Motorcade driving safety intelligent monitoring method and system

Abstract

The invention provides a motorcade driving safety intelligent monitoring method and system, and belongs to the technical field of safety monitoring, and the method comprises the steps: determining a task distribution strategy based on the computing resource and driving position information of each vehicle in a motorcade, and transmitting a data monitoring instruction to each vehicle based on the task distribution strategy. The data monitoring instruction is used for instructing the vehicle to acquire environment data. And in response to the received environment data sent by each vehicle, sending a safety monitoring instruction to each vehicle in the motorcade based on the environment data. The safety monitoring instruction is used for indicating that the vehicle recognizes the driver behavior data and the vehicle behavior data, and if dangerous behavior data is recognized, safety reminding is generated based on the dangerous behavior data. And in response to the received dangerous behavior data of the target vehicle, determining a safety control strategy based on the dangerous behavior data. The target vehicle is a vehicle having a dangerous behavior in the motorcade. According to the invention, the management efficiency of motorcade safety monitoring can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of security monitoring, and more particularly, relates to an intelligent monitoring method and system for the driving safety of a fleet of vehicles. Background Art

[0002] Whether it is a commercial transportation fleet or a military vehicle fleet performing special tasks, ensuring the safety of vehicles during driving is of utmost importance. At the same time, unified scheduling management and safety monitoring during the driving process of the fleet are very important. Meanwhile, when a large number of vehicles upload data simultaneously, it often exceeds the bandwidth carrying capacity of the communication network, resulting in data transmission congestion, affecting the transmission quality and speed of data, making the general control center unable to grasp the vehicle status in real time, and affecting the overall scheduling and safety management efficiency of the fleet. Summary of the Invention

[0003] The purpose of the present disclosure is to provide an intelligent monitoring method and system for the driving safety of a fleet of vehicles to improve the management efficiency of fleet safety monitoring.

[0004] In the first aspect of the embodiments of the present disclosure, an intelligent monitoring method for the driving safety of a fleet of vehicles is provided, including: Determining a task allocation strategy based on the computing resources and driving position information of each vehicle in the fleet, and sending a data monitoring instruction to each vehicle based on the task allocation strategy; the data monitoring instruction is used to instruct the vehicle to obtain environmental data; In response to receiving the environmental data sent by each vehicle, sending a safety monitoring instruction to each vehicle in the fleet based on the environmental data; the safety monitoring instruction is used to instruct: the vehicle to identify driver behavior data and vehicle behavior data, and if dangerous behavior data is identified, generating a safety reminder based on the dangerous behavior data; In response to receiving the dangerous behavior data of the target vehicle, determining a safety control strategy based on the dangerous behavior data; the target vehicle is a vehicle in the fleet that has a dangerous behavior.

[0005] In the second aspect of the embodiments of the present disclosure, an intelligent monitoring system for the driving safety of a fleet of vehicles is provided, including: A task allocation module, configured to determine a task allocation strategy based on the computing resources and driving position information of each vehicle in the fleet, and send a data monitoring instruction to each vehicle based on the task allocation strategy; the data monitoring instruction is used to instruct the vehicle to obtain environmental data; A monitoring and control module, configured to, in response to receiving the environmental data sent by each vehicle, send a safety monitoring instruction to each vehicle in the fleet based on the environmental data; the safety monitoring instruction is used to instruct: the vehicle to identify driver behavior data and vehicle behavior data, and if dangerous behavior data is identified, generating a safety reminder based on the dangerous behavior data; The safety control strategy module is configured to determine a safety control strategy based on the dangerous behavior data received from a target vehicle; the target vehicle is a vehicle in the fleet that has engaged in dangerous behavior.

[0006] In a third aspect of an embodiment of the present disclosure, a fleet monitoring terminal is provided, comprising an electronic device, wherein the electronic device comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for intelligently monitoring fleet driving safety are implemented.

[0007] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for intelligently monitoring the driving safety of a fleet are implemented.

[0008] The beneficial effects of the intelligent fleet driving safety monitoring method and system provided by the disclosed embodiments include: determining a task allocation strategy based on vehicle computing resources and driving location, enabling different vehicles to perform their respective functions, fully utilizing their hardware performance, avoiding resource waste, efficiently completing environmental data collection tasks, and improving overall monitoring effectiveness. Incorporating environmental data improves the accuracy of safety monitoring. Vehicles execute safety monitoring commands, identify dangerous behaviors in real time, and generate safety alerts, ensuring fleet driving safety.

[0009] When dangerous behavior occurs, safety control strategies can be determined based on the dangerous behavior data, and precise response measures can be taken for different types of dangers, such as warnings to illegal drivers, restricting driving rights, adjusting the status of abnormal vehicles, fault diagnosis and emergency response, etc., to achieve effective management and control of the fleet, reduce the impact of dangerous behavior on the fleet, ensure that the fleet can operate continuously, safely and stably, and comprehensively improve the management efficiency of fleet safety monitoring, providing solid guarantees for the safe driving of the fleet. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A flowchart of a method for intelligently monitoring vehicle fleet driving safety according to an embodiment of the present disclosure is provided; Figure 2 This is a structural block diagram of an intelligent monitoring system for fleet driving safety provided by one embodiment of the present disclosure; Figure 3 Schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0012] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0013] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for intelligent monitoring of fleet driving safety provided by an embodiment of the present disclosure. The method may include S101 to S103.

[0015] S101: Determine a task allocation strategy based on the computing resources and driving position information of each vehicle in the fleet, and send a data monitoring instruction to each vehicle based on the task allocation strategy. The data monitoring instruction is used to instruct the vehicle to obtain environmental data.

[0016] In this embodiment, determining a task allocation strategy based on the computing resources and driving position information of each vehicle in the fleet, and sending a data monitoring instruction to each vehicle based on the task allocation strategy includes: Determine the task type based on the computing resources of each vehicle in the fleet.

[0017] Determine the task scope based on the driving position information and task type of each vehicle in the fleet.

[0018] Generate a task allocation strategy based on the task types and task scopes of all vehicles.

[0019] Generate a data monitoring instruction based on the task allocation strategy, and send the data monitoring instruction to each vehicle.

[0020] In this embodiment, the computing resources of a vehicle refer to the data processing capabilities of the vehicle. The driving position information refers to the real-time geographical location of the vehicle during driving. The task types may include meteorological monitoring tasks, road monitoring tasks, etc. The task scope refers to the data monitoring scope determined for each task type, which is represented by the relative distance from the vehicle. The task allocation strategy includes a list of environmental data monitoring task types and task scopes allocated to all vehicles. The data monitoring instruction is generated based on the task allocation strategy, and the data monitoring instruction can be used to indicate the data collection type, collection frequency, collection scope, data upload period, etc. The environmental data may include meteorological data, road surface conditions, road traffic data, etc.

[0021] Exemplarily, a fleet corresponds to a fleet control center for overall management of the driving safety monitoring of the fleet. The fleet control center determines the computing resource information of all vehicles based on a pre-collected vehicle information database. The vehicle sends its own driving position information to the fleet control center in real time.

[0022] The fleet control center compares the vehicle computing resources with the task complexity requirements to determine a suitable task type for each vehicle. Then, based on the vehicle driving position information and combined with the characteristics of the task type, it delimits the task scope of each vehicle. It integrates the task types and task scopes of all vehicles to generate a task allocation strategy. It converts the task allocation strategy into a data monitoring instruction, clarifies the collection requirements, and sends it to each vehicle.

[0023] Exemplarily, on the way to the destination, due to the complex road conditions, the fleet control center determines, according to the computing resources of a vehicle equipped with high-performance computing equipment in the fleet, that its task type is to analyze the road conditions through an on-vehicle camera to monitor for congestion, accidents, etc. Considering its driving position at the front of the fleet, the task scope is determined to be 1 kilometer ahead. For other vehicles with ordinary computing resources, according to their driving positions, tasks such as monitoring the surrounding temperature, humidity, and ground flatness are assigned. The fleet control center summarizes the tasks of all vehicles, generates a task allocation strategy, and then generates a data monitoring instruction to send to each vehicle. Each vehicle starts to collect the corresponding environmental data according to the instruction requirements to ensure the safe and smooth arrival of the fleet at the scenic area.

[0024] S102: In response to receiving the environmental data sent by each vehicle, send a safety monitoring instruction to each vehicle in the fleet based on the environmental data. The safety monitoring instruction is used to indicate that the vehicle identifies the driver behavior data and vehicle behavior data, and if dangerous behavior data is identified, a safety reminder is generated based on the dangerous behavior data.

[0025] In this embodiment, the safety monitoring instructions are generated based on environmental data and include rules and instructions for guiding the vehicle to identify the driver and vehicle dangerous behaviors. The driver behavior data includes relevant information reflecting the driver's driving operations, such as facial expressions and hand movements captured by a camera. The vehicle behavior data may include vehicle operation state data and vehicle traffic behavior data. The vehicle operation state data may include power system data and driving performance data. The vehicle traffic behavior data may include driving trajectory data and special traffic behavior data. The dangerous behavior data refers to abnormal data that affects driving safety identified through analysis, including the type of dangerous behavior (such as fatigue driving, speeding, vehicle failure), occurrence time, and duration. The safety reminder refers to the warning information generated by the vehicle after identifying a dangerous behavior, including a description of the dangerous behavior, danger level, reminder recipients (driver, control center, other vehicles, etc.). The form of the safety reminder may include voice announcements, display screen information pop-ups, sending text messages, etc.

[0026] In this embodiment, generating a safety reminder based on the dangerous behavior data includes: Determining the type of dangerous behavior based on the dangerous behavior data and generating a safety reminder based on the type of dangerous behavior.

[0027] In this embodiment, the types of dangerous behaviors may include fatigue driving, speeding, vehicle failure, illegal lane change, etc.

[0028] Exemplarily, a classification algorithm is used to compare and analyze the collected dangerous behavior data with preset dangerous behavior patterns to determine the type of dangerous behavior to which it belongs. For example, if it is detected that the driver has not performed effective operations for a long time and the vehicle driving trajectory shows a slight deviation, combined with time parameters, it is determined as the fatigue driving type. According to different types of dangerous behaviors, corresponding safety reminder templates and settings can be matched to generate targeted safety reminders and send them to the reminder recipients through corresponding communication channels.

[0029] Exemplarily, the fleet control center collects the environmental data sent by each vehicle and organizes and classifies it.

[0030] Using a data analysis algorithm, safety standards are determined based on the environmental data, and safety monitoring instructions including behavior recognition rules are generated. Through a wireless communication network, the safety monitoring instructions are sent to each vehicle in the fleet. A behavior recognition module is deployed at the vehicle end, which can perform real-time analysis and comparison on the driver and vehicle behavior data according to the requirements of the safety monitoring instructions. If a dangerous behavior is identified, the vehicle generates a safety reminder according to the format and requirements in the safety monitoring instructions, and while reminding the driver, it can also be sent to the control center to take corresponding measures to ensure the safety of the driver and the fleet.

[0031] S103: In response to receiving the dangerous behavior data of the target vehicle, determine a safety control strategy based on the dangerous behavior data. The target vehicle is the vehicle in the fleet that exhibits dangerous behavior.

[0032] In this embodiment, the target vehicle can be located through unique identifiers such as vehicle numbers and license plate numbers. The dangerous behavior data refers to various types of information obtained from the target vehicle indicating its safety risks. The dangerous behavior data may include the type of dangerous behavior, the time of occurrence of the behavior, the duration, and the level of danger. The safety control strategy refers to various response plans formulated for the dangerous behavior of the target vehicle, including the adjustment of the monitoring frequency and the adoption of different danger control measures. For all dangerous behavior data, based on the type of dangerous behavior to which it belongs, preset rules can be adopted to increase the data monitoring frequency of the responding vehicle. At the same time, for the preset types of emergency dangerous behaviors that need to be processed immediately, corresponding danger control measures are taken. For example, if the driver is fatigued, the in-vehicle sound and light alarm can be activated; if the vehicle route continuously deviates, the external danger indicator light of the vehicle can be turned on; if the vehicle breaks down, an alarm message is sent and a dispatching and maintenance request is issued, etc.

[0033] Exemplarily, the control center receives the dangerous behavior data sent by the target vehicle and identifies the type of dangerous behavior and related parameters through a data parsing program. According to the preset correspondence table between dangerous behaviors and control strategies, combined with the dangerous behavior data, a safety control strategy for the target vehicle is generated. For example, if the dangerous behavior is speeding, the speed limit control strategy is matched and the monitoring frequency of the vehicle data is increased. The fleet control center sends a safety control instruction to the target vehicle through wireless communication. After the vehicle receives the instruction, it executes the corresponding control measures, such as increasing its own data collection frequency, automatically limiting the speed, or prompting the driver to stop.

[0034] From the above, it can be concluded that in this embodiment, tasks are determined based on the vehicle computing resources and driving positions, allowing high-performance vehicles to undertake complex tasks, reasonably allocating resources, improving the efficiency and comprehensiveness of environmental data collection, avoiding duplicate data collection, and reducing resource waste. In the safety monitoring link, safety monitoring instructions are generated based on environmental data, accurately identifying the dangerous behaviors of drivers and vehicles, generating safety reminders, and effectively preventing accidents from occurring.

[0035] In the face of dangerous behaviors, quickly determine a safety control strategy based on the dangerous behavior data to ensure the safety of the vehicle and the fleet. Communication connections are established between vehicles and data can be shared. When a certain vehicle exhibits dangerous behavior or abnormal behavior, the other vehicles can also receive the relevant information of this vehicle, which helps to assist in coping with risks and improve the fleet cooperation ability and the overall ability to cope with risks.

[0036] In summary, this embodiment can comprehensively improve the safety management level of fleet driving from data collection to safety warning and then to danger response, ensuring the safe and efficient operation of the fleet.

[0037] In one embodiment of the present disclosure, the environmental data includes road condition information and meteorological data.

[0038] Sending safety monitoring instructions to each vehicle in the fleet based on the environmental data includes: Determining the road risk level based on the road condition information and meteorological data, and determining the safety monitoring frequency and safety monitoring indicators based on the road risk level.

[0039] Generating safety monitoring instructions based on the safety monitoring frequency and safety monitoring indicators, and sending the safety monitoring instructions to each vehicle in the fleet.

[0040] In this embodiment, the road risk level includes a first risk level and a second risk level, and the risk level of the second risk level is higher than that of the first risk level.

[0041] Determining the road risk level based on the road condition information and meteorological data includes: Calculating a road risk assessment index based on the road condition information, meteorological data, and risk prediction function.

[0042] If the road risk assessment index is less than the risk threshold, it is determined that the road risk level is the first risk level.

[0043] If the road risk assessment index is greater than or equal to the risk threshold, it is determined that the road risk level is the second risk level.

[0044] In this embodiment, determining the safety monitoring frequency and safety monitoring indicators based on the road risk level includes: If the road risk level is the first risk level, it is determined that the safety monitoring frequency is the first-level monitoring frequency, and the safety monitoring indicator is the first-level safety monitoring indicator.

[0045] If the road risk level is the second risk level, it is determined that the safety monitoring frequency is the second-level monitoring frequency, and the safety monitoring indicator is the second-level safety monitoring indicator. The second-level monitoring frequency is greater than the first-level monitoring frequency.

[0046] In this embodiment, the road condition information may include road congestion degree, road surface condition, road construction situation, etc. The meteorological data may include temperature, humidity, wind speed, rainfall, visibility, etc. The road risk level refers to the classification of the degree of road safety risk. The first risk level represents a lower risk, and the road is relatively safe; the second risk level represents a higher risk, the environment is relatively harsh, and the possibility of accidents increases. The risk threshold refers to the critical value used to divide the road risk level. The risk threshold can be obtained through analysis of a large amount of historical accident data, road conditions, and meteorological impacts. The risk prediction function can be a linear weighted function, which weights and sums the impacts of road conditions and meteorological data through a linear combination method to obtain a comprehensive risk assessment index.

[0047] The safety monitoring frequency refers to the time frequency at which the vehicle conducts safety monitoring on the driver's behavior and the vehicle's own behavior. The safety monitoring frequency is divided into two frequencies, with the primary monitoring frequency being less than the secondary monitoring frequency. The safety monitoring indicators include specific parameter standards for measuring whether the vehicle and the driver's behavior are safe. The primary safety monitoring indicators are applicable to low-risk road conditions and the standards are relatively loose; the secondary safety monitoring indicators are for high-risk road conditions and the standards are more stringent. For example, in terms of vehicle speed, the allowable speed fluctuation range of the primary indicators is relatively larger than that of the secondary indicators, while the secondary indicators require the vehicle speed to be more stable.

[0048] Exemplarily, the vehicle obtains road condition information through in-vehicle sensors, cameras, and networking with the traffic information system, obtains meteorological data through meteorological sensors or meteorological data service interfaces, and uploads this data to the fleet control center in real time.

[0049] The fleet control center uses a risk prediction function to calculate the received road condition and meteorological data to obtain a road risk assessment index. Compare this index with a preset risk threshold to determine the road risk level. If it is the first risk level, set the safety monitoring frequency to the primary monitoring frequency and the safety monitoring indicators to the primary safety monitoring indicators; if it is the second risk level, set the safety monitoring frequency to the secondary monitoring frequency and the safety monitoring indicators to the secondary safety monitoring indicators.

[0050] According to the determined safety monitoring frequency and indicators, generate a safety monitoring instruction and send it to each vehicle in the fleet through a wireless communication network. After receiving the instruction, the vehicle conducts real-time safety monitoring on the driver behavior data and vehicle behavior data according to the frequency and indicators required by the instruction.

[0051] In this embodiment, by integrating road condition and meteorological data and using a risk prediction function to accurately evaluate the road risk level, different safety monitoring strategies are formulated for different risk situations. Adopt primary monitoring in low-risk situations and make reasonable use of resources; switch to secondary monitoring in high-risk situations, such as stricter vehicle speed control. This ensures that the vehicle can receive appropriate safety monitoring in complex environments, detect potential risks in advance, effectively reduce the accident rate, ensure the safe and efficient driving of the fleet, and improve the overall safety.

[0052] In an embodiment of the present disclosure, the identification of driver behavior data and vehicle behavior data includes: Monitoring driver behavior data and vehicle behavior data based on the safety monitoring frequency.

[0053] Identifying dangerous behaviors based on the safety monitoring indicators for the monitored driver behavior data and vehicle behavior data.

[0054] In this embodiment, the dangerous behavior data includes driver's illegal driving data and vehicle abnormal driving data. The safety reminders include the first safety reminder and the second safety reminder.

[0055] Generating safety reminders based on the dangerous behavior data includes: If the dangerous behavior data is driver's illegal driving data, then generate the first safety reminder based on the driver's illegal driving data.

[0056] If the dangerous behavior data is vehicle abnormal driving data, then generate the second safety reminder based on the vehicle abnormal driving data.

[0057] In this embodiment, the driver behavior data includes the driver's driving operations, facial expressions and hand movements captured by the in-vehicle camera, etc., which can be used to judge the driver's fatigue and distraction status. The driver's illegal driving data can include the type of illegal behavior, the time of occurrence of the violation, and the duration of the violation. The vehicle abnormal driving data can include the type of abnormal behavior, the time of occurrence of the abnormality, and the driving state of the vehicle when the abnormality appears. The first safety reminder includes a description of the illegal behavior, the reminder method, the reminder object, and the reminder urgency. The second safety reminder includes a description of the abnormal behavior, the reminder method, and the reminder urgency.

[0058] In this embodiment, the vehicle continuously collects driver behavior data and vehicle behavior data using various sensors according to the safety monitoring frequency in the safety monitoring instruction. For example, the steering wheel angle sensor obtains the steering wheel rotation data in real time, and the GPS module regularly records the vehicle driving position.

[0059] Compare and analyze the collected behavior data with the safety monitoring indicators. If the driver or vehicle behavior data exceeds the normal range set by the safety monitoring indicators, it is determined that dangerous behavior data is recognized. For example, if the vehicle speed exceeds the limit range of the secondary safety monitoring indicator, it is recognized as an overspeed dangerous behavior. After recognizing the dangerous behavior data, the vehicle generates corresponding safety reminders according to the type of dangerous behavior. The driver's illegal driving data generates the first safety reminder, and the vehicle abnormal driving data generates the second safety reminder.

[0060] Exemplarily, install a steering wheel angle sensor, an accelerator pedal sensor, a brake pedal sensor, an in-vehicle camera, a GPS positioning module, a vehicle fault diagnosis sensor, etc. on the vehicle to collect behavior data. The vehicle collects data in real time through the sensors according to the safety monitoring frequency and transmits it to the in-vehicle data processing unit. The in-vehicle data processing unit compares and analyzes the collected data with the pre-stored safety monitoring indicators to judge whether there is dangerous behavior data. If dangerous behavior data is recognized, corresponding safety reminders are generated according to the behavior type, and reminder information is sent to the driver through the in-vehicle display system and the voice broadcast system, and at the same time, it is uploaded to the fleet control center.

[0061] This embodiment can continuously monitor, promptly detect illegal driving and vehicle anomalies, give early warnings of potential risks, reduce the probability of accidents, and ensure the safety of people's lives and property. It can also remind the driver to correct illegal behaviors in real time, which helps to develop good driving habits and improve the overall driving safety and standardization of the fleet. The vehicle can automatically identify and remind of dangerous behaviors, reducing the cost of manual monitoring, enabling fleet managers to efficiently grasp the vehicle operation status, make timely decisions, and optimize the overall operation of the fleet.

[0062] In one embodiment of the present disclosure, the dangerous behavior data includes driver illegal driving data and vehicle abnormal driving data. Determining a safety control strategy based on the dangerous behavior data includes: If the dangerous behavior data is driver illegal driving data, update the safety monitoring frequency based on the first step length to obtain the first safety monitoring frequency. Send a first safety monitoring instruction to the target vehicle. The first safety monitoring instruction is used to instruct the target vehicle to identify the driver behavior data based on the first safety monitoring frequency.

[0063] If the dangerous behavior data is vehicle abnormal driving data, generate a warning message based on the vehicle abnormal driving data, and send the warning message to the remaining vehicles in the fleet. Update the safety monitoring frequency based on the second step length to obtain the second safety monitoring frequency. Send a second safety monitoring instruction to the target vehicle. The second safety monitoring instruction is used to instruct the target vehicle to identify the vehicle behavior data based on the second safety monitoring frequency.

[0064] In this embodiment, the first step length and the second step length are preset values for adjusting the safety monitoring frequency. They can be set according to factors such as the severity of dangerous behaviors and historical data. The first safety monitoring frequency and the second safety monitoring frequency are both updated safety monitoring frequencies. The first safety monitoring frequency is for driver illegal driving situations, and the second safety monitoring frequency is for vehicle abnormal driving situations. The first safety monitoring instruction and the second safety monitoring instruction are both control instructions sent to the target vehicle.

[0065] The first safety monitoring instruction may include the first safety monitoring frequency, the types of driver behaviors that need to be focused on, etc., and is used to guide the target vehicle to identify the driver behavior according to the new frequency. The second safety monitoring instruction may include the second safety monitoring frequency, the key monitoring directions of vehicle abnormal behaviors, etc., and is used to instruct the target vehicle to identify the vehicle behavior data according to the new frequency. The warning message is a prompt message about the abnormality of the target vehicle sent to the remaining vehicles in the fleet. The warning message may include the location of the target vehicle, the type of abnormality, the expected impact range, etc., so that other vehicles can make preparations in advance.

[0066] Exemplarily, the fleet control center receives the dangerous behavior data uploaded by the target vehicle and determines whether it belongs to driver illegal driving data or vehicle abnormal driving data.

[0067] For a driver's illegal driving: Determine the first step length, calculate the first safety monitoring frequency, generate a first safety monitoring instruction including information such as the new frequency, and send it to the target vehicle via wireless communication.

[0068] For abnormal vehicle driving: Generate warning information based on the abnormal data, including the position of the target vehicle, the type of abnormality, etc., and send it to the other vehicles in the fleet. Determine the second step length, calculate the second safety monitoring frequency, and generate a second safety monitoring instruction to send to the target vehicle.

[0069] After receiving the instruction, the target vehicle identifies the corresponding behavior data according to the new safety monitoring frequency.

[0070] Exemplarily, during a military field training mission, the driver of a vehicle in the fleet was fatigued after driving for a long time, causing the vehicle to start deviating. The vehicle detected the driver's illegal driving data and the abnormal driving data of the vehicle, and immediately sounded an alarm to remind the driver. After the driver woke up, he quickly corrected the direction, avoiding the danger that might be caused by the vehicle deviating from the road.

[0071] At the same time, the engine of a vehicle loaded with supplies suddenly broke down, and the power dropped sharply. After the vehicle sensors detected this abnormal data, they transmitted the fault information to the fleet control center through encrypted communication. After receiving the alarm, the commander of the fleet control center quickly checked the position of the faulty vehicle, combined with the surrounding terrain and mission planning, planned a route to a nearby concealed assembly point, and sent the route information to the driver of the faulty vehicle, instructing him to go to that location and wait. Subsequently, the commander immediately dispatched a support vehicle to carry repair equipment and spare parts to the location of the faulty vehicle.

[0072] In this embodiment, by adjusting the monitoring frequency, it is possible to pay closer attention to the driver's behavior, promptly discover potential risks, correct bad driving habits, and reduce accident hazards. For abnormal vehicle driving, this embodiment can not only promptly warn other vehicles, but also strengthen the monitoring of the faulty vehicle, facilitating the understanding of the development of the fault. At the same time, combined with functions such as route planning and vehicle distance reminder, it comprehensively guarantees the driving safety of the fleet and improves the overall fleet management efficiency.

[0073] In an embodiment of the present disclosure, a method for intelligent monitoring of the driving safety of a fleet further includes: Determine the target driving path based on the road condition information.

[0074] Generate fleet visual monitoring information based on the position information of all vehicles in the fleet and the target driving path, and send the fleet visual monitoring information to each vehicle in the fleet.

[0075] Determine the vehicle spacing based on the fleet visual monitoring information.

[0076] Send a vehicle distance reminder to each vehicle in the fleet based on the comparison result between the vehicle distance of the vehicle and the distance threshold.

[0077] In this embodiment, it further includes: determining the distance threshold based on road condition information and meteorological data.

[0078] Sending a vehicle distance reminder to each vehicle in the fleet based on the comparison result between the vehicle distance of the vehicle and the distance threshold includes: Determine a first distance threshold and a second distance threshold based on road condition information and meteorological data. The first distance threshold is greater than the second distance threshold.

[0079] Send a vehicle falling behind reminder to the vehicles in the fleet whose vehicle distance is greater than or equal to the first distance threshold.

[0080] Send a vehicle distance too close reminder to the vehicles in the fleet whose vehicle distance is less than the second distance threshold.

[0081] In this embodiment, the target driving path is the best driving route of the fleet planned by the fleet control center according to factors such as road conditions and mission requirements. The fleet visualization monitoring information includes information presenting the overall state of the fleet in an intuitive and visual form. The fleet visualization monitoring information may include the relative positions and absolute positions of the vehicles in the fleet, driving directions, speeds, vehicle types, and the visual display of the target driving path. The vehicle distance refers to the distance between adjacent vehicles in the fleet. The distance threshold is the standard distance value for judging whether the vehicle distance meets the requirements. The first distance threshold is the larger distance threshold for judging whether a vehicle has fallen behind. The second distance threshold is the smaller distance threshold for judging whether the vehicle distance is too close.

[0082] Exemplarily, collect road condition information through a traffic information platform, on-vehicle sensors, etc., and use a path planning algorithm to generate the target driving path. Collect vehicle position information, integrate it with the target driving path to generate fleet visualization monitoring information, and send it to each vehicle through wireless communication. The fleet control center determines the first distance threshold and the second distance threshold according to road conditions and meteorological data. Calculate the vehicle distance between every two adjacent vehicles according to the visualization monitoring information, and compare it with the distance threshold. If the corresponding conditions are met, send a vehicle falling behind reminder or a vehicle distance too close reminder to the corresponding vehicle.

[0083] Exemplarily, during the progress of the fleet, the fleet control center monitors the vehicle distance at all times. When it is found that the vehicle distance between two vehicles is too close, send a safe vehicle distance reminder to the two vehicles to prompt them to adjust the vehicle speed in time, maintain a safe distance, and ensure the marching safety of the entire fleet. The fleet control center can also plan the driving route in advance and set a timed reminder according to the mission requirements to ensure that each vehicle arrives at the designated location on time and guarantee the smooth progress of the entire mission.

[0084] Through reasonable vehicle distance reminder, this embodiment can avoid vehicle rear - end collisions or falling behind, reduce accident risks, especially in complex road conditions and adverse weather conditions, and improve the overall safety of the vehicle fleet. Through target driving path planning, it guides the vehicle fleet to avoid congested sections, optimize the driving route, reduce driving time, and improve task execution efficiency. The visual monitoring information of the vehicle fleet enables drivers and management personnel to intuitively understand the status of the vehicle fleet, facilitating unified scheduling and management, and enhancing the command and coordination ability. By dynamically adjusting the vehicle distance threshold according to road conditions and meteorology, the vehicle distance reminder is made more suitable for the actual driving environment, ensuring the safe driving of the vehicle fleet under different conditions.

[0085] A method for intelligent monitoring of the safety of a vehicle fleet traveling corresponding to the above - mentioned embodiment Figure 2 is a structural block diagram of an intelligent monitoring system for the safety of a vehicle fleet traveling provided by an embodiment of the present disclosure. For the sake of convenience of description, only parts related to the embodiments of the present disclosure are shown. Refer to Figure 2 This intelligent monitoring system 20 for the safety of a vehicle fleet traveling includes: a task allocation module 21, a monitoring control module 22, and a safety control strategy module 23.

[0086] Among them, the task allocation module 21 is used to determine a task allocation strategy based on the computing resources and driving position information of each vehicle in the vehicle fleet, and send a data monitoring instruction to each vehicle based on the task allocation strategy. The data monitoring instruction is used to instruct the vehicle to obtain environmental data.

[0087] The monitoring control module 22 is used to, in response to receiving the environmental data sent by each vehicle, send a safety monitoring instruction to each vehicle in the vehicle fleet based on the environmental data. The safety monitoring instruction is used to instruct: the vehicle to identify driver behavior data and vehicle behavior data, and if dangerous behavior data is identified, generate a safety reminder based on the dangerous behavior data.

[0088] The safety control strategy module 23 is used to, in response to receiving the dangerous behavior data of the target vehicle, determine a safety control strategy based on the dangerous behavior data. The target vehicle is the vehicle in the vehicle fleet that has a dangerous behavior.

[0089] In an embodiment of the present disclosure, the environmental data includes road condition information and meteorological data. The monitoring control module 22 is specifically used to determine the road risk level based on the road condition information and meteorological data, and determine the safety monitoring frequency and safety monitoring indicators based on the road risk level.

[0090] Generate a safety monitoring instruction based on the safety monitoring frequency and safety monitoring indicators, and send the safety monitoring instruction to each vehicle in the vehicle fleet.

[0091] In one embodiment of the present disclosure, the road risk level includes a first risk level and a second risk level, and the risk level of the second risk level is higher than that of the first risk level. The monitoring and control module 22 is specifically further configured to calculate a road risk assessment index based on road condition information, meteorological data, and a risk prediction function.

[0092] If the road risk assessment index is less than the risk threshold, it is determined that the road risk level is the first risk level.

[0093] If the road risk assessment index is greater than or equal to the risk threshold, it is determined that the road risk level is the second risk level.

[0094] In one embodiment of the present disclosure, the monitoring and control module 22 is specifically further configured to monitor driver behavior data and vehicle behavior data based on a safety monitoring frequency.

[0095] Identify dangerous behaviors based on safety monitoring indicators for the monitored driver behavior data and vehicle behavior data.

[0096] In one embodiment of the present disclosure, the dangerous behavior data includes driver violation driving data and abnormal vehicle driving data. The safety reminder includes a first safety reminder and a second safety reminder. The monitoring and control module 22 is specifically further configured to, if the dangerous behavior data is driver violation driving data, generate a first safety reminder based on the driver violation driving data.

[0097] If the dangerous behavior data is abnormal vehicle driving data, generate a second safety reminder based on the abnormal vehicle driving data.

[0098] In one embodiment of the present disclosure, the dangerous behavior data includes driver violation driving data and abnormal vehicle driving data. The safety control strategy module 23 is specifically configured to, if the dangerous behavior data is driver violation driving data, update the safety monitoring frequency based on a first step length to obtain a first safety monitoring frequency. Send a first safety monitoring instruction to the target vehicle. The first safety monitoring instruction is used to instruct the target vehicle to identify driver behavior data based on the first safety monitoring frequency.

[0099] If the dangerous behavior data is abnormal vehicle driving data, generate a warning message based on the abnormal vehicle driving data, and send the warning message to the remaining vehicles in the fleet. Update the safety monitoring frequency based on a second step length to obtain a second safety monitoring frequency. Send a second safety monitoring instruction to the target vehicle. The second safety monitoring instruction is used to instruct the target vehicle to identify vehicle behavior data based on the second safety monitoring frequency.

[0100] In one embodiment of the present disclosure, a fleet driving safety intelligent monitoring system 20 further includes: A vehicle distance reminder module, configured to determine a target driving path based on road condition information.

[0101] Generate fleet visualization monitoring information based on the location information and target driving routes of all vehicles in the fleet, and send the fleet visualization monitoring information to each vehicle in the fleet.

[0102] Determine the vehicle spacing based on the fleet visualization monitoring information.

[0103] Send a vehicle spacing reminder to the vehicle based on the comparison result between the vehicle spacing of each vehicle in the fleet and the vehicle spacing threshold.

[0104] See Figure 3 , Figure 3 which is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above system embodiments, for example Figure 2 the functions of the modules 21 to 23 shown.

[0105] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0106] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0107] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0108] In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure may implement the implementation manners described in the first and second embodiments of a method for intelligent monitoring of fleet driving safety provided by the embodiments of the present disclosure, and may also implement the implementation manner of the electronic device 300 described in the embodiments of the present disclosure, which will not be elaborated herein.

[0109] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the method of the above embodiments are implemented. It can also be completed by instructing related hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0110] The computer-readable storage medium may be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store the data that has been output or will be output.

[0111] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this disclosure.

[0112] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0113] In several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces or units, or can be electrical, mechanical, or other forms of connection.

[0114] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this disclosure.

[0115] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0116] The above is only the specific implementation manner of this disclosure, but the protection scope of this disclosure is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by this disclosure, and these modifications or substitutions should be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be subject to the protection scope of the claims.

Claims

1. An intelligent monitoring method for the safe driving of a vehicle fleet, characterized in that, Including: Determine a task allocation strategy based on the computing resources and driving position information of each vehicle in the fleet, and send a data monitoring instruction to each vehicle based on the task allocation strategy; the data monitoring instruction is used to instruct the vehicle to obtain environmental data; In response to receiving the environmental data sent by each vehicle, send a safety monitoring instruction to each vehicle in the fleet based on the environmental data; the safety monitoring instruction is used to instruct: the vehicle to identify driver behavior data and vehicle behavior data, and if dangerous behavior data is identified, generate a safety reminder based on the dangerous behavior data; In response to receiving the dangerous behavior data of the target vehicle, determine a safety control strategy based on the dangerous behavior data; The target vehicle is a vehicle in the fleet that has a dangerous behavior.

2. The intelligent monitoring method for the safe driving of a vehicle fleet according to claim 1, characterized in that The environmental data includes road condition information and meteorological data; The sending a safety monitoring instruction to each vehicle in the fleet based on the environmental data includes: Determine the road risk level based on the road condition information and meteorological data, and determine the safety monitoring frequency and safety monitoring indicators based on the road risk level; Generate a safety monitoring instruction based on the safety monitoring frequency and the safety monitoring indicators, and send the safety monitoring instruction to each vehicle in the fleet.

3. The intelligent monitoring method for the safe driving of a fleet according to claim 2, characterized in that, The road risk level includes a first risk level and a second risk level, and the risk level of the second risk level is higher than that of the first risk level; The determining the road risk level based on the road condition information and meteorological data includes: Calculate a road risk assessment index based on the road condition information, meteorological data and a risk prediction function; If the road risk assessment index is less than the risk threshold, determine that the road risk level is the first risk level; If the road risk assessment index is greater than or equal to the risk threshold, determine that the road risk level is the second risk level.

4. The intelligent monitoring method for the safe driving of a fleet according to claim 2, characterized in that, The identifying the driver behavior data and vehicle behavior data includes: Monitor the driver behavior data and vehicle behavior data based on the safety monitoring frequency; Identify dangerous behaviors in the monitored driver behavior data and vehicle behavior data based on the safety monitoring indicators.

5. The intelligent monitoring method for the safe driving of a vehicle fleet according to claim 1, characterized in that, The dangerous behavior data includes driver violation driving data and vehicle abnormal driving data; The safety reminder includes a first safety reminder and a second safety reminder; The generating a safety reminder based on the dangerous behavior data includes: If the dangerous behavior data is driver violation driving data, generate a first safety reminder based on the driver violation driving data; If the dangerous behavior data is vehicle abnormal driving data, generate a second safety reminder based on the vehicle abnormal driving data.

6. The intelligent monitoring method for the safety of a fleet of vehicles according to claim 2, characterized in that, The dangerous behavior data includes driver violation driving data and vehicle abnormal driving data; The determining a safety control strategy based on the dangerous behavior data includes: If the dangerous behavior data is driver violation driving data, update the safety monitoring frequency based on a first step size to obtain a first safety monitoring frequency; send a first safety monitoring instruction to the target vehicle; the first safety monitoring instruction is used to instruct the target vehicle to identify the driver behavior data based on the first safety monitoring frequency; If the dangerous behavior data is vehicle abnormal driving data, generate a warning message based on the vehicle abnormal driving data, and send the warning message to the remaining vehicles in the fleet; update the safety monitoring frequency based on the second step length to obtain the second safety monitoring frequency; send a second safety monitoring instruction to the target vehicle; the second safety monitoring instruction is used to instruct the target vehicle to identify vehicle behavior data based on the second safety monitoring frequency.

7. The intelligent monitoring method for the safe driving of a fleet according to claim 1, characterized in that, Further included are: Determine a target driving path based on road condition information; Generate fleet visual monitoring information based on the position information of all vehicles in the fleet and the target driving path, and send the fleet visual monitoring information to each vehicle in the fleet; Determine the vehicle spacing based on the fleet visual monitoring information; Send a vehicle spacing reminder to each vehicle in the fleet based on the comparison result between the vehicle spacing of each vehicle in the fleet and the vehicle distance threshold.

8. An intelligent monitoring system for the safe driving of a fleet of vehicles, characterized in that, Included are: A task allocation module, configured to determine a task allocation strategy based on the computing resources and driving position information of each vehicle in the fleet, and send a data monitoring instruction to each vehicle based on the task allocation strategy; the data monitoring instruction is used to instruct the vehicle to obtain environmental data; A monitoring control module, configured to, in response to receiving the environmental data sent by each vehicle, send a safety monitoring instruction to each vehicle in the fleet based on the environmental data; the safety monitoring instruction is used to instruct: the vehicle to identify driver behavior data and vehicle behavior data, and if dangerous behavior data is identified, generate a safety reminder based on the dangerous behavior data; A safety control strategy module, configured to, in response to receiving the dangerous behavior data of the target vehicle, determine a safety control strategy based on the dangerous behavior data; The target vehicle is the vehicle in the fleet that has a dangerous behavior.

9. A fleet monitoring terminal, comprising an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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