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

Abstract

The invention provides a truck formation-oriented vehicle-mounted unstructured data processing method and device, and the method comprises the steps: carrying out the cluster registration of a vehicle-mounted unstructured data processing service in a Nacos service registration center under the condition that the triggering of the vehicle-mounted unstructured data processing service for a target truck formation is determined, and configuring a RabbitMQ message queue service, a MinIO object storage service and an Elasticsearch service for the vehicle-mounted unstructured data processing service, and further performing data processing on video stream data acquired by a vehicle-mounted sensor of each truck in the target truck formation based on the above services. According to the truck formation-oriented vehicle-mounted unstructured data processing method and device provided by the invention, the data processing efficiency of the vehicle-mounted unstructured data generated in a truck formation operation scene, the comprehensiveness of information extraction and the data utilization rate can be improved, and the improvement of the operation stability of the truck formation is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for processing vehicle-mounted unstructured data for a truck convoy. Background Art

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

[0003] With the rapid development of intelligent driving technology, the number of on-board sensors installed on trucks is increasing. During the operation of truck platoons, the on-board sensors of each truck in the platoon will continue to generate a large amount of on-board unstructured data, such as video streams and radar scan images. The above-mentioned on-board unstructured data contains rich operating information, which is of great significance for improving the operating efficiency of truck platoons and ensuring the driving safety of truck platoons.

[0004] Traditional unstructured data processing methods in related technologies can process unstructured data based on big data platforms or machine learning algorithms. However, most of the above traditional unstructured data processing methods focus on general data processing. For the on-board unstructured data generated in the truck platoon operation scenario, there are defects such as low data processing efficiency, incomplete information extraction or insufficient data utilization. Therefore, how to improve the data processing efficiency, comprehensiveness of information extraction and data utilization of on-board unstructured data generated in the truck platoon operation scenario is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] The present invention provides a method and device for processing on-board unstructured data for truck platoons, so as to solve the problems in the prior art of on-board unstructured data generated in truck platoon operation scenarios, such as low data processing efficiency, incomplete information extraction or insufficient data utilization in traditional unstructured data processing methods, so as to improve the data processing efficiency, comprehensiveness of information extraction and data utilization of on-board unstructured data generated in truck platoon operation scenarios.

[0006] The present invention provides a method for processing vehicle-mounted unstructured data for a truck convoy, comprising the following steps.

[0007] In the case where it is determined that the on-board unstructured data processing service for the target truck formation is triggered, registering the on-board unstructured data processing service in the Nacos service registration center cluster, configuring a RabbitMQ message queue service, a MinIO object storage service, and an Elasticsearch service for the on-board unstructured data processing service, and the target truck formation includes multiple trucks; Obtain the video stream data collected by the on-board sensor of each of the trucks, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is obtained, add the metadata of the video stream data collected by the on-board sensor of any truck to the RabbitMQ message queue, and store the video stream data collected by the on-board sensor of any truck to the MinIO cluster, wherein the metadata of the video stream data includes at least one of the identification information, generation time and data size of the video stream data; Monitor the RabbitMQ message queue, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, obtain the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames of 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 key frame images corresponding to the video stream data collected by the on-board sensors of any truck, identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck is generated, and then the key frame 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 key frame images corresponding to the video stream data collected by the on-board sensors of any truck are stored in the Elasticsearch cluster, and the metadata of the key frame images includes a timestamp and a frame number.

[0008] According to a method for processing on-board unstructured data for a truck convoy provided by the present invention, after storing metadata and identification information of key frame images corresponding to video stream data collected by the on-board sensor of any truck in an Elasticsearch cluster, the method further includes: When receiving a data query request input by a user, based on the query condition carried in the data query request, obtaining metadata and identification information of key frame images matching the query condition from an Elasticsearch cluster; Based on the metadata and identification information of the key frame images matching the query condition, the key frame images matching the query condition are obtained from the MinIO cluster.

[0009] According to a method for processing on-board unstructured data for a truck convoy provided by the present invention, the key frame image matching the query condition is obtained from a MinIO cluster based on metadata and identification information of the key frame image matching the query condition, including: Based on the metadata and identification information of the key frame images matching the query condition, determining the key frame images matching the query condition in the MinIO cluster; Using lazy loading technology, the key frame images matching the query condition in the MinIO cluster and the metadata and identification information of the key frame images matching the query condition are loaded into the display interface.

[0010] According to a method for processing on-board unstructured data for a truck convoy provided by the present invention, storing key frame images corresponding to video stream data collected by an on-board sensor of any truck carrying identification information in a MinIO cluster includes: After encrypting the key frame image corresponding to the video stream data collected by the on-board sensor of any truck carrying the identification information, the encrypted data is stored in the MinIO cluster.

[0011] According to a method for processing vehicle-mounted unstructured data for a truck convoy provided by the present invention, the vehicle-mounted sensor includes a radar sensor.

[0012] According to a method for processing on-board unstructured data for a truck formation provided by the present invention, decoding and extracting frames of the video stream data collected by the on-board sensor of any truck to obtain a key frame image corresponding to the video stream data collected by the on-board sensor of any truck includes: Decoding the video stream data collected by the onboard sensor of any truck in a distributed processing manner to obtain point cloud data corresponding to the video stream data collected by the onboard sensor of any truck; Image frame extraction is performed on the point cloud data corresponding to the video stream data collected by the on-board sensor of any truck to obtain a key frame image corresponding to the video stream data collected by the on-board sensor of any truck.

[0013] The present invention also provides a vehicle-mounted unstructured data processing device for truck platoons, comprising the following modules: An initialization module is used to register the on-board unstructured data processing service in the Nacos service registration center cluster when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, and configure RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the on-board unstructured data processing service, wherein the target truck formation includes multiple trucks; A data acquisition module, used to acquire the video stream data collected by the on-board sensor of each of the trucks, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is acquired, the metadata of the video stream data collected by the on-board sensor of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensor of any truck is stored in the MinIO cluster, wherein the metadata of the video stream data includes at least one of the identification information, generation time and data size of the video stream data; A data monitoring module is used to monitor the RabbitMQ message queue, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, the data is obtained from the MinIO cluster, and then the video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained; A data storage module is used to generate identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck based on the metadata of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck, and then store the key frame images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information in the MinIO cluster, and store the metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck in the Elasticsearch cluster, wherein the metadata of the key frame images includes a timestamp and a frame number.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the on-board unstructured data processing method for a truck convoy as described in any one of the above methods is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for processing vehicle-mounted unstructured data for a truck convoy as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned vehicle-mounted unstructured data processing methods for truck platoons.

[0017] The vehicle-mounted unstructured data processing method and device for truck formations provided by the present invention register the vehicle-mounted unstructured data processing service in the Nacos service registration center cluster when it is determined that the vehicle-mounted unstructured data processing service for the target truck formation is triggered, configure the RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the vehicle-mounted unstructured data processing service, obtain the video stream data collected by the vehicle-mounted sensor of each truck, and when the video stream data collected by the vehicle-mounted sensor of any truck in the target truck formation is obtained, the metadata of the video stream data collected by the vehicle-mounted sensor of any truck is added to the RabbitMQ message queue, the video stream data collected by the vehicle-mounted sensor of any truck is stored in the MinIO cluster, the RabbitMQ message queue is monitored, and when the metadata of the video stream data collected by the vehicle-mounted sensor of any truck in the target truck formation arrives, the vehicle-mounted sensor of any truck is obtained from the MinIO cluster. The video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained. Based on the metadata of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck, the identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is generated, and then the key frame image corresponding to the video stream data collected by the on-board sensor of any truck with the identification information is stored in the MinIO cluster, and the metadata and identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck are stored in the Elasticsearch cluster, which can realize the rapid indexing and retrieval of on-board unstructured data, improve the data processing efficiency, comprehensiveness of information extraction and data utilization of on-board unstructured data generated in the truck formation operation scenario, which is conducive to improving the operation stability of truck formations and provides strong technical support for data processing in the field of Internet of Vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is one of the flow charts of the on-board unstructured data processing method for truck platoons provided by the present invention.

[0020] Figure 2 It is a flow chart of service initialization in the vehicle-mounted unstructured data processing method for truck platoons provided by the present invention.

[0021] Figure 3 This is the second flow chart of the on-board unstructured data processing method for truck platoons provided by the present invention.

[0022] Figure 4 It is a structural schematic diagram of the vehicle-mounted unstructured data processing device for truck platoons provided by the present invention.

[0023] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In the description of the invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] In the description of the present application, the terms "first", "second", etc. are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are usually a class, and the number of objects is not limited. For example, the first object can be one or more. In addition, in the description of the present application, "and / or" represents at least one of the connected objects, and the character " / " generally represents that the front and back associated objects are in an "or" relationship.

[0027] Combine the following Figure 1-Figure 3The present invention describes a method for processing on-vehicle unstructured data for truck platoons.

[0028] Figure 1 is one of the flow charts of the vehicle-mounted unstructured data processing method for truck platoons provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101, when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, register the on-board unstructured data processing service in the Nacos service registration center cluster, configure RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the on-board unstructured data processing service, and the target truck formation includes multiple trucks.

[0029] It should be noted that the embodiment of the present invention is implemented by a vehicle-mounted unstructured data processing device for truck platoons. The vehicle-mounted unstructured data processing device for truck platoons may be configured in electronic devices such as computers or servers.

[0030] Specifically, the target truck formation in the embodiment of the present invention includes multiple trucks. The unstructured data generated by the on-board sensors of each truck in the target truck formation is the processing object of the on-board unstructured data processing method for truck formations provided by the present invention. In the embodiment of the present invention, any truck formation can be determined as the target truck formation according to actual needs. In the embodiment of the present invention, the target truck formation is not specifically limited.

[0031] When it is necessary to process the unstructured data generated by the on-board sensors of each truck in the target truck formation based on the on-board unstructured data processing method for truck formations provided by the present invention, the on-board unstructured data processing service for the target truck formation can be triggered in a variety of ways. For example, a user can trigger the on-board unstructured data processing service for the make-up truck formation by inputting a control instruction; or, when it is determined that the target truck formation has started to run, the on-board unstructured data processing service for the target truck formation can be automatically triggered. The specific method of triggering the on-board unstructured data processing service for the target truck formation is not limited in the embodiment of the present invention.

[0032] Figure 2 FIG. 1 is a flow chart of service initialization in the vehicle-mounted unstructured data processing method for truck platoons provided by the present invention. Figure 2As shown, when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, the on-board unstructured data processing service for the target truck formation can be registered in the Nacos service registration center under the Spring Cloud framework to complete the communication preparation between the on-board unstructured data processing service for the target truck formation and other microservices.

[0033] Among them, Spring Cloud is a microservice framework based on Spring Boot, which can provide a series 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.

[0034] Nacos Service Registry Center is a service registration and discovery platform for dynamic service discovery, configuration management and service governance. It is widely used in microservice architecture to solve problems such as service registration, discovery, configuration management and health monitoring.

[0035] After registering the above-mentioned on-board unstructured data processing service for the target truck fleet in the Nacos service registration center, the configuration coordinator can be initialized to configure the RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the above-mentioned on-board unstructured data processing service for the target truck fleet, so as to ensure the unified management and real-time synchronization of the configuration of the above-mentioned on-board unstructured data processing service for the target truck fleet.

[0036] Configure the RabbitMQ message queue service for the above-mentioned on-board unstructured data processing service for the target truck fleet, including starting the RabbitMQ message queue service, automatically creating the required switches, queues and bindings to support the message passing mechanism of tasks such as video parsing and image extraction, and sending asynchronous notifications of task status in a timely manner.

[0037] Configure MinIO object storage service for the above-mentioned on-board unstructured data processing service for the target truck fleet, including creating necessary buckets and setting corresponding access permissions to support the storage of raw video files and processing results.

[0038] Configure the Elasticsearch service for the above-mentioned on-board unstructured data processing service for the target truck formation, including starting the Elasticsearch service, automatically creating an index template, and preparing to store video-related metadata and label information to prepare for subsequent data retrieval and analysis.

[0039] Among them, RabbitMQ is a message middleware that implements the advanced message queuing protocol in the Erlang language. It can support multiple message protocols, including AMQP (Advanced Message Queuing Protocol), MQTT, STOMP, etc.

[0040] MinIO service is a high-performance open source object storage service that is compatible with the Amazon S3 API and is suitable for storing large amounts of unstructured data. MinIO uses a parallel and distributed architecture, supports multi-threading and multi-core processors, and can fully utilize hardware resources to achieve high-speed data read and write operations. The read and write speeds of its object storage are excellent on standard hardware, with read and write speeds of up to 183GB / s and 171GB / s respectively, which can meet business scenarios with high requirements for data access speed, such as processing big data analysis, high-concurrency Internet applications, etc.

[0041] Elasticsearch is a real-time distributed search and analysis engine based on Lucene. It can provide distributed, highly scalable, and highly real-time search and data analysis functions, allowing users to store, search, and analyze large amounts of data in a fast and near real-time manner.

[0042] 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 the present invention, a cluster deployment method of multiple components including Nacos, as well as a cluster configuration of RabbitMQ and Elasticsearch are adopted in the embodiment of the present invention. The cluster deployment not only supports automatic fault switching, but also ensures real-time synchronization of data, thereby improving the stability of the vehicle-mounted unstructured data processing service for the target truck formation and the consistency of data.

[0043] By registering the on-board unstructured data processing service for the target truck fleet in the Nacos service registration center cluster, and configuring the RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the on-board unstructured data processing service for the target truck fleet, the RabbitMQ message queue service, MinIO object storage service and Elasticsearch service can be reasonably configured and initialized, and a document infrastructure can be built for the on-board unstructured data processing service for the target truck fleet, which provides favorable support for subsequent data processing, storage and display.

[0044] Step 102: Obtain the video stream data collected by the on-board sensor of each truck, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is obtained, add the metadata of the video stream data collected by the on-board sensor of any truck to the RabbitMQ message queue, and store the video stream data collected by the on-board sensor of any truck to the MinIO cluster, where the metadata of the video stream data includes at least one of the identification information, generation time and data size of the video stream data.

[0045] Specifically, in an embodiment of the present invention, video stream data collected by the on-board sensor of each truck in the target truck formation can be obtained in a variety of ways. For example, in an embodiment of the present invention, video stream data collected by the on-board sensor of any truck in the target truck formation uploaded by a user through a Web front-end user interface can be received; or, in an embodiment of the present invention, video stream data collected by the on-board sensor of any truck in the target truck formation can be received from the on-board sensor of the above truck.

[0046] It should be noted that when the user uploads the video stream data collected by the on-board sensor of any truck in the target truck formation through the Web front-end user interface, the above user interface not only provides intuitive file selection and upload functions, but also has functions such as progress display and error prompts.

[0047] It should be noted that MinIO cluster is a high-performance, distributed object storage service. MinIO cluster allows multiple hard disks (even on different machines) to form an object storage service, avoiding single point failure and providing a highly available storage solution.

[0048] As an optional embodiment, the vehicle-mounted sensor includes a radar sensor.

[0049] In the embodiment of the present invention, i Identify the trucks in the target truck formation, i Represents a positive integer greater than 0.

[0050] Figure 3 FIG. 2 is a flow chart of the method for processing on-board unstructured data for truck platoons provided by the present invention. Figure 3 As shown, for the first i Truck (any truck), after obtaining the above i In the case of video stream data collected by the on-board sensors of a truck, the above i The video stream data collected by the on-board sensors of the trucks is stored in the MinIO cluster, and the above iThe metadata of the video stream data collected by the on-board sensor of the truck is added to the RabbitMQ message queue, and the above-mentioned i The metadata of the video stream data collected by the on-board sensors of a truck.

[0051] Step 103: monitor the RabbitMQ message queue, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, obtain the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames of 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.

[0052] Specifically, the RabbitMQ message queue is monitored, and upon receiving the first message in the target truck formation, i When the metadata of the video stream data collected by the onboard sensor of a truck (any truck) arrives, it can be i The metadata of the video stream data collected by the onboard sensors of the trucks is downloaded from the MinIO cluster. i Video streaming data collected by the onboard sensors of a truck.

[0053] In the embodiment of the present invention, by monitoring 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 formation is transmitted and arrived through the RabbitMQ message queue, the video stream data collected by the on-board sensor of the above truck is obtained from the MinIO cluster, which not only ensures the immediacy and accuracy of data processing, but also reduces the storage pressure of the main server. The embodiment of the present invention takes the network conditions into consideration, and when obtaining the video stream data collected by the on-board sensor of the above truck from the MinIO cluster, technologies such as breakpoint resume are used to ensure the reliability of data transmission.

[0054] Download the above steps from the MinIO cluster i After the video stream data collected by the on-board sensor of the truck, the above i The video stream data collected by the on-board sensor of the truck is decoded to obtain the above i Point cloud data corresponding to the video stream data collected by the on-board sensors of a truck.

[0055] Obtain the above i After the point cloud data corresponding to the video stream data collected by the on-board sensor of the truck is i The point cloud data corresponding to the video stream data collected by the on-board sensor of the truck is subjected to the image extraction operation to obtain the above-mentionedi The key frame images corresponding to the video stream data collected by the on-board sensors of a truck.

[0056] As an optional embodiment, the video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained, including: using a distributed processing method to decode the video stream data collected by the on-board sensor of any truck, and obtain the point cloud data corresponding to the video stream data collected by the on-board sensor of any truck.

[0057] Image frames are extracted from point cloud data corresponding to the video stream data collected by the on-board sensor of any truck to obtain key frame images corresponding to the video stream data collected by the on-board sensor of any truck.

[0058] It should be noted that in order to improve the above i In order to improve the efficiency of decoding the video stream data collected by the on-board sensor of the truck, the embodiment of the present invention adopts a distributed processing method to decode the above-mentioned i The video stream data collected by the on-board sensors of the trucks is decoded to make full use of computing resources and improve the above i Decoding efficiency of video stream data collected by on-board sensors of a truck.

[0059] It should be noted that in the embodiments of the present invention, i When decoding the video stream data collected by the on-board sensor of a truck, a fault-tolerant mechanism can be introduced to i Appropriate error handling is performed on damaged video segments in the video stream data collected by the on-board sensors of the trucks to ensure the continuity of the processing flow.

[0060] It can be understood that in the embodiment of the present invention, i The number of key frame images corresponding to the video stream data collected by the on-board sensor of a truck may be multiple.

[0061] Step 104: Based on the metadata of the key frame images corresponding to the video stream data collected by the on-board sensor of any truck, generate identification information of the key frame images corresponding to the video stream data collected by the on-board sensor of any truck, and then store the key frame images corresponding to the video stream data collected by the on-board sensor of any truck carrying the identification information in the MinIO cluster, and store the metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensor of any truck in the Elasticsearch cluster, the metadata of the key frame images includes a timestamp and a frame number.

[0062] Specifically, obtain the abovei After the key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are i The metadata of the key frame images corresponding to the video stream data collected by the on-board sensors of the trucks is used to generate the above-mentioned i The identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of the truck. i The combination of the timestamp and frame number of the key frame image corresponding to the video stream data collected by the on-board sensor of the truck is determined as the above i The identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of a truck.

[0063] It should be noted that the metadata of the key frame image may include but is not limited to timestamp, shooting location, image size, resolution, frame rate, sensor model, etc.

[0064] Generate the above i After the identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of the truck is obtained, the above-mentioned first i The key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are stored in the MinIO cluster. i The metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are stored in the Elasticsearch cluster.

[0065] The Elasticsearch cluster is a distributed, real-time search and analysis engine. It is built based on the Lucene library and features high performance, scalability, and ease of use. It is suitable for large-scale data processing and search scenarios.

[0066] In the embodiment of the present invention, the above-mentioned i The key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are stored in the MinIO cluster. i The metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are stored in the Elasticsearch cluster, which not only facilitates the centralized management of data, but also provides a basis for subsequent advanced processing and analysis.

[0067] As an optional embodiment, the key frame images corresponding to the video stream data collected by the on-board sensor of any truck carrying identification information are stored in the MinIO cluster, including: after encrypting the key frame images corresponding to the video stream data collected by the on-board sensor of any truck carrying identification information, the encrypted data is stored in the MinIO cluster.

[0068] Specifically, in the embodiment of the present invention, the security and privacy protection of data storage are taken into consideration. i After the identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of the truck is obtained, the above-mentioned first i The key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are encrypted, and the encrypted data can be stored in the MinIO cluster.

[0069] In the embodiment of the present invention, the above-mentioned first i The key frame images corresponding to the video stream data collected by the on-board sensor of the truck are encrypted. For example, in the embodiment of the present invention, the key frame images corresponding to the video stream data collected by the on-board sensor of the truck can be encrypted based on the Advanced Encryption Standard (AES), an asymmetric encryption algorithm (such as RSA) and a hybrid encryption algorithm. i The key frame images corresponding to the video stream data collected by the on-board sensors of the trucks are encrypted to obtain encrypted data.

[0070] The embodiment of the present invention registers the on-board unstructured data processing service in the Nacos service registration center cluster when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, configures the RabbitMQ message queue service, the MinIO object storage service and the Elasticsearch service for the on-board unstructured data processing service, obtains the video stream data collected by the on-board sensor of each truck, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is obtained, the metadata of the video stream data collected by the on-board sensor of any truck is added to the RabbitMQ message queue, the video stream data collected by the on-board sensor of any truck is stored in the MinIO cluster, the RabbitMQ message queue is monitored, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, the video stream data collected by the on-board sensor of any truck is obtained from the MinIO cluster, Then, the video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained. Based on the metadata of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck, the identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is generated, and then the key frame image corresponding to the video stream data collected by the on-board sensor of any truck carrying the identification information is stored in the MinIO cluster, and the metadata and identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck are stored in the Elasticsearch cluster. This can realize the rapid indexing and retrieval of on-board unstructured data, improve the data processing efficiency, comprehensiveness of information extraction and data utilization of on-board unstructured data generated in the truck formation operation scenario, which is conducive to improving the operation stability of truck formations and provides strong technical support for data processing in the field of Internet of Vehicles.

[0071] The on-board unstructured data processing method for truck formations provided by the present invention adopts the design concept of microservices, registers the on-board unstructured data processing service for the target truck formation in the Nacos service registration center, realizes the flexible expansion and dynamic adjustment of the service, and can realize efficient asynchronous processing of the on-board unstructured data processing service by introducing the RabbitMQ message queue, can buffer and process a large amount of video stream data metadata, avoids the overload of subsequent data processing, and improves the stability and response speed of the system. By storing the video stream data collected by the on-board sensor in the MinIO cluster, redundant backup and distributed storage of the on-board unstructured data are realized, and the reliability and availability of the on-board unstructured data are improved. By storing the metadata of the video stream data and the metadata and identification information of the key frame image in the Elasticsearch cluster, fast indexing and retrieval of the data are realized, resources can be reasonably allocated and processed, and the waste and idleness of resources are avoided, and the utilization efficiency of resources is improved.

[0072] As an optional embodiment, after storing the metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck in the Elasticsearch cluster, the method also includes: upon receiving a data query request input by a user, based on the query conditions carried by the data query request, obtaining the metadata and identification information of the key frame images matching the query conditions from the Elasticsearch cluster.

[0073] Based on the metadata and identification information of the key frame images that match the query conditions, the key frame images that match the query conditions are obtained from the MinIO cluster.

[0074] Specifically, when a user needs to perform a data query, he or she can input a data query request carrying query conditions through the Web front-end user interface.

[0075] When the data query request input by the user is received, metadata and identification information of key frame images matching the query condition may be obtained from the Elasticsearch cluster based on the query condition carried in the data query request.

[0076] After obtaining the metadata and identification information of the key frame images that match the above query conditions, the key frame images that match the above query conditions can be obtained from the MinIO cluster based on the metadata and identification information of the key frame images that match the above query conditions, and then the key frame images that match the above query conditions can be displayed in the user interface for users to view.

[0077] As an optional embodiment, based on the metadata and identification information of the key frame images matching the query conditions, obtaining the key frame images matching the query conditions from the MinIO cluster includes: based on the metadata and identification information of the key frame images matching the query conditions, determining the key frame images matching the query conditions in the MinIO cluster.

[0078] Using lazy loading technology, the key frame images matching the query conditions in the MinIO cluster and the metadata and identification information of the key frame images matching the query conditions are loaded into the display interface.

[0079] It should be noted that lazy loading technology is a front-end optimization technology that aims to delay the loading of resources on the page to improve the loading speed and performance of the page. The core principle of lazy loading technology is to delay the loading of page resources, that is, to load resources only when users need to access them. In the traditional web page loading method, when a user opens a web page, all images, videos, scripts and other resources will be loaded at once, which will cause the page loading time to be too long, especially for large web pages and resource-rich websites. Lazy loading technology determines whether to load a resource by judging whether the user needs it, thereby effectively reducing the loading time of the page.

[0080] In an embodiment of the present invention, after obtaining the metadata and identification information of the key frame images matching the above query conditions from the Elasticsearch cluster, the key frame images matching the above query conditions can be determined in the MinIO cluster based on the metadata and identification information of the key frame images matching the above query conditions.

[0081] After determining the key frame images matching the query conditions in the MinIO cluster, lazy loading calculation can be used to load the key frame images matching the query conditions in the MinIO cluster and the metadata and identification information of the key frame images matching the query conditions to the display interface for the user to view.

[0082] The embodiment of the present invention can quickly respond to the user's data query request, better meet the user's data query needs when the data volume continues to grow, and has stronger scalability and flexibility by obtaining metadata and identification information of key frame images that match the query conditions from the Elasticsearch cluster based on the query conditions carried in the data query request, and obtaining key frame images that match the query conditions from the MinIO cluster based on the metadata and identification information of the key frame images that match the query conditions.

[0083] Figure 4Schematic diagram of the structure of the vehicle-mounted unstructured data processing device for truck platoons provided by the present invention. Figure 4 The vehicle-mounted unstructured data processing device for truck platoons provided by the present invention is described. The vehicle-mounted unstructured data processing device for truck platoons described below and the vehicle-mounted unstructured data processing method for truck platoons provided by the present invention described above can be referred to each other. 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.

[0084] The initialization module 401 is used to register the on-board unstructured data processing service in the Nacos service registration center cluster when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, and configure the RabbitMQ message queue service, the MinIO object storage service and the Elasticsearch service for the on-board unstructured data processing service. The target truck formation includes multiple trucks.

[0085] The data acquisition module 402 is used to acquire the video stream data collected by the on-board sensor of each truck, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is acquired, the metadata of the video stream data collected by the on-board sensor of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensor of any truck is stored in the MinIO cluster. The metadata of the video stream data includes at least one of the identification information of the video stream data, the generation time and the data size.

[0086] 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 formation arrives, the video stream data collected by the on-board sensor of any truck is obtained from the MinIO cluster, and then the video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained.

[0087] The data storage module 404 is used to generate identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck based on the metadata of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck, and then store the key frame images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information in the MinIO cluster, and store the metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck in the Elasticsearch cluster, the metadata of the key frame images includes timestamp and frame number.

[0088] Specifically, the initialization module 401, the data acquisition module 402, the data monitoring module 403 and the data storage module 404 are electrically connected.

[0089] The on-board unstructured data processing device for truck formations in the embodiment of the present invention, when determining that the on-board unstructured data processing service for the target truck formation is triggered, registers the on-board unstructured data processing service in the Nacos service registration center cluster, configures the RabbitMQ message queue service, the MinIO object storage service and the Elasticsearch service for the on-board unstructured data processing service, obtains the video stream data collected by the on-board sensor of each truck, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is obtained, the metadata of the video stream data collected by the on-board sensor of any truck is added to the RabbitMQ message queue, the video stream data collected by the on-board sensor of any truck is stored in the MinIO cluster, and the RabbitMQ message queue is monitored. When the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, the on-board sensor of any truck is obtained from the MinIO cluster. The collected video stream data is decoded and framed for the video stream data collected by the on-board sensor of any truck, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained. Based on the metadata of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck, the identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is generated, and then the key frame image corresponding to the video stream data collected by the on-board sensor of any truck with the identification information is stored in the MinIO cluster, and the metadata and identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck are stored in the Elasticsearch cluster, which can realize the rapid indexing and retrieval of on-board unstructured data, and can improve the data processing efficiency, comprehensiveness of information extraction and data utilization of on-board unstructured data generated in the truck formation operation scenario, which is conducive to improving the operation stability of truck formations and provides strong technical support for data processing in the field of Internet of Vehicles.

[0090] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5As shown, the electronic device may include: a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the on-board unstructured data processing method for the truck formation, the method comprising: when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, registering the on-board unstructured data processing service in the Nacos service registration center cluster, configuring the RabbitMQ message queue service, the MinIO object storage service and the Elasticsearch service for the on-board unstructured data processing service, the target truck formation includes multiple trucks; obtaining the video stream data collected by the on-board sensor of each truck, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is obtained, adding the metadata of the video stream data collected by the on-board sensor of any truck to the RabbitMQ message queue, storing the video stream data collected by the on-board sensor of any truck to the MinIO cluster, the metadata of the video stream data including the identification information, generation time and data size of the video stream data at least one; monitor the RabbitMQ message queue, and upon hearing the arrival of metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation, obtain the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames of the video stream data collected by the on-board sensor of any truck, and 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 key frame image corresponding to the video stream data collected by the on-board sensor of any truck, generate identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck, and then store the key frame image corresponding to the video stream data collected by the on-board sensor of any truck carrying the identification information to the MinIO cluster, and store the metadata and identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck to the Elasticsearch cluster, the metadata of the key frame image includes a timestamp and a frame number.

[0091] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0092] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the vehicle-mounted unstructured data processing method for a truck formation provided by the above methods, the method comprising: when it is determined that the vehicle-mounted unstructured data processing service for a target truck formation is triggered, registering the vehicle-mounted unstructured data processing service in a Nacos service registration center cluster, configuring a RabbitMQ message queue service, a MinIO object storage service and an Elasticsearch service for the vehicle-mounted unstructured data processing service, the target truck formation includes multiple trucks; obtaining video stream data collected by the vehicle-mounted sensor of each truck, and when the video stream data collected by the vehicle-mounted sensor of any truck in the target truck formation is obtained, adding metadata of the video stream data collected by the vehicle-mounted sensor of any truck to the RabbitMQ message queue, storing the video stream data collected by the vehicle-mounted sensor of any truck to the MinIO cluster, The metadata of the video stream data includes at least one of the identification information of the video stream data, the generation time and the data size; the RabbitMQ message queue is monitored, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, the video stream data collected by the on-board sensor of any truck is obtained from the MinIO cluster, and then the video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained; based on the metadata of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck, the identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is generated, and then the key frame image corresponding to the video stream data collected by the on-board sensor of any truck carrying the identification information is stored in the MinIO cluster, and the metadata and identification information of the key frame image corresponding to the video stream data collected by the on-board sensor of any truck are stored in the Elasticsearch cluster, and the metadata of the key frame image includes a timestamp and a frame number.

[0093] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the vehicle-mounted unstructured data processing method for a truck formation provided by the above-mentioned methods, the method comprising: in a case where it is determined that the vehicle-mounted unstructured data processing service for a target truck formation is triggered, registering the vehicle-mounted unstructured data processing service in a Nacos service registration center cluster, configuring a RabbitMQ message queue service, a MinIO object storage service, and an Elasticsearch service for the vehicle-mounted unstructured data processing service, the target truck formation comprising multiple trucks; obtaining video stream data collected by the vehicle-mounted sensor of each truck, and in a case where the video stream data collected by the vehicle-mounted sensor of any truck in the target truck formation is obtained, adding the metadata of the video stream data collected by the vehicle-mounted sensor of any truck to the RabbitMQ message queue, storing the video stream data collected by the vehicle-mounted sensor of any truck to the MinIO cluster, the metadata of the video stream data comprising the video stream data The method comprises the following steps: monitoring the RabbitMQ message queue, and upon hearing the arrival of metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation, obtaining the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decoding and extracting frames of the video stream data collected by the on-board sensor of any truck, and obtaining key frame images corresponding to the video stream data collected by the on-board sensor of any truck; based on the metadata of the key frame images corresponding to the video stream data collected by the on-board sensor of any truck, generating identification information of the key frame images corresponding to the video stream data collected by the on-board sensor of any truck, and then storing the key frame images corresponding to the video stream data collected by the on-board sensor of any truck carrying the identification information in the MinIO cluster, and storing the metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensor of any truck in the Elasticsearch cluster, wherein the metadata of the key frame images includes a timestamp and a frame number.

[0094] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle-borne unstructured data processing method for truck platoons, characterized in that: include: In the case where it is determined that the on-board unstructured data processing service for the target truck formation is triggered, registering the on-board unstructured data processing service in the Nacos service registration center cluster, configuring a RabbitMQ message queue service, a MinIO object storage service, and an Elasticsearch service for the on-board unstructured data processing service, and the target truck formation includes multiple trucks; Obtain the video stream data collected by the on-board sensor of each of the trucks, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is obtained, add the metadata of the video stream data collected by the on-board sensor of any truck to the RabbitMQ message queue, and store the video stream data collected by the on-board sensor of any truck to the MinIO cluster, wherein the metadata of the video stream data includes at least one of the identification information, generation time and data size of the video stream data; Monitor the RabbitMQ message queue, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, obtain the video stream data collected by the on-board sensor of any truck from the MinIO cluster, and then decode and extract frames of 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 key frame images corresponding to the video stream data collected by the on-board sensors of any truck, identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck is generated, and then the key frame 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 key frame images corresponding to the video stream data collected by the on-board sensors of any truck are stored in the Elasticsearch cluster, and the metadata of the key frame images includes a timestamp and a frame number.

2. The vehicle-mounted unstructured data processing method for truck platoons according to claim 1 is characterized in that: After storing the metadata and identification information of the key frame images corresponding to the video stream data collected by the onboard sensor of any truck in the Elasticsearch cluster, the method further includes: When receiving a data query request input by a user, based on the query condition carried in the data query request, obtaining metadata and identification information of key frame images matching the query condition from an Elasticsearch cluster; Based on the metadata and identification information of the key frame images matching the query condition, the key frame images matching the query condition are obtained from the MinIO cluster.

3. The vehicle-mounted unstructured data processing method for truck platoons according to claim 2 is characterized in that: The step of acquiring the key frame image matching the query condition from the MinIO cluster based on the metadata and identification information of the key frame image matching the query condition includes: Based on the metadata and identification information of the key frame images matching the query condition, determining the key frame images matching the query condition in the MinIO cluster; Using lazy loading technology, the key frame images matching the query condition in the MinIO cluster and the metadata and identification information of the key frame images matching the query condition are loaded into the display interface.

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

5. The vehicle-mounted unstructured data processing method for truck platoons according to any one of claims 1 to 4, characterized in that: The vehicle-mounted sensor includes a radar sensor.

6. The method for processing vehicle-borne unstructured data for truck platoons according to claim 5, characterized in that: The decoding and frame extraction of 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 includes: Decoding the video stream data collected by the onboard sensor of any truck in a distributed processing manner to obtain point cloud data corresponding to the video stream data collected by the onboard sensor of any truck; Image frame extraction is performed on the point cloud data corresponding to the video stream data collected by the on-board sensor of any truck to obtain a key frame image corresponding to the video stream data collected by the on-board sensor of any truck.

7. A vehicle-mounted unstructured data processing device for truck platoons, characterized in that: include: An initialization module is used to register the on-board unstructured data processing service in the Nacos service registration center cluster when it is determined that the on-board unstructured data processing service for the target truck formation is triggered, and configure RabbitMQ message queue service, MinIO object storage service and Elasticsearch service for the on-board unstructured data processing service, wherein the target truck formation includes multiple trucks; A data acquisition module, used to acquire the video stream data collected by the on-board sensor of each of the trucks, and when the video stream data collected by the on-board sensor of any truck in the target truck formation is acquired, the metadata of the video stream data collected by the on-board sensor of any truck is added to the RabbitMQ message queue, and the video stream data collected by the on-board sensor of any truck is stored in the MinIO cluster, wherein the metadata of the video stream data includes at least one of the identification information, generation time and data size of the video stream data; A data monitoring module is used to monitor the RabbitMQ message queue, and when the metadata of the video stream data collected by the on-board sensor of any truck in the target truck formation arrives, the data is obtained from the MinIO cluster, and then the video stream data collected by the on-board sensor of any truck is decoded and framed, and the key frame image corresponding to the video stream data collected by the on-board sensor of any truck is obtained; A data storage module is used to generate identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck based on the metadata of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck, and then store the key frame images corresponding to the video stream data collected by the on-board sensors of any truck carrying the identification information in the MinIO cluster, and store the metadata and identification information of the key frame images corresponding to the video stream data collected by the on-board sensors of any truck in the Elasticsearch cluster, wherein the metadata of the key frame images includes a timestamp and a frame number.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for processing vehicle-mounted unstructured data for truck platoons is implemented 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 a processor, the method for processing vehicle-mounted unstructured data for a truck platoon is implemented as claimed 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 a processor, the method for processing vehicle-mounted unstructured data for a truck platoon is implemented as claimed in any one of claims 1 to 6.

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