A vehicle alarm method based on vehicle networking system

Abstract

The present invention discloses a vehicle alarm method based on a vehicle networking system. The vehicle networking system includes a vehicle management platform and an on-board terminal. The vehicle alarm method is implemented by the following steps: S1. The vehicle management platform collects the original alarm data reported by the on-board terminal; S2. The vehicle management platform is preset with three models of speeding, fatigue driving, and dangerous driving. The vehicle management platform summarizes and calculates the alarm information of the vehicle according to the three models combined with multi-dimensional parameters; S3. The dangerous events that really need attention are obtained, and these dangerous events are called alarm events; an alarm event includes multiple alarms of the vehicle and the operating status of the vehicle during the alarm event. The vehicle alarm method of the present invention can quickly obtain the alarm information of vehicles that are really risky, and can filter out a large amount of invalid alarm data. The monitoring personnel can quickly find the vehicles that are really risky, and then effectively remind the driver to achieve the purpose of preventing accidents in advance.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking, and in particular to a vehicle alarm method based on a vehicle networking system. Background Art

[0002] The existing Internet of Vehicles system collects alarm information reported by the on-board terminal during vehicle operation, including ADAS (Advanced driver assistant system), BSD (Blind Spot Detection), DSM (Driver State Monitoring), TPMS (Tire pressure monitoring systems), GPS alarm, etc., and uploads alarm video and alarm picture information at the time of alarm, which is used to monitor the vehicle's working status during driving and various dangerous driving behaviors of the driver.

[0003] However, the existing technical solutions have the following shortcomings and deficiencies in actual operation: on the one hand, due to the limitation of the image recognition algorithm of the vehicle terminal, the system will generate a lot of false alarm data; on the other hand, a single point of alarm is not enough to explain the degree of danger of the actual driving behavior of the vehicle. For example, if the driver yawns while driving, it cannot be said that the driver is already in a state of fatigue driving.

[0004] Therefore, in the existing Internet of Vehicles system monitoring, there will be a large amount of invalid alarm data. In the process of handling alarms, it is difficult for monitoring personnel to find vehicles that are truly risky due to the influence of these invalid data. Therefore, it is difficult to effectively remind drivers and achieve the purpose of preventing accidents in advance. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle alarm method based on a vehicle networking system, which can at least solve one of the above problems.

[0006] According to one aspect of the present invention, a vehicle alarm method based on a vehicle networking system is provided. The vehicle networking system includes a vehicle management platform and a vehicle terminal. The vehicle terminal communicates wirelessly with the vehicle management platform and can upload real-time vehicle information to the vehicle management platform. The vehicle alarm method is implemented by the following steps:

[0007] S1. The vehicle management platform collects the original alarm data reported by the vehicle terminal;

[0008] S2. The vehicle management platform has three preset models: speeding, fatigue driving, and dangerous driving. The vehicle management platform summarizes and calculates the vehicle alarm information based on the three models combined with multi-dimensional parameters;

[0009] S3, get the dangerous events that really need attention, and call these dangerous events alarm events;

[0010] An alarm event includes multiple alarms of the vehicle and the operating status of the vehicle during the alarm event.

[0011] Therefore, the present invention provides a vehicle alarm method with a new structure. The vehicle alarm method is based on the operation of the vehicle networking system. Its specific principle is: the vehicle management platform collects the original alarm data reported by the vehicle terminal, combines the multi-dimensional parameters such as vehicle speed, positioning, weather, time, etc., and designs three models to summarize and calculate the vehicle's speeding, driver fatigue driving, and vehicle dangerous driving events, so as to find out the dangerous events that the monitoring personnel really need to pay attention to. We call it an alarm event. An alarm event contains multiple alarms of the vehicle and the operating status of the vehicle during this alarm event. The monitoring personnel can see the cause of the alarm event and find the corresponding video and picture evidence. They can remind the driver to pay attention to driving safety by sending voice text or voice intercom with the driver; in addition, the vehicle risk level can be generated through the alarm event; and the high-risk driving behavior can be obtained through big data analysis.

[0012] The vehicle alarm method of the present invention has the following advantages:

[0013] 1. Filter out a large number of vehicle alarms that do not require attention;

[0014] 2. Truly identify dangerous events that require special attention during vehicle operation;

[0015] 3. While processing alarm events, the alarms generated by vehicles can be processed in batches, thereby greatly improving the efficiency of monitoring personnel in handling alarms;

[0016] 4. The generated alarm events can be used to assess the risk level of vehicle driving, so that the driving behavior of vehicles with high risk levels can be quickly locked and paid attention to in a timely manner;

[0017] 5. Use voice text delivery and voice intercom to remind drivers to pay attention to driving safety and reduce vehicle driving safety hazards;

[0018] 6. Accurately identify dangerous driving behaviors of vehicles and drivers through big data dashboards, and provide targeted education and training;

[0019] 7. The algorithm model can be expanded to obtain vehicle driving events that meet user needs.

[0020] In some implementations, after step S3, the following steps may also be included:

[0021] S4. Monitoring personnel can see the causes of each alarm event through the vehicle management platform and find the corresponding video and picture evidence.

[0022] Therefore, it is easy to analyze and view with high accuracy and strong credibility.

[0023] In some implementations, after step S4, the following steps may also be included:

[0024] S5. The monitoring personnel can send voice texts through the vehicle management platform or have voice intercoms with the driver to remind the driver to pay attention to driving safety.

[0025] In this way, the driver can be reminded in time to avoid the expansion of risks.

[0026] In some embodiments, the multi-dimensional parameters include vehicle speed, location, weather, and time. Thus, the method takes various conditions into consideration and has high accuracy and reliability.

[0027] In some implementations, step S3 may further include:

[0028] Step S31: The vehicle management platform automatically generates a vehicle risk level based on the alarm event.

[0029] Therefore, the classification of vehicle risk levels makes it easier to quickly identify and focus on the driving behavior of high-risk vehicles.

[0030] In some implementations, after step S31, the following steps may also be included:

[0031] Step S32: deriving high-risk driving behaviors through big data analysis.

[0032] Therefore, the analysis results can be displayed on the big data dashboard, through which dangerous driving behaviors of vehicles and drivers can be accurately identified, facilitating targeted education and training.

[0033] In some implementations, the warning events include speeding events, dangerous driving events, and fatigue driving events.

[0034] In some implementations, the speeding event should be comprehensively calculated based on four dimensions: whether it is speeding, highway / urban area, speed limit sign, and duration. Thus, the speeding event can be determined from multiple aspects with high accuracy and credibility.

[0035] In some implementations, dangerous driving events should be calculated comprehensively including lane departure, collision warning, and vehicle distance too close alarm. Thus, dangerous driving events can be determined from multiple aspects with high accuracy and credibility.

[0036] In some embodiments, fatigue driving events should be calculated comprehensively for four alarms: yawning, closing eyes, distraction, and lane departure. Thus, fatigue driving events can be determined from multiple aspects with high accuracy and credibility, which is sufficient to illustrate the degree of danger of the actual driving behavior of the vehicle.

[0037] In some implementations, the vehicle management platform is a server.

[0038] Beneficial effects of the present invention:

[0039] The present invention provides a vehicle alarm method with a new structure. The vehicle alarm method is based on the operation of the vehicle networking system. The specific principle is: the vehicle management platform collects the original alarm data reported by the vehicle terminal, combines the multi-dimensional parameters such as vehicle speed, positioning, weather, time, etc., and designs three models to summarize and calculate the vehicle's speeding, driver fatigue driving, and vehicle dangerous driving events, so as to find out the dangerous events that the monitoring personnel really need to pay attention to. We call it an alarm event. An alarm event contains multiple alarms of the vehicle and the operating status of the vehicle during this alarm event. The monitoring personnel can see the cause of the alarm event and find the corresponding video and picture evidence. They can remind the driver to pay attention to driving safety by sending voice text or voice intercom with the driver; in addition, the vehicle risk level can be generated through the alarm event; and high-risk driving behavior can be obtained through big data analysis.

[0040] The vehicle alarm method of the present invention can quickly obtain the alarm information of vehicles that are truly at risk, and can filter out a large amount of invalid alarm data. Monitoring personnel can quickly find vehicles that are truly at risk, and then effectively remind drivers, thereby achieving the purpose of preventing accidents in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The system block diagram of a vehicle alarm method based on a vehicle networking system according to an embodiment of the present invention.

[0042] Figure 1 The accompanying drawings are as follows: 100 - vehicle terminal; 200 - vehicle management platform; A - alarm event; A1 - speeding event; A2 - dangerous driving event; A3 - fatigue driving event; a - alarm; b - multi-dimensional parameters. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0044] Figure 1 A vehicle alarm method based on a vehicle networking system according to an embodiment of the present invention is schematically shown.

[0045] The vehicle networking system includes a vehicle management platform 200 and a vehicle terminal 100. The vehicle terminal 100 communicates wirelessly with the vehicle management platform 200 and can upload real-time vehicle information to the vehicle management platform 200. The vehicle terminal 100 is provided with a communication module, and the vehicle management platform 200 is provided with a matching communication module, which can be an existing 4G wireless communication module.

[0046] The vehicle management platform 200 of this embodiment may be a cloud server, and the hardware part may be composed of one or more computers.

[0047] like Figure 1 As shown, the vehicle alarm method based on the Internet of Vehicles system is implemented by the following steps:

[0048] S1, the vehicle management platform 200 collects the original alarm a data reported by the vehicle terminal 100;

[0049] S2. The vehicle management platform 200 presets three models of speeding, fatigue driving, and dangerous driving. The vehicle management platform 200 summarizes and calculates the vehicle alarm a information based on the three models combined with the multi-dimensional parameter b;

[0050] S3, obtaining dangerous events that really need attention, and calling these dangerous events alarm events A;

[0051] An alarm event A includes multiple alarms a of the vehicle and the running status of the vehicle during the alarm event A.

[0052] After step S3, the following steps may also be included:

[0053] S4. Monitoring personnel can see the causes of each alarm event A through the vehicle management platform 200 and find the corresponding video and picture evidence.

[0054] Therefore, it is easy to analyze and view with high accuracy and strong credibility.

[0055] After step S4, the following steps may also be included:

[0056] S5. The monitoring personnel can send voice texts through the vehicle management platform 200 or have a voice conversation with the driver to remind the driver to pay attention to driving safety.

[0057] In this way, the driver can be reminded in time to avoid the expansion of risks.

[0058] The multi-dimensional parameter b includes but is not limited to vehicle speed, location, weather, and time. Thus, the method takes various conditions into consideration and has high accuracy and reliability.

[0059] In step S3, the following steps may also be included:

[0060] Step S31: The vehicle management platform 200 automatically generates a vehicle risk level according to alarm event A.

[0061] Therefore, the classification of vehicle risk levels makes it easier to quickly identify and focus on the driving behavior of high-risk vehicles.

[0062] After step S31, the following steps may also be included:

[0063] Step S32: deriving high-risk driving behaviors through big data analysis.

[0064] Therefore, the analysis results can be displayed on the big data dashboard, through which dangerous driving behaviors of vehicles and drivers can be accurately identified, facilitating targeted education and training.

[0065] The alarm events A of this embodiment include, but are not limited to, speeding events A1, dangerous driving events A2, and fatigue driving events A3.

[0066] Speeding event A1 should comprehensively calculate the four dimensions of whether it is speeding, highway / urban area, speed limit sign, and duration. Thus, speeding event A1 can be determined from multiple aspects with high accuracy and credibility.

[0067] The dangerous driving event A2 should comprehensively calculate the three alarms a of lane departure, collision warning, and vehicle distance too close. Thus, the dangerous driving event A2 can be determined from multiple aspects with high accuracy and credibility.

[0068] Fatigue driving event A3 should comprehensively calculate the four alarms a of yawning, closing eyes, distraction, and lane departure. Thus, fatigue driving event A3 can be determined from multiple aspects with high accuracy and credibility, which is sufficient to illustrate the dangerousness of the actual driving behavior of the vehicle.

[0069] The present invention provides a vehicle alarm method with a new structure. The vehicle alarm method is based on the operation of the vehicle networking system. The specific principle is: the vehicle management platform 200 collects the original alarm a data reported by the vehicle terminal 100, combines the multi-dimensional parameters such as vehicle speed, positioning, weather, time, etc., and designs three models to summarize and calculate the vehicle's speeding, driver fatigue driving, and vehicle dangerous driving events A2, so as to find out the dangerous events that the monitoring personnel really need to pay attention to. We call it alarm event A. An alarm event A contains multiple alarms a of the vehicle and the operating status of the vehicle during this alarm event A. The monitoring personnel can see the cause of the alarm event A and find the corresponding video and picture evidence. They can remind the driver to pay attention to driving safety by sending voice text or voice intercom with the driver; in addition, the vehicle risk level can be generated through the alarm event A; and the driving behavior with high risk level can be obtained through big data analysis.

[0070] The vehicle alarm method of the present invention has the following advantages:

[0071] 1. Filter out a large number of vehicle alarms that do not require attention;

[0072] 2. Truly identify dangerous events that require special attention during vehicle operation;

[0073] 3. While processing alarm event A, the alarm a generated by vehicles can be processed in batches, thereby greatly improving the efficiency of monitoring personnel in processing alarm a;

[0074] 4. The generated alarm event A can be used to evaluate the risk level of vehicle driving, so that the driving behavior of vehicles with high risk levels can be quickly locked and paid attention to in a timely manner;

[0075] 5. Use voice text delivery and voice intercom to remind drivers to pay attention to driving safety and reduce vehicle driving safety hazards;

[0076] 6. Accurately identify dangerous driving behaviors of vehicles and drivers through big data dashboards, and provide targeted education and training;

[0077] 7. The algorithm model can be expanded to obtain vehicle driving events that meet user needs.

[0078] The vehicle alarm method of the present invention can quickly obtain the alarm information of vehicles that are truly at risk, and can filter out a large amount of invalid alarm data. Monitoring personnel can quickly find vehicles that are truly at risk, and then effectively remind drivers, thereby achieving the purpose of preventing accidents in advance.

[0079] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A vehicle alarm method based on a vehicle networking system, characterized in that: The vehicle networking system comprises a vehicle management platform (200) and an on-board terminal (100), wherein the on-board terminal (100) wirelessly communicates with the vehicle management platform (200) and is capable of uploading real-time vehicle information to the vehicle management platform (200), and the vehicle alarm method is implemented by the following steps: S1, the vehicle management platform (200) collects the original alarm (a) data reported by the vehicle terminal (100); S2. The vehicle management platform (200) is preset with three models of speeding, fatigue driving, and dangerous driving. The vehicle management platform (200) summarizes and calculates the vehicle alarm (a) information based on the three models combined with the multi-dimensional parameters (b); S3, obtain the dangerous events that really need attention, and call these dangerous events alarm events (A); An alarm event (A) includes multiple alarms (a) of the vehicle and the operating status of the vehicle during the alarm event (A); S4. The monitoring personnel can see the causes of each alarm event (A) through the vehicle management platform (200) and find the corresponding video and picture evidence; S5. The monitoring personnel can send a voice text or have a voice conversation with the driver through the vehicle management platform (200) to remind the driver to pay attention to driving safety; The multi-dimensional parameters (b) include vehicle speed, location, weather, and time; In step S3, the following steps may also be included: Step S31: the vehicle management platform (200) automatically generates a vehicle risk level according to the alarm event (A); Step S32: deriving high-risk driving behaviors through big data analysis; The alarm events (A) include speeding events (A1), dangerous driving events (A2) and fatigue driving events (A3); The speeding driving event (A1) should be calculated comprehensively in four dimensions: whether it is speeding, highway / urban area, speed limit sign, and duration; The dangerous driving event (A2) should comprehensively calculate the three alarms (a) of lane departure, collision warning and vehicle distance too close; The fatigue driving event (A3) should comprehensively calculate the four alarms (a) of yawning, closing eyes, distraction and lane departure.

Images