information of the road vehicle to the edge cloud equipment.

In some embodiments, the edge device directly reports the vehicle information of the road vehicle to the edge cloud device without processing the vehicle information; in other embodiments, the edge device processes the vehicle information of the road vehicle and reports the processed vehicle information to the edge cloud device.

In other embodiments, when the vehicle information is a vehicle image, the edge device performs image recognition on the vehicle image to obtain an image recognition result, and reports the image recognition result to the edge cloud device. Illustratively, an image recognition model based on machine learning is arranged in the edge equipment, and the edge equipment calls the image recognition model to recognize the vehicle image, so that an image recognition result is obtained. The road vehicles in each vehicle image are one or more.

Correspondingly, the edge cloud equipment receives vehicle information of the road vehicle reported by the edge equipment.

In some embodiments, in order to protect personal privacy of the road vehicle, the edge device encrypts vehicle information of the road vehicle and reports the encrypted vehicle information to the edge cloud device, and the edge cloud device decrypts the vehicle information if the vehicle information is the encrypted information. Alternatively, in the case where the edge cloud device includes the edge cloud device and the center cloud device, only the center cloud device has the decryption authority, or only the edge cloud device is specified to have the decryption authority. For example, the edge element device is divided into a city level edge cloud and a provincial level edge cloud, and only the provincial level edge cloud has decryption authority.

In the present embodiment, the first type of vehicle information including the vehicle image or the image recognition result and the second type of vehicle information including the non-image type of vehicle information are exemplified.

506: the edge cloud equipment obtains first vehicle characteristic information of the road vehicle based on the first type of vehicle information;

Under the condition that the first type of vehicle information comprises an image recognition result, the edge cloud equipment performs data verification on the image recognition result to obtain first vehicle characteristic information of the road vehicle; the image recognition result is obtained by the image recognition of the vehicle image by the edge device.

Optionally, the edge cloud device stores first vehicle characteristic information of the road vehicle, such as license plate, vehicle type, axle and vehicle body color of the vehicle a, and then compares the image recognition result of the edge device with the pre-stored first vehicle characteristic information after receiving the image recognition result of the edge device. If the comparison is successful, the data verification is successful; if the comparison is unsuccessful, the identification of the image identification result of the edge equipment is considered to be inaccurate, or the road vehicle is considered to be an abnormal vehicle. For example, if the comparison is unsuccessful and the recognition probability of the image recognition result of the edge device is greater than a threshold value, the road vehicle is considered to be an abnormal vehicle; for example, if the comparison is unsuccessful and the recognition probability of the image recognition result of the edge device is not greater than a threshold, the recognition of the image recognition result is considered inaccurate, and the threshold is schematically 60%, 70%, 80%, or the like.

And under the condition that the first type of vehicle information comprises a vehicle image, the edge cloud equipment performs image recognition on the vehicle image to obtain first vehicle characteristic information of the road vehicle. The first vehicle characteristic information comprises at least one of license plates, vehicle types, axles and vehicle body characteristics.

Optionally, the edge cloud device stores first vehicle feature information of the road vehicle in advance, and compares the image recognition result with the pre-stored first vehicle feature information after recognizing the image recognition result. If the comparison is successful, the data verification is successful; if the comparison is unsuccessful, the recognition of the image recognition result is considered to be inaccurate, or the road vehicle is considered to be an abnormal vehicle. For example, if the comparison is unsuccessful and the recognition probability of the image recognition result is greater than a threshold value, the road vehicle is considered to be an abnormal vehicle; for example, if the comparison is unsuccessful and the recognition probability of the image recognition result is not greater than a threshold value, the recognition of the image recognition result is considered inaccurate, and the threshold value is schematically 60%, 70%, 80%, or the like.

508: the edge cloud equipment determines a first vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of first vehicle characteristic information;

For a single road vehicle, the edge cloud device determines a plurality of first vehicle characteristic information based on the first vehicle characteristic information having the road vehicle; and further determining deployment positions of the road side acquisition devices reporting the plurality of first vehicle characteristic information, and determining a first vehicle running track of the road vehicle based on the deployment positions of the road side acquisition devices.

510: the edge cloud equipment obtains second vehicle characteristic information of the road vehicle based on the second type of vehicle information;

512: the edge cloud equipment determines a second vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of second vehicle characteristic information;

The edge cloud equipment determines a second vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to a plurality of RSU identifiers of the road vehicle route.

514: the edge cloud equipment fuses the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle;

And the edge cloud equipment fuses the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle. Illustratively, the edge cloud device determines overlapping road segments and non-overlapping road segments on the first vehicle travel track and the second vehicle travel track.

The edge cloud equipment performs de-duplication on the overlapped road sections on the first vehicle running track and the second vehicle running track; and selecting the non-overlapping road section on one of the first vehicle running track and the second vehicle running track as a final road section according to the priority for the non-overlapping road section on the first vehicle running track and the second vehicle running track.

Illustratively, in the case where the confidence of the first vehicle travel track is greater than the confidence of the second vehicle travel track, selecting a non-overlapping road segment in the first vehicle travel track as the final road segment; and selecting the non-overlapping road section in the second vehicle running track as the final road section in the case that the confidence of the first vehicle running track is smaller than that of the second vehicle running track.

The locating points comprise deployment positions of road side acquisition equipment corresponding to the vehicle information.

516: the edge cloud equipment reports the vehicle running track of the road vehicle to the center cloud equipment;

correspondingly, the center cloud equipment receives the vehicle driving track reported by the edge cloud equipment.

518: the center cloud device generates charging information of the road vehicle based on the vehicle travel track.

The center cloud equipment invokes a charging interface of the original charging system, and calculates the charging passing amount according to the running track of the vehicle; or the central cloud equipment reads the rate data and generates the charging information of the road vehicle according to the rate data corresponding to the running track of the vehicle.

In some embodiments, the central cloud device calculates first charging information of the road vehicle, obtains second charging information calculated by the original charging system for the road vehicle, compares and rectifies the first charging information and the second charging information, and generates more accurate charging information. Illustratively, the first charging information is taken as charging information with higher confidence, and deviation correction is performed on the second charging information.

Schematically, image recognition models are deployed on the edge cloud devices, and the center cloud device is also responsible for training and issuing the image recognition models. Optionally, the image recognition model is a federal learning model. The edge cloud equipment regularly updates the local image recognition model and reports the updated local image recognition model to the center cloud equipment; the central cloud equipment updates the global image recognition model based on each local image recognition model, and issues the global image recognition model to each edge cloud equipment, and the process can be executed regularly.

In summary, according to the method provided in the embodiment, by executing the primary task by the edge cloud device, the central cloud device executes the secondary task, and since the number of the edge cloud devices may be multiple, the computing pressure may be shared on each edge cloud device, and the central cloud device is responsible for the secondary task and managing the task.

Fig. 6 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application. The method may include:

602: the edge equipment acquires vehicle information of the road vehicle acquired by the road side acquisition equipment;

604: the edge equipment reports vehicle information of the road vehicle to the edge cloud equipment;

606: the edge cloud equipment pre-processes the vehicle information and reports the pre-processed vehicle information;

illustratively, in the case that the vehicle information includes a vehicle image, performing image recognition on the vehicle image to obtain an image recognition result; reporting the image recognition result to cloud equipment; and under the condition that the vehicle information does not comprise the vehicle image, the edge cloud equipment performs running water combination on the vehicle information of the vehicles on the same road and reports the combined vehicle information to the cloud equipment.

608: the central cloud equipment obtains first vehicle characteristic information of the road vehicle based on the first type of vehicle information;

under the condition that the first type of vehicle information comprises an image recognition result, the central cloud equipment performs data verification on the image recognition result to obtain first vehicle characteristic information of the road vehicle; the image recognition result is obtained by the image recognition of the vehicle image by the edge cloud equipment.

Optionally, the central cloud device stores first vehicle characteristic information of the road vehicle, such as license plate, vehicle type, axle and vehicle body color of the vehicle a, and then compares the image recognition result of the edge device with the pre-stored first vehicle characteristic information after receiving the image recognition result of the edge device. If the comparison is successful, the data verification is successful; if the comparison is unsuccessful, the identification of the image identification result of the edge equipment is considered to be inaccurate, or the road vehicle is considered to be an abnormal vehicle. For example, if the comparison is unsuccessful and the recognition probability of the image recognition result of the edge device is greater than a threshold value, the road vehicle is considered to be an abnormal vehicle; for example, if the comparison is unsuccessful and the recognition probability of the image recognition result of the edge device is not greater than a threshold, the recognition of the image recognition result is considered inaccurate, and the threshold is schematically 60%, 70%, 80%, or the like.

610: the central cloud equipment determines a first vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of first vehicle characteristic information;

for a single road vehicle, the central cloud device screens out a plurality of first vehicle characteristic information of the road vehicle based on the first type of vehicle information with the road vehicle; and further determining deployment positions of the road side acquisition devices reporting the plurality of first vehicle characteristic information, and determining a first vehicle running track of the road vehicle based on the deployment positions of the road side acquisition devices.

612: the central cloud equipment obtains second vehicle characteristic information of the road vehicle based on the second type of vehicle information;

614: the central cloud equipment determines a second vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of second vehicle characteristic information;

the central cloud device determines a second vehicle running track of the road vehicle based on deployment positions of road side acquisition devices corresponding to the plurality of RSU identifiers of the road vehicle route.

616: the central cloud equipment fuses the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle;

since the roadside acquisition device that acquires the first vehicle characteristic information and the roadside acquisition device that acquires the second vehicle characteristic information are different, the first vehicle travel track and the second vehicle travel track may be different.

Referring to fig. 1 in combination, it is assumed that an overpass is provided between the toll gate T2 and the toll gate T3, and a road vehicle makes an error in the route on the overpass, and detours to other routes and then rewinds to the correct route. Since the cameras are provided on the other routes, there is a record of the road vehicle traveling to the wrong route on the first vehicle traveling track, and there is no record of the road vehicle traveling to the wrong route on the second vehicle traveling track.

The central cloud device fuses the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle. Illustratively, the central cloud device determines overlapping road segments and non-overlapping road segments on the first vehicle travel track and the second vehicle travel track.

The central cloud equipment performs de-duplication on the overlapped road sections on the first vehicle running track and the second vehicle running track; and selecting the non-overlapping road section on one of the first vehicle running track and the second vehicle running track as a final road section according to the priority for the non-overlapping road section on the first vehicle running track and the second vehicle running track.

618: the center cloud device generates charging information of the road vehicle based on the vehicle travel track.

Fig. 7 shows a flowchart of a method for charging a road vehicle according to an exemplary embodiment of the present application. The method may include seven stages:

1. a data acquisition stage;

the gateway key node is additionally provided with equipment, and the gateway key node is mainly aimed at provincial gate frames, key gate frames and the like, so that the vehicle charging characteristic information can be obtained. The vehicle charging feature information includes: at least one of license plate, vehicle model, axle, and body characteristics. In some embodiments, the license plate is an essential item in the vehicle charging feature information, and the vehicle type, axle and body features are selectable.

The vehicle charging characteristic information can be identified by an AI vehicle type identification model in the edge device or cloud device.

2. An edge data fusion stage;

and carrying out data fusion on multi-source vehicle information (such as snap shots) acquired by the road side acquisition equipment at the edge cloud equipment to generate portal picture running water.

3. A multi-source data verification stage;

after data fusion, binding of vehicle characteristic information of license plates, vehicle types and the like is achieved, and vehicle information collected by other road side collecting devices (road side gun units and RSU devices) is collected for data analysis, so that an effective communication path is formed, and abnormal vehicles are determined.

4. A path fitting stage;

and calculating the vehicle running track of the road vehicle according to a path restoration algorithm by combining (Geographic Information System, GIS) with the map, and generating the vehicle in-province OD flow data based on the pictures.

5. A fee calculation stage;

and calling a provincial charging interface or rate data, and calculating the charging passing amount according to the vehicle calculation track.

6. A comparison correction stage;

and the charging system is used for comparing and correcting the charging amount of the charging system, the data are complementary, and the accurate amount is accurately received.

7. The result is a show-through stage.

According to the accurate amount of each road vehicle, the data perspective shows the actual flow difference and the charging amount difference, and provides relevant evidence information according to project requirements.

Fig. 8 shows a block diagram of a charging device according to an exemplary embodiment of the present application. The device comprises:

the receiving module 820 is configured to receive vehicle information of the road vehicle reported by an edge device, where the edge device is connected with at least one road side acquisition device;

a determining module 840 for determining a vehicle travel track of the road vehicle based on vehicle information of the road vehicle;

and the generating module 860 is configured to generate charging information of the road vehicle based on the vehicle running track.

In some embodiments, the determining module 840 is configured to, in a case where the vehicle information of the road vehicle is at least two data sources, perform fusion processing on the vehicle information of the at least two data sources, to obtain a vehicle running track of the road vehicle;

wherein the at least two data sources comprise: the system comprises at least two of a toll gate, a road camera, a toll station camera and a roadside camera. Illustratively, the toll gate is provided with an RSU.

In some embodiments, the vehicle information includes: the first type of vehicle information and the second type of vehicle information. The determining module 840 is configured to obtain first vehicle characteristic information of the road vehicle based on the first type of vehicle information; determining a first vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of first vehicle characteristic information; obtaining second vehicle characteristic information of the road vehicle for the second type of vehicle information; determining a second vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of second vehicle characteristic information; and fusing the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle.

In some embodiments, the determining module 840 is configured to, if the first type of vehicle information includes an image recognition result, perform data verification on the image recognition result to obtain first vehicle feature information of the road vehicle; the image recognition result is obtained by the edge equipment performing image recognition on the vehicle image; or, in some embodiments, the determining module 840 is configured to, in a case where the first type of vehicle information includes a vehicle image, perform image recognition on the vehicle image to obtain first vehicle feature information of the road vehicle;

the first vehicle characteristic information comprises at least one of license plates, vehicle types, axles and vehicle body characteristics.

In some embodiments, the determining module 840 is configured to determine an overlapping road segment and a non-overlapping road segment on the first vehicle travel track and the second vehicle travel track; performing de-duplication on the overlapped road sections on the first vehicle running track and the second vehicle running track; and selecting the non-overlapping road section on one of the first vehicle running track and the second vehicle running track as a final road section according to the priority for each pair of the non-overlapping road sections on the first vehicle running track and the second vehicle running track.

In some embodiments, the determining module 840 is configured to determine that the second vehicle travel track has a higher priority than the first vehicle travel track if the number of anchor points in the second vehicle travel track is greater than the number of anchor points in the first vehicle travel track; determining that the priority of the second vehicle travel track is lower than the priority of the first vehicle travel track in the case that the number of anchor points in the second vehicle travel track is less than the number of anchor points in the first vehicle travel track;

wherein the number of anchor points is the number of deployment positions of the roadside acquisition devices involved in determining a vehicle travel track.

In some embodiments, the image recognition result includes at least two of the license plate, vehicle model, axle, body feature;

in some embodiments, the determining module 840 is configured to determine that the road vehicle is an abnormal vehicle when there is a comparison abnormality between the image recognition result of the road vehicle and the pre-stored first vehicle characteristic information.

In some embodiments, the determining module 840 is configured to obtain vehicle positioning information reported by the road vehicle; determining a third vehicle travel track based on vehicle positioning information of the road vehicle; and optimizing the vehicle running track through the third vehicle running track to obtain the optimized vehicle running track.

In some embodiments, the determining module 840 is configured to decrypt the vehicle information if the vehicle information is encrypted information.

Fig. 9 shows a block diagram of a charging device according to an exemplary embodiment of the present application. The device comprises:

an acquiring module 920, configured to acquire vehicle information of the road vehicle acquired by the road side acquisition device;

a sending module 940, configured to report vehicle information of the road vehicle to the cloud device, so that the cloud device determines a vehicle running track of the road vehicle based on the vehicle information of the road vehicle; and generating charging information of the road vehicle based on the vehicle travel track.

In some embodiments, the apparatus further comprises an identification module;

the identification module is used for carrying out image identification on the vehicle image to obtain an image identification result when the vehicle information comprises the vehicle image; reporting the image recognition result to the cloud device;

the image recognition result comprises at least one of license plate, vehicle type, axle and vehicle body characteristics.

Fig. 10 is a schematic structural diagram of a computer device according to an embodiment of the present application. In general, the computer device 1000 includes: a processor 1020 and a memory 1040.

Processor 1020 may include one or more processing cores, such as a 4-core processor, a 10-core processor, and the like. The processor 1020 may be implemented in at least one hardware form of a DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 1020 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 1020 may be integrated with a GPU (Graphics Processing Unit, image processor) for use in responsible for rendering and rendering of content required for display by the display screen. In some embodiments, the processor 1020 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.

Memory 1040 may include one or more computer-readable storage media, which may be non-transitory. Memory 1040 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 1040 is used to store at least one instruction for execution by processor 1020 to implement the methods provided by the method embodiments herein.

The application also provides a computer readable storage medium, wherein at least one instruction, at least one section of program, code set or instruction set is stored in the storage medium, and the at least one instruction, the at least one section of program, the code set or instruction set is loaded and executed by the processor to realize the road vehicle charging method provided by the method embodiment.

Optionally, the present application also provides a computer program product containing instructions which, when run on a computer device, cause the computer device to perform the road vehicle charging method of the above aspects.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

Claims

1. A billing system for a road vehicle, the system comprising: the cloud end equipment and the at least one edge equipment are connected with at least one road side acquisition equipment;

2. A billing system according to claim 1, wherein,

the cloud device is used for fusing the vehicle information of at least two data sources to obtain the vehicle running track of the road vehicle under the condition that the vehicle information of the road vehicle comprises the at least two data sources;

wherein the at least two data sources comprise: the system comprises at least two of a toll gate, a road camera, a toll station camera and a roadside camera.

3. The billing system of claim 2, wherein the vehicle information includes a first type of vehicle information and a second type of vehicle information;

the cloud device is used for acquiring first vehicle characteristic information of the road vehicle based on the first type of vehicle information; determining a first vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of first vehicle characteristic information; acquiring second vehicle characteristic information of the road vehicle based on the second type of vehicle information; determining a second vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of second vehicle characteristic information; and fusing the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle.

4. A charging system according to claim 3, characterized in that,

the cloud device is configured to perform data verification on an image recognition result when the first type of vehicle information includes the image recognition result, so as to obtain the first vehicle feature information of the road vehicle; the image recognition result is obtained by the edge equipment performing image recognition on the vehicle image;

or alternatively, the first and second heat exchangers may be,

the cloud device is configured to, when the first type of vehicle information includes a vehicle image, perform image recognition on the vehicle image to obtain the first vehicle feature information of the road vehicle;

5. A charging system according to claim 3, characterized in that,

the cloud device is used for determining an overlapped road section and a non-overlapped road section on the first vehicle running track and the second vehicle running track;

the cloud device is used for carrying out de-duplication on the overlapped road sections on the first vehicle running track and the second vehicle running track; and selecting the non-overlapping road section on one of the first vehicle running track and the second vehicle running track as a final road section according to the priority for each pair of the non-overlapping road sections on the first vehicle running track and the second vehicle running track.

6. The billing system of claim 5 wherein the billing system comprises a billing system,

the cloud device is further configured to determine that the priority of the second vehicle running track is higher than the priority of the first vehicle running track, if the number of anchor points in the second vehicle running track is greater than the number of anchor points in the first vehicle running track; determining that the priority of the second vehicle travel track is lower than the priority of the first vehicle travel track in the case that the number of anchor points in the second vehicle travel track is less than the number of anchor points in the first vehicle travel track;

wherein the number of anchor points is the number of deployment positions of the roadside acquisition devices involved in determining the vehicle travel track.

7. The billing system of claim 5, wherein the image recognition result comprises at least two of the license plate, vehicle model, axle, body feature;

the cloud device is used for determining that the road vehicle is an abnormal vehicle when the image recognition result of the road vehicle is abnormal in comparison with the pre-stored first vehicle characteristic information.

8. A charging system according to any one of claims 1 to 4, characterized in that,

The cloud device is used for acquiring vehicle positioning information reported by the road vehicle; determining a third vehicle travel track based on vehicle positioning information of the road vehicle; and optimizing the vehicle running track through the third vehicle running track to obtain the optimized vehicle running track.

9. A method for charging a road vehicle, applied to a cloud device, the method comprising:

10. The method of claim 9, wherein the determining the vehicle travel trajectory of the road vehicle based on the vehicle information of the road vehicle comprises:

under the condition that the vehicle information of the road vehicle comprises at least two data sources, carrying out fusion processing on the vehicle information of the at least two data sources to obtain a vehicle running track of the road vehicle;

11. The method of claim 10, wherein the vehicle information includes a first type of vehicle information and a second type of vehicle information;

under the condition that the vehicle information of the road vehicle is at least two data sources, the vehicle information of the at least two data sources is fused to obtain the vehicle running track of the road vehicle, and the method comprises the following steps:

acquiring first vehicle characteristic information of the road vehicle based on the first type of vehicle information; determining a first vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of first vehicle characteristic information;

acquiring second vehicle characteristic information of the road vehicle based on the second type of vehicle information; determining a second vehicle running track of the road vehicle based on deployment positions of road side acquisition equipment corresponding to the plurality of second vehicle characteristic information;

and fusing the first vehicle running track and the second vehicle running track to determine the vehicle running track of the road vehicle.

12. A charging device for a road vehicle, the device comprising:

13. A computer device, the computer device comprising: a processor and a memory storing a computer program that is executed by the processor to cause the computer device to implement the road vehicle charging method according to any one of claims 9 to 11.

14. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform a method of charging a road vehicle according to any one of claims 9 to 11.

15. A computer program product, characterized in that the computer program product contains instructions which, when run on a computer, cause the computer to carry out a method for charging a road vehicle according to any one of claims 9 to 11.