CN115830859A - Traffic data processing method and device, edge cloud server and storage medium - Google Patents

Abstract

The embodiment of the present application provides a traffic data processing method, device, edge cloud server, and storage medium. By marking the target time when the abnormality occurs when the target vehicle is determined to be abnormal, the traffic received within the first time period before the target time is obtained. According to the type of abnormal data, the target time and the abnormal data or the summary of the abnormal data are stored in the block chain, which realizes the The on-chain storage of key traffic data related to vehicle abnormalities not only ensures the credibility of the data, but also reduces the data storage pressure of the blockchain.

Description

Traffic data processing method, device, edge cloud server and storage medium

technical field

The embodiments of the present application relate to the technical field of big data, and in particular to a traffic data processing method, device, edge cloud server, and storage medium.

Background technique

With the continuous development of Internet of Vehicles technology and the rise of intelligent driving, in order to improve driving comfort, active safety of vehicles and meet the needs of handling abnormal events, it is necessary to store relevant traffic data generated by vehicles on the road. .

Since blockchain technology has important features such as non-tamperable, decentralized network, consensus algorithm, and automatic execution of smart contracts, it can solve the trust problem among multiple participants. In order to improve the credibility of data, in the existing technology, through The blockchain stores the traffic data generated by the vehicle while driving on the road.

However, due to the large amount of traffic data generated when the vehicle is driving on the road and the data is continuously generated, the use of existing technology solutions for traffic data storage will cause a greater impact on the blockchain. Storage pressure.

Contents of the invention

Embodiments of the present application provide a traffic data processing method, device, edge cloud server, and storage medium, so as to solve the problem of high storage pressure in the prior art.

In the first aspect, the embodiment of the present application provides a traffic data processing method applied to an edge cloud server, including:

When it is determined that the target vehicle is abnormal, mark the target moment when the abnormality occurs;

Obtaining data received in a first time period before the target time and data received in a second time period after the target time to obtain abnormal data;

According to the type of the abnormal data, the target time and the abnormal data or a summary of the abnormal data are stored in the block chain.

In the second aspect, the embodiment of the present application provides a traffic data processing device, including:

The acquisition module is used to mark the target time when the abnormality occurs when it is determined that the target vehicle is abnormal; acquire the data received in the first time period before the target time and the data received in the second time period after the target time data, get abnormal data;

A processing module, configured to store the target time and the abnormal data or a summary of the abnormal data in a block chain according to the type of the abnormal data.

In the third aspect, an embodiment of the present application provides an edge cloud server, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the above-mentioned first The traffic data processing method described in the aspect.

In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the traffic data processing method as described in the first aspect above is realized.

The traffic data processing method, device, edge cloud server, and storage medium provided in the embodiments of the present application, when it is determined that the target vehicle is abnormal, mark the target time when the abnormality occurs, and obtain the traffic data received within the first time period before the target time. According to the type of abnormal data, the target time and the abnormal data or the summary of the abnormal data are stored in the block chain, realizing the connection with the vehicle The on-chain storage of abnormally relevant transportation key data not only ensures the credibility of the data, but also reduces the data storage pressure of the blockchain.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the present application will be easily understood from the following description.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without creative effort.

FIG. 1 is a schematic structural diagram of a vehicle-road-cloud integrated fusion control system provided by an embodiment of the present application;

FIG. 2 is a schematic flowchart of a traffic data processing method provided in Embodiment 1 of the present application;

FIG. 3 is a schematic diagram of the distribution of abnormal data provided in Embodiment 1 of the present application;

FIG. 4 is a schematic structural diagram of a traffic data processing device provided in Embodiment 2 of the present application;

FIG. 5 is a schematic structural diagram of an edge cloud server provided in Embodiment 3 of the present application.

Detailed ways

In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is an embodiment of a part of the application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

It should be noted that the terms "first", "second", and "object" in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and not necessarily used to describe a specific order or sequentially. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

Based on the technical problems existing in the prior art, the embodiment of the present application provides a traffic data processing method. When it is confirmed that the vehicle is abnormal, by storing the key data related to the abnormality of the vehicle, that is, the abnormal data, into the block chain, Moreover, the data stored in the blockchain can be the abnormal data itself or a summary of the abnormal data according to the needs, thereby reducing the storage pressure of the blockchain.

Exemplarily, FIG. 1 is a schematic structural diagram of a vehicle-road-cloud integrated fusion control system provided in the embodiment of the present application. As shown in FIG. 1 , the vehicle-road-cloud integrated fusion control system mainly includes a cloud control basic platform, Resource platforms, roadside intelligent systems and intelligent connected vehicles, among which, the cloud control basic platform includes central cloud, regional cloud and edge cloud, and the data resource platform includes various application platforms, such as maps, positioning, traffic management, weather and other platforms , the roadside intelligent system can include roadside units such as various communication equipment, computing equipment, cameras, radars, and local positioning equipment installed on both sides of the road. Intelligent networked vehicles refer to various types of vehicles that can drive on the road , including cars, buses, or trucks.

The edge cloud is a fusion information processing node that processes the edge layer, and it can collect and process various types of information from vehicles and roadside intelligent systems. In this embodiment, the edge cloud is composed of edge cloud server clusters, and in this embodiment, block chain nodes are arranged in the edge cloud server clusters. Both intelligent networked vehicles and roadside intelligent systems will send their own data (including vehicle location, vehicle status, still pictures and videos taken by roadside cameras, etc.) to the edge cloud in real time, and the edge cloud will write key data in it as needed Enter the blockchain network to complete the deposit.

Embodiment one

Figure 2 is a schematic flowchart of a traffic data processing method provided in Embodiment 1 of the present application. The method in this embodiment can be executed by the traffic data processing device provided in the embodiment of the present application, and the device can be implemented by software and/or hardware. and can be integrated in the edge cloud server as shown in Figure 1. As shown in Figure 2, the traffic data processing method of the present embodiment includes:

S201. When it is determined that the abnormality occurs in the target vehicle, mark the target time when the abnormality occurs.

Abnormalities may occur at any time when the vehicle is driving on the road. These abnormalities may be hardware, such as tire blowouts, scratches, etc., or software, such as inability to locate, software update failure, etc. These abnormalities are caused by vehicles, Road safety and the key to user experience, therefore, identify and confirm and store evidence.

In this step, the cloud server receives data in real time from the roadside unit, intelligent connected consumer vehicles, and other related equipment or platforms, and when the abnormality of the target vehicle occurs according to the received data, it marks and records the time when the abnormality occurs.

Wherein, the target vehicle refers to an abnormal ICV. Since any ICV may have an abnormality, the target vehicle in this embodiment may be any ICV.

The target time refers to the time when the abnormality occurs, and here mainly refers to the time when the cloud server recognizes that the abnormality occurs.

In this embodiment, the cloud server may determine whether the target vehicle is abnormal by means of external reporting, or by means of active identification based on received vehicle end data and/or roadside data. Among them, the external reporting includes: one or more of vehicle owner reporting, vehicle terminal reporting, roadside terminal reporting, and third-party reporting.

Reporting by car owners mainly refers to the way in which car owners report abnormalities through user equipment. It is understandable that when an abnormality occurs in the target vehicle, such as when a certain button in the vehicle is unavailable, the owner can fill in and report the abnormality through the relevant APP on the user device.

Vehicle-end reporting mainly refers to the method of reporting abnormalities through the on-board unit (OBU) installed on the smart network vehicle. It is understandable that when the OBU detects an abnormality in the target vehicle, such as a software update failure, it will actively report the detected abnormality to the cloud server.

The roadside reporting mainly refers to the way of reporting abnormalities through the roadside unit (roadside unit, RSU) in the roadside intelligent system. It can be understood that when the RSU recognizes that the target vehicle has an abnormality, such as when it collides with other vehicles, it will actively report the identified abnormality to the cloud server.

Third-party reporting mainly refers to the method of reporting abnormalities by other systems or platforms such as road supervision departments and traffic management departments. It can be understood that when the third party finds that the target vehicle has abnormal behavior, such as violating traffic rules, it will actively report the detected abnormality to the cloud server.

In the external reporting method, the reported data will carry an abnormal signal, such as adding an abnormal flag in the packet header, so the edge cloud server can use the data reported by the car owner, and/or, the data reported by the car end, and /or, the data reported by the roadside terminal, and/or, whether the abnormal signal is included in the data reported by the third party, to determine whether the abnormality occurs in the target vehicle.

Specifically, if any one of the data reported by the vehicle owner, the data reported by the vehicle end, the data reported by the roadside end, and the data reported by the third party includes an abnormal signal, it can be determined that the target vehicle is abnormal. In the external reporting method, the edge cloud server determines the time when the abnormal signal is received or the time information carried by the abnormal signal as the target time.

In the active identification mode, the edge cloud server can analyze the data reported by the vehicle end and/or the data reported by the roadside end according to the preset algorithm, obtain the analysis results, and determine whether the target vehicle is abnormal according to whether the analysis results meet expectations.

It should be noted that, depending on the application scenario, the preset algorithm may have different situations, such as an algorithm for judging engine performance, an algorithm for judging the ability of automatic driving, or an algorithm for judging the performance of the vehicle's active safety system, etc. .

In a possible implementation, in this embodiment, on the one hand, the edge cloud server can identify the data received from each data source in real time (whenever data is received), and determine whether the data reported by each data source is Including abnormal signals to determine whether the target vehicle is abnormal. On the other hand, the edge cloud server can periodically (such as every day at zero) use the preset algorithm to analyze the data from various data sources (mainly the vehicle-side data reported by the vehicle-end and the road data). The roadside data reported by the side end) is analyzed to determine whether the target vehicle is abnormal. Through the combination of external reporting and active identification, the abnormal target vehicle can be found in time and the evidence can be stored to ensure the comprehensiveness of data storage. and reliability.

Among them, the vehicle end data can include gateway information (such as SIM card information and GPS positioning information, etc.), electronic control system information (such as body control module BCM information, battery management system BMS information, driving computer ECU information and engine management system EMS information etc.), vehicle overall information (such as vehicle model, battery type, sensor type, etc.), vehicle monitoring information (such as location information, tire pressure information, fault information and CAN data information, etc.), vehicle control information (such as car lock control information, door Control information, vehicle light control information, vehicle window control information, etc.), data statistics information (such as vehicle speed information, power information, and mileage data information, etc.).

Roadside end data can include holographic traffic flow data (such as vehicle speed, queue length, queue time, space occupancy, time occupancy, etc.), vehicle passing data (such as license plate number, model, color, etc.), traffic participant data ( Such as non-motor vehicle information, etc.), traffic violation data (such as violation behavior, violation time, violation location, etc.), traffic event data (such as congestion, parking, roadblocks, construction, traffic accidents, spilled objects, line pressure, retrograde, etc.) , special data (such as road surface water, road icing, wind speed, visibility, temperature and humidity, etc.), raw data (including raw data and raw point cloud data, such as lidar/millimeter-wave radar raw point cloud data, audio recording, camera Take one or more of still photos, camera recording video, etc.).

S202. Obtain data received within a first time period before the target time and data received within a second time period after the target time to obtain abnormal data.

In this step, starting from the target time, trace back for a period of time from the target time to obtain the data received in the first period before the target time, and then continue for a period of time from the target time to obtain the data received after the target time The data received in the second time period, and all the data received in the first time period and the data received in the second time period are regarded as abnormal data, so as to ensure that all abnormal data related to the vehicle are covered. Key data, thus ensuring the validity of the data stored in evidence.

Exemplarily, FIG. 3 is a schematic diagram of distribution of abnormal data provided in Embodiment 1 of the present application. As shown in FIG. 3 , the first time period T1 is the time period between the target time S0 and the first time S1 , and the second time period T2 is the time period between the target time S0 and the second time S2 .

In this embodiment, since the data received in the first time period and the second time period may be vehicle-side data and/or vehicle-side data, the abnormal data may also be vehicle-side data and/or vehicle-side data.

In a possible implementation manner, the edge cloud server stores the received data in the cache according to a sliding time window, and the duration of the first time period is consistent with the window duration of the sliding time window.

Exemplarily, the edge cloud server will reserve a section of data cache in the server for each potential evidence deposit subject (including each roadside unit and the first vehicle unit), and the data volume of the cached data is determined by the window of the sliding time window Duration decision, such as the window duration T0 = 5 minutes, then the edge cloud server retains the last 5 minutes of data received from the ICV and/or the camera.

If there is no abnormality in the intelligent connected car, the edge cloud server will gradually move the data cache window back, and discard the data that was not abnormal 5 minutes ago. Once it is determined that an abnormality has occurred in the intelligent connected car, the edge cloud server will immediately start the action of depositing evidence. Mark the target time, and at the same time, retain the data received in the T1 (T1=T0) time period before the target time and the data in the T2 time period to be received after the target time to obtain abnormal data.

S203. According to the type of the abnormal data, store the target time and the abnormal data or a summary of the abnormal data in the block chain.

In this embodiment, in order to further reduce the storage pressure of the block chain, after the abnormal data is obtained according to S202, according to the actual situation of the type of abnormal data, the target time and the abnormal data or the summary of the abnormal data are sent to the block chain, and It is stored by the blockchain, which not only achieves the purpose of storing key data related to abnormalities through the blockchain, but also reduces the storage pressure of the blockchain.

In a possible implementation manner, in this embodiment, according to whether the abnormal data is image data, it is determined whether to store the original data of the abnormal data or the abstract of the abnormal data on the block chain.

Specifically, if the abnormal data is non-image data, such as text data, store the abnormal data on the block chain; if the abnormal data is image, store the abnormal data in the local database, and store the summary of the abnormal data to the blockchain.

Since the abnormal data may contain both image data and non-image data, in this embodiment, the type of each abnormal data can be judged separately, and according to the judgment result, it is determined whether the original data of the abnormal data or the abnormal data The summary of is stored in the blockchain.

In a possible implementation, before the abnormal data is sent to the blockchain, the abnormal image data and non-image abnormal data can be determined from the abnormal data determined in S202, and then each image is processed by a summary algorithm. Processing abnormal data of image type to obtain the summary of abnormal data of image type. Finally, the target time, summary of abnormal data of image type and non-image type abnormal data are sent to the block chain together for storage by the block chain, thus completing the communication with the vehicle Abnormally related traffic key data is stored on the chain.

In a possible implementation, in this embodiment, the target time and abnormal data are stored in stages. For example, when it is determined that an abnormality occurs, the target time is first stored in the blockchain system, and then the first The original data or summaries of the historical data received in the time period T1 are all stored in the blockchain system, and finally, after the data reception in the second time period T2 is completed, the original data or summaries of all the data received in T2 It is also stored in the blockchain system, so as to complete the on-chain storage of key traffic data related to vehicle abnormalities.

Optionally, in this embodiment, the time stamp system is used to sign the target time, and the time stamped target time is stored in the block chain to prove the validity of the target time.

In this embodiment, by marking the target time when the abnormality occurs when it is determined that the target vehicle is abnormal, the data received in the first time period before the target time and the data received in the second time period after the target time are acquired. According to the type of abnormal data, the target time and the abnormal data or the summary of the abnormal data are stored in the block chain, which realizes the storage of key traffic data related to vehicle abnormalities, which not only guarantees It improves the credibility of the data and reduces the data storage pressure of the blockchain.

Embodiment two

FIG. 4 is a schematic structural diagram of a traffic data processing device provided in Embodiment 2 of the present application. The device can be implemented by software and/or hardware, and can be integrated into an edge cloud server. As shown in Figure 4, the traffic data processing device 10 in this embodiment includes:

An acquisition module 11 and a processing module 12.

The acquisition module 11 is used to mark the target time when the abnormality occurs when it is determined that the target vehicle is abnormal; acquire the data received in the first time period before the target time and the data received in the second time period after the target time Data, get abnormal data;

The processing module 12 is configured to store the target time and the abnormal data or a summary of the abnormal data in the block chain according to the type of the abnormal data.

Optionally, the types of abnormal data include images and non-images, and the processing module 12 is specifically used to include:

If the abnormal data is non-image, store the abnormal data on the blockchain;

If the abnormal data is an image, store the abnormal data in the local database, and store the summary of the abnormal data in the block chain.

Optionally, the abnormal data includes vehicle end data and roadside end data.

Optionally, the obtaining module 11 is also used for:

According to the data reported by the vehicle owner, and/or the data reported by the vehicle end, and/or the data reported by the roadside end, and/or the data reported by the third party, it is determined whether the target vehicle is abnormal.

Optionally, the acquiring module 11 is specifically used for:

According to the data reported by the vehicle owner, and/or, the data reported by the vehicle end, and/or, the data reported by the roadside end, and/or, whether the abnormal signal is included in the data reported by the third party, determine whether the target vehicle is abnormal.

Optionally, the acquiring module 11 is specifically used for:

According to the preset algorithm, analyze the data reported by the vehicle terminal and/or the data reported by the roadside terminal, and obtain the analysis result;

Determine whether the target vehicle is abnormal according to whether the analysis results meet expectations.

Optionally, the edge cloud server stores the received data in the cache according to the sliding time window, and the duration of the first time period is consistent with the window duration of the sliding time window.

The traffic data processing device provided in this embodiment can execute the traffic data processing method provided in the method embodiment above, and has corresponding functional modules and beneficial effects for executing the method. The implementation principles and technical effects of this embodiment are similar to those of the above method embodiments, and will not be repeated here.

Embodiment Three

FIG. 5 is a schematic structural diagram of an edge cloud server provided in Embodiment 3 of the present application. As shown in FIG. 4 , the edge cloud server 20 includes a memory 21, a processor 22, and an Computer program; the number of processors 22 in the edge cloud server 20 can be one or more, taking a processor 22 as an example in Figure 5; the processor 22 and memory 21 in the edge cloud server 20 can be connected by bus or other means , in Figure 5, the connection through the bus is taken as an example.

As a computer-readable storage medium, the memory 21 can be used to store software programs, computer-executable programs and modules, such as program instructions/modules corresponding to the acquisition module 11 and the processing module 12 in the embodiment of the present application. The processor 22 executes various functional applications and data processing of the edge cloud server by running the software programs, instructions and modules stored in the memory 21 , that is, realizes the above traffic data processing method.

The memory 21 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required by a function; the data storage area may store data created according to the use of the terminal, and the like. In addition, the memory 21 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices. In some examples, the memory 21 may further include a memory that is remotely located relative to the processor 22, and these remote memories may be connected to the edge cloud server through a grid. Examples of the aforementioned grids include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Embodiment four

Embodiment 4 of the present application also provides a computer-readable storage medium on which a computer program is stored, and the computer program is used to execute a traffic data processing method when executed by a computer processor. The method includes:

When it is determined that the target vehicle is abnormal, mark the target moment when the abnormality occurs;

Obtain the data received in the first time period before the target time and the data received in the second time period after the target time to obtain abnormal data;

Depending on the type of anomalous data, the target moment and anomalous data or a summary of anomalous data are stored on the blockchain.

Certainly, the computer-readable storage medium provided in the embodiment of the present application is not limited to the operation of the method described above, and its computer program can also perform related operations in the traffic data processing method provided in any embodiment of the present application.

Through the above description about the implementation, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general-purpose hardware, and of course it can also be realized by hardware, but in many cases the former is a better implementation . Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product can be stored in a computer-readable storage medium, such as a floppy disk of a computer , read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), flash memory (FLASH), hard disk or CD, etc., including a number of instructions to make a computer device (which can be a personal computer, A server, or a grid device, etc.) executes the methods described in various embodiments of the present application.

It is worth noting that, in the above-mentioned embodiment of the traffic data processing device, the included units and modules are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be realized; in addition, The specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application.

Note that the above are only preferred embodiments and technical principles used in this application. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can also include more other equivalent embodiments without departing from the concept of the present application, and the present application The scope is determined by the scope of the appended claims.

Claims (10)

1. A traffic data processing method is applied to an edge cloud server and comprises the following steps:

when the target vehicle is determined to be abnormal, marking the target time of the abnormal occurrence;

acquiring data received in a first time period before the target time and data received in a second time period after the target time to obtain abnormal data;

and storing the target time and the abnormal data or the abstract of the abnormal data into a block chain according to the type of the abnormal data.

2. The method of claim 1, wherein the type of the abnormal data comprises an image class and a non-image class, and storing the abnormal data or the summary of the abnormal data onto a blockchain according to the type of the abnormal data comprises:

if the abnormal data is a non-image class, storing the abnormal data to a block chain;

and if the abnormal data is an image class, storing the abnormal data into a local database, and storing the abstract of the abnormal data into a block chain.

3. The method of claim 1, wherein the anomaly data includes vehicle end data and roadside end data.

4. The method according to claim 1, wherein before marking a target time when an abnormality occurs when it is determined that the abnormality occurs in the target vehicle, further comprising:

and determining whether the target vehicle is abnormal or not according to the data reported by the vehicle owner and/or the data reported by the vehicle end and/or the data reported by the road side end and/or the data reported by the third party.

5. The method of claim 4, wherein the determining whether the target vehicle is abnormal according to data reported by a vehicle owner, and/or data reported by a vehicle end, and/or data reported by a road side end, and/or data reported by a third party comprises:

and determining whether the target vehicle is abnormal or not according to data reported by an owner of the vehicle and/or data reported by a vehicle end and/or data reported by a road side end and/or whether the data reported by a third party comprises an abnormal signal or not.

6. The method of claim 4, wherein determining whether the target vehicle is abnormal according to data reported by a vehicle side and/or data reported by a road side comprises:

analyzing the data reported by the vehicle end and/or the data reported by the road side end according to a preset algorithm to obtain an analysis result;

and determining whether the target vehicle is abnormal or not according to whether the analysis result meets the expectation or not.

7. The method according to claim 1, wherein the edge cloud server stores the received data in a cache in a sliding time window manner, and the duration of the first time period is consistent with the window duration of the sliding time window.

8. A traffic data processing device integrated with an edge cloud server, comprising:

the acquisition module is used for marking the target time when the target vehicle is determined to be abnormal; acquiring data received in a first time period before the target time and data received in a second time period after the target time to obtain abnormal data;

and the processing module is used for storing the target time and the abnormal data or the abstract of the abnormal data into a block chain according to the type of the abnormal data.

9. An edge cloud server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the traffic data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a traffic data processing method according to any one of claims 1 to 7.

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