Dangerous driving grade determination system and method based on Internet of Vehicles data

Abstract

The invention discloses a dangerous driving grade determination system and method based on Internet of Vehicles data. The method comprises the following steps: acquiring a vehicle driving state and a self state index, a driver and passenger related index and a road related index based on the Internet of Vehicles data; performing index danger level division on each index according to the collected limit value condition of each index; and making a decision on each collected index and the index danger level corresponding to each index by using an integrated learning algorithm to obtain the dangerous driving level of the vehicle. According to the method, the vehicle, driver, passenger and road state indexes in the Internet of Vehicles data are comprehensively collected, the danger level of each index is divided carefully, the integrated learning algorithm is used for comprehensive evaluation, the dangerous driving level of the vehicle is accurately determined, and the road safety early warning and driving behavior management efficiency is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of automobile safety technology, and in particular relates to a system and method for determining a dangerous driving level based on Internet of Vehicles data. Background Art

[0002] With the continuous advancement of automobile technology and the widespread application of Internet of Vehicles technology, the issue of safe driving has received increasing attention. Internet of Vehicles technology collects information about vehicles, roads and the environment in real time through sensor devices on vehicles and roads, and realizes the interconnection and information sharing of people, vehicles and roads. This provides a rich data source and technical foundation for the development of dangerous driving level determination based on Internet of Vehicles data analysis and further driving safety reminder methods.

[0003] Traditional driving safety reminders mainly rely on infrastructure such as traffic signs and signal lights, as well as the driver's own driving experience and judgment. However, this method has limitations, especially in complex and changing traffic environments, where drivers may not be able to obtain all necessary safety information in a timely and accurate manner. Therefore, it is particularly important to develop a method that can analyze Internet of Vehicles data in real time and remind drivers to drive safely based on the analysis results.

[0004] In recent years, a variety of dangerous driving levels and driving safety reminder methods based on vehicle networking technology have been proposed. For example, some methods use safety traffic signs embedded with digital watermark information and combined with verification by cloud traffic management servers to ensure the legality and correctness of the location of traffic signs, thereby avoiding misoperation of autonomous vehicles. Other methods use communication between vehicles to report and share obstacle information in real time to help drivers avoid obstacle routes in advance. However, most of these methods focus on a single aspect of safety reminders and lack comprehensiveness and systematicness. Summary of the invention

[0005] In order to more comprehensively and accurately evaluate the dangerous driving of a vehicle, the present invention proposes a system and method for determining the dangerous driving level based on Internet of Vehicles data.

[0006] A system for determining a dangerous driving level based on Internet of Vehicles data to achieve one of the purposes of the present invention includes:

[0007] Index collection module: used to collect vehicle driving status and its own status indicators, driver and passenger related indicators and road related indicators based on Internet of Vehicles data;

[0008] The indicator danger level classification module is used to classify each indicator into an indicator danger level according to the limit conditions of each indicator collected by the indicator collection module, so as to obtain the indicator danger level of each indicator;

[0009] Vehicle dangerous driving level decision module: used to use the integrated learning algorithm to make decisions on each collected indicator and the indicator danger level corresponding to each indicator to obtain the vehicle's dangerous driving level.

[0010] The vehicle driving state index is used to indicate the comprehensive performance of the vehicle's motion state and related driving parameters during the vehicle's driving on the road; the vehicle's own state index is used to indicate the working state or health status of each system and component of the vehicle when it is stationary or driving;

[0011] The vehicle driving status indicators include: vehicle ignition status (start or stop), vehicle speed, voltage, SOC, gear, accelerator pedal travel value, brake pedal status; the vehicle's own status includes: door status, window status, vehicle anti-theft status, turn signal status, clearance light status, hazard warning light status, tire pressure status; the turn signal status, clearance light status, hazard warning light status include: normal light, abnormal light; the tire pressure status includes normal tire pressure, abnormal tire pressure; the brake pedal status includes brake pedal pressed, brake pedal released; the door status and window status include: door open or closed, window open or closed Closed; the vehicle body anti-theft status includes: unlocked state: the vehicle is not locked and is in an unprotected state; preset defense state: this is a transitional state. Usually, after pressing the lock key, if the vehicle is not confirmed to be completely locked within a certain period of time, the vehicle will remain in this state; at this time, if conditions such as a legitimate key or the door being opened are detected, the vehicle will return to the unlocked state; if these conditions are not detected within a time limit, it will automatically switch to the armed state; armed state: the vehicle is completely locked and in a security state to protect the property in the vehicle; alarm state: when the vehicle is in the armed state, if someone tries to illegally enter the vehicle, the alarm device will be triggered and an alarm will be issued through sound, light, and other means.

[0012] The beneficial effects of collecting the above vehicle driving status and self-status indicators include:

[0013] Real-time understanding of the vehicle's operating conditions, including key information such as ignition status, vehicle speed, and voltage, can help to promptly detect and remind the driver to deal with potential faults; monitoring the accelerator pedal travel value, brake pedal status, etc. can evaluate the driver's driving behavior and promptly remind the driver to pay attention to driving safety and avoid dangerous operations such as speeding and sudden braking; by collecting indicators such as the body anti-theft status, door status, and window status, the vehicle's anti-theft status can be monitored in real time. Once the vehicle is stolen or illegally invaded, the system can respond quickly and issue an alarm; monitoring the status of turn signals, position lights, and hazard warning lights can ensure that the vehicle's lighting system works normally during driving, improving driving safety at night or in bad weather; by monitoring the tire pressure status, abnormal tire pressure can be detected in time to prevent traffic accidents caused by tire problems.

[0014] The driver and passenger related indicators include: the driver's own driving status, the number of passengers in the car. The driver's own driving status includes: the driver is driving normally, the driver is driving tired, the driver is not wearing a seat belt, and the driver is in an unconscious state.

[0015] The road-related indicators are used to indicate the state of the road on which the vehicle is located, including: the state of the road on which the vehicle is located, the state of the road in front of the vehicle, the state of the road on the left side of the vehicle, and the state of the road on the right side of the vehicle. The road state on which the vehicle is located includes: flat land, desert, urban road, urban elevated road, expressway, whether there is congestion ahead, the road is narrowing, and whether there is a traffic accident ahead; the road state in front of the vehicle, the road state on the left side of the vehicle, and the road state on the right side of the vehicle include: no lane, lane with no vehicles in front and behind the lane, lane with vehicles driving side by side with the vehicle near the lane, and whether there is a traffic accident.

[0016] Furthermore, in the vehicle index danger level decision module, the decision method of the dangerous driving level of the vehicle includes:

[0017] When the indicator danger level of one of the indicators is level 1: the dangerous driving level of the vehicle is defined as level 1; otherwise, proceed to the next step;

[0018] When the indicator danger level of one of the indicators is level 2: the dangerous driving level of the vehicle is defined as level 2; otherwise, proceed to the next step;

[0019] When one of the indicators has an indicator danger level of 3: the dangerous driving level of the vehicle is defined as level 3;

[0020] The vehicle danger level corresponding to the above-mentioned level 1 indicator danger level is higher than the vehicle danger level corresponding to the level 2 indicator danger level, and the vehicle danger level corresponding to the level 2 indicator danger level is higher than the vehicle danger level corresponding to the level 3 indicator danger level.

[0021] The beneficial effects of this decision-making method include: judging the dangerous driving level of the vehicle through a clear and hierarchical decision-making method, and dividing each indicator into different danger levels (level 1, level 2, level 3, with level 1 being the most dangerous) according to the collected vehicle driving status, its own status, driver and passenger related indicators, and road related indicators. This hierarchical decision-making mechanism can respond quickly and accurately to the most dangerous indicators. Once an indicator reaches the level 1 danger level, the dangerous driving level of the vehicle is immediately defined as level 1, and no other indicators need to be considered. If there is no level 1 indicator, the level 2 and level 3 indicators are considered in turn to ensure that each level of danger can be evaluated in a timely and appropriate manner. This method improves the efficiency and accuracy of dangerous driving judgments, and also helps to take corresponding safety measures in a timely manner, reduce the risk of traffic accidents, and ensure driving safety.

[0022] Furthermore, when the dangerous driving level of the vehicle is defined as Level 1:

[0023] If the number of indicators with the current indicator danger level of level 1 is greater than the set number, the current vehicle's dangerous driving level is defined as level 1A;

[0024] If the indicator with a danger level of 1 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B; the danger level of 1A is higher than that of 1B;

[0025] When the dangerous driving level of the vehicle is defined as Level 2:

[0026] If the number of indicators with a current danger level of 2 is greater than the set number, the current dangerous driving level of the vehicle is defined as 2A;

[0027] If the indicator with a danger level of 2 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 2A, otherwise it is 2B; the danger level of 2A is higher than that of 2B;

[0028] When the dangerous driving level of the vehicle is defined as Level 3:

[0029] If the number of indicators with a current danger level of 3 is greater than the set number, the current dangerous driving level of the vehicle is defined as 3A;

[0030] If the indicator with a danger level of 3 accounts for a large proportion in historical data, the dangerous driving level of the vehicle is defined as 3A, otherwise it is 3B; the danger level of 3A is higher than that of 3B.

[0031] The beneficial effects of the above hazard classification include:

[0032] By setting up multiple levels of danger levels (1A, 1B, 2A, 2B, 3A, 3B), the dangerous driving status of the vehicle can be evaluated more finely. This classification not only takes into account the degree of danger of the current indicator, but also combines the statistical results of historical data, making the evaluation results more comprehensive and accurate; when the dangerous driving level of the vehicle reaches a higher level (such as 1A), the system can respond quickly and issue an early warning to remind the driver or relevant managers to take corresponding measures to avoid potential safety risks. This dynamic response mechanism helps to correct dangerous driving behaviors in a timely manner and reduce the incidence of traffic accidents; by considering the proportion of the danger level of each indicator in the historical data, a personalized risk assessment can be provided for each vehicle. This means that even if the current indicator danger level of two vehicles is the same, their dangerous driving levels may be different due to differences in historical data. This personalized assessment helps to more accurately reflect the actual risk status of each vehicle.

[0033] Furthermore, the system also includes a driving safety reminder module: used to provide driving safety reminders according to the dangerous driving level of the vehicle, specifically including:

[0034] When the dangerous driving level of the vehicle is 1A, the corresponding driving safety reminder strategies include: 110 alarm and implementation of intelligent vehicle network operation to assist the driver to achieve safe driving;

[0035] When the dangerous driving level of the vehicle is 1B, the corresponding driving safety reminder strategies include: implementing intelligent operation of the Internet of Vehicles to assist the driver in achieving safe driving;

[0036] When the vehicle's dangerous driving level is 2A, the corresponding driving safety reminder strategy includes: sending an alarm voice to the driver and sending the alarm information to the central control screen;

[0037] When the dangerous driving level of the vehicle is 2B, the corresponding driving safety reminder strategy includes: sending the alarm information to the central control screen;

[0038] When the vehicle's dangerous driving level is 3A, the alarm information will be sent to the central control screen and mobile phone APP;

[0039] When the vehicle's dangerous driving level is level 3B, the corresponding driving safety reminder strategy includes: sending alarm information to a mobile phone APP.

[0040] The beneficial effects of the above safety reminder module include:

[0041] The system adopts different levels of safety reminder strategies according to the dangerous driving level of the vehicle. This hierarchical response mechanism can ensure that the most appropriate intervention measures are taken in different situations of different dangerous levels, without excessively interfering with the driver or ignoring potential safety risks; when the dangerous driving level of the vehicle reaches a higher level (such as level 1A), the system not only sends an alarm message, but also directly triggers the 110 alarm and implements intelligent operation of the Internet of Vehicles to assist the driver in achieving safe driving. This timely and powerful early warning and intervention measure can significantly reduce the risk of traffic accidents; the system sends alarm information through multiple channels such as the central control screen and mobile phone APP to ensure that the driver can receive reminders in time in different scenarios. This multi-channel reminder method helps to improve the driver's alertness, enabling him to respond faster and avoid potential dangers; in the case of a high level of dangerous driving, the system assists the driver in achieving safe driving by implementing intelligent operation of the Internet of Vehicles. This intelligent auxiliary means can reduce the driver's driving burden and improve the safety and comfort during driving. The system adopts different safety reminder strategies according to the different levels of dangerous driving. This personalized reminder method not only meets the actual needs of the driver, but also enhances the user experience to a certain extent, making the driver more willing to use the system.

[0042] Furthermore, in the indicator danger level classification module, the method of classifying each indicator into an indicator danger level includes:

[0043] When the indicator is vehicle speed:

[0044] If the current vehicle speed is greater than the speed limit of the road section × the first set ratio, the danger level of the vehicle speed indicator is level 1;

[0045] If the current vehicle speed is greater than the speed limit of the road section × the second set ratio, and the vehicle speed is less than the speed limit of the road section × the first set ratio, the danger level of the vehicle speed indicator is level 2;

[0046] If the current vehicle speed is greater than the speed limit of the road section × the third set ratio, and the vehicle speed is less than the speed limit of the road section × the second set ratio, the danger level of the vehicle speed indicator is level 3;

[0047] When the vehicle speed index danger level is level 1, the corresponding danger level is higher than the corresponding danger level when the index danger level is level 2 or level 3; when the vehicle speed index danger level is level 2, its danger level is higher than the corresponding danger level when the index danger level is level 3.

[0048] The above beneficial effects of indexing the danger level of vehicle speed include:

[0049] By setting different set ratios (first set ratio, second set ratio, third set ratio), the vehicle speed is divided into three different danger levels (level 1, level 2, and level 3). This refined division helps to more accurately assess the impact of vehicle speed on driving safety, so that when the vehicle speed approaches or exceeds the safety threshold, a warning is issued in time to remind the driver to adjust the speed and ensure driving safety; combining the vehicle speed with the speed limit of the road section, and determining the danger level by comparing the multiple relationship between the current vehicle speed and the speed limit, can ensure that the driver always pays attention to the speed limit of the road section during driving, and avoid traffic accidents caused by speeding. At the same time, through different levels of warnings, the driver can be prompted to slow down in time when the speed is too fast, thereby improving overall driving safety; the flexibility of the setting ratio enables the technical solution to adapt to the speed limit requirements in different sections and different traffic environments. For example, on a highway, the speed limit may be high, so the accuracy and effectiveness of the speed warning can be ensured by adjusting the setting ratio. This adaptability enables the technical solution to play a good role in different scenarios; the method of dividing the danger level of the vehicle speed index is relatively simple and clear, easy to implement and maintain, and by setting a fixed proportional relationship, real-time monitoring and early warning of the vehicle speed can be achieved without complex calculations or data processing.

[0050] A method for determining a dangerous driving level based on Internet of Vehicles data to achieve the second objective of the present invention includes:

[0051] Based on the Internet of Vehicles data, the vehicle's driving status and its own status indicators, driver and passenger related indicators, and road related indicators are collected;

[0052] Classify each indicator into an indicator danger level according to the limit conditions of each indicator collected;

[0053] An integrated learning algorithm is used to make decisions on each collected indicator and the indicator danger level corresponding to each indicator to obtain the vehicle's dangerous driving level.

[0054] Further, including:

[0055] The decision method of the dangerous driving level of the vehicle includes:

[0056] When the indicator danger level of one of the indicators is level 1: the dangerous driving level of the vehicle is defined as level 1; otherwise, proceed to the next step;

[0057] When the indicator danger level of one of the indicators is level 2: the dangerous driving level of the vehicle is defined as level 2; otherwise, proceed to the next step;

[0058] When one of the indicators has an indicator danger level of 3: the dangerous driving level of the vehicle is defined as level 3;

[0059] The vehicle danger level corresponding to the above-mentioned level 1 indicator danger level is higher than the vehicle danger level corresponding to the level 2 indicator danger level, and the vehicle danger level corresponding to the level 2 indicator danger level is higher than the vehicle danger level corresponding to the level 3 indicator danger level.

[0060] Furthermore, when the dangerous driving level of a vehicle is defined as Level 1:

[0061] If the number of indicators with the current indicator danger level of level 1 is greater than the set number, the current vehicle's dangerous driving level is defined as level 1A;

[0062] If the indicator with a danger level of 1 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B; the danger level of 1A is higher than that of 1B;

[0063] When the dangerous driving level of the vehicle is defined as Level 2:

[0064] If the number of indicators with a current danger level of 2 is greater than the set number, the current dangerous driving level of the vehicle is defined as 2A;

[0065] If the indicator with a danger level of 2 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 2A, otherwise it is 2B; the danger level of 2A is higher than that of 2B;

[0066] When the dangerous driving level of the vehicle is defined as Level 3:

[0067] If the number of indicators with a current danger level of 3 is greater than the set number, the current dangerous driving level of the vehicle is defined as 3A;

[0068] If the indicator with a danger level of 3 accounts for a large proportion in historical data, the dangerous driving level of the vehicle is defined as 3A, otherwise it is 3B; the danger level of 3A is higher than that of 3B.

[0069] Furthermore, the method further includes providing a driving safety reminder according to the dangerous driving level of the vehicle, and the driving safety reminder method includes:

[0070] When the dangerous driving level of the vehicle is 1A, the corresponding driving safety reminder strategies include: 110 alarm and implementation of intelligent vehicle network operation to assist the driver to achieve safe driving;

[0071] When the dangerous driving level of the vehicle is 1B, the corresponding driving safety reminder strategies include: implementing intelligent operation of the Internet of Vehicles to assist the driver in achieving safe driving;

[0072] When the vehicle's dangerous driving level is 2A, the corresponding driving safety reminder strategy includes: sending an alarm voice to the driver and sending the alarm information to the central control screen;

[0073] When the dangerous driving level of the vehicle is 2B, the corresponding driving safety reminder strategy includes: sending the alarm information to the central control screen;

[0074] When the vehicle's dangerous driving level is 3A, the alarm information will be sent to the central control screen and mobile phone APP;

[0075] When the vehicle's dangerous driving level is level 3B, the corresponding driving safety reminder strategy includes: sending alarm information to a mobile phone APP.

[0076] The beneficial effects of the present invention include:

[0077] The present invention comprehensively collects vehicle, driver, passenger and road-related indicators through vehicle networking technology, divides the danger level of each indicator according to preset limit conditions, and uses integrated learning algorithms for comprehensive evaluation to accurately determine the vehicle's dangerous driving level, effectively improving the accuracy and real-time performance of driving safety warnings, and providing strong support for road safety management and driving behavior optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the process of the method of the present invention. DETAILED DESCRIPTION

[0079] The following specific implementations are used to explain the technical solutions of the claims of the present invention so that those skilled in the art can understand the claims. The protection scope of the present invention is not limited to the following specific implementation structures. The technical solutions of the claims of the present invention made by those skilled in the art but different from the following specific implementations are also within the protection scope of the present invention.

[0080] Example 1

[0081] A method for determining dangerous driving level based on Internet of Vehicles data

[0082] Step 1: Indicator Collection

[0083] Take the key indicators of a vehicle in the Internet of Vehicles at a certain time period, the key indicators include: vehicle driving status and self-status indicators shown in Table 1, driver and passenger related indicators shown in Table 2, and road related indicators shown in Table 3;

[0084] Table 1

[0085]

[0086]

[0087] Table 2

[0088] Collection indicator content Indicator source Driver’s own driving status recognition Internet of Vehicles (with surveillance cameras in the car) Identification of the number of passengers in the car Internet of Vehicles (with surveillance cameras in the car) …… ……

[0089] Table 3

[0090] Collection indicator content Indicator source The road condition of the vehicle Internet of Vehicles The road conditions ahead of the vehicle Internet of Vehicles Vehicle left side road status Internet of Vehicles Right side of the vehicle road status Internet of Vehicles …… ……

[0091] Step 2: Classification of indicator risk levels

[0092] Each indicator is divided into 1, 2, and 3 levels according to the indicator limit conditions. For example, the vehicle speed is divided into 3 levels according to the vehicle speed and the road section speed limit. The division method is as follows:

[0093] When "vehicle speed>road section speed limit×150%", the vehicle speed is level 1;

[0094] When "road section speed limit × 120% < vehicle speed < road section speed limit × 150%", the vehicle speed is level 2;

[0095] When “road section speed limit × 100% < vehicle speed < road section speed limit × 120%”, the vehicle speed is level 3.

[0096] Step 3: Hazard aggregation output indicator hazard level results

[0097] The ensemble learning algorithm is used to combine the indicators collected in step 1 and the corresponding indicator danger level classification results of each indicator to comprehensively output the current dangerous driving level of the vehicle. Specifically, it includes:

[0098] When one of the indicators has a dangerous level of 1: Combined with the historical frequency of the indicator, the higher the frequency, the lower the danger, but the dangerous driving level of the vehicle is still defined as 1, and divided into 1A and 1B levels according to the proportion; specifically including:

[0099] (1) If the number of indicators with a current indicator danger level of level 1 is greater than the first set number (e.g., there are 5 or more indicators with an indicator danger level of level 1), the current dangerous driving level of the vehicle is defined as level 1A, otherwise, proceed to the next step;

[0100] (2) When the proportion of indicators with a hazard level of 1 in the historical data is relatively large (for example, in the historical data, 100 vehicle speed measurements were collected, and more than 80 of them were at a hazard level of 1), the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B.

[0101] When there is no indicator with a danger level of 1, but there is an indicator with a danger level of 2, combined with the historical frequency of the indicator, the higher the frequency, the lower the danger, but the dangerous driving level of the vehicle is still defined as 2, and divided into 2A and 2B levels according to the proportion; specifically including:

[0102] (1) If the number of indicators with a current indicator danger level of 2 is greater than the second set number (e.g., there are 10 or more indicators with an indicator danger level of 2 at present), the current dangerous driving level of the vehicle is defined as level 2A, otherwise, proceed to the next step;

[0103] (2) When the proportion of indicators with a danger level of 2 in the historical data is relatively large (for example, in the historical data, 100 driver self-driving status identifications were collected, and more than 10 driver self-driving status identifications had an indicator danger level of 2), the vehicle's dangerous driving level is defined as 2A, otherwise it is defined as 2B.

[0104] When there is no indicator with a danger level of 1 or 2, and there is an indicator with a danger level of 3, combined with the historical frequency of the indicator, the higher the frequency, the lower the danger, but the dangerous driving level of the vehicle is still defined as 3, and divided into 3A and 3B levels according to the proportion; specifically including:

[0105] (1) If the number of indicators with a current indicator danger level of level 3 is greater than the third set number (e.g., there are 15 or more indicators with an indicator danger level of level 3), the indicator danger level of the current vehicle is defined as level 3A, otherwise, proceed to the next step;

[0106] (2) When the proportion of indicators with a danger level of 3 in the historical data is relatively large (for example, in the historical data, 100 tire pressure measurements were collected, and more than 10 of them had a danger level of 3), the dangerous driving level of the vehicle is defined as 3A; otherwise, it is defined as 3B.

[0107] Step 4: Output specific reminder decisions

[0108] Using a decision tree algorithm, when the vehicle's dangerous driving level is 1A, the corresponding driving safety reminder strategies include: 110 alarm and implementation of intelligent vehicle network operations to assist the driver in achieving safe driving; when the vehicle's dangerous driving level is 1B, the corresponding driving safety reminder strategies include: implementation of intelligent vehicle network operations to assist the driver in achieving safe driving; when the vehicle's dangerous driving level is 2A, the corresponding driving safety reminder strategies include: sending an alarm voice to the driver and sending the alarm information to the central control screen; when the vehicle's dangerous driving level is 2B, the corresponding driving safety reminder strategies include: sending the alarm information to the central control screen; when the vehicle's dangerous driving level is 3A, the alarm information is sent to the central control screen and mobile phone APP; when the vehicle's dangerous driving level is 3B, the corresponding driving safety reminder strategies include: sending the alarm information to the mobile phone APP.

[0109] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0110] Example 2

[0111] A dangerous driving level determination system based on Internet of Vehicles data, comprising:

[0112] Index collection module: used to collect vehicle driving status and its own status indicators, driver and passenger related indicators and road related indicators based on Internet of Vehicles data;

[0113] The indicator danger level classification module is used to classify each indicator into an indicator danger level according to the limit conditions of each indicator collected by the indicator collection module, so as to obtain the indicator danger level of each indicator;

[0114] Vehicle dangerous driving level decision module: used to use the integrated learning algorithm to make decisions on each collected indicator and the indicator danger level corresponding to each indicator to obtain the vehicle's dangerous driving level.

[0115] The vehicle driving status and self-status indicators include: vehicle ignition status (started or turned off), vehicle speed, voltage, SOC, gear position, accelerator pedal travel value, brake pedal status, door status, window status, vehicle body anti-theft status, turn signal status, clearance light status, hazard warning light status, and tire pressure status.

[0116] In some embodiments, in the vehicle indicator danger level decision module, the method for deciding the dangerous driving level of the vehicle includes:

[0117] When the indicator danger level of one of the indicators is level 1: the dangerous driving level of the vehicle is defined as level 1; otherwise, proceed to the next step;

[0118] When the indicator danger level of one of the indicators is level 2: the dangerous driving level of the vehicle is defined as level 2; otherwise, proceed to the next step;

[0119] When one of the indicators has an indicator danger level of 3: the dangerous driving level of the vehicle is defined as level 3;

[0120] The vehicle danger level corresponding to the above-mentioned level 1 indicator danger level is higher than the vehicle danger level corresponding to the level 2 indicator danger level, and the vehicle danger level corresponding to the level 2 indicator danger level is higher than the vehicle danger level corresponding to the level 3 indicator danger level.

[0121] In some embodiments, when the dangerous driving level of the vehicle is defined as Level 1:

[0122] If the number of indicators with the current indicator danger level of level 1 is greater than the set number, the current vehicle's dangerous driving level is defined as level 1A;

[0123] If the indicator with a danger level of 1 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B; the danger level of 1A is higher than that of 1B;

[0124] When the dangerous driving level of the vehicle is defined as Level 2:

[0125] If the number of indicators with a current danger level of 2 is greater than the set number, the current dangerous driving level of the vehicle is defined as 2A;

[0126] If the indicator with a danger level of 2 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 2A, otherwise it is 2B; the danger level of 2A is higher than that of 2B;

[0127] When the dangerous driving level of the vehicle is defined as Level 3:

[0128] If the number of indicators with a current danger level of 3 is greater than the set number, the current dangerous driving level of the vehicle is defined as 3A;

[0129] If the indicator with a danger level of 3 accounts for a large proportion in historical data, the dangerous driving level of the vehicle is defined as 3A, otherwise it is 3B; the danger level of 3A is higher than that of 3B.

[0130] In some embodiments, the system further includes a driving safety reminder module: used to provide driving safety reminders according to the dangerous driving level of the vehicle, specifically including:

[0131] When the dangerous driving level of the vehicle is 1A, the corresponding driving safety reminder strategies include: 110 alarm and implementation of intelligent vehicle network operation to assist the driver to achieve safe driving;

[0132] When the dangerous driving level of the vehicle is 1B, the corresponding driving safety reminder strategies include: implementing intelligent operation of the Internet of Vehicles to assist the driver in achieving safe driving;

[0133] When the vehicle's dangerous driving level is 2A, the corresponding driving safety reminder strategy includes: sending an alarm voice to the driver and sending the alarm information to the central control screen;

[0134] When the dangerous driving level of the vehicle is 2B, the corresponding driving safety reminder strategy includes: sending the alarm information to the central control screen;

[0135] When the vehicle's dangerous driving level is 3A, the alarm information will be sent to the central control screen and mobile phone APP;

[0136] When the vehicle's dangerous driving level is level 3B, the corresponding driving safety reminder strategy includes: sending alarm information to a mobile phone APP.

[0137] In some embodiments, in the indicator risk level classification module, the method of classifying each indicator into an indicator risk level includes:

[0138] When the indicator is vehicle speed:

[0139] If the current vehicle speed is greater than the speed limit of the road section × the first set ratio, the danger level of the vehicle speed indicator is level 1;

[0140] If the current vehicle speed is greater than the speed limit of the road section × the second set ratio, and the vehicle speed is less than the speed limit of the road section × the first set ratio, the danger level of the vehicle speed indicator is level 2;

[0141] If the current vehicle speed is greater than the road section speed limit × the third set ratio, and the vehicle speed is less than the road section speed limit × the second set ratio, the vehicle speed indicator danger level is level 3.

[0142] Example 3

[0143] A method for determining a dangerous driving level based on Internet of Vehicles data, comprising:

[0144] Based on the Internet of Vehicles data, the vehicle's driving status and its own status indicators, driver and passenger related indicators, and road related indicators are collected;

[0145] Classify each indicator into an indicator danger level according to the limit conditions of each indicator collected;

[0146] An integrated learning algorithm is used to make decisions on each collected indicator and the indicator danger level corresponding to each indicator to obtain the vehicle's dangerous driving level.

[0147] In some embodiments, the method for determining the dangerous driving level of the vehicle includes:

[0148] When the indicator danger level of one of the indicators is level 1: the dangerous driving level of the vehicle is defined as level 1; otherwise, proceed to the next step;

[0149] When the indicator danger level of one of the indicators is level 2: the dangerous driving level of the vehicle is defined as level 2; otherwise, proceed to the next step;

[0150] When one of the indicators has an indicator danger level of 3: the dangerous driving level of the vehicle is defined as level 3;

[0151] The vehicle danger level corresponding to the above-mentioned level 1 indicator danger level is higher than the vehicle danger level corresponding to the level 2 indicator danger level, and the vehicle danger level corresponding to the level 2 indicator danger level is higher than the vehicle danger level corresponding to the level 3 indicator danger level.

[0152] In some embodiments, when the dangerous driving level of the vehicle is defined as Level 1:

[0153] If the number of indicators with the current indicator danger level of level 1 is greater than the set number, the current vehicle's dangerous driving level is defined as level 1A;

[0154] If the indicator with a danger level of 1 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B; the danger level of 1A is higher than that of 1B;

[0155] When the dangerous driving level of the vehicle is defined as Level 2:

[0156] If the number of indicators with a current danger level of 2 is greater than the set number, the current dangerous driving level of the vehicle is defined as 2A;

[0157] If the indicator with a danger level of 2 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 2A, otherwise it is 2B; the danger level of 2A is higher than that of 2B;

[0158] When the dangerous driving level of the vehicle is defined as Level 3:

[0159] If the number of indicators with a current danger level of 3 is greater than the set number, the current dangerous driving level of the vehicle is defined as 3A;

[0160] If the indicator with a danger level of 3 accounts for a large proportion in historical data, the dangerous driving level of the vehicle is defined as 3A, otherwise it is 3B; the danger level of 3A is higher than that of 3B.

[0161] In some embodiments, the method further includes providing a driving safety reminder according to the dangerous driving level of the vehicle, and the driving safety reminder method includes:

[0162] When the dangerous driving level of the vehicle is 1A, the corresponding driving safety reminder strategies include: 110 alarm and implementation of intelligent vehicle network operation to assist the driver to achieve safe driving;

[0163] When the dangerous driving level of the vehicle is 1B, the corresponding driving safety reminder strategies include: implementing intelligent operation of the Internet of Vehicles to assist the driver in achieving safe driving;

[0164] When the vehicle's dangerous driving level is 2A, the corresponding driving safety reminder strategy includes: sending an alarm voice to the driver and sending the alarm information to the central control screen;

[0165] When the dangerous driving level of the vehicle is 2B, the corresponding driving safety reminder strategy includes: sending the alarm information to the central control screen;

[0166] When the vehicle's dangerous driving level is 3A, the alarm information will be sent to the central control screen and mobile phone APP;

[0167] When the vehicle's dangerous driving level is level 3B, the corresponding driving safety reminder strategy includes: sending alarm information to a mobile phone APP.

[0168] Example 4

[0169] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the steps of the method described in the present invention are implemented, which will not be repeated here.

[0170] The computer-readable storage medium may be the data transmission device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (smartmedia card, SMC), a secure digital (secure digital, SD) card, a flash card (flash card), etc., provided on the computer device.

[0171] Furthermore, the computer-readable storage medium may include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data to be output or has been output.

[0172] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0174] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0176] Example 5

[0177] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the method for determining a dangerous driving level based on Internet of Vehicles data.

[0178] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A dangerous driving level determination system based on Internet of Vehicles data, characterized in that: include: Index collection module: used to collect vehicle driving status and its own status indicators, driver and passenger related indicators and road related indicators based on Internet of Vehicles data; The indicator danger level classification module is used to classify each indicator into an indicator danger level according to the limit conditions of each indicator collected by the indicator collection module, so as to obtain the indicator danger level of each indicator; Vehicle dangerous driving level decision module: used to use the integrated learning algorithm to make decisions on each collected indicator and the indicator danger level corresponding to each indicator to obtain the vehicle's dangerous driving level.

2. The dangerous driving level determination system based on Internet of Vehicles data as claimed in claim 1, characterized in that: In the vehicle index danger level decision module, the decision method of the dangerous driving level of the vehicle includes: When the indicator danger level of one of the indicators is level 1: the dangerous driving level of the vehicle is defined as level 1; otherwise, proceed to the next step; When the indicator danger level of one of the indicators is level 2: the dangerous driving level of the vehicle is defined as level 2; otherwise, proceed to the next step; When one of the indicators has an indicator danger level of 3: the dangerous driving level of the vehicle is defined as level 3; The vehicle danger level corresponding to the above-mentioned level 1 indicator danger level is higher than the vehicle danger level corresponding to the level 2 indicator danger level, and the vehicle danger level corresponding to the level 2 indicator danger level is higher than the vehicle danger level corresponding to the level 3 indicator danger level.

3. The dangerous driving level determination system based on Internet of Vehicles data as claimed in claim 2, characterized in that: When the dangerous driving level of the vehicle is defined as Level 1: If the number of indicators with the current indicator danger level of level 1 is greater than the set number, the current vehicle's dangerous driving level is defined as level 1A; If the indicator with a danger level of 1 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B; the danger level of 1A is higher than that of 1B; When the dangerous driving level of the vehicle is defined as Level 2: If the number of indicators with a current danger level of 2 is greater than the set number, the current dangerous driving level of the vehicle is defined as 2A; If the indicator with a danger level of 2 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 2A, otherwise it is 2B; the danger level of 2A is higher than that of 2B; When the dangerous driving level of the vehicle is defined as Level 3: If the number of indicators with a current danger level of 3 is greater than the set number, the current dangerous driving level of the vehicle is defined as 3A; If the indicator with a danger level of 3 accounts for a large proportion in historical data, the dangerous driving level of the vehicle is defined as 3A, otherwise it is 3B; the danger level of 3A is higher than that of 3B.

4. The dangerous driving level determination system based on Internet of Vehicles data as claimed in claim 3, characterized in that: It also includes a driving safety reminder module: used to provide driving safety reminders according to the dangerous driving level of the vehicle, and the driving safety reminder method includes: When the dangerous driving level of the vehicle is 1A, the corresponding driving safety reminder strategies include: alarm and implementation of intelligent vehicle network operation to assist the driver in achieving safe driving; When the dangerous driving level of the vehicle is 1B, the corresponding driving safety reminder strategies include: implementing intelligent operation of the Internet of Vehicles to assist the driver in achieving safe driving; When the vehicle's dangerous driving level is 2A, the corresponding driving safety reminder strategy includes: sending an alarm voice to the driver and sending the alarm information to the central control screen; When the dangerous driving level of the vehicle is 2B, the corresponding driving safety reminder strategy includes: sending the alarm information to the central control screen; When the vehicle's dangerous driving level is 3A, the alarm information will be sent to the central control screen and mobile phone APP; When the vehicle's dangerous driving level is level 3B, the corresponding driving safety reminder strategy includes: sending alarm information to a mobile phone APP.

5. The dangerous driving level determination system based on Internet of Vehicles data as claimed in claim 1, characterized in that: In the indicator danger level classification module, the method of classifying each indicator into an indicator danger level includes: When the indicator is vehicle speed: If the current vehicle speed is greater than the speed limit of the road section × the first set ratio, the danger level of the vehicle speed indicator is level 1; If the current vehicle speed is greater than the speed limit of the road section × the second set ratio, and the vehicle speed is less than the speed limit of the road section × the first set ratio, the danger level of the vehicle speed indicator is level 2; If the current vehicle speed is greater than the speed limit of the road section × the third set ratio, and the vehicle speed is less than the speed limit of the road section × the second set ratio, the danger level of the vehicle speed indicator is level 3; When the vehicle speed index danger level is level 1, the corresponding danger level is higher than the corresponding danger level when the index danger level is level 2 or level 3; when the vehicle speed index danger level is level 2, its danger level is higher than the corresponding danger level when the index danger level is level 3.

6. A method for determining dangerous driving level based on Internet of Vehicles data, characterized in that: include: Based on the Internet of Vehicles data, the vehicle's driving status and its own status indicators, driver and passenger related indicators, and road related indicators are collected; Classify each indicator into an indicator danger level according to the limit conditions of each indicator collected; An integrated learning algorithm is used to make decisions on each collected indicator and the indicator danger level corresponding to each indicator to obtain the vehicle's dangerous driving level.

7. The method for determining the dangerous driving level based on Internet of Vehicles data according to claim 6, characterized in that: The decision method of the dangerous driving level of the vehicle includes: When the indicator danger level of one of the indicators is level 1: the dangerous driving level of the vehicle is defined as level 1; otherwise, proceed to the next step; When the indicator danger level of one of the indicators is level 2: the dangerous driving level of the vehicle is defined as level 2; otherwise, proceed to the next step; When one of the indicators has an indicator danger level of 3: the dangerous driving level of the vehicle is defined as level 3; The vehicle danger level corresponding to the above-mentioned level 1 indicator danger level is higher than the vehicle danger level corresponding to the level 2 indicator danger level, and the vehicle danger level corresponding to the level 2 indicator danger level is higher than the vehicle danger level corresponding to the level 3 indicator danger level.

8. The method for determining the dangerous driving level based on Internet of Vehicles data according to claim 7, characterized in that: When the dangerous driving level of the vehicle is defined as Level 1: If the number of indicators with the current indicator danger level of level 1 is greater than the set number, the current vehicle's dangerous driving level is defined as level 1A; If the indicator with a danger level of 1 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 1A, otherwise it is 1B; the danger level of 1A is higher than that of 1B; When the dangerous driving level of the vehicle is defined as Level 2: If the number of indicators with a current danger level of 2 is greater than the set number, the current dangerous driving level of the vehicle is defined as 2A; If the indicator with a danger level of 2 in the historical data accounts for a large proportion of the indicator danger level, the dangerous driving level of the vehicle is defined as 2A, otherwise it is 2B; the danger level of 2A is higher than that of 2B; When the dangerous driving level of the vehicle is defined as Level 3: If the number of indicators with a current danger level of 3 is greater than the set number, the current dangerous driving level of the vehicle is defined as 3A; If the indicator with a danger level of 3 accounts for a large proportion in historical data, the dangerous driving level of the vehicle is defined as 3A, otherwise it is 3B; the danger level of 3A is higher than that of 3B.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining a dangerous driving level based on Internet of Vehicles data as described in any one of claims 6 to 8 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, any step of the method for determining the dangerous driving level based on vehicle network data described in claims 6 to 8 is implemented.

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