A driving behavior monitoring and early warning method and system based on big data

Abstract

The present invention relates to the field of driving monitoring technology, and specifically discloses a driving behavior monitoring and early warning method and system based on big data. The present invention constructs a three-dimensional driving model; performs a three-dimensional driving simulation; performs basic monitoring analysis and dangerous driving early warning judgment; uploads monitoring and shooting data within the early warning time; if the dangerous warning can be lifted, the dangerous driving warning is stopped; if the dangerous warning cannot be lifted, big data monitoring and analysis and alarm judgment processing are performed. It is capable of multi-angle learning and shooting, constructing a three-dimensional driving model, performing driving monitoring and shooting during driving, realizing three-dimensional driving simulation, and then performing judgment and early warning of dangerous driving behavior offline. At the same time, within the preset early warning time, the monitoring and shooting data is uploaded to assist big data monitoring and analysis, and alarm judgment processing is performed, thereby maintaining the stability of driving behavior monitoring and early warning, and improving the accuracy of driving behavior monitoring and early warning when connected to the Internet.

Description

Technical Field

[0001] The present invention belongs to the technical field of driving monitoring, and in particular relates to a driving behavior monitoring and early warning method and system based on big data. Background Art

[0002] Driving monitoring monitors the driving status of the driver in the vehicle. It uses images obtained by the DSM camera to detect the driver's driving behavior and physiological state through visual tracking, target detection, motion recognition and other technologies. When the driver is in dangerous situations such as fatigue, distraction, making a phone call, smoking, etc., an alarm will be issued within the system set time to avoid accidents. It can effectively regulate the driver's driving behavior and greatly reduce the chance of traffic accidents.

[0003] With the gradual development of intelligent automobiles, existing automobiles need to maintain Internet connection during driving monitoring, accurately monitor driving behavior based on big data, and issue warnings for possible dangerous driving behaviors. However, the driving environment is relatively complex, and a stable Internet connection cannot be guaranteed at all driving moments. Therefore, in some cases where the Internet cannot be stably connected, the function of driving behavior monitoring and warning based on the Internet and big data may not be triggered, and driving behavior monitoring and warning cannot be stably performed. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a driving behavior monitoring and early warning method and system based on big data, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A driving behavior monitoring and early warning method based on big data, the method specifically comprising the following steps:

[0007] Perform multi-angle learning shooting, obtain learning shooting data, and construct a driving three-dimensional model based on the learning shooting data;

[0008] Perform driving monitoring and shooting, obtain monitoring and shooting data, perform driving three-dimensional simulation based on the driving three-dimensional model and the monitoring and shooting data, and generate a driving simulation model in real time;

[0009] Conduct basic monitoring and analysis based on the driving simulation model to determine whether there is any possible dangerous driving behavior, and issue a dangerous driving warning when there is any possible dangerous driving behavior;

[0010] Within the preset warning time, upload the corresponding monitoring data and conduct warning cancellation monitoring to determine whether the danger warning can be lifted;

[0011] If the danger warning can be lifted, the dangerous driving warning will be stopped after the warning time;

[0012] If the danger warning cannot be lifted, the uploaded monitoring and shooting data will be subjected to big data monitoring and analysis to generate big data monitoring results and perform alarm judgment processing.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the multi-angle learning shooting, obtaining learning shooting data, and constructing the driving three-dimensional model based on the learning shooting data specifically include the following steps:

[0014] When unlocking and locking within a preset trigger distance, a learning shot is taken outside the vehicle to generate first shooting data;

[0015] Before starting driving, perform in-car learning shooting to generate second shooting data;

[0016] Combining the plurality of first shooting data and the plurality of second shooting data to obtain learning shooting data;

[0017] A three-dimensional driving model is constructed based on the learning shooting data.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the performing of driving monitoring shooting, obtaining monitoring shooting data, performing a three-dimensional driving simulation based on the three-dimensional driving model and the monitoring shooting data, and generating a driving simulation model in real time specifically include the following steps:

[0019] Conduct driving monitoring and shooting, and obtain monitoring and shooting data;

[0020] Performing personnel identification on the monitoring and shooting data, and extracting personnel shooting data;

[0021] Based on the three-dimensional driving model, a three-dimensional driving simulation is performed on the personnel shooting data to generate a driving simulation model in real time.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, performing basic monitoring and analysis based on the driving simulation model to determine whether there is a possible dangerous driving behavior, and issuing a dangerous driving warning when there is a possible dangerous driving behavior specifically includes the following steps:

[0023] Performing basic monitoring analysis based on the driving simulation model to generate basic analysis results;

[0024] Determine whether there is any possible dangerous driving behavior based on the basic analysis results;

[0025] If there is a possible dangerous driving behavior, a danger warning signal is generated, and a dangerous driving warning is issued according to the danger warning signal;

[0026] If there is no potentially dangerous driving behavior, no hazard warning signal will be generated.

[0027] As a further limitation of the technical solution of the embodiment of the present invention, uploading the corresponding monitoring and shooting data within the preset warning time, performing warning cancellation monitoring, and determining whether the danger warning can be cancelled specifically include the following steps:

[0028] Upload the corresponding monitoring data within the preset warning time;

[0029] Conduct early warning release monitoring within the preset early warning time and generate early warning monitoring data;

[0030] According to the warning monitoring data, determine whether the danger warning can be lifted.

[0031] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing big data monitoring and analysis on the uploaded monitoring and shooting data, generating big data monitoring results, and performing alarm judgment processing specifically include the following steps:

[0032] Conduct big data monitoring and analysis on the uploaded monitoring data to generate big data monitoring results;

[0033] Determining whether dangerous driving can be confirmed based on the big data monitoring results;

[0034] If dangerous driving can be confirmed, a dangerous warning signal is generated, and a dangerous driving warning is issued according to the dangerous warning signal;

[0035] If dangerous driving cannot be confirmed, the danger warning will be cancelled.

[0036] A driving behavior monitoring and warning system based on big data, comprising a three-dimensional model construction unit, a simulation model generation unit, a basic monitoring and judgment unit, a warning cancellation judgment unit, a danger warning cancellation unit, and a big data analysis and processing unit, wherein:

[0037] A three-dimensional model construction unit, configured to perform multi-angle learning shooting, obtain learning shooting data, and construct a three-dimensional driving model based on the learning shooting data;

[0038] a simulation model generating unit, configured to perform driving monitoring and shooting, obtain monitoring and shooting data, perform a three-dimensional driving simulation based on the three-dimensional driving model and the monitoring and shooting data, and generate a driving simulation model in real time;

[0039] A basic monitoring and judgment unit is used to perform basic monitoring and analysis based on the driving simulation model to determine whether there is a possible dangerous driving behavior and to issue a dangerous driving warning when there is a possible dangerous driving behavior;

[0040] The warning cancellation judgment unit is used to upload the corresponding monitoring and shooting data within the preset warning time, and perform warning cancellation monitoring to determine whether the danger warning can be cancelled;

[0041] a danger warning cancellation unit, configured to stop the dangerous driving warning after the warning time when the danger warning can be cancelled;

[0042] The big data analysis and processing unit is used to perform big data monitoring and analysis on the uploaded monitoring and shooting data when the danger warning cannot be lifted, generate big data monitoring results, and perform alarm judgment processing.

[0043] As a further limitation of the technical solution of the embodiment of the present invention, the three-dimensional model building unit specifically includes:

[0044] The vehicle exterior shooting module is used to perform exterior learning shooting when unlocking and locking within a preset trigger distance to generate first shooting data;

[0045] The in-car shooting module is used to shoot in-car learning before driving starts and generate second shooting data;

[0046] a data integration module, configured to integrate the plurality of first shooting data and the plurality of second shooting data to obtain learning shooting data;

[0047] The model building module is used to build a three-dimensional driving model based on the learning shooting data.

[0048] As a further limitation of the technical solution of the embodiment of the present invention, the simulation model generation unit specifically includes:

[0049] Monitoring and shooting module, used for driving monitoring and shooting, and obtaining monitoring and shooting data;

[0050] A personnel identification module is used to identify personnel from the monitoring and shooting data and extract personnel shooting data;

[0051] The driving simulation module is used to perform a three-dimensional driving simulation on the personnel shooting data based on the three-dimensional driving model and generate a driving simulation model in real time.

[0052] As a further limitation of the technical solution of the embodiment of the present invention, the basic monitoring and judgment unit specifically includes:

[0053] A monitoring and analysis module, configured to perform basic monitoring and analysis based on the driving simulation model and generate basic analysis results;

[0054] A risk judgment module, configured to judge whether there is a possible dangerous driving behavior according to the basic analysis results;

[0055] The driving warning module is used to generate a danger warning signal when there is a possible dangerous driving behavior, and to provide a dangerous driving warning according to the danger warning signal; when there is no possible dangerous driving behavior, no danger warning signal is generated.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The embodiment of the present invention constructs a three-dimensional driving model; performs a three-dimensional driving simulation; performs basic monitoring and analysis and dangerous driving warning judgment; uploads monitoring and shooting data within the warning time; stops the dangerous driving warning if the dangerous warning can be lifted; and performs big data monitoring and analysis and alarm judgment processing if the dangerous warning cannot be lifted. It is capable of multi-angle learning and shooting, constructing a three-dimensional driving model, performing driving monitoring and shooting during driving, achieving three-dimensional driving simulation, and then performing offline judgment and warning of dangerous driving behavior. At the same time, within the preset warning time, it uploads monitoring and shooting data to assist in big data monitoring and analysis and perform alarm judgment processing, thereby maintaining the stability of driving behavior monitoring and warning, and improving the accuracy of driving behavior monitoring and warning when connected to the Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0059] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0060] Figure 2 A flowchart of constructing a three-dimensional driving model in the method provided by an embodiment of the present invention is shown.

[0061] Figure 3 A flow chart of generating a driving simulation model in the method provided by an embodiment of the present invention is shown.

[0062] Figure 4 A flowchart of basic monitoring, analysis and judgment in the method provided by an embodiment of the present invention is shown.

[0063] Figure 5 The flowchart of early warning release monitoring judgment in the method provided by the embodiment of the present invention is shown.

[0064] Figure 6 The flowchart of big data monitoring and analysis processing in the method provided by the embodiment of the present invention is shown.

[0065] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0066] Figure 8 The figure shows a structural block diagram of a three-dimensional model building unit in a system provided by an embodiment of the present invention.

[0067] Figure 9 The structure block diagram of the simulation model generation unit in the system provided by the embodiment of the present invention is shown.

[0068] Figure 10 It shows a structural block diagram of a basic monitoring and judgment unit in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0070] It is understandable that in existing technologies, the process of driving monitoring of automobiles requires maintaining an Internet connection, accurately monitoring driving behavior based on big data, and issuing warnings for possible dangerous driving behaviors. However, the automobile driving environment is relatively complex, and a stable Internet connection cannot be guaranteed at all driving moments. Therefore, in some cases where a stable Internet connection cannot be achieved, the function of driving behavior monitoring and warning based on the Internet and big data may not be triggered, and driving behavior monitoring and warning cannot be performed stably.

[0071] To address the above-mentioned issues, embodiments of the present invention construct a three-dimensional driving model by learning and shooting from multiple angles; perform a three-dimensional driving simulation; conduct basic monitoring and analysis and dangerous driving warning judgment; upload monitoring and shooting data within the warning time; if the dangerous warning can be lifted, the dangerous driving warning is stopped; if the dangerous warning cannot be lifted, big data monitoring and analysis and alarm judgment processing are performed. It is possible to learn and shoot from multiple angles, construct a three-dimensional driving model, perform driving monitoring and shooting during driving, implement a three-dimensional driving simulation, and then perform offline judgment and warning of dangerous driving behavior. At the same time, within the preset warning time, it uploads monitoring and shooting data to assist in big data monitoring and analysis and perform alarm judgment processing, thereby maintaining the stability of driving behavior monitoring and warning, and improving the accuracy of driving behavior monitoring and warning when connected to the Internet.

[0072] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0073] Specifically, a driving behavior monitoring and early warning method based on big data includes the following steps:

[0074] Step S101 : Perform multi-angle learning shooting to obtain learning shooting data, and construct a driving three-dimensional model based on the learning shooting data.

[0075] In an embodiment of the present invention, when the driver approaches the vehicle to unlock and lock the vehicle, the outside of the vehicle is photographed at a distance of 1-2m from the vehicle to obtain first shooting data of the driver. After the driver enters the vehicle and before driving starts, the driver is photographed at the front and left and right sides of the cockpit to obtain second shooting data. By combining multiple first shooting data and multiple second shooting data, multi-angle photos of the driver in various behavioral states can be obtained, and learning shooting data can be obtained. Then, a three-dimensional model is constructed according to the learning shooting data to obtain a three-dimensional driving model of the driver.

[0076] It is understandable that, since the first shooting data and the second shooting data can be continuously acquired, the driving three-dimensional model can be continuously optimized.

[0077] Specifically, Figure 2 A flowchart of constructing a three-dimensional driving model in the method provided by an embodiment of the present invention is shown.

[0078] In a preferred embodiment of the present invention, the multi-angle learning shooting, obtaining learning shooting data, and constructing a driving three-dimensional model based on the learning shooting data specifically include the following steps:

[0079] Step S1011 , when unlocking and locking within a preset trigger distance, performing learning shooting outside the vehicle to generate first shooting data.

[0080] Step S1012: Before driving starts, perform in-car learning shooting to generate second shooting data.

[0081] Step S1013 : Combining the plurality of first shooting data and the plurality of second shooting data to obtain learning shooting data.

[0082] Step S1014: constructing a three-dimensional driving model based on the learned shooting data.

[0083] Furthermore, the driving behavior monitoring and early warning method based on big data further includes the following steps:

[0084] Step S102 : Perform driving monitoring shooting to obtain monitoring shooting data, perform driving three-dimensional simulation based on the driving three-dimensional model and the monitoring shooting data, and generate a driving simulation model in real time.

[0085] In an embodiment of the present invention, after the driver starts the vehicle to drive, the driver is photographed at the front and left and right sides of the cockpit to obtain monitoring shooting data. By identifying the driver in the monitoring shooting data, the personnel shooting data containing only the driver is extracted from the monitoring shooting data, and then based on the driving three-dimensional model, the driving three-dimensional state is simulated according to the personnel shooting data, and a driving simulation model identical to the driver's current behavior state is generated in real time.

[0086] Specifically, Figure 3 A flow chart of generating a driving simulation model in the method provided by an embodiment of the present invention is shown.

[0087] In a preferred embodiment of the present invention, the performing of driving monitoring and shooting, obtaining monitoring and shooting data, performing a three-dimensional driving simulation based on the three-dimensional driving model and the monitoring and shooting data, and generating a driving simulation model in real time specifically include the following steps:

[0088] Step S1021: Perform driving monitoring shooting to obtain monitoring shooting data.

[0089] Step S1022: performing personnel identification on the monitoring shooting data and extracting personnel shooting data.

[0090] Step S1023: Based on the three-dimensional driving model, a three-dimensional driving simulation is performed on the personnel shooting data to generate a driving simulation model in real time.

[0091] Furthermore, the driving behavior monitoring and early warning method based on big data further includes the following steps:

[0092] Step S103: Perform basic monitoring and analysis based on the driving simulation model to determine whether there is any possible dangerous driving behavior, and issue a dangerous driving warning if there is any possible dangerous driving behavior.

[0093] In an embodiment of the present invention, an offline basic monitoring analysis is performed according to a driving simulation model to generate basic analysis results, and then according to the basic analysis results, it is judged whether the driver has possible dangerous driving behaviors (including: fatigue driving, smoking, making phone calls, distracted driving, abnormal conditions, etc.), and when there are possible dangerous driving behaviors, a danger warning signal is generated, and a dangerous driving warning is issued in the car according to the danger warning signal, and the dangerous driving warning at this time is a mild dangerous driving reminder; when there are no possible dangerous driving behaviors, no danger warning signal and dangerous driving warning are generated.

[0094] Specifically, Figure 4 A flowchart of basic monitoring, analysis and judgment in the method provided by an embodiment of the present invention is shown.

[0095] Among them, in the preferred embodiment provided by the present invention, the basic monitoring and analysis based on the driving simulation model to determine whether there is a possible dangerous driving behavior, and when there is a possible dangerous driving behavior, issuing a dangerous driving warning specifically includes the following steps:

[0096] Step S1031 : performing basic monitoring analysis based on the driving simulation model to generate basic analysis results.

[0097] Step S1032: Determine whether there is any possible dangerous driving behavior based on the basic analysis results.

[0098] Step S1033: If there is a possible dangerous driving behavior, a danger warning signal is generated, and a dangerous driving warning is issued according to the danger warning signal.

[0099] Step S1034: If there is no possible dangerous driving behavior, no danger warning signal is generated.

[0100] Furthermore, the driving behavior monitoring and early warning method based on big data further includes the following steps:

[0101] Step S104: within the preset warning time, the corresponding monitoring and shooting data are uploaded, and warning cancellation monitoring is performed to determine whether the danger warning can be cancelled.

[0102] In an embodiment of the present invention, the corresponding monitoring and shooting data is uploaded within the preset warning time (15s), and within the preset warning time, it is determined in real time whether the dangerous driving warning can be lifted.

[0103] Specifically, Figure 5 The flowchart of early warning release monitoring judgment in the method provided by the embodiment of the present invention is shown.

[0104] Among them, in the preferred embodiment provided by the present invention, uploading the corresponding monitoring and shooting data within the preset warning time, and performing warning cancellation monitoring, and determining whether the danger warning can be cancelled specifically include the following steps:

[0105] Step S1041: Upload corresponding monitoring and shooting data within a preset warning time.

[0106] Step S1042: Perform warning cancellation monitoring within the preset warning time and generate warning monitoring data.

[0107] Step S1043: Determine whether the danger warning can be lifted according to the warning monitoring data.

[0108] Furthermore, the driving behavior monitoring and early warning method based on big data further includes the following steps:

[0109] Step S105: If the danger warning can be cancelled, the dangerous driving warning is stopped after the warning time.

[0110] Step S106: If the danger warning cannot be lifted, the uploaded monitoring and shooting data is subjected to big data monitoring and analysis to generate big data monitoring results and perform alarm judgment processing.

[0111] In an embodiment of the present invention, when the danger warning cannot be lifted, based on big data technology, big data monitoring and analysis is performed on the uploaded monitoring and shooting data to generate big data monitoring results, and according to the big data monitoring results, an accurate judgment is made on whether dangerous driving can be confirmed. When dangerous driving can be confirmed, a danger warning signal is generated, and according to the danger warning signal, a dangerous driving warning is issued after a preset warning time, and the dangerous driving warning at this time is a severe dangerous driving reminder; when dangerous driving cannot be confirmed, the danger warning is lifted, and no dangerous driving warning is issued after the preset warning time.

[0112] Specifically, Figure 6 The flowchart of big data monitoring and analysis processing in the method provided by the embodiment of the present invention is shown.

[0113] Among them, in the preferred embodiment provided by the present invention, the big data monitoring and analysis of the uploaded monitoring and shooting data, the generation of big data monitoring results, and the alarm judgment processing specifically include the following steps:

[0114] Step S1061: perform big data monitoring analysis on the uploaded monitoring and shooting data to generate big data monitoring results.

[0115] Step S1062: Determine whether dangerous driving can be confirmed based on the big data monitoring results.

[0116] Step S1063: If dangerous driving can be confirmed, a danger warning signal is generated, and a dangerous driving warning is issued according to the danger warning signal.

[0117] Step S1064: If dangerous driving cannot be confirmed, the danger warning is canceled.

[0118] Further, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0119] Among them, in another preferred embodiment provided by the present invention, a driving behavior monitoring and early warning system based on big data includes:

[0120] The three-dimensional model construction unit 101 is used to perform multi-angle learning shooting, obtain learning shooting data, and construct a driving three-dimensional model based on the learning shooting data.

[0121] In an embodiment of the present invention, the three-dimensional model construction unit 101 shoots outside the vehicle at a distance of 1-2 meters from the vehicle when the driver approaches the vehicle to unlock and lock, and obtains first shooting data of the driver. After the driver enters the vehicle and before driving starts, the driver is photographed at the front and left and right sides of the cockpit to obtain second shooting data. By combining multiple first shooting data and multiple second shooting data, multi-angle photos of the driver in various behavioral states can be obtained, and learning shooting data can be obtained. Then, a three-dimensional model is constructed according to the learning shooting data to obtain a three-dimensional driving model of the driver.

[0122] Specifically, Figure 8 It shows a structural block diagram of the three-dimensional model building unit 101 in the system provided by an embodiment of the present invention.

[0123] In a preferred embodiment of the present invention, the three-dimensional model building unit 101 specifically includes:

[0124] The vehicle exterior shooting module 1011 is used to perform exterior learning shooting when the vehicle is unlocked or locked within a preset trigger distance to generate first shooting data.

[0125] The in-car shooting module 1012 is used to perform in-car learning shooting before driving starts to generate second shooting data.

[0126] The data integration module 1013 is used to integrate the plurality of first shooting data and the plurality of second shooting data to obtain learning shooting data.

[0127] The model building module 1014 is used to build a three-dimensional driving model based on the learning shooting data.

[0128] Furthermore, the driving behavior monitoring and early warning system based on big data also includes:

[0129] The simulation model generating unit 102 is used to perform driving monitoring shooting, obtain monitoring shooting data, perform driving three-dimensional simulation according to the driving three-dimensional model and the monitoring shooting data, and generate a driving simulation model in real time.

[0130] In an embodiment of the present invention, after the driver starts the vehicle to drive, the simulation model generation unit 102 photographs the driver on the front and left and right sides of the cockpit to obtain monitoring shooting data, and through personnel identification of the driver in the monitoring shooting data, extracts the personnel shooting data containing only the driver in the monitoring shooting data, and then based on the three-dimensional driving model, performs a three-dimensional driving state simulation according to the personnel shooting data, and generates a driving simulation model that is the same as the driver's current behavior state in real time.

[0131] Specifically, Figure 9FIG. 1 shows a structural block diagram of the simulation model generating unit 102 in the system provided by an embodiment of the present invention.

[0132] In a preferred embodiment of the present invention, the simulation model generating unit 102 specifically includes:

[0133] The monitoring and shooting module 1021 is used to perform driving monitoring and shooting and obtain monitoring and shooting data.

[0134] The personnel identification module 1022 is used to perform personnel identification on the monitoring and shooting data and extract personnel shooting data.

[0135] The driving simulation module 1023 is used to perform a three-dimensional driving simulation on the personnel shooting data based on the three-dimensional driving model, and generate a driving simulation model in real time.

[0136] Furthermore, the driving behavior monitoring and early warning system based on big data also includes:

[0137] The basic monitoring and judgment unit 103 is used to perform basic monitoring and analysis based on the driving simulation model to determine whether there is a possible dangerous driving behavior and to issue a dangerous driving warning when there is a possible dangerous driving behavior.

[0138] In an embodiment of the present invention, the basic monitoring and judgment unit 103 performs offline basic monitoring and analysis according to the driving simulation model, generates basic analysis results, and then judges whether the driver has possible dangerous driving behaviors (including: fatigue driving, smoking, making phone calls, distracted driving, abnormal conditions, etc.) according to the basic analysis results, and generates a danger warning signal when there is possible dangerous driving behavior, and issues a dangerous driving warning in the car according to the danger warning signal, and the dangerous driving warning at this time is a mild dangerous driving reminder; when there is no possible dangerous driving behavior, no danger warning signal and dangerous driving warning are generated.

[0139] Specifically, Figure 10 It shows a structural block diagram of a basic monitoring and judgment unit in a system provided by an embodiment of the present invention.

[0140] In a preferred embodiment of the present invention, the basic monitoring and judgment unit 103 specifically includes:

[0141] The monitoring and analysis module 1031 is used to perform basic monitoring and analysis based on the driving simulation model and generate basic analysis results.

[0142] The danger judgment module 1032 is used to judge whether there is a possible dangerous driving behavior according to the basic analysis result.

[0143] The driving warning module 1033 is used to generate a danger warning signal when there is a possible dangerous driving behavior, and to provide a dangerous driving warning according to the danger warning signal; when there is no possible dangerous driving behavior, no danger warning signal is generated.

[0144] Furthermore, the driving behavior monitoring and early warning system based on big data also includes:

[0145] The warning cancellation judgment unit 104 is used to upload the corresponding monitoring and shooting data within the preset warning time, and perform warning cancellation monitoring to determine whether the danger warning can be cancelled.

[0146] In an embodiment of the present invention, the warning cancellation judgment unit 104 uploads the corresponding monitoring and shooting data within the preset warning time (15s), and determines in real time whether the dangerous driving warning can be cancelled within the preset warning time.

[0147] The danger warning cancellation unit 105 is configured to stop the dangerous driving warning after the warning time when the danger warning can be cancelled.

[0148] The big data analysis and processing unit 106 is used to perform big data monitoring and analysis on the uploaded monitoring and shooting data when the danger warning cannot be lifted, generate big data monitoring results, and perform alarm judgment processing.

[0149] In an embodiment of the present invention, when the danger warning cannot be cancelled, the big data analysis and processing unit 106 performs big data monitoring and analysis on the uploaded monitoring and shooting data based on big data technology, generates big data monitoring results, and makes an accurate judgment on whether dangerous driving can be confirmed according to the big data monitoring results. When dangerous driving can be confirmed, a danger warning signal is generated, and according to the danger warning signal, a dangerous driving warning is issued after a preset warning time, and the dangerous driving warning at this time is a severe dangerous driving reminder; when dangerous driving cannot be confirmed, the danger warning is cancelled, and no dangerous driving warning is issued after the preset warning time.

[0150] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0151] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0152] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A driving behavior monitoring and early warning method based on big data, characterized in that: The method specifically comprises the following steps: Perform multi-angle learning shooting, obtain learning shooting data, and construct a driving three-dimensional model based on the learning shooting data; Perform driving monitoring and shooting, obtain monitoring and shooting data, perform driving three-dimensional simulation based on the driving three-dimensional model and the monitoring and shooting data, and generate a driving simulation model in real time; Conduct basic monitoring and analysis based on the driving simulation model to determine whether there is any possible dangerous driving behavior, and issue a dangerous driving warning when there is any possible dangerous driving behavior; Within the preset warning time, upload the corresponding monitoring data and conduct warning cancellation monitoring to determine whether the danger warning can be lifted; If the danger warning can be lifted, the dangerous driving warning will be stopped after the warning time; If the danger warning cannot be lifted, the uploaded monitoring data will be analyzed for big data monitoring, and big data monitoring results will be generated, and alarm judgment and processing will be carried out; The process of uploading the corresponding monitoring data within the preset warning time, performing warning cancellation monitoring, and determining whether the danger warning can be cancelled specifically includes the following steps: Upload the corresponding monitoring data within the preset warning time; Conduct early warning release monitoring within the preset early warning time and generate early warning monitoring data; Determining whether the danger warning can be lifted according to the warning monitoring data; The steps of performing big data monitoring and analysis on the uploaded monitoring and shooting data, generating big data monitoring results, and performing alarm judgment processing specifically include the following steps: Conduct big data monitoring and analysis on the uploaded monitoring data to generate big data monitoring results; Determining whether dangerous driving can be confirmed based on the big data monitoring results; If dangerous driving can be confirmed, a dangerous warning signal is generated, and a dangerous driving warning is issued according to the dangerous warning signal; If dangerous driving cannot be confirmed, the danger warning will be cancelled.

2. The driving behavior monitoring and early warning method based on big data according to claim 1 is characterized in that: The multi-angle learning shooting, obtaining learning shooting data, and constructing a driving three-dimensional model based on the learning shooting data specifically include the following steps: When unlocking and locking within a preset trigger distance, a learning shot is taken outside the vehicle to generate first shooting data; Before starting driving, perform in-car learning shooting to generate second shooting data; Combining the plurality of first shooting data and the plurality of second shooting data to obtain learning shooting data; A three-dimensional driving model is constructed based on the learning shooting data.

3. The driving behavior monitoring and early warning method based on big data according to claim 1 is characterized in that: The driving monitoring and shooting, obtaining monitoring and shooting data, performing a three-dimensional driving simulation based on the three-dimensional driving model and the monitoring and shooting data, and generating a driving simulation model in real time specifically include the following steps: Conduct driving monitoring and shooting, and obtain monitoring and shooting data; Performing personnel identification on the monitoring and shooting data, and extracting personnel shooting data; Based on the three-dimensional driving model, a three-dimensional driving simulation is performed on the personnel shooting data to generate a driving simulation model in real time.

4. The driving behavior monitoring and early warning method based on big data according to claim 1 is characterized in that: The basic monitoring and analysis based on the driving simulation model to determine whether there is a possible dangerous driving behavior and issuing a dangerous driving warning when there is a possible dangerous driving behavior specifically includes the following steps: Performing basic monitoring analysis based on the driving simulation model to generate basic analysis results; Determine whether there is any possible dangerous driving behavior based on the basic analysis results; If there is a possible dangerous driving behavior, a danger warning signal is generated, and a dangerous driving warning is issued according to the danger warning signal; If there is no potentially dangerous driving behavior, no hazard warning signal will be generated.

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