Driving behavior evaluation method, device, equipment and storage medium

Abstract

The application discloses a driving behavior evaluation method and device, equipment and a storage medium, and relates to the technical field of driving behavior scoring. The method comprises the following steps: acquiring driving state data collected by each electronic control system in a vehicle during driving; scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data, obtaining the driving score of the user, and adjusting the preset scoring mechanism in real time based on real vehicle working conditions and user feedback through big data analysis; generating a driving evaluation based on the driving score, and displaying the driving evaluation to the user. In the application, the preset driving scoring mechanism is adjusted in real time, and the driving behavior of the user driving the vehicle is scored based on the driving state data, so that the preset driving scoring mechanism is prevented from deviating from the actual vehicle working conditions, and the driving score is prevented from being unable to convince the user. Therefore, the credibility of the user to the score is improved, and the effect of improving the driving behavior of the user by using the driving behavior score is improved.

Description

Technical Field

[0001] This application relates to the field of driving behavior scoring technology, and in particular to a driving behavior evaluation method, device, equipment and storage medium. Background Technology

[0002] With the rapid development of the logistics industry, commercial vehicles play a crucial role. Currently, freight rates in the logistics industry fluctuate significantly, and the operating costs of commercial vehicles directly determine the user's profits. Commercial vehicles operate under complex conditions, and drivers' habits and skill levels vary. Poor driving behavior increases fuel consumption, directly raising vehicle operating costs.

[0003] To better correct poor driving behaviors, some car manufacturers fuse vehicle positioning technology with driving data collected by accelerometers. This fused data captures various driving behaviors, which are then scored based on the frequency of these behaviors and the vehicle's mileage. However, this scoring mechanism, once set up, may deviate from real-world driving conditions, causing users to disregard the scoring suggestions and resulting in low effectiveness in improving driving behavior. Summary of the Invention

[0004] The main purpose of this application is to provide a driving behavior evaluation method, device, equipment and storage medium, which aims to solve the technical problem that the existing technology may deviate from the actual driving conditions, causing users to not care about the rating suggestions, resulting in low effectiveness in improving users' driving behavior.

[0005] To achieve the above objectives, this application provides a driving behavior evaluation method, the driving behavior evaluation method comprising:

[0006] Acquire driving status data collected by various electronic control systems in the vehicle during driving;

[0007] The driving behavior of the user driving the vehicle is scored based on a preset scoring mechanism and the driving status data to obtain the user's driving score. The preset scoring mechanism is adjusted in real time based on real driving conditions and user feedback through big data analysis.

[0008] A driving evaluation is generated based on the driving score, and the driving evaluation is displayed to the user.

[0009] Optionally, before the step of acquiring the driving status data collected by each electronic control system in the vehicle during driving, the method further includes:

[0010] Real-time acquisition of reference data on vehicle driving behavior under different driving conditions, user feedback data, and the current scoring mechanism;

[0011] The current scoring mechanism is adjusted based on the reference data and the return visit data to obtain the latest preset scoring mechanism.

[0012] Optionally, the step of adjusting the current scoring mechanism based on the reference data and the follow-up data to obtain the latest preset scoring mechanism includes:

[0013] Based on the reference data and the return visit data, an initial adjustment plan for the current scoring mechanism is determined;

[0014] Obtain big data on the vehicle usage conditions;

[0015] Based on the big data, the initial adjustment plan is adjusted to obtain the final adjustment plan;

[0016] The current scoring mechanism is adjusted based on the final adjustment plan to obtain the latest preset scoring mechanism.

[0017] Optionally, the step of determining the initial adjustment scheme of the current scoring mechanism based on the reference data and the follow-up data includes:

[0018] Based on the reference data, we have compiled the target driving behaviors required under different driving conditions and the frequency of occurrence of each target driving behavior.

[0019] Based on the analysis of the return visit data, adjustment suggestions are made for historical users using the current rating mechanism;

[0020] Based on the target driving behavior, the frequency of occurrence, and the adjustment suggestions, an initial adjustment scheme is determined for the scores and rating coefficients corresponding to each driving behavior in the current scoring mechanism.

[0021] Optionally, the step of scoring the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data to obtain the user's driving score includes:

[0022] Based on the driving status data, determine whether the user's driving behavior is inappropriate;

[0023] If it is the aforementioned misconduct, then determine whether the misconduct meets the preset deduction threshold;

[0024] If the conditions are not met, the score and rating coefficient corresponding to the misbehavior will be obtained from the preset scoring mechanism.

[0025] Based on the score and the rating coefficient, determine the score that the user needs to increase;

[0026] The increased score is added to the user's original score to obtain the user's driving score.

[0027] Optionally, after the step of determining whether the misconduct meets the preset deduction threshold, if it is the misconduct, the method further includes:

[0028] If the condition is met, the score corresponding to the bad behavior is subtracted from the original score to obtain the user's driving score.

[0029] Optionally, if the vehicle is in fleet mode, the step of generating a driving evaluation based on the driving score and displaying the driving evaluation to the user includes:

[0030] The user's driving behavior data is generated based on the driving score;

[0031] Obtain the formation of the convoy;

[0032] Based on the formation and the driving behavior data, a driving evaluation for each member of the fleet is generated, and the driving evaluation is displayed to the fleet's management user.

[0033] In addition, to achieve the above objectives, this application also provides a driving behavior evaluation device, which includes:

[0034] The first acquisition module is used to acquire driving status data collected by various electronic control systems in the vehicle during the driving process;

[0035] The scoring module is used to score the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data, and to obtain the user's driving score. The preset scoring mechanism is adjusted in real time based on real driving conditions and user feedback through big data analysis.

[0036] The prompt module is used to generate a driving evaluation based on the driving score and display the driving evaluation to the user.

[0037] In addition, to achieve the above objectives, this application also proposes a driving behavior evaluation device, the device comprising: a memory, a processor, and a driving behavior evaluation program stored in the memory and executable on the processor, the driving behavior evaluation program being configured to implement the steps of the driving behavior evaluation method as described above.

[0038] In addition, to achieve the above objectives, this application also proposes a storage medium storing a driving behavior evaluation program, which, when executed by a processor, implements the steps of the driving behavior evaluation method described above.

[0039] This application provides a driving behavior evaluation method, apparatus, device, and storage medium. Compared with existing technologies that may deviate from real-world driving conditions, leading to users disregarding rating suggestions and resulting in low effectiveness in improving user driving behavior, this application acquires driving status data collected by various electronic control systems in the vehicle during operation; scores the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data, obtaining the user's driving score. The preset scoring mechanism is adjusted in real-time based on real-world driving conditions and user feedback through big data analysis; a driving evaluation is generated based on the driving score and displayed to the user. In this application, by using a real-time adjusted preset driving scoring mechanism with driving status data as a reference to score the user's driving behavior, this avoids the preset driving scoring mechanism being out of touch with actual driving conditions, resulting in driving scores that are not convincing to users. Therefore, it increases user confidence in the score and improves the effectiveness of using driving behavior scores to improve user driving behavior. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the driving behavior evaluation device structure in the hardware operating environment involved in the embodiments of this application;

[0043] Figure 2 This is a network topology diagram of the driving behavior evaluation device in this application;

[0044] Figure 3 This is a flowchart illustrating the first embodiment of the driving behavior evaluation method of this application;

[0045] Figure 4 This is a table showing the scores and coefficients for some driving behaviors in the economic scoring mechanism of the driving behavior evaluation method in this application.

[0046] Figure 5 This is a flowchart illustrating the second embodiment of the driving behavior evaluation method of this application;

[0047] Figure 6 This is a flowchart illustrating the third embodiment of the driving behavior evaluation method of this application;

[0048] Figure 7 This is a flowchart illustrating the fourth embodiment of the driving behavior evaluation method of this application;

[0049] Figure 8 This is a schematic diagram of the structural configuration of the driving behavior evaluation device of this application.

[0050] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0052] Reference Figure 1 , Figure 1 This is a schematic diagram of the driving behavior evaluation device structure in the hardware operating environment involved in the embodiments of this application.

[0053] like Figure 1 As shown, the driving behavior evaluation device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0054] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the driving behavior evaluation device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0055] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a driving behavior evaluation program.

[0056] exist Figure 1 In the driving behavior evaluation device shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the driving behavior evaluation device of this application can be set in the driving behavior evaluation device. The driving behavior evaluation device calls the driving behavior evaluation program stored in the memory 1005 through the processor 1001 and executes the driving behavior evaluation method provided in the embodiment of this application.

[0057] refer to Figure 2 , Figure 2 This is a network topology diagram of the driving behavior evaluation device in this application.

[0058] like Figure 2 As shown, in its specific implementation, the driving behavior evaluation equipment also includes an in-vehicle terminal, a vehicle-to-everything (V2X) platform, a gateway, an EMS (Engine Management System), a TCU (Transmission Control Unit), an ABS (Anti-lock Brake System), instrument clusters, and an MMI (Multimedia Interface), as well as wires connecting these electronic control systems. Specifically, the TCU, EMS, ABS, MMI, and instrument clusters are all connected to the gateway via wires. The gateway is connected to the in-vehicle terminal, and the in-vehicle terminal is wirelessly connected to the V2X platform.

[0059] In its implementation, the vehicle-mounted terminal receives signals from various components sent by the gateway to determine whether the vehicle has triggered driving behavior events, including prolonged idling, high throttle, low gear at high speed, excessive engine speed, economical driving speed, coasting in neutral, prolonged driving, emergency braking, and sharp turns. It then uploads these events to the vehicle network platform and simultaneously transmits them to the CAN (Controller Area Network). It also receives data from the vehicle network platform and sends it to the CAN network. The vehicle network platform receives driving behavior event information uploaded by the vehicle-mounted terminal, performs statistical analysis and comprehensive scoring, and sends the statistical data back to the vehicle-mounted terminal. The EMS (Engine Management System) sends engine speed, throttle opening, engine torque mode, and clutch pedal status signals to the bus. The TCU (Transmission Control Unit) sends the current gear position signal to the bus. The gateway forwards signals required by various components. The ABS (Anti-lock Braking System) sends brake pedal status and vehicle speed signals. The MMI (Manual Management Interface) receives data from the vehicle-mounted terminal and displays it graphically. The instrument panel is used to send vehicle speed signals, transmission output shaft speed signals, etc. to the bus, and at the same time, it receives driving behavior events sent by the vehicle terminal to promptly prompt the user to correct or maintain the current driving behavior.

[0060] This application provides a driving behavior evaluation method, referring to... Figure 3 , Figure 3 This is a flowchart illustrating the first embodiment of a driving behavior evaluation method according to this application.

[0061] It should be noted that the executing entity in this embodiment can be the driving behavior evaluation device, which can be a vehicle network platform and electronic devices such as personal computers, smartphones, and tablets connected to the vehicle network extension, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment and the following embodiments, the driving behavior evaluation device is used as an example to illustrate the driving behavior evaluation method of this application.

[0062] In this embodiment, the driving behavior evaluation method includes:

[0063] Step S10: Obtain driving status data collected by various electronic control systems in the vehicle during driving.

[0064] The various electronic control systems may include vehicle terminals, EMS, TCU, ABS, instrument clusters, MMI, etc., without any specific limitations.

[0065] The driving status data includes, but is not limited to, signals such as engine speed, throttle opening, engine torque mode, clutch pedal status, current gear position of the transmission, brake pedal status, vehicle speed, and transmission output shaft speed.

[0066] It should be noted that, since engine speed and vehicle speed can be used to determine whether a vehicle is idling for an extended period; throttle opening, engine speed, engine torque mode, and vehicle speed signals can be used to determine the throttle opening size; the current gear of the transmission, the current acceleration of the vehicle, throttle opening, engine speed, and the economic speed corresponding to the engine model can be used to determine whether the vehicle is in a low gear at high speed; the current engine speed, the economic speed of the engine model, and the throttle opening size can be used to determine whether the engine speed is too high; and vehicle speed, clutch pedal status, engine torque request mode, engine speed, and transmission gear ratio can be used to determine whether the vehicle is in neutral coasting, driving status data can be collected in a targeted manner through various electronic control systems.

[0067] It should be noted that if other driving behaviors need to be determined, the remaining driving status data collected by the corresponding electronic control system can be obtained so that the user can determine the required driving behavior according to their needs.

[0068] In practice, during vehicle operation, the gateway forwards the trip status data collected by various electronic control systems to the vehicle terminal, and finally receives the trip status data uploaded by the vehicle terminal to score the user's driving behavior based on the trip status data.

[0069] Step S20: Based on the preset scoring mechanism and the driving status data, the driving behavior of the user driving the vehicle is scored to obtain the user's driving score. The preset scoring mechanism is adjusted in real time based on real driving conditions and user feedback through big data analysis.

[0070] The preset scoring mechanism can be further divided into an economic scoring mechanism and a security scoring mechanism.

[0071] Driving behaviors can include prolonged idling, heavy acceleration, low gear at high speed, excessive engine speed, driving at economical speeds, and coasting in neutral, etc., without specific limitations.

[0072] It should be noted that the driving data and scores obtained through the economy scoring mechanism can remind users to drive properly to reduce vehicle fuel consumption and transportation costs; while the driving data and scores obtained through the safety scoring mechanism can remind users to drive safely.

[0073] It should be noted that the initial preset scoring mechanism is generally set based on theoretical data from research. However, since users have different driving experiences and usage conditions when driving different vehicles, the scoring mechanism set based on research may deviate from real-world usage conditions. This can lead to the final score not accurately reflecting the user's driving behavior, causing users to distrust the score. To ensure that the preset scoring mechanism can reasonably reflect the user's driving behavior based on real-world usage conditions, it should be adjusted in real time based on user feedback and actual usage conditions. This adjustment can be minor, but it can make the preset scoring mechanism more reasonable.

[0074] In practical implementation, the economic scoring mechanism for evaluating user driving behavior can be as follows: When the vehicle engine is idling, the vehicle speed is maintained at 0 km / h, and the battery voltage and coolant temperature are within the normal range for a predetermined time, the onboard terminal determines that the vehicle has been idling for an extended period. At this point, the instrument panel prompts the user to turn off the engine and wait, and starts timing. If the vehicle remains idling after the predetermined time, then... Figure 4 The score is calculated based on the duration of idling, i.e., S. N =S N-1 -A. If the vehicle is turned off within the scheduled time, then S N =S N-1 +α1A. If no prolonged idling event occurs during driving, then SN =S N-1 +A. Where S N S is the user's current driving rating. N-1 The previous driving score is given, with km / h representing speed in kilometers per hour.

[0075] When the vehicle's throttle opening exceeds a set value, the onboard terminal combines engine speed signals, engine torque mode signals, and instrument panel speed signals to determine if a high-throttle event has been triggered. If the event is triggered, the instrument panel prompts the user to reduce the throttle promptly. If the number of high-throttle events triggered exceeds a specified limit (e.g., 5 times) within a specified mileage range (e.g., 100km), then according to... Figure 4 Medium to high throttle is scored according to a corresponding score, i.e., S. N =S N-1 -B. If the number of times a high-throttle event is triggered within the specified mileage is less than the specified number (e.g., 2 times), then S N =S N-1 +α2B; If the vehicle does not trigger a high-throttle event within the specified mileage, then S N =S N-1 +B.

[0076] The onboard terminal periodically monitors signals such as the current gear position of the transmission, the current acceleration of the vehicle, the throttle opening, and the engine speed. It then uses this information, along with the engine model's optimal RPM, to determine if the vehicle is in a low-gear, high-speed position. If so, an event is triggered, and the instrument panel prompts the user to upshift or reduce throttle. If, within a specified mileage (e.g., 100km), the low-gear, high-speed event is triggered more than a specified number of times (e.g., 5 times) or continues to trigger for more than a specified time (e.g., 10 minutes), then... Figure 4 The low-to-mid range and high speed are scored according to a corresponding score, namely S. N =S N-1 -C. If the number of low-gear high-speed events triggered within the specified mileage is less than the specified number (e.g., 2 times), then S N =S N-1 +α3C; If the low-gear high-speed event is not triggered within the specified mileage, then S N =S N-1 +C.

[0077] When the onboard terminal detects that the current engine speed is higher than a certain economic speed for that engine model, it simultaneously determines the throttle opening and comprehensively judges whether an excessively high engine speed event has been triggered. If this event is triggered, the instrument panel prompts the user to reduce the throttle in time. Within a specified mileage (e.g., 100km), if the number of times the excessively high engine speed event is triggered exceeds a specified number (e.g., 5 times) or if the excessively high engine speed event continues to trigger for a specified time (e.g., 10 minutes), then according to... Figure 4 Excessive mid-range speed is scored accordingly, i.e., S. N =S N-1-D. If the number of times the high RPM event is triggered is less than the specified number (e.g., 2 times) within the specified mileage, then SN = S. N-1 +α4D; If no high RPM event is triggered within the specified mileage, then S N =S N-1 +D.

[0078] When the onboard terminal detects that the vehicle is within the economical speed range, an economical speed driving event is triggered. The instrument panel prompts the user to maintain this speed. After the vehicle has maintained the economical speed for T1(s), then according to... Figure 4 The vehicle is scored based on its speed at which it travels at the most economical speed, i.e., S. N =S N-1 +α5E, after the vehicle maintains an economical speed for T2(s), S N =S N-1 +α5(1+α5)E(T2>T1).

[0079] When the vehicle speed exceeds a certain set value, the onboard terminal judges signals such as clutch status, engine torque request mode, engine speed, and transmission ratio to confirm whether the vehicle is coasting in neutral. When a coasting event is triggered, the instrument panel prompts the user that they are coasting in neutral and asks them to pay attention to safety. Simultaneously, according to... Figure 4 The score is awarded based on the distance traveled in neutral, which is known as S. N =S N-1 +α6F.

[0080] In practical implementation, a safety scoring mechanism can be used to score a user's driving behavior. For example, coasting in neutral is an economical but risky driving behavior. When this event is triggered, the user is advised to pay attention to safety. If this event is triggered too frequently within a certain trip (e.g., 5 times in 10 kilometers), the safety score decreases. If the number of triggers within a certain trip is less than a specified value or there is no trigger, the safety score increases. When the on-board terminal detects that the vehicle's turning angle is greater than θ (e.g., 15°) N times consecutively, and the vehicle speed is greater than a set value (e.g., 30 km / h), the on-board terminal judges it as a sharp turn and will trigger a sharp turn event. The instrument panel will then warn the user to pay attention to sharp turns and advise them to slow down. If the number of sharp turn events triggered within a certain trip exceeds a specified number, the safety score decreases. If the number of sharp turn events triggered within a certain trip is less than a specified value or there is no trigger, the safety score increases. Driving behaviors that need to be scored by the safety scoring mechanism can also include safety events such as sudden braking and sharp turns. The scoring method for sudden braking and sharp turns, as well as other safety events, is basically the same as the scoring method for coasting in neutral.

[0081] Step S30: Generate a driving evaluation based on the driving score, and display the driving evaluation to the user.

[0082] It should be noted that improvement suggestions can be generated based on driving scores and corresponding score data. The improvement suggestions, driving scores, and score data are then combined into a driving evaluation, which is displayed to the user through MMI or mobile terminal so that the user can improve their driving behavior in a timely manner based on the driving evaluation.

[0083] It should be noted that, in order to avoid driving behavior events being triggered too frequently under certain conditions, which may cause user annoyance, a driving behavior evaluation prompt switch has been added to the instrument panel. Users can choose to turn off all prompts or turn off prompts for a specific event.

[0084] In practice, improvement suggestions are generated based on the driving score and corresponding score data. The improvement suggestions, driving score, and score data are then combined into a driving evaluation. If the evaluation prompt switch on the instrument panel is on, the driving evaluation is displayed to the user through the MMI. If the evaluation prompt switch on the instrument panel is off, the driving evaluation can be sent to the associated mobile terminal so that the user can view it through the mobile terminal during rest.

[0085] This embodiment provides a driving behavior evaluation method. Compared with existing technologies, which may deviate from real-world driving conditions, causing users to disregard the scoring suggestions and resulting in low effectiveness in improving user driving behavior, this application acquires driving status data collected by various electronic control systems in the vehicle during operation. Based on a preset scoring mechanism and the driving status data, the user's driving behavior is scored to obtain a driving score. The preset scoring mechanism is adjusted in real-time based on real-world driving conditions and user feedback through big data analysis. A driving evaluation is generated based on the driving score and displayed to the user. In this application, by using a real-time adjusted preset driving scoring mechanism with driving status data as a reference to score the user's driving behavior, the method avoids the preset scoring mechanism being out of touch with actual driving conditions, thus increasing user confidence in the score and improving the effectiveness of using driving behavior scoring to improve user driving behavior.

[0086] refer to Figure 5 , Figure 5 This is a flowchart illustrating the second embodiment of the driving behavior evaluation method of this application.

[0087] Based on the above embodiments, in this embodiment, before the step of acquiring the driving status data collected by each electronic control system in the vehicle during driving, the method further includes:

[0088] Step S01: Real-time acquisition of reference data on vehicle driving behavior under different driving conditions, user feedback data, and the current scoring mechanism;

[0089] Step S02: Adjust the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism.

[0090] The reference data can be data recorded by the electronic control systems of different vehicles under different driving conditions during normal operation.

[0091] The current rating mechanism can be understood as the rating mechanism currently in use, that is, the latest preset rating mechanism after the last adjustment.

[0092] The follow-up data may include whether the score for a certain driving behavior is too high or too low, or whether the deduction threshold for a certain driving behavior is too high or too low.

[0093] It should be noted that adjusting the current scoring mechanism using reference data on driving behavior under different driving conditions and user feedback data can make the preset scoring mechanism more in line with users' wishes, making the final driving score more reasonable and easier for users to believe, thereby encouraging users to improve their driving behavior based on the driving rating.

[0094] It should be noted that since the preset scoring mechanism is a scoring mechanism, to avoid frequent adjustments, settings can be made for the feedback data and the adjustment precision. For example, the preset scoring mechanism can be adjusted only when a certain number of feedback data points (100) are reached and the adjustment precision meets a certain range (greater than or equal to 0.1). Setting the feedback data not only avoids frequent adjustments to the preset scoring mechanism but also prevents data extremes caused by insufficient feedback data. Setting the adjustment precision also helps prevent frequent adjustments, thus avoiding extremes in the data used and preventing a decrease in the reference value of the preset scoring mechanism.

[0095] In practice, the system acquires reference data, user feedback data, and the currently used preset scoring mechanism in real time from various electronic control systems under different driving conditions. It uses the reference data and feedback data to determine the score and scoring coefficient that need to be adjusted for each driving behavior, or to determine the deduction threshold that needs to be adjusted for each driving behavior. The system then adjusts the current scoring mechanism according to the score and scoring coefficient or the deduction threshold that needs to be adjusted, and obtains the latest preset scoring mechanism.

[0096] Optionally, the step of adjusting the current scoring mechanism based on the reference data and the follow-up data to obtain the latest preset scoring mechanism includes:

[0097] Step S021: Determine the initial adjustment plan for the current scoring mechanism based on the reference data and the return visit data;

[0098] Step S022: Obtain big data on the vehicle usage conditions;

[0099] Step S023: Adjust the initial adjustment plan based on the big data to obtain the final adjustment plan;

[0100] Step S024: Adjust the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism.

[0101] It should be noted that since the reference data and the number of return visits cannot represent all users, after determining the initial adjustment plan for the current rating mechanism based on the reference data and the number of return visits, it is also necessary to obtain big data on vehicle usage conditions. This is so that the final adjustment plan for all users can be represented through big data analysis, and the current rating mechanism adjusted according to the final adjustment plan can be used by as many users as possible.

[0102] In the specific implementation, the required driving behaviors for each driving condition and the frequency of different driving behaviors are compiled based on reference data. User suggestions for adjusting the scores of each driving behavior are analyzed based on feedback data. Based on the required driving behaviors for each driving condition, the frequency of different driving behaviors, and adjustment suggestions, an initial adjustment plan for the current scoring mechanism is determined. Then, big data on each driving condition is obtained, and the initial adjustment plan is adjusted based on the big data to obtain the final adjustment plan. Finally, the current scoring mechanism is adjusted based on the final adjustment plan to obtain the latest preset scoring mechanism.

[0103] Furthermore, the step of determining the initial adjustment scheme of the current scoring mechanism based on the reference data and the follow-up data includes:

[0104] Step S0211: Based on the reference data, organize the target driving behaviors required under different vehicle use conditions and the frequency of occurrence of each target driving behavior;

[0105] Step S0212: Based on the feedback data analysis, provide adjustment suggestions for historical users using the current rating mechanism;

[0106] Step S0213: Based on the target driving behavior, the number of occurrences, and the adjustment suggestions, determine an initial adjustment scheme for the scores and scoring coefficients corresponding to each driving behavior in the current scoring mechanism.

[0107] In the specific implementation, the driving behaviors required for each vehicle use condition and the frequency of different driving behaviors are compiled based on reference data. Based on the feedback data, user suggestions for adjusting the scores of each driving behavior are analyzed. Based on the driving behaviors required for each vehicle use condition, the frequency of different driving behaviors, and the adjustment suggestions, the initial adjustment scheme for the corresponding scores and scoring coefficients of driving behaviors in the current scoring mechanism is determined.

[0108] refer to Figure 6 , Figure 6 This is a flowchart illustrating the third embodiment of the driving behavior evaluation method of this application.

[0109] Based on the above embodiments, in this embodiment, the step of scoring the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data to obtain the user's driving score includes:

[0110] Step S21: Determine whether the user's driving behavior is inappropriate based on the driving status data;

[0111] Step S22: If it is the bad behavior, then determine whether the bad behavior meets the preset deduction threshold;

[0112] Step S23: If the condition is not met, obtain the score and scoring coefficient corresponding to the bad behavior from the preset scoring mechanism;

[0113] Step S24: Determine the score that the user needs to have increased based on the score and the rating coefficient;

[0114] Step S25: Add the increased score to the user's original score to obtain the user's driving score.

[0115] It should be noted that after identifying a user's poor driving behavior, the system determines whether the behavior meets the deduction threshold. If not, incentive points are added to the user's original score to encourage the user to continue to improve, enhance the user's confidence in improving their driving behavior, and increase the user's motivation to improve their driving behavior.

[0116] In practice, the system determines whether a user's driving behavior is inappropriate based on driving status data. If it is not inappropriate, the system adds the corresponding score from the preset scoring mechanism to the user's original score to obtain the user's driving score. If the user's driving behavior is inappropriate, the system again determines whether the inappropriate behavior meets the preset deduction threshold. If it does not meet the threshold, the system determines the additional score based on the corresponding score and scoring coefficient, and adds the original score and the additional score to obtain the user's driving score to encourage the user.

[0117] Furthermore, after the step of determining whether the misconduct meets the preset deduction threshold, if it is the misconduct, the method further includes:

[0118] Step S23a: If the condition is met, the score corresponding to the bad behavior is subtracted from the original score to obtain the user's driving score.

[0119] In practice, if a bad behavior does not meet the preset deduction threshold within a certain time or a certain journey, the original score is used to reduce the score corresponding to the bad behavior, thereby obtaining the user's driving score and alerting the user to improve their driving behavior.

[0120] refer to Figure 7 , Figure 7 This is a flowchart illustrating the fourth embodiment of the driving behavior evaluation method of this application.

[0121] Based on the above embodiments, in this embodiment, if the vehicle is in fleet mode, the step of generating a driving evaluation based on the driving score and displaying the driving evaluation to the user includes:

[0122] Step S1: Generate the user's driving behavior data based on the driving score;

[0123] Step S2: Obtain the formation of the convoy;

[0124] Step S3: Generate driving evaluations for each member of the fleet based on the formation and driving behavior data, and display the driving evaluations to the fleet management user.

[0125] It should be noted that if a vehicle is in a fleet and fleet mode is enabled, when displaying driving evaluations to the user, the driving evaluations of all team members in the fleet can be displayed to the fleet manager. This allows the manager to have a clearer view of the fleet's operation, promptly correct drivers with lower driving skills, and facilitate fleet management.

[0126] It should be noted that vehicles in different positions within the same convoy will experience different driving conditions when traversing a section of road. For example, when the convoy encounters a turn, the driver of the lead vehicle may not have any vehicles ahead of them or may be far away from the vehicles in front. When turning, the driver of this vehicle does not need to consider the vehicles following or approaching, and their driving behavior may be relatively less. The driver of the vehicle in the middle of the convoy needs to consider both the vehicles behind and the vehicles in front, and therefore their driving behavior may be relatively more. The driver of the last vehicle at the rear of the convoy does not need to consider the vehicles behind them, and their driving behavior differs from that of the driver at the front of the convoy. Therefore, when vehicles are in convoy mode, the convoy formation can also be used as a reference for scoring, making the scoring more applicable to the convoy.

[0127] This application also provides a driving behavior evaluation device, for reference Figure 8 The driving behavior evaluation device includes:

[0128] The first acquisition module 801 is used to acquire driving status data collected by various electronic control systems in the vehicle during the driving process;

[0129] The scoring module 802 is used to score the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data, and to obtain the user's driving score. The preset scoring mechanism is adjusted in real time based on real driving conditions and user feedback through big data analysis.

[0130] The prompt module 803 is used to generate a driving evaluation based on the driving score and display the driving evaluation to the user.

[0131] Optionally, the driving behavior evaluation device also includes:

[0132] The second acquisition module 804 is used to acquire in real time reference data of vehicle driving behavior under different driving conditions, user feedback data and the current scoring mechanism;

[0133] The adjustment module 805 is used to adjust the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism.

[0134] Optionally, the adjustment module 805 is further configured to determine an initial adjustment scheme for the current scoring mechanism based on the reference data and the return visit data; acquire big data on the vehicle usage conditions; adjust the initial adjustment scheme based on the big data to obtain a final adjustment scheme; and adjust the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism.

[0135] Optionally, the adjustment module 805 is further configured to: organize the target driving behaviors required for different driving conditions and the frequency of occurrence of each target driving behavior based on the reference data; analyze the adjustment suggestions of historical users using the current scoring mechanism based on the return visit data; and determine an initial adjustment scheme for the scores and scoring coefficients corresponding to each driving behavior in the current scoring mechanism based on the target driving behaviors, the frequency of occurrence, and the adjustment suggestions.

[0136] Optionally, the scoring module 802 is further configured to determine whether the user's driving behavior is a bad behavior based on the driving status data; if it is a bad behavior, then determine whether the bad behavior meets a preset deduction threshold; if it does not meet the threshold, then obtain the score and scoring coefficient corresponding to the bad behavior from a preset scoring mechanism; determine the score to be added to the user based on the score and the scoring coefficient; and add the added score to the user's original score to obtain the user's driving score.

[0137] Optionally, the scoring module 802 is further configured to, if satisfied, subtract the score corresponding to the bad behavior from the original score to obtain the user's driving score.

[0138] Optionally, if the vehicle is in fleet mode, the prompting module 803 is further configured to generate the user's driving behavior data based on the driving score; obtain the fleet formation; generate the driving evaluation of each member of the fleet based on the formation and the driving behavior data, and display the driving evaluation to the fleet management user.

[0139] The specific implementation of the driving behavior evaluation device in this application is basically the same as the embodiments of the driving behavior evaluation method described above, and will not be repeated here.

[0140] This application provides a storage medium that stores one or more programs, which can be executed by one or more processors to implement the steps of the driving behavior evaluation method described above.

[0141] The specific implementation of the storage medium in this application is basically the same as the embodiments of the driving behavior evaluation method described above, and will not be repeated here.

[0142] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one, etc." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0143] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0145] The above are merely preferred embodiments of this application and do not limit the scope of this patent application. Any equivalent structural or procedural changes made based on the description and drawings of this application, or any direct or indirect application in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A method for evaluating driving behavior, characterized in that, The driving behavior evaluation method includes: Acquire driving status data collected by various electronic control systems in the vehicle during driving; The driving behavior of the user driving the vehicle is scored based on a preset scoring mechanism and the driving status data to obtain the user's driving score. The preset scoring mechanism is adjusted in real time based on real driving conditions and user feedback through big data analysis. A driving evaluation is generated based on the driving score, and the driving evaluation is displayed to the user. Prior to the step of acquiring the driving status data collected by the various electronic control systems in the vehicle during driving, the method further includes: Real-time acquisition of reference data on vehicle driving behavior under different driving conditions, user feedback data, and the current scoring mechanism; The current scoring mechanism is adjusted based on the reference data and the return visit data to obtain the latest preset scoring mechanism; The step of adjusting the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism includes: Based on the reference data and the return visit data, an initial adjustment plan for the current scoring mechanism is determined; Obtain big data on the vehicle usage conditions; Based on the big data, the initial adjustment plan is adjusted to obtain the final adjustment plan; The current scoring mechanism is adjusted based on the final adjustment plan to obtain the latest preset scoring mechanism; The step of determining the initial adjustment scheme of the current scoring mechanism based on the reference data and the return visit data includes: Based on the reference data, we have compiled the target driving behaviors required under different driving conditions and the frequency of occurrence of each target driving behavior. Based on the analysis of the return visit data, adjustment suggestions are made for historical users using the current rating mechanism; Based on the target driving behavior, the frequency of occurrence, and the adjustment suggestions, an initial adjustment scheme is determined for the scores and rating coefficients corresponding to each driving behavior in the current scoring mechanism.

2. The driving behavior evaluation method as described in claim 1, characterized in that, The step of scoring the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data, and obtaining the user's driving score, includes: Based on the driving status data, determine whether the user's driving behavior is inappropriate; If it is the aforementioned misconduct, then determine whether the misconduct meets the preset deduction threshold; If the conditions are not met, the score and rating coefficient corresponding to the misbehavior will be obtained from the preset scoring mechanism. Based on the score and the rating coefficient, determine the score that the user needs to increase; The increased score is added to the user's original score to obtain the user's driving score.

3. The driving behavior evaluation method as described in claim 2, characterized in that, After determining whether the misconduct meets the preset deduction threshold, the method further includes: If the condition is met, the score corresponding to the bad behavior is subtracted from the original score to obtain the user's driving score.

4. The driving behavior evaluation method as described in claim 1, characterized in that, If the vehicle is in fleet mode, the step of generating a driving evaluation based on the driving score and displaying the driving evaluation to the user includes: The user's driving behavior data is generated based on the driving score; Obtain the formation of the convoy; Based on the formation and the driving behavior data, a driving evaluation for each member of the fleet is generated, and the driving evaluation is displayed to the fleet's management user.

5. A driving behavior evaluation device, characterized in that, The driving behavior evaluation device includes: The first acquisition module is used to acquire driving status data collected by various electronic control systems in the vehicle during the driving process; The scoring module is used to score the driving behavior of the user driving the vehicle based on a preset scoring mechanism and the driving status data, and to obtain the user's driving score. The preset scoring mechanism is adjusted in real time based on real driving conditions and user feedback through big data analysis. The prompt module is used to generate a driving evaluation based on the driving score and display the driving evaluation to the user; The second acquisition module is used to acquire reference data on vehicle driving behavior under different driving conditions, user feedback data, and the current scoring mechanism in real time. The adjustment module is used to adjust the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism; The adjustment module is further configured to determine an initial adjustment scheme for the current scoring mechanism based on the reference data and the return visit data; acquire big data on the vehicle usage conditions; adjust the initial adjustment scheme based on the big data to obtain a final adjustment scheme; and adjust the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism. The adjustment module is further configured to: organize the target driving behaviors required for different driving conditions and the frequency of occurrence of each target driving behavior based on the reference data; analyze the adjustment suggestions of historical users using the current scoring mechanism based on the return visit data; and determine the initial adjustment scheme for the scores and scoring coefficients corresponding to each driving behavior in the current scoring mechanism based on the target driving behaviors, the frequency of occurrence, and the adjustment suggestions.

6. A driving behavior evaluation device, characterized in that, The driving behavior evaluation device includes: a memory, a processor, and a driving behavior evaluation program stored in the memory and executable on the processor, the driving behavior evaluation program being configured to implement the steps of the driving behavior evaluation method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a program that implements the driving behavior evaluation method, and the program that implements the driving behavior evaluation method is executed by a processor to implement the steps of the driving behavior evaluation method as described in any one of claims 1 to 4.

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