Vehicle information collection system

Abstract

The present invention relates to the technical field of data collection, and in particular to a vehicle information collection system, the system comprising: a data collection module for collecting vehicle information, road section information, vehicle distance information and driving environment information; a driving analysis module for analyzing the driving state of the vehicle; a road section analysis module for optimizing the analysis process of the driving state of the vehicle; a vehicle distance analysis module for analyzing the abnormality of the distance between the vehicle and the preceding vehicle; a level classification module for classifying the abnormal levels of the vehicle driving environment; an early warning module for evaluating the operation risk of the vehicle and providing a driving risk early warning to the user according to the evaluation result; an information acquisition module for obtaining the number of accident vehicles and the average driving distance of sample vehicles within a monitoring period; and a system optimization module for optimizing the process of providing a driving risk early warning to the user within the next monitoring period. The present invention improves the evaluation accuracy of the vehicle operation risk.

Description

Technical Field

[0001] The present invention relates to the technical field of data collection, and in particular to a vehicle information collection system. Background Art

[0002] With the continuous development of road traffic, the risk of vehicle operation is also increasing. In order to ensure traffic safety, it is necessary to evaluate and manage the risk of vehicle operation. At present, some vehicle operation risk assessment systems have appeared at home and abroad, such as vehicle status monitoring systems based on sensor data. However, most of these systems can only evaluate a single factor and lack the ability to comprehensively consider multiple factors. Therefore, a vehicle operation risk assessment system that can comprehensively consider multiple factors is needed.

[0003] Chinese patent publication number CN109094482A discloses a vehicle operation risk assessment information collection system, a risk assessment system and a method, which calculates vehicle spacing index data and steering wheel steering entropy index data according to the current vehicle speed, acceleration, the vehicle distance between the current vehicle and the vehicle in front, and the driver's line of sight transfer time, scanning speed, and blinking frequency; and judges the corresponding risks according to the various indicators in each judgment cycle and the corresponding risk indicator thresholds, and then constructs a Bayesian network, and calculates the conditional probability through learning to obtain the vehicle operation risk value; It can be seen that when the scheme collects information for vehicle operation risk assessment, it does not analyze environmental factors and road section types, and there is a problem of low accuracy in vehicle operation risk assessment. Summary of the invention

[0004] To this end, the present invention provides a vehicle information collection system to overcome the problem of low accuracy in vehicle operation risk assessment in the prior art.

[0005] To achieve the above object, the present invention provides a vehicle information collection system, the system comprising: a data collection module for collecting vehicle information, road section information, vehicle distance information and driving environment information;

[0006] A driving analysis module is used to analyze the driving status of the vehicle based on the collected vehicle information;

[0007] A road section analysis module is used to optimize the analysis process of vehicle driving status based on the collected road section information;

[0008] The vehicle distance analysis module is used to analyze the abnormality of the distance between the vehicle and the preceding vehicle based on the collected vehicle distance information;

[0009] A level classification module is used to classify the abnormal level of the vehicle driving environment according to the collected driving environment information;

[0010] The early warning module is used to evaluate the vehicle's operating risk based on the analysis results of the vehicle's driving state, the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle, and the classification results of the abnormality level of the vehicle's driving environment, and to issue a driving risk early warning to the user based on the evaluation results;

[0011] An information acquisition module is used to obtain the number of accident vehicles and the average driving distance of sample vehicles during the monitoring period;

[0012] The system optimization module is used to optimize the process of providing driving risk warnings to users in the next monitoring period based on the number of vehicle accidents and the average driving distance of sample vehicles obtained during the monitoring period.

[0013] Further, the state analysis unit is used to analyze the abnormality of the vehicle driving state according to the analysis results of the abnormality of the vehicle speed, the abnormality of the vehicle acceleration and the abnormality of the vehicle steering wheel angle, wherein:

[0014] When w1×Cy+w2×Ay+w3×Jy≤u0, the state analysis unit determines that the vehicle driving state is normal;

[0015] When w1×Cy+w2×Ay+w3×Jy>u0, the state analysis unit determines that the vehicle driving state is abnormal, and sets the vehicle driving abnormality index to xs, setting xs=w1×Cy+w2×Ay+w3×Jy-u0;

[0016] Among them, w1 is the speed weight, w2 is the acceleration weight, w3 is the turning angle weight, w1<w3≤w2, and u0 is the preset driving abnormality coefficient.

[0017] Furthermore, the road section analysis module optimizes the analysis process of the abnormality of the vehicle driving state according to the collected road section information, wherein:

[0018] When the road section where the vehicle is traveling is a normal road section, the road section analysis module does not optimize the analysis process of the abnormality of the vehicle's driving state;

[0019] When the road section where the vehicle is traveling is a special road section, the road section analysis module optimizes the analysis process of the abnormality of the vehicle's driving state, sets the optimized preset vehicle speed to v2, and sets the optimized preset acceleration to a2.

[0020] Furthermore, the vehicle distance analysis module compares the collected vehicle distance t0 between the vehicle and the preceding vehicle with the preset vehicle distance t1, and analyzes the abnormality of the vehicle distance between the vehicle and the preceding vehicle according to the comparison result, wherein:

[0021] When t0≤t1, the vehicle distance analysis module determines that the distance between the vehicle and the preceding vehicle is abnormal, and sets the abnormal distance index to cj, setting cj=exp[3×(t1-t0) / t1-3];

[0022] When t0>t1, the vehicle distance analysis module determines that the distance between the vehicle and the preceding vehicle is normal.

[0023] Furthermore, the grading module is provided with an environmental analysis unit and a grading unit, wherein the environmental analysis unit is used to compare the collected precipitation k0 with the preset precipitation k1, and analyze the abnormality of the environmental precipitation according to the comparison result, wherein:

[0024] When k0≤k1, the environmental analysis unit determines that the environmental precipitation is normal;

[0025] When k0>k1, the environmental analysis unit determines that the environmental precipitation is abnormal, and sets the precipitation abnormality index to ky, setting ky=α×(k0-k1) / k1;

[0026] The environmental analysis unit compares the collected visibility d0 with the preset visibility d1, and analyzes the abnormality of the environmental visibility according to the comparison result, wherein:

[0027] When d0≤d1, the environment analysis unit determines that the environment visibility is abnormal, and sets the visibility abnormality index to dy, setting dy=β×(d1-d0) / d1;

[0028] When d0>d1, the environment analysis unit determines that the environment visibility is normal;

[0029] Wherein, α is the first preset adjustment coefficient, and β is the second preset adjustment coefficient.

[0030] Furthermore, the level classification unit is used to classify the abnormal level of the driving environment abnormality according to the analysis results of the abnormality of environmental precipitation and the analysis results of the abnormality of environmental visibility, wherein:

[0031] When ky+dy≤p1, the level classification unit determines that the abnormal level of the driving environment is level one;

[0032] When ky+dy>p1, the level classification unit determines that the abnormal level of the driving environment is level 2;

[0033] Among them, p1 is the preset environmental abnormality coefficient.

[0034] Furthermore, the early warning module is provided with a risk assessment unit and an early warning unit, and the risk assessment unit is used to assess the operation risk of the vehicle according to the analysis results of the vehicle driving state and the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle, wherein:

[0035] When the driving state of the vehicle is normal and the distance between the vehicle and the preceding vehicle is normal, the risk assessment unit sets the running risk of the vehicle to R1, and sets R1=0;

[0036] When the driving state of the vehicle is normal and the distance between the vehicle and the preceding vehicle is abnormal, the risk assessment unit sets the running risk of the vehicle to R2, and sets R2={arctan[sin(cj)]}×(π / 2);

[0037] When the driving state of the vehicle is abnormal and the distance between the vehicle and the preceding vehicle is normal, the risk assessment unit sets the running risk of the vehicle to R3, and sets R3={exp[sin(△u-u0)]-1} / 2;

[0038] When the driving state of the vehicle is abnormal and the distance between the vehicle and the preceding vehicle is abnormal, the risk assessment unit sets the running risk of the vehicle to R4, and sets R4={exp[sin(△u-u0)]-1} / 2+{arctan[sin(cj)]}×(π / 2);

[0039] Among them, △u=w1×Cy+w2×Ay+w3×Jy-u0.

[0040] Furthermore, the warning unit is used to provide a driving risk warning to the user according to the evaluation result of the vehicle running risk and the classification result of the driving environment abnormality level, wherein:

[0041] When the driving environment abnormality level is level one, if 0<Rr≤F1, the warning unit will issue a low-risk driving warning to the user; if F1<Rr≤F2, the warning unit will issue a medium-risk driving warning to the user; if Rr>F2, the warning unit will issue a high-risk driving warning to the user;

[0042] When the driving environment abnormality level is level 2, if 0<Rr≤F3, the warning unit will issue a low-risk driving warning to the user; if F3<Rr≤F4, the warning unit will issue a medium-risk driving warning to the user; if Rr>F4, the warning unit will issue a high-risk driving warning to the user;

[0043] Among them, F1 is the first preset risk coefficient, F2 is the second preset risk coefficient, F3 is the third preset risk coefficient, F4 is the fourth preset risk coefficient, Rr is the classification result of the abnormal level of the driving environment, and F3<F1<F4<F2.

[0044] Furthermore, the system optimization module is provided with a first optimization unit and a second optimization unit, wherein the first optimization unit is used to optimize the process of providing driving risk warning to the user based on the number of accident vehicles sg1 obtained during the monitoring period, wherein:

[0045] When sg1 / sg0≤B, the first optimization unit determines that the number of accident vehicles in the current monitoring period is normal and does not perform optimization;

[0046] When sg1 / sg0>B, the first optimization unit determines that the number of accident vehicles in the current monitoring period is abnormal, and optimizes the process of providing driving risk warning to the user, and sets the optimized preset risk coefficient as Ff', setting Ff'=Ff-f0×{1-[(sg1 / sg0-B)+1] (sg1 / sg0-B)+1 -1} / 3};

[0047] Among them, Ff is the preset risk factor, f=1,2,3,4; f0 is the preset adjustment value, B is the preset abnormal proportion, and sg0 is the number of sample vehicles.

[0048] Furthermore, the second optimization unit compares the average driving distance q0 of the sample vehicles in the monitoring period with the preset distance q1, and performs secondary optimization on the process of providing driving risk warning to the user according to the comparison result, wherein:

[0049] When q0≤q1, the second optimization unit determines that the driving distance of the sample vehicles in the current monitoring period is normal and does not perform optimization;

[0050] When q0>q1, the second optimization unit determines that the driving distance of the sample vehicle in the current monitoring period is abnormal, and performs secondary optimization on the process of providing driving risk warning to the user, and sets the optimized preset adjustment value to f0', setting f0'=f0×[1-η×(q0-q1) / (q0+q1)];

[0051] Wherein, η is the preset correction coefficient.

[0052] Compared with the prior art, the beneficial effects of the present invention are that the driving analysis module improves the accuracy of vehicle driving state analysis by setting a preset vehicle speed, a preset acceleration and a preset angle, and improves the accuracy of vehicle driving state analysis by setting various weights and a preset driving abnormality coefficient, thereby improving the accuracy of vehicle operation risk assessment; the vehicle distance analysis module improves the accuracy of abnormality analysis of the vehicle distance to the preceding vehicle by setting a preset vehicle distance, thereby improving the accuracy of vehicle operation risk assessment; the grading module improves the accuracy of driving environment abnormality grading by setting a preset precipitation, a preset visibility and a preset adjustment coefficient, thereby improving the accuracy of vehicle operation risk assessment; the accuracy of driving environment abnormality grading is improved by setting a preset environmental abnormality coefficient, Thereby improving the accuracy of vehicle operation risk assessment, thereby improving the accuracy of driving risk warning to users. The warning module improves the accuracy of driving risk warning to users by setting various preset risk coefficients, thereby improving the accuracy of vehicle operation risk assessment and improving the safety of vehicle driving. The system optimization module improves the accuracy of driving risk warning to users by setting preset adjustment values ​​and preset abnormality ratios, thereby improving the accuracy of vehicle operation risk assessment. The preset distance and preset correction coefficient are set to reduce the impact of sample vehicle driving distance abnormalities on driving risk warning optimization, thereby improving the accuracy of driving risk warning to users, thereby improving the accuracy of vehicle operation risk assessment and improving the safety of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the structure of the vehicle information collection system of this embodiment.

[0054] Figure 2 Schematic diagram of the structure of the driving analysis module of this embodiment.

[0055] Figure 3 It is a structural diagram of the level division module of this embodiment.

[0056] Figure 4 Schematic diagram of the structure of the early warning module of this embodiment.

[0057] Figure 5 Schematic diagram of the structure of the system optimization module of this embodiment. DETAILED DESCRIPTION

[0058] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0060] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] See also Figure 1 As shown, it is a schematic diagram of the structure of the vehicle information collection system of this embodiment, and the system includes:

[0062] The data acquisition module is used to collect vehicle information, road section information, vehicle distance information and driving environment information; the vehicle information includes vehicle speed, vehicle acceleration and vehicle steering wheel angle; the road section information includes normal road sections and special road sections, the special road sections are hospital road sections and school road sections, and the normal road sections are non-special road sections; the vehicle distance information is the distance between the vehicle and the front vehicle, which is expressed by a time interval, that is, the time difference between the front and rear vehicles passing the same position, in seconds; the driving environment information includes environmental precipitation and visibility; in this embodiment, the collection method of vehicle information, road section information, vehicle distance information and driving environment information is not specifically limited, and those skilled in the art can freely set it, as long as the collection requirements of vehicle information, road section information, vehicle distance information and driving environment information are met, wherein the vehicle information, road section information and form environment information can be collected by the on-board computer, and the driving environment information can be collected by the meteorological website;

[0063] A driving analysis module, used to analyze the driving state of the vehicle according to the collected vehicle information, and the driving analysis module is connected to the data collection module;

[0064] A road section analysis module, used for optimizing the analysis process of the vehicle driving state according to the collected road section information, the road section analysis module is connected to the driving analysis module;

[0065] A vehicle distance analysis module, used for analyzing the abnormality of the distance between the vehicle and the preceding vehicle according to the collected vehicle distance information, the vehicle distance analysis module being connected to the data collection module;

[0066] A level classification module, used to classify the abnormal level of the vehicle driving environment according to the collected driving environment information, and the level classification module is connected to the data collection module;

[0067] An early warning module is used to evaluate the operation risk of the vehicle according to the analysis results of the vehicle driving state, the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle, and the classification results of the abnormality level of the vehicle driving environment, and to issue a driving risk early warning to the user according to the evaluation results. The early warning module is connected to the driving analysis module, the road section analysis module, and the level classification module;

[0068] An information acquisition module is used to obtain the number of accident vehicles and the average driving distance of sample vehicles within a monitoring period, and the information acquisition module is connected to the early warning module; the sample vehicle described in this embodiment is a test vehicle for applying the vehicle information collection system, and the accident vehicle is a sample vehicle involved in a traffic accident; in this embodiment, no specific limitation is made to the setting of the monitoring period, and those skilled in the art can set it freely, as long as the setting requirements of the monitoring period are met, wherein the monitoring period can be set to 60 days, 90 days, etc.; in this embodiment, no specific limitation is made to the method for obtaining the number of accident vehicles and the average driving distance of sample vehicles within the monitoring period, and those skilled in the art can set it freely, as long as the requirements for obtaining the number of accident vehicles and the average driving distance of sample vehicles within the monitoring period are met, wherein the number of accident vehicles and the average driving distance of sample vehicles within the monitoring period can be obtained through user interactive input;

[0069] The system optimization module is used to optimize the process of providing driving risk warning to users in the next monitoring period according to the number of vehicle accidents and the average driving distance of sample vehicles in the monitoring period. The system optimization module is connected to the information acquisition module.

[0070] See also Figure 2 As shown, it is a schematic diagram of the structure of the driving analysis module of this embodiment, and the driving analysis module includes:

[0071] A driving analysis unit, used to analyze the abnormality of the vehicle speed, the abnormality of the vehicle acceleration and the abnormality of the vehicle steering wheel angle according to the collected vehicle information;

[0072] The state analysis unit is used to analyze the abnormality of the vehicle driving state according to the analysis results of the vehicle speed abnormality, the vehicle acceleration abnormality and the vehicle steering wheel angle abnormality. The state analysis unit is connected to the driving analysis unit.

[0073] See also Figure 3 As shown, it is a schematic diagram of the structure of the level division module of this embodiment, and the level division module includes:

[0074] An environmental analysis unit, used to analyze the abnormality of environmental precipitation and the abnormality of environmental visibility according to the collected driving environment information;

[0075] The level classification unit is used to classify the abnormal level of the driving environment abnormality according to the analysis results of the abnormality of environmental precipitation and the analysis results of the abnormality of environmental visibility. The level classification unit is connected to the environmental analysis unit.

[0076] See also Figure 4 As shown, it is a schematic diagram of the structure of the early warning module of this embodiment, and the early warning module includes:

[0077] A risk assessment unit, used to assess the operation risk of the vehicle based on the analysis results of the vehicle's driving state and the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle;

[0078] The early warning unit is used to issue a driving risk early warning to the user according to the evaluation result of the vehicle operation risk and the classification result of the abnormal level of the driving environment. The early warning unit is connected to the risk evaluation unit.

[0079] See also Figure 5 As shown, it is a schematic diagram of the structure of the system optimization module of this embodiment, and the system optimization module includes:

[0080] The first optimization unit is used to optimize the process of providing driving risk warning to users according to the number of accident vehicles obtained during the monitoring period;

[0081] The second optimization unit is used to perform secondary optimization on the process of providing driving risk warning to users according to the average driving distance of sample vehicles obtained during the monitoring period. The second optimization unit is connected to the first optimization unit.

[0082] Specifically, the vehicle information collection system described in this embodiment is applied to the on-board computer of vehicles traveling on urban roads. It analyzes the vehicle's driving status, vehicle distance, and driving environment, and issues driving risk warnings to users based on the analysis results. It optimizes the warning process based on the acquired accident information, thereby improving the accuracy of vehicle operation risk assessment and the safety of vehicle driving.

[0083] Specifically, the driving analysis unit compares the collected vehicle speed v0 with the preset speed v1, and analyzes the abnormality of the vehicle speed according to the comparison result, wherein:

[0084] When v0<v1, the driving analysis unit determines that the vehicle speed is normal;

[0085] When v0>v1, the driving analysis unit determines that the vehicle speed is abnormal, and sets the abnormal speed coefficient to Cy, setting Cy=sin[(v0-v1)×(π / 2) / (v1+v0)];

[0086] The driving analysis unit compares the collected vehicle acceleration a0 with the preset acceleration a1, and analyzes the abnormality of the vehicle acceleration according to the comparison result, wherein:

[0087] When a0≤a1, the driving analysis unit determines that the vehicle acceleration is normal;

[0088] When a0>a1, the driving analysis unit determines that the vehicle acceleration is abnormal, and sets the acceleration abnormality coefficient to Ay, setting Ay=(a0-a1) / a1;

[0089] The driving analysis unit compares the collected vehicle steering wheel angle j0 with the preset angle j1, and analyzes the abnormality of the vehicle steering wheel angle according to the comparison result, wherein:

[0090] When j0≤j1, the driving analysis unit determines that the vehicle steering wheel angle is normal;

[0091] When j0>j1, the driving analysis unit determines that the steering wheel angle of the vehicle is abnormal, and sets the angle abnormality coefficient to Jy, setting Jy=(j0-j1) / j1.

[0092] Specifically, the driving analysis unit improves the accuracy of vehicle driving state analysis by setting a preset speed, preset acceleration and preset angle, thereby improving the accuracy of vehicle operation risk assessment, thereby improving the accuracy of driving risk warning to users, and ultimately improving the accuracy of vehicle operation risk assessment, while improving vehicle driving safety; in this embodiment, the values ​​of the preset speed, preset acceleration and preset angle are not specifically limited, and technicians in this field can set them freely as long as the value requirements of the preset speed, preset acceleration and preset angle are met. Among them, the optimal value of v1 is 55km / h, the optimal value of a1 is 3 m / s², and the optimal value of j1 is 180°.

[0093] Specifically, the state analysis unit analyzes the abnormality of the vehicle driving state according to the analysis results of the abnormality of the vehicle speed, the abnormality of the vehicle acceleration and the abnormality of the vehicle steering wheel angle, wherein:

[0094] When w1×Cy+w2×Ay+w3×Jy≤u0, the state analysis unit determines that the vehicle driving state is normal;

[0095] When w1×Cy+w2×Ay+w3×Jy>u0, the state analysis unit determines that the vehicle driving state is abnormal, and sets the vehicle driving abnormality index to xs, setting xs=w1×Cy+w2×Ay+w3×Jy-u0;

[0096] Among them, w1 is the speed weight, w2 is the acceleration weight, w3 is the angle weight, w1<w3≤w2, u0 is the preset driving abnormality coefficient, when the vehicle speed is normal, Cy=0, when the vehicle acceleration is normal, Ay=0, when the vehicle steering wheel angle is normal, Jy=0.

[0097] Specifically, the state analysis unit improves the accuracy of the vehicle driving state analysis by setting various weights and preset driving abnormality coefficients, thereby improving the accuracy of the vehicle operation risk assessment, thereby improving the accuracy of the driving risk warning to the user, and ultimately improving the accuracy of the vehicle operation risk assessment, while improving the safety of vehicle driving; in this embodiment, no specific limitation is made to the values ​​of each weight and the preset driving abnormality coefficient, and technical personnel in this field can set them freely, as long as the value requirements of each weight and the preset driving abnormality coefficient are met, among which the best value of w1 is 0.6, the best value of w2 is 1.1, the best value of w3 is 1, and the best value of u0 is 0.18.

[0098] Specifically, the road section analysis module optimizes the analysis process of the abnormality of the vehicle driving state according to the collected road section information, wherein:

[0099] When the road section where the vehicle is traveling is a normal road section, the road section analysis module does not optimize the analysis process of the abnormality of the vehicle's driving state;

[0100] When the road section where the vehicle is traveling is a special road section, the road section analysis module optimizes the analysis process of the abnormality of the vehicle's driving state, sets the optimized preset vehicle speed to v2, and sets the optimized preset acceleration to a2.

[0101] Specifically, this embodiment does not impose any specific restrictions on the values ​​of v2 and a2. Those skilled in the art can set them freely as long as the value requirements of v2 and a2 are met. Among them, the optimal value of v2 is 20 km / h, and the optimal value of a2 is 1 m / s².

[0102] Specifically, the vehicle distance analysis module compares the collected vehicle distance t0 between the vehicle and the preceding vehicle with the preset vehicle distance t1, and analyzes the abnormality of the vehicle distance between the vehicle and the preceding vehicle according to the comparison result, wherein:

[0103] When t0≤t1, the vehicle distance analysis module determines that the distance between the vehicle and the preceding vehicle is abnormal, and sets the abnormal distance index to cj, setting cj=exp[3×(t1-t0) / t1-3];

[0104] When t0>t1, the vehicle distance analysis module determines that the distance between the vehicle and the preceding vehicle is normal.

[0105] Specifically, the vehicle distance analysis module improves the accuracy of abnormality analysis of the distance between the vehicle and the vehicle in front by setting a preset vehicle distance, thereby improving the accuracy of vehicle operation risk assessment, thereby improving the accuracy of driving risk warning to users, and ultimately improving the accuracy of vehicle operation risk assessment, while improving vehicle driving safety; in this embodiment, the value of the preset vehicle distance is not specifically limited, and technicians in this field can set it freely as long as the value requirement of the preset vehicle distance is met, among which the optimal value of t1 is 1.8s.

[0106] Specifically, the environmental analysis unit compares the collected precipitation k0 with the preset precipitation k1, and analyzes the abnormality of the environmental precipitation according to the comparison result, wherein:

[0107] When k0≤k1, the environmental analysis unit determines that the environmental precipitation is normal;

[0108] When k0>k1, the environmental analysis unit determines that the environmental precipitation is abnormal, and sets the precipitation abnormality index to ky, setting ky=α×(k0-k1) / k1;

[0109] The environmental analysis unit compares the collected visibility d0 with the preset visibility d1, and analyzes the abnormality of the environmental visibility according to the comparison result, wherein:

[0110] When d0≤d1, the environment analysis unit determines that the environment visibility is abnormal, and sets the visibility abnormality index to dy, setting dy=β×(d1-d0) / d1;

[0111] When d0>d1, the environment analysis unit determines that the environment visibility is normal;

[0112] Wherein, α is the first preset adjustment coefficient, β is the second preset adjustment coefficient, 0.4<α<1, 0.6<β<1.5.

[0113] Specifically, the environmental analysis unit improves the accuracy of the driving environment abnormality level classification by setting a preset precipitation, preset visibility and a preset adjustment coefficient, thereby improving the accuracy of the vehicle operation risk assessment, thereby improving the accuracy of the driving risk warning to the user, and ultimately improving the accuracy of the vehicle operation risk assessment, while improving the safety of vehicle driving; in this embodiment, there is no specific limitation on the values ​​of the preset precipitation, preset visibility and preset adjustment coefficient, and technical personnel in this field can set them freely as long as the value requirements of the preset precipitation, preset visibility and preset adjustment coefficient are met. Among them, the optimal value of k1 is 50 mm, the optimal value of d1 is 500 meters, the optimal value of α is 0.64, and the optimal value of β is 1.1.

[0114] Specifically, the level classification unit classifies the abnormality level of the driving environment abnormality according to the analysis results of the abnormality of environmental precipitation and the analysis results of the abnormality of environmental visibility, wherein:

[0115] When ky+dy≤p1, the level classification unit determines that the abnormal level of the driving environment is level one;

[0116] When ky+dy>p1, the level classification unit determines that the abnormal level of the driving environment is level 2;

[0117] Among them, p1 is the preset environmental anomaly coefficient. When the environmental precipitation is normal, the value of ky is 0, and when the environmental visibility is normal, the value of dy is 0.

[0118] Specifically, the grading unit improves the accuracy of the driving environment abnormality grading by setting a preset environmental abnormality coefficient, thereby improving the accuracy of the vehicle operation risk assessment, thereby improving the accuracy of the driving risk warning to the user, and ultimately improving the accuracy of the vehicle operation risk assessment, while improving the safety of vehicle driving; in this embodiment, there is no specific limitation on the value of the preset environmental abnormality coefficient, and technical personnel in this field can set it freely, as long as the value requirement of the preset environmental abnormality coefficient is met, among which the optimal value of p1 is 0.2.

[0119] Specifically, the risk assessment unit assesses the operation risk of the vehicle according to the analysis results of the vehicle driving state and the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle, wherein:

[0120] When the driving state of the vehicle is normal and the distance between the vehicle and the preceding vehicle is normal, the risk assessment unit sets the running risk of the vehicle to R1, and sets R1=0;

[0121] When the driving state of the vehicle is normal and the distance between the vehicle and the preceding vehicle is abnormal, the risk assessment unit sets the running risk of the vehicle to R2, and sets R2={arctan[sin(cj)]}×(π / 2);

[0122] When the driving state of the vehicle is abnormal and the distance between the vehicle and the preceding vehicle is normal, the risk assessment unit sets the running risk of the vehicle to R3, and sets R3={exp[sin(△u-u0)]-1} / 2;

[0123] When the driving state of the vehicle is abnormal and the distance between the vehicle and the preceding vehicle is abnormal, the risk assessment unit sets the running risk of the vehicle to R4, and sets R4={exp[sin(△u-u0)]-1} / 2+{arctan[sin(cj)]}×(π / 2);

[0124] Among them, △u=w1×Cy+w2×Ay+w3×Jy-u0.

[0125] Specifically, the warning unit issues a driving risk warning to the user based on the evaluation result of the vehicle operation risk and the classification result of the driving environment abnormality level, wherein:

[0126] When the driving environment abnormality level is level one, if 0<Rr≤F1, the warning unit will issue a low-risk driving warning to the user; if F1<Rr≤F2, the warning unit will issue a medium-risk driving warning to the user; if Rr>F2, the warning unit will issue a high-risk driving warning to the user;

[0127] When the driving environment abnormality level is level 2, if 0<Rr≤F3, the warning unit will issue a low-risk driving warning to the user; if F3<Rr≤F4, the warning unit will issue a medium-risk driving warning to the user; if Rr>F4, the warning unit will issue a high-risk driving warning to the user;

[0128] Among them, F1 is the first preset risk coefficient, F2 is the second preset risk coefficient, F3 is the third preset risk coefficient, F4 is the fourth preset risk coefficient, Rr is the classification result of the abnormal level of the driving environment, and F3<F1<F4<F2.

[0129] Specifically, the warning unit improves the accuracy of driving risk warnings to users by setting preset risk coefficients, thereby improving the accuracy of vehicle operation risk assessment and improving the safety of vehicle driving. In this embodiment, there is no specific limitation on the value of each preset risk coefficient, and technical personnel in this field can set it freely as long as the value requirements of each preset risk coefficient are met. Among them, the best value of F1 is 0.12, the best value of F2 is 0.2, the best value of F3 is 0.08, and the best value of F4 is 0.16.

[0130] Specifically, the first optimization unit optimizes the process of providing driving risk warning to the user based on the number of accident vehicles sg1 obtained during the monitoring period, wherein:

[0131] When sg1 / sg0≤B, the first optimization unit determines that the number of accident vehicles in the current monitoring period is normal and does not perform optimization;

[0132] When sg1 / sg0>B, the first optimization unit determines that the number of accident vehicles in the current monitoring period is abnormal, and optimizes the process of providing driving risk warning to the user, and sets the optimized preset risk coefficient as Ff', setting Ff'=Ff-f0×{1-[(sg1 / sg0-B)+1] (sg1 / sg0-B)+1 -1} / 3};

[0133] Among them, Ff is the preset risk factor, f=1,2,3,4; f0 is the preset adjustment value, B is the preset abnormal proportion, and sg0 is the number of sample vehicles.

[0134] Specifically, the first optimization unit sets a preset adjustment value and a preset abnormal ratio to improve the accuracy of driving risk warning to users, thereby improving the accuracy of vehicle operation risk assessment and improving vehicle driving safety. In this embodiment, there is no specific limitation on the values ​​of the preset adjustment value and the preset abnormal ratio, and those skilled in the art can set them freely as long as the value requirements of the preset adjustment value and the preset abnormal ratio are met. Among them, the optimal value of f0 is 0.05, and the optimal value of B is 0.08.

[0135] Specifically, the second optimization unit compares the average driving distance q0 of the sample vehicles in the monitoring period with the preset distance q1, and performs secondary optimization on the process of providing driving risk warning to the user according to the comparison result, wherein:

[0136] When q0≤q1, the second optimization unit determines that the driving distance of the sample vehicles in the current monitoring period is normal and does not perform optimization;

[0137] When q0>q1, the second optimization unit determines that the driving distance of the sample vehicle in the current monitoring period is abnormal, and performs secondary optimization on the process of providing driving risk warning to the user, and sets the optimized preset adjustment value to f0', setting f0'=f0×[1-η×(q0-q1) / (q0+q1)];

[0138] Wherein, η is the preset correction coefficient.

[0139] Specifically, the second optimization unit optimizes the driving risk warning by setting a preset distance and a preset correction coefficient to reduce the impact of abnormal driving distance of sample vehicles, thereby improving the accuracy of driving risk warning to users, thereby improving the accuracy of vehicle operation risk assessment, and at the same time improving the safety of vehicle driving; in this embodiment, there is no specific limitation on the values ​​of the preset distance and the preset correction coefficient, and technical personnel in this field can set them freely, as long as the value requirements of the preset distance and the preset correction coefficient are met. Among them, when the monitoring period is 60 days, the optimal value of q1 is 900km, and the optimal value of η is 0.7.

[0140] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A vehicle information collection system, characterized in that: include, Data collection module, used to collect vehicle information, road section information, vehicle distance information and driving environment information; A driving analysis module is used to analyze the driving status of the vehicle based on the collected vehicle information; A road section analysis module is used to optimize the analysis process of vehicle driving status based on the collected road section information; The vehicle distance analysis module is used to analyze the abnormality of the distance between the vehicle and the preceding vehicle based on the collected vehicle distance information; A level classification module is used to classify the abnormal level of the vehicle driving environment according to the collected driving environment information; The early warning module is used to evaluate the vehicle's operating risk based on the analysis results of the vehicle's driving state, the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle, and the classification results of the abnormality level of the vehicle's driving environment, and to issue a driving risk early warning to the user based on the evaluation results; An information acquisition module is used to obtain the number of accident vehicles and the average driving distance of sample vehicles during the monitoring period; The system optimization module is used to optimize the process of providing driving risk warnings to users in the next monitoring period based on the number of vehicle accidents and the average driving distance of sample vehicles obtained in the monitoring period; The driving analysis module is provided with a state analysis unit, and the state analysis unit is used to analyze the abnormality of the vehicle driving state according to the analysis results of the abnormality of the vehicle speed, the abnormality of the vehicle acceleration and the abnormality of the vehicle steering wheel angle, wherein: When w1×Cy+w2×Ay+w3×Jy≤u0, the state analysis unit determines that the vehicle driving state is normal; When w1×Cy+w2×Ay+w3×Jy>u0, the state analysis unit determines that the vehicle driving state is abnormal, and sets the vehicle driving abnormality index to xs, setting xs=w1×Cy+w2×Ay+w3×Jy-u0; Wherein, w1 is the speed weight, w2 is the acceleration weight, w3 is the turning angle weight, w1<w3≤w2, u0 is the preset driving abnormality coefficient, Cy is the vehicle speed abnormality coefficient, Ay is the acceleration abnormality coefficient, and Jy is the turning angle abnormality coefficient; The road section analysis module optimizes the analysis process of the abnormality of the vehicle driving state according to the collected road section information, wherein: When the road section on which the vehicle is traveling is a normal road section, the road section analysis module does not optimize the analysis process of the abnormality of the vehicle's driving state; When the road section on which the vehicle is traveling is a special road section, the road section analysis module optimizes the analysis process of the abnormality of the vehicle's driving state, sets the optimized preset vehicle speed to v2, and sets the optimized preset acceleration to a2; The vehicle distance analysis module compares the collected vehicle distance t0 between the vehicle and the preceding vehicle with the preset vehicle distance t1, and analyzes the abnormality of the vehicle distance between the vehicle and the preceding vehicle according to the comparison result, wherein: When t0≤t1, the vehicle distance analysis module determines that the distance between the vehicle and the preceding vehicle is abnormal, and sets the abnormal distance index to cj, setting cj=exp[3×(t1-t0) / t1-3]; When t0>t1, the vehicle distance analysis module determines that the distance between the vehicle and the preceding vehicle is normal; The grading module is provided with an environment analysis unit and a grading unit. The environment analysis unit is used to compare the collected precipitation k0 with the preset precipitation k1, and analyze the abnormality of the environmental precipitation according to the comparison result, wherein: When k0≤k1, the environmental analysis unit determines that the environmental precipitation is normal; When k0>k1, the environmental analysis unit determines that the environmental precipitation is abnormal, and sets the precipitation abnormality index to ky, setting ky=α×(k0-k1) / k1; The environmental analysis unit compares the collected visibility d0 with the preset visibility d1, and analyzes the abnormality of the environmental visibility according to the comparison result, wherein: When d0≤d1, the environment analysis unit determines that the environment visibility is abnormal, and sets the visibility abnormality index to dy, setting dy=β×(d1-d0) / d1; When d0>d1, the environment analysis unit determines that the environment visibility is normal; Wherein, α is the first preset adjustment coefficient, and β is the second preset adjustment coefficient; The level classification unit is used to classify the abnormal level of the driving environment abnormality according to the analysis results of the abnormality of environmental precipitation and the analysis results of the abnormality of environmental visibility, wherein: When ky+dy≤p1, the level classification unit determines that the abnormal level of the driving environment is level one; When ky+dy>p1, the level classification unit determines that the abnormal level of the driving environment is level 2; Among them, p1 is the preset environmental abnormality coefficient; The early warning module is provided with a risk assessment unit and an early warning unit, and the risk assessment unit is used to assess the operation risk of the vehicle according to the analysis results of the vehicle driving state and the analysis results of the abnormality of the distance between the vehicle and the preceding vehicle, wherein: When the driving state of the vehicle is normal and the distance between the vehicle and the preceding vehicle is normal, the risk assessment unit sets the running risk of the vehicle to R1, and sets R1=0; When the driving state of the vehicle is normal and the distance between the vehicle and the preceding vehicle is abnormal, the risk assessment unit sets the running risk of the vehicle to R2, and sets R2={arctan[sin(cj)]}×(π / 2); When the driving state of the vehicle is abnormal and the distance between the vehicle and the preceding vehicle is normal, the risk assessment unit sets the running risk of the vehicle to R3, and sets R3={exp[sin(△u-u0)]-1} / 2; When the driving state of the vehicle is abnormal and the distance between the vehicle and the preceding vehicle is abnormal, the risk assessment unit sets the running risk of the vehicle to R4, and sets R4={exp[sin(△u-u0)]-1} / 2+{arctan[sin(cj)]}×(π / 2); Among them, △u=w1×Cy+w2×Ay+w3×Jy-u0; The warning unit is used to provide a driving risk warning to the user according to the evaluation result of the vehicle operation risk and the classification result of the driving environment abnormality level, wherein: When the driving environment abnormality level is level one, if 0<Rr≤F1, the warning unit will issue a low-risk driving warning to the user; if F1<Rr≤F2, the warning unit will issue a medium-risk driving warning to the user; if Rr>F2, the warning unit will issue a high-risk driving warning to the user; When the driving environment abnormality level is level 2, if 0<Rr≤F3, the warning unit will issue a low-risk driving warning to the user; if F3<Rr≤F4, the warning unit will issue a medium-risk driving warning to the user; if Rr>F4, the warning unit will issue a high-risk driving warning to the user; Among them, F1 is the first preset risk coefficient, F2 is the second preset risk coefficient, F3 is the third preset risk coefficient, F4 is the fourth preset risk coefficient, Rr is the evaluation result of the vehicle's operating risk, and F3<F1<F4<F2.

2. The vehicle information collection system according to claim 1, characterized in that: The system optimization module is provided with a first optimization unit and a second optimization unit. The first optimization unit is used to optimize the process of providing driving risk warning to the user based on the number of accident vehicles sg1 obtained during the monitoring period, wherein: When sg1 / sg0≤B, the first optimization unit determines that the number of accident vehicles in the current monitoring period is normal and does not perform optimization; When sg1 / sg0>B, the first optimization unit determines that the number of accident vehicles in the current monitoring period is abnormal, and optimizes the process of providing driving risk warning to the user, and sets the optimized preset risk coefficient as Ff', setting Ff'=Ff-f0×{1-[(sg1 / sg0-B)+1] (sg1 / sg0-B)+1 -1} / 3}; Among them, Ff is the preset risk factor, f=1,2,3,4; f0 is the preset adjustment value, B is the preset abnormal proportion, and sg0 is the number of sample vehicles.

3. The vehicle information collection system according to claim 2, characterized in that: The second optimization unit compares the average driving distance q0 of the sample vehicles in the monitoring period with the preset distance q1, and performs secondary optimization on the process of providing driving risk warning to the user according to the comparison result, wherein: When q0≤q1, the second optimization unit determines that the driving distance of the sample vehicles in the current monitoring period is normal and does not perform optimization; When q0>q1, the second optimization unit determines that the driving distance of the sample vehicle in the current monitoring period is abnormal, and performs secondary optimization on the process of providing driving risk warning to the user, and sets the optimized preset adjustment value to f0', setting f0'=f0×[1-η×(q0-q1) / (q0+q1)]; Wherein, η is the preset correction coefficient.

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