Fleet management system, method, apparatus, device, and storage medium

Abstract

The application discloses a vehicle fleet management system, method, device, equipment and storage medium, and relates to the technical field of automobiles. The system comprises a first server, at least one vehicle end device corresponding to at least one vehicle, and the first server and the vehicle end device are connected through a network; the vehicle end device is used for collecting first data in real time, the first data comprises at least one of vehicle state data, position data, driving data and sensing data; the first data is transmitted to the first server through the network; the first server is used for receiving the first data sent by the vehicle end device; the first data is stored in a local storage space; the first data is analyzed to obtain a first analysis result; a first instruction is obtained based on the first analysis result, the first instruction is used for instructing driving of the at least one vehicle; the first instruction is sent to the vehicle end device; and the vehicle end device is also used for receiving the first instruction, so that the vehicle fleet management efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a fleet management system, method, apparatus, equipment, and storage medium. Background Technology

[0002] With the development of vehicle-to-everything (V2X) technology, information exchange between vehicles has become more convenient. However, vehicle owners need a fleet management system to provide assistance in areas such as dispatching, monitoring, and safety.

[0003] In related technologies, remote monitoring of vehicle locations allows managers to centrally manage a fleet, enabling the issuance of unified dispatch instructions to direct the fleet's movement.

[0004] However, the above methods rely on the subjective decisions of managers, making it difficult to guarantee the accuracy of decisions and resulting in low fleet management efficiency. Summary of the Invention

[0005] This application provides a fleet management system, method, apparatus, equipment, and storage medium that can improve fleet management efficiency. The technical solution is as follows.

[0006] On the one hand, a fleet management system is provided, the system including a first server and at least one vehicle-side device corresponding to each vehicle, the first server and the vehicle-side device being connected via a network;

[0007] The vehicle-side device is used to collect first data corresponding to each of the at least one vehicle in real time, the first data including at least one of vehicle status data, location data, driving data, and sensor data; and transmit the first data to the first server via a network.

[0008] The first server is configured to receive the first data sent by the vehicle-end devices corresponding to the at least one vehicle; store the first data in local storage space; analyze the first data to obtain a first analysis result, the first analysis result being used to indicate the vehicle status of the at least one vehicle; obtain a first instruction based on the first analysis result, the first instruction being used to instruct the at least one vehicle to drive; and send the first instruction to the vehicle-end devices.

[0009] The vehicle-side device is also used to receive the first instruction.

[0010] On the other hand, a fleet management method is provided, the method comprising:

[0011] Acquire first data, which is collected by vehicle-side devices corresponding to at least one vehicle, and the first data includes at least one of vehicle status data, location data, driving data, and sensor data;

[0012] Analyze the first data to obtain a first analysis result, which is used to indicate the vehicle status of the at least one vehicle;

[0013] A first instruction is obtained based on the first analysis result, and the first instruction is used to instruct the driving of the at least one vehicle;

[0014] Send the first instruction to the vehicle-side device.

[0015] On the other hand, a fleet management device is provided, the device comprising:

[0016] The acquisition module is used to acquire first data, which is collected by vehicle-end devices corresponding to at least one vehicle. The first data includes at least one of vehicle status data, location data, driving data, and sensor data.

[0017] An analysis module is used to analyze the first data and obtain a first analysis result, the first analysis result being used to indicate the vehicle status of the at least one vehicle;

[0018] The acquisition module is further configured to acquire a first instruction based on the first analysis result, the first instruction being used to instruct the driving of the at least one vehicle;

[0019] The sending module is used to send the first instruction to the vehicle-side device.

[0020] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the fleet management method as described in any of the embodiments of this application above.

[0021] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the fleet management method as described in any of the embodiments of this application above.

[0022] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the fleet management methods described in the above embodiments.

[0023] The beneficial effects of the technical solutions provided in this application include at least the following:

[0024] By automatically analyzing the received and stored vehicle data through the server, analytical results indicating vehicle status are obtained. Instructions are then automatically generated based on these results to guide vehicle movement, improving efficiency and enhancing the automation and objectivity of decision-making in fleet management. Objective data analysis improves fleet management efficiency. Furthermore, real-time collection of vehicle status, location, driving, and sensor data from vehicle-side devices enhances the comprehensiveness of data collection, thereby improving the accuracy of instructions derived from the analysis results and further improving fleet management efficiency. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of an implementation environment provided by an exemplary embodiment of this application;

[0027] Figure 2 This is a flowchart of a fleet management method provided in an exemplary embodiment of this application;

[0028] Figure 3 This is a schematic diagram of permission allocation interaction provided in an exemplary embodiment of this application;

[0029] Figure 4 This is a schematic diagram of the interaction process of a fleet distributed management system provided in an exemplary embodiment of this application;

[0030] Figure 5 This is a flowchart of a fleet management method provided in an exemplary embodiment of this application;

[0031] Figure 6 This is a schematic diagram of a fleet management system provided in an exemplary embodiment of this application;

[0032] Figure 7 This is a structural block diagram of a fleet management device provided in an exemplary embodiment of this application;

[0033] Figure 8 This is a structural block diagram of a terminal provided in an exemplary embodiment of this application. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0035] It should be understood that although the terms first, second, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, a first parameter may also be referred to as a second parameter without departing from the scope of this disclosure, and similarly, a second parameter may also be referred to as a first parameter. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0036] With the development of vehicle-to-everything (V2X) technology, information exchange between vehicles has become more convenient. However, vehicle owners need a fleet management system to assist in dispatching, monitoring, and safety. One related technology involves remotely monitoring vehicle locations for unified fleet management by administrators, enabling the issuance of dispatch instructions and directing fleet movement. However, this method relies on the administrator's subjective decision-making, making accuracy difficult to guarantee and resulting in low fleet management efficiency.

[0037] The fleet management system provided in this application embodiment automatically analyzes the received and stored vehicle data through a server to obtain analysis results that indicate vehicle status. Then, it automatically obtains instructions based on the analysis results to instruct vehicle movement, which can improve instruction efficiency and enhance the automation and objectivity of decision-making in the fleet management process. By achieving fleet management through objective data analysis, fleet management efficiency can be improved. On the other hand, by collecting vehicle status data, location data, driving data, and sensor data in real time through vehicle-side devices, the comprehensiveness of data collection can be improved, thereby helping to improve the accuracy of instructions obtained based on analysis results and improve fleet management efficiency.

[0038] First, the implementation environment of this application will be introduced. Please refer to... Figure 1 The illustration shows an implementation environment provided by an exemplary embodiment of this application, which includes: a terminal 110, a server 120 and a communication network 130.

[0039] Terminal 110 is a vehicle-side device corresponding to at least one vehicle, and server 120 is a central server used to deploy a fleet management system and manage at least one vehicle. Terminal 110 and server 120 transmit data through communication network 130.

[0040] Terminal 110 can collect first data corresponding to at least one vehicle in real time. The first data includes at least one of vehicle status data, location data, driving data, and sensor data. Terminal 110 transmits the first data to server 120 through communication network 130. Server 120 receives the first data, stores the first data in its local storage space, analyzes the first data, obtains a first analysis result, which is used to indicate the vehicle status of at least one vehicle. Based on the first analysis result, server 120 obtains a first instruction, which is used to instruct the driving of at least one vehicle, and sends the first instruction to terminal 110. Terminal 110 receives the first instruction.

[0041] Optionally, the terminal 110 can drive automatically according to the first instruction, or it can transmit instruction information to the driver by displaying the first instruction, allowing the driver to make the driving decision.

[0042] The aforementioned terminal is optional and can be a vehicle-mounted terminal, desktop computer, laptop computer, mobile phone, tablet computer, e-book reader, Moving Picture Experts Group Audio Layer III (MP3) player, Moving Picture Experts Group Audio Layer IV (MP4) player, smart TV, smart vehicle, and other terminal devices. This application embodiment does not limit the specific terminal device to these.

[0043] It is worth noting that the aforementioned servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud security, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0044] Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or local area network to achieve data computing, storage, processing, and sharing.

[0045] In some embodiments, the server described above can also be implemented as a node in a blockchain system.

[0046] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant regions. For example, vehicle data involved in this application was obtained with full authorization.

[0047] To further explain, this application can display a prompt interface, pop-up window, or output voice prompts before and during the collection of user-related data (e.g., vehicle status data, location data, driving data, sensor data, etc. involved in this application). These prompt interfaces, pop-ups, or voice prompts are used to inform the user that their relevant data is being collected. This ensures that the application only begins executing the steps related to acquiring user-related data after receiving confirmation from the user regarding the prompt interface or pop-up window; otherwise (i.e., without receiving confirmation from the user), the steps to acquire user-related data end, meaning no user-related data is acquired. In other words, all user data collected in this application is collected with the user's consent and authorization, and the collection, use, and processing of related user data must comply with the relevant laws, regulations, and standards of the relevant regions.

[0048] This is illustrative; please refer to it. Figure 2 It illustrates an interactive flowchart of a fleet management system provided in an exemplary embodiment of this application, such as... Figure 2 As shown, the system includes a first server and at least one vehicle-side device corresponding to each vehicle. The interaction process of the system includes the following steps:

[0049] Step 211: The vehicle-side device collects first data corresponding to at least one vehicle in real time.

[0050] The first data includes at least one of vehicle status data, location data, driving data, and sensor data.

[0051] Optionally, the vehicle-side device can automatically collect the first data in real time, or it can collect the first data according to the instructions sent by the first server. This application embodiment does not limit this.

[0052] In some embodiments, at least one vehicle is equipped with a sensor for collecting the aforementioned sensing data in real time. The sensing data can be used to indicate collision conditions, speeding conditions, etc., of at least one vehicle.

[0053] At least one vehicle may also be equipped with a Global Positioning System (GPS) device, which collects the location and driving data of at least one vehicle in real time.

[0054] The driving data can include information such as the vehicle's speed, trajectory, and direction.

[0055] At least one vehicle may also be equipped with a condition detection device, which can detect the vehicle's condition in real time.

[0056] The vehicle status includes vehicle speed, vehicle mileage, and vehicle energy consumption. Vehicle energy consumption includes fuel consumption, electricity consumption, etc. The specific type of energy consumption is related to the type of energy used by the vehicle, and this application does not limit this.

[0057] Step 212: The vehicle-side device transmits the first data to the first server via the network.

[0058] The first server is used to uniformly manage at least one vehicle.

[0059] Optionally, the first server can be deployed in the same cloud server, or in a designated vehicle in at least one vehicle, or it can be deployed in a distributed manner in roadside equipment corresponding to different regions.

[0060] To illustrate, taking a first server comprising multiple server nodes, distributed across roadside equipment in different regions, as an example, to reduce issues such as slow data transmission speed during remote data transmission and improve data transmission rate, when at least one vehicle travels to the first region, it can automatically associate with the server node corresponding to the first region. When transmitting first data, the first data is transmitted to the server node in the first region via the network. Furthermore, the distance between the vehicle and the roadside equipment (server node) can be determined based on the vehicle's location, and the first data is transmitted to the nearest server node.

[0061] Optionally, when the distance between the vehicle and the roadside equipment (server node) is less than a preset distance threshold, the system can switch to Bluetooth connection to transmit the first data through the Bluetooth connection between the vehicle and the roadside equipment. The distance threshold is determined based on the Bluetooth signal coverage range of the vehicle and the roadside equipment.

[0062] Step 221: The first server receives first data sent by the vehicle terminal device corresponding to at least one vehicle.

[0063] Indicatively, the first server is connected to at least one vehicle via a network, enabling remote data transmission.

[0064] Optionally, the first server can be deployed in the same cloud server, or in a designated vehicle in at least one vehicle, or it can be deployed in a distributed manner in roadside equipment corresponding to different regions.

[0065] To illustrate, taking a first server comprising multiple server nodes, which are distributed and deployed in roadside equipment corresponding to different regions, in order to reduce problems such as slow data transmission speed during remote data transmission and improve data transmission rate, when at least one vehicle travels to the first region, it can automatically associate with the server node corresponding to the first region, and the roadside equipment (server node) in the first region can receive the first data sent by at least one vehicle in the first region.

[0066] Step 222: The first server stores the first data in its local storage space.

[0067] In some embodiments, the first server can store and process a large amount of vehicle data. Due to the large amount of data, it may be prone to lag or other issues. The first server may be equipped with multiple data buffers to cache the received first data.

[0068] To illustrate, the first server manages at least one vehicle, and can build a corresponding buffer for each vehicle. When the first vehicle connects to the first server, the first server creates a first buffer corresponding to the first vehicle locally. When it receives the first data sent by the first vehicle, it caches the first data in the first buffer. During data storage, the first data is read from the first buffer and written to the local storage space.

[0069] Multiple buffers can receive data sent by their respective vehicles in parallel, or write data to local storage space in parallel or sequentially. This application does not limit this.

[0070] Step 223: The first server analyzes the first data and obtains the first analysis result.

[0071] The first analysis result is used to indicate the vehicle status of at least one vehicle.

[0072] In some embodiments, a first analysis result is obtained by automatically analyzing the first data through a preset program.

[0073] For example, a preset program can automatically analyze a vehicle's speeding situation, determine the vehicle's current location based on the vehicle's location data, obtain the speed limit information of the current location, and use the speed limit information to indicate the driving speed threshold of the current area. Based on the driving data in the first data and the aforementioned speed limit information, the vehicle's current driving speed is compared with the driving speed threshold. When the driving speed is greater than the driving speed threshold, a first analysis result is determined, and the first analysis result is used to indicate that the vehicle is currently speeding.

[0074] The first server also enables visual management functions.

[0075] In some embodiments, the first server can display the location information of at least one vehicle in a map in real time based on location data.

[0076] The map image is shown schematically, and at least one location marker is displayed in the map image. The at least one location marker is used to indicate the current real-time location of at least one vehicle on the map. Each location marker corresponds to a vehicle marker. The vehicle marker can be a license plate number or other identification information that can uniquely identify the corresponding vehicle. For example, different vehicle location markers can be identified by different colors, or by the driver's headshot of the vehicle, etc. The embodiments of this application do not limit this.

[0077] In some embodiments, the first server may display vehicle status information corresponding to at least one vehicle based on at least one of vehicle status data and driving data.

[0078] Optionally, the vehicle status information includes at least one of the following: vehicle speed, mileage, and energy consumption.

[0079] The illustration shows the vehicle status interface, which displays the vehicle status information for at least one vehicle.

[0080] Optionally, in order to simplify the interface presentation and improve the efficiency of information transmission, vehicle status indicators can be displayed in the vehicle status interface. The vehicle status indicators include normal status indicators and abnormal status indicators. The display methods of the normal status indicators and the abnormal status indicators are different. The visual significance of the display method corresponding to the abnormal status indicators is higher than that of the display method corresponding to the normal status indicators.

[0081] Indicatively, during the display of at least one location marker on the map, a green location marker indicates that the corresponding vehicle is in normal condition, while a red location marker indicates that the corresponding vehicle is in abnormal condition. Clicking on a red location marker will display the vehicle's status information. Specifically, only abnormal vehicle status information can be displayed, for example, if a vehicle is speeding, the vehicle's current speed will be displayed.

[0082] Step 224: The first server obtains the first instruction based on the first analysis result.

[0083] The first instruction is used to direct the movement of at least one vehicle.

[0084] Optionally, the first server has a pre-set instruction library, and the first server can automatically determine the instruction that matches the first analysis result from the instruction library as the first instruction through an artificial intelligence model.

[0085] Indicatively, the instructions in the instruction library correspond to type labels. For example, an instruction to instruct a vehicle to adjust its speed can correspond to a speed label, and an instruction to instruct a vehicle to travel a path can correspond to a path label. The first analysis result can be implemented as text content, which includes type keywords. For example, when the first analysis result obtained based on the first data is "Vehicle A is currently speeding by 5 kilometers per hour and needs to slow down", the artificial intelligence model detects the keywords "speeding" and "slow down", determines the speed type label corresponding to the instruction to be matched, and matches the speed reduction instruction from the instruction library as the first instruction based on the keywords.

[0086] The first server can also implement anomaly detection functionality.

[0087] In some embodiments, the first server detects an abnormal state corresponding to at least one vehicle based on the first data, generates an alarm signal in response to the detection of the abnormal state, determines an emergency instruction based on the alarm signal, the emergency instruction is used to instruct at least one vehicle to switch from the abnormal state to the normal state, and sends the emergency instruction to the vehicle-side device.

[0088] The alarm signal is used to instruct the first server to send emergency instructions to at least one vehicle.

[0089] Regarding the process of determining the above-mentioned emergency instructions, the first server can determine the first exception type corresponding to the abnormal state, and match the first candidate instruction from the first instruction library as the emergency instruction based on the first exception type.

[0090] Optionally, the anomaly type includes, but is not limited to, at least one of the following: speed anomaly, energy consumption anomaly, collision anomaly, location anomaly, and route anomaly.

[0091] Among them, speed anomaly refers to the vehicle's speed not meeting the preset speed requirement, or not meeting the speed range specified for the area where the vehicle is currently located; energy consumption anomaly refers to an abnormal amount of energy consumed by the vehicle, specifically including but not limited to the remaining energy being less than the preset energy threshold, the energy consumption rate being greater than the preset consumption rate, or the energy consumption curve not conforming to the preset curve even when the vehicle's trajectory is normal within a preset time period; collision anomaly refers to the vehicle being involved in a collision, or the vehicle being subjected to an impact greater than the preset pressure; position anomaly refers to the displacement distance between the vehicle's current position and its previous position being different from the displacement that can be generated by the driving speed, or the vehicle being prohibited from driving in the area where the vehicle is currently located; route anomaly refers to the vehicle's current driving route being different from the preset route, or the route indicated by the instruction.

[0092] The first instruction library is used to store multiple candidate instructions preset based on a first exception type. The multiple candidate instructions include a first candidate instruction, and the matching degree between the first candidate instruction and the aforementioned exception state reaches a preset matching degree.

[0093] The matching degree between the first candidate instruction and the aforementioned abnormal state is calculated by an artificial intelligence model based on the simulation data corresponding to the switching of at least one vehicle from the abnormal state to the normal state when the first candidate instruction is executed under the condition that at least one vehicle is in an abnormal state.

[0094] Optionally, the simulation data includes at least one of the following: switching success rate, switching time, and the amount of energy required for switching.

[0095] Schematic illustration: For multiple vehicles in a target convoy, a first server detects an abnormal state of vehicle A in the target convoy based on first data sent by the target convoy. This first data includes the current vehicle position arrangement information of the target convoy and the convoy's regional location. The target convoy has a preset driving route and a preset vehicle position arrangement during the driving process. The first server determines the preset vehicle position arrangement corresponding to the current target convoy based on the convoy's regional location. In response to vehicle A's position not conforming to the preset vehicle position arrangement, the server determines vehicle A's abnormal state as a position anomaly and generates an alarm signal. This alarm signal instructs the first server to issue a position adjustment command to vehicle A. During the command issuance process, the first server determines that vehicle A's abnormal state belongs to the position anomaly type based on the aforementioned abnormal state. Based on the position anomaly type, the server matches a first candidate command from a first command library as an emergency command. The first command library stores multiple candidate commands with corresponding position tags. These multiple candidate commands are multiple position adjustment commands preset based on the position anomaly type. Among these multiple candidate commands is the first candidate command "accelerate to the location of vehicle B and exchange positions with vehicle B." The matching degree between the first candidate command and the aforementioned abnormal state reaches a preset matching degree. Specifically, an artificial intelligence model simulates vehicle A accelerating to the location of vehicle B under the current abnormal state, exchanging positions with vehicle B, and obtaining simulation data showing that the target convoy's vehicle positions conform to a preset arrangement. This simulation data includes at least one of the following: switching success rate, switching time, and energy consumption required for the switching. The switching success rate indicates the probability that the target convoy's vehicle positions conform to the preset arrangement under the simulated conditions. The switching time indicates the duration required for vehicle A to execute the emergency command. The energy consumption required for the switching refers to the fuel consumption required for vehicle A to execute the emergency command. The matching degree is calculated based on the simulation data using the artificial intelligence model. The switching success rate is positively correlated with the matching degree, while the switching time and energy consumption are negatively correlated with the matching degree, respectively.

[0096] Step 225: The first server sends a first instruction to the vehicle-side device.

[0097] To illustrate, when the distance between the vehicle and the first server is within Bluetooth coverage, a Bluetooth connection is established between the first server and the vehicle-side device, and the first server sends a first command to the vehicle-side device based on the Bluetooth connection; when the distance between the vehicle and the first server is outside Bluetooth coverage, the first server sends a first command to the vehicle-side device based on a network connection.

[0098] Step 213: The vehicle-side device receives the first instruction.

[0099] Optionally, the vehicle can automatically verify and execute the received first instruction based on the autonomous driving system, or the first instruction can be broadcast by voice and executed by the vehicle driver. This application embodiment does not limit this.

[0100] In summary, the system provided in this application embodiment automatically analyzes the received and stored vehicle data through a server to obtain analysis results that indicate vehicle status. Then, it automatically obtains instructions based on the analysis results to direct vehicle movement, improving instruction efficiency and enhancing the automation and objectivity of decision-making in fleet management. By implementing fleet management through objective data analysis, fleet management efficiency is improved. Furthermore, the real-time collection of vehicle status data, location data, driving data, and sensor data from vehicle-side devices enhances the comprehensiveness of data collection, thereby improving the accuracy of instructions obtained based on the analysis results and further increasing fleet management efficiency.

[0101] In some embodiments, the first server can also implement permission allocation functionality, assigning management permissions to vehicles in the fleet to achieve distributed fleet management. For illustrative purposes, please refer to [reference needed]. Figure 3 , Figure 3 This is a schematic diagram of permission allocation interaction provided in an exemplary embodiment of this application, such as... Figure 3 As shown, the fleet management system includes a first server and at least one vehicle-side device corresponding to each vehicle. The access control process includes the following steps:

[0102] Step 310: The first server assigns management permissions to at least one vehicle based on the first data.

[0103] Specifically, a vehicle whose first data meets the preset management conditions is identified as a first vehicle, and other vehicles other than the first vehicle are identified as second vehicles. The first vehicle has management authority, which is used to instruct the first vehicle to manage the second vehicle.

[0104] The first vehicle has a corresponding first vehicle-side device, and the second vehicle has a corresponding second vehicle-side device.

[0105] To illustrate, taking management authority as navigation authority as an example, the first server assigns navigation authority to the target convoy based on the first data, designating the first vehicle in the target convoy as the lead vehicle. The lead vehicle is used to lead the target convoy and take over the route planning function of the first server, guiding the target convoy's driving route. For example, based on the first data reported by the target convoy, the first server determines the lead vehicle as the first vehicle, which has reached a preset speed, consumed less than a preset energy consumption, and whose driver has navigation experience. The other vehicles in the target convoy are designated as second vehicles, and management authority is assigned to the first vehicle. The first vehicle can then instruct the second vehicles on their driving routes based on this management authority.

[0106] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the interaction process of a fleet distributed management system provided in an exemplary embodiment of this application, such as... Figure 4 As shown, the system includes a first server and vehicle-side devices corresponding to at least one vehicle. The at least one vehicle includes a first vehicle and a second vehicle. The first vehicle corresponds to a first vehicle-side device, and the second vehicle corresponds to a second vehicle-side device. The interaction process includes the following steps:

[0107] Step 411: The first server sends a first permission instruction to the first vehicle terminal device.

[0108] The first permission instruction is used to assign management permissions to the first vehicle.

[0109] Optionally, the first authority instruction includes a first instruction. Taking the navigation authority instruction as an example, the first authority instruction is used to instruct the first vehicle to obtain navigation authority, including the first instruction, which is used to instruct the first vehicle to drive in front of the target convoy and maintain navigation driving for a preset time period.

[0110] Step 421: The first vehicle terminal device receives the first instruction, obtains management authority according to the first instruction, generates a second instruction based on the management authority, and sends the second instruction to the second vehicle terminal device.

[0111] The management authority is used to instruct the first vehicle terminal device to issue instructions to the second vehicle terminal device, and the second instruction is used to instruct the second vehicle to drive.

[0112] Indicatively, after the first vehicle obtains navigation permission, it generates a second instruction. This second instruction is used to instruct the second vehicle to follow the first vehicle. Optionally, the second instruction can instruct the second vehicle to obtain the location data of the first vehicle at a preset period, determine the distance between the second vehicle and the first vehicle based on the location data, and automatically determine the driving speed based on the distance.

[0113] In some embodiments, when the first vehicle-side device obtains management authority, it displays a fleet management interface, which includes an indicator control for triggering the automatic sending of a second instruction to the second vehicle. The indicator control includes a formation indicator control. In response to receiving a trigger operation for the formation indicator control, the device automatically sends a formation adjustment instruction to the second vehicle-side device. The formation adjustment instruction instructs the second vehicle to automatically adjust its driving position until it meets the preset formation arrangement conditions.

[0114] Among them, the formation indicator control is automatically displayed by the artificial intelligence model deployed in the first vehicle terminal device based on the first data sent by multiple vehicles. When the fleet has preset formation arrangement conditions, such as the fleet driving order, the artificial intelligence model automatically detects whether the driving position, formation arrangement, driving speed, etc. of multiple vehicles meet the preset formation arrangement conditions based on the first data. When it is detected that the first data does not meet the preset formation arrangement conditions, the formation indicator control is automatically displayed.

[0115] Optionally, the fleet management interface can also display vehicle location identifiers corresponding to multiple vehicles. These vehicle location identifiers are used to indicate the location of multiple vehicles on the map. The first vehicle terminal device can automatically generate and send a movement command to the second vehicle terminal device by receiving a movement operation for the vehicle location identifier corresponding to the second vehicle.

[0116] Step 431: The second vehicle-side device receives the second instruction.

[0117] Optionally, when the distance between the first vehicle and the second vehicle remains within a preset distance range, the second vehicle receives a second command via a Bluetooth connection with the first vehicle; when the distance exceeds the preset distance range, the second vehicle receives a second command via a network connection with the first vehicle.

[0118] In summary, the system provided in this application embodiment realizes a distributed management function for a fleet. It can flexibly allocate some management permissions to designated vehicles in the fleet based on fleet data, so that designated vehicles can manage the fleet in a timely manner according to the actual driving situation of the fleet, improve management efficiency, reduce the situation of untimely remote management and decision-making errors caused by remote data transmission and data analysis errors, and improve fleet management efficiency.

[0119] Please refer to Figure 5 This document illustrates a flowchart of a fleet management method provided in an exemplary embodiment of this application. This method can be executed by a terminal, a server, or both simultaneously. This embodiment uses the execution of the method by a first server in a fleet management system as an example for illustration. Figure 5 As shown, the method includes the following steps:

[0120] Step 510: Obtain the first data.

[0121] The first data is collected by the vehicle-side equipment corresponding to at least one vehicle, and the first data includes at least one of vehicle status data, location data, driving data, and sensor data.

[0122] Optionally, the vehicle-side device can automatically collect the first data in real time, or it can collect the first data according to the instructions sent by the first server. This application embodiment does not limit this.

[0123] In some embodiments, at least one vehicle is equipped with a sensor for collecting the aforementioned sensing data in real time. The sensing data can be used to indicate collision conditions, speeding conditions, etc., of at least one vehicle.

[0124] At least one vehicle may also be equipped with a Global Positioning System (GPS) device, which collects real-time location and driving data of at least one vehicle.

[0125] The driving data can include information such as the vehicle's speed, trajectory, and direction.

[0126] At least one vehicle may also be equipped with a condition detection device, which can detect the vehicle's condition in real time.

[0127] The vehicle status includes vehicle speed, vehicle mileage, and vehicle energy consumption. Vehicle energy consumption includes fuel consumption, electricity consumption, etc. The specific type of energy consumption is related to the type of energy used by the vehicle, and this application does not limit this.

[0128] Optionally, the first data can be received via Bluetooth or via a network connection. Specifically, the data communication connection method can be determined based on parameters such as the distance between the vehicle-side device and the first server, signal strength requirements, and data transmission rate. This application does not limit this method.

[0129] This is illustrative; please refer to it. Figure 6 , Figure 6 This is a schematic diagram of a fleet management system provided in an exemplary embodiment of this application, such as... Figure 6As shown, the fleet management system includes a fleet system 610, a vehicle-mounted infotainment system 620, and a communication device 630. The fleet system 610 refers to the fleet management program deployed on the first server. The vehicle-mounted infotainment system 620 is a vehicle-end device deployed in at least one vehicle. The communication device 630 is a device in at least one vehicle used to manage remote vehicle communication. The fleet system 610 and the vehicle-mounted infotainment system 620 are connected via a network, and the vehicle-mounted infotainment system 620 and the communication device 630 are connected via a Controller Area Network (CAN). The communication device 630 can be a Telematics Box (TBOX) capable of performing functions such as vehicle positioning and navigation, remote control and monitoring, fault diagnosis and remote diagnostics, security and anti-theft, and vehicle networking services.

[0130] Step 520: Analyze the first data to obtain the first analysis result.

[0131] The first analysis result is used to indicate the vehicle status of at least one vehicle.

[0132] In some embodiments, a first analysis result is obtained by automatically analyzing the first data through a preset program.

[0133] For example, a preset program can automatically analyze a vehicle's speeding situation, determine the vehicle's current location based on the vehicle's location data, obtain the speed limit information of the current location, and use the speed limit information to indicate the driving speed threshold of the current area. Based on the driving data in the first data and the aforementioned speed limit information, the vehicle's current driving speed is compared with the driving speed threshold. When the driving speed is greater than the driving speed threshold, a first analysis result is determined, and the first analysis result is used to indicate that the vehicle is currently speeding.

[0134] The first server also enables visual management functions.

[0135] In some embodiments, the first server can display the location information of at least one vehicle in a map in real time based on location data.

[0136] The map image is shown schematically, and at least one location marker is displayed in the map image. The at least one location marker is used to indicate the current real-time location of at least one vehicle on the map. Each location marker corresponds to a vehicle marker. The vehicle marker can be a license plate number or other identification information that can uniquely identify the corresponding vehicle. For example, different vehicle location markers can be identified by different colors, or by the driver's headshot of the vehicle, etc. The embodiments of this application do not limit this.

[0137] In some embodiments, the first server may display vehicle status information corresponding to at least one vehicle based on at least one of vehicle status data and driving data.

[0138] Optionally, the vehicle status information includes at least one of the following: vehicle speed, mileage, and energy consumption.

[0139] The illustration shows the vehicle status interface, which displays the vehicle status information for at least one vehicle.

[0140] Optionally, in order to simplify the interface presentation and improve the efficiency of information transmission, vehicle status indicators can be displayed in the vehicle status interface. The vehicle status indicators include normal status indicators and abnormal status indicators. The display methods of the normal status indicators and the abnormal status indicators are different. The visual significance of the display method corresponding to the abnormal status indicators is higher than that of the display method corresponding to the normal status indicators.

[0141] Indicatively, during the display of at least one location marker on the map, a green location marker indicates that the corresponding vehicle is in normal condition, while a red location marker indicates that the corresponding vehicle is in abnormal condition. Clicking on a red location marker will display the vehicle's status information. Specifically, only abnormal vehicle status information can be displayed, for example, if a vehicle is speeding, the vehicle's current speed will be displayed.

[0142] Step 530: Obtain the first instruction based on the first analysis result.

[0143] The first instruction is used to direct the movement of at least one vehicle.

[0144] Optionally, the first server has a pre-set instruction library, and the first server can automatically determine the instruction that matches the first analysis result from the instruction library as the first instruction through an artificial intelligence model.

[0145] Indicatively, the instructions in the instruction library correspond to type labels. For example, an instruction to instruct a vehicle to adjust its speed can correspond to a speed label, and an instruction to instruct a vehicle to travel a path can correspond to a path label. The first analysis result can be implemented as text content, which includes type keywords. For example, when the first analysis result obtained based on the first data is "Vehicle A is currently speeding by 5 kilometers per hour and needs to slow down", the artificial intelligence model detects the keywords "speeding" and "slow down", determines the speed type label corresponding to the instruction to be matched, and matches the speed reduction instruction from the instruction library as the first instruction based on the keywords.

[0146] The first server can also implement anomaly detection functionality.

[0147] In some embodiments, the first server detects an abnormal state corresponding to at least one vehicle based on the first data, generates an alarm signal in response to the detection of the abnormal state, determines an emergency instruction based on the alarm signal, the emergency instruction is used to instruct at least one vehicle to switch from the abnormal state to the normal state, and sends the emergency instruction to the vehicle-side device.

[0148] The alarm signal is used to instruct the first server to send emergency instructions to at least one vehicle.

[0149] Regarding the process of determining the above-mentioned emergency instructions, the first server can determine the first exception type corresponding to the abnormal state, and match the first candidate instruction from the first instruction library as the emergency instruction based on the first exception type.

[0150] Optionally, the anomaly type includes, but is not limited to, at least one of the following: speed anomaly, energy consumption anomaly, collision anomaly, location anomaly, and route anomaly.

[0151] Among them, speed anomaly refers to the vehicle's speed not meeting the preset speed requirement, or not meeting the speed range specified for the area where the vehicle is currently located; energy consumption anomaly refers to an abnormal amount of energy consumed by the vehicle, specifically including but not limited to the remaining energy being less than the preset energy threshold, the energy consumption rate being greater than the preset consumption rate, or the energy consumption curve not conforming to the preset curve even when the vehicle's trajectory is normal within a preset time period; collision anomaly refers to the vehicle being involved in a collision, or the vehicle being subjected to an impact greater than the preset pressure; position anomaly refers to the displacement distance between the vehicle's current position and its previous position being different from the displacement that can be generated by the driving speed, or the vehicle being prohibited from driving in the area where the vehicle is currently located; route anomaly refers to the vehicle's current driving route being different from the preset route, or the route indicated by the instruction.

[0152] The first instruction library is used to store multiple candidate instructions preset based on a first exception type. The multiple candidate instructions include a first candidate instruction, and the matching degree between the first candidate instruction and the aforementioned exception state reaches a preset matching degree.

[0153] The matching degree between the first candidate instruction and the aforementioned abnormal state is calculated by an artificial intelligence model based on the simulation data corresponding to the switching of at least one vehicle from the abnormal state to the normal state when the first candidate instruction is executed under the condition that at least one vehicle is in an abnormal state.

[0154] Optionally, the simulation data includes at least one of the following: switching success rate, switching time, and the amount of energy required for switching.

[0155] Step 540: Send the first instruction to the vehicle-side device.

[0156] Optionally, a first instruction may be sent to the vehicle-side device according to the data communication connection method for receiving the first data.

[0157] In summary, the method provided in this application automatically analyzes received and stored vehicle data to obtain analysis results that indicate vehicle status. Based on these results, it automatically obtains instructions to direct vehicle movement, improving instruction efficiency and enhancing the automation and objectivity of decision-making in fleet management. By implementing objective data analysis, fleet management efficiency is improved. Furthermore, the real-time collection of vehicle status data, location data, driving data, and sensor data from vehicle-side devices enhances the comprehensiveness of data collection, thereby improving the accuracy of instructions obtained based on analysis results and further increasing fleet management efficiency.

[0158] Figure 7 This is a structural block diagram of a fleet management device provided in an exemplary embodiment of this application, such as... Figure 7 As shown, the device includes the following parts:

[0159] The acquisition module 710 is used to acquire first data, which is collected by vehicle-end devices corresponding to at least one vehicle. The first data includes at least one of vehicle status data, location data, driving data, and sensor data.

[0160] Analysis module 720 is used to analyze the first data and obtain a first analysis result, the first analysis result being used to indicate the vehicle status of the at least one vehicle;

[0161] The acquisition module 710 is further configured to acquire a first instruction based on the first analysis result, the first instruction being used to instruct the driving of the at least one vehicle;

[0162] The sending module 730 is used to send the first instruction to the vehicle terminal device.

[0163] In summary, the device provided in this application automatically analyzes the received and stored vehicle data to obtain analysis results that indicate vehicle status. Based on these results, it automatically obtains instructions to direct vehicle movement, improving instruction efficiency and enhancing the automation and objectivity of decision-making in fleet management. By implementing objective data analysis, fleet management efficiency is improved. Furthermore, the real-time collection of vehicle status data, location data, driving data, and sensor data from vehicle-side devices enhances the comprehensiveness of data collection, thereby improving the accuracy of instructions obtained based on the analysis results and further increasing fleet management efficiency.

[0164] It should be noted that the fleet management device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0165] Figure 8 This illustration shows a structural block diagram of a terminal 800 provided in an exemplary embodiment of this application. The terminal 800 may be a smartphone, tablet computer, MP3 player, MP4 player, laptop computer, or desktop computer. The terminal 800 may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other names.

[0166] Typically, terminal 800 includes a processor 801 and a memory 802.

[0167] Processor 801 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 801 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). Processor 801 may also include a main processor and a coprocessor. The main processor is used to process data in the wake-up state, also known as the central processing unit (CPU); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 801 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 801 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0168] Memory 802 may include one or more computer-readable storage media, which may be non-transitory. Memory 802 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in memory 802 is used to store at least one instruction, which is executed by processor 801 to implement the fleet management method provided in the method embodiments of this application.

[0169] In some embodiments, the terminal 800 also includes other components, as those skilled in the art will understand. Figure 8 The structure shown does not constitute a limitation on terminal 800 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0170] Embodiments of this application also provide a computer device that can be implemented as follows: Figure 1 The terminal or server shown. The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement the fleet management method provided in the above-described method embodiments.

[0171] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the fleet management method provided in the above-described method embodiments.

[0172] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the fleet management method provided in the above-described method embodiments.

[0173] Optionally, the computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments in this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0174] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0175] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A fleet management system, characterized in that, The system includes a first server and at least one vehicle-side device corresponding to each vehicle, wherein the first server and the vehicle-side device are connected via a network. The vehicle-side device is used to collect first data corresponding to each of the at least one vehicle in real time, the first data including at least one of vehicle status data, location data, driving data, and sensor data; and transmit the first data to the first server via a network. The first server is configured to receive the first data sent by the vehicle-end device corresponding to the at least one vehicle; Store the first data in local storage space; The process involves analyzing the first data to obtain a first analysis result, which indicates the vehicle status of at least one vehicle; obtaining a first instruction based on the first analysis result, which instructs the at least one vehicle to drive; sending the first instruction to the vehicle-side device; wherein, the location information corresponding to the at least one vehicle is displayed in real time on a map based on the location data; displaying vehicle status information corresponding to the at least one vehicle based on at least one of the vehicle status data and the driving data, the vehicle status information including at least one of the driving speed, mileage, and energy consumption of the at least one vehicle; detecting abnormal states corresponding to the at least one vehicle based on the first data; generating an alarm signal in response to detecting the abnormal state; determining an emergency instruction based on the alarm signal, the emergency instruction instructing the at least one vehicle to switch from the abnormal state to a normal state; and sending the emergency instruction to the vehicle-side device. The vehicle-side device is also used to receive the first instruction.

2. The system according to claim 1, characterized in that, The first server is further configured to determine a first anomaly type corresponding to the abnormal state, and match a first candidate instruction from a first instruction library as the emergency instruction based on the first anomaly type. The first instruction library is configured to store a plurality of candidate instructions preset based on the first anomaly type, the plurality of candidate instructions including the first candidate instruction, and the matching degree between the first candidate instruction and the abnormal state reaches a preset matching degree. The matching degree between the first candidate instruction and the abnormal state is calculated by an artificial intelligence model based on the simulation data corresponding to the switching of the at least one vehicle from the abnormal state to the normal state when the first candidate instruction is executed under the condition of the at least one vehicle being in the abnormal state. The simulation data includes at least one of the following: switching success rate, switching time, and the amount of energy consumed for switching.

3. The system according to claim 1 or 2, characterized in that, The first server is further configured to assign management permissions to the at least one vehicle based on the first data, wherein the vehicle whose first data meets the preset management conditions among the at least one vehicles is identified as the first vehicle, and the other vehicles among the at least one vehicles besides the first vehicle are identified as the second vehicles, the first vehicle corresponds to the management permission, the management permission is used to instruct the first vehicle to manage the second vehicle, the first vehicle corresponds to the first vehicle terminal device, and the second vehicle corresponds to the second vehicle terminal device.

4. The system according to claim 3, characterized in that, The first server is also used to send a first permission instruction to the first vehicle terminal device; The first vehicle-side device is configured to receive the first permission instruction and obtain the management permission according to the first permission instruction. A second instruction is generated based on the aforementioned management authority, and the second instruction is used to instruct the second vehicle to drive. Send the second instruction to the second vehicle terminal device; The second vehicle-side device is used to receive the second instruction.

5. The system according to claim 4, characterized in that, The first vehicle-side device is further configured to display a fleet management interface when the management authority is obtained. The fleet management interface includes an indicator control, which is used to trigger the automatic sending of the second instruction to the second vehicle-side device. The indicator control includes a formation indicator control. In response to receiving a trigger operation for the formation indicator control, the device automatically sends a formation adjustment instruction to the second vehicle-side device. The formation adjustment instruction is used to instruct the second vehicle to automatically adjust its driving position until it meets the preset formation arrangement conditions.

6. A fleet management method, characterized in that, The method includes: Acquire first data, which is collected by vehicle-side devices corresponding to at least one vehicle, and the first data includes at least one of vehicle status data, location data, driving data, and sensor data; Analyze the first data to obtain a first analysis result, which is used to indicate the vehicle status of the at least one vehicle; A first instruction is obtained based on the first analysis result, and the first instruction is used to instruct the driving of the at least one vehicle; Send the first instruction to the vehicle-side device; Specifically, the system displays the location information of at least one vehicle in real time on a map based on the location data; displays vehicle status information of at least one vehicle based on at least one of the vehicle status data and the driving data, wherein the vehicle status information includes at least one of the driving speed, mileage, and energy consumption of the at least one vehicle; detects abnormal states of at least one vehicle based on the first data; generates an alarm signal in response to detecting the abnormal state; determines an emergency command based on the alarm signal, wherein the emergency command instructs the at least one vehicle to switch from the abnormal state to a normal state; and sends the emergency command to the vehicle-side device.

7. A fleet management device, characterized in that, The device includes: The acquisition module is used to acquire first data, which is collected by vehicle-end devices corresponding to at least one vehicle. The first data includes at least one of vehicle status data, location data, driving data, and sensor data. An analysis module is used to analyze the first data and obtain a first analysis result, the first analysis result being used to indicate the vehicle status of the at least one vehicle; The acquisition module is further configured to acquire a first instruction based on the first analysis result, the first instruction being used to instruct the at least one vehicle to drive; The sending module is used to send the first instruction to the vehicle terminal device; Specifically, the system displays the location information of at least one vehicle in real time on a map based on the location data; displays vehicle status information of at least one vehicle based on at least one of the vehicle status data and the driving data, wherein the vehicle status information includes at least one of the driving speed, mileage, and energy consumption of the at least one vehicle; detects abnormal states of at least one vehicle based on the first data; generates an alarm signal in response to detecting the abnormal state; determines an emergency command based on the alarm signal, wherein the emergency command instructs the at least one vehicle to switch from the abnormal state to a normal state; and sends the emergency command to the vehicle-side device.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one computer program, which is loaded and executed by the processor to implement the fleet management method as described in claim 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one computer program, which is loaded and executed by a processor to implement the fleet management method as described in claim 6.

Images