CN114757058A - Automobile time domain load extrapolation method and device based on particle swarm optimization - Google Patents

Automobile time domain load extrapolation method and device based on particle swarm optimization Download PDF

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CN114757058A
CN114757058A CN202210665472.3A CN202210665472A CN114757058A CN 114757058 A CN114757058 A CN 114757058A CN 202210665472 A CN202210665472 A CN 202210665472A CN 114757058 A CN114757058 A CN 114757058A
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丁鼎
韩广宇
张永仁
卢放
马德慧
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Voyah Automobile Technology Co Ltd
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Abstract

The invention discloses an automobile time domain load extrapolation method and device based on a particle swarm algorithm, which are applied to the field of vehicle endurance tests, and the method comprises the following steps: collecting load signal data of a vehicle on a public road; establishing a time domain load extrapolation calculation model according to load signal data of the public road, wherein the time domain load extrapolation calculation model comprises a super-threshold probability distribution function and a super-threshold probability density function; and solving the super-threshold probability density function by adopting a particle swarm algorithm, and carrying out time-domain load extrapolation according to the solved result of the super-threshold probability density function to obtain load signal data of the vehicle in the whole life cycle. The invention solves the technical problem that the load signal data obtained by the time domain extrapolation method is inaccurate.

Description

基于粒子群算法的汽车时域载荷外推方法及装置Method and device for vehicle time-domain load extrapolation based on particle swarm optimization

技术领域technical field

本发明属于车辆耐久试验领域，尤其涉及一种基于粒子群算法的汽车时域载荷外推方法及装置。The invention belongs to the field of vehicle durability test, and in particular relates to a method and device for extrapolating vehicle time domain load based on particle swarm algorithm.

背景技术Background technique

汽车在研发阶段会在试车场内进行大量的整车耐久道路试验。而采集汽车在公共道路的载荷信号是制定汽车在试车场内进行整车耐久道路试验的重要输入。汽车整车的设计寿命一般为24万公里到30万公里，而汽车在公共道路进行载荷信号采集往往由于时间及费用的限制条件，最多只能采集几万公里的载荷信号，所以采集得到的汽车在公共道路的载荷信号需要进行外推，从而估算出汽车在整个生命周期内的载荷信号，才能更加合理的应用于制定整车耐久道路试验规范。During the research and development stage, a large number of vehicle durability road tests will be carried out in the test field. The collection of the load signal of the car on the public road is an important input for the vehicle to carry out the vehicle durability road test in the test field. The design life of the entire vehicle is generally 240,000 kilometers to 300,000 kilometers, and the collection of load signals on public roads is often limited by time and cost, and at most, only tens of thousands of kilometers of load signals can be collected. The load signal on the public road needs to be extrapolated to estimate the load signal of the vehicle in the whole life cycle, which can be more reasonably applied to formulate the vehicle durability road test specification.

时域外推方法是直接在时域信号上进行外推，时域信号极值符合广义帕累托分布，进行时域信号外推的核心步骤就是求解广义帕累托分布概率密度函数对应的参数。而以往求解广义帕累托分布概率密度函数的参数往往不是解空间内的最优解、且精度不够，会导致通过时域外推方法所得到的载荷信号数据不准确。The time-domain extrapolation method is to extrapolate directly on the time-domain signal. The extreme value of the time-domain signal conforms to the generalized Pareto distribution. The core step of time-domain signal extrapolation is to solve the parameters corresponding to the probability density function of the generalized Pareto distribution. In the past, the parameters for solving the probability density function of generalized Pareto distribution are often not the optimal solution in the solution space, and the precision is not enough, which will lead to the inaccuracy of the load signal data obtained by the time-domain extrapolation method.

发明内容SUMMARY OF THE INVENTION

鉴于现有技术存在上述技术问题，本发明实施例提供了一种基于粒子群算法的汽车时域载荷外推方法及装置。In view of the above-mentioned technical problems in the prior art, the embodiments of the present invention provide a method and device for extrapolating a vehicle time-domain load based on a particle swarm algorithm.

第一方面，本发明实施例提供了一种基于粒子群算法的汽车时域载荷外推方法，包括：In a first aspect, an embodiment of the present invention provides a particle swarm algorithm-based vehicle time-domain load extrapolation method, including:

采集车辆在公共道路的载荷信号数据；Collect vehicle load signal data on public roads;

根据所述在公共道路的载荷信号数据建立时域载荷外推计算模型，其中，所述时域载荷外推计算模型包括超阀值概率分布函数和超阀值概率密度函数；A time-domain load extrapolation calculation model is established according to the load signal data on the public road, wherein the time-domain load extrapolation calculation model includes a super-threshold probability distribution function and a super-threshold probability density function;

采用粒子群算法对所述超阀值概率密度函数进行求解，并根据对所述超阀值概率密度函数的求解结果进行时域载荷外推，得到所述车辆在全生命周期内的载荷信号数据。The particle swarm algorithm is used to solve the over-threshold probability density function, and the time-domain load extrapolation is performed according to the solution result of the over-threshold probability density function to obtain the load signal data of the vehicle in the whole life cycle. .

可选地，所述采集车辆在公共道路的载荷信号数据，包括：Optionally, the collecting the load signal data of the vehicle on the public road includes:

在所述车辆上布置轮心六分力传感器和三向加速度传感器、在所述车辆的传动轴上布置非接触式传动轴扭矩传感器、以及在所述车辆的悬架杆件上布置杆件力传感器；A wheel center six-component force sensor and a three-way acceleration sensor are arranged on the vehicle, a non-contact drive shaft torque sensor is arranged on a drive shaft of the vehicle, and a rod force is arranged on a suspension rod of the vehicle sensor;

规划在公共道路行驶的合计里程及在每种公共道路的行驶路线，其中，所述合计里程中，城市道路的里程占比为

Figure 954806DEST_PATH_IMAGE001
，高速道路的里程占比为
Figure 903171DEST_PATH_IMAGE002
，郊区道路的里程占比为
Figure 606814DEST_PATH_IMAGE003
，国省道道路的里程占比为
Figure 171788DEST_PATH_IMAGE004
，坏路道路的里程占比为
Figure 352233DEST_PATH_IMAGE005
，山区道路的里程占比为
Figure 951842DEST_PATH_IMAGE006
，其中：
Figure 808808DEST_PATH_IMAGE007
；The total mileage planned on public roads and the driving route on each public road, wherein, in the total mileage, the proportion of urban road mileage is
Figure 954806DEST_PATH_IMAGE001
, the mileage ratio of expressways is
Figure 903171DEST_PATH_IMAGE002
, the proportion of mileage of suburban roads is
Figure 606814DEST_PATH_IMAGE003
, the proportion of mileage of national and provincial roads is
Figure 171788DEST_PATH_IMAGE004
, the mileage proportion of bad roads is
Figure 352233DEST_PATH_IMAGE005
, the mileage ratio of mountain roads is
Figure 951842DEST_PATH_IMAGE006
,in:
Figure 808808DEST_PATH_IMAGE007
;

在所述车辆行驶于所述公共道路过程中，通过如下任意一种方式采集在公共道路的载荷信号数据：所述车辆上布置的轮心六分力传感器采集轮心六分力信号、通过适应于车辆上布置的三向加速度传感器采集轮心三向加速度信号、通过所述车辆的传动轴上布置的非接触式传动轴扭矩传感器采集传动轴扭矩信号、以及通过所述车辆的悬架杆件上布置杆件力传感器采集杆件力信号；When the vehicle is driving on the public road, the load signal data on the public road is collected in any one of the following ways: the wheel center six-component force sensor arranged on the vehicle collects the wheel center six-component force signal, and by adapting The three-way acceleration sensor arranged on the vehicle collects the three-way acceleration signal of the wheel center, the non-contact transmission shaft torque sensor arranged on the transmission shaft of the vehicle collects the transmission shaft torque signal, and the suspension rod of the vehicle collects the transmission shaft torque signal. The rod force sensor is arranged on the upper part to collect the rod force signal;

对所述在公共道路的载荷信号数据进行检查和清洗。Check and clean the load signal data on the public road.

可选地，还包括：Optionally, also include:

设定所述车辆在全生命周期内的目标里程；setting the target mileage of the vehicle throughout its life cycle;

根据所述目标里程和所述车辆在多种公共道路上行驶的合计里程，确定对所述在公共道路的载荷信号进行外推的倍数N。A multiple N for extrapolating the load signal on the public road is determined according to the target distance and the total distance traveled by the vehicle on various public roads.

可选地，所述根据所述在公共道路的载荷信号数据建立时域载荷外推计算模型，包括：Optionally, establishing a time-domain load extrapolation calculation model according to the load signal data on the public road, including:

定义所述在公共道路的载荷信号数据；define the load signal data on the public road;

定义阀值参数、形状参数以及尺寸参数；Define threshold parameters, shape parameters and size parameters;

定义大于所述阀值参数的载荷信号数据为超阀值；Define the load signal data larger than the threshold parameter as the over-threshold value;

根据所述公共道路的载荷信号数据、所述阀值参数、所述形状参数以及所述尺寸参数，建立所述超阀值概率分布函数和所述超阀值概率密度函数。The super-threshold probability distribution function and the super-threshold probability density function are established according to the load signal data of the public road, the threshold parameter, the shape parameter and the size parameter.

可选地，所述采用粒子群算法对所述超阀值概率密度函数进行求解，包括：Optionally, using particle swarm algorithm to solve the super-threshold probability density function, including:

步骤1：均匀随机产生粒子构成粒子群集合，其中，所述粒子群集合中每一个粒子包括位置向量及速度向量；Step 1: uniformly and randomly generating particles to form a particle swarm set, wherein each particle in the particle swarm set includes a position vector and a velocity vector;

步骤2：计算所述粒子群集合中每一个粒子的适应度函数；Step 2: Calculate the fitness function of each particle in the particle swarm set;

步骤3：定义个体最优粒子位置及全局最优粒子位置；Step 3: Define the individual optimal particle position and the global optimal particle position;

步骤4：针对所述粒子群集合所有粒子进行变异操作；Step 4: perform mutation operation on all particles in the particle swarm set;

步骤5：针对粒子进行速度向量及位置向量更新；Step 5: Update the velocity vector and position vector for the particle;

步骤6：判断是否满足迭代结束条件，如果满足则终止迭代，并求解得到粒子的位置向量解集合，如果不满足则跳转至执行所述步骤2、步骤3、步骤4以及步骤5，直到满足迭代结束条件或者达到最大迭代次数；Step 6: Determine whether the iteration end condition is met, if so, terminate the iteration, and solve to obtain the particle's position vector solution set, if not, jump to execute the steps 2, 3, 4 and 5 until the The iteration end condition or the maximum number of iterations is reached;

步骤7：取所述位置向量解集合中阀值参数最大的粒子位置作为载荷信号超阀值的概率密度函数的求解结果。Step 7: Take the particle position with the largest threshold parameter in the position vector solution set as the solution result of the probability density function of the load signal exceeding the threshold.

可选地，所述个体最优粒子位置定义为针对个体粒子在迭代过程中适应度数值最大时对应的粒子位置；所述全局最优粒子位置定义为针对粒子群在迭代过程中适应度数值最大对应的粒子位置。Optionally, the individual optimal particle position is defined as the particle position corresponding to the maximum fitness value for the individual particle in the iterative process; the global optimal particle position is defined as the maximum fitness value for the particle swarm in the iterative process. the corresponding particle position.

可选地，所述根据对所述超阀值概率密度函数的求解结果进行时域载荷外推，得到所述车辆在全生命周期内的载荷信号数据，包括：Optionally, the time-domain load extrapolation is performed according to the solution result of the over-threshold probability density function to obtain the load signal data of the vehicle in the whole life cycle, including:

从所述在公共道路的载荷信号数据中，提取超过阀值参数的数据；from the load signal data on the public road, extracting data exceeding the threshold parameter;

针对所述超过阀值参数的数据，采用超阀值概率密度函数的求解结果重复进行N次操作，每次操作随机产生新的载荷信号数据进行替换原数据；For the data exceeding the threshold parameter, the operation is repeated for N times using the solution result of the probability density function exceeding the threshold, and new load signal data is randomly generated for each operation to replace the original data;

将重复进行N次操作所生成的载荷信号数据进行首尾相连，得到外推N倍的时域载荷信号；Connect the payload signal data generated by repeating N times of operations end-to-end to obtain a time-domain payload signal that is extrapolated N times;

将所述外推N倍的时域载荷信号作为所述车辆在全生命周期内的载荷信号数据。The time-domain load signal extrapolated by N times is used as the load signal data of the vehicle in the whole life cycle.

第二方面，本发明实施例提供了一种基于粒子群算法的汽车时域载荷外推装置，包括：In a second aspect, an embodiment of the present invention provides a particle swarm algorithm-based vehicle time-domain load extrapolation device, including:

数据采集单元，用于采集车辆在公共道路的载荷信号数据；The data acquisition unit is used to collect the load signal data of the vehicle on the public road;

模型建立单元，用于根据所述在公共道路的载荷信号数据建立时域载荷外推计算模型，其中，所述时域载荷外推计算模型包括超阀值概率分布函数和超阀值概率密度函数；A model establishment unit for establishing a time-domain load extrapolation calculation model according to the load signal data on the public road, wherein the time-domain load extrapolation calculation model includes an over-threshold probability distribution function and an over-threshold probability density function ;

模型求解单元，用于采用粒子群算法对所述超阀值概率密度函数进行求解；a model solving unit, used for solving the super-threshold probability density function by using the particle swarm algorithm;

外推执行单元，用于根据对所述超阀值概率密度函数的求解结果进行时域载荷外推，得到所述车辆在全生命周期内的载荷信号数据。The extrapolation execution unit is configured to extrapolate the load in the time domain according to the solution result of the probability density function of the over-threshold value, so as to obtain the load signal data of the vehicle in the whole life cycle.

第三方面，本发明实施例提供了一种基于粒子群算法进行汽车时域载荷外推的电子设备，包括：存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的代码，所述处理器在执行所述代码时实现第一方面任一实施方式所述方法。In a third aspect, an embodiment of the present invention provides an electronic device for extrapolating vehicle time-domain load based on particle swarm algorithm, including: a memory, a processor, and an electronic device stored in the memory and running on the processor code, the processor implements the method of any one of the embodiments of the first aspect when executing the code.

第四方面，本发明实施例提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现第一方面任一实施方式所述方法。In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the method described in any one of the implementation manners of the first aspect.

本发明实施例提供的一个或者多个技术方案，至少实现了如下技术效果或者优点：One or more technical solutions provided by the embodiments of the present invention achieve at least the following technical effects or advantages:

通过粒子群算法采集车辆在公共道路的载荷信号数据；根据在公共道路的载荷信号数据建立时域载荷外推计算模型，时域载荷外推计算模型包括超阀值概率分布函数和超阀值概率密度函数；采用粒子群算法对超阀值概率密度函数进行求解，并根据对超阀值概率密度函数的求解结果进行时域载荷外推，得到车辆在全生命周期内的载荷信号数据。采用粒子群算法求解得到的广义帕累托分布概率密度函数，能够满足与采集数据的误差精度，求解精度较高，适用于任何时域载荷信号的外推。因此，实现了自动化外推载荷信号，且得到的全生命周期内的载荷信号数据更准确。The load signal data of the vehicle on the public road is collected by the particle swarm algorithm; the time-domain load extrapolation calculation model is established according to the load signal data on the public road. The time-domain load extrapolation calculation model includes the over-threshold probability distribution function and the over-threshold probability. Density function; the particle swarm algorithm is used to solve the over-threshold probability density function, and according to the solution result of the over-threshold probability density function, the time-domain load is extrapolated to obtain the load signal data of the vehicle in the whole life cycle. The generalized Pareto distribution probability density function obtained by the particle swarm algorithm can meet the error accuracy of the collected data, and the solution accuracy is high, which is suitable for the extrapolation of any time-domain load signal. Therefore, the automatic extrapolation of the load signal is realized, and the obtained load signal data in the whole life cycle is more accurate.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative work.

图1为本发明实施例中基于粒子群算法的汽车时域载荷外推方法的流程图；1 is a flowchart of a method for extrapolating vehicle time-domain load based on particle swarm algorithm in an embodiment of the present invention;

图2为本发明实施例中基于粒子群算法的汽车时域载荷外推装置的结构示意图；2 is a schematic structural diagram of a vehicle time-domain load extrapolation device based on a particle swarm algorithm in an embodiment of the present invention;

图3为本发明实施例中基于粒子群算法的电子设备的结构示意图。FIG. 3 is a schematic structural diagram of an electronic device based on a particle swarm algorithm in an embodiment of 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作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 purposes, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

参考图1所示，本发明实施例提供了一种基于粒子群算法的汽车时域载荷外推方法，包括如下步骤S101~S103：Referring to FIG. 1 , an embodiment of the present invention provides a method for extrapolating vehicle time-domain load based on a particle swarm algorithm, including the following steps S101 to S103:

S101、采集车辆在公共道路的载荷信号数据。S101. Collect load signal data of a vehicle on a public road.

在一些实施方式下，步骤S101具体包括如下多个子步骤：In some embodiments, step S101 specifically includes the following sub-steps:

S1011、在车辆上布置轮心六分力传感器和三向加速度传感器、在车辆的传动轴上布置非接触式传动轴扭矩传感器、以及在车辆的悬架杆件上布置杆件力传感器。S1011 , arranging a wheel center six-component force sensor and a three-way acceleration sensor on the vehicle, arranging a non-contact transmission shaft torque sensor on a transmission shaft of the vehicle, and arranging a rod force sensor on a suspension rod of the vehicle.

应当理解的是，可以根据需要采集的载荷信号数据的类型仅布置其中一种或者多种传感器。It should be understood that only one or more of the sensors may be arranged according to the type of load signal data to be collected.

S1012、规划在公共道路行驶的合计里程及在每种公共道路的行驶路线。S1012 , planning the total mileage traveled on the public road and the travel route on each public road.

其中，在合计里程A中，城市道路的里程占比为

Figure 595499DEST_PATH_IMAGE008
，高速道路的里程占比为
Figure 579635DEST_PATH_IMAGE009
，郊区道路的里程占比为
Figure 33750DEST_PATH_IMAGE010
，国省道道路的里程占比为
Figure 609088DEST_PATH_IMAGE011
，坏路道路的里程占比为
Figure 132342DEST_PATH_IMAGE012
，山区道路的里程占比为
Figure 654590DEST_PATH_IMAGE013
，且满足
Figure 697633DEST_PATH_IMAGE014
。Among them, in the total mileage A, the proportion of urban road mileage is
Figure 595499DEST_PATH_IMAGE008
, the mileage ratio of expressways is
Figure 579635DEST_PATH_IMAGE009
, the proportion of mileage of suburban roads is
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, the proportion of mileage of national and provincial roads is
Figure 609088DEST_PATH_IMAGE011
, the mileage proportion of bad roads is
Figure 132342DEST_PATH_IMAGE012
, the mileage ratio of mountain roads is
Figure 654590DEST_PATH_IMAGE013
, and satisfy
Figure 697633DEST_PATH_IMAGE014
.

具体的，需要针对如下每种公共道路规划各自的行驶路线：城市道路、高速道路、郊区道路、国省道道路、坏路道路、山区道路规划行驶路线。Specifically, it is necessary to plan respective driving routes for each of the following public roads: urban roads, expressways, suburban roads, national and provincial roads, bad roads, and mountain roads.

S1013、在所述车辆行驶于所述公共道路过程中，通过如下任意一种或者多种方式采集在公共道路的载荷信号数据：所述车辆上布置的轮心六分力传感器采集轮心六分力信号、通过适应于车辆上布置的三向加速度传感器采集轮心三向加速度信号、通过所述车辆的传动轴上布置的非接触式传动轴扭矩传感器采集传动轴扭矩信号、以及通过所述车辆的悬架杆件上布置杆件力传感器采集杆件力信号；S1013. While the vehicle is driving on the public road, collect the load signal data on the public road by any one or more of the following methods: the wheel center six-component force sensor arranged on the vehicle collects the wheel center six-point force sensor force signal, collecting the wheel center three-way acceleration signal through a three-way acceleration sensor arranged on the vehicle, collecting the drive shaft torque signal through a non-contact drive shaft torque sensor arranged on the drive shaft of the vehicle, and collecting the drive shaft torque signal through the vehicle The rod force sensor is arranged on the suspension rod to collect the rod force signal;

S1014、对所采集的在公共道路的载荷信号数据进行检查和清洗。S1014. Check and clean the collected load signal data on the public road.

具体而言，对采集到的在公共道路的载荷信号，需要经过数据有效性核查、去毛刺、漂移等数据检查及清洗。Specifically, the collected load signals on public roads need to be checked and cleaned by data validation, deburring, drift and other data.

进一步的，在步骤S101中，还包括：Further, in step S101, it also includes:

设定车辆在全生命周期内的目标里程；根据目标里程和车辆在多种公共道路上行驶的合计里程，确定对在公共道路的载荷信号进行外推的倍数N。Set the target mileage of the vehicle in the whole life cycle; according to the target mileage and the total mileage of the vehicle on various public roads, determine the multiple N for extrapolating the load signal on the public road.

设定车辆在全生命周期的目标里程为B，需要针对采集的载荷信号进行外推N倍，其表达式如下：The target mileage of the vehicle in the whole life cycle is set as B, which needs to be extrapolated by N times for the collected load signal, and its expression is as follows:

Figure 647134DEST_PATH_IMAGE015
Figure 647134DEST_PATH_IMAGE015

其中：

Figure 923264DEST_PATH_IMAGE016
表示车辆在全生命周期的目标里程，
Figure 452465DEST_PATH_IMAGE017
表示采集载荷信号的合计里程，
Figure 881172DEST_PATH_IMAGE018
表示外推的倍数。in:
Figure 923264DEST_PATH_IMAGE016
Indicates the target mileage of the vehicle in the whole life cycle,
Figure 452465DEST_PATH_IMAGE017
represents the total mileage of the collected load signal,
Figure 881172DEST_PATH_IMAGE018
Represents a multiple of extrapolation.

S102、根据所述在公共道路的载荷信号数据建立时域载荷外推计算模型，其中，所述时域载荷外推计算模型包括超阀值概率分布函数和超阀值概率密度函数。S102. Establish a time-domain load extrapolation calculation model according to the load signal data on the public road, wherein the time-domain load extrapolation calculation model includes a super-threshold probability distribution function and a super-threshold probability density function.

采集的载荷信号数据可以是上述步骤S101所采集的轮心六分力载荷信号或者轮心三向加速度信号或者传动轴扭矩信号或者杆件力信号。The collected load signal data may be the wheel center six-component force load signal or the wheel center three-directional acceleration signal or the transmission shaft torque signal or the rod force signal collected in the above step S101 .

定义所述在公共道路的载荷信号数据；定义阀值参数、形状参数以及尺寸参数；定义大于所述阀值参数的载荷信号数据为超阀值；根据所述公共道路的载荷信号数据、所述阀值参数、所述形状参数以及所述尺寸参数，建立所述超阀值概率分布函数和所述超阀值概率密度函数。Define the load signal data on the public road; define threshold parameters, shape parameters and size parameters; define the load signal data greater than the threshold parameter as an over-threshold value; according to the load signal data of the public road, the The threshold parameter, the shape parameter, and the size parameter establish the over-threshold probability distribution function and the over-threshold probability density function.

具体来讲，定义采集的载荷信号数据为

Figure 516422DEST_PATH_IMAGE019
，定义
Figure 499421DEST_PATH_IMAGE020
为数据阀值参数，定义采集的载荷信号数据中，绝对值大于数据阀值参数
Figure 363472DEST_PATH_IMAGE020
的载荷信号数据为超阀值
Figure 646686DEST_PATH_IMAGE021
，其表达式如下：Specifically, the collected load signal data is defined as
Figure 516422DEST_PATH_IMAGE019
,definition
Figure 499421DEST_PATH_IMAGE020
For the data threshold parameter, it defines that in the collected load signal data, the absolute value is greater than the data threshold parameter
Figure 363472DEST_PATH_IMAGE020
The load signal data of is over-threshold
Figure 646686DEST_PATH_IMAGE021
, whose expression is as follows:

Figure 734728DEST_PATH_IMAGE022
Figure 734728DEST_PATH_IMAGE022

其中，

Figure 719870DEST_PATH_IMAGE023
表示超阀值，即绝对值大于阀值参数u的载荷信号数据，
Figure 387612DEST_PATH_IMAGE024
表示采集的载荷信号数据；
Figure 525332DEST_PATH_IMAGE025
表示阀值参数。in,
Figure 719870DEST_PATH_IMAGE023
Indicates the super-threshold, that is, the load signal data whose absolute value is greater than the threshold parameter u,
Figure 387612DEST_PATH_IMAGE024
Indicates the collected load signal data;
Figure 525332DEST_PATH_IMAGE025
Represents the threshold parameter.

其中，采集的载荷信号数据的超阀值概率分布函数

Figure 721958DEST_PATH_IMAGE026
及概率密度函数
Figure 194397DEST_PATH_IMAGE027
表达式如下：Among them, the super-threshold probability distribution function of the collected load signal data
Figure 721958DEST_PATH_IMAGE026
and the probability density function
Figure 194397DEST_PATH_IMAGE027
The expression is as follows:

Figure 462567DEST_PATH_IMAGE028
Figure 462567DEST_PATH_IMAGE028

Figure 454794DEST_PATH_IMAGE029
Figure 454794DEST_PATH_IMAGE029

Figure 87901DEST_PATH_IMAGE030
Figure 87901DEST_PATH_IMAGE030

其中，

Figure 532788DEST_PATH_IMAGE031
表示超阀值概率分布函数；
Figure 791600DEST_PATH_IMAGE032
表示超阀值概率密度函数；
Figure 638333DEST_PATH_IMAGE033
表示超阀值，即绝对值大于阀值参数u的载荷信号数据；
Figure 504658DEST_PATH_IMAGE034
——表示采集的载荷信号数据；
Figure 436842DEST_PATH_IMAGE035
表示阀值参数；
Figure 250077DEST_PATH_IMAGE036
表示形状参数；
Figure 206444DEST_PATH_IMAGE037
表示尺寸参数。in,
Figure 532788DEST_PATH_IMAGE031
represents the probability distribution function of the super-threshold;
Figure 791600DEST_PATH_IMAGE032
represents the super-threshold probability density function;
Figure 638333DEST_PATH_IMAGE033
Indicates the over-threshold, that is, the load signal data whose absolute value is greater than the threshold parameter u;
Figure 504658DEST_PATH_IMAGE034
——represents the collected load signal data;
Figure 436842DEST_PATH_IMAGE035
Represents the threshold parameter;
Figure 250077DEST_PATH_IMAGE036
represents the shape parameter;
Figure 206444DEST_PATH_IMAGE037
Indicates the size parameter.

S103、采用粒子群算法对所述超阀值概率密度函数进行求解，并根据对所述超阀值概率密度函数的求解结果进行时域载荷外推，得到所述车辆在全生命周期内的载荷信号数据。S103. Use the particle swarm algorithm to solve the over-threshold probability density function, and extrapolate the time-domain load according to the solution result of the over-threshold probability density function to obtain the load of the vehicle in the entire life cycle signal data.

在步骤S103中，所述采用粒子群算法对所述超阀值概率密度函数进行求解，包括：In step S103, the particle swarm algorithm is used to solve the super-threshold probability density function, including:

步骤1：均匀随机产生粒子构成粒子群集合，其中，所述粒子群集合中每一个粒子包括位置向量及速度向量；Step 1: uniformly and randomly generating particles to form a particle swarm set, wherein each particle in the particle swarm set includes a position vector and a velocity vector;

具体而言，在步骤1中，均匀随机产生

Figure 181353DEST_PATH_IMAGE038
个粒子构成粒子群集合：Specifically, in step 1, uniformly randomly generated
Figure 181353DEST_PATH_IMAGE038
The particles form a particle swarm set:

Figure 600833DEST_PATH_IMAGE039
，
Figure 600833DEST_PATH_IMAGE039
,

其中，粒子群集合中任意一个粒子

Figure 952180DEST_PATH_IMAGE040
的位置向量
Figure 570244DEST_PATH_IMAGE041
及速度向量
Figure 965322DEST_PATH_IMAGE042
其表达式如下：Among them, any particle in the particle swarm set
Figure 952180DEST_PATH_IMAGE040
the position vector of
Figure 570244DEST_PATH_IMAGE041
and velocity vector
Figure 965322DEST_PATH_IMAGE042
Its expression is as follows:

Figure 872098DEST_PATH_IMAGE043
Figure 872098DEST_PATH_IMAGE043

Figure 27136DEST_PATH_IMAGE044
Figure 27136DEST_PATH_IMAGE044

Figure 702968DEST_PATH_IMAGE045
Figure 702968DEST_PATH_IMAGE045

Figure 81996DEST_PATH_IMAGE046
Figure 81996DEST_PATH_IMAGE046

Figure 725336DEST_PATH_IMAGE047
Figure 725336DEST_PATH_IMAGE047

Figure 152906DEST_PATH_IMAGE048
Figure 152906DEST_PATH_IMAGE048

Figure 948824DEST_PATH_IMAGE049
Figure 948824DEST_PATH_IMAGE049

Figure 436437DEST_PATH_IMAGE050
Figure 436437DEST_PATH_IMAGE050

Figure 567073DEST_PATH_IMAGE051
Figure 567073DEST_PATH_IMAGE051

其中：in:

Figure 860651DEST_PATH_IMAGE052
表示粒子群集合中粒子的个数合计，为使最终求解的结果更加多样性，
Figure 511075DEST_PATH_IMAGE052
的数值可以取较大数值；
Figure 860651DEST_PATH_IMAGE052
represents the total number of particles in the particle swarm set. In order to make the final solution result more diverse,
Figure 511075DEST_PATH_IMAGE052
The value of can take a larger value;

Figure 435169DEST_PATH_IMAGE053
表示粒子群集合中任意一个粒子
Figure 538254DEST_PATH_IMAGE054
的位置向量；
Figure 435169DEST_PATH_IMAGE053
Represents any particle in the particle swarm set
Figure 538254DEST_PATH_IMAGE054
the position vector of ;

Figure 822474DEST_PATH_IMAGE055
表示粒子群集合中任意一个粒子
Figure 327405DEST_PATH_IMAGE054
的速度向量；
Figure 822474DEST_PATH_IMAGE055
Represents any particle in the particle swarm set
Figure 327405DEST_PATH_IMAGE054
the velocity vector of ;

Figure 422400DEST_PATH_IMAGE056
表示粒子群集合中任意一个粒子i的位置向量的第一行数值、第二行数值、第三行数值；
Figure 422400DEST_PATH_IMAGE056
Represents the first row value, the second row value, and the third row value of the position vector of any particle i in the particle swarm set;

Figure 12781DEST_PATH_IMAGE057
表示粒子群集合中任意一个粒子i的速度向量第一行数值、第二行数值、第三行数值；
Figure 12781DEST_PATH_IMAGE057
Represents the first row value, the second row value, and the third row value of the velocity vector of any particle i in the particle swarm set;

Figure 648162DEST_PATH_IMAGE058
表示粒子的位置向量与速度向量的相关系数常数参数；
Figure 648162DEST_PATH_IMAGE058
Represents the constant parameter of the correlation coefficient between the position vector and the velocity vector of the particle;

Figure 522446DEST_PATH_IMAGE059
及
Figure 788342DEST_PATH_IMAGE060
表示粒子群集合的位置向量第j行数值的最小值及最大值常数；后续进行粒子位置向量迭代时，当粒子位置向量第j行数值小于最小值常数或者大于最大值常数，则将最小值常数或者最大值常数赋值给粒子位置向量的第j行数值；
Figure 522446DEST_PATH_IMAGE059
and
Figure 788342DEST_PATH_IMAGE060
Represents the minimum and maximum constants of the value in the jth row of the position vector of the particle swarm set; when the particle position vector is iterated later, when the value of the jth row of the particle position vector is smaller than the minimum value constant or greater than the maximum value constant, the minimum value constant Or the maximum value constant is assigned to the value of the jth row of the particle position vector;

Figure 866019DEST_PATH_IMAGE061
及
Figure 977195DEST_PATH_IMAGE062
表示粒子群集合的速度向量第j行数值的最小值及最大值常数；后续进行粒子位置速度迭代时，当粒子速度向量第j行数值小于最小值常数或者大于最大值常数，则将最小值常数或者最大值常数赋值给粒子速度向量的第j行数值；
Figure 866019DEST_PATH_IMAGE061
and
Figure 977195DEST_PATH_IMAGE062
Represents the minimum and maximum constants of the value in the jth row of the velocity vector of the particle swarm set; in the subsequent iteration of particle position and velocity, when the value of the jth row of the particle velocity vector is smaller than the minimum value constant or greater than the maximum value constant, the minimum value constant Or the maximum value constant is assigned to the value of the jth row of the particle velocity vector;

Figure 705985DEST_PATH_IMAGE063
表示粒子i的位置向量对应的超阀值概率密度函数的阀值参数；
Figure 705985DEST_PATH_IMAGE063
represents the threshold parameter of the super-threshold probability density function corresponding to the position vector of particle i;

Figure 611624DEST_PATH_IMAGE064
表示粒子i的位置向量对应的超阀值概率密度函数的形状参数；
Figure 611624DEST_PATH_IMAGE064
represents the shape parameter of the super-threshold probability density function corresponding to the position vector of particle i;

Figure 442177DEST_PATH_IMAGE065
表示粒子i的位置向量对应的超阀值概率密度函数的尺寸参数。
Figure 442177DEST_PATH_IMAGE065
Represents the size parameter of the super-threshold probability density function corresponding to the position vector of particle i.

步骤2：计算所述粒子群集合中每一个粒子的适应度函数。其中，任意一个粒子i的适应度函数的表达式如下：Step 2: Calculate the fitness function of each particle in the particle swarm set. Among them, the expression of the fitness function of any particle i is as follows:

Figure 75153DEST_PATH_IMAGE066
Figure 75153DEST_PATH_IMAGE066

迭代结束条件：

Figure 409182DEST_PATH_IMAGE067
Iteration end condition:
Figure 409182DEST_PATH_IMAGE067

其中：in:

Figure 282460DEST_PATH_IMAGE068
表示表示超阀值，即绝对值大于阀值参数u的载荷信号数据；
Figure 578138DEST_PATH_IMAGE069
表示粒子i的位置向量对应的超阀值概率密度函数对应的数据；m表示载荷信号数据个数；
Figure 296695DEST_PATH_IMAGE070
表示任意粒子i的适应度数值；
Figure 485231DEST_PATH_IMAGE071
表示迭代误差常数。
Figure 282460DEST_PATH_IMAGE068
Indicates the over-threshold, that is, the load signal data whose absolute value is greater than the threshold parameter u;
Figure 578138DEST_PATH_IMAGE069
Represents the data corresponding to the super-threshold probability density function corresponding to the position vector of particle i; m represents the number of load signal data;
Figure 296695DEST_PATH_IMAGE070
represents the fitness value of any particle i;
Figure 485231DEST_PATH_IMAGE071
represents the iteration error constant.

需要说明的是，均匀随机产生D个粒子的规则为：针对粒子的位置向量及速度向量的每一行的最小值及最大值区间分割为E段，取每一段的中心为初始数据。It should be noted that the rule for generating D particles uniformly and randomly is: the minimum and maximum intervals of each row of the particle's position vector and velocity vector are divided into E segments, and the center of each segment is taken as the initial data.

步骤3：定义个体最优粒子位置及全局最优粒子位置。Step 3: Define the individual optimal particle position and the global optimal particle position.

其中，个体最优粒子位置定义为针对个体粒子在迭代过程中适应度数值最大时对应的粒子位置，全局最优粒子位置定义为针对粒子群在迭代过程中适应度数值最大对应的粒子位置。Among them, the individual optimal particle position is defined as the particle position corresponding to the maximum fitness value for the individual particle during the iteration process, and the global optimal particle position is defined as the particle position corresponding to the maximum fitness value for the particle swarm during the iteration process.

步骤4：针对所述粒子群集合所有粒子进行变异操作。Step 4: Perform mutation operation on all particles in the particle swarm set.

其中，在步骤4中，针对粒子群集合中每一个粒子i，分别随机生产一个区间在

Figure 60569DEST_PATH_IMAGE072
之间的均匀随机数
Figure 600135DEST_PATH_IMAGE073
。根据产生的均匀随机数以及变异概率对粒子i进行变异操作。Among them, in step 4, for each particle i in the particle swarm set, randomly generate an interval in
Figure 60569DEST_PATH_IMAGE072
uniform random number between
Figure 600135DEST_PATH_IMAGE073
. Perform mutation operation on particle i according to the generated uniform random number and mutation probability.

在具体实施过程中，变异概率

Figure 106071DEST_PATH_IMAGE074
的表达式如下：In the specific implementation process, the mutation probability
Figure 106071DEST_PATH_IMAGE074
The expression is as follows:

Figure 414693DEST_PATH_IMAGE075
；
Figure 414693DEST_PATH_IMAGE075
;

Figure 364194DEST_PATH_IMAGE076
；
Figure 364194DEST_PATH_IMAGE076
;

Figure 391056DEST_PATH_IMAGE077
；
Figure 391056DEST_PATH_IMAGE077
;

Figure 248154DEST_PATH_IMAGE078
；
Figure 248154DEST_PATH_IMAGE078
;

Figure 660549DEST_PATH_IMAGE079
；
Figure 660549DEST_PATH_IMAGE079
;

其中：

Figure 46531DEST_PATH_IMAGE080
表示变异概率；
Figure 29531DEST_PATH_IMAGE081
为区间
Figure 159161DEST_PATH_IMAGE082
的随机数；
Figure 426063DEST_PATH_IMAGE083
及
Figure 717367DEST_PATH_IMAGE084
表示误差常数参数；
Figure 453242DEST_PATH_IMAGE085
表示误差计算变量；
Figure 120984DEST_PATH_IMAGE086
表示粒子群适应度数值的平均值；
Figure 773551DEST_PATH_IMAGE087
表示粒子i的适应度数值；
Figure 235756DEST_PATH_IMAGE088
表示粒子i个体最优位置的适应度数值；
Figure 458927DEST_PATH_IMAGE038
表示粒子群集合的粒子个数；
Figure 664780DEST_PATH_IMAGE089
表示粒子的适应度函数的理论最优解；
Figure 47220DEST_PATH_IMAGE090
表示表示迭代误差常数；
Figure 742644DEST_PATH_IMAGE091
表示表示粒子的适应度中间计算变量。in:
Figure 46531DEST_PATH_IMAGE080
represents the probability of mutation;
Figure 29531DEST_PATH_IMAGE081
for the interval
Figure 159161DEST_PATH_IMAGE082
the random number;
Figure 426063DEST_PATH_IMAGE083
and
Figure 717367DEST_PATH_IMAGE084
Represents the error constant parameter;
Figure 453242DEST_PATH_IMAGE085
represents the error calculation variable;
Figure 120984DEST_PATH_IMAGE086
Represents the average value of particle swarm fitness values;
Figure 773551DEST_PATH_IMAGE087
represents the fitness value of particle i;
Figure 235756DEST_PATH_IMAGE088
Represents the fitness value of the individual optimal position of particle i;
Figure 458927DEST_PATH_IMAGE038
Represents the number of particles in the particle swarm set;
Figure 664780DEST_PATH_IMAGE089
represents the theoretical optimal solution of the fitness function of the particle;
Figure 47220DEST_PATH_IMAGE090
represents the iteration error constant;
Figure 742644DEST_PATH_IMAGE091
Represents an intermediate computational variable representing the fitness of the particle.

进行变异操作具体是指：如果

Figure 702378DEST_PATH_IMAGE092
，则针对粒子i的个体最优位置进行更新，再随机产生一个符合正太分布
Figure 180764DEST_PATH_IMAGE093
的随机数
Figure 89814DEST_PATH_IMAGE094
，粒子i的个体最优位置的表达式如下：The mutation operation specifically refers to: if
Figure 702378DEST_PATH_IMAGE092
, then update the individual optimal position of particle i, and then randomly generate a random distribution that conforms to the normal distribution
Figure 180764DEST_PATH_IMAGE093
random number of
Figure 89814DEST_PATH_IMAGE094
, the expression of the individual optimal position of particle i is as follows:

Figure 893822DEST_PATH_IMAGE095
Figure 893822DEST_PATH_IMAGE095

其中：

Figure 826006DEST_PATH_IMAGE096
表示粒子i的位置向量在第k次迭代时的个体最优位置的第j行数据；
Figure 622930DEST_PATH_IMAGE097
表示符合正太分布
Figure 386486DEST_PATH_IMAGE098
的随机数。in:
Figure 826006DEST_PATH_IMAGE096
The jth row of data representing the individual optimal position of the position vector of particle i at the kth iteration;
Figure 622930DEST_PATH_IMAGE097
Indicates that it fits the normal distribution
Figure 386486DEST_PATH_IMAGE098
of random numbers.

步骤5：针对粒子进行速度向量及位置向量更新。Step 5: Update the velocity vector and the position vector for the particle.

具体来讲，可以基于如下表达式对粒子i进行速度向量及位置向量更新：Specifically, the velocity vector and position vector of particle i can be updated based on the following expressions:

Figure 626975DEST_PATH_IMAGE099
Figure 626975DEST_PATH_IMAGE099

Figure 780876DEST_PATH_IMAGE100
Figure 780876DEST_PATH_IMAGE100

Figure 132223DEST_PATH_IMAGE101
Figure 132223DEST_PATH_IMAGE101

Figure 750286DEST_PATH_IMAGE102
Figure 750286DEST_PATH_IMAGE102

Figure 416803DEST_PATH_IMAGE103
Figure 416803DEST_PATH_IMAGE103

Figure 323579DEST_PATH_IMAGE104
Figure 323579DEST_PATH_IMAGE104

其中：in:

Figure 947458DEST_PATH_IMAGE105
表示粒子i的速度向量的第j行数据在第k+1次迭代的数值；
Figure 947458DEST_PATH_IMAGE105
The value of the jth row of data representing the velocity vector of particle i at the k+1th iteration;

Figure 888869DEST_PATH_IMAGE106
表示粒子i的速度向量的第j行数据在第k次迭代的数值；
Figure 888869DEST_PATH_IMAGE106
The value of the jth row of data representing the velocity vector of particle i at the kth iteration;

Figure 267898DEST_PATH_IMAGE107
表示第k次迭代的惯性参数；
Figure 267898DEST_PATH_IMAGE107
Represents the inertia parameter of the k-th iteration;

Figure 380079DEST_PATH_IMAGE108
及
Figure 73229DEST_PATH_IMAGE109
表示惯性参数的最大值常数参数及最小值常数参数，一般最大值取0.9，最小值取0.4；
Figure 380079DEST_PATH_IMAGE108
and
Figure 73229DEST_PATH_IMAGE109
Indicates the maximum value constant parameter and the minimum value constant parameter of the inertia parameter, generally the maximum value is 0.9, and the minimum value is 0.4;

Figure 56097DEST_PATH_IMAGE110
表示迭代次数；
Figure 56097DEST_PATH_IMAGE110
Indicates the number of iterations;

Figure 606027DEST_PATH_IMAGE111
表示最大迭代次数常数参数；
Figure 606027DEST_PATH_IMAGE111
Indicates the constant parameter of the maximum number of iterations;

Figure 487396DEST_PATH_IMAGE112
及
Figure 984236DEST_PATH_IMAGE113
表示学习因子参数；
Figure 487396DEST_PATH_IMAGE112
and
Figure 984236DEST_PATH_IMAGE113
represents the learning factor parameter;

Figure 634660DEST_PATH_IMAGE114
及
Figure 542442DEST_PATH_IMAGE115
表示学习因子初始值参数；
Figure 634660DEST_PATH_IMAGE114
and
Figure 542442DEST_PATH_IMAGE115
represents the initial value parameter of the learning factor;

Figure 645527DEST_PATH_IMAGE116
及
Figure 680480DEST_PATH_IMAGE117
表示学习因子终止值参数；
Figure 645527DEST_PATH_IMAGE116
and
Figure 680480DEST_PATH_IMAGE117
represents the learning factor termination value parameter;

Figure 450989DEST_PATH_IMAGE118
表示时间因子参数，
Figure 529673DEST_PATH_IMAGE119
及
Figure 385633DEST_PATH_IMAGE120
表示
Figure 224276DEST_PATH_IMAGE121
之间的随机数；
Figure 450989DEST_PATH_IMAGE118
represents the time factor parameter,
Figure 529673DEST_PATH_IMAGE119
and
Figure 385633DEST_PATH_IMAGE120
express
Figure 224276DEST_PATH_IMAGE121
random numbers between;

Figure 583713DEST_PATH_IMAGE122
表示粒子i的位置向量在第k次迭代时的个体最优位置的第j行数据；
Figure 583713DEST_PATH_IMAGE122
The jth row of data representing the individual optimal position of the position vector of particle i at the kth iteration;

Figure 98877DEST_PATH_IMAGE123
表示粒子群位置向量在第k次迭代时的全局最优位置的第j行数据；
Figure 98877DEST_PATH_IMAGE123
The jth row of data representing the global optimal position of the particle swarm position vector at the kth iteration;

Figure 176555DEST_PATH_IMAGE124
表示粒子i的位置向量的第j行数据在第k+1次迭代的数值；
Figure 176555DEST_PATH_IMAGE124
The value of the jth row of data representing the position vector of particle i at the k+1th iteration;

Figure 818889DEST_PATH_IMAGE125
表示粒子i的位置向量的第j行数据在第k次迭代的数值。
Figure 818889DEST_PATH_IMAGE125
The value of the jth row of data representing the position vector of particle i at the kth iteration.

步骤6：判断是否满足迭代结束条件，如果满足迭代结束条件则终止迭代，并求解得到粒子的位置向量解集合，如果不满足迭代结束条件跳转至执行步骤2、步骤3、步骤4以及步骤5，直到满足迭代结束条件或者达到最大迭代次数时，停止迭代。Step 6: Determine whether the iteration end condition is met. If the iteration end condition is met, the iteration is terminated, and the solution set of the particle's position vector is obtained by solving. If the iteration end condition is not met, jump to step 2, step 3, step 4 and step 5. , stop the iteration until the iteration end condition is met or the maximum number of iterations is reached.

粒子群且前为止搜索到的最优位置满足适应度函数的最小允许误差或者达到迭代最大次数

Figure 563991DEST_PATH_IMAGE126
，都会结束迭代。为使最终求解的结果更加多样性，迭代最大次数
Figure 453318DEST_PATH_IMAGE127
的数值可以取较大数值。Particle swarm and the optimal position searched so far satisfies the minimum allowable error of the fitness function or reaches the maximum number of iterations
Figure 563991DEST_PATH_IMAGE126
, will end the iteration. In order to make the final solution more diverse, the maximum number of iterations
Figure 453318DEST_PATH_IMAGE127
can take larger values.

具体而言，求解得到粒子的位置向量解集合

Figure 752713DEST_PATH_IMAGE128
，其表达式如下：Specifically, the solution set of the position vector of the particle is obtained by solving
Figure 752713DEST_PATH_IMAGE128
, whose expression is as follows:

Figure 182426DEST_PATH_IMAGE129
Figure 182426DEST_PATH_IMAGE129

Figure 250876DEST_PATH_IMAGE130
Figure 250876DEST_PATH_IMAGE130

Figure 858575DEST_PATH_IMAGE131
Figure 858575DEST_PATH_IMAGE131

Figure 910844DEST_PATH_IMAGE132
Figure 910844DEST_PATH_IMAGE132

Figure 161827DEST_PATH_IMAGE133
Figure 161827DEST_PATH_IMAGE133

其中：in:

Figure 615942DEST_PATH_IMAGE134
表示粒子的位置向量解集合；
Figure 615942DEST_PATH_IMAGE134
represents the position vector solution set of the particle;

Figure 128963DEST_PATH_IMAGE135
表示粒子j的位置向量解；
Figure 128963DEST_PATH_IMAGE135
represents the position vector solution of particle j;

Figure 668528DEST_PATH_IMAGE136
表示粒子j的位置向量第一行数值解；
Figure 668528DEST_PATH_IMAGE136
Represents the numerical solution of the first row of the position vector of particle j;

Figure 440044DEST_PATH_IMAGE137
表示粒子j的位置向量第二行数值解；
Figure 440044DEST_PATH_IMAGE137
represents the second row of the numerical solution of the position vector of particle j;

Figure 748666DEST_PATH_IMAGE138
表示粒子j的位置向量第三行数值解；
Figure 748666DEST_PATH_IMAGE138
Represents the numerical solution of the third row of the position vector of particle j;

Figure 963746DEST_PATH_IMAGE139
表示粒子j的位置向量对应的超阀值的概率密度函数的阀值参数；
Figure 963746DEST_PATH_IMAGE139
Threshold parameter representing the probability density function of the super-threshold corresponding to the position vector of particle j;

Figure 725029DEST_PATH_IMAGE140
表示粒子j的位置向量对应的超阀值的概率密度函数的形状参数；
Figure 725029DEST_PATH_IMAGE140
The shape parameter of the probability density function representing the super-threshold value corresponding to the position vector of particle j;

Figure 300236DEST_PATH_IMAGE141
表示粒子j的位置向量对应的超阀值的概率密度函数的尺寸参数。
Figure 300236DEST_PATH_IMAGE141
The size parameter of the probability density function representing the super-threshold value corresponding to the position vector of particle j.

步骤7：取所述位置向量解集合中阀值参数最大的粒子位置作为载荷信号超阀值的概率密度函数的求解结果。Step 7: Take the particle position with the largest threshold parameter in the position vector solution set as the solution result of the probability density function of the load signal exceeding the threshold.

取位置向量解集合

Figure 197785DEST_PATH_IMAGE142
中，阀值参数最大的粒子位置作为载荷信号超阀值的概率密度函数的参数，这些参数包括：阀值参数
Figure 318187DEST_PATH_IMAGE143
、形状参数
Figure 832345DEST_PATH_IMAGE144
、尺寸参数
Figure 945664DEST_PATH_IMAGE145
。Take the position vector solution set
Figure 197785DEST_PATH_IMAGE142
, the particle position with the largest threshold parameter is used as the parameter of the probability density function of the load signal exceeding the threshold. These parameters include: threshold parameter
Figure 318187DEST_PATH_IMAGE143
, shape parameters
Figure 832345DEST_PATH_IMAGE144
,Size parameters
Figure 945664DEST_PATH_IMAGE145
.

在根据对所述超阀值概率密度函数的求解结果进行时域载荷外推，得到所述车辆在全生命周期内的载荷信号数据，包括：Carrying out the time-domain load extrapolation according to the solution result of the probability density function of the over-threshold value, to obtain the load signal data of the vehicle in the whole life cycle, including:

从在公共道路的载荷信号数据中提取超过阀值参数的数据；针对所述超过阀值参数的数据，采用超阀值概率密度函数的求解结果重复进行N次操作，每次操作随机产生新的载荷信号数据进行替换原数据；将重复进行N次操作所生成的载荷信号数据进行首尾相连，得到外推N倍的时域载荷信号；将所述外推N倍的时域载荷信号作为所述车辆在全生命周期内的载荷信号数据。Extract the data exceeding the threshold parameter from the load signal data on the public road; for the data exceeding the threshold parameter, repeat the operation N times using the solution result of the probability density function exceeding the threshold, and each operation randomly generates a new The load signal data is replaced with the original data; the load signal data generated by repeating N times of operations are connected end to end to obtain an extrapolated N times time domain load signal; The load signal data of the vehicle in the whole life cycle.

具体来讲，针对采集的载荷信号数据为

Figure 963298DEST_PATH_IMAGE146
，提取其中超过阀值参数
Figure 520182DEST_PATH_IMAGE147
的数据；针对这些超过阀值参数
Figure 256056DEST_PATH_IMAGE148
的数据，采用超阀值概率密度函数
Figure 173066DEST_PATH_IMAGE149
随机产生数据替换原数据，将此过程重复N次（N为外推倍数），再将重复进行N次操作生成的载荷信号数据进行首尾相连，即得到外推N倍的时域载荷信号。求解的超阀值概率密度函数
Figure 45207DEST_PATH_IMAGE150
，其表达式如下：Specifically, the collected load signal data is:
Figure 963298DEST_PATH_IMAGE146
, extract the parameters which exceed the threshold
Figure 520182DEST_PATH_IMAGE147
data; for these over-threshold parameters
Figure 256056DEST_PATH_IMAGE148
data, using the over-threshold probability density function
Figure 173066DEST_PATH_IMAGE149
Randomly generate data to replace the original data, repeat this process N times (N is the extrapolation multiple), and then connect the payload signal data generated by repeating N times of operations to the end to obtain the extrapolated time-domain payload signal N times. Solved overthreshold probability density function
Figure 45207DEST_PATH_IMAGE150
, whose expression is as follows:

Figure 772991DEST_PATH_IMAGE151
Figure 772991DEST_PATH_IMAGE151

Figure 996162DEST_PATH_IMAGE152
Figure 996162DEST_PATH_IMAGE152

其中：

Figure 451283DEST_PATH_IMAGE153
表示超阀值，即大于阀值参数u的载荷信号数据；
Figure 240248DEST_PATH_IMAGE154
表示采集的载荷信号数据；
Figure 342196DEST_PATH_IMAGE155
表示阀值参数；
Figure 787084DEST_PATH_IMAGE156
表示形状参数；
Figure 593366DEST_PATH_IMAGE157
表示尺寸参数。in:
Figure 451283DEST_PATH_IMAGE153
Indicates the over-threshold, that is, the load signal data greater than the threshold parameter u;
Figure 240248DEST_PATH_IMAGE154
Indicates the collected load signal data;
Figure 342196DEST_PATH_IMAGE155
Represents the threshold parameter;
Figure 787084DEST_PATH_IMAGE156
represents the shape parameter;
Figure 593366DEST_PATH_IMAGE157
Indicates the size parameter.

本发明实施例由于应用粒子群寻优求解算法，从而提高了求解精度，求解得到的广义帕累托分布概率密度函数能够满足与采集数据的误差精度，并且求解的是解空间的所有解集合，解决以往求解阀值参数不能得到解空间中的最优解问题；适用于任何时域载荷信号的外推；且本发明实施例流程化程度较高，基于软件编程可以实现时域载荷信号外推自动化处理工作。Due to the application of the particle swarm optimization solution algorithm in the embodiment of the present invention, the solution accuracy is improved, the obtained generalized Pareto distribution probability density function can satisfy the error accuracy with the collected data, and the solution is all solution sets in the solution space, Solve the problem that the optimal solution in the solution space cannot be obtained by solving the threshold parameters in the past; it is suitable for the extrapolation of any time-domain load signal; and the embodiment of the present invention has a high degree of process flow, and can realize the time-domain load signal extrapolation based on software programming Automate processing work.

基于同一发明构思，本发明实施例提供了一种基于粒子群算法的汽车时域载荷外推装置，参考图2所示，该装置包括：Based on the same inventive concept, an embodiment of the present invention provides a vehicle time-domain load extrapolation device based on particle swarm algorithm. Referring to FIG. 2 , the device includes:

数据采集单元201，用于采集车辆在公共道路的载荷信号数据；The data collection unit 201 is used to collect the load signal data of the vehicle on the public road;

模型建立单元202，用于根据所述在公共道路的载荷信号数据建立时域载荷外推计算模型，其中，所述时域载荷外推计算模型包括超阀值概率分布函数和超阀值概率密度函数；A model establishment unit 202, configured to establish a time-domain load extrapolation calculation model according to the load signal data on the public road, wherein the time-domain load extrapolation calculation model includes an over-threshold probability distribution function and an over-threshold probability density function;

模型求解单元203，用于采用粒子群算法对所述超阀值概率密度函数进行求解；A model solving unit 203, configured to solve the super-threshold probability density function by using a particle swarm algorithm;

外推执行单元204，用于根据对所述超阀值概率密度函数的求解结果进行时域载荷外推，得到所述车辆在全生命周期内的载荷信号数据。The extrapolation execution unit 204 is configured to perform time-domain load extrapolation according to the solution result of the over-threshold probability density function to obtain the load signal data of the vehicle in the whole life cycle.

在一些实施方式下，数据采集单元201，包括：In some embodiments, the data collection unit 201 includes:

布置子单元，用于在所述车辆上布置轮心六分力传感器和三向加速度传感器、在所述车辆的传动轴上布置非接触式传动轴扭矩传感器、以及在所述车辆的悬架杆件上布置杆件力传感器；arranging subunits for arranging wheel center six-component force sensors and three-way acceleration sensors on the vehicle, arranging a non-contact drive shaft torque sensor on the drive shaft of the vehicle, and a suspension rod on the vehicle The rod force sensor is arranged on the piece;

规划子单元，用于规划在公共道路行驶的合计里程及在每种公共道路的行驶路线，其中，所述合计里程中，城市道路的里程占比为

Figure 954946DEST_PATH_IMAGE158
，高速道路的里程占比为
Figure 758954DEST_PATH_IMAGE159
，郊区道路的里程占比为
Figure 956717DEST_PATH_IMAGE160
，国省道道路的里程占比为
Figure 504373DEST_PATH_IMAGE161
，坏路道路的里程占比为
Figure 454880DEST_PATH_IMAGE162
，山区道路的里程占比为
Figure 429789DEST_PATH_IMAGE163
，其中：
Figure 911586DEST_PATH_IMAGE164
；The planning sub-unit is used to plan the total mileage on public roads and the driving route on each public road, wherein, in the total mileage, the proportion of urban road mileage is
Figure 954946DEST_PATH_IMAGE158
, the mileage ratio of expressways is
Figure 758954DEST_PATH_IMAGE159
, the proportion of mileage of suburban roads is
Figure 956717DEST_PATH_IMAGE160
, the proportion of mileage of national and provincial roads is
Figure 504373DEST_PATH_IMAGE161
, the mileage proportion of bad roads is
Figure 454880DEST_PATH_IMAGE162
, the mileage ratio of mountain roads is
Figure 429789DEST_PATH_IMAGE163
,in:
Figure 911586DEST_PATH_IMAGE164
;

采集子单元，用于在所述车辆行驶于所述公共道路过程中，通过如下任意一种方式采集在公共道路的载荷信号数据：所述车辆上布置的轮心六分力传感器采集轮心六分力信号、通过适应于车辆上布置的三向加速度传感器采集轮心三向加速度信号、通过所述车辆的传动轴上布置的非接触式传动轴扭矩传感器采集传动轴扭矩信号、以及通过所述车辆的悬架杆件上布置杆件力传感器采集杆件力信号；The collection subunit is used to collect the load signal data on the public road in any one of the following ways when the vehicle is driving on the public road: the wheel center six-component force sensor arranged on the vehicle collects the wheel center six-component force sensor. component force signal, collecting the three-way acceleration signal of the wheel center through a three-way acceleration sensor arranged on the vehicle, collecting the transmission shaft torque signal through the non-contact transmission shaft torque sensor arranged on the transmission shaft of the vehicle, and collecting the transmission shaft torque signal through the A rod force sensor is arranged on the suspension rod of the vehicle to collect the rod force signal;

处理子单元，用于对所述在公共道路的载荷信号数据进行检查和清洗。The processing sub-unit is used for checking and cleaning the load signal data on the public road.

在一些实施方式下，数据采集单元201，包括：In some embodiments, the data collection unit 201 includes:

设定子单元，用于设定所述车辆在全生命周期内的目标里程；a setting subunit, used for setting the target mileage of the vehicle in the whole life cycle;

倍数确定子单元，用于根据所述目标里程和所述车辆在多种公共道路上行驶的合计里程，确定对所述在公共道路的载荷信号进行外推的倍数N。The multiple determination subunit is configured to determine the multiple N for extrapolating the load signal on the public road according to the target distance and the total distance traveled by the vehicle on various public roads.

在一些实施方式下，模型建立单元202具体用于：定义所述在公共道路的载荷信号数据；定义阀值参数、形状参数以及尺寸参数；定义大于所述阀值参数的载荷信号数据为超阀值；根据所述公共道路的载荷信号数据、所述阀值参数、所述形状参数以及所述尺寸参数，建立所述超阀值概率分布函数和所述超阀值概率密度函数。In some embodiments, the model building unit 202 is specifically configured to: define the load signal data on the public road; define a threshold parameter, a shape parameter and a size parameter; define a load signal data larger than the threshold parameter as an over-valve value; establishing the super-threshold probability distribution function and the super-threshold probability density function according to the load signal data of the public road, the threshold parameter, the shape parameter and the size parameter.

在一些实施方式下，模型求解单元203具体用于执行如下步骤1~7：In some embodiments, the model solving unit 203 is specifically configured to perform the following steps 1-7:

步骤1：均匀随机产生粒子构成粒子群集合，其中，所述粒子群集合中每一个粒子包括位置向量及速度向量；Step 1: uniformly and randomly generating particles to form a particle swarm set, wherein each particle in the particle swarm set includes a position vector and a velocity vector;

步骤2：计算所述粒子群集合中每一个粒子的适应度函数；Step 2: Calculate the fitness function of each particle in the particle swarm set;

步骤3：定义个体最优粒子位置及全局最优粒子位置；Step 3: Define the individual optimal particle position and the global optimal particle position;

步骤4：针对所述粒子群集合所有粒子进行变异操作；Step 4: perform mutation operation on all particles in the particle swarm set;

步骤5：针对粒子进行速度向量及位置向量更新；Step 5: Update the velocity vector and position vector for the particle;

步骤6：判断是否满足迭代结束条件，如果满足则终止迭代，并求解得到粒子的位置向量解集合，如果不满足则跳转至执行所述步骤2、步骤3、步骤4以及步骤5，直到足迭代结束条件或者达到最大迭代次数。Step 6: Judging whether the iteration end condition is met, if so, terminate the iteration, and solve the set of position vector solutions of the particles. The iteration end condition or the maximum number of iterations is reached.

步骤7：取所述位置向量解集合中阀值参数最大的粒子位置作为载荷信号超阀值的概率密度函数的求解结果。Step 7: Take the particle position with the largest threshold parameter in the position vector solution set as the solution result of the probability density function of the load signal exceeding the threshold.

在一些实施方式下，所述个体最优粒子位置定义为针对个体粒子在迭代过程中适应度数值最大时对应的粒子位置；所述全局最优粒子位置定义为针对粒子群在迭代过程中适应度数值最大对应的粒子位置。In some embodiments, the individual optimal particle position is defined as the particle position corresponding to the individual particle in the iterative process when the fitness value is the largest; the global optimal particle position is defined as the fitness of the particle swarm in the iterative process The particle position corresponding to the largest value.

在一些实施方式下，外推执行单元204具体用于：从所述在公共道路的载荷信号数据中，提取超过阀值参数的数据；针对所述超过阀值参数的数据，采用超阀值概率密度函数的求解结果重复进行N次操作，每次操作随机产生新的载荷信号数据进行替换原数据；将重复进行N次操作所生成的载荷信号数据进行首尾相连，得到外推N倍的时域载荷信号；将所述外推N倍的时域载荷信号作为所述车辆在全生命周期内的载荷信号数据。In some embodiments, the extrapolation execution unit 204 is specifically configured to: extract data exceeding the threshold parameter from the load signal data on the public road; for the data exceeding the threshold parameter, adopt the probability of exceeding the threshold The solution result of the density function is repeated for N times, and new load signal data is randomly generated for each operation to replace the original data; the load signal data generated by repeated N times of operations are connected end to end, and the time domain of extrapolation N times is obtained. Load signal; take the time-domain load signal extrapolated by N times as the load signal data of the vehicle in the whole life cycle.

上述基于粒子群算法的汽车时域载荷外推装置为用于执行前述基于粒子群算法的汽车时域载荷外推方法的装置，该装置的更多实施细节可以参考前述基于粒子群算法的汽车时域载荷外推方法实施例，在此不再赘述。The above-mentioned vehicle time-domain load extrapolation device based on particle swarm algorithm is a device for executing the aforementioned method for vehicle time-domain load extrapolation based on particle swarm optimization algorithm. Embodiments of the domain load extrapolation method are not repeated here.

基于同一发明构思，本发明实施例提供了一种基于粒子群算法进行汽车时域载荷外推的电子设备，如图3所示，该电子设备包括存储器304、处理器302及存储在存储器304上并可在处理器302上运行的计算机程序，所述处理器302执行所述程序时实现前述基于粒子群算法的汽车时域载荷外推方法。Based on the same inventive concept, an embodiment of the present invention provides an electronic device for extrapolating vehicle time-domain load based on particle swarm algorithm. As shown in FIG. And a computer program that can be run on the processor 302, when the processor 302 executes the program, the aforementioned method for extrapolating the vehicle time domain load based on the particle swarm algorithm is implemented.

其中，在图3中，总线300可以包括任意数量的互联的总线和桥，总线300将包括由处理器302代表的一个或多个处理器和存储器304代表的存储器的各种电路链接在一起。总线300还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路链接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口306在总线300和接收器301和发送器303之间提供接口。接收器301和发送器303可以是同一个元件，即收发机，提供用于在传输介质上与各种其他装置通信的单元。处理器302负责管理总线300和通常的处理，而存储器304可以被用于存储处理器302在执行操作时所使用的数据。3, the bus 300 may include any number of interconnected buses and bridges that link together various circuits including one or more processors, represented by processors 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, etc., which are well known in the art and thus will not be described further herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303 . The receiver 301 and the transmitter 303 may be the same element, a transceiver, providing a means for communicating with various other devices over the transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 in performing operations.

基于同一发明构思，本发明实施例提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现前述基于粒子群算法的汽车时域载荷外推方法。Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the aforementioned particle swarm algorithm-based vehicle time-domain load extrapolation method.

尽管已描述了本发明的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。Although the preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be construed to include the preferred embodiment and all changes and modifications that fall within the scope of the present invention.

显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (10)

1. An automobile time domain load extrapolation method based on a particle swarm algorithm is characterized by comprising the following steps:
collecting load signal data of a vehicle on a public road;
establishing a time domain load extrapolation calculation model according to the load signal data of the public road, wherein the time domain load extrapolation calculation model comprises a super-threshold probability distribution function and a super-threshold probability density function;
And solving the super-threshold probability density function by adopting a particle swarm algorithm, and carrying out time-domain load extrapolation according to the solved result of the super-threshold probability density function to obtain load signal data of the vehicle in the whole life cycle.
2. The method of claim 1, wherein collecting load signal data of a vehicle on a public road comprises:
arranging a wheel center six-component force sensor and a three-way acceleration sensor on the vehicle, arranging a non-contact transmission shaft torque sensor on a transmission shaft of the vehicle, and arranging a rod force sensor on a suspension rod of the vehicle;
planning a total mileage for driving on public roads and a driving route on each public road, wherein the mileage of the urban road accounts for the total mileage
Figure 206811DEST_PATH_IMAGE001
The mileage of the expressway is expressed as
Figure 301806DEST_PATH_IMAGE002
The mileage of suburb roads is
Figure 688925DEST_PATH_IMAGE003
The mileage of the national provincial road is in proportion
Figure 527568DEST_PATH_IMAGE004
The mileage of the bad road is
Figure 136272DEST_PATH_IMAGE005
The mileage of the mountain road is
Figure 402169DEST_PATH_IMAGE006
Wherein:
Figure 479846DEST_PATH_IMAGE007
；
during the process that the vehicle runs on the public road, load signal data on the public road are collected in any one of the following modes: the method comprises the following steps that a wheel center six-component force sensor arranged on a vehicle acquires wheel center six-component force signals, a three-way acceleration sensor adapted to the arrangement on the vehicle acquires wheel center three-way acceleration signals, a non-contact transmission shaft torque sensor arranged on a transmission shaft of the vehicle acquires transmission shaft torque signals, and a rod force sensor arranged on a suspension rod of the vehicle acquires rod force signals;
And checking and cleaning the load signal data on the public road.
3. The method of claim 2, further comprising:
setting a target mileage of the vehicle in a full life cycle;
and determining the multiple N for extrapolating the load signals of the public roads according to the target mileage and the total mileage of the vehicles on various public roads.
4. The method of claim 3, wherein said building a time-domain load extrapolation computation model based on said load signal data on the public road comprises:
defining the load signal data on the public road;
defining a threshold parameter, a shape parameter and a size parameter;
defining the load signal data larger than the threshold parameter as a super threshold;
and establishing the above-threshold probability distribution function and the above-threshold probability density function according to the load signal data of the public road, the threshold parameter, the shape parameter and the size parameter.
5. The method of claim 3, wherein solving the above-threshold probability density function using a particle swarm algorithm comprises:
step 1: uniformly and randomly generating particles to form a particle swarm, wherein each particle in the particle swarm comprises a position vector and a velocity vector;
And 2, step: calculating a fitness function of each particle in the particle swarm set;
and 3, step 3: defining an individual optimal particle position and a global optimal particle position;
and 4, step 4: performing mutation operation on all particles in the particle swarm;
and 5: updating a velocity vector and a position vector for the particles;
and 6: judging whether an iteration ending condition is met, if so, terminating iteration, and solving to obtain a position vector solution set of the particles, and if not, skipping to executing the step 2, the step 3, the step 4 and the step 5 until the iteration ending condition is met or the maximum iteration frequency is reached;
and 7: and taking the position of the particle with the maximum threshold parameter in the position vector solution set as a solving result of the probability density function of the load signal exceeding the threshold.
6. The method of claim 5, wherein:
the individual optimal particle position is defined as the corresponding particle position when the fitness value of the individual particle is maximum in the iteration process;
the global optimal particle position is defined as the particle position corresponding to the maximum fitness value in the iterative process of the particle swarm.
7. The method of claim 3, wherein said time-domain load extrapolation from the solution to the above-threshold probability density function to obtain load signal data of the vehicle over a full life cycle comprises:
Extracting data exceeding a threshold parameter from the load signal data on the public road;
aiming at the data exceeding the threshold parameter, repeating the operation for N times by adopting the solving result of the probability density function exceeding the threshold, and randomly generating new load signal data for replacing the original data in each operation;
carrying out end-to-end connection on load signal data generated by repeating the operations for N times to obtain an extrapolated N-times time domain load signal;
and taking the time domain load signal of which the extrapolation time is N times as the load signal data of the vehicle in the full life cycle.
8. The utility model provides an automobile time domain load extrapolation device based on particle swarm algorithm which characterized in that includes:
the data acquisition unit is used for acquiring load signal data of the vehicle on a public road;
the model establishing unit is used for establishing a time domain load extrapolation calculation model according to the load signal data of the public road, wherein the time domain load extrapolation calculation model comprises a super-threshold probability distribution function and a super-threshold probability density function;
the model solving unit is used for solving the probability density function exceeding the threshold value by adopting a particle swarm algorithm;
and the extrapolation execution unit is used for carrying out time-domain load extrapolation according to the solving result of the super-threshold probability density function to obtain load signal data of the vehicle in the full life cycle.
9. An electronic device for carrying out automobile time domain load extrapolation based on a particle swarm algorithm is characterized by comprising the following components: a memory, a processor, and code stored on the memory and executable on the processor, the processor implementing the method of any of claims 1-7 when executing the code.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
CN202210665472.3A 2022-06-14 2022-06-14 Automobile time domain load extrapolation method and device based on particle swarm optimization Pending CN114757058A (en)

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