阅读说明：本技术 一种基于人工智能的电动汽车安全预警方法及系统、电动汽车 (Electric vehicle safety early warning method and system based on artificial intelligence and electric vehicle ) 是由 王亚鹏 于 2020-06-27 设计创作，主要内容包括：本发明提供一种基于人工智能的电动汽车安全预警方法与系统,其采集了包括历史行驶数据和历史维修数据在内的多种不同类型的涉及到汽车安全行驶的数据,并分别采用不同的人工智能模型进行训练,得到对应的汽车安全预警结果,并拟合两种汽车安全预警结果得到最终的超载安全预警结果,从而大大提高了汽车的安全预警的效率和准确性；此外,通过检测超载汽车的行驶轨迹数据预测同型号的其他汽车的超载安全预警结果,从而无需为每个汽车安装超载检测装置来进行超载检测,从而大大降低了检测设备的研发、安装和维护成本；最后,本发明充分利用了电动汽车的各种车身传感器来获取车辆数据,以用于汽车安全预警。(The invention provides an electric vehicle safety early warning method and system based on artificial intelligence, which collects various different types of data related to vehicle safety driving, including historical driving data and historical maintenance data, and respectively trains by adopting different artificial intelligence models to obtain corresponding vehicle safety early warning results, and fits two vehicle safety early warning results to obtain a final overload safety early warning result, thereby greatly improving the efficiency and accuracy of vehicle safety early warning; in addition, the overload safety early warning results of other automobiles with the same model are predicted by detecting the running track data of the overloaded automobile, so that overload detection is carried out without installing an overload detection device for each automobile, and the research, development, installation and maintenance costs of detection equipment are greatly reduced; finally, the invention fully utilizes various body sensors of the electric automobile to acquire the vehicle data so as to be used for automobile safety early warning.)

1. An electric vehicle safety early warning method based on artificial intelligence is characterized by comprising the following steps:

s1, obtaining automobile models, automobile license plates, historical maintenance data, maintenance safety early warning information, historical driving data and driving safety early warning information of a plurality of electric automobiles in a plurality of sampling time periods;

s2, acquiring the historical driving data and the driving safety early warning information according to the automobile model to train an electric automobile driving safety early warning Bayes prediction model of the automobile model; the travel data includes: tire pressure, braking acceleration, braking distance, unloading power and power consumption of the tire; the driving safety early warning information comprises overload and non-overload;

s3, acquiring the historical maintenance data and the maintenance safety early warning information according to the automobile license plate to train an electric automobile maintenance safety early warning regression prediction model; the repair data includes: tire replacement mileage, brake pad replacement mileage and battery replacement mileage; the maintenance safety early warning information comprises normal loss and abnormal loss;

s4, obtaining the automobile model of the electric automobile to be early-warned, obtaining a corresponding electric automobile driving safety early-warning Bayes prediction model according to the automobile model, collecting current driving data of the electric automobile to be early-warned in the current time period, importing the current driving data into the electric automobile driving safety early-warning Bayes prediction model, and obtaining the driving safety early-warning result of the electric automobile; acquiring a maintenance safety early warning result of the electric automobile according to the automobile license plate number of the electric automobile to be early warned;

and S5, obtaining the current overload safety early warning result of the electric automobile to be early warned according to the driving safety early warning result and the maintenance safety early warning result of the electric automobile to be early warned.

2. The method according to claim 1, wherein obtaining the vehicle models, the historical driving data and the safety warning information thereof of the plurality of electric vehicles in a plurality of sampling time periods specifically comprises:

the sampling time period comprises a driving sampling time period and a maintenance sampling time period;

acquiring running time data of an electric automobile, calculating a running period of the electric automobile according to the running time data, and determining a running sampling time period of the electric automobile according to a non-idle time period in the running period;

and acquiring maintenance time data of the electric automobile, and determining a maintenance sampling time period of the electric automobile according to the maintenance time data and the maintenance period.

3. The method according to claim 2, wherein the obtaining of the maintenance safety early warning result of the electric vehicle according to the number of the electric vehicle to be early warned specifically comprises:

and acquiring the maintenance data according to the license plate number of the electric automobile to be early-warned in the maintenance time period of the electric automobile, importing the maintenance data into the electric automobile maintenance safety early-warning regression prediction model, and acquiring the maintenance safety early-warning result of the electric automobile.

4. The method according to claim 1, wherein the step of collecting the historical driving data and the driving safety early warning information according to the automobile model to train an electric automobile driving safety early warning Bayesian prediction model of the automobile model specifically comprises:

acquiring the non-overload driving data and non-overload driving safety early warning information according to the automobile model to train an electric automobile driving safety early warning Bayes prediction model of the automobile model;

and acquiring the overload driving data and overload driving safety early warning information according to the automobile model so as to train an electric automobile driving safety early warning Bayesian prediction model of the automobile model.

5. The method of claim 1, wherein obtaining a current overload safety early warning result of the electric vehicle to be early warned according to a driving safety early warning result and the maintenance safety early warning result of the electric vehicle to be early warned, further comprises:

the current overload safety early warning result Z = K1M + K2N of the electric automobile to be early warned, wherein M is a driving safety early warning result, N is a maintenance safety early warning result, K1 and K2 are weights, and K1+ K2= 1.

6. The method of claim 1, further comprising:

if the current overload safety early warning result of the electric automobile to be early warned is overload, acquiring the running track of the electric automobile, if a plurality of electric automobiles with the same running track exist in the same time period, acquiring the automobile models of the plurality of electric automobiles according to the automobile brands of the plurality of electric automobiles, and sending the automobile brands and overload warning early warning information of the electric automobiles with the same models as the electric automobiles to be early warned to a safety early warning server;

and the safety early warning server sends out overload warning early warning information of the automobile license plate.

7. The method of claim 6, wherein the safety precaution server issues an overload precaution message for the car license plate number, further comprising:

acquiring and comparing the driving image data of the electric automobile to be pre-warned and the electric automobiles with the same model on the same road section; if the similarity of the carriage images in the image data is greater than or equal to a first similarity threshold value, the safety early warning server adjusts overload warning early warning information of the license plate number of the electric automobile corresponding to the image data into overload information;

and if the similarity of the carriage images in the image data is smaller than a first similarity threshold value, the safety early warning server removes overload warning early warning information of the automobile license plate number of the electric automobile corresponding to the image data.

8. The method of claim 7, wherein the safety precaution server issues an overload precaution message for the car license plate number, further comprising:

acquiring and comparing the speeds of the electric automobile to be pre-warned and the electric automobile of the same model on the same road section, and if the similarity of the speeds of the electric automobile to be pre-warned and the electric automobile of the same model is greater than or equal to a second similarity threshold value, adjusting the overload warning pre-warning information of the automobile license plate of the electric automobile of the same model into overload information by the safety pre-warning server;

and if the similarity of the speed of the electric automobile to be early-warned and the electric automobile of the same model is smaller than a first similarity threshold value, the safety early-warning server removes the overload warning early-warning information of the automobile brand of the electric automobile of the same model.

9. The method of claim 7, wherein the safety precaution server issues an overload precaution message for the car license plate number, further comprising:

acquiring and comparing the driving image data of the electric automobile to be pre-warned and the electric automobiles with the same model on the same road section, and acquiring the driving sequence of the electric automobile to be pre-warned and the electric automobiles with the same model according to the image data; if a plurality of electric automobiles with consistent running sequences including the electric automobile to be pre-warned exist, the safety pre-warning server adjusts overload warning pre-warning information of automobile marks of the electric automobiles with consistent running sequences into overload information;

and if a plurality of electric automobiles with consistent running sequence including the electric automobile to be early-warned do not exist, the safety early-warning server removes the overload warning early-warning information of the automobile license plate number of the electric automobile with inconsistent running sequence.

10. The utility model provides an electric automobile safety precaution system based on artificial intelligence which characterized in that, the system includes following module:

the sampling module is used for acquiring automobile models, automobile license plates, historical maintenance data, maintenance safety early warning information, historical driving data and driving safety early warning information of a plurality of electric automobiles in a plurality of sampling time periods;

the historical driving data training module is used for acquiring the historical driving data and the driving safety early warning information according to the automobile model so as to train an electric automobile driving safety early warning Bayesian prediction model of the automobile model; the travel data includes: tire pressure, braking acceleration, braking distance, unloading power and power consumption of the tire; the driving safety early warning information comprises overload and non-overload;

the historical maintenance data training module is used for acquiring the historical maintenance data and the maintenance safety early warning information according to the automobile license plate so as to train an electric automobile maintenance safety early warning regression prediction model; the repair data includes: tire replacement mileage, brake pad replacement mileage and battery replacement mileage; the maintenance safety early warning information comprises normal loss and abnormal loss;

the early warning judgment module is used for acquiring the automobile model of the electric automobile to be early warned, acquiring a corresponding electric automobile driving safety early warning Bayes prediction model according to the automobile model, acquiring current driving data of the electric automobile to be early warned in the current time period, importing the current driving data into the electric automobile driving safety early warning Bayes prediction model, and acquiring a driving safety early warning result of the electric automobile; acquiring a maintenance safety early warning result of the electric automobile according to the automobile license plate number of the electric automobile to be early warned;

and the early warning module is used for obtaining the current overload safety early warning result of the electric automobile to be early warned according to the driving safety early warning result and the maintenance safety early warning result of the electric automobile to be early warned.

Technical Field

The invention relates to the technical field of artificial intelligence and electric automobiles, in particular to an electric automobile safety early warning method and system based on artificial intelligence and an electric automobile.

Background

Although in recent years, various government departments pay more and more attention to traffic safety and have more and more attention to behaviors (such as overload) impairing traffic safety, the overload condition of the automobile is still rare, and the following points are mainly considered for the reasons: 1. sometimes the driver makes a wrong estimate of the weight carried by the vehicle and some overload behavior is not known by the driver himself. 2. The driver knows that the vehicle is overloaded with weight, but this is for economic reasons. For any reason, the overload behavior brings harm to the social traffic safety.

The new energy automobile adopts unconventional automobile fuel as a power source (or adopts conventional automobile fuel and a novel vehicle-mounted power device), integrates advanced technologies in the aspects of power control and driving of the automobile, and forms an automobile with advanced technical principle, new technology and new structure. The new energy automobile comprises a pure electric automobile, an extended range electric automobile, a hybrid electric automobile, a fuel cell electric automobile, a hydrogen engine automobile and the like. The new energy automobile is also called as an alternative fuel automobile, and comprises a pure electric automobile, a fuel cell electric automobile and other automobiles which all use non-petroleum fuel, and also comprises a hybrid electric automobile, an ethanol gasoline automobile and other automobiles which partially use non-petroleum fuel. All new energy automobiles existing at present are included in the concept, and are specifically divided into six categories: hybrid electric vehicles, pure electric vehicles, fuel cell vehicles, alcohol ether fuel vehicles, natural gas vehicles, and the like. The pure Electric Vehicles (BEV) are Vehicles using a single battery as an energy storage power source, and the battery is used as the energy storage power source to provide Electric energy to the motor through the battery to drive the motor to run, so as to drive the Vehicles to run. The rechargeable battery of the pure electric vehicle mainly comprises a lead-acid battery, a nickel-cadmium battery, a nickel-hydrogen battery, a lithium ion battery and the like, and the batteries can provide power for the pure electric vehicle. Meanwhile, the pure electric vehicle stores electric energy through the battery, drives the motor to operate, and enables the vehicle to normally run.

Disclosure of Invention

The invention provides an electric vehicle safety early warning method based on artificial intelligence, which comprises the following steps:

s1, obtaining automobile models, automobile license plates, historical maintenance data, maintenance safety early warning information, historical driving data and driving safety early warning information of a plurality of electric automobiles in a plurality of sampling time periods;

s2, acquiring the historical driving data and the driving safety early warning information according to the automobile model to train an electric automobile driving safety early warning Bayes prediction model of the automobile model; the travel data includes: tire pressure, braking acceleration, braking distance, unloading power and power consumption of the tire; the driving safety early warning information comprises overload and non-overload;

s3, acquiring the historical maintenance data and the maintenance safety early warning information according to the automobile license plate to train an electric automobile maintenance safety early warning regression prediction model; the repair data includes: tire replacement mileage, brake pad replacement mileage and battery replacement mileage; the maintenance safety early warning information comprises normal loss and abnormal loss;

s4, obtaining the automobile model of the electric automobile to be early-warned, obtaining a corresponding electric automobile driving safety early-warning Bayes prediction model according to the automobile model, collecting current driving data of the electric automobile to be early-warned in the current time period, importing the current driving data into the electric automobile driving safety early-warning Bayes prediction model, and obtaining the driving safety early-warning result of the electric automobile; acquiring a maintenance safety early warning result of the electric automobile according to the automobile license plate number of the electric automobile to be early warned;

and S5, obtaining the current overload safety early warning result of the electric automobile to be early warned according to the driving safety early warning result and the maintenance safety early warning result of the electric automobile to be early warned.

As a preferred embodiment, acquiring the automobile models, the historical driving data and the safety warning information thereof of a plurality of sampling time periods of a plurality of electric automobiles specifically comprises:

the sampling time period comprises a driving sampling time period and a maintenance sampling time period;

acquiring running time data of an electric automobile, calculating a running period of the electric automobile according to the running time data, and determining a running sampling time period of the electric automobile according to a non-idle time period in the running period;

and acquiring maintenance time data of the electric automobile, and determining a maintenance sampling time period of the electric automobile according to the maintenance time data and the maintenance period.

As a preferred embodiment, the method for obtaining the maintenance safety early warning result of the electric vehicle according to the license plate number of the electric vehicle to be early warned specifically includes:

and acquiring the maintenance data according to the license plate number of the electric automobile to be early-warned in the maintenance time period of the electric automobile, importing the maintenance data into the electric automobile maintenance safety early-warning regression prediction model, and acquiring the maintenance safety early-warning result of the electric automobile.

As a preferred embodiment, the method for training the electric vehicle driving safety early warning bayesian prediction model of the vehicle model by collecting the historical driving data and the driving safety early warning information according to the vehicle model specifically includes:

acquiring the non-overload driving data and non-overload driving safety early warning information according to the automobile model to train an electric automobile driving safety early warning Bayes prediction model of the automobile model;

and acquiring the overload driving data and overload driving safety early warning information according to the automobile model so as to train an electric automobile driving safety early warning Bayesian prediction model of the automobile model.

As a preferred embodiment, obtaining the current overload safety early warning result of the electric vehicle to be early warned according to the driving safety early warning result and the maintenance safety early warning result of the electric vehicle to be early warned, further includes:

the current overload safety early warning result Z = K1M + K2N of the electric automobile to be early warned, wherein M is a driving safety early warning result, N is a maintenance safety early warning result, K1 and K2 are weights, and K1+ K2= 1.

As a preferred embodiment, the method further comprises:

if the current overload safety early warning result of the electric automobile to be early warned is overload, acquiring the running track of the electric automobile, if a plurality of electric automobiles with the same running track exist in the same time period, acquiring the automobile models of the plurality of electric automobiles according to the automobile brands of the plurality of electric automobiles, and sending the automobile brands and overload warning early warning information of the electric automobiles with the same models as the electric automobiles to be early warned to a safety early warning server;

and the safety early warning server sends out overload warning early warning information of the automobile license plate.

As a preferred embodiment, the method for sending the overload warning information of the license plate number by the safety warning server further includes:

acquiring and comparing the driving image data of the electric automobile to be pre-warned and the electric automobiles with the same model on the same road section; if the similarity of the carriage images in the image data is greater than or equal to a first similarity threshold value, the safety early warning server adjusts overload warning early warning information of the license plate number of the electric automobile corresponding to the image data into overload information;

and if the similarity of the carriage images in the image data is smaller than a first similarity threshold value, the safety early warning server removes overload warning early warning information of the automobile license plate number of the electric automobile corresponding to the image data.

As a preferred embodiment, the method for sending the overload warning information of the license plate number by the safety warning server further includes:

acquiring and comparing the speeds of the electric automobile to be pre-warned and the electric automobile of the same model on the same road section, and if the similarity of the speeds of the electric automobile to be pre-warned and the electric automobile of the same model is greater than or equal to a second similarity threshold value, adjusting the overload warning pre-warning information of the automobile license plate of the electric automobile of the same model into overload information by the safety pre-warning server;

and if the similarity of the speed of the electric automobile to be early-warned and the electric automobile of the same model is smaller than a first similarity threshold value, the safety early-warning server removes the overload warning early-warning information of the automobile brand of the electric automobile of the same model.

As a preferred embodiment, the method for sending the overload warning information of the license plate number by the safety warning server further includes:

acquiring and comparing the driving image data of the electric automobile to be pre-warned and the electric automobiles with the same model on the same road section, and acquiring the driving sequence of the electric automobile to be pre-warned and the electric automobiles with the same model according to the image data; if a plurality of electric automobiles with consistent running sequences including the electric automobile to be pre-warned exist, the safety pre-warning server adjusts overload warning pre-warning information of automobile marks of the electric automobiles with consistent running sequences into overload information;

and if a plurality of electric automobiles with consistent running sequence including the electric automobile to be early-warned do not exist, the safety early-warning server removes the overload warning early-warning information of the automobile license plate number of the electric automobile with inconsistent running sequence.

The invention provides an artificial intelligence-based electric vehicle safety early warning method, which collects various different types of data related to vehicle safety driving, including historical driving data and historical maintenance data, and respectively trains by adopting different artificial intelligence models to obtain corresponding vehicle safety early warning results, and fits two vehicle safety early warning results to obtain a final overload safety early warning result, so that the efficiency and the accuracy of vehicle safety early warning are greatly improved; in addition, the overload safety early warning results of other automobiles with the same model are predicted by detecting the running track data of the overloaded automobile, so that overload detection is carried out without installing an overload detection device for each automobile, and the research, development, installation and maintenance costs of detection equipment are greatly reduced; finally, the invention fully utilizes various body sensors of the electric automobile to acquire the vehicle data so as to be used for automobile safety early warning.

As another embodiment, the present invention provides an electric vehicle safety early warning system based on artificial intelligence, which includes the following modules:

the sampling module is used for acquiring automobile models, automobile license plates, historical maintenance data, maintenance safety early warning information, historical driving data and driving safety early warning information of a plurality of electric automobiles in a plurality of sampling time periods;

the historical driving data training module is used for acquiring the historical driving data and the driving safety early warning information according to the automobile model so as to train an electric automobile driving safety early warning Bayesian prediction model of the automobile model; the travel data includes: tire pressure, braking acceleration, braking distance, unloading power and power consumption of the tire; the driving safety early warning information comprises overload and non-overload;

the historical maintenance data training module is used for acquiring the historical maintenance data and the maintenance safety early warning information according to the automobile license plate so as to train an electric automobile maintenance safety early warning regression prediction model; the repair data includes: tire replacement mileage, brake pad replacement mileage and battery replacement mileage; the maintenance safety early warning information comprises normal loss and abnormal loss;

the early warning judgment module is used for acquiring the automobile model of the electric automobile to be early warned, acquiring a corresponding electric automobile driving safety early warning Bayes prediction model according to the automobile model, acquiring current driving data of the electric automobile to be early warned in the current time period, importing the current driving data into the electric automobile driving safety early warning Bayes prediction model, and acquiring a driving safety early warning result of the electric automobile; acquiring a maintenance safety early warning result of the electric automobile according to the automobile license plate number of the electric automobile to be early warned;

and the early warning module is used for obtaining the current overload safety early warning result of the electric automobile to be early warned according to the driving safety early warning result and the maintenance safety early warning result of the electric automobile to be early warned.

As a preferred embodiment, acquiring the automobile models, the historical driving data and the safety warning information thereof of a plurality of sampling time periods of a plurality of electric automobiles specifically comprises:

the sampling time period comprises a driving sampling time period and a maintenance sampling time period;

acquiring running time data of an electric automobile, calculating a running period of the electric automobile according to the running time data, and determining a running sampling time period of the electric automobile according to a non-idle time period in the running period;

and acquiring maintenance time data of the electric automobile, and determining a maintenance sampling time period of the electric automobile according to the maintenance time data and the maintenance period.

As a preferred embodiment, the method for obtaining the maintenance safety early warning result of the electric vehicle according to the license plate number of the electric vehicle to be early warned specifically includes:

and acquiring the maintenance data according to the license plate number of the electric automobile to be early-warned in the maintenance time period of the electric automobile, importing the maintenance data into the electric automobile maintenance safety early-warning regression prediction model, and acquiring the maintenance safety early-warning result of the electric automobile.

As a preferred embodiment, the method for training the electric vehicle driving safety early warning bayesian prediction model of the vehicle model by collecting the historical driving data and the driving safety early warning information according to the vehicle model specifically includes:

acquiring the non-overload driving data and non-overload driving safety early warning information according to the automobile model to train an electric automobile driving safety early warning Bayes prediction model of the automobile model;

and acquiring the overload driving data and overload driving safety early warning information according to the automobile model so as to train an electric automobile driving safety early warning Bayesian prediction model of the automobile model.

As a preferred embodiment, obtaining the current overload safety early warning result of the electric vehicle to be early warned according to the driving safety early warning result and the maintenance safety early warning result of the electric vehicle to be early warned, further includes:

the current overload safety early warning result Z = K1M + K2N of the electric automobile to be early warned, wherein M is a driving safety early warning result, N is a maintenance safety early warning result, K1 and K2 are weights, and K1+ K2= 1.

As a preferred embodiment, the method further comprises:

if the current overload safety early warning result of the electric automobile to be early warned is overload, acquiring the running track of the electric automobile, if a plurality of electric automobiles with the same running track exist in the same time period, acquiring the automobile models of the plurality of electric automobiles according to the automobile brands of the plurality of electric automobiles, and sending the automobile brands and overload warning early warning information of the electric automobiles with the same models as the electric automobiles to be early warned to a safety early warning server;

and the safety early warning server sends out overload warning early warning information of the automobile license plate.

As a preferred embodiment, the method for sending the overload warning information of the license plate number by the safety warning server further includes:

acquiring and comparing the driving image data of the electric automobile to be pre-warned and the electric automobiles with the same model on the same road section; if the similarity of the carriage images in the image data is greater than or equal to a first similarity threshold value, the safety early warning server adjusts overload warning early warning information of the license plate number of the electric automobile corresponding to the image data into overload information;

and if the similarity of the carriage images in the image data is smaller than a first similarity threshold value, the safety early warning server removes overload warning early warning information of the automobile license plate number of the electric automobile corresponding to the image data.

As a preferred embodiment, the method for sending the overload warning information of the license plate number by the safety warning server further includes:

acquiring and comparing the speeds of the electric automobile to be pre-warned and the electric automobile of the same model on the same road section, and if the similarity of the speeds of the electric automobile to be pre-warned and the electric automobile of the same model is greater than or equal to a second similarity threshold value, adjusting the overload warning pre-warning information of the automobile license plate of the electric automobile of the same model into overload information by the safety pre-warning server;

and if the similarity of the speed of the electric automobile to be early-warned and the electric automobile of the same model is smaller than a first similarity threshold value, the safety early-warning server removes the overload warning early-warning information of the automobile brand of the electric automobile of the same model.

As a preferred embodiment, the method for sending the overload warning information of the license plate number by the safety warning server further includes:

acquiring and comparing the driving image data of the electric automobile to be pre-warned and the electric automobiles with the same model on the same road section, and acquiring the driving sequence of the electric automobile to be pre-warned and the electric automobiles with the same model according to the image data; if a plurality of electric automobiles with consistent running sequences including the electric automobile to be pre-warned exist, the safety pre-warning server adjusts overload warning pre-warning information of automobile marks of the electric automobiles with consistent running sequences into overload information;

and if a plurality of electric automobiles with consistent running sequence including the electric automobile to be early-warned do not exist, the safety early-warning server removes the overload warning early-warning information of the automobile license plate number of the electric automobile with inconsistent running sequence.

The invention provides an artificial intelligence-based electric vehicle safety early warning method, which collects various different types of data related to vehicle safety driving, including historical driving data and historical maintenance data, and respectively trains by adopting different artificial intelligence models to obtain corresponding vehicle safety early warning results, and fits two vehicle safety early warning results to obtain a final overload safety early warning result, so that the efficiency and the accuracy of vehicle safety early warning are greatly improved; in addition, the overload safety early warning results of other automobiles with the same model are predicted by detecting the running track data of the overloaded automobile, so that overload detection is carried out without installing an overload detection device for each automobile, and the research, development, installation and maintenance costs of detection equipment are greatly reduced; finally, the invention fully utilizes various body sensors of the electric automobile to acquire the vehicle data so as to be used for automobile safety early warning.

The invention provides an electric vehicle safety early warning method and system based on artificial intelligence, which collects various different types of data related to vehicle safety driving, including historical driving data and historical maintenance data, and respectively trains by adopting different artificial intelligence models to obtain corresponding vehicle safety early warning results, and fits two vehicle safety early warning results to obtain a final overload safety early warning result, thereby greatly improving the efficiency and accuracy of vehicle safety early warning; in addition, the overload safety early warning results of other automobiles with the same model are predicted by detecting the running track data of the overloaded automobile, so that overload detection is carried out without installing an overload detection device for each automobile, and the research, development, installation and maintenance costs of detection equipment are greatly reduced; finally, the invention fully utilizes various body sensors of the electric automobile to acquire the vehicle data so as to be used for automobile safety early warning.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the embodiments and the drawings used in the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

Fig. 1 is a schematic step diagram of an electric vehicle safety early warning method based on artificial intelligence according to the present invention.

Fig. 2 is a schematic structural diagram of an embodiment of an artificial intelligence-based electric vehicle safety warning system according to the present invention.

Detailed Description

The embodiments of the present invention are further described below with reference to the drawings.