Multi-vehicle dynamic vehicle following management method and system based on cloud collaboration

Abstract

The invention provides a multi-vehicle dynamic vehicle-following management method and system based on cloud collaboration, and belongs to the technical field of multi-vehicle vehicle-following management. Generating room information according to the data of the plurality of target vehicles, storing the room information in a cloud end, and synchronizing the room information to the plurality of target vehicles in real time; the room information comprises a member list, head car information and a planned route. According to the invention, the stability, the safety and the intelligent level of the collective travel fleet are improved. According to the invention, the vehicle following system is more efficient and reliable, is suitable for various scenes, and provides an innovative solution for intelligent network connection fleet management.

Description

Technical Field

[0001] This invention relates to the field of multi-vehicle following management technology, and in particular to a cloud-based collaborative method and system for dynamic multi-vehicle following management. Background Technology

[0002] In group travel scenarios, such as convoy travel, logistics transportation, convoy operations, or emergency rescue, multiple vehicles need to maintain a stable formation and share routes, marker information, and real-time voice communication during the journey. However, existing fleet management methods have the following problems:

[0003] 1. Difficulty in maintaining formation: Traditional convoy driving relies on driver experience, making it difficult to maintain a stable formation, especially in complex road conditions where vehicles may fall behind or become too far apart.

[0004] 2. Information synchronization lag: The lack of a unified communication platform makes it difficult for vehicles to share routes, the location of other vehicles, and important road marking information in real time.

[0005] 3. The selection method for the lead vehicle is singular: a certain vehicle is usually fixed as the lead vehicle, and it is impossible to dynamically adjust the lead vehicle according to the actual situation (such as driver experience, road condition adaptability, etc.).

[0006] 4. Lack of intelligent decision-making: Unable to optimize fleet driving strategies based on factors such as driving behavior analysis, vehicle speed adjustment, and remaining driving range.

[0007] 5. Inadequate energy replenishment planning: Failure to make reasonable energy replenishment plans based on vehicle range, energy type, and distance may result in some vehicles leaving the convoy due to insufficient energy. Summary of the Invention

[0008] The present invention aims to solve at least one of the above-mentioned problems in the prior art. The present invention proposes a cloud-based collaborative multi-vehicle following management method that can achieve intelligent following management, information sharing, intelligent power replenishment and network anomaly handling.

[0009] In a first aspect, embodiments of the present invention provide a multi-vehicle dynamic following management method based on cloud collaboration, including:

[0010] Acquire data from multiple target vehicles;

[0011] Room information is generated based on data from multiple target vehicles, the room information is stored in the cloud, and synchronized to the multiple target vehicles in real time;

[0012] The room information includes a member list, lead vehicle information, and planned route.

[0013] In a preferred embodiment, the method further includes: acquiring online status signals periodically sent by each target vehicle; recording the disconnection time when the first target vehicle disconnects due to network problems; and synchronizing the missing data to the first target vehicle after the network is restored.

[0014] The missing data includes at least the planned route updated after the disconnection time, to ensure that the first target vehicle travels along the current planned route.

[0015] In a preferred embodiment, the method further includes: acquiring at least one of the tagging information and image data uploaded by multiple target vehicles, storing it in the cloud, and synchronizing it to the multiple target vehicles in real time;

[0016] The tagging information includes: tag type, geographic location, and timestamp;

[0017] The image data includes at least one of the following: still images captured by the target vehicle's camera and recorded short videos.

[0018] In a preferred embodiment, the step of synchronizing the missing data to the first target vehicle after the first target vehicle recovers the network includes:

[0019] When the first target vehicle recovers the network after a time greater than or equal to a first preset time, the optimal following path for the first target vehicle is replanned, and recovery suggestions are provided.

[0020] The missing data also includes: the marker information and image data that the first target vehicle did not receive after the disconnection time;

[0021] The missing data also includes the real-time positions of the lead vehicle and following vehicles among the multiple target vehicles.

[0022] In a preferred embodiment, the method further includes: performing intelligent automatic tracking and formation adjustment;

[0023] The intelligent automatic tracking and formation adjustment steps include at least one of the following steps:

[0024] Based on the location and speed information of each target vehicle, the position of the lead vehicle is monitored in real time. When the speed of the lead vehicle is more than 30% lower than the average speed of all target vehicles and the duration exceeds a preset time threshold, or when the distance between the lead vehicle and the last target vehicle decreases to less than or equal to a preset safety threshold, the recommended acceleration value of the lead vehicle is calculated, the lead vehicle is prompted to accelerate, and a target speed for the lead vehicle is suggested. When the lead vehicle stops unexpectedly or the network disconnection time exceeds a second preset time, the automatic lead vehicle election mechanism is triggered to set the second target vehicle as the lead vehicle.

[0025] The real-time distance between each target vehicle is obtained through vehicle-to-vehicle communication. When the distance to the following vehicle exceeds the first distance, it is recommended that the following vehicle accelerate and the target speed of the following vehicle is calculated. When the distance to the following vehicle is less than the second distance, it is recommended that the following vehicle decelerate and the target speed of the following vehicle is calculated.

[0026] Based on the current speed of the following vehicles, the target vehicle speed, and road conditions, calculate the estimated time to restore the formation, notify each target vehicle to display the estimated restoration time, and provide driving suggestions;

[0027] The convoy speed is dynamically adjusted based on the driving habits, vehicle condition, and current road conditions of each target vehicle.

[0028] By combining traffic flow data, road speed limits, and traffic signals, the optimal speed is calculated and provided.

[0029] In a preferred embodiment, the method further includes: recording and analyzing the driver's driving habits, and based on the driving profile, suggesting the optimal driving speed, the optimal driving route, and the lead vehicle selection scheme for the convoy.

[0030] In a preferred embodiment, the method further includes: performing intelligent energy replenishment planning;

[0031] The intelligent energy replenishment planning step includes at least one of the following steps:

[0032] Range calculation: Obtain the current fuel level, battery level, and average energy consumption reported by each target vehicle through the on-board diagnostic system or CAN bus. Combine this with the historical driving data of each target vehicle to predict the remaining driving range of each target vehicle. Calculate the matching degree between the current trip and the driving range of each target vehicle in real time. If the range cannot support the entire trip, trigger the replenishment plan.

[0033] Data acquisition of charging stations: Information on charging stations along the route is obtained through cloud databases or real-time APIs, suitable charging stations are selected, and a comprehensive ranking is conducted based on distance, waiting time, charging power, and fuel station availability.

[0034] Pure electric vehicle charging planning: Calculate the optimal charging station based on the remaining battery power, estimated driving range, and distribution of fast charging stations along the route; dynamically recommend charging points based on station charging power, queuing status, and station availability; calculate the estimated charging time based on the vehicle's battery capacity, current remaining battery power, and target charging percentage, and notify each target vehicle of the estimated charging end time; if the queuing time at the recommended charging station is too long, replan backup charging stations.

[0035] Fuel vehicle refueling planning: Based on fuel level, fuel consumption, road conditions, and gas station distribution, the optimal gas station is recommended; refueling suggestions are provided in conjunction with fuel prices and vehicle queue conditions; the driving range with remaining fuel in the tank is calculated, and the appropriate refueling time is suggested based on the distribution of the nearest gas station.

[0036] Fleet Recharge Synchronization Mechanism: When the third target vehicle is about to enter recharge mode, a notification is sent to all target vehicles; the overall fleet navigation is adjusted to recharge mode; if multiple vehicles need recharge, it is recommended to use a unified recharge point.

[0037] Intelligent charging route optimization: If some vehicles in the fleet have short range while others do not need charging, it is recommended that some vehicles go to the charging station first and provide a merging point; if charging affects the overall journey, alternative routes are calculated.

[0038] In a second aspect, embodiments of the present invention provide a cloud-based collaborative multi-vehicle dynamic following management system, configured to implement any of the methods described in the first aspect, the system comprising:

[0039] The acquisition module is used to acquire data from multiple target vehicles;

[0040] The management module is used to generate room information based on data from multiple target vehicles, store the room information in the cloud, and synchronize it with the multiple target vehicles in real time; the room information includes a member list, lead vehicle information, and planned route.

[0041] Thirdly, embodiments of the present invention provide an electronic device, including:

[0042] One or more processors;

[0043] Memory, used to store one or more programs;

[0044] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described in the first aspect.

[0045] Fourthly, embodiments of the present invention provide a computer-readable medium storing a computer program that, when executed by a processor, implements the steps of any of the methods described in the first aspect.

[0046] Beneficial effects of this invention:

[0047] This invention improves the stability, safety, and intelligence of group travel fleets.

[0048] This invention makes the vehicle following system more efficient and reliable, applicable to a variety of scenarios, and provides an innovative solution for intelligent connected vehicle fleet management. Attached Figure Description

[0049] Figure 1 This is one of the flowcharts of a cloud-based collaborative multi-vehicle dynamic following management method provided in an embodiment of the present invention.

[0050] Figure 2 This is the second schematic diagram of a cloud-based collaborative multi-vehicle dynamic following management method provided in an embodiment of the present invention.

[0051] Figure 3 This is the third schematic diagram of a cloud-based collaborative multi-vehicle dynamic following management method provided in an embodiment of the present invention.

[0052] Figure 4 This is the fourth schematic diagram of a cloud-based collaborative multi-vehicle dynamic following management system provided in an embodiment of the present invention.

[0053] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0055] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0056] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0057] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0058] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0059] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0060] Figure 1 This is one of the flowcharts illustrating a cloud-based collaborative multi-vehicle dynamic following management method provided in an embodiment of the present invention; such as... Figure 1 As shown, the method includes:

[0061] Acquire data from multiple target vehicles;

[0062] Room information is generated based on data from multiple target vehicles, the room information is stored in the cloud, and synchronized to the multiple target vehicles in real time;

[0063] The room information includes a member list, lead vehicle information, and planned route.

[0064] When acquiring data from multiple target vehicles, the vehicles and the cloud use the MQTT protocol for low-latency data communication to ensure the real-time performance and stability of data updates.

[0065] In some embodiments, such as Figure 2 As shown, it also includes:

[0066] Acquire the online status signals sent periodically by each target vehicle; record the disconnection time when the first target vehicle disconnects due to network problems; and synchronize the missing data to the first target vehicle after the network is restored.

[0067] The missing data includes at least the planned route updated after the disconnection time, to ensure that the first target vehicle travels along the current planned route.

[0068] Specifically, when a network error occurs or a member disconnects, other members are notified that the vehicle is temporarily offline, and a voice announcement is triggered: "Vehicle XX has disconnected, please maintain platoon spacing." If a vehicle cannot reconnect for an extended period, all members are notified, and the administrator is allowed to decide whether to continue waiting or adjust the driving plan.

[0069] In some embodiments, such as Figure 3 As shown, it also includes:

[0070] At least one of the tagging information and image data uploaded by multiple target vehicles is stored in the cloud and synchronized to the multiple target vehicles in real time;

[0071] The marking information includes: marking type (such as intersection, accident, construction, obstacle, etc.), geographical location (GPS coordinates, road name), timestamp (recording marking time), and user annotation information (optional, such as "pothole ahead").

[0072] The image data includes at least one of the following: still images captured by the target vehicle's camera and recorded short videos.

[0073] Each target vehicle can mark key road sections (such as intersections, U-turn points, construction areas, accident scenes, etc.) using voice commands. Examples of voice commands are as follows: "Mark the intersection ahead" → The vehicle marks its current location as an important intersection; "Record construction ahead" → The vehicle marks its current location and adds a "construction" label; "Capture the image ahead" → The vehicle activates its camera system, records and uploads the image data. Voice commands are parsed by the in-vehicle AI assistant, and the marked point information is stored in a local cache, then uploaded to the cloud and stored in a cloud database.

[0074] When a new marker is created, multiple target vehicles are alerted via in-vehicle voice prompts and a HUD / in-vehicle display. For example, the voice prompt might say, "There is a U-turn point 100 meters ahead; please pay attention to your route," or the HUD might display, "Construction ahead; we recommend taking a detour." Target vehicles can view historical markers and replay key images on the navigation interface, improving driving safety.

[0075] The system allows each target vehicle to communicate via real-time voice, and each vehicle can manually turn its microphone on or off to ensure flexibility in information exchange.

[0076] In some embodiments, the step of synchronizing the missing data to the first target vehicle after the first target vehicle recovers the network includes:

[0077] When the first target vehicle recovers the network after a time equal to or greater than a first preset time, the optimal following path for the first target vehicle is replanned, and recovery suggestions are provided, including but not limited to the current vehicle speed and the estimated catch-up time, such as: "Please accelerate at XX km / h and expect to rejoin the team in YY seconds."

[0078] The missing data also includes: the marker information and image data that the first target vehicle did not receive after the disconnection time, to ensure that the first target vehicle obtains complete driving information;

[0079] The missing data also includes the real-time positions of the lead vehicle and following vehicles among the multiple target vehicles, to prevent the first target vehicle from deviating too far from the convoy;

[0080] The first preset time can be set to 30 seconds. A disconnection time of less than 30 seconds is set as a short disconnection, and a disconnection time of more than or equal to 30 seconds is set as a long disconnection.

[0081] In some embodiments, such as Figure 4 As shown, it also includes: intelligent automatic tracking and formation adjustment. The intelligent automatic tracking and formation adjustment mechanism ensures formation stability and improves convoy safety and coordination efficiency by monitoring the position, speed, and distance of vehicles in the convoy in real time.

[0082] In some embodiments, the intelligent automatic tracking and formation adjustment step includes at least one of the following steps:

[0083] Based on the location and speed information of each target vehicle, the position of the lead vehicle is monitored in real time. When the speed of the lead vehicle is more than 30% lower than the average speed of all target vehicles and the duration exceeds a preset time threshold, or when the distance between the lead vehicle and the last target vehicle decreases to less than or equal to a preset safety threshold, the recommended acceleration value of the lead vehicle is calculated, prompting the lead vehicle to accelerate and suggesting a target speed for the lead vehicle. When the lead vehicle unexpectedly stops or the network disconnection time exceeds a second preset time, an automatic lead vehicle election mechanism is triggered to set the second target vehicle as the lead vehicle. The automatic lead vehicle election mechanism includes: manual designation: the administrator can directly designate the lead vehicle; automatic priority switching: the lead vehicle is automatically switched according to a preset switching priority; and automatic election: the lead vehicle is automatically selected based on the current relative positions of the vehicles.

[0084] The system obtains real-time distances between target vehicles via V2V vehicle-to-vehicle communication. When the distance to the following vehicle exceeds a first distance (e.g., first distance > 100m, considered too large), it suggests acceleration and calculates the target speed of the following vehicle, with a voice prompt: "Please accelerate to XX km / h to restore formation within YY seconds." When the distance to the following vehicle is less than a second distance (e.g., second distance < 10m, considered too close), it suggests deceleration and calculates the target speed of the following vehicle to prevent rear-end collisions, with a voice prompt: "Please decelerate appropriately and maintain a safe following distance."

[0085] Based on the current and target speeds of the following vehicles and road conditions, the estimated time to restore formation is calculated, and the estimated restoration time is displayed on the vehicle's HUD, along with driving suggestions such as: "Estimated to restore to standard formation in 15 seconds".

[0086] The convoy speed is dynamically adjusted based on the driving habits, vehicle condition, and current road conditions of each target vehicle; if some vehicles have difficulty accelerating (such as electric vehicles in low battery mode), the overall speed of the convoy is reduced to ensure the consistency of the convoy.

[0087] By combining traffic flow data, road speed limits, and traffic signals, the system calculates the optimal speed and provides a prompt, such as a voice prompt: "It is recommended that the entire team maintain a speed of XX km / h to maintain optimal driving efficiency."

[0088] In some embodiments, the method further includes: recording and analyzing the driver's driving habits, such as speed stability and braking frequency, and suggesting the optimal driving speed, optimal driving route, and lead vehicle selection scheme based on the driving profile.

[0089] In some embodiments, the system further includes intelligent refueling planning. During long-haul convoy operations, different vehicles may use different energy types (fuel, electric, hybrid, etc.) and have different driving ranges. Intelligent refueling planning ensures that all members of the convoy can refuel efficiently, preventing the overall journey from being affected by insufficient range in a single vehicle.

[0090] In some embodiments, the intelligent energy replenishment planning step includes at least one of the following steps:

[0091] Range calculation: Obtain information such as current fuel level, battery level, and average energy consumption reported by each target vehicle through OBD (On-Board Diagnostics) or CAN bus, combine it with the historical driving data of each target vehicle, predict the remaining driving range of each target vehicle, calculate the matching degree between the current trip and the driving range of each target vehicle in real time, and trigger the energy replenishment plan if it is expected that it cannot support the entire trip.

[0092] Energy replenishment site data acquisition: Obtain information on energy replenishment sites along the route through cloud databases or real-time APIs (such as Gaode and Baidu Maps), filter suitable energy replenishment sites, and rank them comprehensively based on parameters such as distance, waiting time, charging power (applicable to electric vehicles), and fuel station supply.

[0093] Pure electric vehicle charging planning: Based on SOC (State of Charge), estimated driving range, and the distribution of fast charging stations along the route, the optimal charging station is calculated; combining information such as station charging power (fast charging / slow charging), queue status, and station availability, charging points are dynamically recommended with voice prompts: "It is recommended to charge at XX charging station, estimated charging time YY minutes"; based on the vehicle's battery capacity, current SOC, and target charging percentage, the estimated charging time is calculated, and the estimated charging end time is displayed on the HUD or in-vehicle navigation, such as: "Estimated charging time 20 minutes, to 80%"; if the queue time at the recommended charging station is too long, an alternative charging station is replanned to ensure that the journey is not affected;

[0094] Fuel vehicle refueling planning: Based on fuel level, fuel consumption, road conditions, and gas station distribution, recommend the best gas station; combine fuel prices and vehicle queue conditions to provide more economical and efficient refueling suggestions; calculate the driving range with the remaining fuel in the tank and suggest a suitable refueling time based on the distribution of the nearest gas station; voice prompt: "It is recommended to refuel at XX gas station, estimated refueling time YY minutes";

[0095] Fleet Refueling Synchronization Mechanism: When the third target vehicle is about to refuel, a notification is sent to all target vehicles to prevent other vehicles from leaving the fleet due to unknown circumstances. The voice prompt is: "Fleet member vehicle XX is about to refuel, estimated stay time YY minutes"; the fleet's overall navigation is switched to refueling mode so that other members know the current waiting time and can dynamically adjust the fleet's pace; if multiple vehicles need to refuel, it is recommended to use a unified refueling point to avoid the fleet being scattered and causing unnecessary waiting.

[0096] Intelligent charging route optimization: If some vehicles in the fleet have short range while others do not need charging, it is recommended that some vehicles go to the charging station first and provide a merging point to avoid excessive waiting time for the entire fleet. The voice prompt will say: "Some vehicles need to charge, it is recommended that they charge first, and other vehicles can wait to merge at XX location"; If charging affects the overall journey, alternative routes will be calculated and a better solution will be provided, such as: "Due to the charging plan, it is recommended to adjust the driving route and go to the destination via XX road, which is expected to save 10 minutes."

[0097] Based on the same inventive concept, embodiments of the present invention also provide a cloud-based collaborative multi-vehicle dynamic following management system, configured to implement any of the methods described in the above embodiments, the system comprising:

[0098] The acquisition module is used to acquire data from multiple target vehicles;

[0099] The management module is used to generate room information based on data from multiple target vehicles, store the room information in the cloud, and synchronize it with the multiple target vehicles in real time; the room information includes a member list, lead vehicle information, and planned route.

[0100] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0101] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0102] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0103] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0104] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable medium. This computer-readable medium stores a computer program, wherein, when executed by a processor, the program implements the steps of any of the methods described in the above embodiments. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0105] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0106] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0107] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0108] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0109] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0110] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0111] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0112] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0114] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A cloud-based cooperative multi-vehicle dynamic following management method, characterized in that, Comprising: Obtaining data of a plurality of target vehicles; Generating room information according to the data of the plurality of target vehicles, saving the room information in the cloud, and synchronizing the room information to the plurality of target vehicles in real time; The room information includes a member list, head vehicle information, and a planned route.

2. The method of claim 1, wherein, Further comprising: Obtaining online status signals periodically sent by each target vehicle, recording the disconnection time when the first target vehicle is disconnected due to network problems, and synchronizing the missing data to the first target vehicle when the first target vehicle resumes the network; The missing data at least includes the updated planned route after the disconnection time to ensure that the first target vehicle travels according to the current planned route.

3. The method of claim 2, wherein, Further comprising: Saving at least one of the mark information and image data uploaded by the plurality of target vehicles in the cloud and synchronizing the mark information and image data to the plurality of target vehicles in real time; The mark information includes: mark type, geographic location, and timestamp; The image data includes: at least one of still pictures taken by the camera of the target vehicle and recorded short videos.

4. The method of claim 3, wherein, The step of synchronizing the missing data to the first target vehicle when the first target vehicle resumes the network comprises: When the first target vehicle resumes the network after more than or equal to a first preset time, re-planning the optimal following path of the first target vehicle and providing a recovery suggestion; The missing data further includes: mark information and image data that the first target vehicle has not received since the disconnection time; The missing data further includes: real-time positions of the head vehicle and the following vehicles in the plurality of target vehicles.

5. The method of claim 1, 2, or 3, wherein, Further comprising: Performing intelligent automatic tracking and formation adjustment; The step of performing intelligent automatic tracking and formation adjustment comprises at least one of the following steps: According to the position and speed information of each target vehicle, monitoring the position of the head vehicle in real time, calculating the recommended acceleration value of the head vehicle when the speed of the head vehicle is more than 30% lower than the average speed of each target vehicle and the duration exceeds a preset time threshold, or the distance between the head vehicle and the last vehicle in each target vehicle is reduced to less than or equal to a preset safety threshold, prompting the head vehicle to accelerate, and suggesting a target speed of the head vehicle; when the head vehicle is unexpectedly parked or the network disconnection time exceeds a second preset time, triggering the head vehicle automatic election mechanism to set the second target vehicle as the head vehicle; Obtaining real-time distances between target vehicles through vehicle-mounted communication, suggesting the following vehicle to accelerate when the distance between the following vehicle and the head vehicle exceeds a first distance, and calculating the target speed of the following vehicle; suggesting the following vehicle to decelerate when the distance between the following vehicle and the head vehicle is less than a second distance, and calculating the target speed of the following vehicle; According to the current speed and target speed of the following vehicle and road condition information, calculating the time of expected recovery of the formation, notifying each target vehicle to display the expected recovery time, and providing driving suggestions; According to the driving habits, vehicle conditions, and current road conditions of each target vehicle, dynamically adjusting the driving speed of the vehicle formation; Combining traffic flow data, road speed limit, and traffic signals to calculate the optimal speed and provide suggestions.

6. The method of claim 1, 2, or 3, wherein, Further comprising: Recording and analyzing the driving habits of the driver, and suggesting the best driving speed, optimal driving route, and head vehicle election scheme of the vehicle formation according to the driving portrait.

7. The method of claim 1, 2, or 3, wherein, Further comprising: Performing intelligent energy supplement planning; The step of performing intelligent energy supplement planning comprises at least one of the following steps: Range calculation: Obtain the current fuel level, battery level, and average energy consumption reported by each target vehicle through the on-board diagnostic system or CAN bus. Combine this with the historical driving data of each target vehicle to predict the remaining driving range of each target vehicle. Calculate the matching degree between the current trip and the driving range of each target vehicle in real time. If the range cannot support the entire trip, trigger the replenishment plan. Data acquisition of charging stations: Information on charging stations along the route is obtained through cloud databases or real-time APIs, suitable charging stations are selected, and a comprehensive ranking is conducted based on distance, waiting time, charging power, and fuel station availability. Pure electric vehicle charging planning: Calculate the optimal charging station based on the remaining battery power, estimated driving range, and distribution of fast charging stations along the route; dynamically recommend charging points based on station charging power, queuing situation, and station availability; calculate the estimated charging time based on the vehicle's battery capacity, current remaining battery power, and target charging percentage, and notify each target vehicle to display the estimated charging end time. If the recommended charging station has too long a queue, a backup charging station should be planned. Fuel vehicle refueling planning: Based on fuel level, fuel consumption, road conditions, and gas station distribution, the optimal gas station is recommended; refueling suggestions are provided in conjunction with fuel prices and vehicle queue conditions; Calculate the driving range with the remaining fuel in the tank, and suggest a suitable refueling time based on the distribution of the nearest gas stations; Fleet Recharge Synchronization Mechanism: When the third target vehicle is about to enter recharge mode, a notification is sent to all target vehicles; the overall fleet navigation is adjusted to recharge mode; if multiple vehicles need recharge, it is recommended to use a unified recharge point. Intelligent charging route optimization: If some vehicles in the fleet have short range while others do not need charging, it is recommended that some vehicles go to the charging station first and provide a merging point; if charging affects the overall journey, alternative routes are calculated.

8. A cloud-based cooperative multi-vehicle dynamic following management system, characterized in that, The system, configured to implement the method as described in any one of claims 1 to 7, comprises: The acquisition module is used to acquire data from multiple target vehicles; The management module is used to generate room information based on data from multiple target vehicles, store the room information in the cloud, and synchronize it with the multiple target vehicles in real time; the room information includes a member list, lead vehicle information, and planned route.

9. An electronic device, comprising: include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer readable medium having stored thereon a computer program, characterized in that When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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