CN114819244A - Vehicle chassis weight prediction method and device - Google Patents
Vehicle chassis weight prediction method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting vehicle chassis weight, wherein the method comprises the steps of obtaining chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters; grouping chassis influence parameters based on target screening conditions to obtain a training set and a test set; inputting the training set into a neural network for training to obtain an initial neural network model; inputting the test set into the initial neural network model and continuously correcting, and outputting a corresponding weight fitting curve when the accuracy of the test set reaches a target condition; and calculating the weight of the chassis according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user. According to the method and the device for predicting the weight of the vehicle chassis, provided by the embodiment of the invention, the accuracy rate of predicting the weight of the vehicle chassis is improved through a neural network technology and a specific algorithm, so that the research and development efficiency and progress of a new vehicle type project are ensured.
Description
Technical Field
The invention relates to the technical field of automobile servicing quality, in particular to a method and a device for predicting the weight of a vehicle chassis.
Background
The chassis of the vehicle has the functions of supporting and mounting an automobile engine and parts thereof to form the integral shape of the automobile, and receiving the power of the engine to enable the automobile to move and ensure normal running, so that the weight of the chassis can bring important influence on the actual running performance of the automobile in the research and development design process of new automobile types.
Because the types of parts related to the vehicle chassis are various, in the prior art, the chassis weight of a newly developed vehicle type is estimated by researching the chassis weight ratio of a standard vehicle type in the current market, however, only the research on the standard vehicle type cannot provide global judgment of integrity, and the existing chassis weight prediction method has large errors and low accuracy, so that the phenomenon that the performance setting of the parts and the weight setting are in game in the subsequent development process, the weight target needs to be changed or the performance of the chassis needs to be sacrificed, and the development progress of the new project vehicle type is seriously influenced.
Disclosure of Invention
The invention provides a vehicle chassis weight prediction method and a vehicle chassis weight prediction device, which are used for solving the technical problem of low accuracy of the existing chassis weight prediction method, and the accuracy of the vehicle chassis weight prediction is improved through a neural network technology and a specific algorithm, so that the research and development efficiency and progress of a new vehicle type project are ensured.
In order to solve the above technical problem, an embodiment of the present invention provides a method for predicting a vehicle chassis weight, including:
acquiring chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters;
grouping the chassis influence parameters based on target screening conditions to respectively obtain a corresponding training set and a corresponding test set;
inputting the training set into a neural network for training to obtain an initial neural network model;
inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve;
and calculating the chassis weight of the vehicle according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user.
As one of the preferable schemes, the chassis influencing parameters at least include: a transmission system influencing parameter, a suspension system influencing parameter, a steering system influencing parameter and a brake system influencing parameter.
As one of the preferable schemes, the step of grouping the chassis influence parameters based on the target screening condition to obtain a corresponding training set and a corresponding test set respectively includes:
and screening the chassis influence parameters according to the total data amount of the chassis influence parameters to respectively obtain the training set with a first data proportion and the test set with a second data proportion.
As one of the preferable schemes, the step of inputting the training set to a neural network for training to obtain an initial neural network model specifically includes:
carrying out normalization processing on the training set;
configuring training parameters of the neural network;
and training the training set after the normalization processing by using the training parameters to obtain the initial neural network model.
As one of the preferable schemes, the step of inputting the test set into the initial neural network model and continuously correcting until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve specifically includes:
inputting the test set into the initial neural network model, and calculating a corresponding real-time decision coefficient through weight correction;
when the real-time decision coefficient is greater than or equal to a preset threshold value, judging that the target condition is met;
and if the target condition is met, outputting a corresponding weight fitting curve.
Another embodiment of the present invention provides a vehicle chassis weight prediction apparatus, including a controller configured to:
acquiring chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters;
grouping the chassis influence parameters based on target screening conditions to respectively obtain a corresponding training set and a corresponding test set;
inputting the training set into a neural network for training to obtain an initial neural network model;
inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve;
and calculating the chassis weight of the vehicle according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user.
As one of the preferable schemes, the chassis influencing parameters at least include: a transmission system influencing parameter, a suspension system influencing parameter, a steering system influencing parameter and a brake system influencing parameter.
As one of the preferable schemes, the controller is further configured to:
and screening the chassis influence parameters according to the total data amount of the chassis influence parameters to respectively obtain the training set with a first data proportion and the test set with a second data proportion.
As one of the preferable schemes, the controller is further configured to:
carrying out normalization processing on the training set;
configuring training parameters of the neural network;
and training the training set after the normalization processing by using the training parameters to obtain the initial neural network model.
As one of the preferable schemes, the controller is further configured to:
inputting the test set into the initial neural network model, and calculating a corresponding real-time decision coefficient through weight correction;
when the real-time decision coefficient is greater than or equal to a preset threshold value, judging that the target condition is met;
and if the target condition is met, outputting a corresponding weight fitting curve.
Compared with the prior art, the method has the advantages that the chassis influence parameters of all vehicle types are obtained, the specific prediction algorithm is adopted to process data based on the neural network technology, the chassis weight of the new vehicle type is finally obtained, compared with the method only depending on the standard vehicle type in the prior art, the data setting range is wider, overall judgment can be obtained, the chassis prediction accuracy of the new vehicle type can reach 98% based on the specific algorithm of the neural network, the error range is within 5KG, further the phenomenon that the performance setting of parts and the weight setting are in game in the subsequent research and development process after the chassis weight of the new vehicle type is predicted is prevented, the weight target needs to be changed or the performance of the chassis needs to be sacrificed, and the research and development efficiency and accuracy of the new vehicle type are effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting vehicle chassis weight in one embodiment of the present invention;
FIG. 2 is a data chart of chassis impact parameters in one embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of the present application, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In the description of the present application, it is to be noted that, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention, as those skilled in the art will recognize the specific meaning of the terms used in the present application in a particular context.
An embodiment of the present invention provides a method for predicting a vehicle chassis weight, and specifically, referring to fig. 1, fig. 1 is a schematic flow chart of a method for predicting a vehicle chassis weight according to an embodiment of the present invention, where the method includes:
s1, acquiring chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters;
s2, grouping the chassis influence parameters based on target screening conditions to respectively obtain a corresponding training set and a corresponding test set;
s3, inputting the training set into a neural network for training to obtain an initial neural network model;
s4, inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve;
s5, calculating the chassis weight of the vehicle according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user; the parameters input by the user are related parameters which are specified by the project of the new vehicle type and have influence on the weight of the chassis, for example, the total vehicle weight, the front and rear wheel track, the weight of the wheel assembly, the presence or absence of a spare tire, the power type, the driving type and the like are input according to the project requirement of the new vehicle type, and finally, the corresponding predicted weight value of the chassis is output.
It should be noted that, in the process of research and design of a new automobile type, a corresponding target weight is obtained from each large system of the automobile and the like for the weight part, and then corresponding adjustment is made according to the positioning requirement of the market on a target automobile type. In the chassis prediction method in the prior art, only one or two vehicle types can be referred for analysis, the target setting range is small, the integrity is lacked, a large error exists between a predicted value obtained by the existing prediction algorithm and a final weight value, the accuracy is low, in addition, the project progress delay can be caused by frequently changing the weight value of the chassis in the subsequent research and development processes, in view of the above, the embodiment of the invention obtains the chassis influence parameters of all the vehicle types, processes data by adopting the specific prediction algorithm based on the neural network technology, and finally obtains the chassis weight of a new vehicle type, compared with the prior art which only depends on a standard vehicle type, the data setting range is wider, overall judgment can be obtained, and the chassis prediction accuracy of the new vehicle type can reach 98% and the error range is within 5KG by the specific algorithm based on the neural network, and then, the phenomenon that the weight target needs to be changed or the performance of the chassis needs to be sacrificed when the performance setting and the weight setting of the parts are played in the subsequent research and development process after the weight of the chassis of the new vehicle type is predicted is avoided, and the research and development efficiency and accuracy of the new vehicle type are effectively improved.
For the above step S1, preferably, a big data technology may be used to collect and arrange the chassis weight influence parameter data of all vehicle types in the company existing inventory database, wherein, considering that the chassis mainly consists of four parts, namely, a transmission system, a suspension system, a steering system and a braking system, the relevant weights of the four parts can be preferentially obtained, and for the transmission system, the weight influence parameters of the transmission system include a clutch, a transmission, a universal transmission, a main reducer, a differential, a half shaft, and the like; the influence parameters of the weight of the suspension system (including the auxiliary frame) comprise front and rear wheel axle loads, front and rear wheel tracks, front and rear suspension types and the like; the influence parameters of the weight of the steering system comprise front wheel axle load, front wheel track, tire width, EPS type, adjusting mode of a steering wheel, driving mode and arrangement mode of a steering engine and the like; the parameters influencing the weight of the brake system include the overall vehicle weight, the material of the shaft joint, whether a rear shaft joint exists, and the like. In addition, the influence parameters of the total weight of the chassis include the whole vehicle prepared weight, the front and rear wheel tracks, the weight of the wheel assembly, whether a spare tire exists, the power type and the driving type, and each weight influence parameter also includes more detailed specific types, for example, the influence parameters of the rim weight include the whole vehicle full load weight, the rim width, the rim diameter and the rim material, which all affect the prediction of the chassis of a new vehicle type, before such a huge amount of data is obtained, the influence parameters of the chassis of the vehicle are more rapidly, more variously and accurately screened out based on a data acquisition method of big data, and the influence parameters have higher accuracy and signal to noise ratio, so that the method is favorable for uniformly classifying, sorting, planning and analyzing the weight of relevant parts of the chassis of each vehicle type, and meanwhile, the research ideas of each vehicle type are not consistent, the invention is preferably based on the database stored in the company, the novel vehicle type is more accurate in positioning, and the accuracy of chassis weight prediction is greatly improved.
In addition, preferably, corresponding influence coefficients are generated according to the driving type, the power type and the spare tire type in the chassis influence parameters, linear regression can be performed according to the data types in the database, and the values of the corresponding influence coefficients are calculated.
Further, in the above embodiment, the chassis affecting parameters at least include: a transmission system influencing parameter, a suspension system influencing parameter, a steering system influencing parameter and a brake system influencing parameter. For example, referring to fig. 2 for ease of understanding, fig. 2 shows a data chart of chassis influence parameters in one embodiment of the present invention, wherein 28 vehicle models (i.e., items in the figure) in a company are listed, and several chassis influence parameters are preferred: and calculating the whole vehicle finishing quality, the wheel assembly, the front and rear wheel tracks, the driving type, the presence or absence of a spare tire and the power type according to the vehicle chassis weight prediction method, thereby outputting a final new vehicle type chassis weight prediction value.
Further, in the above embodiment, regarding step S2, the step of grouping the chassis influence parameters based on the target screening condition to obtain a corresponding training set and a corresponding test set respectively includes:
and screening the chassis influence parameters according to the total data amount of the chassis influence parameters to respectively obtain the training set with a first data proportion and the test set with a second data proportion. The embodiment preferably uses the data proportion as the target screening condition, for example, 95% of the total data amount is divided into a training set, and 5% of the total data amount is divided into a test set, although the division stage may also be randomly allocated and determined by the actual algorithm requirement.
In one aspect of the present invention, for step S3, the step of inputting the training set to a neural network for training to obtain an initial neural network model specifically includes:
s31, carrying out normalization processing on the training set; the normalization processing is to transform the dimensional expression into a dimensionless expression to become a scalar, thereby simplifying the calculation and improving the calculation precision;
s32, configuring the training parameters of the neural network; preferably, the number of hidden layers of the neural network is 10, the output layer is 1, the maximum training frequency is 1000, the training requirement precision is 1e-3, the learning rate is 0.01 (the neural network of each chassis system has different set parameters, wherein the number of the hidden layers is set according to the original data quantity and is used for ensuring the fitting rate of discrete data, 10 is obtained by an empirical value, the maximum training frequency is determined according to the diversity of the original data set, the higher the diversity is, the higher the value is)
And S33, training the training set after the normalization processing by using the training parameters to obtain the initial neural network model.
In one aspect of the present invention, for step S4, the step of inputting the test set into the initial neural network model and continuously correcting until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve specifically includes:
s41, inputting the test set into the initial neural network model, and calculating a corresponding real-time decision coefficient through weight correction;
s42, when the real-time decision coefficient is larger than or equal to a preset threshold value, judging that the target condition is met; preferably, the embodiment uses the value of the decision coefficient as the evaluation index of the accuracy, and if the decision coefficient is smaller than a preset threshold, such as 0.98, or the absolute difference between the predicted value calculated by the neural network model and the actual value in the test set is greater than 5, the correction is continued until the decision coefficient is greater than or equal to 0.98 (0.98 is only an example value, and the closer the decision coefficient is to 1, the better the performance of the neural network model is, the higher the accuracy is).
And S43, if the target condition is met, outputting a corresponding weight fitting curve.
In the method for predicting the weight of the vehicle chassis in the embodiment, for convenience of processing, a visual human-computer interaction interface can be realized by means of a signal processing and analyzing tool kit and a GUI (graphical user interface) in the MATLAB, so that data processing is more convenient.
Another embodiment of the present invention provides a vehicle chassis weight prediction apparatus, including a controller configured to:
acquiring chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters;
grouping the chassis influence parameters based on target screening conditions to respectively obtain a corresponding training set and a corresponding test set;
inputting the training set into a neural network for training to obtain an initial neural network model;
inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve;
and calculating the chassis weight of the vehicle according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user.
Further, the chassis influencing parameters comprise at least: a transmission system influencing parameter, a suspension system influencing parameter, a steering system influencing parameter and a brake system influencing parameter.
In the above embodiment, the controller is further configured to:
and screening the chassis influence parameters according to the total data amount of the chassis influence parameters to respectively obtain the training set with a first data proportion and the test set with a second data proportion.
In the above embodiment, the controller is further configured to:
carrying out normalization processing on the training set;
configuring training parameters of the neural network;
and training the training set after the normalization processing by using the training parameters to obtain the initial neural network model.
In the above embodiment, the controller is further configured to:
inputting the test set into the initial neural network model, and calculating a corresponding real-time decision coefficient through weight correction;
when the real-time decision coefficient is greater than or equal to a preset threshold value, judging that the target condition is met;
and if the target condition is met, outputting a corresponding weight fitting curve.
The method and the device for predicting the weight of the vehicle chassis have the advantages that the chassis influence parameters of all vehicle types are obtained, the data are processed by adopting a specific prediction algorithm based on a neural network technology, and the weight of the chassis of the new vehicle type is finally obtained.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method of predicting vehicle chassis weight, comprising:
acquiring chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters;
grouping the chassis influence parameters based on target screening conditions to respectively obtain a corresponding training set and a corresponding test set;
inputting the training set into a neural network for training to obtain an initial neural network model;
inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve;
and calculating the chassis weight of the vehicle according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user.
2. The method for predicting vehicle chassis weight according to claim 1, wherein the chassis influence parameters include at least: a transmission system influencing parameter, a suspension system influencing parameter, a steering system influencing parameter and a brake system influencing parameter.
3. The method for predicting the vehicle chassis weight according to claim 1, wherein the step of grouping the chassis influence parameters based on the target screening condition to obtain a corresponding training set and a corresponding test set respectively comprises:
and screening the chassis influence parameters according to the total data amount of the chassis influence parameters to respectively obtain the training set with a first data proportion and the test set with a second data proportion.
4. The method for predicting the vehicle chassis weight according to claim 1, wherein the step of inputting the training set into a neural network for training to obtain an initial neural network model comprises:
carrying out normalization processing on the training set;
configuring training parameters of the neural network;
and training the training set after the normalization processing by using the training parameters to obtain the initial neural network model.
5. The method for predicting the vehicle chassis weight according to claim 1, wherein the step of inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve comprises the following steps:
inputting the test set into the initial neural network model, and calculating a corresponding real-time decision coefficient through weight correction;
when the real-time decision coefficient is greater than or equal to a preset threshold value, judging that the target condition is met;
and if the target condition is met, outputting a corresponding weight fitting curve.
6. An apparatus for predicting vehicle chassis weight, comprising a controller, wherein the controller is configured to:
acquiring chassis influence parameters of all vehicle types from a preset chassis weight database, and generating corresponding influence coefficients according to a driving type, a power type and a spare tire type in the chassis influence parameters;
grouping the chassis influence parameters based on target screening conditions to respectively obtain a corresponding training set and a corresponding test set;
inputting the training set into a neural network for training to obtain an initial neural network model;
inputting the test set into the initial neural network model and continuously correcting the test set until the accuracy of the test set reaches a target condition, and outputting a corresponding weight fitting curve;
and calculating the chassis weight of the vehicle according to the influence coefficient, the weight fitting curve and the weight demand parameter input by the user.
7. The vehicle chassis weight prediction apparatus of claim 6, wherein the chassis influence parameters include at least: a transmission system influencing parameter, a suspension system influencing parameter, a steering system influencing parameter and a brake system influencing parameter.
8. The vehicle chassis weight prediction apparatus of claim 6, wherein the controller is further configured to:
and screening the chassis influence parameters according to the total data amount of the chassis influence parameters to respectively obtain the training set with a first data proportion and the test set with a second data proportion.
9. The vehicle chassis weight prediction apparatus of claim 6, wherein the controller is further configured to:
carrying out normalization processing on the training set;
configuring training parameters of the neural network;
and training the training set after the normalization processing by using the training parameters to obtain the initial neural network model.
10. The vehicle chassis weight prediction apparatus of claim 6, wherein the controller is further configured to:
inputting the test set into the initial neural network model, and calculating a corresponding real-time decision coefficient through weight correction;
when the real-time decision coefficient is greater than or equal to a preset threshold value, judging that the target condition is met;
and if the target condition is met, outputting a corresponding weight fitting curve.
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