Vehicle driving safety management method and system and storage medium

Abstract

The invention provides a vehicle driving safety management method and system and a storage medium, and relates to the technical field of vehicle driving safety management, and the method comprises the steps: carrying out the state data collection of a target vehicle through a vehicle-mounted sensor set, and obtaining a vehicle real-time state data set; performing preprocessing to obtain a vehicle standard state data set; according to the vehicle standard state data set, multi-risk synchronous identification is carried out, multiple risk event identification results are obtained, and the multiple risk event identification results have multiple risk scores; according to a preset risk alarm index, carrying out risk grade evaluation on the plurality of risk scores to obtain a plurality of risk grades; and carrying out risk event processing according to the plurality of risk levels and a preset multi-level risk response mechanism. The technical problems that traditional driving safety management mostly depends on manual observation or simple data recording, real-time and comprehensive monitoring cannot be achieved, feedback lagging of bad behaviors of a driver is caused, and then safety is insufficient are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle driving safety management, and in particular to a vehicle driving safety management method, system and storage medium. Background Art

[0002] With the widespread application of commercial vehicles in logistics, engineering and other industries, driving safety and risk control are increasingly becoming key links in corporate management. Traditional vehicle driving safety management relies heavily on manual observation and simple data recording, which makes it difficult to effectively identify potential risks in real time, and there is a delay in feedback on drivers' bad behaviors. This approach not only reduces driving safety, but may also lead to increased corporate operating costs and higher accident rates.

[0003] In recent years, with the rapid development of sensor technology, the Internet of Things and artificial intelligence, intelligent driving behavior monitoring systems based on real-time data collection and analysis have emerged. Such systems can monitor drivers' behaviors in real time, such as smoking, fatigue driving, using mobile phones, sudden braking and acceleration, and identify potential risk behaviors through data analysis. However, existing technologies still have deficiencies in risk information generation and feedback mechanisms, and cannot achieve comprehensive and timely risk control. Summary of the invention

[0004] The present application provides a vehicle driving safety management method, system and storage medium, aiming to solve the technical problem that traditional driving safety management relies heavily on manual observation or simple data recording, cannot achieve real-time and comprehensive monitoring, leads to delayed feedback on the driver's bad behavior, and thus leads to insufficient safety.

[0005] The first aspect disclosed in the present application provides a vehicle driving safety management method, the method comprising: collecting status data of a target vehicle through a set of on-board sensors to obtain a real-time status data set of the vehicle; preprocessing the real-time status data set of the vehicle to obtain a standard status data set of the vehicle; performing simultaneous identification of multiple risks based on the standard status data set of the vehicle to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores; performing risk level assessment on the multiple risk scores according to preset risk alarm indicators to obtain multiple risk levels; and performing risk event processing according to a preset multi-level risk response mechanism based on the multiple risk levels.

[0006] The second aspect disclosed in the present application provides a vehicle driving safety management system, which is used for the above-mentioned vehicle driving safety management method, and the system includes: a data acquisition module, which is used to collect status data of a target vehicle through a set of on-board sensors to obtain a real-time status data set of the vehicle; a preprocessing module, which is used to preprocess the real-time status data set of the vehicle to obtain a standard status data set of the vehicle; a multi-risk synchronous identification module, which is used to perform multi-risk synchronous identification according to the standard status data set of the vehicle to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores; a risk level assessment module, which is used to perform risk level assessment on the multiple risk scores according to preset risk alarm indicators to obtain multiple risk levels; a risk event processing module, which is used to perform risk event processing according to a preset multi-level risk response mechanism according to the multiple risk levels.

[0007] According to a third aspect disclosed in the present application, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any step of the first aspect disclosed in the present application is implemented.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The state data of the target vehicle is collected through the on-board sensor set to obtain the real-time state data set of the vehicle, which provides an accurate data source and provides sufficient data support for subsequent risk identification and analysis; the collected real-time state data of the vehicle is preprocessed to obtain a standardized data set, which ensures the accuracy and reliability of the subsequent analysis and risk assessment results; by performing multi-risk synchronous identification on the standard state data set, multiple potential risk events can be identified at the same time, and a risk score is assigned to each event. This synchronous identification can comprehensively evaluate various potential dangers during vehicle driving and improve the accuracy and coverage of risk identification; by evaluating the risk score according to the preset risk alarm indicators, multiple risk levels are generated. Such an evaluation can accurately distinguish which events are urgent and which events are relatively minor, thereby providing a basis for the subsequent response mechanism and ensuring the orderliness of risk management; according to the multiple risk levels obtained by the evaluation, the risk events are handled according to the preset multi-level risk response mechanism, and different levels of response measures are automatically executed according to different risk levels. This multi-level response mechanism ensures that high-risk events are handled in a timely and effective manner, and low-risk events can also be monitored and prevented as necessary, thereby realizing intelligent and automated risk handling.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flow chart of a vehicle driving safety management method provided in an embodiment of the present application.

[0012] Figure 2 A schematic diagram of the structure of a vehicle driving safety management system provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: data acquisition module 10, preprocessing module 20, multi-risk synchronous identification module 30, risk level assessment module 40, risk event processing module 50. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a vehicle driving safety management method to solve the technical problem that traditional driving safety management relies heavily on manual observation or simple data recording, cannot achieve real-time and comprehensive monitoring, leads to delayed feedback on the driver's bad behavior, and thus leads to insufficient safety.

[0015] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0016] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a vehicle driving safety management method, the method comprising:

[0017] The status data of the target vehicle is collected through a set of on-board sensors to obtain a real-time status data set of the vehicle.

[0018] The vehicle-mounted sensor set includes different types of sensors for collecting data on the vehicle and its surrounding environment. The main function of these sensors is to monitor various status parameters of the vehicle in real time, including but not limited to GPS modules, camera systems, gyroscope sensors and radar sensors, which are used to collect the vehicle's location information and driving speed, monitor driver behavior and road environment, detect dynamic posture changes of the vehicle, etc. The data information collected by all sensors is integrated and stored as a real-time vehicle status data set, which contains various dynamic information of the vehicle as the basis for subsequent processing.

[0019] The vehicle real-time status data set is preprocessed to obtain a vehicle standard status data set.

[0020] The vehicle real-time status data set is preprocessed, where preprocessing includes data cleaning and data standardization. Specifically, during the data collection stage, there may be some noise or invalid data, such as missing values, outliers, redundant data, etc. The purpose of data cleaning is to ensure the quality of the data so that subsequent analysis can draw accurate conclusions. The specific cleaning steps include removing redundant data, correcting erroneous data, filling missing data, and outlier detection and processing. After cleaning the data, the next step is to standardize the data. The purpose of standardization is to uniformly convert different types of sensor data into comparable standard forms so that different data sources can be effectively compared and analyzed. Common standardization methods include normalization, that is, scaling the data to a fixed range, such as between 0 and 1, so as to divide the data into the same scale for comparison. The standard status data set obtained after preprocessing has higher data quality and can be used for subsequent analysis such as simultaneous identification of multiple risks.

[0021] Based on the vehicle standard status data set, multiple risks are simultaneously identified to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores.

[0022] Acquire multiple risk factors, which are key factors affecting vehicle driving safety, including driver behavior, such as speeding, sudden braking, and sharp turns; vehicle status, such as whether the braking system is normal, tire wear, oil level, etc.; road environment, such as weather conditions, road conditions, traffic flow, etc.; external factors, such as the behavior of other vehicles and the presence of pedestrians.

[0023] Analyze multiple risk factors and evaluate their potential impact on vehicle safety. Through data mining or machine learning models, combined with historical data and existing vehicle status, analyze the risk characteristics of these factors in different scenarios. For example, speeding may bring greater risks on rainy days, while night driving may have a higher risk of fatigue driving. Assign weights to each factor according to the degree of impact of different risk factors.

[0024] Based on the vehicle standard status data set and the weights of multiple risk factors, multiple risks are identified simultaneously, which means that all identified risk factors are evaluated at the same time, and the risk score of each risk event is calculated by comprehensively considering the weight of each factor. These risk scores reflect the severity of each risk event.

[0025] According to the preset risk alarm indicators, risk level assessment is performed on the multiple risk scores to obtain multiple risk levels;

[0026] The preset risk alarm indicators contain pre-set thresholds, which can be set according to specific industry standards or experimental data to assess the severity of the risk score. For example, a score below 30 is set as low risk, which usually does not require an immediate response; a score between 30 and 70 is set as medium risk, which requires attention and may require some preventive measures; a score over 70 is set as high risk, which requires an immediate response and may initiate emergency procedures.

[0027] Based on different risk scores and according to the preset risk alarm indicators, a risk level assessment is performed. Each risk event is assigned a risk level based on its score. For example, sudden braking may have a higher risk score, while other smaller events may have a lower risk score.

[0028] According to the multiple risk levels, risk event processing is performed according to a preset multi-level risk response mechanism.

[0029] The preset multi-level risk response mechanism defines different measures to be taken at different risk levels. For example, low risk can remind the driver through warnings and suggest taking some preventive measures, such as reminding the driver to pay attention to road conditions through the on-board display or sound; medium risk requires further intervention, such as issuing a stronger warning to remind the driver to slow down and keep a safe distance; high risk requires immediate emergency response measures, such as automatically starting emergency braking, adjusting vehicle speed, and enabling automatic obstacle avoidance in high-risk situations. These risk event handling measures can range from simple warnings to complex automatic interventions to ensure the safety of vehicle driving.

[0030] Furthermore, the method of collecting status data of the target vehicle through the vehicle-mounted sensor set to obtain the real-time status data set of the vehicle includes:

[0031] The vehicle-mounted sensor set includes but is not limited to a GPS module, a camera system, a gyroscope sensor and a radar sensor; through the GPS module, the vehicle location information and real-time driving speed are collected to obtain the vehicle driving status data; through the camera system, the driver's behavior status, road environment, and obstacles ahead are monitored to obtain the driver status data and road environment status data; through the gyroscope sensor, the dynamic posture changes and direction adjustments of the vehicle are detected to obtain the vehicle posture status data; through the radar sensor, surrounding objects are detected, the distance and relative speed of objects are measured, and the surrounding environment status data are generated; data integration is performed to obtain the vehicle real-time status data set.

[0032] The vehicle-mounted sensor collection includes but is not limited to GPS modules, camera systems, gyroscope sensors and radar sensors. The GPS module is used to collect the vehicle's location information and driving speed, the camera system is used to monitor driver behavior, road environment, obstacles ahead and other information, the gyroscope sensor is used to detect the vehicle's dynamic posture changes, steering and other information, and the radar sensor is used to detect surrounding objects, measure object distances and relative speeds, etc.

[0033] The GPS module calculates the specific location (latitude and longitude) and driving speed of the vehicle by receiving signals from satellites. It communicates with signals from multiple satellites and uses the principle of triangulation to determine the geographic location of the vehicle. The vehicle driving status data obtained includes the vehicle's current location and speed. This information is critical for judging the vehicle's driving status. For example, the vehicle's driving trajectory, driving direction, and whether there is abnormal behavior, such as speeding or sudden deceleration, can be analyzed based on the changing trends of speed and position.

[0034] The camera system monitors the driver's behavior, road environment and obstacles ahead in real time through cameras installed on the front, side or inside the vehicle. These cameras combine image recognition algorithms and machine learning technologies to analyze the captured image data. For example, by monitoring the driver's facial expressions, eye movements, head position, etc., it can identify whether the driver is focused on driving, such as whether he is drowsy, distracted (such as making a phone call), or obeying traffic regulations. The driver status data and road environment status data collected by the camera are used to analyze in real time whether the driver's behavior is safe and whether there are dangers in the road environment. These data can help evaluate the driver's behavior patterns, concentration and road safety.

[0035] A gyroscope sensor is a device used to measure changes in the angle of an object. It can detect the vehicle's pitch angle (up and down movement), yaw angle (left and right turn of the vehicle), and roll angle (side tilt of the vehicle) in real time. Through these measurements, the gyroscope can determine the vehicle's dynamic posture, including whether the vehicle makes sharp turns, emergency braking, or other posture changes during driving. The vehicle posture status data from the gyroscope sensor can provide dynamic stability information of the vehicle, such as whether slippage occurs, body tilt when cornering, or abnormal posture. This information is crucial for determining whether the vehicle has dangerous driving behavior.

[0036] Radar sensors detect surrounding objects by emitting electromagnetic waves and receiving reflected signals. Radar signals will reflect from surrounding objects, and the position, distance, relative speed, etc. of the objects will be determined by calculating the time difference and intensity of the reflected signals. The surrounding environment status data collected by the radar sensor includes the position, distance and speed of objects around the vehicle. These data are very important for collision warning, automatic braking, adaptive cruise control, etc. By monitoring the surrounding environment in real time, radar can help determine the movement trends and potential risks of surrounding objects.

[0037] Integrate the data collected by all sensors to obtain a real-time vehicle status data set. The data from these sensors complement each other and provide comprehensive information support for real-time vehicle status monitoring, risk assessment and safety management.

[0038] Furthermore, the method of preprocessing the vehicle real-time status data set to obtain the vehicle standard status data set includes:

[0039] The vehicle real-time status data set is subjected to data cleaning processing to obtain a cleaned vehicle real-time status data set, wherein the data cleaning processing includes removing redundant data, correcting erroneous data, filling missing data, and detecting and processing outliers; and the cleaned vehicle real-time status data set is subjected to data standardization processing to obtain the vehicle standard status data set.

[0040] Data cleaning processing includes removing redundant data, correcting erroneous data, filling missing data, and detecting and processing outliers. Removing redundant data means deleting repeated or multiple recorded values. For example, multiple sensors may collect the same information in a very short time. At this time, a valid record should be retained to remove redundancy. Correcting erroneous data. If a sensor fails during data collection, it may cause data errors, such as the speed displayed as a negative number or the GPS positioning deviation is large. At this time, it is necessary to correct or mark it as an outlier through an algorithm. Filling missing data. In some cases, some sensors may not provide data in time, resulting in missing values. Interpolation or other data filling methods can be used to process missing data to ensure data continuity. Detecting and processing outliers. Some extreme data (such as the speed suddenly jumping to an abnormally high value) may be outliers caused by sensor failure or extreme conditions. These outliers are identified and processed through statistical analysis methods, such as the Z-score method. After data cleaning processing, a set of cleaned vehicle real-time status data is obtained to ensure the quality of the data and improve the accuracy of subsequent analysis.

[0041] The cleaned vehicle real-time status data set is subjected to data standardization. Standardization methods include normalization methods, which scale the data to a fixed range, such as between 0 and 1, which is suitable for situations where comparisons need to be made at the same scale, or using standardization methods to convert the data into a standard normal distribution with zero mean and unit variance, which is suitable for situations where there are large differences in data. After data standardization, a vehicle standard status data set is obtained, so that different types of sensor data can be uniformly converted into comparable standard forms, so that different data sources can be effectively compared and analyzed.

[0042] Furthermore, the method of performing multi-risk synchronous identification based on the vehicle standard state data set to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores, includes:

[0043] Acquire the vehicle type and generate a vehicle type influence coefficient; acquire multiple risk factors, perform risk characteristic analysis on the multiple risk factors, assign weights based on the analysis results, and generate multiple risk factor weights; based on the vehicle type influence coefficient and the multiple risk factor weights, perform multi-risk synchronous identification and risk score calculation on the vehicle standard state data set to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores.

[0044] Different types of vehicles, such as cars, trucks, SUVs, etc., have different behavioral characteristics and risk factors when driving. For example, trucks are more susceptible to the impact of overloading than cars, and SUVs may be prone to rollover due to their higher center of gravity. Therefore, when conducting risk assessment, it is first necessary to generate a vehicle type impact coefficient based on the type of vehicle. This coefficient can affect the weight distribution in subsequent risk assessments. The vehicle type impact coefficient is a coefficient generated based on the characteristics of the vehicle type and is used to adjust the risk identification and assessment results. The coefficient can be obtained by analyzing historical data or expert experience.

[0045] Risk factors are key factors that affect driving safety, including but not limited to: driver behavior, such as speeding, sudden braking, sharp turns, etc.; vehicle status, such as whether the braking system is normal, tire wear, oil level, etc.; road environment, such as weather conditions, road conditions, traffic flow, etc.; external factors, such as the behavior of other vehicles, the presence of pedestrians, etc.

[0046] Risk characteristics analysis is performed on each risk factor, with the focus on the impact of each factor on vehicle safety under different circumstances. For example, "fatigue driving" in driver behavior may have higher risks at night and during long driving, but less impact during daytime and short driving; "slippery road surface" in the road environment has a greater impact on braking distance and vehicle stability, especially in rainy and snowy weather. Data mining technology is used to analyze the severity, frequency of occurrence and scope of impact of each risk factor, so as to assign corresponding weights to each factor and generate multiple risk factor weights. The purpose of weight allocation is to make high-risk, common and difficult-to-control factors have a greater impact on the final risk score, while low-risk and rare factors have a smaller impact on the score.

[0047] Through the simultaneous identification of multiple risks, multiple possible risk events are identified at the same time. This process relies on machine learning algorithms, such as classifiers, which can simultaneously consider multiple factors such as vehicle status, driver behavior, road environment, etc., and identify multiple potential risk events. After the risk event is identified, each risk event is scored according to the vehicle type impact coefficient and the risk factor weight. Specifically, according to the weight of each risk factor, the risk level of its contribution is weighted, and the final risk score is adjusted according to the vehicle type impact coefficient. For example, the risk of a large truck making a sharp turn needs to be weighted to reflect its greater rollover risk. The scores of multiple risk factors are integrated to obtain multiple risk scores. In this way, each risk event will receive a risk score, which reflects the degree of danger of each event.

[0048] Furthermore, it also includes:

[0049] The frequency of occurrence of risk events within a preset period is counted; and the multiple risk scores are compensated and adjusted according to the frequency of occurrence of the risk events.

[0050] The preset period is a fixed time range, which can be 12 hours, one day, one week, etc., and can be set as needed. Within the preset period, the frequency of all identified risk events is counted. For example, in one day, the number of all risk events such as sudden braking, speeding, and fatigue driving that have occurred can be recorded. For each risk event category, the number of times it occurs is counted to obtain the frequency of occurrence of risk events.

[0051] The frequency of risk events will affect their actual risk level. For example, some risk events, such as speeding, may occur frequently, but the actual danger each time they occur may not be high; while other low-frequency events, such as major collisions, are very dangerous each time they occur, but they occur less frequently. If some risk events occur frequently, their single risk scores can be reduced to avoid excessive scores due to their frequent occurrence, which will affect the judgment of the system. For example, if speeding events occur once a day, but they are only slightly speeding, you can consider adjusting the score in some way to avoid over-emphasizing its risk. You can reduce the risk score of high-frequency risk events by assigning an attenuation factor to them, and increase the score of low-frequency high-risk events by assigning an amplification factor to them, so as to make compensatory adjustments to multiple risk scores.

[0052] Furthermore, the risk event processing is performed according to the preset multi-level risk response mechanism based on the multiple risk levels, including:

[0053] The preset multi-level risk response mechanism includes a multi-level risk event response mechanism and a common risk event response mechanism; according to the multi-level risk event response mechanism, in combination with the multiple risk levels, multi-level risk event processing is performed; according to the common risk event response mechanism, in combination with the multiple risk levels, common risk event processing is performed.

[0054] The preset multi-level risk response mechanism is a mechanism for handling risk events in a layered manner. It provides different levels of response according to the severity of the risk event, including a multi-level risk event response mechanism and a general risk event response mechanism. Among them, the multi-level risk event response mechanism includes multiple response levels, such as low-risk event response, medium-risk event response, and high-risk event response; the general risk event response mechanism is for common, lower-risk events that occur frequently but generally do not cause serious consequences. Therefore, the response to such events is relatively simple.

[0055] In the multi-level risk response mechanism, according to the risk level assessment of risk events in the previous steps, events can be divided into multiple levels such as low, medium, and high, and different levels of response are carried out according to the level of risk events. For example, low-risk event response, such as warning the driver to pay attention, but no emergency response is required; medium-risk event response, such as prompting the driver to take certain actions, such as slowing down, adjusting the distance between vehicles, etc., or enabling assisted driving functions; high-risk event response, such as immediately initiating emergency measures, such as automatic braking, alarms, or emergency steering, etc. Through hierarchical processing, high-risk events are ensured to receive priority and emergency response, while for low-risk events, only conventional warnings or auxiliary measures are required, which can effectively avoid the waste of system resources while ensuring timely response to high-risk events.

[0056] For common risk events, such as slight speeding, frequent acceleration or braking, etc., although these events are relatively common, they generally do not lead to serious accidents. Such events will be handled through the general risk response mechanism. The focus of this mechanism is to prevent the events from evolving into more serious risks through prompts and preventive measures. For example, when the driver slightly exceeds the speed limit or brakes suddenly within a short period of time, the driver can be reminded through dashboard prompts, voice warnings or vibrations.

[0057] Furthermore, the processing of risk events according to the plurality of risk levels and a preset multi-level risk response mechanism also includes:

[0058] The preset multi-level risk response mechanism also includes preset multi-level risk uploading rules; connecting multiple communication terminals; and uploading the multiple risk levels to the multiple communication terminals according to the preset multi-level risk uploading rules.

[0059] The preset multi-level risk upload rules are how to upload relevant information to different communication terminals according to the level of risk events after risk events are identified and level assessed. The basis for setting includes the level of risk events, event type, frequency and timing of upload, etc.

[0060] Multiple communication terminals refer to all devices connected to the system that can receive risk data. According to the preset multi-level risk upload rules, multiple risk levels are uploaded to the multiple communication terminals. For example, risk events are divided into three levels, with level one being the highest risk and level three being the lowest. When a level one serious risk event occurs, the system will automatically dial the driver's preset emergency contact number and broadcast the current risk situation; for level two serious risk events, the system will generate corresponding text reminders and convert them into voice, and deliver the reminder content by dialing the driver's phone; if a level three risk event occurs, the system will send the relevant text information to the driver's emergency contact number in the form of a text message or send a DingTalk group message reminder. These responses can be set by managers, which is more flexible. At the same time, all serious risk events will be uploaded to the platform for managers to view.

[0061] Furthermore, it also includes:

[0062] Integrate the vehicle real-time status data set, the multiple risk event identification results, the multiple risk scores, and the multiple risk levels to generate a vehicle driving safety management report, wherein the vehicle driving safety management report includes a first format and a second format; send the first format to a first user group, and send the second format to a second user group.

[0063] All the data obtained from the above analysis, including the real-time vehicle status data set, the multiple risk event identification results, the multiple risk scores, the multiple risk levels, etc., are summarized into a complete vehicle driving safety management report. According to different user needs, the content, data items and display methods in the report can be customized to generate a first format and a second format, and the different formats are sent to the corresponding user groups. For example, when summarizing the report, the data of the previous day is used for statistics to generate two formats of reports: Word document and H5 page. The Word document is mainly for fleet personnel to view, while the H5 page is for business owners and managers. The report content can be accurate to the specific situation of each vehicle, and can also present the summary data of the entire fleet. Through these reports, drivers and business owners can intuitively and clearly understand the current risk situation, so as to take corresponding measures in time to ensure safety and efficiency.

[0064] In summary, the vehicle driving safety management method provided by the embodiment of the present application has the following technical effects:

[0065] The state data of the target vehicle is collected through the on-board sensor set to obtain the real-time state data set of the vehicle, which provides an accurate data source and provides sufficient data support for subsequent risk identification and analysis; the collected real-time state data of the vehicle is preprocessed to obtain a standardized data set, which ensures the accuracy and reliability of the subsequent analysis and risk assessment results; by performing multi-risk synchronous identification on the standard state data set, multiple potential risk events can be identified at the same time, and a risk score is assigned to each event. This synchronous identification can comprehensively evaluate various potential dangers during vehicle driving and improve the accuracy and coverage of risk identification; by evaluating the risk score according to the preset risk alarm indicators, multiple risk levels are generated. Such an evaluation can accurately distinguish which events are urgent and which events are relatively minor, thereby providing a basis for the subsequent response mechanism and ensuring the orderliness of risk management; according to the multiple risk levels obtained by the evaluation, the risk events are handled according to the preset multi-level risk response mechanism, and different levels of response measures are automatically executed according to different risk levels. This multi-level response mechanism ensures that high-risk events are handled in a timely and effective manner, and low-risk events can also be monitored and prevented as necessary, thereby realizing intelligent and automated risk handling.

[0066] Embodiment 2 is based on the same inventive concept as a vehicle driving safety management method in the above embodiment. Figure 2 As shown, an embodiment of the present application provides a vehicle driving safety management system, the system comprising:

[0067] The data acquisition module 10 is used to collect status data of the target vehicle through a set of on-board sensors to obtain a real-time status data set of the vehicle; the preprocessing module 20 is used to preprocess the real-time status data set of the vehicle to obtain a standard status data set of the vehicle; the multi-risk synchronous identification module 30 is used to perform multi-risk synchronous identification based on the standard status data set of the vehicle to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores; the risk level assessment module 40 is used to perform risk level assessment on the multiple risk scores according to preset risk alarm indicators to obtain multiple risk levels; the risk event processing module 50 is used to process risk events according to a preset multi-level risk response mechanism based on the multiple risk levels.

[0068] Furthermore, the system further includes a vehicle real-time status data set acquisition module to perform the following operation steps:

[0069] The vehicle-mounted sensor set includes but is not limited to a GPS module, a camera system, a gyroscope sensor and a radar sensor; through the GPS module, the vehicle location information and real-time driving speed are collected to obtain the vehicle driving status data; through the camera system, the driver's behavior status, road environment, and obstacles ahead are monitored to obtain the driver status data and road environment status data; through the gyroscope sensor, the dynamic posture changes and direction adjustments of the vehicle are detected to obtain the vehicle posture status data; through the radar sensor, surrounding objects are detected, the distance and relative speed of objects are measured, and the surrounding environment status data are generated; data integration is performed to obtain the vehicle real-time status data set.

[0070] Furthermore, the system further includes a vehicle standard state data set acquisition module to perform the following operation steps:

[0071] The vehicle real-time status data set is subjected to data cleaning processing to obtain a cleaned vehicle real-time status data set, wherein the data cleaning processing includes removing redundant data, correcting erroneous data, filling missing data, and detecting and processing outliers; and the cleaned vehicle real-time status data set is subjected to data standardization processing to obtain the vehicle standard status data set.

[0072] Furthermore, the system also includes a risk score calculation module to perform the following steps:

[0073] Acquire the vehicle type and generate a vehicle type influence coefficient; acquire multiple risk factors, perform risk characteristic analysis on the multiple risk factors, assign weights based on the analysis results, and generate multiple risk factor weights; based on the vehicle type influence coefficient and the multiple risk factor weights, perform multi-risk synchronous identification and risk score calculation on the vehicle standard state data set to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores.

[0074] Furthermore, the system further includes a compensation adjustment module to perform the following operation steps:

[0075] The frequency of occurrence of risk events within a preset period is counted; and the multiple risk scores are compensated and adjusted according to the frequency of occurrence of the risk events.

[0076] Furthermore, the system also includes a risk event processing module to perform the following operation steps:

[0077] The preset multi-level risk response mechanism includes a multi-level risk event response mechanism and a common risk event response mechanism; according to the multi-level risk event response mechanism, in combination with the multiple risk levels, multi-level risk event processing is performed; according to the common risk event response mechanism, in combination with the multiple risk levels, common risk event processing is performed.

[0078] Furthermore, the system also includes a risk upload module to perform the following operation steps:

[0079] The preset multi-level risk response mechanism also includes preset multi-level risk uploading rules; connecting multiple communication terminals; and uploading the multiple risk levels to the multiple communication terminals according to the preset multi-level risk uploading rules.

[0080] Furthermore, the system further includes a report sending module to perform the following operation steps:

[0081] Integrate the vehicle real-time status data set, the multiple risk event identification results, the multiple risk scores, and the multiple risk levels to generate a vehicle driving safety management report, wherein the vehicle driving safety management report includes a first format and a second format; send the first format to a first user group, and send the second format to a second user group.

[0082] Through the above detailed description of a vehicle driving safety management method in this specification, those skilled in the art can clearly understand a vehicle driving safety management system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0083] Embodiment 3 provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the aforementioned vehicle driving safety management method is implemented.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle driving safety management method, characterized in that: The method comprises: Collect status data of the target vehicle through a set of on-board sensors to obtain a real-time status data set of the vehicle; Preprocessing the vehicle real-time status data set to obtain a vehicle standard status data set; According to the vehicle standard state data set, multiple risks are synchronously identified to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores; According to the preset risk alarm indicators, risk level assessment is performed on the multiple risk scores to obtain multiple risk levels; According to the multiple risk levels, risk event processing is performed according to a preset multi-level risk response mechanism.

2. A vehicle driving safety management method as claimed in claim 1, characterized in that: The method of collecting state data of the target vehicle through a set of vehicle-mounted sensors to obtain a set of real-time state data of the vehicle includes: The vehicle-mounted sensor set includes but is not limited to a GPS module, a camera system, a gyroscope sensor, and a radar sensor; Through the GPS module, the vehicle location information and real-time driving speed are collected to obtain the vehicle driving status data; The camera system is used to monitor the driver's behavior, road environment, and obstacles ahead, and obtain driver status data and road environment status data; The gyro sensor is used to detect the dynamic posture change and direction adjustment of the vehicle and obtain the vehicle posture state data; By using the radar sensor, the surrounding objects are detected, the distance and relative speed of the objects are measured, and the surrounding environment status data is generated; Perform data integration to obtain a real-time status data set of the vehicle.

3. A vehicle driving safety management method as claimed in claim 1, characterized in that: The method of preprocessing the vehicle real-time status data set to obtain the vehicle standard status data set includes: Performing data cleaning processing on the vehicle real-time status data set to obtain a cleaned vehicle real-time status data set, wherein the data cleaning processing includes removing redundant data, correcting erroneous data, filling missing data, and detecting and processing outliers; The real-time status data set of the cleaning vehicles is subjected to data standardization processing to obtain the vehicle standard status data set.

4. A vehicle driving safety management method as claimed in claim 1, characterized in that: The method of performing multi-risk synchronous identification according to the vehicle standard state data set to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores, includes: Obtain vehicle type and generate vehicle type influence coefficient; Acquire multiple risk factors, perform risk characteristic analysis on the multiple risk factors, assign weights according to the analysis results, and generate multiple risk factor weights; According to the vehicle type influence coefficient and the weights of the multiple risk factors, multiple risks are simultaneously identified and risk scores are calculated for the vehicle standard state data set to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores.

5. A vehicle driving safety management method as claimed in claim 4, characterized in that: Also includes: Count the frequency of risk events within a preset period; The multiple risk scores are compensated and adjusted according to the frequency of occurrence of the risk event.

6. A vehicle driving safety management method as claimed in claim 1, characterized in that: The risk event processing is performed according to the multiple risk levels and a preset multi-level risk response mechanism, including: The preset multi-level risk response mechanism includes a multi-level risk event response mechanism and a common risk event response mechanism; According to the multi-level risk event response mechanism, combined with the multiple risk levels, multi-level risk event processing is performed; According to the common risk event response mechanism and in combination with the multiple risk levels, common risk events are handled.

7. A vehicle driving safety management method as claimed in claim 1, characterized in that: The processing of risk events according to the plurality of risk levels and the preset multi-level risk response mechanism also includes: The preset multi-level risk response mechanism also includes preset multi-level risk upload rules; Connect multiple communication terminals; According to the preset multi-level risk uploading rule, the multiple risk levels are uploaded to the multiple communication terminals.

8. A vehicle driving safety management method as claimed in claim 1, characterized in that: Also includes: Integrate the vehicle real-time status data set, the multiple risk event identification results, the multiple risk scores, and the multiple risk levels to generate a vehicle driving safety management report, wherein the vehicle driving safety management report includes a first format and a second format; The first format is sent to a first user group, and the second format is sent to a second user group.

9. A vehicle driving safety management system, characterized in that: A vehicle driving safety management method for implementing any one of claims 1 to 8, the system comprising: The data acquisition module is used to collect status data of the target vehicle through a set of vehicle-mounted sensors to obtain a set of real-time status data of the vehicle; A preprocessing module, used for preprocessing the vehicle real-time status data set to obtain a vehicle standard status data set; A multi-risk synchronous identification module, used to perform multi-risk synchronous identification according to the vehicle standard state data set to obtain multiple risk event identification results, wherein the multiple risk event identification results have multiple risk scores; A risk level assessment module, used to perform risk level assessment on the multiple risk scores according to preset risk alarm indicators to obtain multiple risk levels; The risk event processing module is used to process risk events according to the multiple risk levels and a preset multi-level risk response mechanism.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a vehicle driving safety management method described in any one of claims 1 to 8 are implemented.

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