技术领域technical field
本发明涉及通信技术领域，尤其涉及一种驾驶员识别方法和装置。The invention relates to the field of communication technology, in particular to a driver identification method and device.
背景技术Background technique
随着通信、移动互联网技术的普及，以及智能移动终端的高度覆盖，联网车机市场蓬勃发展，出现了许多个性化、定制化的车载服务。以地图导航为例，除了提供道路规划、航线、常用地址管理、实时交通状况等服务，还提供线路收藏、基于用户检索历史的消息推荐等服务。With the popularization of communication and mobile Internet technologies, and the high coverage of smart mobile terminals, the connected car market is booming, and many personalized and customized car services have emerged. Taking map navigation as an example, in addition to providing services such as road planning, routes, common address management, and real-time traffic conditions, it also provides services such as route collection and message recommendation based on user search history.
然而，上述各种服务的提供，依赖于OBD终端等采集的车辆数据或驾驶数据等，基于这些数据进行分析，只能针对车辆提供服务。而驾驶车辆的驾驶员可以有多个，OBD终端等难以分辨当前驾驶车辆的驾驶员身份，因此，难以提供针对驾驶员的个性化服务，例如，针对特定驾驶员的疲劳监控或驾驶提醒等。However, the provision of the above-mentioned various services depends on vehicle data or driving data collected by OBD terminals, etc., and analysis based on these data can only provide services for vehicles. However, there may be multiple drivers driving the vehicle, and it is difficult for OBD terminals to distinguish the identity of the driver currently driving the vehicle. Therefore, it is difficult to provide personalized services for drivers, such as fatigue monitoring or driving reminders for specific drivers.
发明内容Contents of the invention
本发明提供一种驾驶员识别方法和装置，用于解决现有技术中难以提供针对驾驶员的个性化服务的问题。The invention provides a driver identification method and device, which are used to solve the problem in the prior art that it is difficult to provide personalized services for drivers.
本发明的第一个方面是提供一种驾驶员识别方法，包括：A first aspect of the present invention provides a driver identification method, comprising:
获取当前车辆的驾驶数据，所述驾驶数据包括：车辆行车数据、驾驶员状态数据以及所述当前车辆的行驶路段数据；Acquiring driving data of the current vehicle, the driving data including: vehicle driving data, driver status data, and driving section data of the current vehicle;
对所述当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；performing quantitative processing on the driving data of the current vehicle to obtain driving data in a preset form;
将所述预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员；Inputting the driving data in the preset form into a pre-created regression classification model, judging whether the driver of the current vehicle is a preset driver according to the output result of the regression classification model;
若当前车辆的驾驶员为预设驾驶员，则基于所述驾驶数据以及所述预设驾驶员对应的驾驶习惯数据为所述预设驾驶员提供服务。If the current driver of the vehicle is a preset driver, then provide services for the preset driver based on the driving data and the driving habit data corresponding to the preset driver.
进一步的，所述将所述预设形式的驾驶数据输入至预先创建的回归分类模型中之前，还包括：Further, before inputting the driving data in the preset form into the pre-created regression classification model, it also includes:
获取驾驶当前车辆的各个驾驶员的第一样本驾驶数据以及所述第一样本驾驶数据对应的模型理论输出值；Acquiring the first sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the first sample driving data;
对所述第一样本驾驶数据进行量化处理，得到预设形式的第一样本驾驶数据；performing quantitative processing on the first sample driving data to obtain the first sample driving data in a preset form;
将所述预设形式的第一样本驾驶数据输出至初始回归分类模型中，根据初始回归分类模型的输出结果以及所述第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到所述回归分类模型。Outputting the first sample driving data in the preset form to the initial regression classification model, according to the output result of the initial regression classification model and the theoretical output value of the model corresponding to the first sample driving data to the initial regression classification model The regression coefficients are adjusted to obtain the regression classification model.
进一步的，所述将所述预设形式的第一样本驾驶数据输出至初始回归分类模型中，根据初始回归分类模型的输出结果以及所述第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到所述回归分类模型之后，还包括：Further, the first sample driving data in the preset form is output to the initial regression classification model, according to the output result of the initial regression classification model and the model theoretical output value pair corresponding to the first sample driving data The regression coefficient of the initial regression classification model is adjusted, and after obtaining the regression classification model, it also includes:
获取驾驶当前车辆的各个驾驶员的检测样本驾驶数据以及所述检测样本驾驶数据对应的模型理论输出值；Acquiring the detection sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the detection sample driving data;
对所述检测样本驾驶数据进行量化处理，得到预设形式的检测样本驾驶数据；Carrying out quantitative processing on the test sample driving data to obtain test sample driving data in a preset form;
将所述预设形式的检测样本驾驶数据输出至所述回归分类模型中，判断所述回归分类模型的输出结果与所述检测样本驾驶数据对应的模型理论输出值是否匹配；Outputting the test sample driving data in the preset form to the regression classification model, and judging whether the output result of the regression classification model matches the model theoretical output value corresponding to the test sample driving data;
若对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率，则不对所述回归分类模型的回归系数进行调整。If the ratio of the detection sample driving data whose corresponding output result matches the corresponding model theoretical output value is greater than or equal to a preset ratio, then the regression coefficient of the regression classification model is not adjusted.
进一步的，所述将所述预设形式的第一样本驾驶数据输出至初始回归分类模型中，根据初始回归分类模型的输出结果以及所述第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到所述回归分类模型之后，还包括：Further, the first sample driving data in the preset form is output to the initial regression classification model, according to the output result of the initial regression classification model and the model theoretical output value pair corresponding to the first sample driving data The regression coefficient of the initial regression classification model is adjusted, and after obtaining the regression classification model, it also includes:
若对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率小于预设比率，则获取驾驶当前车辆的各个驾驶员的第二样本驾驶数据以及所述第二样本驾驶数据对应的模型理论输出值，基于所述第二样本驾驶数据以及所述第二样本驾驶数据对应的模型理论输出值对所述回归分类模型的回归系数进行调整，直至对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率为止。If the ratio of the corresponding output result and the detection sample driving data matching the corresponding model theoretical output value is less than the preset ratio, then obtain the second sample driving data of each driver driving the current vehicle and the corresponding second sample driving data. Model theoretical output value, adjusting the regression coefficient of the regression classification model based on the second sample driving data and the model theoretical output value corresponding to the second sample driving data until the corresponding output result is the same as the corresponding model theoretical output until the ratio of the detection sample driving data whose value matches is greater than or equal to the preset ratio.
进一步的，所述回归分类模型的公式为，Further, the formula of the regression classification model is,
其中，z＝w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；Among them, z=w0 HB+w1 ST+w2 SB+w3 RC+w4 SD+w5 RL+w6 FD+w7 AS+w8 FKM;
其中，σ(z)为回归分类模型的输出结果，HB、ST、SB、RC、SD、RL、FD、AS和FKM为预设形式的驾驶数据中的各个参数。Among them, σ(z) is the output result of the regression classification model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are the parameters in the preset driving data.
进一步的，所述车辆行车数据包括：加速度、速度、方向盘转角、平均时速和油耗；Further, the vehicle driving data includes: acceleration, speed, steering wheel angle, average speed and fuel consumption;
驾驶员状态数据包括：安全带佩戴状态和疲劳状态；Driver status data includes: seat belt wearing status and fatigue status;
行驶路段数据包括：限速数据和红绿灯数据。The driving section data includes: speed limit data and traffic light data.
本发明中，提供一种驾驶员识别方法，通过获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据；对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员，在当前车辆的驾驶员为预设驾驶员时，基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供个性化的服务。In the present invention, a driver identification method is provided. By acquiring the driving data of the current vehicle, the driving data includes: vehicle driving data, driver status data, and driving section data of the current vehicle; quantifying the driving data of the current vehicle, Get the driving data in the preset form; input the driving data in the preset form into the pre-created regression classification model, judge whether the driver of the current vehicle is the preset driver according to the output result of the regression classification model, and drive the current vehicle When the driver is the preset driver, the preset driver is provided with personalized services based on the driving data and the corresponding driving habit data of the preset driver.
本发明的第二个方面是提供一种驾驶员识别装置，包括：A second aspect of the present invention is to provide a driver identification device, comprising:
第一获取模块，用于获取当前车辆的驾驶数据，所述驾驶数据包括：车辆行车数据、驾驶员状态数据以及所述当前车辆的行驶路段数据；The first acquisition module is used to acquire the driving data of the current vehicle, the driving data including: vehicle driving data, driver status data and driving section data of the current vehicle;
第一处理模块，用于对所述当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；The first processing module is used to quantify the driving data of the current vehicle to obtain driving data in a preset form;
输入模块，用于将所述预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员；The input module is used to input the driving data in the preset form into the pre-created regression classification model, and judge whether the driver of the current vehicle is a preset driver according to the output result of the regression classification model;
提供模块，用于在当前车辆的驾驶员为预设驾驶员时，基于所述驾驶数据以及所述预设驾驶员对应的驾驶习惯数据为所述预设驾驶员提供服务。A module is provided for providing services for the preset driver based on the driving data and the driving habit data corresponding to the preset driver when the current driver of the vehicle is the preset driver.
进一步的，所述的装置还包括：Further, the device also includes:
第二获取模块，用于在所述输入模块将所述预设形式的驾驶数据输入至预先创建的回归分类模型中之前，获取驾驶当前车辆的各个驾驶员的第一样本驾驶数据以及所述第一样本驾驶数据对应的模型理论输出值；The second obtaining module is used to obtain the first sample driving data of each driver driving the current vehicle and the described The theoretical output value of the model corresponding to the first sample driving data;
第二处理模块，用于对所述第一样本驾驶数据进行量化处理，得到预设形式的第一样本驾驶数据；The second processing module is configured to perform quantitative processing on the first sample driving data to obtain the first sample driving data in a preset form;
调整模块，用于将所述预设形式的第一样本驾驶数据输出至初始回归分类模型中，根据初始回归分类模型的输出结果以及所述第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到所述回归分类模型。An adjustment module, configured to output the first sample driving data in the preset form to the initial regression classification model, and according to the output result of the initial regression classification model and the model theoretical output value pair corresponding to the first sample driving data The regression coefficient of the initial regression classification model is adjusted to obtain the regression classification model.
进一步的，所述的装置还包括：Further, the device also includes:
第三获取模块，用于在所述调整模块根据初始回归分类模型的输出结果以及所述第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到所述回归分类模型之后，获取驾驶当前车辆的各个驾驶员的检测样本驾驶数据以及所述检测样本驾驶数据对应的模型理论输出值；The third acquisition module is used to adjust the regression coefficient of the initial regression classification model in the adjustment module according to the output result of the initial regression classification model and the model theoretical output value corresponding to the first sample driving data, to obtain the regression After the classification model, the detection sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the detection sample driving data are obtained;
第三处理模块，用于对所述检测样本驾驶数据进行量化处理，得到预设形式的检测样本驾驶数据；The third processing module is used to quantify the detection sample driving data to obtain the detection sample driving data in a preset form;
判断模块，用于将所述预设形式的检测样本驾驶数据输出至所述回归分类模型中，判断所述回归分类模型的输出结果与所述检测样本驾驶数据对应的模型理论输出值是否匹配；A judging module, configured to output the test sample driving data in a preset format to the regression classification model, and judge whether the output result of the regression classification model matches the model theoretical output value corresponding to the test sample driving data;
操作模块，用于在对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率时，不对所述回归分类模型的回归系数进行调整。The operation module is used to not adjust the regression coefficient of the regression classification model when the ratio of the corresponding output result to the corresponding model theoretical output value matches the detection sample driving data is greater than or equal to the preset ratio.
进一步的，所述回归分类模型的公式为，Further, the formula of the regression classification model is,
其中，z＝w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；Among them, z=w0 HB+w1 ST+w2 SB+w3 RC+w4 SD+w5 RL+w6 FD+w7 AS+w8 FKM;
其中，σ(z)为回归分类模型的输出结果，HB、ST、SB、RC、SD、RL、FD、AS和FKM为预设形式的驾驶数据中的各个参数。Among them, σ(z) is the output result of the regression classification model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are the parameters in the preset driving data.
本发明中，提供一种驾驶员识别装置，通过获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据；对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员，在当前车辆的驾驶员为预设驾驶员时，基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供个性化的服务。In the present invention, a driver identification device is provided. By acquiring the driving data of the current vehicle, the driving data includes: vehicle driving data, driver status data, and driving section data of the current vehicle; quantifying the driving data of the current vehicle, Get the driving data in the preset form; input the driving data in the preset form into the pre-created regression classification model, judge whether the driver of the current vehicle is the preset driver according to the output result of the regression classification model, and drive the current vehicle When the driver is the preset driver, the preset driver is provided with personalized services based on the driving data and the corresponding driving habit data of the preset driver.
附图说明Description of drawings
图1为本发明提供的驾驶员识别方法一个实施例的流程图；Fig. 1 is the flowchart of an embodiment of the driver identification method provided by the present invention;
图2为本发明提供的驾驶员识别方法又一个实施例的流程图；Fig. 2 is the flow chart of another embodiment of the driver identification method provided by the present invention;
图3为本发明提供的驾驶员识别装置一个实施例的结构示意图；Fig. 3 is a structural schematic diagram of an embodiment of the driver identification device provided by the present invention;
图4为本发明提供的驾驶员识别装置又一个实施例的结构示意图；Fig. 4 is a structural schematic diagram of another embodiment of the driver identification device provided by the present invention;
图5为本发明提供的驾驶员识别装置又一个实施例的结构示意图。Fig. 5 is a structural schematic diagram of another embodiment of the driver identification device provided by the present invention.
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
图1为本发明提供的驾驶员识别方法一个实施例的流程图，如图1所示，包括：Fig. 1 is a flowchart of an embodiment of the driver identification method provided by the present invention, as shown in Fig. 1, comprising:
101、获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据。101. Acquire the driving data of the current vehicle, the driving data includes: vehicle driving data, driver status data, and driving section data of the current vehicle.
本发明提供的驾驶员识别方法的执行主体为驾驶员识别装置，驾驶员识别装置具体可以为车载终端或者与车载终端连接的车载服务器等，驾驶员识别装置还可以为安装在车载终端或者车载服务器上的软件等。The execution subject of the driver identification method provided by the present invention is a driver identification device. The driver identification device may specifically be a vehicle-mounted terminal or a vehicle-mounted server connected to the vehicle-mounted terminal. The driver identification device may also be a vehicle-mounted terminal or a vehicle-mounted server. software etc.
其中，获取当前车辆的驾驶数据的方式至少可以有三种：车载智能终端OBD、驾驶员的移动终端上安装的车载应用或者车载操作系统。车辆行车数据具体可以为加速度、速度、方向盘转角、平均时速和油耗等；驾驶员状态数据可以包括：安全带佩戴状态和疲劳状态等；行驶路段数据可以包括：限速数据和红绿灯数据等。Among them, there are at least three ways to obtain the driving data of the current vehicle: the vehicle-mounted intelligent terminal OBD, the vehicle-mounted application or the vehicle-mounted operating system installed on the driver's mobile terminal. Vehicle driving data can specifically include acceleration, speed, steering wheel angle, average speed and fuel consumption, etc.; driver status data can include: seat belt wearing status and fatigue status, etc.; driving section data can include: speed limit data and traffic light data, etc.
进一步的，车辆行车数据还可以包括：车道线信号、方向灯信号、油门信号、离合器信号、挡位信号和陀螺仪数据等。以上这些参数中的任意一个或者相互结合可以体现驾驶员的以下驾驶行为：急刹车、急转弯、安全带佩戴状态、是否飞速抢灯、是否超速行驶、是否压车道行驶、是否疲劳驾驶、平均时速以及百公里油耗等。Further, the vehicle driving data may also include: lane line signals, direction light signals, accelerator signals, clutch signals, gear signals, gyroscope data, and the like. Any one of the above parameters or a combination of them can reflect the following driving behaviors of the driver: sudden braking, sharp turn, seat belt wearing status, whether to grab the light at high speed, whether to speed, whether to press the lane, whether to drive fatigued, average speed and fuel consumption per 100 kilometers.
102、对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据。102. Quantify the driving data of the current vehicle to obtain driving data in a preset form.
具体的，对当前车辆的驾驶数据进行量化处理，得到的预设形式的驾驶数据具体可以为：百公里急刹车数、百公里急转弯数、安全带佩戴情况、飞速抢灯频率、百公里超速行驶率、百公里压车道线行驶数、百公里疲劳驾驶数、百公里平均时速和百公里油耗等。Specifically, the driving data of the current vehicle is quantitatively processed, and the driving data obtained in a preset form can be: the number of sudden brakes per 100 kilometers, the number of sharp turns per 100 kilometers, the wearing of seat belts, the frequency of rushing to the lights, and the speeding per 100 kilometers Driving rate, the number of driving on 100 kilometers, the number of fatigue driving per 100 kilometers, the average speed per 100 kilometers, and the fuel consumption per 100 kilometers.
103、将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员。103. Input the driving data in the preset form into the pre-created regression classification model, and judge whether the driver of the current vehicle is the default driver according to the output result of the regression classification model.
其中，回归分类模型的公式可以为，Among them, the formula of the regression classification model can be,
其中，z＝w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；Among them, z=w0 HB+w1 ST+w2 SB+w3 RC+w4 SD+w5 RL+w6 FD+w7 AS+w8 FKM;
其中，σ(z)为回归分类模型的输出结果，HB、ST、SB、RC、SD、RL、FD、AS和FKM为预设形式的驾驶数据中的各个参数。Among them, σ(z) is the output result of the regression classification model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are the parameters in the preset driving data.
对应的，HB为百公里急刹车数；ST为百公里急转弯数；SB为安全带佩戴情况；RC为飞速抢灯频率；SD为百公里超速行驶率；RL为百公里压车道线行驶数；FD为百公里疲劳驾驶数；AS为百公里平均时速；FKM为百公里油耗。W0、W1、W2、W3、W4、W5、W6、W7、W8依次为百公里急刹车数、百公里急转弯数、安全带佩戴情况、飞速抢灯频率、百公里超速行驶率、百公里压车道线行驶数、百公里疲劳驾驶数、百公里平均时速和百公里油耗的回归系数。Correspondingly, HB is the number of sudden brakes per 100 kilometers; ST is the number of sharp turns per 100 kilometers; SB is the wearing status of seat belts; RC is the frequency of speeding lights; SD is the speeding rate per 100 kilometers; ; FD is the number of fatigue driving per 100 kilometers; AS is the average speed per 100 kilometers; FKM is the fuel consumption per 100 kilometers. W0, W1, W2, W3, W4, W5, W6, W7, W8 are the number of sudden braking per 100 kilometers, the number of sharp turns per 100 kilometers, the wearing condition of seat belts, the frequency of rushing to lights, the speeding rate per 100 kilometers, and the pressure per 100 kilometers. The regression coefficient of the number of lanes driven, the number of fatigue driving per 100 kilometers, the average speed per 100 kilometers and the fuel consumption per 100 kilometers.
具体的，将预设形式的驾驶数据输入至预先创建的回归分类模型中之后，可以得到一个范围在0-1之间的输出值，当输出值大于等于0.5时，表示当前车辆的驾驶员为车主；当输出值小于0.5时，表示当前车辆的驾驶员为非车主。Specifically, after inputting the driving data in the preset form into the pre-created regression classification model, an output value ranging from 0 to 1 can be obtained. When the output value is greater than or equal to 0.5, it means that the driver of the current vehicle is Vehicle owner; when the output value is less than 0.5, it means that the driver of the current vehicle is not the vehicle owner.
104、若当前车辆的驾驶员为预设驾驶员，则基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供服务。104. If the driver of the current vehicle is the preset driver, provide services for the preset driver based on the driving data and the driving habit data corresponding to the preset driver.
其中，预设驾驶员可以为车主或者非车主。Wherein, the preset driver can be the owner or non-owner of the vehicle.
本实施例中，提供一种驾驶员识别方法，通过获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据；对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员，在当前车辆的驾驶员为预设驾驶员时，基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供个性化的服务。In this embodiment, a method for identifying a driver is provided. By acquiring the driving data of the current vehicle, the driving data includes: vehicle driving data, driver status data, and driving section data of the current vehicle; quantitative processing is performed on the driving data of the current vehicle , to obtain the driving data in the preset form; input the driving data in the preset form into the pre-created regression classification model, and judge whether the driver of the current vehicle is the preset driver according to the output result of the regression classification model, in the current vehicle When the driver is the preset driver, personalized services are provided for the preset driver based on the driving data and the corresponding driving habit data of the preset driver.
图2为本发明提供的驾驶员识别方法有一个实施例的流程图，如图2所示，在图1所示实施例的基础上，步骤103之前，还可以包括：Fig. 2 has a flow chart of an embodiment of the driver identification method provided by the present invention, as shown in Fig. 2, on the basis of the embodiment shown in Fig. 1, before step 103, may also include:
105、获取驾驶当前车辆的各个驾驶员的第一样本驾驶数据以及第一样本驾驶数据对应的模型理论输出值。105. Acquire the first sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the first sample driving data.
其中，第一样本驾驶数据具体可以为当前时间之前一段时间内驾驶当前车辆各个驾驶员的历史驾驶数据。该段时间的长度可以根据需要进行设定。Wherein, the first sample driving data may specifically be historical driving data of each driver driving the current vehicle in a period of time before the current time. The length of this period of time can be set as required.
106、对第一样本驾驶数据进行量化处理，得到预设形式的第一样本驾驶数据。106. Quantify the first sample driving data to obtain the first sample driving data in a preset form.
其中，此处对第一样本驾驶数据的量化处理可以参考对当前车辆的驾驶数据的处理方式，此处不再进行详细说明。Wherein, the quantization processing of the driving data of the first sample here may refer to the processing method of the driving data of the current vehicle, which will not be described in detail here.
107、将预设形式的第一样本驾驶数据输出至初始回归分类模型中，根据初始回归分类模型的输出结果以及第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到回归分类模型。107. Output the first sample driving data in the preset form to the initial regression classification model, and perform the regression coefficient of the initial regression classification model according to the output result of the initial regression classification model and the theoretical output value of the model corresponding to the first sample driving data Adjustments are made to obtain a regression classification model.
进一步的，步骤107之后，还可以包括：获取驾驶当前车辆的各个驾驶员的检测样本驾驶数据以及检测样本驾驶数据对应的模型理论输出值；对检测样本驾驶数据进行量化处理，得到预设形式的检测样本驾驶数据；将预设形式的检测样本驾驶数据输出至回归分类模型中，判断回归分类模型的输出结果与检测样本驾驶数据对应的模型理论输出值是否匹配；若对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率，则不对回归分类模型的回归系数进行调整。Further, after step 107, it may also include: acquiring the test sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the test sample driving data; performing quantization processing on the test sample driving data to obtain a preset form of Detect sample driving data; output the test sample driving data in the preset form to the regression classification model, and judge whether the output result of the regression classification model matches the theoretical output value of the model corresponding to the detection sample driving data; if the corresponding output result matches the corresponding If the ratio of the driving data of the detection sample matching the theoretical output value of the model is greater than or equal to the preset ratio, the regression coefficient of the regression classification model will not be adjusted.
另外，需要进行说明的是，若对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率小于预设比率，则获取驾驶当前车辆的各个驾驶员的第二样本驾驶数据以及第二样本驾驶数据对应的模型理论输出值，基于第二样本驾驶数据以及第二样本驾驶数据对应的模型理论输出值对回归分类模型的回归系数进行调整，直至对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率为止。In addition, it needs to be explained that if the ratio of the corresponding output result of the detected sample driving data matching the corresponding model theoretical output value is less than the preset ratio, the second sample driving data and the first sample driving data of each driver driving the current vehicle are obtained. The model theoretical output value corresponding to the two-sample driving data, based on the second sample driving data and the model theoretical output value corresponding to the second sample driving data, the regression coefficient of the regression classification model is adjusted until the corresponding output result is consistent with the corresponding model theoretical output until the ratio of the detection sample driving data whose value matches is greater than or equal to the preset ratio.
本实施例中，提供一种驾驶员识别方法，通过获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据；对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；获取驾驶当前车辆的各个驾驶员的第一样本驾驶数据以及第一样本驾驶数据对应的模型理论输出值；基于当前车辆的各个驾驶员的第一样本驾驶数据以及第一样本驾驶数据对应的模型理论输出值对初始回归分类模型中的各个回归系数进行调整，得到回归分类模型；将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员，在当前车辆的驾驶员为预设驾驶员时，基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供个性化的服务。In this embodiment, a method for identifying a driver is provided. By acquiring the driving data of the current vehicle, the driving data includes: vehicle driving data, driver status data, and driving section data of the current vehicle; quantitative processing is performed on the driving data of the current vehicle , to obtain the driving data in the preset form; obtain the first sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the first sample driving data; based on the first sample of each driver of the current vehicle The driving data and the theoretical output value of the model corresponding to the first sample driving data are adjusted to each regression coefficient in the initial regression classification model to obtain a regression classification model; the driving data in a preset form is input into the pre-created regression classification model, According to the output result of the regression classification model, it is judged whether the driver of the current vehicle is the default driver. When the driver of the current vehicle is the default driver, based on the driving data and the driving habit data corresponding to the preset driver, it is the default driving provide personalized service.
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成。前述的程序可以存储于一计算机可读取存储介质中。该程序在执行时，执行包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by program instructions and related hardware. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above-mentioned method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other various media that can store program codes.
图3为本发明提供的驾驶员识别装置一个实施例的结构示意图，如图3所示，包括：Fig. 3 is a schematic structural diagram of an embodiment of a driver identification device provided by the present invention, as shown in Fig. 3 , including:
第一获取模块31，用于获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据；The first obtaining module 31 is used to obtain the driving data of the current vehicle, and the driving data includes: vehicle driving data, driver state data and driving section data of the current vehicle;
第一处理模块32，用于对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；The first processing module 32 is used to quantify the driving data of the current vehicle to obtain driving data in a preset form;
输入模块33，用于将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员；The input module 33 is used for inputting the driving data of the preset form into the regression classification model created in advance, and judging whether the driver of the current vehicle is a preset driver according to the output result of the regression classification model;
提供模块34，用于在当前车辆的驾驶员为预设驾驶员时，基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供服务。A module 34 is provided for providing services for the preset driver based on the driving data and the driving habit data corresponding to the preset driver when the current driver of the vehicle is the preset driver.
本发明提供的驾驶员识别装置具体可以为车载终端或者与车载终端连接的车载服务器等，驾驶员识别装置还可以为安装在车载终端或者车载服务器上的软件等。The driver identification device provided by the present invention may specifically be a vehicle-mounted terminal or a vehicle-mounted server connected to the vehicle-mounted terminal, and the driver identification device may also be software installed on the vehicle-mounted terminal or the vehicle-mounted server.
其中，获取当前车辆的驾驶数据的方式至少可以有三种：车载智能终端OBD、驾驶员的移动终端上安装的车载应用或者车载操作系统。车辆行车数据具体可以为加速度、速度、方向盘转角、平均时速和油耗等；驾驶员状态数据可以包括：安全带佩戴状态和疲劳状态等；行驶路段数据可以包括：限速数据和红绿灯数据等。Among them, there are at least three ways to obtain the driving data of the current vehicle: the vehicle-mounted intelligent terminal OBD, the vehicle-mounted application or the vehicle-mounted operating system installed on the driver's mobile terminal. Vehicle driving data can specifically include acceleration, speed, steering wheel angle, average speed and fuel consumption, etc.; driver status data can include: seat belt wearing status and fatigue status, etc.; driving section data can include: speed limit data and traffic light data, etc.
进一步的，车辆行车数据还可以包括：车道线信号、方向灯信号、油门信号、离合器信号、挡位信号和陀螺仪数据等。以上这些参数中的任意一个或者相互结合可以体现驾驶员的以下驾驶行为：急刹车、急转弯、安全带佩戴状态、是否飞速抢灯、是否超速行驶、是否压车道行驶、是否疲劳驾驶、平均时速以及百公里油耗等。Further, the vehicle driving data may also include: lane line signals, direction light signals, accelerator signals, clutch signals, gear signals, gyroscope data, and the like. Any one of the above parameters or a combination of them can reflect the following driving behaviors of the driver: sudden braking, sharp turn, seat belt wearing status, whether to grab the light at high speed, whether to speed, whether to press the lane, whether to drive fatigued, average speed and fuel consumption per 100 kilometers.
具体的，对当前车辆的驾驶数据进行量化处理，得到的预设形式的驾驶数据具体可以为：百公里急刹车数、百公里急转弯数、安全带佩戴情况、飞速抢灯频率、百公里超速行驶率、百公里压车道线行驶数、百公里疲劳驾驶数、百公里平均时速和百公里油耗等。Specifically, the driving data of the current vehicle is quantitatively processed, and the driving data obtained in a preset form can be: the number of sudden brakes per 100 kilometers, the number of sharp turns per 100 kilometers, the wearing of seat belts, the frequency of rushing to the lights, and the speeding per 100 kilometers Driving rate, the number of driving on 100 kilometers, the number of fatigue driving per 100 kilometers, the average speed per 100 kilometers, and the fuel consumption per 100 kilometers.
进一步的，回归分类模型的公式可以为，Further, the formula of the regression classification model can be,
其中，z＝w0HB+w1ST+w2SB+w3RC+w4SD+w5RL+w6FD+w7AS+w8FKM；Among them, z=w0 HB+w1 ST+w2 SB+w3 RC+w4 SD+w5 RL+w6 FD+w7 AS+w8 FKM;
其中，σ(z)为回归分类模型的输出结果，HB、ST、SB、RC、SD、RL、FD、AS和FKM为预设形式的驾驶数据中的各个参数。Among them, σ(z) is the output result of the regression classification model, and HB, ST, SB, RC, SD, RL, FD, AS and FKM are the parameters in the preset driving data.
对应的，HB为百公里急刹车数；ST为百公里急转弯数；SB为安全带佩戴情况；RC为飞速抢灯频率；SD为百公里超速行驶率；RL为百公里压车道线行驶数；FD为百公里疲劳驾驶数；AS为百公里平均时速；FKM为百公里油耗。W0、W1、W2、W3、W4、W5、W6、W7、W8依次为百公里急刹车数、百公里急转弯数、安全带佩戴情况、飞速抢灯频率、百公里超速行驶率、百公里压车道线行驶数、百公里疲劳驾驶数、百公里平均时速和百公里油耗的回归系数。Correspondingly, HB is the number of sudden brakes per 100 kilometers; ST is the number of sharp turns per 100 kilometers; SB is the wearing status of seat belts; RC is the frequency of speeding lights; SD is the speeding rate per 100 kilometers; ; FD is the number of fatigue driving per 100 kilometers; AS is the average speed per 100 kilometers; FKM is the fuel consumption per 100 kilometers. W0, W1, W2, W3, W4, W5, W6, W7, W8 are the number of sudden braking per 100 kilometers, the number of sharp turns per 100 kilometers, the wearing condition of seat belts, the frequency of rushing to lights, the speeding rate per 100 kilometers, and the pressure per 100 kilometers. The regression coefficient of the number of lanes driven, the number of fatigue driving per 100 kilometers, the average speed per 100 kilometers and the fuel consumption per 100 kilometers.
具体的，将预设形式的驾驶数据输入至预先创建的回归分类模型中之后，可以得到一个范围在0-1之间的输出值，当输出值大于等于0.5时，表示当前车辆的驾驶员为车主；当输出值小于0.5时，表示当前车辆的驾驶员为非车主。Specifically, after inputting the driving data in the preset form into the pre-created regression classification model, an output value ranging from 0 to 1 can be obtained. When the output value is greater than or equal to 0.5, it means that the driver of the current vehicle is Vehicle owner; when the output value is less than 0.5, it means that the driver of the current vehicle is not the vehicle owner.
进一步的，图4为本发明提供的驾驶员识别装置又一个实施例的结构示意图，如图4所示，在图3所示实施例的基础上，所述的驾驶员识别装置还包括：Further, Fig. 4 is a schematic structural diagram of another embodiment of the driver identification device provided by the present invention. As shown in Fig. 4, on the basis of the embodiment shown in Fig. 3, the driver identification device further includes:
第二获取模块35，用于在输入模块将预设形式的驾驶数据输入至预先创建的回归分类模型中之前，获取驾驶当前车辆的各个驾驶员的第一样本驾驶数据以及第一样本驾驶数据对应的模型理论输出值；The second obtaining module 35 is used to obtain the first sample driving data and the first sample driving data of each driver who drives the current vehicle before the input module inputs the driving data of the preset form into the pre-created regression classification model. The theoretical output value of the model corresponding to the data;
第二处理模块36，用于对第一样本驾驶数据进行量化处理，得到预设形式的第一样本驾驶数据；The second processing module 36 is configured to perform quantitative processing on the first sample driving data to obtain the first sample driving data in a preset form;
调整模块37，用于将预设形式的第一样本驾驶数据输出至初始回归分类模型中，根据初始回归分类模型的输出结果以及第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到回归分类模型。The adjustment module 37 is configured to output the first sample driving data in a preset form to the initial regression classification model, and perform the initial regression classification according to the output result of the initial regression classification model and the theoretical output value of the model corresponding to the first sample driving data The regression coefficient of the model is adjusted to obtain the regression classification model.
进一步的，图5为本发明提供的驾驶员识别装置又一个实施例的结构示意图，如图5所示，在图4所示实施例的基础上，所述的驾驶员识别装置还包括：Further, Fig. 5 is a structural schematic diagram of another embodiment of the driver identification device provided by the present invention. As shown in Fig. 5, on the basis of the embodiment shown in Fig. 4, the driver identification device further includes:
第三获取模块38，用于在调整模块根据初始回归分类模型的输出结果以及第一样本驾驶数据对应的模型理论输出值对初始回归分类模型的回归系数进行调整，得到回归分类模型之后，获取驾驶当前车辆的各个驾驶员的检测样本驾驶数据以及检测样本驾驶数据对应的模型理论输出值；The third acquisition module 38 is used to adjust the regression coefficient of the initial regression classification model according to the output result of the initial regression classification model and the model theoretical output value corresponding to the first sample driving data in the adjustment module to obtain the regression classification model. The detection sample driving data of each driver driving the current vehicle and the model theoretical output value corresponding to the detection sample driving data;
第三处理模块39，用于对检测样本驾驶数据进行量化处理，得到预设形式的检测样本驾驶数据；The third processing module 39 is used to quantify the detection sample driving data to obtain the detection sample driving data in a preset form;
判断模块40，用于将预设形式的检测样本驾驶数据输出至回归分类模型中，判断回归分类模型的输出结果与检测样本驾驶数据对应的模型理论输出值是否匹配；A judging module 40, configured to output the test sample driving data in a preset form to the regression classification model, and judge whether the output result of the regression classification model matches the model theoretical output value corresponding to the test sample driving data;
操作模块41，用于在对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率时，不对回归分类模型的回归系数进行调整。The operation module 41 is configured to not adjust the regression coefficient of the regression classification model when the ratio of the corresponding output result to the corresponding model theoretical output value matching the detection sample driving data is greater than or equal to the preset ratio.
另外，需要进行说明的是，若对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率小于预设比率，则获取驾驶当前车辆的各个驾驶员的第二样本驾驶数据以及第二样本驾驶数据对应的模型理论输出值，基于第二样本驾驶数据以及第二样本驾驶数据对应的模型理论输出值对回归分类模型的回归系数进行调整，直至对应的输出结果与对应的模型理论输出值匹配的检测样本驾驶数据的比率大于等于预设比率为止。In addition, it needs to be explained that if the ratio of the corresponding output result of the detected sample driving data matching the corresponding model theoretical output value is less than the preset ratio, the second sample driving data and the first sample driving data of each driver driving the current vehicle are obtained. The model theoretical output value corresponding to the two-sample driving data, based on the second sample driving data and the model theoretical output value corresponding to the second sample driving data, the regression coefficient of the regression classification model is adjusted until the corresponding output result is consistent with the corresponding model theoretical output until the ratio of the detection sample driving data whose value matches is greater than or equal to the preset ratio.
本实施例中，提供一种驾驶员识别装置，通过获取当前车辆的驾驶数据，驾驶数据包括：车辆行车数据、驾驶员状态数据以及当前车辆的行驶路段数据；对当前车辆的驾驶数据进行量化处理，得到预设形式的驾驶数据；将预设形式的驾驶数据输入至预先创建的回归分类模型中，根据回归分类模型的输出结果判断当前车辆的驾驶员是否为预设驾驶员，在当前车辆的驾驶员为预设驾驶员时，基于驾驶数据以及预设驾驶员对应的驾驶习惯数据为预设驾驶员提供个性化的服务。In this embodiment, a driver identification device is provided. By acquiring the driving data of the current vehicle, the driving data includes: vehicle driving data, driver status data, and driving section data of the current vehicle; quantitative processing is performed on the driving data of the current vehicle , to obtain the driving data in the preset form; input the driving data in the preset form into the pre-created regression classification model, and judge whether the driver of the current vehicle is the preset driver according to the output result of the regression classification model, in the current vehicle When the driver is the preset driver, personalized services are provided for the preset driver based on the driving data and the corresponding driving habit data of the preset driver.
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present invention. scope.
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| CN201610513833.7ACN106128099B (en) | 2016-07-01 | 2016-07-01 | Driver's recognition methods and device |
| Application Number | Priority Date | Filing Date | Title |
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| CN106128099A CN106128099A (en) | 2016-11-16 |
| CN106128099Btrue CN106128099B (en) | 2018-12-07 |
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| CN201610513833.7AActiveCN106128099B (en) | 2016-07-01 | 2016-07-01 | Driver's recognition methods and device |
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