CN114348008A - A vehicle control method, device, electronic device and storage medium - Google Patents

A vehicle control method, device, electronic device and storage medium Download PDF

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CN114348008A
CN114348008A CN202111517650.XA CN202111517650A CN114348008A CN 114348008 A CN114348008 A CN 114348008A CN 202111517650 A CN202111517650 A CN 202111517650A CN 114348008 A CN114348008 A CN 114348008A
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vehicle
gear
neural network
parameter values
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王臣
邓金涛
连凤霞
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Weichai Power Co Ltd
Weifang Weichai Power Technology Co Ltd
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Weichai Power Co Ltd
Weifang Weichai Power Technology Co Ltd
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Abstract

The application provides a vehicle control method, a vehicle control device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring a plurality of running parameter values of a target vehicle under a current running condition; inputting the plurality of driving parameter values into a pre-constructed gear determining model to obtain a target gear corresponding to the target vehicle under the current driving condition; and controlling the target vehicle to run according to the target gear. Compared with the prior art, the optimal gear under the current working condition can be decided according to the specific running working condition through the scheme, and the vehicle can better give consideration to the economy, the dynamic property and the comfort of the whole vehicle when running at the optimal gear.

Description

一种车辆控制方法、装置、电子设备及存储介质A vehicle control method, device, electronic device and storage medium

技术领域technical field

本申请涉及车辆控制技术领域,具体涉及一种车辆控制方法、装置、电子 设备以及存储介质。The present application relates to the technical field of vehicle control, and in particular, to a vehicle control method, device, electronic device, and storage medium.

背景技术Background technique

车辆在不同工况的道路中行驶的过程中,合适的档位将直接影响车辆的动 力性、经济性和乘坐舒适性。When the vehicle is running on roads with different working conditions, the appropriate gear will directly affect the power, economy and ride comfort of the vehicle.

因此,在半自动或者自动驾驶车辆中,如何能够选择合理的档位为亟需解 决的技术问题。Therefore, in a semi-autonomous or autonomous vehicle, how to select a reasonable gear is a technical problem that needs to be solved urgently.

发明内容SUMMARY OF THE INVENTION

本申请的目的是提供一种车辆控制方法、装置、电子设备及存储介质。The purpose of this application is to provide a vehicle control method, device, electronic device and storage medium.

本申请第一方面提供一种车辆控制方法,包括:A first aspect of the present application provides a vehicle control method, including:

获取目标车辆在当前行驶工况下的多个行驶参数值;Obtain multiple driving parameter values of the target vehicle under the current driving condition;

将所述多个行驶参数值输入预先构建的档位确定模型,得到与所述目标车 辆在当前行驶状况下对应的目标档位;Inputting the plurality of driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under current driving conditions;

控制所述目标车辆按照所述目标档位进行行驶。The target vehicle is controlled to travel according to the target gear.

在一种可能的实现方式中,在本申请提供的上述车辆控制方法中,所述档 位确定模型按照以下方式训练得到:In a possible implementation, in the above-mentioned vehicle control method provided by the present application, the gear determination model is obtained by training in the following manner:

获取样本车辆在不同工况下行驶时的多个样本行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple sample driving parameter values when the sample vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经 网络,得到所述样本车辆在该工况下行驶时对应的预测档位;Inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition;

基于所述样本车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量;determining the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

基于所述预测误差量调整所述神经网络的参数值后,返回执行将所述样本 车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经网络的步骤,直 至所述神经网络的预测误差量小于预设误差量,或者训练次数达到预设次数后, 得到所述档位确定模型。After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the neural network The predicted error amount is less than the preset error amount, or after the number of training times reaches the preset number of times, the gear determination model is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制方法中,所述神 经网络包含输入层、隐含层和输出层;In a possible implementation manner, in the above-mentioned vehicle control method provided in the present application, the neural network includes an input layer, a hidden layer and an output layer;

所述将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入 神经网络,得到所述样本车辆在该工况下行驶时对应的预测档位,包括:Described inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition, including:

将所述多个样本行驶参数值输入所述神经网络的输入层,通过所述神经网 络中输入层和隐含层之间的神经元的传递函数,得到所述隐含层输出的中间值;The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

基于所述隐含层输出的中间值,和所述神经网络中隐含层和输出层之间的 神经元的传递函数,得到所述输出层输出的预测档位。Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制方法中,所述基 于所述预测误差量调整所述神经网络的参数值,包括:In a possible implementation manner, in the above-mentioned vehicle control method provided by the present application, the adjustment of the parameter value of the neural network based on the prediction error amount includes:

基于所述预测误差量,对所述神经网络的输出层和隐含层的参数值进行反 向修正。Based on the prediction error amount, the parameter values of the output layer and the hidden layer of the neural network are reversely corrected.

在一种可能的实现方式中,在本申请提供的上述车辆控制方法中,所述车 辆控制方法还包括:In a possible implementation, in the above-mentioned vehicle control method provided by the present application, the vehicle control method further includes:

获取验证车辆在不同工况下行驶时的多个验证行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述验证车辆在每种工况下行驶时对应的多个验证行驶参数值输入神经 网络,得到所述验证车辆在该工况下行驶时对应的预测档位;Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

基于所述验证车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量小于预设误差量的情况下,得到所述档位确定模型。Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制方法中,所述行 驶参数值包括:整车传感器的测量值、整车模拟信号的输入值、变速箱控制信 息、发动机控制信息以及换挡杆控制信息。In a possible implementation manner, in the above-mentioned vehicle control method provided by the present application, the driving parameter values include: the measured value of the vehicle sensor, the input value of the vehicle simulation signal, the transmission control information, the engine control information and shift lever control information.

在一种可能的实现方式中,在本申请提供的上述车辆控制方法中,所述整 车传感器包括:弯道角度传感器、车速传感器、坡度传感器、温度传感器、压 力传感器以及重力传感器。In a possible implementation manner, in the above-mentioned vehicle control method provided by the present application, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

本申请第二方面提供一种车辆控制装置,包括:A second aspect of the present application provides a vehicle control device, comprising:

获取模块,用于获取目标车辆在当前行驶工况下的多个行驶参数值;an acquisition module for acquiring multiple driving parameter values of the target vehicle under the current driving condition;

确定模块,用于将所述多个行驶参数值输入预先构建的档位确定模型,得 到与所述目标车辆在当前行驶状况下对应的目标档位;a determination module, configured to input the plurality of driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under current driving conditions;

控制模块,用于控制所述目标车辆按照所述目标档位进行行驶。The control module is configured to control the target vehicle to drive according to the target gear.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,还包括: 模型训练模块,用于按照以下方式训练得到所述档位确定模型:In a possible implementation manner, the above-mentioned vehicle control device provided by the present application further includes: a model training module, configured to obtain the gear determination model by training in the following manner:

获取样本车辆在不同工况下行驶时的多个样本行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple sample driving parameter values when the sample vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经 网络,得到所述样本车辆在该工况下行驶时对应的预测档位;Inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition;

基于所述样本车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量;determining the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

基于所述预测误差量调整所述神经网络的参数值后,返回执行将所述样本 车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经网络的步骤,直 至所述神经网络的预测误差量小于预设误差量,或者训练次数达到预设次数后, 得到所述档位确定模型。After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the neural network The predicted error amount is less than the preset error amount, or after the number of training times reaches the preset number of times, the gear determination model is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述神 经网络包含输入层、隐含层和输出层;In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the neural network includes an input layer, a hidden layer and an output layer;

所述将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入 神经网络,得到所述样本车辆在该工况下行驶时对应的预测档位,包括:Described inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition, including:

将所述多个样本行驶参数值输入所述神经网络的输入层,通过所述神经网 络中输入层和隐含层之间的神经元的传递函数,得到所述隐含层输出的中间值;The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

基于所述隐含层输出的中间值,和所述神经网络中隐含层和输出层之间的 神经元的传递函数,得到所述输出层输出的预测档位。Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述模 型训练模块,具体用于:In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is specifically used for:

基于所述预测误差量,对所述神经网络的输出层和隐含层的参数值进行反 向修正。Based on the prediction error amount, the parameter values of the output layer and the hidden layer of the neural network are reversely corrected.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述模 型训练模块,还具体用于:In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is also specifically used for:

获取验证车辆在不同工况下行驶时的多个验证行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述验证车辆在每种工况下行驶时对应的多个验证行驶参数值输入神经 网络,得到所述验证车辆在该工况下行驶时对应的预测档位;Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

基于所述验证车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量小于预设误差量的情况下,得到所述档位确定模型。Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述行 驶参数值包括:整车传感器的测量值、整车模拟信号的输入值、变速箱控制信 息、发动机控制信息以及换挡杆控制信息。In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the driving parameter values include: measurement values of sensors of the entire vehicle, input values of analog signals of the entire vehicle, transmission control information, and engine control information and shift lever control information.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述整 车传感器包括:弯道角度传感器、车速传感器、坡度传感器、温度传感器、压 力传感器以及重力传感器。In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

本申请第三方面提供一种电子设备,包括:存储器、处理器及存储在所述 存储器上并可在所述处理器上运行的计算机程序,所述处理器运行所述计算机 程序时执行以实现本申请第一方面所述的方法。A third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement The method described in the first aspect of the present application.

本申请第四方面提供一种计算机可读介质,其上存储有计算机可读指令, 所述计算机可读指令可被处理器执行以实现本申请第一方面所述的方法。A fourth aspect of the present application provides a computer-readable medium having computer-readable instructions stored thereon, where the computer-readable instructions can be executed by a processor to implement the method described in the first aspect of the present application.

相较于现有技术,本申请提供的车辆控制方法、装置、电子设备及存储介 质,获取目标车辆在当前行驶工况下的多个行驶参数值;将所述多个行驶参数 值输入预先构建的档位确定模型,得到与所述目标车辆在当前行驶状况下对应 的目标档位;控制所述目标车辆按照所述目标档位进行行驶。相较于现有技术, 通过本方案,可以根据具体的行驶工况决策出当前工况下的最优挡位,车辆以 最优挡位行驶可以更好的兼顾整车经济性、动力性和舒适性。Compared with the prior art, the vehicle control method, device, electronic device and storage medium provided by the present application can obtain multiple driving parameter values of the target vehicle under the current driving condition; The target gear corresponding to the current driving condition of the target vehicle is obtained; the target vehicle is controlled to drive according to the target gear. Compared with the prior art, through this solution, the optimal gear under the current operating conditions can be determined according to the specific driving conditions, and the vehicle can be driven in the optimal gear to better take into account the economy, power and performance of the vehicle. comfort.

附图说明Description of drawings

通过阅读下文优选实施方式的详细描述,各种其他的优点和益处对于本领 域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的,而并 不认为是对本申请的限制。而且在整个附图中,用相同的参考符号表示相同的 部件。在附图中:Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for purposes of illustrating preferred embodiments only and are not to be considered limiting of the application. Also, the same reference numerals are used for the same parts throughout the drawings. In the attached image:

图1示出了本申请提供的一种车辆控制方法的流程图;FIG. 1 shows a flowchart of a vehicle control method provided by the present application;

图2示出了本申请提供的档位确定模型训练过程的流程图;Fig. 2 shows the flow chart of the gear determination model training process provided by the present application;

图3示出了本申请提供的神经网络的示意图;Fig. 3 shows the schematic diagram of the neural network provided by this application;

图4示出了本申请提供的一种具体的档位确定模型的训练过程的流程图;Fig. 4 shows the flow chart of the training process of a specific gear determination model provided by the present application;

图5示出了本申请提供的一种车辆控制装置的示意图;FIG. 5 shows a schematic diagram of a vehicle control device provided by the present application;

图6示出了本申请提供的各个部件之间交互信息信号流的示意图。FIG. 6 shows a schematic diagram of the signal flow of interactive information between various components provided in this application.

具体实施方式Detailed ways

下面将参照附图更详细地描述本公开的示例性实施方式。虽然附图中显示 了本公开的示例性实施方式,然而应当理解,可以以各种形式实现本公开而不 应被这里阐述的实施方式所限制。相反,提供这些实施方式是为了能够更透彻 地理解本公开,并且能够将本公开的范围完整的传达给本领域的技术人员。Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art.

需要注意的是,除非另有说明,本申请使用的技术术语或者科学术语应当 为本申请所属领域技术人员所理解的通常意义。It should be noted that, unless otherwise specified, the technical or scientific terms used in the present application should have the usual meanings understood by those skilled in the art to which the present application belongs.

另外,术语“第一”和“第二”等是用于区别不同对象,而不是用于描述 特定顺序。此外,术语“包括”和“具有”以及它们任何变形,意图在于覆盖 不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设 备没有限定于已列出的步骤或单元,而是可选地还包括没有列出的步骤或单元, 或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。In addition, the terms "first" and "second" etc. are used to distinguish different objects, and not to describe a particular order. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally also includes For other steps or units inherent to these processes, methods, products or devices.

本申请实施例提供一种车辆控制方法及装置、一种电子设备以及计算机可 读存储介质,下面结合附图进行说明。Embodiments of the present application provide a vehicle control method and device, an electronic device, and a computer-readable storage medium, which are described below with reference to the accompanying drawings.

请参考图1,其示出了本申请的一些实施方式所提供的一种车辆控制方法 的流程图,如图1所示,该车辆控制方法可以包括以下步骤:Please refer to FIG. 1 , which shows a flowchart of a vehicle control method provided by some embodiments of the present application. As shown in FIG. 1 , the vehicle control method may include the following steps:

S101、获取目标车辆在当前行驶工况下的多个行驶参数值;S101, acquiring multiple driving parameter values of the target vehicle under the current driving condition;

S102、将所述多个行驶参数值输入预先构建的档位确定模型,得到与所述 目标车辆在当前行驶状况下对应的目标档位;S102, inputting the multiple driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under current driving conditions;

S103、控制所述目标车辆按照所述目标档位进行行驶。S103. Control the target vehicle to travel according to the target gear.

上述方法的执行主体可以是车辆VCU(Vehicle control unit,整车控制器)。The execution subject of the above method may be a vehicle VCU (Vehicle control unit, vehicle controller).

具体的,所述行驶参数值包括:整车传感器的测量值、整车模拟信号的输 入值、变速箱控制信息、发动机控制信息以及换挡杆控制信息。Specifically, the driving parameter values include: the measurement value of the vehicle sensor, the input value of the vehicle simulation signal, the transmission control information, the engine control information and the shift lever control information.

具体的,所述整车传感器包括:弯道角度传感器、车速传感器、坡度传感 器、温度传感器、压力传感器以及重力传感器。Specifically, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

整车模拟信号可以是踏板开度(踏板电压值),例如刹车踏板开度、油门 踏板开度等。The vehicle simulation signal can be pedal opening (pedal voltage value), such as brake pedal opening, accelerator pedal opening and so on.

变速箱控制信息可以从TCU(Transmission Control Unit,变速箱控制器) 处获得,发动机控制信息可以从发动机ECU(Electronic Control Unit,电子控 制器)处获得,换挡杆控制信息可以从换挡杆执行机构处获得档杆位置,例如 前进挡、后退档。如图6所示为各个部件之间交互信息信号流的示意图。Transmission control information can be obtained from TCU (Transmission Control Unit, transmission controller), engine control information can be obtained from engine ECU (Electronic Control Unit, electronic controller), shift lever control information can be executed from the shift lever The gear lever position is obtained at the mechanism, such as forward gear, reverse gear. Figure 6 is a schematic diagram of the signal flow of the interactive information between the various components.

因此,上述多个行驶参数可以包括车速、踏板开度、载重、坡度、弯道角 度、发动机转速、档杆位置、温度、压强等车辆行驶参数,这些车辆行驶参数 为车辆在不同工况下最优档位的影响因子。Therefore, the above-mentioned multiple driving parameters may include vehicle driving parameters such as vehicle speed, pedal opening, load, slope, curve angle, engine speed, gear lever position, temperature, pressure, etc. The impact factor of the excellent grade.

本申请中,基于上述最优档位的影响因子预先训练得到了档位确定模型, 如图2所示,所述档位确定模型按照以下方式训练得到:In the present application, a gear determination model is obtained by pre-training based on the influence factor of the above-mentioned optimal gear. As shown in FIG. 2 , the gear determination model is obtained by training in the following manner:

S201、获取样本车辆在不同工况下行驶时的多个样本行驶参数值,以及在 每种工况下行驶时对应的最优档位;S201, acquiring multiple sample driving parameter values when the sample vehicle is driving under different working conditions, and the corresponding optimal gear when driving under each working condition;

S202、将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输 入神经网络,得到所述样本车辆在该工况下行驶时对应的预测档位;S202, input a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, and obtain the corresponding predicted gear when the sample vehicle is driving under this working condition;

S203、基于所述样本车辆在每种工况下行驶时的最优档位和预测档位,确 定所述神经网络的预测误差量;S203, determine the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

S204、基于所述预测误差量调整所述神经网络的参数值后,返回执行将所 述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经网络的步 骤,直至所述神经网络的预测误差量小于预设误差量,或者训练次数达到预设 次数后,得到所述档位确定模型。S204. After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the The gear determination model is obtained after the prediction error amount of the neural network is less than the preset error amount, or after the number of training times reaches the preset number of times.

具体的,样本行驶参数值可以包括车速、踏板开度、载重、坡度、弯道角 度、发动机转速、档杆位置、温度、压强等车辆行驶参数值。Specifically, the sample driving parameter values may include vehicle driving parameter values such as vehicle speed, pedal opening, load, slope, curve angle, engine speed, gear lever position, temperature, and pressure.

具体的,上述神经网络可以采用BP(Back Propagation,反向传播)神经 网络。如图3所示,所述神经网络包含输入层、隐含层和输出层。Specifically, the above-mentioned neural network may adopt a BP (Back Propagation, back propagation) neural network. As shown in Figure 3, the neural network includes an input layer, a hidden layer and an output layer.

上述步骤S202,具体包括以下步骤:The above step S202 specifically includes the following steps:

将所述多个样本行驶参数值输入所述神经网络的输入层,通过所述神经网 络中输入层和隐含层之间的神经元的传递函数,得到所述隐含层输出的中间值;The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

基于所述隐含层输出的中间值,和所述神经网络中隐含层和输出层之间的 神经元的传递函数,得到所述输出层输出的预测档位。Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

上述步骤S204中基于所述预测误差量调整所述神经网络的参数值,具体 包括:基于所述预测误差量,对所述神经网络的输出层和隐含层的参数值进行 反向修正。Adjusting the parameter values of the neural network based on the prediction error in the above-mentioned step S204 specifically includes: performing reverse correction on the parameter values of the output layer and the hidden layer of the neural network based on the prediction error.

上述档位确定模型的训练方法中还包括以下模型验证步骤:The training method for the above gear determination model further includes the following model verification steps:

获取验证车辆在不同工况下行驶时的多个验证行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述验证车辆在每种工况下行驶时对应的多个验证行驶参数值输入神经 网络,得到所述验证车辆在该工况下行驶时对应的预测档位;Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

基于所述验证车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量小于预设误差量的情况下,得到所述档位确定模型。Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

如图4所示,以BP神经网络为例,对上述档位确定模型的训练过程进行 详细介绍。As shown in Figure 4, taking the BP neural network as an example, the training process of the above gear determination model is introduced in detail.

确定影响因子,也就是确定影响不同工况下最优档位的行驶参数;Determine the influencing factors, that is, determine the driving parameters that affect the optimal gear under different working conditions;

获取样本数据,也就是获取影响不同工况下最优档位的行驶参数值,形成 样本数据;Obtain sample data, that is, obtain the driving parameter values that affect the optimal gear under different working conditions, and form sample data;

创建BP神经网络模型,先将BP神经网络模型初始化并设置误差上限值, 设置不同层的神经元个数。To create a BP neural network model, first initialize the BP neural network model, set the upper limit of the error, and set the number of neurons in different layers.

用大量样本数据训练模型,将不同工况下最优挡位对应的样本行驶参数值, 输入至神经网络输入层,对神经网络模型进行训练。A large amount of sample data is used to train the model, and the sample driving parameter values corresponding to the optimal gears under different working conditions are input to the input layer of the neural network to train the neural network model.

在正向运算决策最优挡位的过程中,实际计算的最优挡位值和样本数据中 理想挡位值之间的差,若大于设定的误差范围,则对神经网络模型进行逆向误 差修正,按极小化误差的方式对权值和阈值进行后向调整。In the process of deciding the optimal gear by the forward operation, if the difference between the actually calculated optimal gear value and the ideal gear value in the sample data is larger than the set error range, the neural network model will be reversed for error. Correction, the weights and thresholds are adjusted backwards in a way that minimizes the error.

输出层节点误差修正量:

Figure BDA0003407331110000071
k∈{1,2,…,m}。其 中,Tk表示输出层节点理想输出值,
Figure BDA0003407331110000072
表示输出层节点输出值。Output layer node error correction:
Figure BDA0003407331110000071
k∈{1,2,…,m}. Among them, T k represents the ideal output value of the output layer node,
Figure BDA0003407331110000072
Represents the output value of the output layer node.

隐含层节点误差修正量:

Figure BDA0003407331110000073
其中,
Figure BDA0003407331110000074
表示隐 含层节点输出值,Wjk表示隐含层权值。Error correction of hidden layer nodes:
Figure BDA0003407331110000073
in,
Figure BDA0003407331110000074
represents the output value of the hidden layer node, and W jk represents the hidden layer weight.

通过计算出的节点误差修正量,对不同层的神经元的权重和阈值进行修正。The weights and thresholds of neurons in different layers are corrected through the calculated node error correction.

输出层和隐含层之间权重和阈值修正:

Figure BDA0003407331110000081
θk(t+1)=θk(t)+βδk。Weight and threshold correction between output layer and hidden layer:
Figure BDA0003407331110000081
θ k (t+1)=θ k (t)+βδ k .

隐含层和输入样本之间权重和阈值修正:

Figure BDA0003407331110000082
θj(t+1)=θj(t)+βδj。Weight and threshold correction between hidden layer and input samples:
Figure BDA0003407331110000082
θ j (t+1)=θ j (t)+βδ j .

最后,利用大量不同工况下的最优挡位实车数据对模型进行训练以及验证, 得到适应复杂行驶工况的最优挡位选择控制策略。Finally, a large number of optimal gear real vehicle data under different working conditions are used to train and verify the model, and the optimal gear selection control strategy for complex driving conditions is obtained.

通过这种智能选档控制方法,使车辆换挡不再受限于特定的换挡线(标定 的工况与档位对应曲线),可以根据具体的行驶工况决策出当前工况下的最优 挡位,可以更好的兼顾整车经济性、动力性和舒适性。Through this intelligent gear selection control method, the shift of the vehicle is no longer limited by a specific shift line (the curve corresponding to the calibrated operating condition and the gear position), and the optimal driving condition under the current operating condition can be determined according to the specific driving condition. The optimal gear can better take into account the economy, power and comfort of the vehicle.

首先,目标挡位选择不再单纯的依靠换挡线,而是由众多影响因子确定的 非线性关系决定,因此这种智能选档控制方法更能适应复杂多变的实际行驶工 况;其次,该方法针对不同硬件选型只需要通过样本数据的训练就可得到工况 与最优挡位之间的对应关系,因此通用性和扩展性较强。First, the target gear selection is no longer purely dependent on the shift line, but is determined by a nonlinear relationship determined by many influencing factors, so this intelligent gear selection control method is more suitable for complex and changeable actual driving conditions; second, The method can obtain the corresponding relationship between the working condition and the optimal gear only through the training of sample data for different hardware selection, so it has strong versatility and scalability.

本申请实施例提供的车辆控制方法,获取目标车辆在当前行驶工况下的多 个行驶参数值;将所述多个行驶参数值输入预先构建的档位确定模型,得到与 所述目标车辆在当前行驶状况下对应的目标档位;控制所述目标车辆按照所述 目标档位进行行驶。相较于现有技术,通过本方案,可以根据具体的行驶工况 决策出当前工况下的最优挡位,车辆以最优挡位行驶可以更好的兼顾整车经济 性、动力性和舒适性。The vehicle control method provided by the embodiment of the present application obtains multiple driving parameter values of the target vehicle under the current driving condition; inputs the multiple driving parameter values into a pre-built gear determination model, and obtains The target gear corresponding to the current driving condition; the target vehicle is controlled to drive according to the target gear. Compared with the prior art, through this solution, the optimal gear under the current operating conditions can be determined according to the specific driving conditions, and the vehicle can travel in the optimal gear to better take into account the economy, power and performance of the vehicle. comfort.

在上述的实施例中,提供了一种车辆控制方法,与之相对应的,本申请还 提供一种车辆控制装置,该车辆控制装置可以通过软件、硬件或软硬结合的方 式来实现。例如,该车辆控制装置可以包括集成的或分开的功能模块或单元来 执行上述各方法中的对应步骤。请参考图5,其示出了本申请的一些实施方式 所提供的一种车辆控制装置的示意图。由于装置实施例基本相似于方法实施例, 所以描述得比较简单,相关之处参见方法实施例的部分说明即可。下述描述的 装置实施例仅仅是示意性的。In the above-mentioned embodiments, a vehicle control method is provided, and correspondingly, the present application also provides a vehicle control device, which can be implemented by software, hardware or a combination of software and hardware. For example, the vehicle control device may include integrated or separate functional modules or units to perform corresponding steps in the above-described methods. Please refer to FIG. 5, which shows a schematic diagram of a vehicle control device provided by some embodiments of the present application. Since the apparatus embodiments are basically similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for related parts. The apparatus embodiments described below are merely illustrative.

如图5所示,所述车辆控制装置10,可以包括:As shown in FIG. 5 , the vehicle control device 10 may include:

获取模块101,用于获取目标车辆在当前行驶工况下的多个行驶参数值;an obtaining module 101, configured to obtain a plurality of driving parameter values of the target vehicle under the current driving condition;

确定模块102,用于将所述多个行驶参数值输入预先构建的档位确定模型, 得到与所述目标车辆在当前行驶状况下对应的目标档位;A determination module 102, configured to input the plurality of driving parameter values into a pre-built gear determination model to obtain a target gear corresponding to the target vehicle under the current driving condition;

控制模块103,用于控制所述目标车辆按照所述目标档位进行行驶。The control module 103 is configured to control the target vehicle to drive according to the target gear.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,还包括: 模型训练模块,用于按照以下方式训练得到所述档位确定模型:In a possible implementation manner, the above-mentioned vehicle control device provided by the present application further includes: a model training module, configured to obtain the gear determination model by training in the following manner:

获取样本车辆在不同工况下行驶时的多个样本行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple sample driving parameter values when the sample vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经 网络,得到所述样本车辆在该工况下行驶时对应的预测档位;Inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition;

基于所述样本车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量;determining the prediction error amount of the neural network based on the optimal gear and the predicted gear when the sample vehicle is running under each operating condition;

基于所述预测误差量调整所述神经网络的参数值后,返回执行将所述样本 车辆在每种工况下行驶时对应的多个样本行驶参数值输入神经网络的步骤,直 至所述神经网络的预测误差量小于预设误差量,或者训练次数达到预设次数后, 得到所述档位确定模型。After adjusting the parameter values of the neural network based on the prediction error amount, return to the step of inputting a plurality of sample driving parameter values corresponding to the sample vehicle driving under each working condition into the neural network, until the neural network The predicted error amount is less than the preset error amount, or after the number of training times reaches the preset number of times, the gear determination model is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述神 经网络包含输入层、隐含层和输出层;In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the neural network includes an input layer, a hidden layer and an output layer;

所述将所述样本车辆在每种工况下行驶时对应的多个样本行驶参数值输入 神经网络,得到所述样本车辆在该工况下行驶时对应的预测档位,包括:Described inputting a plurality of sample driving parameter values corresponding to the sample vehicle when driving under each working condition into the neural network, to obtain the corresponding predicted gear when the sample vehicle is driving under the working condition, including:

将所述多个样本行驶参数值输入所述神经网络的输入层,通过所述神经网 络中输入层和隐含层之间的神经元的传递函数,得到所述隐含层输出的中间值;The multiple sample driving parameter values are input into the input layer of the neural network, and the intermediate value of the output of the hidden layer is obtained through the transfer function of the neurons between the input layer and the hidden layer in the neural network;

基于所述隐含层输出的中间值,和所述神经网络中隐含层和输出层之间的 神经元的传递函数,得到所述输出层输出的预测档位。Based on the intermediate value of the output of the hidden layer and the transfer function of the neurons between the hidden layer and the output layer in the neural network, the predicted level of the output of the output layer is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述模 型训练模块,具体用于:In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is specifically used for:

基于所述预测误差量,对所述神经网络的输出层和隐含层的参数值进行反 向修正。Based on the prediction error amount, the parameter values of the output layer and the hidden layer of the neural network are reversely corrected.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述模 型训练模块,还具体用于:In a possible implementation, in the above-mentioned vehicle control device provided by the present application, the model training module is also specifically used for:

获取验证车辆在不同工况下行驶时的多个验证行驶参数值,以及在每种工 况下行驶时对应的最优档位;Obtain multiple verification driving parameter values when the verification vehicle is running under different working conditions, and the corresponding optimal gear when driving under each working condition;

将所述验证车辆在每种工况下行驶时对应的多个验证行驶参数值输入神经 网络,得到所述验证车辆在该工况下行驶时对应的预测档位;Inputting a plurality of verification driving parameter values corresponding to the verification vehicle when driving under each working condition into the neural network to obtain the corresponding predicted gear position of the verification vehicle when driving under the operating condition;

基于所述验证车辆在每种工况下行驶时的最优档位和预测档位,确定所述 神经网络的预测误差量小于预设误差量的情况下,得到所述档位确定模型。Based on the optimal gear and the predicted gear when the verification vehicle is running under each working condition, it is determined that the predicted error amount of the neural network is less than the preset error amount, and the gear determination model is obtained.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述行 驶参数值包括:整车传感器的测量值、整车模拟信号的输入值、变速箱控制信 息、发动机控制信息以及换挡杆控制信息。In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the driving parameter values include: measured values of sensors of the entire vehicle, input values of analog signals of the entire vehicle, transmission control information, and engine control information and shift lever control information.

在一种可能的实现方式中,在本申请提供的上述车辆控制装置中,所述整 车传感器包括:弯道角度传感器、车速传感器、坡度传感器、温度传感器、压 力传感器以及重力传感器。In a possible implementation manner, in the above-mentioned vehicle control device provided in the present application, the vehicle sensors include: a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor, and a gravity sensor.

本申请实施例提供的车辆控制装置,可以根据具体的行驶工况决策出当前 工况下的最优挡位,车辆以最优挡位行驶可以更好的兼顾整车经济性、动力性 和舒适性。The vehicle control device provided by the embodiment of the present application can decide the optimal gear under the current working condition according to the specific driving condition, and the vehicle can better take into account the economy, power and comfort of the whole vehicle when running in the optimal gear. sex.

本申请实施方式还提供一种与前述实施方式所提供的车辆控制方法对应的 电子设备,所述电子设备可以是车辆VCU、手机、笔记本电脑、平板电脑、 台式机电脑等,以执行上述车辆控制方法。The embodiments of the present application further provide an electronic device corresponding to the vehicle control method provided by the foregoing embodiments, the electronic device may be a vehicle VCU, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to perform the above-mentioned vehicle control method.

本申请实施例提供的电子设备与本申请实施例提供的车辆控制方法出于相 同的发明构思,具有与其采用、运行或实现的方法相同的有益效果。The electronic device provided by the embodiment of the present application and the vehicle control method provided by the embodiment of the present application are based on the same inventive concept, and have the same beneficial effects as the method adopted, operated or realized.

本申请实施方式还提供一种与前述实施方式所提供的车辆控制方法对应的 计算机可读存储介质,其上存储有计算机程序(即程序产品),所述计算机程 序在被处理器运行时,会执行前述任意实施方式所提供的车辆控制方法。Embodiments of the present application also provide a computer-readable storage medium corresponding to the vehicle control method provided by the foregoing embodiments, on which a computer program (ie, a program product) is stored, and when the computer program is run by a processor, the The vehicle control method provided by any of the foregoing embodiments is executed.

需要说明的是,所述计算机可读存储介质的例子还可以包括,但不限于相 变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦 除可编程只读存储器(EEPROM)、快闪记忆体或其他光学、磁性存储介质,在 此不再一一赘述。It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random Access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media will not be repeated here.

本申请的上述实施例提供的计算机可读存储介质与本申请实施例提供的车 辆控制方法出于相同的发明构思,具有与其存储的应用程序所采用、运行或实 现的方法相同的有益效果。The computer-readable storage medium provided by the above-mentioned embodiments of the present application and the vehicle control method provided by the embodiments of the present application are based on the same inventive concept, and have the same beneficial effects as the methods used, run or implemented by the stored application programs.

需要说明的是,附图中的流程图和框图显示了根据本申请的多个实施例的 系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上, 流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分,所述 模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执 行指令。也应当注意,在有些作为替换的实现中,方框中所标注的功能也可以 以不同于附图中所标注的顺序发生。例如,两个连续的方框实际上可以基本并 行地执行,它们有时也可以按相反的顺序执行,这依所涉及的功能而定。也要 注意的是,框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的 组合,可以用执行规定的功能或动作的专用的基于硬件的系统来实现,或者可 以用专用硬件与计算机指令的组合来实现。It should be noted that the flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function(s). executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.

最后应说明的是:以上各实施例仅用以说明本申请的技术方案,而非对其 限制;尽管参照前述各实施例对本申请进行了详细的说明,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申请各实施例技术方案的范围,其均应涵盖在本申请 的权利要求和说明书的范围当中。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features thereof can be equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application. scope, which shall be included in the scope of the claims and description of the present application.

Claims (10)

1. A vehicle control method characterized by comprising:
acquiring a plurality of running parameter values of a target vehicle under a current running condition;
inputting the plurality of driving parameter values into a pre-constructed gear determining model to obtain a target gear corresponding to the target vehicle under the current driving condition;
and controlling the target vehicle to run according to the target gear.
2. The vehicle control method according to claim 1, characterized in that the gear determination model is trained in the following manner:
obtaining a plurality of sample running parameter values of a sample vehicle running under different working conditions and a corresponding optimal gear when the sample vehicle runs under each working condition;
inputting a plurality of sample running parameter values corresponding to the running of the sample vehicle under each working condition into a neural network to obtain a corresponding predicted gear when the sample vehicle runs under the working condition;
determining a prediction error amount of the neural network based on an optimal gear and a predicted gear when the sample vehicle runs under each working condition;
and after the parameter values of the neural network are adjusted based on the predicted error amount, returning to the step of inputting a plurality of corresponding sample running parameter values of the sample vehicle in running under each working condition into the neural network until the predicted error amount of the neural network is smaller than a preset error amount or the training times reach preset times, and obtaining the gear determination model.
3. The vehicle control method according to claim 2, characterized in that the neural network includes an input layer, a hidden layer, and an output layer;
the step of inputting a plurality of sample running parameter values corresponding to the sample vehicle running under each working condition into a neural network to obtain a corresponding predicted gear when the sample vehicle runs under the working condition comprises the following steps:
inputting the plurality of sample driving parameter values into an input layer of the neural network, and obtaining an intermediate value output by a hidden layer through a transfer function of a neuron between the input layer and the hidden layer in the neural network;
and obtaining a prediction gear output by the output layer based on the intermediate value output by the hidden layer and the transfer function of the neuron between the hidden layer and the output layer in the neural network.
4. The vehicle control method according to claim 3, wherein the adjusting the parameter value of the neural network based on the prediction error amount includes:
and reversely correcting the parameter values of the output layer and the hidden layer of the neural network based on the prediction error quantity.
5. The vehicle control method according to claim 3 or 4, characterized by further comprising:
acquiring a plurality of verified driving parameter values when the verified vehicle drives under different working conditions and corresponding optimal gears when the verified vehicle drives under each working condition;
inputting a plurality of corresponding verified running parameter values of the verified vehicle running under each working condition into a neural network to obtain a corresponding predicted gear of the verified vehicle running under the working condition;
and obtaining the gear determination model under the condition that the prediction error amount of the neural network is determined to be smaller than a preset error amount on the basis of the optimal gear and the predicted gear when the verified vehicle runs under each working condition.
6. The vehicle control method according to claim 1, characterized in that the running parameter value includes:
the control system comprises measured values of a whole vehicle sensor, input values of whole vehicle analog signals, gearbox control information, engine control information and gear lever control information.
7. The vehicle control method according to claim 6, wherein the entire vehicle sensor includes:
a curve angle sensor, a vehicle speed sensor, a gradient sensor, a temperature sensor, a pressure sensor and a gravity sensor.
8. A vehicle control apparatus characterized by comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of running parameter values of a target vehicle under a current running condition;
the determining module is used for inputting the plurality of driving parameter values into a gear determining model which is constructed in advance to obtain a target gear corresponding to the target vehicle under the current driving condition;
and the control module is used for controlling the target vehicle to run according to the target gear.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes when executing the computer program to implement the method according to any of claims 1 to 7.
10. A computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method of any one of claims 1 to 7.
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