Vehicle information alarm system based on intelligent vehicle networking

Abstract

The invention discloses a vehicle information alarm system based on an intelligent vehicle connection, and relates to the technical field of vehicle information, and the system comprises a vehicle driving monitoring module, a driving hidden danger analysis module, a highlighted information alarm module and an information alarm display module, and is provided with the vehicle driving monitoring module, the driving hidden danger analysis module and the highlighted information alarm module. Various types of vehicle driving information can be collected in the vehicle driving process, whether the vehicle has driving hidden dangers or not is comprehensively analyzed, and when the vehicle has the driving hidden dangers, the alarm optimization score of each piece of potential alarm information is analyzed through the vehicle driving simulation model, so that the vehicle driving safety is improved. According to the technical scheme, the information needing to be subjected to alarm display is judged based on the alarm optimization score, the information alarm display module is arranged, it is guaranteed that the highlighted alarm information can be displayed in order, a driver can make a more accurate and timely driving decision based on more comprehensive information, and the coping capacity for the complex driving environment is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of vehicle information technology, and more specifically, to a vehicle information warning system based on smart vehicle networking. Background Art

[0002] With the rapid development of the automobile industry and the continuous advancement of intelligent technology, vehicle driving safety has become the focus of social attention. Traditional vehicle safety systems mainly rely on the reliability of mechanical components and the operating skills of drivers, but their limitations gradually emerge in the face of complex and changing driving environments. In order to further improve the safety of vehicle driving, smart car networking technology has emerged. This technology integrates advanced technologies such as vehicle sensors, communication modules, data processing and analysis algorithms, and realizes real-time monitoring and intelligent analysis of vehicle driving status.

[0003] However, in the current smart car networking system, the collection and processing of vehicle driving information mostly remains at the basic level, lacking in-depth analysis and accurate warning of driving hazards. Specifically, although the system can obtain various types of driving information of the vehicle in real time, it often lacks an effective mechanism to comprehensively evaluate this information to determine whether the vehicle has potential driving hazards. In addition, even if the system can identify potential hazards, it is often difficult for drivers to respond quickly and accurately because of too much and disorganized warning information. Summary of the invention

[0004] In view of the deficiencies in the prior art, an object of the present invention is to provide a vehicle information warning system based on smart vehicle networking.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A vehicle information warning system based on smart car networking, including a vehicle driving monitoring module, a driving hidden danger analysis module, a prominent information warning module, and an information warning display module;

[0007] The vehicle driving monitoring module: during the driving process of the vehicle, regularly collects various types of vehicle driving information of the vehicle in the current period, and combines the various types of vehicle driving information in the form of a data set to form a driving information set;

[0008] The driving hazard analysis module is configured to obtain a driving hazard determination score of the vehicle based on the driving information set, and determine whether to mark the vehicle as a driving hazard vehicle according to the driving hazard determination score;

[0009] The prominent information warning module is used to obtain the warning optimization score of each potential warning information of the vehicle with driving hazards, and determine whether to mark the potential warning information as prominent warning information;

[0010] The information warning display module obtains the warning sequence number of each prominent warning information, sorts all the prominent warning information in descending order according to the value of the warning sequence number, and displays the sorted prominent warning information in the vehicle's car-connected central control interface.

[0011] Furthermore, a driving hazard determination score of the vehicle is obtained based on the driving information set, specifically: the driving information set is used as input data of a driving hazard determination model, and the driving process determination model outputs the driving hazard determination score.

[0012] Furthermore, the driving hazard determination score is used to determine whether to mark the vehicle as a driving hazard vehicle. Specifically, the driving hazard determination standard score is obtained, and when the driving hazard determination score is greater than or equal to the driving hazard determination standard score, the vehicle is marked as a driving hazard vehicle, and the driving information of all types of vehicles in the current period is simultaneously marked as potential warning information; when the driving hazard determination score is less than the driving hazard determination standard score, no corresponding processing is performed.

[0013] Furthermore, the warning optimization score of the potential warning information is constructed by the following steps: selecting a potential warning information, obtaining the information type of the potential warning information, obtaining the preset driving information of the type of information, marking the remaining potential warning information other than the type of information as warning control information, collecting the basic information of the vehicle, and constructing a vehicle driving simulation model based on the preset driving information, the warning control information and the basic information of the vehicle, simulating the driving of the vehicle based on the vehicle driving simulation model, setting the simulated driving time to d, setting a plurality of driving nodes with equal time intervals within the simulated driving time, generating a simulated driving record at each driving node, the simulated driving record including the driving hazard determination score and the determination time, sorting all the simulated driving records in the order of the determination time, and then obtaining the number of driving hazard continuous sections LMs, the number of driving hazard turbulence EPk and the average driving hazard score SRg, and using the formula The alarm optimization score Wdg of the potential alarm information is obtained, where ut is the continuous paragraph coefficient, um is the hidden danger turbulence coefficient, and ue is the average hidden danger coefficient.

[0014] Furthermore, the number of driving hazard continuous sections LMs is obtained through the following steps: summing up the driving hazard judgment scores of two adjacent simulated driving records after sorting to obtain a driving hazard continuous score, setting a driving hazard continuous standard score, and when the driving hazard continuous score is greater than or equal to the driving hazard continuous standard score, increasing the number of driving hazard continuous sections by one; when the driving hazard continuous score is less than the driving hazard continuous standard score, no corresponding processing is performed, and the number of driving hazard continuous sections is marked as LMs.

[0015] Furthermore, the number of driving hazard turbulence EPk is obtained through the following steps: the driving hazard judgment scores of two adjacent simulated driving records after sorting are differenced and the absolute value is taken to obtain the driving hazard turbulence score, and the driving hazard turbulence standard score is set. When the driving hazard turbulence score is greater than or equal to the driving hazard turbulence standard score, the number of driving hazard turbulence is increased by one; when the driving hazard turbulence score is less than the driving hazard turbulence standard score, no corresponding processing is performed, and the number of driving hazard turbulence is marked as EPk.

[0016] Furthermore, the average driving hazard score SRg is obtained by the following steps: the driving hazard judgment scores of all simulated driving records are summed up and averaged to obtain the average driving hazard score SRg.

[0017] Furthermore, an alarm optimization basic score is set. When the alarm optimization score is greater than or equal to the alarm optimization basic score, the potential alarm information is marked as prominent alarm information. When the alarm optimization score is less than the alarm optimization basic score, the mark of the potential alarm information is cancelled.

[0018] Furthermore, the warning ordinal number of the prominent warning information is constructed by the following steps: obtaining the warning optimization score Ryh of a prominent warning information, obtaining the vehicle driving information relationship spectrum, and when the prominent warning information has a relationship with another prominent warning information in the vehicle driving information relationship spectrum, increasing the number of driving relationships by one, marking the number of driving relationships as Fsx, and using the formula The warning sequence number M iy of the prominent warning information is obtained, wherein ds is the optimization scoring coefficient and dt is the driving relationship coefficient.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The vehicle driving monitoring module, driving hazard analysis module and prominent information warning module are set up to collect driving information of various types of vehicles during driving, and comprehensively analyze whether the vehicle has driving hazards. When the vehicle has driving hazards, the warning optimization score of each potential warning information is analyzed through the vehicle driving simulation model, and the information that needs to be displayed as an alarm is determined based on the warning optimization score;

[0021] An information warning display module is set up to ensure that prominent warning information can be displayed in an orderly manner, so that drivers can make more accurate and timely driving decisions based on more comprehensive information, enhance the ability to cope with complex driving environments, and reduce the information processing burden on drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a system flow chart of a vehicle information warning system based on smart car networking;

[0023] Figure 2 The module block diagram of a vehicle information warning system based on smart car networking. DETAILED DESCRIPTION

[0024] Reference Figure 1-Figure 2 A vehicle information warning system based on smart car networking includes a vehicle driving monitoring module, a driving hidden danger analysis module, a prominent information warning module, and an information warning display module.

[0025] Vehicle driving monitoring module: When the vehicle is driving, various types of vehicle driving information within the current period are regularly collected. The types of vehicle driving information include ABS (anti-lock braking system) status information, ESP (electronic stability program) status information, energy consumption information (fuel consumption information or power consumption information), engine operation information, and brake operation information. The above vehicle driving information is key information to ensure the safe driving of the vehicle. The various types of vehicle driving information are combined in the form of a data set to form a driving information set (the various types of vehicle driving information are collected, integrated and organized to form a structured and systematic data set. Before forming the data set, the corresponding vehicle driving information needs to be cleaned).

[0026] Driving hazard analysis module: the driving information set is used as the input data of the driving hazard judgment model, the driving process judgment model outputs the driving hazard judgment score, and the driving hazard judgment standard score is obtained (the driving hazard judgment standard score is a score preset by the system). When the driving hazard judgment score is greater than or equal to the driving hazard judgment standard score, the vehicle is marked as a driving hazard vehicle, and the driving information of all types of vehicles in the current period is simultaneously marked as potential warning information. When the driving hazard judgment score is less than the driving hazard judgment standard score, no corresponding processing is performed.

[0027] The driving process judgment model is constructed through the following steps: collecting j driving information sets, building a neural network model, and using the driving information sets as training data for the neural network model. The vehicle driving information contained in each formal information set can be real collected information or virtually set information. A driving hazard judgment score is assigned to each training data. The driving hazard judgment score takes values ​​greater than zero. The higher the driving hazard judgment score, the greater the safety hazard of vehicle driving. The training data is iteratively trained, and the driving process judgment model is obtained after the training is completed.

[0028] Prominent information alarm module: obtain the alarm optimization score of each potential alarm information, set the alarm optimization basic score (the alarm optimization basic score is a score preset by the system), when the alarm optimization score is greater than or equal to the alarm optimization basic score, mark the potential alarm information as prominent alarm information, when the alarm optimization score is less than the alarm optimization basic score, cancel the mark of the potential alarm information.

[0029] The alarm optimization score of potential alarm information is constructed by the following steps: selecting a potential alarm information, obtaining the information type of the potential alarm information, obtaining the preset driving information of the type of information (the preset driving information is the system preset information, which is the type of information of the vehicle under the ideal driving state. Taking the ABS state information as an example, the preset driving information of the ABS state information is the information collected by the ABS state information under the ideal driving state), marking the remaining potential alarm information other than the type of information as alarm control information (if the information type of the selected potential alarm information is energy consumption information, then the preset driving information of the energy consumption information is obtained, and the remaining potential alarm information other than the energy consumption information is marked as alarm control information), collecting the basic information of the vehicle (the basic information includes the size, mass, inertia parameters, etc. of the vehicle), and constructing a vehicle driving simulation model based on the preset driving information, the alarm control information and the basic information of the vehicle (the construction of the vehicle driving simulation model can use CarS im software), simulates the driving of the vehicle based on the vehicle driving simulation model, sets the simulation driving time to be d, sets multiple driving nodes with equal time intervals within the simulation driving time, generates a simulation driving record at each driving node, and the simulation driving record includes a driving hazard determination score and a determination time (the determination time corresponding to the driving hazard determination score), sorts all the simulation driving records in the order of the determination time, sums up the driving hazard determination scores of the two adjacent simulation driving records after sorting, and obtains a driving hazard duration score, sets a driving hazard duration standard score (the driving hazard duration standard score is a score preset by the system), and when the driving hazard duration score is greater than or equal to the driving hazard duration standard score, the driving hazard The number of continuous sections is increased by one. When the driving hazard continuous score is less than the driving hazard continuous standard score, no corresponding processing is performed, and the number of driving hazard continuous sections is marked as LMs. The driving hazard judgment scores of the two adjacent simulated driving records after sorting are calculated by difference and the absolute value is taken to obtain the driving hazard turbulence score. The driving hazard turbulence standard score is set. When the driving hazard turbulence score is greater than or equal to the driving hazard turbulence standard score, the number of driving hazard turbulence is increased by one. When the driving hazard turbulence score is less than the driving hazard turbulence standard score, no corresponding processing is performed, and the number of driving hazard turbulence is marked as EPk. The driving hazard judgment scores of all simulated driving records are summed and averaged to obtain the average driving hazard score SRg. The formula is used The alarm optimization score Wdg of the potential alarm information is obtained, where ut is the continuous paragraph coefficient, um is the hidden danger turbulence coefficient, ue is the average hidden danger coefficient, the value of ut is 1.42, the value of um is 1.08, and the value of ut is 1.27.

[0030] The vehicle driving monitoring module, the driving hazard analysis module and the prominent information warning module are set up to collect driving information of various types of vehicles during the driving process, and comprehensively analyze whether the vehicle has driving hazards. When the vehicle has driving hazards, the alarm optimization score of each potential alarm information is analyzed through the vehicle driving simulation model, and the information that needs to be displayed as an alarm is determined based on the alarm optimization score.

[0031] Information warning display module: obtain the warning sequence number of each prominent warning information, sort all the prominent warning information in descending order according to the value of the warning sequence number, and display the sorted prominent warning information in the vehicle's car-connected central control interface (the driver can take corresponding measures according to the corresponding prominent warning information).

[0032] The warning ordinal number of the prominent warning information is constructed by the following steps: obtaining the warning optimization score Ryh of a prominent warning information, obtaining the vehicle driving information relationship spectrum, and when the prominent warning information has a relationship with another prominent warning information in the vehicle driving information relationship spectrum, the number of driving relationships is increased by one (for example, when the prominent warning information has a relationship with three prominent warning information in the vehicle driving information relationship spectrum, the number of driving relationships is three), marking the number of driving relationships as Fsx, and using the formula The warning sequence number Miy of the prominent warning information is obtained, where ds is the optimization score coefficient, dt is the driving relationship coefficient, the value of ds is 2.56, and the value of dt is 3.11.

[0033] The vehicle driving information relationship spectrum includes various types of vehicle driving information, and the relationship between various types of vehicle driving information is displayed in the vehicle driving information relationship spectrum. For example, ABS (anti-lock braking system) status information and brake operation information jointly ensure the effectiveness and safety of the braking system, so there is a relationship between the ABS (anti-lock braking system) status information and the brake operation information in the vehicle driving information relationship spectrum.

[0034] An information warning display module is set up to ensure that prominent warning information can be displayed in an orderly manner, so that drivers can make more accurate and timely driving decisions based on more comprehensive information, enhance the ability to cope with complex driving environments, and reduce the information processing burden on drivers.

[0035] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0037] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not 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 embodiments of the present application.

[0038] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0040] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0041] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0042] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle information warning system based on smart car networking, characterized in that: It includes vehicle driving monitoring module, driving hidden danger analysis module, prominent information warning module and information warning display module; The vehicle driving monitoring module: during the driving process of the vehicle, regularly collects various types of vehicle driving information of the vehicle in the current period, and combines the various types of vehicle driving information in the form of a data set to form a driving information set; The driving hazard analysis module: obtains a driving hazard determination score of the vehicle based on the driving information set, and determines whether to mark the vehicle as a driving hazard vehicle according to the driving hazard determination score; The prominent information warning module is used to obtain the warning optimization score of each potential warning information of the vehicle with driving hazards, and determine whether to mark the potential warning information as prominent warning information; The information warning display module obtains the warning sequence number of each prominent warning information, sorts all the prominent warning information in descending order according to the value of the warning sequence number, and displays the sorted prominent warning information in the vehicle's car-connected central control interface.

2. The vehicle information warning system based on intelligent vehicle networking according to claim 1 is characterized in that: The driving hazard determination score of the vehicle is obtained based on the driving information set, specifically: the driving information set is used as input data of the driving hazard determination model, and the driving process determination model outputs the driving hazard determination score.

3. The vehicle information warning system based on intelligent vehicle networking according to claim 1 is characterized in that: The driving hazard determination score is used to determine whether to mark a vehicle as a driving hazard vehicle. Specifically, the driving hazard determination standard score is obtained. When the driving hazard determination score is greater than or equal to the driving hazard determination standard score, the vehicle is marked as a driving hazard vehicle, and the driving information of all types of vehicles in the current period is simultaneously marked as potential warning information. When the driving hazard determination score is less than the driving hazard determination standard score, no corresponding processing is performed.

4. The vehicle information warning system based on intelligent vehicle networking according to claim 1 is characterized in that: The warning optimization score of potential warning information is constructed by the following steps: select a potential warning information, obtain the information type of the potential warning information, obtain the preset driving information of the type of information, mark the remaining potential warning information other than the type of information as warning control information, collect the basic information of the vehicle, and build a vehicle driving simulation model based on the preset driving information, warning control information and basic information of the vehicle. Simulate the driving of the vehicle based on the vehicle driving simulation model, set the simulation driving time to d, set multiple driving nodes with equal time intervals within the simulation driving time, and generate a simulation driving record at each driving node. The simulation driving record includes the driving hazard judgment score and judgment time. All simulation driving records are sorted in the order of the judgment time, and then the number of driving hazard continuous sections LMs, the number of driving hazard turbulence EPk and the average driving hazard score SRg are obtained. The formula is used The alarm optimization score Wdg of the potential alarm information is obtained, where ut is the continuous paragraph coefficient, um is the hidden danger turbulence coefficient, and ue is the average hidden danger coefficient.

5. The vehicle information warning system based on intelligent vehicle networking according to claim 4 is characterized in that: The number of driving hazard continuous sections LMs is obtained by the following steps: summing up the driving hazard judgment scores of two adjacent simulated driving records after sorting to obtain a driving hazard continuous score, setting a driving hazard continuous standard score, and when the driving hazard continuous score is greater than or equal to the driving hazard continuous standard score, increasing the number of driving hazard continuous sections by one; when the driving hazard continuous score is less than the driving hazard continuous standard score, no corresponding processing is performed, and the number of driving hazard continuous sections is marked as LMs.

6. The vehicle information warning system based on intelligent vehicle networking according to claim 4 is characterized in that: The number of driving hazard turbulence EPk is obtained through the following steps: the driving hazard judgment scores of two adjacent simulated driving records after sorting are calculated by difference and the absolute value is taken to obtain the driving hazard turbulence score, and the driving hazard turbulence standard score is set. When the driving hazard turbulence score is greater than or equal to the driving hazard turbulence standard score, the number of driving hazard turbulence is increased by one; when the driving hazard turbulence score is less than the driving hazard turbulence standard score, no corresponding processing is performed, and the number of driving hazard turbulence is marked as EPk.

7. The vehicle information warning system based on intelligent vehicle networking according to claim 4 is characterized in that: The average driving hazard score SRg is obtained by the following steps: the driving hazard judgment scores of all simulated driving records are summed up and averaged to obtain the average driving hazard score SRg.

8. The vehicle information warning system based on intelligent vehicle networking according to claim 1 is characterized in that: Set an alarm optimization basic score. When the alarm optimization score is greater than or equal to the alarm optimization basic score, mark the potential alarm information as prominent alarm information. When the alarm optimization score is less than the alarm optimization basic score, cancel the mark of the potential alarm information.

9. The vehicle information warning system based on intelligent vehicle networking according to claim 1 is characterized in that: The warning ordinal number of the prominent warning information is constructed by the following steps: obtaining the warning optimization score Ryh of a prominent warning information, obtaining the vehicle driving information relationship spectrum, and when the prominent warning information has a relationship with another prominent warning information in the vehicle driving information relationship spectrum, increasing the number of driving relationships by one, marking the number of driving relationships as Fsx, and using the formula The warning sequence number Miy of the prominent warning information is obtained, wherein ds is the optimization scoring coefficient and dt is the driving relationship coefficient.

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