A method of analyzing driving data of a heavy truck

Abstract

The application relates to a driving data analysis method of a heavy truck. The method comprises the following steps: acquiring road surface parameters and vehicle parameters of a target vehicle in a preset time period; inputting the vehicle parameters into a preset vehicle force model to obtain a target vehicle force model of the target vehicle; for any preset time point in the preset time period, determining a force condition of the target vehicle according to the road surface parameters corresponding to the preset time point and the target vehicle force model, and determining a displacement of a driver of the target vehicle corresponding to the road surface parameters according to the force condition of the target vehicle; generating a displacement curve of the driver of the target vehicle in the preset time period according to the displacements corresponding to the preset time points, and determining driving data of the target vehicle according to the displacement curve. The method can improve the analysis accuracy of vehicle data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a driving data analysis method of a heavy truck. BACKGROUND

[0002] The heavy truck has problems such as large self weight, large load variation, complex working conditions, and long working time. The ride comfort of the heavy truck is directly related to the running characteristics of the vehicle, such as running efficiency, fuel economy, safety, and driver comfort. Therefore, it is of great significance to put forward a simple and effective analysis method of the running characteristics of the heavy truck to optimize the ride comfort of the heavy truck and improve the overall performance of the vehicle.

[0003] The prior art analyzes the running characteristics of the heavy truck through a running characteristic test or a simulation model analysis method. The running characteristic test is mostly carried out on a test field after the physical prototype is completed. The test conditions are greatly affected by subjective and objective factors, and the test process cannot be reproduced. The test cycle is long, the cost is high, and the risk is great. At present, there is no simulation model analysis method for the running characteristics of the heavy truck. Generally, a general automobile model is used for simulation model analysis, which leads to poor accuracy of the analysis results. SUMMARY

[0004] Therefore, it is necessary to provide a driving data analysis method of a heavy truck to solve the above technical problems.

[0005] In a first aspect, the present application provides a driving data analysis method of a heavy truck. The method comprises:

[0006] obtaining road surface parameters and vehicle parameters of a target vehicle in a preset time period;

[0007] inputting the vehicle parameters into a preset vehicle force model to obtain a target vehicle force model for the target vehicle;

[0008] for any preset time point in the preset time period, determining a force condition of the target vehicle according to the road surface parameters corresponding to the preset time point and the target vehicle force model, and determining a displacement of a driver of the target vehicle corresponding to the road surface parameters according to the force condition of the target vehicle;

[0009] generating a displacement curve of the driver of the target vehicle in the preset time period according to the displacements corresponding to each of the preset time points, and determining driving data of the target vehicle according to the displacement curve.

[0010] In one of the embodiments, the force condition of the target vehicle is determined according to the road surface parameters corresponding to the preset time point and the force model of the target vehicle, including:

[0011] For any road surface contact point of the target vehicle, the vibration displacement corresponding to the road surface contact point is determined according to the preset time point and the road surface parameters corresponding to the road surface contact point.

[0012] The force condition of the target vehicle is determined according to the vibration displacement corresponding to each road surface contact point and the force model of the target vehicle.

[0013] In one of the embodiments, the vibration displacement corresponding to the road surface contact point is determined according to the road surface parameters corresponding to the preset time point, including:

[0014] The phase of the road noise signal corresponding to the road surface contact point at the preset time point is taken as the vibration displacement corresponding to the road surface contact point.

[0015] The road noise signal is generated according to the road surface parameters in the preset time period and the preset driving speed of the force model of the target vehicle.

[0016] In one of the embodiments, the force model of the target vehicle is composed of at least one force constraint function, and each force constraint function is constructed according to each preset force constraint function and the vehicle parameters.

[0017] In one of the embodiments, the preset force constraint function includes a driver pitch vibration function and a driver force function.

[0018] The driver pitch vibration function is used to represent the relationship between the distance between the driver and the vehicle center of mass, the rotational inertia of the driver, the displacement of the driver and the displacement of the vehicle center of mass.

[0019] The driver force function is used to represent the relationship between the mass of the driver, the seat parameters of the driver seat, the displacement of the driver and the displacement of the vehicle center of mass.

[0020] In one of the embodiments, the driving data of the target vehicle is determined according to the displacement curve, including:

[0021] The driver vibration acceleration curve is determined according to the displacement curve.

[0022] The driving data of the target vehicle is determined according to the driver vibration acceleration curve and the preset weighting function.

[0023] In one of the embodiments, after the driving data of the target vehicle is determined according to the displacement curve, the method further comprises:

[0024] determining a plurality of target seat parameters according to the seat parameter in the vehicle parameter;

[0025] for any of the target seat parameters, generating a target vehicle force model for the target seat parameter based on the target seat parameter and the vehicle parameter, and jumping to the step of determining the force condition of the target vehicle force model according to the road surface parameter corresponding to the preset time point and the target vehicle force model for any of the preset time points in the preset time period until a preset condition is met;

[0026] generating a relationship curve between each of the target seat parameters and the driving data according to the driving data corresponding to each of the target seat parameters.

[0027] In a second aspect, the present application also provides a driving data analysis device for a heavy truck. The device comprises:

[0028] an acquisition module configured to acquire road surface parameters in a preset time period and vehicle parameters of a target vehicle;

[0029] an input module configured to input the vehicle parameters into a preset vehicle force model to obtain a target vehicle force model for the target vehicle;

[0030] a first determination module configured to, for any of the preset time points in the preset time period, determine a force condition of the target vehicle according to the road surface parameter corresponding to the preset time point and the target vehicle force model, and determine a displacement of a driver of the target vehicle corresponding to the road surface parameter according to the force condition of the target vehicle;

[0031] a first generation module configured to generate a displacement curve of the driver of the target vehicle in the preset time period according to the displacement corresponding to each of the preset time points, and determine driving data of the target vehicle according to the displacement curve.

[0032] In one of the embodiments, the first determination module is further configured to:

[0033] for any of the road surface contact points of the target vehicle, determine a vibration displacement corresponding to the road surface contact point according to the preset time point and the road surface parameter corresponding to the road surface contact point;

[0034] determine a force condition of the target vehicle according to the vibration displacement corresponding to each of the road surface contact points and the target vehicle force model.

[0035] In one of the embodiments, the first determining module is further configured to:

[0036] a phase of the road surface noise signal corresponding to the road surface contact point at the preset time point as a vibration displacement corresponding to the road surface contact point;

[0037] The road surface noise signal is generated according to a road surface parameter in the preset time period and a preset driving speed of the target vehicle force model.

[0038] In one of the embodiments, the target vehicle force model is composed of at least one force constraint function, and each force constraint function is constructed according to each preset force constraint function and the vehicle parameter.

[0039] In one of the embodiments, the preset force constraint function includes a driver pitch vibration function and a driver force function.

[0040] The driver pitch vibration function is used to represent the relationship between the distance between the driver and the vehicle center of mass, the rotational inertia of the driver, the displacement of the driver and the displacement of the vehicle center of mass.

[0041] The driver force function is used to represent the relationship between the mass of the driver, the seat parameters of the driver seat, the displacement of the driver and the displacement of the vehicle center of mass.

[0042] In one of the embodiments, the first generating module is further configured to:

[0043] According to the displacement curve, a driver vibration acceleration curve is determined.

[0044] According to the driver vibration acceleration curve and a preset weighting function, driving data of the target vehicle is determined.

[0045] In one of the embodiments, the device further includes:

[0046] A second determining module is configured to determine a plurality of target seat parameters according to seat parameters in the vehicle parameters.

[0047] A processing module is configured to, for any target seat parameter, generate a target vehicle force model for the target seat parameter based on the target seat parameter and the vehicle parameter, and jump to the step of determining the force condition of the target vehicle force model according to the road surface parameter corresponding to the preset time point and the target vehicle force model for any preset time point in the preset time period until a preset condition is met.

[0048] The second generating module is configured to generate a relationship curve between each target seat parameter and the driving data according to the driving data corresponding to each target seat parameter.

[0049] In a third aspect, the present application provides a computer device. The computer device comprises a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement any of the above methods.

[0050] In a fourth aspect, the present application provides a computer readable storage medium. The computer readable storage medium stores a computer program. The computer program is executed by a processor to implement any of the above methods.

[0051] In a fifth aspect, the present application provides a computer program product. The computer program product comprises a computer program. The computer program is executed by a processor to implement any of the above methods.

[0052] The driving data analysis method of the heavy truck comprises the following steps: constructing a target vehicle force model for a target vehicle according to vehicle parameters and a preset vehicle force model; performing force analysis on a driver of the target vehicle according to road surface parameters at a preset time point and the target vehicle force model, to obtain displacement of the driver of the target vehicle; and obtaining driving data of the target vehicle according to a displacement curve of the driver of the target vehicle. The preset vehicle force model is constructed for the heavy truck, and when used, the vehicle parameters to be analyzed are input into the preset vehicle force model, and the obtained target vehicle force model is subjected to accurate force analysis, to calculate the displacement of the driver of the target vehicle. Then, the driving data of the target vehicle is generated according to the calculated displacement. The displacement of the driver is calculated through force analysis, and the accuracy of the obtained displacement is better than that of the data obtained through simulation experiments, so that the driving data analysis accuracy of the target vehicle can be further improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 FIG. 1 is a flowchart of a driving data analysis method of a heavy truck in an embodiment;

[0054] Figure 2 FIG. 2 is a schematic diagram of a preset vehicle force model in an embodiment;

[0055] Figure 3 FIG. 3 is a flowchart of step 106 in an embodiment;

[0056] Figure 4 FIG. 4 is a schematic diagram of a driver displacement curve in an embodiment;

[0057] Figure 5 FIG. 5 is a schematic diagram of a driver acceleration curve in an embodiment;

[0058] Figure 6 a flowchart of step 108 in one embodiment;

[0059] Figure 7 a curve diagram of the relationship between vehicle data and target seat parameters in one embodiment;

[0060] Figure 8 a structural block diagram of a driving data analysis device of a heavy truck in one embodiment;

[0061] Figure 9 an internal structure diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0063] In one embodiment, as shown in Figure 1 a driving data analysis method of a heavy truck is provided. The present embodiment takes the method applied to a server as an example. It should be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is realized through the interaction of the terminal and the server. In the present embodiment, the method includes the following steps:

[0064] Step 102, acquiring road surface parameters and vehicle parameters of a target vehicle in a preset time period.

[0065] In the present embodiment, the target vehicle is a vehicle that needs to be analyzed for running characteristics. The vehicle parameters of the target vehicle correspond to the parameters required by a preset vehicle force model. The parameters required by the preset vehicle force model can be parameters related to the force calculation of each part of the target vehicle, such as vehicle mass, number of vehicle wheels, vehicle wheel track, tire rigidity, tire damping coefficient, etc.

[0066] The road surface parameters are determined according to the road surface to which the running characteristic analysis is directed, and are used to characterize the unevenness of the road surface. In one example, a road surface unevenness function corresponding to the target road surface can be constructed, and the road surface unevenness function is taken as the road surface parameter. The road surface unevenness function characterizes the change trend of the road surface height along the road surface. In another example, road surface unevenness functions corresponding to the left and right wheels of the target vehicle can be constructed respectively. When analyzing the force condition of the target vehicle force model, the left wheel corresponding road surface unevenness function is used for the left wheel of the target vehicle, and the right wheel corresponding road surface unevenness function is used for the right wheel of the target vehicle, so as to simulate the case of poor road conditions.

[0067] The preset time period can be set by a person skilled in the art according to the actual need of the running characteristic analysis time length, for example, set to 10 seconds, 1 minute, etc. The road surface parameter in the preset time period refers to the road surface parameter corresponding to the road surface on which the target vehicle travels in the preset time period.

[0068] In step 104, the vehicle parameters are input into the preset vehicle force model to obtain the target vehicle force model for the target vehicle.

[0069] In the embodiments of the present application, the preset vehicle force model is a vehicle dynamics model, which is used to describe the relationship that should be met between the force, kinetic energy and momentum of each part of the vehicle. According to the principle of dynamics, each equation that should be met by the vehicle in motion can be listed, and the set of equations is taken as the preset vehicle force model. By inputting the vehicle parameters into the preset vehicle force model, each equation that should be met by the target vehicle in motion can be obtained, that is, the target vehicle force model.

[0070] In step 106, for any preset time point in the preset time period, the force condition of the target vehicle is determined according to the road surface parameter corresponding to the preset time point and the target vehicle force model, and the displacement of the target vehicle driver corresponding to the road surface parameter is determined according to the force condition of the target vehicle.

[0071] In the embodiments of the present application, at a specific preset time point in the preset time period, the target vehicle is subjected to force analysis according to the target vehicle force model and the road surface parameter, the force condition of the target vehicle is obtained, and the displacement of the target vehicle driver at the preset time point is determined according to the force condition of the target vehicle. The displacement here refers to the distance between the driver and the reference height, which is a pre-set horizontal plane and is irrelevant to the height of the road surface.

[0072] The displacement of the target vehicle caused by the road surface parameter can be calculated according to the road surface parameter, and then the force condition of the target vehicle is calculated according to the displacement. Taking the road surface unevenness function as an example, the position of the road surface where the target vehicle travels to at the preset time point can be calculated according to the speed of the target vehicle, the preset time point and the road surface unevenness function, and the value of the road surface unevenness function at the position is taken as the displacement of the target vehicle caused by the road surface parameter.

[0073] The force condition of the target vehicle refers to all external forces acting on the target vehicle when the target vehicle is analyzed as a whole. In order to facilitate subsequent calculation, for the external forces whose exact size can be calculated according to the vehicle parameters, these external forces are represented as numerical values; for the external forces whose size cannot be calculated according to the vehicle parameters, these external forces are represented by the displacement of a part (or multiple parts) of the target vehicle, and one or more vehicle parameters and one or more road surface parameters.

[0074] For example, for gravity, since the gravity that the target vehicle receives can be directly calculated according to the mass of the target vehicle, the gravity that the target vehicle receives can be represented as a numerical value. For example, the mass of the target vehicle is 50 tons, and according to the formula of gravity, the gravity that the target vehicle receives can be calculated to be about 490 kilo-newtons.

[0075] For example, for the ground force that the target vehicle receives, since the ground force that the target vehicle receives cannot be directly calculated, according to Newton's second law, the relationship between the ground force that the target vehicle receives and the displacement of the target vehicle in the vertical direction can be determined, and the ground force that the target vehicle receives can be represented as an expression related to the displacement of the target vehicle in the vertical direction. Newton's second law states that the acceleration of an object is directly proportional to the size of the force applied to the object and inversely proportional to the mass of the object, that is, F = ma. And the acceleration of the object can be obtained by twice differentiating the displacement of the object with respect to time, that is, Z represents the displacement of the object. According to the above principle, the total force that the target vehicle receives in the vertical direction can be represented as the product of the mass of the target vehicle and the second derivative of the displacement of the target vehicle in the vertical direction. The ground force that the target vehicle receives can be obtained according to the difference between the total force that the target vehicle receives in the vertical direction and the gravity that the target vehicle receives.

[0076] After obtaining the force condition of the target vehicle, the force conditions of each part inside the target vehicle can be analyzed to obtain the displacement of the vehicle driver corresponding to the road surface parameters.

[0077] In one embodiment, a mass-spring model of various types of vehicles is constructed, the displacement of the vehicle driver corresponding to the force condition of each type of vehicle is obtained according to simulation experiments, and for each type of vehicle, a relationship function between the force condition of the vehicle and the displacement of the vehicle driver is fitted. The relationship function corresponding to the vehicle type of the target vehicle is selected, and the force condition of the target vehicle is input into the relationship function to obtain the displacement of the target vehicle driver.

[0078] In another embodiment, the target vehicle force model is composed of at least one force constraint function, and each force constraint function is constructed according to each preset force constraint function and the vehicle parameters. At least one of the preset force constraint functions is related to the displacement of the vehicle driver. By solving each force constraint function, the displacement of the target vehicle driver is obtained.

[0079] The example is described in detail as follows. Each preset force constraint function constitutes a preset vehicle force model. The preset vehicle force model as a whole is used to describe the relationship between the force conditions of each part of the vehicle, for example, the relationship between the ground force received by the four tires of the vehicle and the ground force received by the whole vehicle, and the relationship between the force received by the driver of the vehicle through the seat of the vehicle and the ground force received by the whole vehicle. Each preset force constraint function is constructed according to the principle of dynamics.

[0080] The preset force constraint function is exemplarily described according to the construction of the second law of Newton. A set of equations satisfying the second law of Newton can be constructed for the driver of the vehicle and the vehicle (see equation (I)):

[0081]

[0082] wherein m4 is the mass of the driver of the vehicle, Z4 is the displacement of the driver of the vehicle, is the second derivative of the displacement of the driver of the vehicle, that is, the acceleration of the driver of the vehicle in the vertical direction. F1 is the total force received by the driver of the vehicle, which can be regarded as the force exerted by the seat of the vehicle on the driver of the vehicle. m3 is the mass of the vehicle, and Z3 is the displacement of the mass center of the vehicle, is the second derivative of the displacement of the mass center of the vehicle, that is, the acceleration of the vehicle in the vertical direction. F2 is the total ground force received by the vehicle, and g is the acceleration of gravity.

[0083] The ground force received by the vehicle and the force exerted by the seat of the vehicle on the driver of the vehicle can be calculated according to the relationship between the stiffness of the object, the deformation of the object and the force received by the object (see equation (II)):

[0084]

[0085] wherein K3 is the stiffness of the vehicle, which can be set according to the material of the vehicle, q3 is the road height represented by the road parameter, and K5 is the stiffness of the seat of the vehicle, which can be set according to the material of the seat of the vehicle, etc.

[0086] The above equation (I) and equation (II) are preset force constraint functions. The vehicle parameters of the target vehicle are substituted into the above equation (I) and equation (II) to obtain the force constraint function, and the force constraint function is solved to obtain Z4, that is, the displacement of the driver of the vehicle.

[0087] In one embodiment, the preset force constraint function includes a driver pitch vibration function and a driver force function.

[0088] The driver pitch vibration function is used to represent the relationship between the distance between the driver and the mass center of the vehicle, the moment of inertia of the driver, the displacement of the driver and the displacement of the mass center of the vehicle.

[0089] The driver force function is used to represent the relationship between the mass of the driver, the seat parameters of the driver seat, the displacement of the driver, and the displacement of the vehicle center of mass.

[0090] In the embodiments of the present application, the preset force constraint function is constructed by Newton's second law and the relationship between the moment and the rotational inertia.

[0091] The driver pitch vibration function is constructed according to the relationship between the moment and the rotational inertia. For a system in which a force acts on an object to make the object rotate around an axis, the moment of the force is equal to the product of the rotational inertia of the object and the angular acceleration of the object. When the vehicle is pitching, it can be regarded as rotating around the vehicle center of mass, so the moment of the force acting on the driver can be regarded as the product of the size of the force and the distance between the driver and the vehicle center of mass. The force acting on the driver is provided by the driver seat, and the seat parameters include a plurality of parameters for calculating the size of the force exerted by the driver seat on the driver. Depending on the situation to be considered, the parameters included in the seat parameters can also be different. For example, in the case of only considering the stiffness of the driver seat, the force exerted by the driver seat on the driver can be calculated according to the stiffness of the driver seat and the deformation of the driver seat, and in this case, the seat parameters only include the stiffness of the driver seat. In the case of considering the damping of the driver seat, the damping force of the driver seat can be calculated according to the damping coefficient of the seat and the speed of the vertical movement of the driver, and the product of the stiffness of the driver seat and the deformation of the driver seat, and the sum of the damping force, as the force exerted by the driver seat on the driver, and in this case, the seat parameters include the stiffness of the driver seat and the damping coefficient of the driver seat.

[0092] In one example, the driver pitch vibration function includes a driver longitudinal pitch vibration function and a driver lateral pitch vibration function, respectively used to represent the case where the driver pitches in the direction of the road surface extension (longitudinal direction) and the case where the driver pitches in the direction perpendicular to the direction of the road surface extension (lateral direction). Referring to formula (three) shown in the following:

[0093]

[0094] wherein I x is the rotational inertia in the longitudinal direction of the vehicle, θ is the rotational angle of the vehicle in the longitudinal direction, is the angular acceleration of the vehicle in the longitudinal direction, K5 is the stiffness of the driver seat, Z3 is the displacement of the driver in the vertical direction, is the first derivative of the displacement of the driver in the vertical direction, Similarly, d3 is the distance between the driver and the vehicle's center of mass in the longitudinal direction, Z4 is the displacement of the vehicle in the vertical direction, and C3 is the damping coefficient of the driver's seat;

[0095] I y is the moment of inertia of the vehicle in the lateral direction, and a is the rotation angle of the vehicle in the lateral direction, is the angular acceleration of the vehicle in the lateral direction, and d4 is the distance between the driver and the vehicle's center of mass in the lateral direction.

[0096] The driver force function is constructed according to Newton's second law. The force exerted by the driver's seat on the driver is equal to the product of the acceleration of the driver in the vertical direction and the mass of the driver (see equation (four)):

[0097]

[0098] Solving the driver pitch vibration function and the driver force function can obtain Z4, i.e., the displacement of the vehicle driver.

[0099] In one embodiment, the preset force constraint function further includes a tire force function and a vehicle force function.

[0100] In the embodiments of the present application, the tire force function and the vehicle force function are both constructed according to Newton's second law. The tire force function is used to represent the relationship between the unsprung mass at the position of the tire, the parameters of the tire, the displacement of the tire load mass (sprung mass), the elastic force output by the tire, and the road surface parameters. The vehicle force function is used to represent the relationship between the mass of the vehicle, the displacement of the vehicle, the elastic force output by each tire of the vehicle, and the force exerted by the driver's seat on the driver.

[0101] A tire force function can be constructed for each tire of the vehicle. The tire force function defines that the product of the mass of the tire and the acceleration of the tire load mass in the vertical direction is equal to the difference between the force exerted on the tire by the road surface, the gravity of the unsprung mass, the output damping force of the suspension, and the elastic force output by the tire.

[0102] The vehicle force function defines that the product of the mass of the vehicle and the acceleration of the vehicle in the vertical direction is equal to the difference between the sum of the elastic forces output by all the tires of the vehicle and the gravity of the vehicle, and the force exerted by the driver's seat on the driver.

[0103] Taking a vehicle including four tires as an example, the preset force constraint function including the tire force function, the vehicle force function, the driver pitch vibration function, and the driver force function is shown in equation (five):

[0104]

[0105] wherein, q 1L is the front left wheel road excitation input (the road excitation input is determined according to the road parameters, and is the displacement of the wheel caused by the road parameters), q 1R is the front right wheel road excitation input, q 2L is the rear left wheel road excitation input, q 2R is the rear right wheel road excitation input, m 1L is the front left unsprung mass, m 1R is the front right unsprung mass, m 2L is the rear left unsprung mass, m 2R is the rear right unsprung mass, Z 1L is the front left unsprung mass displacement, Z 1R is the front right unsprung mass displacement, Z 2L is the rear left unsprung mass displacement, Z 2R is the rear left unsprung mass displacement, Z3 is the sprung mass displacement, F c1L is the output damping force of the front left suspension, F c1R is the output damping force of the front right suspension, F c2L is the output damping force of the rear left suspension, F c2R is the output damping force of the rear right suspension, K 1L is the vertical stiffness of the front left tire, K 1R is the vertical stiffness of the front right tire, K 2L is the vertical stiffness of the rear left tire, K 2R is the vertical stiffness of the rear right tire, F k3L is the output elastic force of the front left suspension, F k3R is the output elastic force of the front right suspension, F k4L is the output elastic force of the rear left suspension, F k4R is the output elastic force of the rear right suspension, d1 is the lateral distance between the front axle and the vehicle mass center, d2 is the lateral distance between the rear axle and the vehicle mass center, and y1 is the wheel track.

[0106] Referring to Figure 2 , it is a schematic diagram of a preset vehicle force model constructed based on formula (five).

[0107] The formula (V) actually defines a six-degree-of-freedom preset vehicle force model. The prior art widely adopts a four-degree-of-freedom vehicle dynamics model and a five-degree-of-freedom vehicle dynamics model. The four-degree-of-freedom vehicle dynamics model is constructed based on the symmetry of the heavy truck about the longitudinal axis and assumes that the unevenness functions of the left and right wheel tracks are equal. Under this assumption, the four-degree-of-freedom model mainly considers the vertical vibration and the pitching vibration of the body of the heavy truck. However, in actual applications, since the heavy truck often runs in an environment with poor road conditions, the simplified four-degree-of-freedom model often cannot accurately reflect the real dynamics characteristics of the heavy truck. Therefore, the existing four-degree-of-freedom vehicle dynamics model has a large error with the actual situation. Although the five-degree-of-freedom model has made some progress in the consideration of vibration modes, it still ignores the driver model which is a key factor. The six-degree-of-freedom vehicle dynamics model of the heavy truck including the driver model established based on the embodiments of the present application can objectively and accurately analyze the running characteristics of the heavy truck.

[0108] In one embodiment, as shown in FIG. 1, step 106, according to the road surface parameters corresponding to the preset time point and the target vehicle force model, the force condition of the target vehicle is determined, including: Figure 3

[0109] Step 302, for any road surface contact point of the target vehicle, according to the preset time point and the road surface parameters corresponding to the road surface contact point, the vibration displacement corresponding to the road surface contact point is determined.

[0110] Step 304, according to the vibration displacement corresponding to each road surface contact point and the target vehicle force model, the force condition of the target vehicle is determined.

[0111] In the embodiments of the present application, the target vehicle has multiple road surface contact points. Generally speaking, one tire of the target vehicle corresponds to one road surface contact point. In the case where the preset vehicle force model includes the tire force function of each tire of the vehicle, the vibration displacement corresponding to each road surface contact point can be determined respectively to analyze the force condition of the target vehicle more accurately.

[0112] The road surface parameters can be set for the left and right tires respectively, and according to the driving speed of the target vehicle, the position of each tire of the target vehicle at the preset time point is determined, then the vibration displacement corresponding to the tire is determined according to the road surface parameters of the position, and then the force condition of each tire of the target vehicle is calculated according to the tire force function for each tire respectively.

[0113] Taking the tire force function provided in formula (V) as an example, if the vibration displacement is calculated for each tire respectively, then q 1L , q 1R , q 2L , q​2R The tire elasticity and damping force analyzed in this way are more accurate, and the accuracy of the calculated driver displacement is improved.

[0114] In step 108, a displacement curve of the driver of the target vehicle in the preset time period is generated according to the displacement corresponding to each preset time point, and driving data of the target vehicle is determined according to the displacement curve.

[0115] In the embodiment of the present application, after the displacement of the target vehicle corresponding to the preset time point is obtained, the displacement curve of the driver of the target vehicle in the preset time period is generated, as shown in FIG. 6. The driving data of the target vehicle can be calculated according to the displacement curve. The driving data is used to represent the comfort level of the driver driving the target vehicle. It can be understood that the smaller the fluctuation of the displacement curve, the higher the driving comfort level. Therefore, a parameter capable of describing the fluctuation of the displacement curve can be used as the driving data, so that the fluctuation of the displacement curve is negatively correlated with the driving data. Figure 4

[0116] In one embodiment, the driving data of the target vehicle is determined according to one or more parameters of the peak value, the standard deviation, the skewness, and the change rate of the displacement curve. For example, any one of the above parameters can be used as the driving data, or weights are set for each parameter, and the driving data is obtained by weighted sum of each parameter.

[0117] In another embodiment, the driver vibration acceleration curve is determined according to the displacement curve, and the driving data of the target vehicle is determined according to the driver vibration acceleration curve and a preset weighting function.

[0118] In the embodiment of the present application, the vibration acceleration is used to describe the driving comfort level of the driver. The driver vibration acceleration curve can be obtained by taking the second derivative of the displacement curve, as shown in FIG. 7. Figure 5

[0119] The preset weighting function is determined according to the boundary value of the human body feeling to the vibration frequency. The boundary value can include a first boundary value and a second boundary value. The first boundary value represents the minimum vibration frequency that can be felt by the human body, and the second boundary value represents the maximum vibration frequency that can be felt by the human body. When the vibration frequency is less than the first boundary value, the preset weighting function takes the value of 0, because the human body cannot feel the vibration less than the vibration frequency. When the vibration frequency is greater than the second boundary value, the preset weighting function can take the ratio of the second boundary value to the vibration frequency, because when the vibration frequency is greater than the second boundary value, the higher the vibration frequency, the more the human body cannot feel the vibration, and thus the more comfortable it is. In one example, the first boundary value is 4 Hz, the second boundary value is 12.5 Hz, and the preset weighting function is shown in formula (six):

[0120] ​​

[0121] wherein w(f) is a preset weighting function, and f is a vibration frequency.

[0122] The frequency of the driver vibration acceleration curve is obtained, and driver data is calculated according to the frequency of the driver vibration acceleration curve and the preset weighting function.

[0123] In one embodiment, a weighted acceleration root mean square value is determined according to the driver vibration acceleration curve and the preset weighting function, and the weighted acceleration root mean square value is taken as the driver data.

[0124] In the embodiments of the present application, the power spectral density curve of the driver vibration acceleration curve is obtained by performing Fourier transform on the driver vibration acceleration curve, the power spectral density curve is multiplied by the square of the preset weighting function, the result of the multiplication is integrated and then squared to obtain the weighted acceleration root mean square value (see formula (7)):

[0125]

[0126] wherein a w is the weighted acceleration root mean square value, t1 is one end of a preset time period, t2 is the other end of the preset time period, G q (f) is the power spectral density curve.

[0127] Referring to Table 1 below, the correspondence between the weighted acceleration root mean square value and the subjective feeling of the driver is given by ISO 2631-1:

[0128] Table 1

[0129] Weighted acceleration root mean square a w (m / s -2 )]]> Driver's subjective feeling <0.315 No discomfort 0.315～0.63 Some discomfort 0.5～1.0 Quite a lot of discomfort 0.8～1.6 A lot of discomfort 1.25～2.5 Very uncomfortable >2.0 Extremely uncomfortable

[0130] The driving data analysis method for a heavy truck provided in the embodiments of the present application constructs a target vehicle force model for a target vehicle according to vehicle parameters and a preset vehicle force model, and performs force analysis on a driver of the target vehicle according to road surface parameters in a preset time point and the target vehicle force model to obtain the displacement of the driver of the target vehicle, and then obtains the driving data of the target vehicle according to the displacement curve of the driver of the target vehicle. The embodiments of the present application construct a preset vehicle force model for a heavy truck, and when used, input vehicle parameters that need to be analyzed into the preset vehicle force model, and perform accurate force analysis on the obtained target vehicle force model to calculate the displacement of the driver of the target vehicle. Then, the driving data of the target vehicle is generated according to the calculated displacement. The displacement of the driver is calculated through force analysis, and the accuracy of the displacement obtained is better than the data obtained through simulation experiments, so the driving data analysis accuracy of the target vehicle can be further improved.

[0131] In one embodiment, the phase of the road surface noise signal at the preset time point corresponding to the road surface contact point is taken as the vibration displacement of the road surface contact point;

[0132] The road surface noise signal is generated according to the road surface parameters in the preset time period and the preset driving speed of the target vehicle force model.

[0133] In the embodiments of the present application, the road surface noise signal is used to generate the vibration displacement. The road surface noise signal can be a spatial signal, which is generated according to the preset driving speed and the road surface parameters, and is used to represent the vibration condition of the target vehicle during driving over time.

[0134] The road surface noise signal can be generated by using a limited bandwidth white noise. The power spectral density of the limited bandwidth white noise is shown in formula (eight):

[0135] G q (n)=G q (n0)(n / n0) (-W) Formula (eight)

[0136] Wherein, G q (n) is the power spectral density, G q (n0) is the road roughness coefficient, which can be set by the road surface analyzed by those skilled in the art as needed. n is the spatial frequency, n0 is the reference spatial frequency, which is 0.1, and W is the frequency index.

[0137] The power spectrum of the limited bandwidth white noise is shown in formula (nine):

[0138]

[0139] Wherein, f is the time frequency, and v is the preset driving speed.

[0140] The power spectrum of the limited bandwidth white noise is integrated to obtain the road surface noise signal.

[0141] In one embodiment, as shown in Figure 6 After determining the driving data of the target vehicle according to the displacement curve in step 108, the method further includes:

[0142] Step 602, determining a plurality of target seat parameters according to the seat parameters in the vehicle parameters;

[0143] Step 604, for any target seat parameter, generating a target vehicle force model for the target seat parameter based on the target seat parameter and the vehicle parameters, and jumping to the step of determining the force condition of the target vehicle force model according to the road surface parameters corresponding to the preset time point and the target vehicle force model for any preset time point in the preset time period until the preset condition is met.

[0144] In step 606, a relationship curve between each target seat parameter and driving data is generated according to the driving data corresponding to each target seat parameter.

[0145] In the embodiments of the present application, the relationship curve between the seat parameter and the driving data can be generated by changing the seat parameter, and the relationship between the seat parameter and the driving data in the target vehicle can be analyzed through the relationship curve, thereby providing data support for selecting a suitable seat parameter.

[0146] A plurality of target seat parameters can be determined according to the seat parameter. For example, a parameter step can be set in advance, and the sum of the seat parameter and the parameter step and the difference between the seat parameter and the parameter step are taken as the target seat parameter. Further, a seat parameter range centered on the seat parameter can be determined, and a target seat parameter is taken every parameter step from one end point of the seat parameter range until the other end point of the seat parameter range is reached. Other ways of obtaining the target seat parameter can also be used, which are not limited in the embodiments of the present application.

[0147] In the case where the seat parameter includes a plurality of kinds of parameters, a plurality of target parameters are generated for each kind of parameter, and each target seat parameter includes one target parameter of each kind. At least one target parameter is different between two different target seat parameters. For example, in the case where the seat parameter includes a seat damping coefficient and a seat stiffness, target parameters A, B and C are generated for the seat damping coefficient, and target parameters D, E and F are generated for the seat stiffness, and a target seat parameter is composed of one target parameter of the seat damping coefficient and one target parameter of the seat stiffness. AD, AE, BE and BF are all different target seat parameters.

[0148] For each target seat parameter, a target vehicle force model is generated again according to the target seat parameter, and the newly generated target vehicle force model is analyzed again until a preset condition is met. The preset condition can be that the driving data corresponding to each target seat parameter is generated, or the number of obtained driving data has reached a preset number threshold, which is not limited in the embodiments of the present application.

[0149] Based on the driving data corresponding to each target seat parameter, a relationship curve between the target seat parameter and the driving data is plotted. If the target seat parameters include multiple types of parameters, a relationship curve can be generated for each type of parameter separately, or a multi-dimensional relationship curve between the target seat parameters and the driving data can be generated. For example, if the target seat parameters include seat damping coefficient and seat stiffness, separate relationship curves between seat damping coefficient and driving data, and between seat stiffness and driving data can be generated. Alternatively, a two-dimensional relationship curve (a surface) can be generated between seat damping coefficient, seat stiffness, and driving data. Figure 7 The figure shows the relationship curves between seat damping coefficient and weighted root mean square acceleration, and between seat stiffness and weighted root mean square acceleration, when the driving data is weighted root mean square acceleration.

[0150] The driving data analysis method for heavy-duty trucks provided in this application generates a relationship curve between target seat parameters and driving data, which can show the user the relationship between seat parameters and driving data, making it easier for the user to select appropriate seat parameters for the target vehicle based on the relationship curve.

[0151] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0152] Based on the same inventive concept, this application also provides a heavy-duty truck driving data analysis device for implementing the above-described heavy-duty truck driving data analysis method. The solution provided by this device is similar to the implementation described in the above-described method. Therefore, the specific limitations of one or more embodiments of the heavy-duty truck driving data analysis device provided below can be found in the limitations of the heavy-duty truck driving data analysis method described above, and will not be repeated here.

[0153] In one embodiment, such as Figure 8 As shown, a driving data analysis device 800 for heavy trucks is provided, comprising: an acquisition module 802, an input module 804, a first determination module 806, and a first generation module 808, wherein:

[0154] The acquisition module 802 is configured to acquire road surface parameters and vehicle parameters of a target vehicle in a preset time period.

[0155] The input module 804 is configured to input the vehicle parameters into a preset vehicle force model to obtain a target vehicle force model for the target vehicle.

[0156] The first determination module 806 is configured to, for any preset time point in the preset time period, determine a force condition of the target vehicle according to the road surface parameters corresponding to the preset time point and the target vehicle force model, and determine a displacement of a driver of the target vehicle corresponding to the road surface parameters according to the force condition of the target vehicle.

[0157] The first generation module 808 is configured to generate a displacement curve of the driver of the target vehicle in the preset time period according to the displacements corresponding to each of the preset time points, and determine driving data of the target vehicle according to the displacement curve.

[0158] The driving data analysis device for a heavy truck provided by the embodiments of the present application constructs a target vehicle force model for a target vehicle according to vehicle parameters and a preset vehicle force model, performs force analysis on a driver of the target vehicle according to road surface parameters in a preset time point and the target vehicle force model, obtains a displacement of the driver of the target vehicle, and further obtains driving data of the target vehicle according to a displacement curve of the driver of the target vehicle. The embodiments of the present application construct a preset vehicle force model for a heavy truck, input vehicle parameters that need to be analyzed into the preset vehicle force model when in use, perform accurate force analysis on the obtained target vehicle force model, and calculate the displacement of the driver of the target vehicle. Further, the driving data of the target vehicle is generated according to the calculated displacement. The displacement of the driver is calculated through force analysis, and the accuracy of the obtained displacement is better than the data accuracy obtained through simulation experiments, so that the driving data analysis accuracy of the target vehicle can be further improved.

[0159] In one of the embodiments, the first determination module 806 is further configured to:

[0160] For any road surface contact point of the target vehicle, the vibration displacement corresponding to the road surface contact point is determined according to the preset time point and the road surface parameters corresponding to the road surface contact point.

[0161] The force condition of the target vehicle is determined according to the vibration displacements corresponding to each of the road surface contact points and the target vehicle force model.

[0162] In one of the embodiments, the first determination module 806 is further configured to:

[0163] a phase of a road surface noise signal at the preset time point corresponding to the road surface contact point as a vibration displacement corresponding to the road surface contact point;

[0164] The road surface noise signal is generated according to a road surface parameter in the preset time period and a preset driving speed of the target vehicle force model.

[0165] In one of the embodiments, the target vehicle force model is composed of at least one force constraint function, and each force constraint function is constructed according to each preset force constraint function and the vehicle parameter.

[0166] In one of the embodiments, the preset force constraint function includes a driver pitch vibration function and a driver force function.

[0167] The driver pitch vibration function is used to represent the relationship between the distance between the driver and the vehicle center of mass, the rotational inertia of the driver, the displacement of the driver, and the displacement of the vehicle center of mass.

[0168] The driver force function is used to represent the relationship between the mass of the driver, the seat parameters of the driver seat, the displacement of the driver, and the displacement of the vehicle center of mass.

[0169] In one of the embodiments, the first generation module 808 is further configured to:

[0170] determine a driver vibration acceleration curve according to the displacement curve;

[0171] determine the driving data of the target vehicle according to the driver vibration acceleration curve and a preset weighting function.

[0172] In one of the embodiments, the device further includes:

[0173] a second determination module configured to determine a plurality of target seat parameters according to seat parameters in the vehicle parameters;

[0174] a processing module configured to, for any target seat parameter, generate a target vehicle force model for the target seat parameter based on the target seat parameter and the vehicle parameters, and jump to the step of determining the force condition of the target vehicle force model according to the road surface parameter corresponding to the preset time point and the target vehicle force model for any preset time point in the preset time period until a preset condition is met.

[0175] a second generation module configured to generate a relationship curve between each target seat parameter and the driving data according to the driving data corresponding to each target seat parameter.

[0176] Each of the modules in the above apparatus can be implemented by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to the above modules.

[0177] In an embodiment, a computer device, which can be a server, has an internal structure diagram as shown in Figure 9 The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a method for analyzing driving data of a heavy truck.

[0178] Those skilled in the art can understand that Figure 9 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components than those shown in the diagram, or combine certain components, or have a different arrangement of components.

[0179] In an embodiment, a computer device includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps in the above method embodiments.

[0180] In an embodiment, a computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0181] In an embodiment, a computer program product includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0182] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0183] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0184] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0185] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method of analyzing driving data of a heavy-duty truck, characterized by, The method comprises: acquiring road surface parameters and vehicle parameters of a target vehicle in a preset time period; inputting the vehicle parameters into a preset vehicle force model to obtain a target vehicle force model for the target vehicle; for any preset time point in the preset time period, determining a force condition of the target vehicle according to the road surface parameters corresponding to the preset time point and the target vehicle force model, and determining a displacement of a driver of the target vehicle corresponding to the road surface parameters according to the force condition of the target vehicle; generating a displacement curve of the driver of the target vehicle in the preset time period according to the displacements corresponding to each preset time point, and determining driving data of the target vehicle according to the displacement curve; determining a seat parameter range centered on a seat parameter in the vehicle parameters, and taking a target seat parameter every parameter step in the seat parameter range; for any target seat parameter, generating a target vehicle force model for the target seat parameter based on the target seat parameter and the vehicle parameters, and jumping to the step of determining a force condition of the target vehicle force model according to the road surface parameters corresponding to any preset time point in the preset time period and the target vehicle force model until a preset condition is met; generating a relationship curve for representing the influence of changes in the seat parameter on the driving data according to the driving data corresponding to each target seat parameter.

2. The method of claim 1, wherein, The determining of the force condition of the target vehicle according to the road surface parameters corresponding to the preset time point and the target vehicle force model comprises: for any road surface contact point of the target vehicle, determining a vibration displacement corresponding to the road surface contact point according to the preset time point and the road surface parameters corresponding to the road surface contact point; determining the force condition of the target vehicle according to the vibration displacements corresponding to each road surface contact point and the target vehicle force model.

3. The method of claim 2, wherein, The determining of the vibration displacement corresponding to the road surface contact point according to the road surface parameters corresponding to the preset time point comprises: taking a phase of a road noise signal at the road surface contact point corresponding to the preset time point as the vibration displacement corresponding to the road surface contact point; wherein the road noise signal is generated according to the road surface parameters in the preset time period and a preset driving speed of the target vehicle force model.

4. The method according to any one of claims 1 to 3, characterized in that, The target vehicle force model is composed of at least one force constraint function, and each force constraint function is constructed according to each preset force constraint function and the vehicle parameters.

5. The method of claim 4, wherein, The preset force constraint function includes a driver pitch vibration function and a driver force function; the driver pitch vibration function is used to represent the relationship between the distance between the driver and the vehicle center of mass, the rotational inertia of the driver, the displacement of the driver and the displacement of the vehicle center of mass; the driver force function is used to represent the relationship between the mass of the driver, the seat parameter of the driver seat, the displacement of the driver and the displacement of the vehicle center of mass.

6. The method of claim 1, wherein, The determining of the driving data of the target vehicle according to the displacement curve comprises: According to the displacement curve, a driver vibration acceleration curve is determined; According to the driver vibration acceleration curve and a preset weighting function, driving data of the target vehicle is determined.

7. A driving data analysis device for a heavy-duty truck, characterized by comprising: The device comprises: An acquisition module is configured to acquire road surface parameters and vehicle parameters of a target vehicle in a preset time period; An input module is configured to input the vehicle parameters into a preset vehicle force model to obtain a target vehicle force model for the target vehicle; A first determination module is configured to, for any preset time point in the preset time period, determine a force condition of the target vehicle according to road surface parameters corresponding to the preset time point and the target vehicle force model, and determine a displacement of a driver of the target vehicle corresponding to the road surface parameters according to the force condition of the target vehicle; A first generation module is configured to generate a displacement curve of the driver of the target vehicle in the preset time period according to the displacements corresponding to each preset time point, and determine driving data of the target vehicle according to the displacement curve; A second determination module is configured to determine a seat parameter range with a seat parameter in the vehicle parameters as a center, and take a target seat parameter every parameter step in the seat parameter range; A processing module is configured to, for any target seat parameter, generate a target vehicle force model for the target seat parameter based on the target seat parameter and the vehicle parameters, and jump to the step of determining the force condition of the target vehicle force model according to the road surface parameters corresponding to any preset time point in the preset time period and the target vehicle force model until a preset condition is met; A second generation module is configured to generate a relationship curve for representing an influence of a change of a seat parameter on the driving data according to the driving data corresponding to each target seat parameter.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

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