CN115186541A - Vehicle data optimization method, device, equipment and storage medium - Google Patents
Vehicle data optimization method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of vehicle type design, in particular to a finished vehicle data optimization method, a device, equipment and a storage medium.
Description
Technical Field
The invention relates to the technical field of vehicle model design, in particular to a method, a device, equipment and a storage medium for optimizing vehicle data.
Background
In the process of vehicle model research and development, in order to reduce the vehicle production cost, a mode of reducing the vehicle mass or simplifying the vehicle structure can be adopted to reduce the production cost on the premise of not influencing the vehicle performance, but the optimization of the vehicle body mass generally carries out data processing aiming at single optimization performance, the optimization space is limited, and the data processing calculation workload involved in the vehicle body light weight is large and the data processing efficiency is lower on the premise of not influencing the vehicle body performance.
The above is only for the purpose of assisting understanding of the technical solution of the present invention, and does not represent an admission that the above is the prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for optimizing vehicle data, and aims to solve the technical problems of large data processing and calculation workload and low data processing efficiency in light weight design of a vehicle body in the prior art.
In order to achieve the purpose, the invention provides a complete vehicle data optimization method, which comprises the following steps:
acquiring variable factors of a target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors;
determining corresponding initial weight information according to the variable factor and the initial thickness information;
establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information;
carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information;
and determining the mass of the target whole vehicle through the mass response surface according to the target thickness information.
Optionally, the vehicle operating conditions include: structural rigidity working condition, modal working condition, NVH working condition and safe collision working condition;
the lightweight processing of the initial thickness information through a preset lightweight model to obtain target thickness information comprises the following steps:
acquiring a constraint factor and a whole vehicle target value of the target vehicle under each vehicle working condition;
performing first data optimization on the initial thickness information according to the finished automobile target value and the constraint factor to obtain first thickness information;
updating a torsional rigidity parameter and a torsional mode parameter in the constraint factor according to a preset performance data image;
performing second data optimization on the first thickness information according to the updated torsional rigidity parameter and the updated torsional mode parameter to obtain second thickness information;
and performing third data optimization on the second thickness information through a preset lightweight model to obtain target thickness information.
Optionally, the constraint factor comprises: the structural rigidity working condition constraint factor comprises a structural rigidity working condition constraint factor, a modal working condition constraint factor, an NVH working condition constraint factor and a safe collision constraint factor, wherein the structural rigidity working condition constraint factor comprises: torsional stiffness and bending stiffness, and the modal operating condition constraint factor comprises: a torsional mode and a bending mode;
the first data optimization of the initial thickness information according to the finished automobile target value and the constraint factor comprises the following steps:
extracting the whole vehicle collision displacement in the whole vehicle target value;
covering the safety collision working condition constraint factor according to the whole vehicle collision displacement;
covering the NVH working condition constraint factor according to the torsional rigidity and the torsional mode;
and performing first data optimization on the initial thickness information according to the torsional rigidity, the bending rigidity, the torsional mode, the bending mode and the whole vehicle collision displacement.
Optionally, the NVH operating condition restriction factor includes: vibration transfer information; the updating of the torsional rigidity parameter and the torsional modal parameter in the constraint factor according to the preset performance data image includes:
determining a proportionality coefficient among the vibration transfer information, the torsional mode and the torsional rigidity according to the preset performance data image;
and adjusting the torsional rigidity parameter and the torsional mode parameter according to the proportionality coefficient.
Optionally, the safe collision condition constraint factor includes: the amount and rate of invasion; the NVH working condition constraint factor further comprises: the dynamic stiffness parameter and the noise transfer information;
and performing third-time data optimization on the second thickness information through a preset lightweight model, wherein the third-time data optimization comprises the following steps of:
and performing third-time data optimization on the second thickness information through a preset lightweight model based on the torsional rigidity, the bending rigidity, the torsional mode, the bending mode, the dynamic rigidity parameter, the vibration transmission information, the noise transmission information, the intrusion amount and the intrusion speed.
Optionally, the obtaining of the variable factor of the target vehicle under each vehicle operating condition and the initial thickness information corresponding to the variable factor includes:
determining initial variable factors of a target vehicle under various vehicle working conditions and partial derivatives of the initial variable factors;
carrying out sensitivity analysis on the initial variable factor according to the partial derivative to obtain a sensitivity analysis result;
and screening the initial variable factor according to the sensitivity analysis result to obtain a variable factor, and obtaining initial thickness information corresponding to the variable factor.
Optionally, the establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information, and the initial weight information includes:
acquiring the running condition of the target vehicle;
determining a target response surface establishment strategy according to the operation working condition;
establishing a strategy through a target response surface according to the variable factor, the initial thickness information and the initial weight information to establish a quality response surface of the target vehicle;
acquiring precision information of the quality response surface;
and when the precision information is not less than a preset precision threshold value, executing lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information.
In addition, in order to achieve the above object, the present invention further provides a vehicle data optimization device, where the vehicle data optimization device includes:
the information acquisition module is used for acquiring variable factors of the target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors;
the weight determining module is used for determining corresponding initial weight information according to the variable factor and the initial thickness information;
the response surface establishing module is used for establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information;
the light weight processing module is used for carrying out light weight processing on the initial thickness information through a preset light weight model to obtain target thickness information;
and the mass query module is used for determining the mass of the target whole vehicle through the mass response surface according to the target thickness information.
In addition, in order to achieve the above object, the present invention further provides a vehicle data optimization device, where the vehicle data optimization device includes: a memory, a processor and a vehicle data optimization program stored on the memory and executable on the processor, the vehicle data optimization program being configured to implement the steps of the vehicle data optimization method as described above.
In addition, in order to achieve the above object, the present invention further provides a storage medium, where a whole vehicle data optimization program is stored on the storage medium, and when executed by a processor, the whole vehicle data optimization program implements the steps of the whole vehicle data optimization method described above.
The invention discloses a vehicle data optimization method, which comprises the following steps: acquiring variable factors of a target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors; determining corresponding initial weight information according to the variable factor and the initial thickness information; establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information; carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information; compared with the prior art, the method and the device have the advantages that variable factors and initial thickness information of the target vehicle under various vehicle working conditions are obtained, corresponding initial weight information is determined according to the variable factors and the initial thickness information, a quality response surface is further established, association of the variable factors, the initial thickness information and the weight information is established, data optimization is achieved through light-weighted processing of the initial thickness information, the optimal target thickness is obtained, and the target vehicle mass corresponding to the target thickness is finally determined.
Drawings
Fig. 1 is a schematic structural diagram of a vehicle data optimization device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a vehicle data optimization method according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a vehicle data optimization method according to a second embodiment of the present invention;
FIG. 4 is a schematic diagram of first-time data optimization performance parameters according to an embodiment of a vehicle data optimization method of the present invention;
FIG. 5 is a schematic diagram of the relationship among torsional rigidity, mode and vibration transmission working conditions in the embodiment of the vehicle data optimization method;
FIG. 6 is a schematic diagram of a second data optimization performance parameter of the vehicle data optimization method according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of a third data optimization performance parameter according to an embodiment of the vehicle data optimization method of the present invention;
fig. 8 is a block diagram of a first embodiment of the vehicle data optimization apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle data optimization device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the vehicle data optimization device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation on the overall vehicle data optimization device, and may include more or fewer components than shown, or some components combined, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a full vehicle data optimization program.
In the entire vehicle data optimization device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the vehicle data optimization device of the present invention may be disposed in the vehicle data optimization device, and the vehicle data optimization device calls the vehicle data optimization program stored in the memory 1005 through the processor 1001 and executes the vehicle data optimization method provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for optimizing vehicle data, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the method for optimizing vehicle data according to the present invention.
In this embodiment, the vehicle data optimization method includes the following steps:
step S10: and acquiring variable factors of the target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors.
It should be noted that the execution subject in the method of this embodiment may be a device having functions of data acquisition, data transmission, and data processing, for example: the present embodiment is not particularly limited to this, and in the present embodiment and the following embodiments, the test device will be taken as an example for explanation.
In this embodiment, the set variable factors may be the same under different single conditions, so when the thickness value of the variable factor is changed, the performance optimization results may be opposite, and vehicle body quality optimization is performed based on single-condition investigation performance, and the obtained quality parameters may not be the optimal quality parameters in the current performance state.
It should be noted that the variable factor refers to a structural attachment that has an influence on the vehicle body mass under at least one of the operating conditions, such as the mode, the structural rigidity, the NVH, and the crash safety, and the setting of the variable factor may be different according to the vehicle optimization performance, for example: in the NHV working condition, the variable factors can be accessories such as left and right suspensions, a frame, a spring seat and the like; in the safe collision working condition, the variable factors can be structural accessories of B columns, front doors, rear doors and other areas; in the structural stiffness condition, the variable factor may be set as a structural accessory in a region such as a threshold or a longitudinal beam, which is not particularly limited in this embodiment.
In the specific implementation, because the influence degrees of partial structural accessories of the vehicle on the working condition of the whole vehicle are different, some accessories which have smaller influence on the vehicle performance exist, and when the variable factors are set, all the structural accessories which have influence on the vehicle are used as the variable factors, in order to reduce the processing amount of invalid data and improve the data processing efficiency, the variable factors can be screened, and the variable factors which have smaller influence degrees on the vehicle performance are removed, so that the data processing efficiency is improved.
Further, in order to eliminate the variable factor having a small influence on the vehicle performance, the step S10 includes:
determining initial variable factors of a target vehicle under various vehicle working conditions and partial derivatives of the initial variable factors;
carrying out sensitivity analysis on the initial variable factor according to the partial derivative to obtain a sensitivity analysis result;
and screening the initial variable factor according to the sensitivity analysis result to obtain a variable factor, and obtaining initial thickness information corresponding to the variable factor.
It should be noted that the initial variable factor refers to a structural accessory having an influence on the investigation performance of a single working condition of the vehicle; the partial derivatives are used for quantifying the influence degree of each variable factor on the investigation performance of each working condition of the vehicle.
In the concrete implementation, according to the design sensitivity analysis and the partial derivative of the design response to the optimization variable, the influence degree of each variable factor on the investigation performance of each working condition of the vehicle can be determined.
Step S20: and determining corresponding initial weight information according to the variable factor and the initial thickness information.
It can be understood that a set of accurate performance parameters and the initial weight of the vehicle body can be obtained by calculating the variable factor and the initial thickness information corresponding to the variable factor, in the process, the variable factor and the initial thickness information can be uploaded to a preset cloud platform for calculation, and in the calculation process, the material attributes of various accessories of the vehicle body can be referred to, so that the initial weight information of the vehicle can be obtained.
Step S30: and establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information.
It should be understood that the mass response surface refers to a link response surface between the variable factor, the initial thickness information and the initial weight information, wherein when the thickness information is taken as a variable, mass, mode, structural rigidity and the like have strong linear characteristics, and a polynomial method can be adopted for fitting; for the NVH and the safe collision working condition, because the nonlinear relationship is strong, a kriging method or a neural network method may be adopted to establish the quality response surface, which is not specifically limited in this embodiment.
Further, in order to improve the reliability of the quality response surface, the establishing the quality response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information includes:
acquiring the running condition of the target vehicle;
determining a target response surface establishment strategy according to the operation working condition;
establishing a strategy through a target response surface according to the variable factor, the initial thickness information and the initial weight information to establish a quality response surface of the target vehicle;
acquiring precision information of the quality response surface;
and when the precision information is not less than a preset precision threshold value, executing lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information.
It should be noted that the accuracy information of the quality response surface is used to quantify a fitting degree between an estimated value or a predicted value of the trend line and actual data, the higher the fitting degree is, the larger the accuracy information is, the preset accuracy threshold may be a value set to be not greater than 1, where the preset accuracy threshold may be set to be 0.8, and this embodiment does not specifically limit this.
In the concrete implementation, after the quality response surfaces of all the working conditions are established, the precision information of the quality response surfaces of all the working conditions needs to be calculated, when the precision information of the quality response surfaces of all the working conditions is not less than 0.8, all the quality response surfaces are output, and the initial thickness information is subjected to light weight processing through a preset light weight model, so that the target thickness information is obtained; if the accuracy information of the quality response surface of each working condition is less than 0.8, the scheme adjustment of the quality response surface needs to be performed again, and the adjustment scheme may be to re-screen the variable factor, replace the approximate model building method, increase the number of samples, and the like, which is not specifically limited in this embodiment.
Step S40: and carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information.
It should be noted that the preset lightweight model is used for performing data optimization on the initial thickness information of the variable factor, and performing lightweight processing to obtain the lightest vehicle mass without affecting the vehicle performance.
It can be understood that the traditional light-weight treatment is generally restricted by a target value specified by the existing working conditions, so that an optimization result is difficult to obtain or is not ideal; in the present embodiment, the data of the target vehicle is subjected to the weight reduction processing by the three-round data optimization.
Step S50: and determining the mass of the target whole vehicle through the mass response surface according to the target thickness information.
It should be appreciated that after determining each target variable factor and corresponding target thickness information, a corresponding accessory name may be determined from the quality response surface, and a mass value of the finished vehicle may be determined by determining the mass of the accessory from the accessory name and the accessory thickness and the material properties of the accessory.
The embodiment discloses a vehicle data optimization method, which comprises the following steps: acquiring variable factors of a target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors; determining corresponding initial weight information according to the variable factor and the initial thickness information; establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information; carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information; according to the embodiment, the variable factor and the initial thickness information of the target vehicle under each vehicle working condition are obtained, the corresponding initial weight information is determined according to the variable factor and the initial thickness information, a quality response surface is further established, the association among the variable factor, the initial thickness information and the weight information is established, the data optimization is realized through the light-weighted processing on the initial thickness information, the optimal target thickness is further obtained, and the target vehicle quality corresponding to the target thickness is finally determined.
Referring to fig. 3, fig. 3 is a schematic flow chart of a vehicle data optimization method according to a second embodiment of the present invention.
Based on the first embodiment, in this embodiment, the step S40 includes:
step S401: and acquiring the constraint factors and the whole vehicle target value of the target vehicle under each vehicle working condition.
It should be noted that the constraint factors under each vehicle operating condition include: structural rigidity working condition constraint factors, modal working condition constraint factors, NVH working condition constraint factors and safe collision constraint factors; the structural rigidity working condition constraint factors comprise torsional rigidity and bending rigidity; the modal working condition constraint factors comprise a torsional mode and a bending mode; the NVH working condition constraint factors comprise dynamic stiffness parameters, vibration transmission information and noise transmission information; the safe collision condition constraint factors comprise an intrusion amount and an intrusion speed.
It should be noted that the overall target value may be an overall collision displacement, a torsional mode, a torsional rigidity, and the like, and this embodiment is not particularly limited thereto.
Step S402: and performing first data optimization on the initial thickness information according to the finished automobile target value and the constraint factor to obtain first thickness information.
It should be noted that, before the initial thickness information is subjected to the first data optimization, the target value of the entire vehicle may be substituted for the target value of a single working condition, for example: the intrusion amount of the frontal collision displacement is replaced by the collision displacement of the whole vehicle, and the relative performance of NVH (noise vibration and harshness) is approximately replaced by the mode and the torsional rigidity, so that the working conditions with a plurality of target values exist, the processing amount is reduced in the first data optimization process, and the data processing efficiency is improved.
Further, the step S402 includes:
extracting the whole vehicle collision displacement in the whole vehicle target value;
covering the safety collision working condition constraint factor according to the whole vehicle collision displacement;
covering the NVH working condition constraint factor according to the torsional rigidity and the torsional mode;
and performing first data optimization on the initial thickness information according to the torsional rigidity, the bending rigidity, the torsional mode, the bending mode and the whole vehicle collision displacement.
It can be understood that, in the first data optimization process, five constraint factors of the entire vehicle working condition remain five of torsional rigidity, bending rigidity, torsional mode, bending mode and entire vehicle collision displacement, and the vehicle is subjected to the first data optimization according to the five constraint factors, so that the number of the investigation performance is reduced, the optimization space is improved, and the optimization time is shortened, wherein, because the requirements of the investigation performance are inconsistent, the data optimization process can refer to the parameter requirements of the investigation performance, refer to fig. 4, and fig. 4 is the parameter requirements of part of the investigation performance in the first data optimization process.
Step S403: and updating the torsional rigidity parameter and the torsional mode parameter in the constraint factor according to a preset performance data image.
It should be understood that, in the second data optimization process, the investigation performance corresponding to the safe collision is improved to the initial level, but the performance of the vibration transmission working condition is still poor, and the optimization needs to be continued aiming at the performance of the vibration transmission working condition, if all the performances of the vibration transmission working condition are constrained within a qualified range, an ideal optimization result is difficult to find, or the lightweight effect is poor, so that the relation among the torsional rigidity, the mode and the vibration transmission working condition in the first data optimization process needs to be obtained.
Further, the step S403 includes:
determining a proportionality coefficient among the vibration transfer information, the torsional mode and the torsional rigidity according to the preset performance data image;
and adjusting the torsional rigidity parameter and the torsional mode parameter according to the proportionality coefficient.
In a specific implementation, referring to fig. 5, fig. 5 is a schematic diagram of torsional stiffness, mode and vibration transmission condition performance, and it can be seen from fig. 5 that various examined performances of the vibration transmission condition are proportional to the torsional mode and the stiffness, so that the torsional stiffness and the torsional mode can be adjusted by determining a proportionality coefficient between the vibration transmission information and the torsional mode as well as the torsional stiffness, so as to improve the effect of the vibration transmission condition of the entire vehicle, for example: the torsional rigidity constraint is improved from 13000Nm/deg to 14000Nm/deg, and the torsional mode constraint is improved from 31.94HZ to 32.4HZ, which is not specifically limited in this embodiment, and the investigation performance of the second data optimization after the torsional rigidity and the torsional mode are adjusted refers to FIG. 6.
Step S404: and performing second data optimization on the first thickness information according to the updated torsional rigidity parameter and the updated torsional modal parameter to obtain second thickness information.
Step S405: and performing third data optimization on the second thickness information through a preset lightweight model to obtain target thickness information.
It can be understood that the preset lightweight model can be a data processing model based on a mitigation optimization algorithm and used for carrying out third-time data optimization on the thickness information, and in the third-time data optimization process, the quality lightweight processing is carried out according to all the investigation performances of all the working conditions, and the whole vehicle target value is not used for replacing the constraint factor of a single working condition.
Further, the step S405 includes:
and performing third-time data optimization on the second thickness information through a preset lightweight model based on the torsional rigidity, the bending rigidity, the torsional mode, the bending mode, the dynamic rigidity parameter, the vibration transmission information, the noise transmission information, the intrusion amount and the intrusion speed.
In a specific implementation, the parameter requirements of performance are examined with reference to the operation conditions of the third data optimization in fig. 7.
The embodiment discloses that a constraint factor and a whole vehicle target value of the target vehicle under each vehicle working condition are obtained; performing first data optimization on the initial thickness information according to the finished automobile target value and the constraint factor to obtain first thickness information; updating a torsional rigidity parameter and a torsional mode parameter in the constraint factor according to a preset performance data image; performing second data optimization on the first thickness information according to the updated torsional rigidity parameter and the updated torsional mode parameter to obtain second thickness information; and carrying out third-time data optimization on the second thickness information through a preset light-weight model to obtain target thickness information, and realizing vehicle light weight to the greatest extent on the premise of ensuring the performance of each working condition through the third-time data optimization in the embodiment, and avoiding the problem that the optimized data of the single working condition investigation performance possibly conflict.
In addition, an embodiment of the present invention further provides a storage medium, where a vehicle data optimization program is stored on the storage medium, and when executed by a processor, the vehicle data optimization program implements the steps of the vehicle data optimization method described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
Referring to fig. 8, fig. 8 is a structural block diagram of a first embodiment of the vehicle data optimization device of the present invention.
As shown in fig. 8, the whole vehicle data optimization apparatus provided in the embodiment of the present invention includes:
the information obtaining module 100 is configured to obtain variable factors of a target vehicle under various vehicle operating conditions and initial thickness information corresponding to the variable factors.
A weight determining module 200, configured to determine corresponding initial weight information according to the variable factor and the initial thickness information.
A response surface establishing module 300, configured to establish a mass response surface of the target vehicle according to the variable factor, the initial thickness information, and the initial weight information.
And the weight reduction processing module 400 is used for carrying out weight reduction processing on the initial thickness information through a preset weight reduction model to obtain target thickness information.
And the mass query module 500 is used for determining the mass of the target whole vehicle through the mass response surface according to the target thickness information.
The embodiment discloses a method for optimizing data of a whole vehicle, which comprises the following steps: acquiring variable factors of a target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors; determining corresponding initial weight information according to the variable factor and the initial thickness information; establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information; carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information; according to the embodiment, the variable factor and the initial thickness information of the target vehicle under each vehicle working condition are obtained, the corresponding initial weight information is determined according to the variable factor and the initial thickness information, a quality response surface is further established, the association among the variable factor, the initial thickness information and the weight information is established, the data optimization is realized through the light-weighted processing on the initial thickness information, the optimal target thickness is further obtained, and the target vehicle quality corresponding to the target thickness is finally determined.
In an embodiment, the light weight processing module 400 is further configured to obtain a constraint factor and a vehicle-finished target value of the target vehicle under each vehicle operating condition; performing first data optimization on the initial thickness information according to the finished automobile target value and the constraint factor to obtain first thickness information; updating a torsional rigidity parameter and a torsional mode parameter in the constraint factor according to a preset performance data image; performing second data optimization on the first thickness information according to the updated torsional rigidity parameter and the updated torsional mode parameter to obtain second thickness information; and performing third data optimization on the second thickness information through a preset lightweight model to obtain target thickness information.
In an embodiment, the lightweight processing module 400 is further configured to extract a finished vehicle collision displacement in the finished vehicle target value; covering the safe collision working condition constraint factor according to the whole vehicle collision displacement; covering the NVH working condition constraint factor according to the torsional rigidity and the torsional mode; and performing first data optimization on the initial thickness information according to the torsional rigidity, the bending rigidity, the torsional mode, the bending mode and the whole vehicle collision displacement.
In an embodiment, the weight reduction processing module 400 is further configured to determine a proportionality coefficient between the vibration transmission information, the torsional mode, and the torsional stiffness according to the preset performance data image; and adjusting the torsional rigidity parameter and the torsional mode parameter according to the proportionality coefficient.
In an embodiment, the weight reduction processing module 400 is further configured to perform a third data optimization on the second thickness information through a preset weight reduction model based on the torsional stiffness, the bending stiffness, the torsional mode, the bending mode, the dynamic stiffness parameter, the vibration transmission information, the noise transmission information, an intrusion amount, and an intrusion speed.
In an embodiment, the information obtaining module 100 is further configured to determine an initial variable factor of the target vehicle under each vehicle operating condition and a partial derivative of the initial variable factor; carrying out sensitivity analysis on the initial variable factor according to the partial derivative to obtain a sensitivity analysis result; and screening the initial variable factor according to the sensitivity analysis result to obtain a variable factor, and obtaining initial thickness information corresponding to the variable factor.
In an embodiment, the response surface establishing module 300 is further configured to obtain an operation condition of the target vehicle; determining a target response surface establishment strategy according to the operation working condition; establishing a strategy through a target response surface according to the variable factor, the initial thickness information and the initial weight information to establish a quality response surface of the target vehicle; acquiring precision information of the quality response surface; and when the precision information is not less than a preset precision threshold value, performing lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited in this respect.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the entire vehicle data optimization method provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. a Read Only Memory (ROM)/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The finished automobile data optimization method is characterized by comprising the following steps:
acquiring variable factors of a target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors;
determining corresponding initial weight information according to the variable factor and the initial thickness information;
establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information;
carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information;
and determining the target whole vehicle mass through the mass response surface according to the target thickness information.
2. The vehicle data optimization method of claim 1, wherein the vehicle operating conditions include: structural rigidity working condition, modal working condition, NVH working condition and safe collision working condition;
the processing of the initial thickness information in a lightweight manner through a preset lightweight model to obtain target thickness information comprises the following steps:
acquiring a constraint factor and a whole vehicle target value of the target vehicle under each vehicle working condition;
performing first data optimization on the initial thickness information according to the finished automobile target value and the constraint factor to obtain first thickness information;
updating a torsional rigidity parameter and a torsional mode parameter in the constraint factor according to a preset performance data image;
performing second data optimization on the first thickness information according to the updated torsional rigidity parameter and the updated torsional modal parameter to obtain second thickness information;
and performing third data optimization on the second thickness information through a preset lightweight model to obtain target thickness information.
3. The vehicle data optimization method of claim 2, wherein the constraint factors include: the structural rigidity working condition constraint factor comprises a structural rigidity working condition constraint factor, a modal working condition constraint factor, an NVH working condition constraint factor and a safe collision constraint factor, wherein the structural rigidity working condition constraint factor comprises: torsional stiffness and bending stiffness, and the modal behavior constraint factors include: a torsional mode and a bending mode;
the first data optimization of the initial thickness information according to the finished automobile target value and the constraint factor comprises the following steps:
extracting the whole vehicle collision displacement in the whole vehicle target value;
covering the safety collision working condition constraint factor according to the whole vehicle collision displacement;
covering the NVH working condition constraint factor according to the torsional rigidity and the torsional mode;
and performing first data optimization on the initial thickness information according to the torsional rigidity, the bending rigidity, the torsional mode, the bending mode and the whole vehicle collision displacement.
4. The vehicle data optimization method according to claim 3, wherein the NVH condition constraint factor comprises: vibration transfer information;
the updating of the torsional rigidity parameter and the torsional mode parameter in the constraint factor according to the preset performance data image comprises:
determining a proportionality coefficient among the vibration transfer information, the torsional mode and the torsional rigidity according to the preset performance data image;
and adjusting the torsional rigidity parameter and the torsional mode parameter according to the proportionality coefficient.
5. The vehicle data optimization method of claim 4, wherein the crash safety constraints comprise: the amount and rate of invasion; the NVH working condition constraint factor further comprises: the dynamic stiffness parameter and the noise transfer information;
and performing third-time data optimization on the second thickness information through a preset lightweight model, wherein the third-time data optimization comprises the following steps of:
and performing third-time data optimization on the second thickness information through a preset lightweight model based on the torsional rigidity, the bending rigidity, the torsional mode, the bending mode, the dynamic rigidity parameter, the vibration transmission information, the noise transmission information, the intrusion amount and the intrusion speed.
6. The vehicle data optimization method of claim 1, wherein the obtaining of the variable factor of the target vehicle under each vehicle condition and the initial thickness information corresponding to the variable factor comprises:
determining initial variable factors of a target vehicle under various vehicle working conditions and partial derivatives of the initial variable factors;
performing sensitivity analysis on the initial variable factor according to the partial derivative to obtain a sensitivity analysis result;
and screening the initial variable factor according to the sensitivity analysis result to obtain a variable factor, and obtaining initial thickness information corresponding to the variable factor.
7. The vehicle data optimization method according to any one of claims 1 to 6, wherein the establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information, and the initial weight information includes:
acquiring the running condition of the target vehicle;
determining a target response surface establishment strategy according to the operation working condition;
establishing a mass response surface of the target vehicle through a target response surface establishing strategy according to the variable factor, the initial thickness information and the initial weight information;
acquiring precision information of the quality response surface;
and when the precision information is not less than a preset precision threshold value, executing lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information.
8. The utility model provides a whole car data optimization device which characterized in that, whole car data optimization device includes:
the information acquisition module is used for acquiring variable factors of the target vehicle under various vehicle working conditions and initial thickness information corresponding to the variable factors;
the weight determining module is used for determining corresponding initial weight information according to the variable factor and the initial thickness information;
the response surface establishing module is used for establishing a mass response surface of the target vehicle according to the variable factor, the initial thickness information and the initial weight information;
the lightweight processing module is used for carrying out lightweight processing on the initial thickness information through a preset lightweight model to obtain target thickness information;
and the mass query module is used for determining the mass of the target whole vehicle through the mass response surface according to the target thickness information.
9. The utility model provides a whole car data optimizing apparatus which characterized in that, whole car data optimizing apparatus includes: a memory, a processor, and a vehicle data optimization program stored on the memory and executable on the processor, the vehicle data optimization program configured to implement the vehicle data optimization method of any one of claims 1 to 7.
10. A storage medium having stored thereon a complete vehicle data optimization program which, when executed by a processor, implements a complete vehicle data optimization method according to any one of claims 1 to 7.
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| CN202210795099.3A CN115186541A (en) | 2022-07-07 | 2022-07-07 | Vehicle data optimization method, device, equipment and storage medium |
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| CN202210795099.3A CN115186541A (en) | 2022-07-07 | 2022-07-07 | Vehicle data optimization method, device, equipment and storage medium |
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