CN114999134B - Driving behavior early warning method, device and system - Google Patents
Driving behavior early warning method, device and system Download PDFInfo
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Abstract
The application discloses a driving behavior early warning method, a driving behavior early warning device and a driving behavior early warning system, which relate to the technical field of driving safety, wherein the method comprises the following steps: acquiring current driving data of an electric automobile, wherein the driving data is related to driving behaviors of a user; predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time length; scoring the driving behavior of the user according to the target prediction driving data; and outputting early warning information under the condition that the scoring result is smaller than the preset score. The scheme of the application realizes early warning of potential safety hazards caused by driving behaviors.
Description
Technical Field
The application relates to the technical field of driving safety, in particular to a driving behavior early warning method, device and system.
Background
At present, the holding quantity of automobiles in China is close to 3 hundred million, and due to the current technical condition limitation, drivers are still the main control body of the automobiles in the road running process in the past and future. However, due to the unpredictability of driving behavior and possibly abnormal driving behavior of a user, the service life and the use economy of the vehicle are reduced due to the light weight of the vehicle, and serious traffic accidents are caused due to the heavy weight of the vehicle. It is estimated that the vehicle has exceeded a third of its energy consumption and that traffic accidents, on average up to 20 tens of thousands of per year, cause immeasurable losses. How to pre-warn the potential safety hazards existing in the running process of the vehicle in advance based on the driving behaviors of the user becomes a technical problem to be solved at present.
Disclosure of Invention
The application aims to provide a driving behavior early warning method, device and system, so as to solve the problem that potential safety hazards caused by driving behaviors of users cannot be early warned in advance in the prior art.
In order to achieve the above object, an embodiment of the present application provides a driving behavior early warning method, including:
acquiring current driving data of an electric automobile, wherein the driving data is related to driving behaviors of a user;
Predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time length;
scoring the driving behavior of the user according to the target prediction driving data;
and outputting early warning information under the condition that the scoring result is smaller than the preset score.
Optionally, predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile within a preset time length in the future, including:
Preprocessing the current driving data;
Inputting the preprocessed current running data into a user driving behavior analysis and prediction model to obtain initial predicted running data;
And correcting the initial predicted running data according to a pre-stored data distribution curve related to the user to obtain the target predicted running data.
Optionally, preprocessing the current driving data, including at least one of the following:
Removing lost data in the data transmission process from the current driving data;
And removing abnormal outlier data in the current driving data by using an unsupervised learning clustering algorithm.
Optionally, scoring the driving behavior of the user according to the target predicted driving data includes:
according to the target prediction running data, determining first information of influence degree of the driving behavior of the user on the power battery of the electric automobile;
Determining second information of influence degree of driving behaviors of the user on traffic accidents of the electric automobile according to the target prediction driving data;
and scoring the driving behavior of the user according to the first information and the second information.
Optionally, determining, according to the target predicted driving data, first information of a degree of influence of the driving behavior of the user on the power battery of the electric vehicle includes:
inputting the target predicted running data into a battery life correlation analysis model to obtain the first information; the first information comprises the electricity consumption of the electric automobile and the battery health of the power battery.
Optionally, determining, according to the target predicted driving data, second information of the degree of influence of the driving behavior of the user on the traffic accident of the electric automobile includes:
acquiring current road condition data of the electric automobile;
And inputting the target predicted driving data and the road condition data into a traffic accident related relation analysis model to obtain the second information, wherein the second information comprises an accident type and the probability of accident.
Optionally, scoring the driving behavior of the user according to the first information and the second information includes:
And inputting the first information and the second information into a user driving behavior evaluation feedback model to obtain a grading result.
Optionally, the current driving data includes: at least one of vehicle speed, accelerator pedal opening, and steering angle.
In order to achieve the above object, an embodiment of the present application further provides a driving behavior early warning device, including:
The first acquisition module is used for acquiring current running data of the electric automobile, wherein the running data is related to the driving behavior of a user;
The prediction module is used for predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time period;
the scoring module is used for scoring the driving behaviors of the user according to the target prediction driving data;
and the output module is used for outputting early warning information under the condition that the scoring result is smaller than the preset score value.
In order to achieve the above object, an embodiment of the present application further provides a driving behavior early warning system, including: a processor, a memory, and a program or instructions stored on the memory and executable on the processor; the processor, when executing the program or instructions, implements the steps of the driving behavior early warning method as described in the first aspect above.
The technical scheme of the application has at least the following beneficial effects:
According to the driving behavior early warning method, firstly, current driving data of an electric automobile are obtained, and the driving data are related to the driving behavior of a user; secondly, predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time length; in this way, the prediction of the driving behavior of the user is realized, and the driving behavior of the user is scored again according to the target prediction driving data; and finally, outputting early warning information under the condition that the scoring result is smaller than the preset score. Therefore, whether potential safety hazards exist in the running process of the vehicle or not is judged based on the predicted driving behaviors, and the user is reminded through outputting the early warning information when the potential safety hazards exist, so that safety accidents are avoided, and the driving safety of the vehicle is improved.
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Fig. 1 is a schematic flow chart of a driving behavior early warning method according to an embodiment of the application;
fig. 2 is a schematic structural diagram of a driving behavior early warning device according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the application may be practiced otherwise than as specifically illustrated or described herein. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
The driving behavior early warning method, device and system provided by the embodiment of the application are described in detail through specific embodiments and application scenes thereof with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a driving behavior early warning method according to an embodiment of the present application, where the method includes:
Step 101, current running data of an electric automobile is obtained, wherein the running data is related to driving behaviors of a user;
Here, the travel data related to the driving behavior of the user may be, for example: vehicle speed, accelerator pedal opening, steering wheel angle, etc.; the driving parameters in this step can thus be obtained from sensors mounted on the electric vehicle, wherein each sensor is used for the real-time acquisition of the corresponding driving data.
Step 102, predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time period;
Here, it should be noted that, depending on the rapid development of the internet of vehicles technology and the gradual maturity of vehicle monitoring platforms in countries, places and enterprises, especially, the running and usage data of the electric automobile form a huge rule body, on this basis, the step can analyze the driving habit of the user by using big data, and on this basis, predict the target prediction running data of the electric automobile within the future preset duration by using the current running data.
Step 103, scoring the driving behavior of the user according to the target prediction driving data;
in the step, the driving behavior of the user is scored based on the target prediction driving data, so that the probability of dangerous driving operation of the user can be reduced, the accident occurrence probability and risk are further reduced, the promotion of the driving technology of the user and the transition of the driving style can be directly promoted, and the use economy of the vehicle is further improved.
And 104, outputting early warning information under the condition that the scoring result is smaller than the preset score value.
In this step, when the scoring result is smaller than the preset score, the driving behavior of the user is predicted to possibly cause potential safety hazard in the driving process, so that the user is reminded by outputting the early warning information.
Specifically, the method can remind the user in a mode of displaying early warning information on a vehicle screen or in a mode of outputting warning sound on a vehicle-mounted buzzer.
According to the driving behavior early warning method, firstly, current driving data of an electric automobile are obtained, and the driving data are related to the driving behavior of a user; secondly, predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time length; in this way, the prediction of the driving behavior of the user is realized, and the driving behavior of the user is scored again according to the target prediction driving data; and finally, outputting early warning information under the condition that the scoring result is smaller than the preset score. Therefore, whether potential safety hazards exist in the driving process of the vehicle or not is judged based on the predicted driving behaviors, and the user is reminded in a mode of outputting early warning information when the potential safety hazards exist, so that safety accidents are avoided, the driving safety of the vehicle is improved, and meanwhile the driving technology of the user is improved.
As an optional implementation manner, step 102, predicting, according to the current driving data, the driving behavior of the user to obtain target predicted driving data of the electric automobile within a preset time period in the future, where the predicting includes:
(1) Preprocessing the current driving data;
According to the method, invalid data in the current running data can be removed by preprocessing the current running data, and only data valid for a prediction process are reserved.
(2) Inputting the preprocessed current running data into a user driving behavior analysis and prediction model to obtain initial predicted running data;
Here, it should be noted that, the user driving behavior analysis prediction model is a learning algorithm for artificial intelligence, such as: long and short term memory networks (Long Short Term Memory Network, LSTM), wherein the data required for training may be historical data obtained based on big data techniques. The specific training process is as follows: and (3) importing a historical vehicle speed curve taking a time axis as an abscissa and an accelerator pedal opening curve into a long-short-time memory network (a user driving behavior analysis and prediction model) as learning data, and learning through a neural network to obtain longitudinal driving behaviors of different users. The vehicle speed curve and the accelerator pedal opening curve of the whole vehicle in a period of time in the future can be predicted by the learned user driving behavior analysis prediction model;
(3) And correcting the initial predicted running data according to a pre-stored data distribution curve related to the user to obtain the target predicted running data.
Here, the generation process of the data distribution curve related to the user may be: aiming at each user, a numerical value statistics method is applied to obtain columnar distribution of driving data related to driving behaviors of the user; and obtaining a data distribution curve of the opportunity by using a polynomial fitting method.
In the step, the initial prediction running data is corrected by utilizing a pre-stored data distribution curve related to the user to obtain target prediction running data, so that the target prediction running data is more in line with the driving habit of the user, and the prediction accuracy is further improved.
As a specific implementation manner, preprocessing the current running data includes at least one of the following:
removing lost data in the data transmission process from the current driving data; namely: removing blank points in the current driving data;
And removing abnormal outlier data in the current driving data by using an unsupervised learning clustering algorithm.
According to the specific implementation mode, invalid data in the current driving data are removed, so that data used in a prediction process are all effective, and the accuracy of prediction is improved.
Here, an implementation manner of the step of predicting the driving behavior of the user according to the current driving data to obtain the target predicted driving data of the electric vehicle within a preset time period in the future is described:
a) Early preparation:
Firstly, determining quantitative analysis indexes of driving behaviors of a user, wherein the quantitative analysis indexes mainly comprise longitudinal speed, longitudinal acceleration, yaw angle, yaw rate and the like;
Secondly, correlating the driving behavior quantitative analysis index obtained by analysis with the existing data of the vehicle; specifically, a longitudinal speed-related vehicle speed, a longitudinal acceleration-related accelerator pedal opening, a yaw angle, a yaw rate-related steering wheel handoff, and the like; wherein, the data are all driving data related to driving behaviors in the application;
Thirdly, aiming at each user, a numerical value statistics method is applied to obtain a histogram distribution of driving data related to the driving behaviors of the user, and a polynomial fitting method is applied to obtain an actual data distribution curve;
b) The prediction process comprises the following steps:
First, the driving data related to the driving behavior is processed, specifically including: rejecting blank spots in the driving data related to the driving behavior of the user (i.e. lost data in transmission); screening out abnormal outliers in the driving data related to the driving behaviors of the user through an unsupervised learning clustering algorithm, and removing the driving data related to the driving behaviors of the user to be processed;
Secondly, the processed running data related to the driving behavior of the user is input into a driving behavior analysis and prediction model of the user to conduct prediction, initial prediction running data is obtained, for example, the initial prediction running data can be a longitudinal driving behavior prediction curve;
And thirdly, correcting the predicted longitudinal driving behavior prediction curve obtained by prediction by utilizing an actual data distribution curve obtained by a fitting method in the earlier stage to obtain a final prediction curve (target predicted driving data).
As an optional implementation manner, step 103, scoring the driving behavior of the user according to the target predicted driving data includes:
according to the target prediction running data, determining first information of influence degree of the driving behavior of the user on the power battery of the electric automobile;
Determining second information of influence degree of driving behaviors of the user on traffic accidents of the electric automobile according to the target prediction driving data;
and scoring the driving behavior of the user according to the first information and the second information.
In the alternative implementation mode, the driving behavior of the user is scored based on the influence degree of the driving behavior on the power battery and the influence degree of the traffic accident, so that the use economy of the whole vehicle can be improved, and the risk of the accident can be reduced.
As a specific implementation manner, determining, according to the target predicted driving data, first information of a degree of influence of the driving behavior of the user on the power battery of the electric vehicle includes:
inputting the target predicted running data into a battery life correlation analysis model to obtain the first information; the first information comprises the electricity consumption of the electric automobile and the battery health of the power battery.
In this specific implementation manner, the power consumption is the power consumption condition of the electric automobile, for example: the power consumption of the electric automobile per hour; the battery health may include: remaining driving mileage of the battery, charge and discharge times of the battery, and the like; the relation between the driving data and the power consumption of the battery/the health degree of the battery can be obtained through self-learning of a neural network; next, a process of establishing and training a battery life correlation analysis model will be described:
firstly, establishing theory and formula definition of the power consumption and the battery life of the whole vehicle;
Secondly, calculating a previous whole vehicle electricity consumption curve and a battery life curve according to historical driving data and battery state data of each user;
thirdly, a correlation analysis and principal component analysis method is applied, and the knowledge in the field of pure electric vehicles is combined, so that driving behavior influence factors (comprising strong positive correlation and strong negative correlation) which are strongly correlated with the power consumption of the whole vehicle and the service life of a battery are extracted from driving behavior related data, and the factors such as the running speed, the acceleration, the running temperature, the battery charge and discharge power of the whole vehicle are analyzed to be relevant main influence factors; wherein, the running speed and the acceleration of the whole vehicle are related to the driving behavior of the user;
and finally, establishing a prediction model of the influence factors and the power consumption and the battery life of the whole vehicle by a deep circulation neural network method.
As another specific implementation manner, determining, according to the target predicted driving data, second information of an influence degree of the driving behavior of the user on the traffic accident of the electric automobile includes:
(1) Acquiring current road condition data of the electric automobile;
in this step, the road condition information may be the road condition around the electric vehicle detected by the vehicle-mounted detecting device (vehicle-mounted camera, radar, etc.), such as the environmental information around the electric vehicle (e.g., static obstacle), road participants (surrounding vehicles, pedestrians), traffic information (traffic signal lamp, road guidance sign, lane line);
(2) And inputting the target predicted driving data and the road condition data into a traffic accident related relation analysis model to obtain the second information, wherein the second information comprises an accident type and the probability of accident.
The following describes the process of building and training the traffic accident correlation analysis model:
firstly, acquiring vehicle accident related data, wherein the data acquisition modes are divided into two types, namely vehicle data reproduction after accident occurrence and vehicle data uploading during accident occurrence;
Secondly, accident types are automatically classified according to the acquired data, accidents related to driving behaviors of users are screened out, and the accidents are classified into three types according to severity level, namely high (the safety air bags are fully opened), medium (the safety air bags are partially opened) and low (slight scratch and emergency braking);
thirdly, aiming at accidents with different severity, the distribution of corresponding driving behavior related data is counted, and the purpose is to acquire the correlation of accidents caused by acceleration and steering behaviors of different users;
Then, for the vehicle with radar, camera and vehicle event data recorder, record its video stream and radar information data simultaneously, wherein discern lane line, pedestrian, traffic signal lamp, traffic sign and road barrier etc. in the video stream through CNN and semantic segmentation technique. Acquiring whether other vehicles exist around the vehicle and relative distance and relative speed information of the other vehicles through radar data;
And then, the real scene of the accident can be directly reproduced by fusing the information acquired from the video stream and the radar data, so that a complete association database of the accident scene and the driving behavior of the user can be obtained.
And finally, training a traffic accident related relation analysis model based on the data in the established related database of the accident scene and the driving behavior of the user.
Here, another implementation manner of obtaining the second information will be described again: firstly, determining the speed/acceleration of a target in predicted driving data and the type of traffic accidents possibly caused by road condition information around an electric automobile in a table look-up mode; secondly, extracting the pre-stored nominal vehicle speed/acceleration when the traffic accident happens, and nominal road condition information and nominal probability; thirdly, calculating the ratio of the vehicle speed in the target predicted running data to the nominal vehicle speed, the ratio of the acceleration in the target predicted running data to the nominal acceleration, and the ratio of the road condition information to the nominal road condition information; finally, based on these ratios and the nominal probability, the probability of an accident is calculated.
As still another specific implementation, scoring the driving behavior of the user according to the first information and the second information includes:
And inputting the first information and the second information into a user driving behavior evaluation feedback model to obtain a grading result.
Here, the user driving behavior evaluation feedback model may specifically score by means of weighted summation or the like. The weights of the first information and the second information can be preset or dynamically adjusted, wherein if the weights are dynamically adjusted, the weights can be adjusted according to the emergency degree, and if the probability of occurrence of a traffic accident is high, the weight occupied by the second information is high.
Furthermore, the embodiment of the application can further upload the grading result to the cloud server so as to realize ranking of driving technologies of different users and increase the interestingness of driving.
In short, the implementation process of the driving behavior early warning method of the embodiment of the application is as follows:
first, a driving parameter related to a driving behavior of a user detected by a vehicle-mounted detecting device is obtained, for example: vehicle speed, accelerator pedal opening, steering wheel angle, etc.;
Secondly, preprocessing the acquired driving parameters related to the driving behavior of the user to remove invalid data, such as: removing lost data in the transmission process; removing abnormal outlier data and the like;
thirdly, inputting the preprocessed data into a user driving behavior analysis and prediction model to predict initial predicted driving data of the electric automobile in a future preset time period;
fourth, the initial predicted running data is corrected by utilizing a pre-stored data distribution curve, and target predicted running data is obtained;
Fifthly, inputting target prediction driving data into a battery life correlation analysis model to obtain power consumption and battery health;
sixthly, inputting the target predicted driving data and the acquired road condition information into a traffic accident correlation analysis model to obtain accident types and the probability of occurrence of accidents;
seventhly, inputting the power consumption, the battery health degree, the accident type and the probability of accident occurrence into a user driving behavior evaluation feedback model, and grading the driving behavior of the user to obtain a grading result;
eighth, when the scoring result is smaller than the preset score, outputting early warning information through a locomotive screen or a buzzer;
and ninth, uploading the grading result to a cloud server, and ranking according to the advantages and disadvantages of the driving behavior.
It should be noted that, in the driving behavior early-warning method provided by the embodiment of the present application, the execution body may be a driving behavior early-warning device, or a control module in the driving behavior early-warning device for executing the loading driving behavior early-warning method. In the embodiment of the application, the driving behavior early-warning device is taken as an example to execute the loading driving behavior early-warning method, and the driving behavior early-warning method provided by the embodiment of the application is described.
According to the driving behavior early warning method, the predicted driving parameters of the electric automobile in a future period of time are predicted based on the driving parameters related to the driving behaviors of the user, so that the driving behaviors of the user are scored based on the predicted driving parameters in terms of influences on a battery and possibility of traffic accidents, and early warning information is output when the scores are low, and therefore the probability of dangerous driving operations of the user can be reduced, and the accident occurrence probability and risk are further reduced; the method can also directly promote the improvement of the driving technology and the conversion of the driving style of the user, thereby improving the use economy and the service life of the battery of the vehicle.
As shown in fig. 2, an embodiment of the present application further provides a driving behavior early warning device, including:
a first obtaining module 201, configured to obtain current driving data of an electric vehicle, where the driving data is related to driving behavior of a user;
The prediction module 202 is configured to predict driving behavior of the user according to the current driving data, and obtain target predicted driving data of the electric vehicle within a preset time period in the future;
The scoring module 203 is configured to score driving behaviors of the user according to the target prediction driving data;
And the output module 204 is configured to output early warning information when the scoring result is smaller than a preset score.
According to the driving behavior early warning device provided by the embodiment of the application, firstly, the first acquisition module 201 acquires current driving data of the electric automobile, wherein the driving data is related to the driving behavior of a user; secondly, the prediction module 202 predicts the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time period; in this way, prediction of the driving behavior of the user is achieved, and again, the scoring module 203 scores the driving behavior of the user according to the target predicted driving data; finally, the output module 204 outputs the early warning information when the scoring result is smaller than the preset score. Therefore, whether potential safety hazards exist in the driving process of the vehicle or not is judged based on the predicted driving behaviors, and the user is reminded in a mode of outputting early warning information when the potential safety hazards exist, so that safety accidents are avoided, the driving safety of the vehicle is improved, and meanwhile the driving technology of the user is improved.
Optionally, the prediction module 202 includes:
the processing sub-module is used for preprocessing the current running data;
The prediction sub-module is used for inputting the preprocessed current running data into a user driving behavior analysis prediction model to obtain initial predicted running data;
And the correction sub-module is used for correcting the initial predicted running data according to a pre-stored data distribution curve related to the user to obtain the target predicted running data.
Optionally, the processing sub-module is specifically configured to perform at least one of:
Removing lost data in the data transmission process from the current driving data;
And removing abnormal outlier data in the current driving data by using an unsupervised learning clustering algorithm.
Optionally, the scoring module 203 includes:
The first determining submodule is used for determining first information of influence degree of driving behaviors of the user on a power battery of the electric automobile according to the target prediction driving data;
The second determining submodule is used for determining second information of the influence degree of the driving behavior of the user on the traffic accident of the electric automobile according to the target prediction driving data;
and the scoring module is used for scoring the driving behaviors of the user according to the first information and the second information.
Optionally, the first determining submodule is specifically configured to:
inputting the target predicted running data into a battery life correlation analysis model to obtain the first information; the first information comprises the electricity consumption of the electric automobile and the battery health of the power battery.
Optionally, the second determining submodule includes:
the first acquisition unit is used for acquiring road condition data of the electric automobile at present;
And the second acquisition unit is used for inputting the target prediction driving data and the road condition data into a traffic accident related relation analysis model to acquire the second information, wherein the second information comprises an accident type and the probability of accident occurrence.
Optionally, the evaluation module is specifically configured to: and inputting the first information and the second information into a user driving behavior evaluation feedback model to obtain a grading result.
Optionally, the current driving data includes: at least one of vehicle speed, accelerator pedal opening, and steering angle.
The embodiment of the application also provides a driving behavior early warning system, which comprises: a processor, a memory, and a program or instructions stored on the memory and executable on the processor; the processor implements each process of the driving behavior early warning method embodiment described above when executing the program or the instruction, and can achieve the same technical effects, so that repetition is avoided, and no description is repeated here.
The embodiment of the application also provides a readable storage medium, and a program is stored on the readable storage medium, and when the program is executed by a processor, the program realizes the various processes of the driving behavior early warning method embodiment, and can achieve the same technical effects, so that repetition is avoided, and no repeated description is provided here. The readable storage medium is, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, an optical disk, or the like.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
While the foregoing is directed to the preferred embodiments of the present application, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations are intended to be comprehended within the scope of the present application.
Claims (9)
1. A driving behavior early warning method, characterized by comprising:
acquiring current driving data of an electric automobile, wherein the driving data is related to driving behaviors of a user;
Predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time length;
scoring the driving behavior of the user according to the target prediction driving data;
outputting early warning information under the condition that the scoring result is smaller than a preset score value;
Wherein scoring the driving behavior of the user according to the target predicted driving data comprises:
according to the target prediction running data, determining first information of influence degree of the driving behavior of the user on the power battery of the electric automobile;
Determining second information of influence degree of driving behaviors of the user on traffic accidents of the electric automobile according to the target prediction driving data;
and scoring the driving behavior of the user according to the first information and the second information.
2. The method of claim 1, wherein predicting the driving behavior of the user based on the current driving data to obtain target predicted driving data of the electric vehicle within a predetermined time period in the future comprises:
Preprocessing the current driving data;
Inputting the preprocessed current running data into a user driving behavior analysis and prediction model to obtain initial predicted running data;
And correcting the initial predicted running data according to a pre-stored data distribution curve related to the user to obtain the target predicted running data.
3. The method of claim 2, wherein preprocessing the current travel data comprises at least one of:
Removing lost data in the data transmission process from the current driving data;
And removing abnormal outlier data in the current driving data by using an unsupervised learning clustering algorithm.
4. The method of claim 1, wherein determining first information of a degree of influence of the driving behavior of the user on a power battery of the electric vehicle based on the target predicted travel data comprises:
inputting the target predicted running data into a battery life correlation analysis model to obtain the first information; the first information comprises the electricity consumption of the electric automobile and the battery health of the power battery.
5. The method according to claim 1, wherein determining second information of the degree of influence of the driving behavior of the user on the occurrence of the traffic accident of the electric vehicle based on the target predicted driving data includes:
acquiring current road condition data of the electric automobile;
And inputting the target predicted driving data and the road condition data into a traffic accident related relation analysis model to obtain the second information, wherein the second information comprises an accident type and the probability of accident.
6. The method of claim 1, wherein scoring the driving behavior of the user based on the first information and the second information comprises:
And inputting the first information and the second information into a user driving behavior evaluation feedback model to obtain a grading result.
7. The method according to any one of claims 1 to 6, wherein the current travel data comprises: at least one of vehicle speed, accelerator pedal opening, and steering angle.
8. A driving behavior early warning device, characterized by comprising:
The first acquisition module is used for acquiring current running data of the electric automobile, wherein the running data is related to the driving behavior of a user;
The prediction module is used for predicting the driving behavior of the user according to the current driving data to obtain target predicted driving data of the electric automobile in a future preset time period;
the scoring module is used for scoring the driving behaviors of the user according to the target prediction driving data;
The output module is used for outputting early warning information under the condition that the scoring result is smaller than a preset score value;
wherein the scoring module comprises:
The first determining submodule is used for determining first information of influence degree of driving behaviors of the user on a power battery of the electric automobile according to the target prediction driving data;
The second determining submodule is used for determining second information of the influence degree of the driving behavior of the user on the traffic accident of the electric automobile according to the target prediction driving data;
and the scoring module is used for scoring the driving behaviors of the user according to the first information and the second information.
9. A driving behavior early warning system, comprising: a processor, a memory, and a program or instructions stored on the memory and executable on the processor; the method according to any one of claims 1 to 7, characterized in that the processor executes the program or instructions to implement the steps of the driving behavior early warning method.
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