Truck platoon-oriented vehicle-mounted unstructured data processing method and device

Abstract

The application provides a truck platoon-oriented vehicle-mounted unstructured data processing method and device, the method comprising: in the case of determining that a vehicle-mounted unstructured data processing service for a target truck platoon is triggered, registering the vehicle-mounted unstructured data processing service in a Nacos service registration center cluster, configuring the vehicle-mounted unstructured data processing service with a RabbitMQ message queue service, a MinIO object storage service and an Elasticsearch service, and then performing data processing on video stream data collected by vehicle-mounted sensors of each truck in the target truck platoon based on the above services. The truck platoon-oriented vehicle-mounted unstructured data processing method and device provided by the application can improve the data processing efficiency, comprehensiveness of information extraction and data utilization rate of vehicle-mounted unstructured data generated in a truck platoon operation scenario, and is conducive to improving the operation stability of the truck platoon.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an onboard unstructured data processing method and apparatus for truck platooning. Background Technology

[0002] Truck platooning refers to a mode of operation in which multiple trucks are grouped into a queue for coordinated operation through wireless communication between them. Each truck in the platoon can control its own movement based on data collected from onboard sensors such as radar and cameras, as well as data received via wireless communication. Truck platooning can improve the operational efficiency of highway transportation, enhance safety, reduce energy consumption in truck operation, improve the working environment for drivers, and reduce their workload.

[0003] With the rapid development of intelligent driving technology, the number of onboard sensors on trucks is increasing daily. During truck platooning operations, the onboard sensors of each truck in the platoon continuously generate a large amount of unstructured onboard data, such as video streams and radar scan images. This unstructured onboard data contains rich operational information, which is of great significance for improving the operational efficiency of truck platoons and ensuring their driving safety.

[0004] Traditional unstructured data processing methods in related technologies can be based on big data platforms or machine learning algorithms to process unstructured data. However, most of these traditional methods focus on general data processing and suffer from drawbacks such as low data processing efficiency, incomplete information extraction, or insufficient data utilization when dealing with unstructured data generated in truck platooning scenarios. Therefore, improving the data processing efficiency, the comprehensiveness of information extraction, and the data utilization rate of unstructured data generated in truck platooning scenarios is a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0005] This invention provides a method and apparatus for processing unstructured vehicle data in truck platooning scenarios. This addresses the shortcomings of existing methods for processing unstructured vehicle data generated in truck platooning operations, which suffer from low processing efficiency, incomplete information extraction, and insufficient data utilization. The invention aims to improve the processing efficiency, the comprehensiveness of information extraction, and the utilization rate of unstructured vehicle data generated in truck platooning operations.

[0006] This invention provides an onboard unstructured data processing method for truck platooning, comprising the following steps.

[0007] If it is determined that the vehicle-mounted unstructured data processing service for the target truck convoy has been triggered, the vehicle-mounted unstructured data processing service is registered in the Nacos service registry cluster, and RabbitMQ message queue service, MinIO object storage service and Elasticsearch service are configured for the vehicle-mounted unstructured data processing service. The target truck convoy includes multiple trucks.

[0008] The video stream data collected by the on-board sensors of each truck is obtained. If the video stream data collected by the on-board sensors of any truck in the target truck platoon is obtained, the metadata of the video stream data collected by the on-board sensors of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensors of any truck is stored in the MinIO cluster. The metadata of the video stream data includes at least one of the following: the identification information of the video stream data, the generation time, and the data size.

[0009] Listen to the RabbitMQ message queue. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck platoon arrives, retrieve the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames from the video stream data collected by the on-board sensor of any truck to obtain the key frame image corresponding to the video stream data collected by the on-board sensor of any truck.

[0010] Based on the metadata of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck, identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck is generated. Then, the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information are stored in the MinIO cluster, and the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck are stored in the Elasticsearch cluster. The metadata of the keyframe images includes timestamps and frame numbers.

[0011] According to the present invention, a method for processing unstructured vehicle data for truck platooning, after storing the metadata and identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck into an Elasticsearch cluster, the method further includes:

[0012] Upon receiving a data query request from a user, based on the query conditions carried in the data query request, the metadata and identification information of the keyframe image matching the query conditions are obtained from the Elasticsearch cluster.

[0013] Based on the metadata and identification information of the keyframe images that match the query conditions, the keyframe images that match the query conditions are obtained from the MinIO cluster.

[0014] According to the present invention, an onboard unstructured data processing method for truck platooning includes retrieving keyframe images matching the query conditions from a MinIO cluster based on metadata and identification information of keyframe images matching the query conditions.

[0015] Based on the metadata and identification information of the keyframe image that matches the query conditions, the keyframe image that matches the query conditions is determined in the MinIO cluster;

[0016] Lazy loading technology is used to load the keyframe images in the MinIO cluster that match the query conditions, as well as the metadata and identification information of the keyframe images that match the query conditions, onto the display interface.

[0017] According to the present invention, an onboard unstructured data processing method for truck platooning includes storing keyframe images corresponding to video stream data collected by onboard sensors of any truck carrying identification information to a MinIO cluster, comprising:

[0018] After encrypting the keyframe images corresponding to the video stream data collected by the on-board sensors of any of the trucks carrying identification information, the encrypted data is stored in the MinIO cluster.

[0019] According to the present invention, an onboard unstructured data processing method for truck platooning is provided, wherein the onboard sensors include radar sensors.

[0020] According to the present invention, an onboard unstructured data processing method for truck platooning includes decoding and frame extraction of video stream data collected by the onboard sensors of any truck to obtain keyframe images corresponding to the video stream data collected by the onboard sensors of any truck, comprising:

[0021] A distributed processing approach is used to decode the video stream data collected by the on-board sensors of any truck to obtain the point cloud data corresponding to the video stream data collected by the on-board sensors of any truck.

[0022] Image frame extraction is performed on the point cloud data corresponding to the video stream data collected by the on-board sensors of any truck to obtain the keyframe image corresponding to the video stream data collected by the on-board sensors of any truck.

[0023] The present invention also provides an onboard unstructured data processing device for truck platooning, comprising the following modules:

[0024] An initialization module is used to register the vehicle-mounted unstructured data processing service in the Nacos service registry cluster when it is determined that the vehicle-mounted unstructured data processing service for the target truck convoy has been triggered, and to configure RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the vehicle-mounted unstructured data processing service. The target truck convoy includes multiple trucks.

[0025] The data acquisition module is used to acquire video stream data collected by the on-board sensors of each truck, and when the video stream data collected by the on-board sensors of any truck in the target truck platoon is acquired, the metadata of the video stream data collected by the on-board sensors of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensors of any truck is stored in the MinIO cluster. The metadata of the video stream data includes at least one of the following: the identification information of the video stream data, the generation time, and the data size.

[0026] The data monitoring module is used to monitor the RabbitMQ message queue. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck platoon arrives, it obtains the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decodes and extracts frames from the video stream data collected by the on-board sensor of any truck to obtain the key frame image corresponding to the video stream data collected by the on-board sensor of any truck.

[0027] The data storage module is used to generate identification information for the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck based on the metadata of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck. Then, the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information are stored in the MinIO cluster, and the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck are stored in the Elasticsearch cluster. The metadata of the keyframe images includes timestamps and frame numbers.

[0028] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the onboard unstructured data processing method for truck platooning as described above.

[0029] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the onboard unstructured data processing method for truck platooning as described above.

[0030] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the onboard unstructured data processing method for truck platooning as described above.

[0031] The present invention provides a method and apparatus for processing unstructured vehicle data for truck platoons. Upon determining that an unstructured vehicle data processing service for a target truck platoon has been triggered, the method registers the unstructured vehicle data processing service in a Nacos service registry cluster, configures RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the service, acquires video stream data collected by the onboard sensors of each truck, and, upon acquiring video stream data collected by the onboard sensors of any truck in the target truck platoon, adds the metadata of the video stream data collected by the onboard sensors of that truck to the RabbitMQ message queue, stores the video stream data collected by the onboard sensors of that truck in the MinIO cluster, listens to the RabbitMQ message queue, and upon detecting the arrival of the metadata of the video stream data collected by the onboard sensors of any truck in the target truck platoon, retrieves the onboard data of that truck from the MinIO cluster. The system collects video stream data from sensors on any truck, then decodes and extracts frames to obtain keyframe images corresponding to these images. Based on the metadata of these keyframe images, it generates identification information for each keyframe image. This identification information is then stored in a MinIO cluster, and the metadata and identification information are stored in an Elasticsearch cluster. This process enables rapid indexing and retrieval of unstructured vehicle data, improving data processing efficiency, comprehensiveness of information extraction, and data utilization in truck platooning scenarios. It also enhances the operational stability of truck platoons and provides strong technical support for data processing in the field of vehicle networking. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is one of the flowcharts illustrating the onboard unstructured data processing method for truck platooning provided by the present invention.

[0034] Figure 2 This is a schematic diagram of the service initialization process in the vehicle-mounted unstructured data processing method for truck platooning provided by the present invention.

[0035] Figure 3 This is the second flowchart of the onboard unstructured data processing method for truck platooning provided by the present invention.

[0036] Figure 4 This is a schematic diagram of the structure of the on-board unstructured data processing device for truck platooning provided by the present invention.

[0037] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0039] In the description of the invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0040] In the description of this application, the terms "first," "second," etc., are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, in the description of this application, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0041] The following is combined with Figures 1-3 This invention describes an onboard unstructured data processing method for truck platooning.

[0042] Figure 1 This is one of the flowcharts illustrating the onboard unstructured data processing method for truck platooning provided by the present invention, such as... Figure 1 As shown, the method includes the following steps: Step 101: If it is determined that the vehicle-mounted unstructured data processing service for the target truck convoy has been triggered, register the vehicle-mounted unstructured data processing service in the Nacos service registry cluster, and configure RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the vehicle-mounted unstructured data processing service. The target truck convoy includes multiple trucks.

[0043] It should be noted that the execution subject of this embodiment of the invention is an on-board unstructured data processing device for truck platooning. This on-board unstructured data processing device for truck platooning can be configured in electronic devices such as computers or servers.

[0044] Specifically, the target truck platoon in this embodiment of the invention includes multiple trucks. The unstructured data generated by the onboard sensors of each truck in the target truck platoon is the processing object of the onboard unstructured data processing method for truck platoons provided by this invention. In this embodiment of the invention, any truck platoon can be determined as the target truck platoon according to actual needs. This embodiment of the invention does not specifically limit the target truck platoon.

[0045] When it is necessary to process the unstructured data generated by the onboard sensors of each truck in a target truck platoon based on the onboard unstructured data processing method for truck platoons provided by this invention, the onboard unstructured data processing service for the target truck platoon can be triggered in various ways. For example, the user can trigger the onboard unstructured data processing service for the target truck platoon by inputting control commands; or, the onboard unstructured data processing service for the target truck platoon can be automatically triggered when the target truck platoon is determined to be running. The specific method for triggering the onboard unstructured data processing service for the target truck platoon is not limited in the embodiments of this invention.

[0046] Figure 2 This is a schematic diagram of the service initialization process in the onboard unstructured data processing method for truck platooning provided by this invention. For example... Figure 2 As shown, once it is determined that the on-board unstructured data processing service for the target truck platoon has been triggered, the on-board unstructured data processing service for the target truck platoon can be registered in the Nacos service registry under the Spring Cloud framework to complete the communication preparation between the on-board unstructured data processing service for the target truck platoon and other microservices.

[0047] Spring Cloud is a microservice framework based on Spring Boot that provides a range of tools and components to help developers quickly build and deploy cloud-native applications and solve common problems in distributed systems, such as configuration management, service registration and discovery, routing, load balancing, circuit breakers, and distributed tracing.

[0048] Nacos Service Registry is a service registration and discovery platform used to implement dynamic service discovery, configuration management, and service governance. It is widely used in microservice architectures to solve problems such as service registration, discovery, configuration management, and health monitoring.

[0049] After registering the aforementioned vehicle-mounted unstructured data processing service for the target truck convoy in the Nacos service registry, the coordinator can be initialized and configured to provide RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the vehicle-mounted unstructured data processing service for the target truck convoy, so as to ensure unified management and real-time synchronization of the configuration of the vehicle-mounted unstructured data processing service for the target truck convoy.

[0050] Configure RabbitMQ message queue service for the above-mentioned on-board unstructured data processing service for target truck platoons, including starting RabbitMQ message queue service, automatically creating the required exchanges, queues and bindings to support message passing mechanisms for tasks such as video parsing and image extraction, and to send asynchronous notifications of task status in a timely manner.

[0051] Configure the MinIO object storage service for the above-mentioned onboard unstructured data processing service for the target truck convoy, including creating the necessary buckets and setting the corresponding access permissions to support the storage of raw video files and processing results.

[0052] Configure the Elasticsearch service for the above-mentioned onboard unstructured data processing service for the target truck platoon, including starting the Elasticsearch service, automatically creating index templates, and preparing to store video-related metadata and tag information, in order to prepare for subsequent data retrieval and analysis.

[0053] RabbitMQ is a message middleware that uses an advanced message queuing protocol implemented in Erlang. It supports multiple message protocols, including AMQP (Advanced Message Queuing Protocol), MQTT, and STOMP.

[0054] MinIO is a high-performance, open-source object storage service compatible with the Amazon S3 API, suitable for storing large volumes of unstructured data. MinIO uses a parallel and distributed architecture, supporting multi-threading and multi-core processors, fully utilizing hardware resources to achieve high-speed data read and write operations. Its object storage exhibits excellent read and write speeds on standard hardware, reaching 183GB / s and 171GB / s respectively, meeting the needs of business scenarios with high data access speed requirements, such as big data analytics and high-concurrency internet applications.

[0055] Elasticsearch is a real-time distributed search and analytics engine based on Lucene. It provides distributed, highly scalable, and real-time search and data analysis capabilities, allowing users to store, search, and analyze large amounts of data quickly and in near real-time.

[0056] It should be noted that, in order to enhance the reliability and load-bearing capacity of the vehicle-mounted unstructured data processing method provided by this invention, the embodiments of this invention employ a cluster deployment of multiple components, including Nacos, and a clustered configuration of RabbitMQ and Elasticsearch. This cluster deployment not only supports automated failover but also ensures real-time data synchronization, thereby improving the stability and data consistency of the vehicle-mounted unstructured data processing service for target truck platoons.

[0057] By registering the onboard unstructured data processing service for the target truck convoy in the Nacos service registry cluster, and configuring RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the aforementioned onboard unstructured data processing service for the target truck convoy, the RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service can be properly configured and initialized. This can build a document infrastructure for the aforementioned onboard unstructured data processing service for the target truck convoy, providing favorable support for subsequent data processing, storage, and display.

[0058] Step 102: Obtain video stream data collected by the on-board sensors of each truck. If the video stream data collected by the on-board sensors of any truck in the target truck platoon is obtained, add the metadata of the video stream data collected by the on-board sensors of any truck to the RabbitMQ message queue and store the video stream data collected by the on-board sensors of any truck to the MinIO cluster. The metadata of the video stream data includes at least one of the following: the identification information of the video stream data, the generation time, and the data size.

[0059] Specifically, in this embodiment of the invention, video stream data collected by the onboard sensors of each truck in the target truck convoy can be obtained in various ways. For example, in this embodiment of the invention, video stream data collected by the onboard sensors of any truck in the target truck convoy can be received by the user through a web front-end user interface; or, in this embodiment of the invention, video stream data collected by the onboard sensors of the aforementioned truck can also be received by the onboard sensors of any truck in the target truck convoy.

[0060] It should be noted that when a user uploads video stream data collected by the onboard sensors of any truck in the target truck convoy through the web front-end user interface, the aforementioned user interface not only provides intuitive file selection and upload functions, but also features progress display and error prompts.

[0061] It should be noted that MinIO Cluster is a high-performance, distributed object storage service. MinIO Cluster allows multiple hard drives (even on different machines) to be combined into an object storage service, avoiding single points of failure and providing a highly available storage solution.

[0062] As an optional embodiment, the vehicle-mounted sensor includes a radar sensor.

[0063] In the embodiments of the present invention, it is possible to i Identify the trucks in the target truck platoon. i Represents a positive integer greater than 0.

[0064] Figure 3 This is the second flowchart illustrating the onboard unstructured data processing method for truck platooning provided by this invention. Figure 3 As shown, for the first in the target truck platoon i Any truck, after obtaining the above-mentioned number i In the case of video stream data collected by the onboard sensors of a truck, the above-mentioned... i The video stream data collected by the truck's onboard sensors is stored in the MinIO cluster, and the aforementioned... i Metadata of the video stream data collected by the truck's onboard sensors is added to a RabbitMQ message queue, and the aforementioned RabbitMQ message queue is used to transmit the data. i Metadata of video stream data collected by the onboard sensors of a truck.

[0065] Step 103: Listen to the RabbitMQ message queue. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck platoon arrives, retrieve the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames from the video stream data collected by the on-board sensor of any truck to obtain the key frame image corresponding to the video stream data collected by the on-board sensor of any truck.

[0066] Specifically, monitor the RabbitMQ message queue mentioned above, and when the first message in the target truck convoy is detected... i When the metadata of the video stream data collected by the onboard sensors of any truck arrives, it can be processed according to the above-mentioned... i Metadata of the video stream data collected by the truck's onboard sensors is downloaded from the MinIO cluster. i Video stream data collected by the onboard sensors of a truck.

[0067] In this embodiment of the invention, by monitoring the RabbitMQ message queue, when the metadata of the video stream data collected by the onboard sensors of any truck in the target truck platoon arrives via the RabbitMQ message queue, the video stream data collected by the onboard sensors of that truck is retrieved from the MinIO cluster. This not only ensures the timeliness and accuracy of data processing but also reduces the storage pressure on the main server. This embodiment of the invention also considers network conditions, employing techniques such as breakpoint resumption to ensure the reliability of data transmission when retrieving the video stream data collected by the onboard sensors of the truck from the MinIO cluster.

[0068] Download the above steps from the MinIO cluster. iAfter collecting the video stream data from the truck's onboard sensors, the aforementioned... i The video stream data collected by the onboard sensors of the truck is decoded to obtain the aforementioned... i Point cloud data corresponding to video stream data collected by the onboard sensors of a truck.

[0069] Obtain the above-mentioned i After collecting the point cloud data corresponding to the video stream data from the truck's onboard sensors, the above-mentioned... i The point cloud data corresponding to the video stream data collected by the onboard sensors of the truck is used for image frame extraction to obtain the aforementioned first... i Keyframe images corresponding to video stream data collected by the onboard sensors of a truck.

[0070] As an optional embodiment, decoding and frame extraction of video stream data collected by the on-board sensors of any truck to obtain keyframe images corresponding to the video stream data collected by the on-board sensors of any truck includes: decoding the video stream data collected by the on-board sensors of any truck using a distributed processing method to obtain point cloud data corresponding to the video stream data collected by the on-board sensors of any truck.

[0071] Image frame extraction is performed on the point cloud data corresponding to the video stream data collected by the on-board sensors of any truck to obtain the keyframe image corresponding to the video stream data collected by the on-board sensors of any truck.

[0072] It should be noted that, in order to improve the understanding of the above-mentioned... i To improve the efficiency of decoding the video stream data collected by the onboard sensors of the truck, this embodiment of the invention employs a distributed processing approach. i The video stream data collected by the truck's onboard sensors is decoded to fully utilize computing resources and improve the performance of the aforementioned... i Decoding efficiency of video stream data collected by onboard sensors of a truck.

[0073] It should be noted that, in the embodiments of the present invention, the above-mentioned first... i When decoding the video stream data collected by the truck's onboard sensors, a fault-tolerant mechanism can be introduced to address the aforementioned issues. i Damaged video segments in the video stream data collected by the truck's onboard sensors are properly error-handled to ensure the continuity of the processing flow.

[0074] It is understood that, in the embodiments of the present invention, the above-mentioned first... i The number of keyframe images corresponding to the video stream data collected by the on-board sensors of a truck can be multiple.

[0075] Step 104: Based on the metadata of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck, generate the identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck. Then, store the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information to the MinIO cluster, and store the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck to the Elasticsearch cluster. The metadata of the keyframe images includes timestamps and frame numbers.

[0076] Specifically, to obtain the above-mentioned first i After acquiring keyframe images corresponding to the video stream data from the truck's onboard sensors, the data can be used based on the aforementioned... i The metadata of keyframe images corresponding to the video stream data collected by the onboard sensors of the truck is used to generate the above-mentioned... i The identification information of keyframe images corresponding to the video stream data collected by the onboard sensors of a truck. For example, the above-mentioned... i The combination of timestamps and frame numbers of keyframe images corresponding to the video stream data collected by the onboard sensors of the truck is used to determine the aforementioned [number]. i Identification information of keyframe images corresponding to video stream data collected by the onboard sensors of a truck.

[0077] It should be noted that the metadata of keyframe images may include, but is not limited to, timestamps, shooting location, image size, resolution, frame rate, and sensor model.

[0078] Generate the above-mentioned number i After obtaining the identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of the truck, the aforementioned first... i The keyframe images corresponding to the video stream data collected by the truck's onboard sensors are stored in the MinIO cluster, and the above-mentioned... i The metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of a truck are stored in an Elasticsearch cluster.

[0079] Elasticsearch Cluster is a distributed, real-time search and analytics engine built on the Lucene library. It features high performance, scalability, and ease of use, making it suitable for large-scale data processing and search scenarios.

[0080] The embodiments of the present invention use the above-mentioned first method carrying identification information. i The keyframe images corresponding to the video stream data collected by the truck's onboard sensors are stored in the MinIO cluster, and the above-mentioned...i The metadata and identification information of keyframe images corresponding to the video stream data collected by the on-board sensors of a truck are stored in an Elasticsearch cluster. This not only facilitates centralized data management but also provides a foundation for subsequent advanced processing and analysis.

[0081] As an optional embodiment, storing the keyframe image corresponding to the video stream data collected by the on-board sensor of any truck carrying identification information to the MinIO cluster includes: encrypting the keyframe image corresponding to the video stream data collected by the on-board sensor of any truck carrying identification information, and then storing the encrypted data to the MinIO cluster.

[0082] Specifically, in this embodiment of the invention, considering the security and privacy protection of data storage, when generating the above-mentioned... i After obtaining the identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of the truck, the aforementioned first... i The keyframe images corresponding to the video stream data collected by the on-board sensors of a truck are encrypted, and then the obtained encrypted data can be stored in the MinIO cluster.

[0083] In the embodiments of the present invention, the above-mentioned first method carrying identification information can be implemented in various ways. i The keyframe images corresponding to the video stream data collected by the onboard sensors of the truck are encrypted. For example, in this embodiment of the invention, the encryption can be based on Advanced Encryption Standard (AES), asymmetric encryption algorithms (such as RSA), and hybrid encryption algorithms to encrypt the aforementioned keyframe images carrying identification information. i The keyframe images corresponding to the video stream data collected by the on-board sensors of a truck are encrypted to obtain encrypted data.

[0084] This invention, in its embodiments, registers an onboard unstructured data processing service with the Nacos service registry cluster when it is determined that the onboard unstructured data processing service for a target truck convoy has been triggered. The service is configured with RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service. It acquires video stream data collected by the onboard sensors of each truck. Upon acquiring video stream data from the onboard sensors of any truck in the target truck convoy, it adds the metadata of that truck's video stream data to the RabbitMQ message queue and stores the video stream data to the MinIO cluster. It listens to the RabbitMQ message queue, and upon receiving the metadata of the video stream data from any truck in the target truck convoy, it retrieves the video stream data from the MinIO cluster. The system then decodes and extracts frames from the video stream data collected by the onboard sensors of any truck to obtain keyframe images corresponding to the video stream data collected by the onboard sensors of any truck. Based on the metadata of the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck, identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck is generated. The keyframe images corresponding to the video stream data collected by the onboard sensors of any truck carrying the identification information are then stored in a MinIO cluster, and the metadata and identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck are stored in an Elasticsearch cluster. This enables fast indexing and retrieval of unstructured onboard data, improves the data processing efficiency, information extraction comprehensiveness, and data utilization of unstructured onboard data generated in truck platooning operation scenarios, and is conducive to improving the operational stability of truck platoons, providing strong technical support for data processing in the field of vehicle networking.

[0085] The present invention provides a method for processing unstructured vehicle data for truck platoons. It adopts a microservices design, registering the unstructured vehicle data processing service for the target truck platoon in the Nacos service registry, enabling flexible service expansion and dynamic adjustment. By introducing a RabbitMQ message queue, it achieves efficient asynchronous processing of the unstructured vehicle data service, buffering and processing large amounts of video stream metadata, avoiding overload in subsequent data processing, and improving system stability and response speed. By storing video stream data collected by vehicle sensors in a MinIO cluster, it achieves redundant backup and distributed storage of unstructured vehicle data, improving the reliability and availability of the data. By storing the metadata of the video stream data and the metadata and identification information of keyframe images in an Elasticsearch cluster, it achieves fast data indexing and retrieval, rationally allocating and processing resources, avoiding resource waste and idleness, and improving resource utilization efficiency.

[0086] As an optional embodiment, after storing the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck into the Elasticsearch cluster, the method further includes: upon receiving a data query request input by a user, retrieving the metadata and identification information of the keyframe images matching the query conditions from the Elasticsearch cluster based on the query conditions carried in the data query request.

[0087] Based on the metadata and identification information of the keyframe images that match the query conditions, the keyframe images that match the query conditions are obtained from the MinIO cluster.

[0088] Specifically, when users need to perform data queries, they can enter a data query request with query conditions through the web front-end user interface.

[0089] Upon receiving the aforementioned data query request from the user, the metadata and identification information of the keyframe image matching the aforementioned query conditions can be retrieved from the Elasticsearch cluster based on the query conditions carried in the data query request.

[0090] After obtaining the metadata and identification information of the keyframe images that match the above query conditions, the keyframe images that match the above query conditions can be retrieved from the MinIO cluster based on the metadata and identification information of the keyframe images that match the above query conditions. Then, the keyframe images that match the above query conditions can be displayed on the user interface for the user to view.

[0091] As an optional embodiment, obtaining keyframe images that match the query conditions from the MinIO cluster based on the metadata and identification information of the keyframe images that match the query conditions includes: determining keyframe images that match the query conditions in the MinIO cluster based on the metadata and identification information of the keyframe images that match the query conditions.

[0092] Lazy loading technology is used to load the keyframe images that match the query conditions in the MinIO cluster, as well as the metadata and identification information of the keyframe images that match the query conditions, into the display interface.

[0093] It's important to note that lazy loading is a front-end optimization technique designed to delay the loading of resources on a webpage, thereby improving page loading speed and performance. The core principle of lazy loading is to delay loading page resources; that is, a resource is only loaded when the user needs it. In traditional webpage loading methods, when a user opens a webpage, all resources such as images, videos, and scripts are loaded at once, leading to excessively long page loading times, especially for large webpages and resource-rich websites. Lazy loading, however, determines whether a resource is needed by the user and thus decides whether to load it, effectively reducing page loading time.

[0094] In this embodiment of the invention, after obtaining the metadata and identification information of the keyframe image that matches the above query conditions from the Elasticsearch cluster, the keyframe image that matches the above query conditions can be determined in the MinIO cluster based on the metadata and identification information of the keyframe image that matches the above query conditions.

[0095] After identifying the keyframe images that match the above query conditions in the MinIO cluster, lazy loading can be used to load the keyframe images that match the above query conditions, along with their metadata and identification information, into the display interface for users to view.

[0096] This invention, upon receiving a user's data query request, retrieves metadata and identification information of keyframe images matching the query conditions from the Elasticsearch cluster based on the query conditions carried in the data query request. Then, it retrieves keyframe images matching the query conditions from the MinIO cluster based on the metadata and identification information of the keyframe images matching the query conditions. This allows for rapid response to user data query requests and better meets user data query needs even with continuously growing data volumes, offering greater scalability and flexibility.

[0097] Figure 4This is a schematic diagram of the onboard unstructured data processing device for truck platooning provided by the present invention. The following is in conjunction with... Figure 4 The present invention describes an onboard unstructured data processing apparatus for truck platooning. The onboard unstructured data processing apparatus for truck platooning described below corresponds to the onboard unstructured data processing method for truck platooning provided above. Figure 4 As shown, the device includes: an initialization module 401, a data acquisition module 402, a data monitoring module 403, and a data storage module 404.

[0098] Initialization module 401 is used to register the vehicle-mounted unstructured data processing service in the Nacos service registry cluster when it is determined that the vehicle-mounted unstructured data processing service for the target truck convoy has been triggered. It configures RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the vehicle-mounted unstructured data processing service. The target truck convoy includes multiple trucks.

[0099] The data acquisition module 402 is used to acquire video stream data collected by the on-board sensors of each truck, and when the video stream data collected by the on-board sensors of any truck in the target truck platoon is acquired, the metadata of the video stream data collected by the on-board sensors of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensors of any truck is stored in the MinIO cluster. The metadata of the video stream data includes at least one of the following: the identification information of the video stream data, the generation time, and the data size.

[0100] The data monitoring module 403 is used to monitor the RabbitMQ message queue. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck platoon arrives, it retrieves the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decodes and extracts frames from the video stream data collected by the on-board sensor of any truck to obtain the key frame image corresponding to the video stream data collected by the on-board sensor of any truck.

[0101] The data storage module 404 is used to generate identification information for the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck based on the metadata of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck. Then, it stores the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information to the MinIO cluster, and stores the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck to the Elasticsearch cluster. The metadata of the keyframe images includes timestamps and frame numbers.

[0102] Specifically, the initialization module 401, the data acquisition module 402, the data monitoring module 403, and the data storage module 404 are electrically connected.

[0103] The vehicle-mounted unstructured data processing device for truck platoons in this embodiment of the invention, upon determining that a vehicle-mounted unstructured data processing service for a target truck platoon has been triggered, registers the vehicle-mounted unstructured data processing service with the Nacos service registry cluster, configures RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the vehicle-mounted unstructured data processing service, acquires video stream data collected by the vehicle-mounted sensors of each truck, and, upon acquiring video stream data collected by the vehicle-mounted sensors of any truck in the target truck platoon, adds the metadata of the video stream data collected by the vehicle-mounted sensors of any truck to the RabbitMQ message queue, stores the video stream data collected by the vehicle-mounted sensors of any truck in the target truck platoon to the MinIO cluster, listens to the RabbitMQ message queue, and, upon detecting the arrival of the metadata of the video stream data collected by the vehicle-mounted sensors of any truck in the target truck platoon, retrieves the vehicle-mounted sensor data of any truck from the MinIO cluster. The system collects video stream data and then decodes and extracts frames from the video stream data collected by the onboard sensors of any truck to obtain keyframe images corresponding to the video stream data collected by the onboard sensors of any truck. Based on the metadata of the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck, it generates identification information for the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck. Then, it stores the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck carrying identification information in a MinIO cluster, and stores the metadata and identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of any truck in an Elasticsearch cluster. This enables fast indexing and retrieval of unstructured onboard data, improves the data processing efficiency, information extraction comprehensiveness, and data utilization of unstructured onboard data generated in truck platooning operation scenarios, and is conducive to improving the operational stability of truck platoons, providing strong technical support for data processing in the field of vehicle networking.

[0104] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other through the communications bus 540. The processor 510 can call logical instructions in the memory 530 to execute an onboard unstructured data processing method for truck platoons. This method includes: upon determining that an onboard unstructured data processing service for a target truck platoon has been triggered, registering the onboard unstructured data processing service in the Nacos service registry cluster; configuring RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the onboard unstructured data processing service; the target truck platoon includes multiple trucks; acquiring video stream data collected by the onboard sensors of each truck; and, upon acquiring video stream data collected by the onboard sensors of any truck in the target truck platoon, adding the metadata of the video stream data collected by the onboard sensors of any truck to the RabbitMQ message queue; and storing the video stream data collected by the onboard sensors of any truck to the MinIO cluster. The metadata of the video stream data includes the identification information, generation time, and data size of the video stream data. At least one of the following: Listening to the RabbitMQ message queue, upon receiving the metadata of the video stream data collected by the onboard sensors of any truck in the target truck convoy, retrieving the video stream data collected by the onboard sensors of any truck from the MinIO cluster, then decoding and extracting frames from the video stream data collected by the onboard sensors of any truck to obtain the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck; based on the metadata of the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck, generating identification information for the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck, then storing the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck carrying the identification information to the MinIO cluster, and storing the metadata and identification information of the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck to the Elasticsearch cluster. The metadata of the keyframe image includes a timestamp and a frame number.

[0105] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the onboard unstructured data processing method for truck platoons provided by the above methods. The method includes: when it is determined that an onboard unstructured data processing service for a target truck platoon has been triggered, registering the onboard unstructured data processing service in the Nacos service registry cluster, configuring RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the onboard unstructured data processing service, wherein the target truck platoon includes multiple trucks; acquiring video stream data collected by the onboard sensors of each truck, and when the video stream data collected by the onboard sensors of any truck in the target truck platoon is acquired, adding the metadata of the video stream data collected by the onboard sensors of any truck to the RabbitMQ message queue, and storing the video stream data collected by the onboard sensors of any truck to the MinIO cluster. The metadata of the video stream data includes at least one of the following: identification information, generation time, and data size. The system listens to the RabbitMQ message queue. Upon receiving the metadata of the video stream data collected by the onboard sensors of any truck in the target truck platoon, it retrieves the video stream data from the MinIO cluster. Then, it decodes and extracts frames from the video stream data to obtain the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck. Based on the metadata of the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck, it generates identification information for the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck. The keyframe image carrying the identification information is then stored in the MinIO cluster. The metadata and identification information of the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck are stored in the Elasticsearch cluster. The metadata of the keyframe image includes a timestamp and frame number.

[0107] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the onboard unstructured data processing method for truck platoons provided by the methods described above. This method includes: upon determining that an onboard unstructured data processing service for a target truck platoon has been triggered, registering the onboard unstructured data processing service in a Nacos service registry cluster; configuring RabbitMQ message queue service, MinIO object storage service, and Elasticsearch service for the onboard unstructured data processing service; the target truck platoon comprising multiple trucks; acquiring video stream data collected by the onboard sensors of each truck; and, upon acquiring video stream data collected by the onboard sensors of any truck in the target truck platoon, adding the metadata of the video stream data collected by the onboard sensors of any truck to the RabbitMQ message queue; and storing the video stream data collected by the onboard sensors of any truck to the MinIO cluster; the metadata of the video stream data includes video stream numbers. The system retrieves at least one of the following: identification information, generation time, and data size; monitors the RabbitMQ message queue; upon receiving metadata for video stream data collected by the onboard sensors of any truck in the target truck convoy, it retrieves the video stream data collected by the onboard sensors of any truck from the MinIO cluster, decodes and extracts frames from the video stream data collected by the onboard sensors of any truck to obtain the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck; based on the metadata of the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck, it generates identification information for the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck, and stores the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck carrying the identification information in the MinIO cluster; it stores the metadata and identification information of the keyframe image corresponding to the video stream data collected by the onboard sensors of any truck in the Elasticsearch cluster. The metadata of the keyframe image includes a timestamp and a frame number.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing unstructured vehicle data in truck platooning, characterized in that, include: If it is determined that the vehicle-mounted unstructured data processing service for the target truck convoy has been triggered, the vehicle-mounted unstructured data processing service is registered in the Nacos service registry cluster, and RabbitMQ message queue service, MinIO object storage service and Elasticsearch service are configured for the vehicle-mounted unstructured data processing service. The target truck convoy includes multiple trucks. The video stream data collected by the on-board sensors of each truck is obtained. If the video stream data collected by the on-board sensors of any truck in the target truck platoon is obtained, the metadata of the video stream data collected by the on-board sensors of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensors of any truck is stored in the MinIO cluster. The metadata of the video stream data includes at least one of the following: the identification information of the video stream data, the generation time, and the data size. Listen to the RabbitMQ message queue. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck platoon arrives, retrieve the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames from the video stream data collected by the on-board sensor of any truck to obtain the key frame image corresponding to the video stream data collected by the on-board sensor of any truck. Based on the metadata of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck, identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck is generated. Then, the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information are stored in the MinIO cluster, and the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck are stored in the Elasticsearch cluster. The metadata of the keyframe images includes timestamps and frame numbers.

2. The onboard unstructured data processing method for truck platooning according to claim 1, characterized in that, After storing the metadata and identification information of the keyframe images corresponding to the video stream data collected by the onboard sensors of any of the trucks into the Elasticsearch cluster, the method further includes: Upon receiving a data query request from a user, based on the query conditions carried in the data query request, the metadata and identification information of the keyframe image matching the query conditions are obtained from the Elasticsearch cluster. Based on the metadata and identification information of the keyframe images that match the query conditions, the keyframe images that match the query conditions are obtained from the MinIO cluster.

3. The onboard unstructured data processing method for truck platooning according to claim 2, characterized in that, The process of retrieving keyframe images matching the query conditions from the MinIO cluster based on the metadata and identification information of the keyframe images matching the query conditions includes: Based on the metadata and identification information of the keyframe image that matches the query conditions, the keyframe image that matches the query conditions is determined in the MinIO cluster; Lazy loading technology is used to load the keyframe images in the MinIO cluster that match the query conditions, as well as the metadata and identification information of the keyframe images that match the query conditions, onto the display interface.

4. The onboard unstructured data processing method for truck platooning according to claim 1, characterized in that, The step of storing the keyframe image corresponding to the video stream data collected by the onboard sensors of any of the trucks carrying identification information to the MinIO cluster includes: After encrypting the keyframe images corresponding to the video stream data collected by the on-board sensors of any of the trucks carrying identification information, the encrypted data is stored in the MinIO cluster.

5. The onboard unstructured data processing method for truck platooning according to any one of claims 1 to 4, characterized in that, The vehicle-mounted sensors include radar sensors.

6. The onboard unstructured data processing method for truck platooning according to claim 5, characterized in that, The step of decoding and frame extraction of the video stream data collected by the on-board sensors of any truck to obtain the keyframe image corresponding to the video stream data collected by the on-board sensors of any truck includes: A distributed processing approach is used to decode the video stream data collected by the on-board sensors of any truck, thereby obtaining the point cloud data corresponding to the video stream data collected by the on-board sensors of any truck. Image frame extraction is performed on the point cloud data corresponding to the video stream data collected by the on-board sensors of any truck to obtain the keyframe image corresponding to the video stream data collected by the on-board sensors of any truck.

7. An onboard unstructured data processing device for truck platooning, characterized in that, include: An initialization module is used to register the vehicle-mounted unstructured data processing service in the Nacos service registry cluster when it is determined that the vehicle-mounted unstructured data processing service for the target truck convoy has been triggered, and to configure RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the vehicle-mounted unstructured data processing service. The target truck convoy includes multiple trucks. The data acquisition module is used to acquire video stream data collected by the on-board sensors of each truck, and when the video stream data collected by the on-board sensors of any truck in the target truck platoon is acquired, the metadata of the video stream data collected by the on-board sensors of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensors of any truck is stored in the MinIO cluster. The metadata of the video stream data includes at least one of the following: the identification information of the video stream data, the generation time, and the data size. The data monitoring module is used to monitor the RabbitMQ message queue. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck platoon arrives, it obtains the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decodes and extracts frames from the video stream data collected by the on-board sensor of any truck to obtain the key frame image corresponding to the video stream data collected by the on-board sensor of any truck. The data storage module is used to generate identification information for the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck based on the metadata of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck. Then, the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information are stored in the MinIO cluster, and the metadata and identification information of the keyframe images corresponding to the video stream data collected by the on-board sensors of any truck are stored in the Elasticsearch cluster. The metadata of the keyframe images includes timestamps and frame numbers.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the onboard unstructured data processing method for truck platooning as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the onboard unstructured data processing method for truck platooning as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the onboard unstructured data processing method for truck platooning as described in any one of claims 1 to 6.

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