Vehicle part parameter optimization method, device, equipment and medium
By using in-vehicle road noise prediction model and response sensitivity analysis during vehicle development, the problem of difficult to predict in-vehicle road noise in the early stage of vehicle development is solved, and the development design cost is reduced.
Patent Information
- Application Number
- CN202311207920.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-18
- Publication Date
- 2025-03-25
AI Technical Summary
The prior art is difficult to predict in-vehicle road noise in the early stages of vehicle development, resulting in an increase in vehicle development design costs.
By obtaining the current parameters of the target components during vehicle development, using the trained in-vehicle road noise prediction model for prediction, and calculating the response sensitivity based on the prediction results, adjusting the component parameters until the preset target level is met.
It realizes the prediction and optimization of in-vehicle road noise in the early stages of vehicle development, reducing the adjustment workload and development and design costs after vehicle trial production.
Smart Images
Figure CN119691881A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of vehicles, and in particular to a method, device, equipment and medium for optimizing vehicle component parameters. Background Art
[0002] In the prior art, for the prediction method of in-vehicle road noise, various component parameters of the vehicle driving process are obtained through experimental testing as input parameters of the prediction model, such as the front and rear bushing stiffness of the front swing arm, the front and rear shock absorber damping coefficients, the vertical stiffness of the front coil spring and other component parameters. The prediction model predicts the in-vehicle road noise based on the input parameters.
[0003] However, the above prediction method is mainly used after the detailed design stage of vehicle development, that is, the vehicle has been trial-produced and the corresponding characteristics of the in-vehicle road noise are fixed. Predicting the in-vehicle road noise of the vehicle according to the prediction model and improving the vehicle performance according to the prediction results will increase the cost of vehicle development and design. Therefore, how to predict the in-vehicle road noise in the early stage of vehicle development to reduce the cost of vehicle development and design has become an urgent problem to be solved. Summary of the invention
[0004] In view of this, embodiments of the present invention provide a vehicle component parameter optimization method, device, equipment and medium to solve the problem of how to predict in-vehicle road noise in the early stage of vehicle development, thereby reducing vehicle development and design issues.
[0005] In a first aspect, an embodiment of the present invention provides a method for optimizing vehicle component parameters, the method comprising:
[0006] In the vehicle development process, current parameters of the target component in the current development result are obtained, and based on the current parameters, the trained in-vehicle road noise prediction model is used to predict the in-vehicle road noise level of the vehicle to obtain a predicted level result;
[0007] detecting whether the level result satisfies a preset target level, and if it is detected that the level result does not satisfy the target level, calculating, according to the current parameter and the level result, a response sensitivity characterizing the influence of the target component on the road noise inside the vehicle;
[0008] Adjusting the current parameter according to the response sensitivity to obtain an adjusted parameter, and updating the target component in the current development result according to the adjusted parameter;
[0009] Return to the step of obtaining the current parameters of the target component in the current development result until it is detected that the level result meets the target level, and determine that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component.
[0010] In a second aspect, an embodiment of the present invention provides a component parameter optimization device for a vehicle, the component parameter optimization device comprising:
[0011] A noise prediction module is used to obtain current parameters of target components in the current development results during vehicle development, and to predict the in-vehicle road noise level of the vehicle using the trained in-vehicle road noise prediction model based on the current parameters to obtain a predicted level result;
[0012] an impact analysis module, configured to detect whether the level result satisfies a preset target level, and if it is detected that the level result does not satisfy the target level, calculate a response sensitivity representing the impact of the target component on the in-vehicle road noise according to the current parameters and the level result;
[0013] A parameter adjustment module, used for adjusting the current parameter according to the response sensitivity to obtain the adjusted parameter, and updating the target component in the current development result according to the adjusted parameter;
[0014] A parameter determination module is used to return to the step of obtaining the current parameters of the target component in the current development result until it is detected that the level result meets the target level, and determine that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component.
[0015] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the component parameter optimization method as described in the first aspect when executing the computer program.
[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the component parameter optimization method as described in the first aspect is implemented.
[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:
[0018] In the process of vehicle development, the present invention obtains the current parameters of the target component in the current development result, predicts the in-vehicle road noise level of the vehicle based on the current parameters using a trained in-vehicle road noise prediction model, and obtains a predicted level result; detects whether the level result meets a preset target level, and if it is detected that the level result does not meet the target level, calculates the response sensitivity characterizing the influence of the target component on the in-vehicle road noise according to the current parameters and the level result; adjusts the current parameters according to the response sensitivity to obtain the adjusted parameters, and updates the target component in the current development result according to the adjusted parameters; returns to execute the step of obtaining the current parameters of the target component in the current development result, until it is detected that the level result meets the target level, and determines that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component. Among them, by obtaining the current parameters of the target components of the vehicle during the development period, the vehicle's in-vehicle road noise level is predicted, and when the road noise performance does not meet the target performance, the response sensitivity of the target components to the impact of the in-vehicle road noise under the current parameters is analyzed, and the current parameters of the target components of the vehicle are adjusted according to the response sensitivity to ensure that the vehicle's road noise performance meets the target performance. By predicting the road noise of the vehicle in the early stage of development and timely adjusting the component parameters in the vehicle to optimize the road noise, the adjustment workload after the vehicle trial production is reduced, and the cost of vehicle development and design is also reduced. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.
[0020] Figure 1 This is a schematic diagram of an application environment of a vehicle component parameter optimization method provided by Embodiment 1 of the present invention;
[0021] Figure 2 is a flow chart of a method for optimizing vehicle component parameters provided by the first embodiment of the present invention;
[0022] Figure 3 is a flow chart of a method for optimizing vehicle component parameters provided by the second embodiment of the present invention;
[0023] Figure 4 is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 3 of the present invention;
[0024] Figure 5 is a flow chart of a method for optimizing vehicle component parameters provided by a fourth embodiment of the present invention;
[0025] Figure 6 is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 5 of the present invention;
[0026] Figure 7 is a flow chart of a method for optimizing vehicle component parameters provided by a sixth embodiment of the present invention;
[0027] Figure 8 It is a structural schematic diagram of a vehicle component parameter optimization device provided by Embodiment 7 of the present invention;
[0028] Fig. 9 It is a structural diagram of a computer device provided in Embodiment 8 of the present invention. DETAILED DESCRIPTION
[0029] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.
[0030] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.
[0031] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
[0032] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.
[0033] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.
[0034] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.
[0035] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0036] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.
[0037] A vehicle component parameter optimization method provided by the first embodiment of the present invention can be applied in the following aspects: Figure 1 In an application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud terminal devices, personal digital assistants (PDAs) and other computer devices. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.
[0038] See also Figure 2 , is a flow chart of a method for optimizing vehicle component parameters provided by the first embodiment of the present invention. The above-mentioned method for optimizing vehicle component parameters can be applied to Figure 1 The client in the client, the computer device corresponding to the client connects to the server to obtain the corresponding background data. For example, the current parameters of the target component in the current development result and the trained in-vehicle road noise prediction model are used to optimize the current parameters of the target component based on the in-vehicle road noise level predicted by the trained in-vehicle road noise prediction model.
[0039] like Figure 2As shown, the component parameter optimization method may include the following steps:
[0040] Step S201, during the vehicle development process, obtain the current parameters of the target component in the current development result, and based on the current parameters, use the trained in-vehicle road noise prediction model to predict the in-vehicle road noise level of the vehicle to obtain a predicted level result.
[0041] Among them, the target component refers to the selected component in the vehicle related to the in-vehicle road noise, and includes but is not limited to the body, suspension, tire and bushing. The current parameter refers to the component parameters of the target component determined in the current development result, and includes but is not limited to the vehicle model, power mode; suspension form, tire size and other parameters. The trained in-vehicle road noise prediction model refers to an in-vehicle road noise prediction model that has been trained and has accurate prediction capabilities. When faced with new data, it can accurately predict the corresponding in-vehicle road noise level. The in-vehicle road noise level is divided into the first level, the second level, ..., the nth level in the order of noise from small to large, where n>0.
[0042] In an embodiment of the present invention, during the vehicle development process, firstly, the target components of the vehicle related to the road noise are obtained, and the current parameters of the target components are set. After the current parameters of the target components selected in the vehicle are set, the current parameters are input into a trained in-vehicle road noise prediction model to predict the in-vehicle road noise level of the vehicle, and the predicted level result is output accordingly, so as to effectively manage and control the parameters of the target components in the vehicle during the vehicle development process, so that the in-vehicle road noise of the subsequently developed vehicle meets the expected requirements.
[0043] Step S202 , whether the level result meets the preset target level. If it is detected that the level result does not meet the target level, the response sensitivity characterizing the impact of the target component on the road noise inside the vehicle is calculated based on the current parameters and the level result.
[0044] The target level refers to the expected level of road noise inside the vehicle, that is, the level of road noise inside the vehicle that the vehicle should meet after development. Response sensitivity is used to characterize the degree of influence of components on road noise inside the vehicle. The greater the response sensitivity, the easier it is for the corresponding component to affect road noise inside the vehicle.
[0045] In the embodiment of the present invention, after the level result of the vehicle interior road noise under the current parameters is predicted, it is detected whether the level result meets the preset target level. If the level result meets the preset target level, it means that the current parameter setting of the target component is reasonable and meets the expected requirements of the interior road noise. The design can be carried out according to the current parameters of the target component when the sample vehicle is trial-produced. If the level result does not meet the preset target level, it means that the current parameter setting of the target component is unreasonable and the interior road noise does not meet the expected requirements. Therefore, when it is detected that the level result does not meet the target level, the response sensitivity characterizing the impact of the target component on the interior road noise is calculated according to the current parameters of the target component and the corresponding level result, so as to clarify whether the impact of the target component on the interior road noise is easy.
[0046] Step S203, adjusting the current parameters according to the response sensitivity to obtain the adjusted parameters, and updating the target components in the current development result according to the adjusted parameters.
[0047] Among them, the greater the response sensitivity, the easier it is for the corresponding component to affect the vehicle's interior road noise. The easier it is to change the vehicle's interior road noise by adjusting the current parameters according to the response sensitivity. Therefore, in an embodiment of the present invention, the current parameters are adjusted according to the response sensitivity to obtain adjusted parameters, and the target components in the current development results are updated according to the adjusted parameters, so that the interior road noise can meet the expected requirements by adjusting the parameters of the target components.
[0048] Step S204, returning to the step of obtaining the current parameters of the target component in the current development result, until it is detected that the level result meets the target level, and determining that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component.
[0049] Among them, the parameter optimization result refers to the actual parameters of the target component when the in-vehicle road noise meets the expected requirements. Therefore, in the embodiment of the present invention, after updating the target component in the current development result according to the adjusted parameters, the step of predicting the in-vehicle road noise level of the vehicle using the trained in-vehicle road noise prediction model based on the current parameters of the target component is repeated to obtain the predicted level result, that is, the adjusted parameters are input into the trained in-vehicle road noise prediction model, and the in-vehicle road noise level of the vehicle is predicted again to obtain the corresponding predicted level result, until it is detected that the level result meets the target level, and the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component. By adjusting the component parameters in the vehicle to optimize the road noise, the adjustment workload after the vehicle trial production is reduced, and the cost of vehicle development and design is also reduced.
[0050] In a vehicle development process, an embodiment of the present invention obtains current parameters of a target component in a current development result, predicts an interior road noise level of the vehicle using a trained interior road noise prediction model based on the current parameters, and obtains a predicted level result; detects whether the level result meets a preset target level, and if it is detected that the level result does not meet the target level, calculates a response sensitivity that characterizes the impact of the target component on the interior road noise based on the current parameters and the level result; adjusts the current parameters based on the response sensitivity to obtain adjusted parameters, and updates the target component in the current development result based on the adjusted parameters; returns to execute the step of obtaining the current parameters of the target component in the current development result until it is detected that the level result meets the target level, and determines that the current parameters corresponding to the level result meeting the target level are parameter optimization results of the target component. Among them, by obtaining the current parameters of the target components of the vehicle during the development period, the vehicle's in-vehicle road noise level is predicted, and when the road noise performance does not meet the target performance, the response sensitivity of the target components to the impact of the in-vehicle road noise under the current parameters is analyzed, and the current parameters of the target components of the vehicle are adjusted according to the response sensitivity to ensure that the vehicle's road noise performance meets the target performance. By predicting the road noise of the vehicle in the early stage of development and timely adjusting the component parameters in the vehicle to optimize the road noise, the adjustment workload after the vehicle trial production is reduced, and the cost of vehicle development and design is also reduced.
[0051] In one embodiment, referring to Figure 3 , is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 2 of the present invention, such as Figure 3 As shown, in step S202, the calculation of the response sensitivity of the target component to the impact of the road noise inside the vehicle according to the current parameter and the level result includes the following specific steps:
[0052] Step S301, obtaining a standard vibration-noise transmission degree corresponding to the target component under the current parameters, and adjusting the standard vibration-noise transmission degree using a preset adjustment coefficient to obtain an adjusted vibration-noise transmission degree.
[0053] In the embodiment of the present invention, the standard vibration and noise transfer degree corresponding to the target component under the current parameters is obtained through the transfer function, and the calculation expression of the transfer function is: G(jω)=P(jω) / F(jω), wherein G(jω) represents the transfer function, P(jω) represents the vibration or noise at the receiver, and F(jω) represents the vibration or noise at the vibration source.
[0054] It should be noted that the transfer function of vibration and noise is a function that describes the characteristics and laws of vibration and noise in the transmission process. It describes the attenuation and amplification characteristics of vibration and noise in the transmission process between different systems. The transfer function can be used to determine the main influencing factors and key parts in the transmission process, so as to effectively control and reduce vibration and noise.
[0055] The standard vibration and noise transmission degree of the target component is adjusted using a preset adjustment coefficient to obtain an adjusted vibration and noise transmission degree, so as to reduce the vibration and noise transmission of the target component, thereby reducing the road noise inside the vehicle. It is worth noting that different adjustment coefficients can be set for different components. For example, the adjustment coefficient of the tire can be 15%, and the adjustment coefficient of the suspension can be 10%.
[0056] Step S302, calculating a vibration-noise transmission degree change between the standard vibration-noise transmission degree and the adjusted vibration-noise transmission degree.
[0057] In the embodiment of the present invention, the absolute value of the difference between the standard vibration-noise transmission degree and the adjusted vibration-noise transmission degree is calculated as the vibration-noise transmission degree change.
[0058] Step S303 , obtaining a response sensitivity characterizing the influence of the target component on the road noise inside the vehicle according to the ratio between the change in the vibration noise transmission degree and the grade result.
[0059] In the embodiment of the present invention, the level result is used as the numerator and the change in the vibration noise transmission degree is used as the denominator to obtain a corresponding ratio, and the ratio is used as a representation of the response sensitivity of the target component to the impact of the road noise inside the vehicle.
[0060] In one embodiment, referring to Figure 4 , is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 3 of the present invention, such as Figure 4 As shown, in step S203, the target component includes at least two components, and the current parameter is adjusted according to the response sensitivity to obtain the adjusted parameter, including the following specific steps:
[0061] Step S401 , sorting the response sensitivities corresponding to all components in descending order to obtain a corresponding sorting result.
[0062] Step S402: According to the sorting result, the current parameters of any component in the target components are adjusted in turn to obtain the adjusted parameters of the component.
[0063] With respect to step S401 and step S402, in an embodiment of the present invention, when the target component includes multiple components, since the response sensitivity of each component to the influence of the road noise inside the vehicle is different, the response sensitivities corresponding to all components are sorted in order from large to small to obtain the corresponding sorting results, and then, based on the sorting results, the current parameters of any component in the target component are adjusted in turn to obtain the adjusted parameters of the component. By preferentially adjusting the current parameters of components with high response sensitivity, the workload of parameter optimization can be reduced.
[0064] In one embodiment, referring to Figure 5 , is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 4 of the present invention, such as Figure 5 As shown, in step S402, adjusting the current parameters of any component in the target component in turn according to the sorting result to obtain the adjusted parameters of the component includes the following specific steps:
[0065] Step S501, obtaining a first response sensitivity in the sorting result, and determining a component to be adjusted corresponding to the first response sensitivity in the target component.
[0066] Step S502, adjusting the current parameters of the component to be adjusted to obtain the adjusted parameters of the component to be adjusted, and updating the target component in the current development result using the adjusted parameters of the component to be adjusted to obtain the updated target component.
[0067] Step S503, returning to the step of obtaining the current parameters of the target component in the current development result for the updated target component, obtaining a corresponding new level result, and detecting whether the new level result meets the target level.
[0068] Step S504: If it is detected that the new level result does not meet the target level, the second response sensitivity in the sorting result is obtained, and the component to be adjusted corresponding to the second response sensitivity is determined in the updated target component, and the step of performing parameter adjustment on the current parameters of the component to be adjusted to obtain the adjusted parameters of the component to be adjusted is returned to.
[0069] With respect to step S501 to step S504, the first response sensitivity is the maximum response sensitivity of all components. Therefore, in the embodiment of the present invention, the current parameters of the component corresponding to the maximum response sensitivity are first adjusted, and the current parameters of other components remain unchanged, thereby achieving the update of the target component and also updating the current parameters of the target component. After the target component is updated, the trained in-vehicle road noise prediction model is used again to predict the in-vehicle road noise level of the vehicle to obtain a new predicted level result.
[0070] Check whether the new grade result meets the target grade. If so, no current parameter optimization is performed on other components. If not, based on this update, obtain the component corresponding to the second response sensitivity, adjust the current parameters of the component corresponding to the second response sensitivity, and keep the current parameters of other components unchanged to update the current parameters of the target component again. Continue to reuse the trained in-vehicle road noise prediction model to predict the in-vehicle road noise level of the vehicle to obtain a predicted new grade result. Check whether the new grade result meets the target grade. If so, no current parameter optimization is performed on other components. If not, obtain the component corresponding to the third response sensitivity based on this update, adjust the current parameters of the component corresponding to the third response sensitivity, and so on. According to the sorting result, adjust the current parameters of any component in the target component to obtain the adjusted parameters of the component.
[0071] In one embodiment, referring to Figure 6 , is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 5 of the present invention, such as Figure 6 As shown, in step S402 or step S502, the parameter adjustment method includes the following specific steps:
[0072] Step S601, for any component, respectively obtain the standard vibration and noise transmission degree corresponding to the component under each other parameter, and for any standard vibration and noise transmission degree, calculate the similarity between the standard vibration and noise transmission degree and the corresponding adjusted vibration and noise transmission degree of the component.
[0073] In the embodiment of the present invention, for any component, the transfer function in step S301 is used to obtain the standard vibration noise transfer degree corresponding to the component under each other parameter. It is worth noting that the standard vibration noise transfer degree is the inherent characteristic corresponding to the component under each parameter, that is, one parameter corresponds to one standard vibration noise transfer degree. Therefore, for any standard vibration noise transfer degree, the absolute value of the difference between the standard vibration noise transfer degree and the corresponding adjusted vibration noise transfer degree of the component is calculated, and the absolute value of the difference is used as the similarity. The larger the absolute value of the difference, the smaller the similarity.
[0074] Step S602: obtaining the maximum similarity among all similarities, and taking other parameters corresponding to the maximum similarity as adjusted parameters of the component.
[0075] In the embodiment of the present invention, according to step S601, the similarity between each standard vibration noise transmission degree and the corresponding adjusted vibration noise transmission degree of the component can be obtained, and the maximum similarity is determined among all similarities, so that other parameters corresponding to the maximum similarity are used as adjusted parameters.
[0076] In one embodiment, referring to Figure 7 , is a flow chart of a method for optimizing vehicle component parameters provided by Embodiment 6 of the present invention, such as Figure 7 As shown, in step S201, the step of obtaining the trained in-vehicle road noise prediction model includes the following specific steps:
[0077] Step S701 , obtaining historical parameters of target components of a developed vehicle and historical level results of corresponding in-vehicle road noise.
[0078] Step S702: Based on the historical parameters, the in-vehicle road noise prediction model is used to predict the in-vehicle road noise level of the developed vehicle to obtain a predicted sample level result, and a mean square error loss function is used to calculate the loss between the sample level result and the historical level result.
[0079] Step S703 , reversely adjusting the model parameters of the interior road noise prediction model using a stochastic gradient descent algorithm until the loss converges, thereby obtaining a trained interior road noise prediction model.
[0080] With respect to steps S701 to S703, in an embodiment of the present invention, based on the developed vehicles, for any developed vehicle, the historical parameters corresponding to the target components in the developed vehicle and the historical level results of the in-vehicle road noise corresponding to the developed vehicle are obtained, and the historical level results are used as labels of the historical parameters. The historical parameters with labels of the target components are obtained, and based on the historical parameters, the in-vehicle road noise prediction model is used to predict the in-vehicle road noise level of the developed vehicle to obtain the predicted sample level results, the mean square error loss function is used to calculate the loss between the sample level results and the labels, and the model parameters of the in-vehicle road noise prediction model are reversely adjusted using the stochastic gradient descent algorithm until the loss converges, thereby obtaining a trained in-vehicle road noise prediction model.
[0081] Corresponding to the component parameter optimization method of the above embodiment, Figure 8 The structural block diagram of a vehicle component parameter optimization device provided by the seventh embodiment of the present invention is shown. The component parameter optimization device is applied to Figure 1The client in the present invention is connected to the server by a computer device corresponding to the client to obtain the corresponding background data. For example, the current parameters of the target component in the current development result and the trained in-vehicle road noise prediction model are used to optimize the current parameters of the target component based on the in-vehicle road noise level predicted by the trained in-vehicle road noise prediction model. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown.
[0082] See also Figure 8 , the component parameter optimization device comprises:
[0083] The noise prediction module 81 is used to obtain the current parameters of the target components in the current development results during the vehicle development process, and predict the in-vehicle road noise level of the vehicle using the trained in-vehicle road noise prediction model based on the current parameters to obtain a predicted level result;
[0084] An impact analysis module 82, configured to detect whether the level result satisfies a preset target level, and if it is detected that the level result does not satisfy the target level, calculate a response sensitivity representing the impact of the target component on the vehicle interior road noise according to the current parameters and the level result;
[0085] A parameter adjustment module 83, configured to adjust the current parameter according to the response sensitivity to obtain an adjusted parameter, and update the target component in the current development result according to the adjusted parameter;
[0086] The parameter determination module 84 is used to return to the step of obtaining the current parameters of the target component in the current development result until it is detected that the level result meets the target level, and determine that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component.
[0087] Optionally, the impact analysis module 82 includes:
[0088] a characteristic adjustment unit, used for obtaining a standard vibration-noise transfer degree corresponding to the target component under the current parameters, and adjusting the standard vibration-noise transfer degree using a preset adjustment coefficient to obtain an adjusted vibration-noise transfer degree;
[0089] a change analysis unit, used to calculate a change in the vibration-noise transmission degree between the standard vibration-noise transmission degree and the adjusted vibration-noise transmission degree;
[0090] The influence calculation unit is used to obtain the response sensitivity characterizing the influence of the target component on the road noise inside the vehicle according to the ratio between the change in the vibration noise transmission degree and the grade result.
[0091] Optionally, the target component includes at least two components, and the parameter adjustment module 73 includes:
[0092] A sorting unit is used to sort the response sensitivities corresponding to all components in descending order to obtain a corresponding sorting result;
[0093] An adjustment unit is used to adjust the current parameters of any component in the target components in turn according to the sorting result to obtain the adjusted parameters of the component.
[0094] Optionally, the adjustment unit includes:
[0095] A component selection subunit, used for obtaining a first response sensitivity in the sorting result, and determining a component to be adjusted corresponding to the first response sensitivity in the target component;
[0096] A target updating subunit, configured to adjust the current parameters of the component to be adjusted to obtain the adjusted parameters of the component to be adjusted, and to update the target component in the current development result by using the adjusted parameters of the component to be adjusted to obtain the updated target component;
[0097] A level detection subunit, configured to return to the step of obtaining the current parameters of the target component in the current development result for the updated target component, obtain a corresponding new level result, and detect whether the new level result meets the target level;
[0098] A loop adjustment unit is used to obtain a second response sensitivity in the sorting result if it is detected that the new level result does not meet the target level, and determine the component to be adjusted corresponding to the second response sensitivity in the updated target component, and return to execute the step of adjusting the current parameters of the component to be adjusted to obtain the adjusted parameters of the component to be adjusted.
[0099] Optionally, the parameter adjustment module 83 includes:
[0100] A similarity analysis unit is used to obtain, for any component, a standard vibration-noise transmission degree corresponding to the component under each other parameter, and, for any standard vibration-noise transmission degree, calculate a similarity between the standard vibration-noise transmission degree and the corresponding adjusted vibration-noise transmission degree of the component;
[0101] The parameter determination unit is used to obtain the maximum similarity among all similarities, and use other parameters corresponding to the maximum similarity as adjusted parameters of the component.
[0102] Optionally, the noise prediction module 81 includes:
[0103] A sample acquisition unit, used to acquire historical parameters of historical target components of a developed vehicle and historical level results of corresponding in-vehicle road noise;
[0104] a loss analysis unit, configured to predict the in-vehicle road noise level of the developed vehicle using an in-vehicle road noise prediction model based on the historical parameters, obtain a predicted sample level result, and calculate a loss between the sample level result and the historical level result using a mean square error loss function;
[0105] The model training unit is used to reversely adjust the model parameters of the in-vehicle road noise prediction model using a stochastic gradient descent algorithm until the loss converges to obtain a trained in-vehicle road noise prediction model.
[0106] It should be noted that the information interaction, execution process, etc. between the above-mentioned modules, units, and sub-units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.
[0107] Fig. 9 This is a schematic diagram of the structure of a computer device provided in Embodiment 8 of the present invention. Fig. 9 As shown, the computer device of this embodiment includes: at least one processor ( Fig. 9 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned component parameter optimization method embodiments are implemented.
[0108] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Fig. 9 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.
[0109] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0110] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.
[0111] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.
[0112] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.
[0113] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0114] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0115] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0117] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.
Claims
1. A method for optimizing vehicle component parameters, characterized in that: The component parameter optimization includes: In the vehicle development process, current parameters of the target component in the current development result are obtained, and based on the current parameters, the trained in-vehicle road noise prediction model is used to predict the in-vehicle road noise level of the vehicle to obtain a predicted level result; detecting whether the level result satisfies a preset target level, and if it is detected that the level result does not satisfy the target level, calculating, according to the current parameter and the level result, a response sensitivity characterizing the influence of the target component on the road noise inside the vehicle; Adjusting the current parameter according to the response sensitivity to obtain an adjusted parameter, and updating the target component in the current development result according to the adjusted parameter; Return to the step of obtaining the current parameters of the target component in the current development result until it is detected that the level result meets the target level, and determine that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component.
2. The component parameter optimization method according to claim 1, characterized in that: The calculating, according to the current parameter and the grade result, the response sensitivity representing the impact of the target component on the road noise inside the vehicle comprises: Obtaining a standard vibration-noise transfer degree corresponding to the target component under the current parameters, and adjusting the standard vibration-noise transfer degree using a preset adjustment coefficient to obtain an adjusted vibration-noise transfer degree; calculating a vibration-noise transmission degree change between the standard vibration-noise transmission degree and the adjusted vibration-noise transmission degree; According to the ratio between the vibration noise transmission degree change and the grade result, the response sensitivity characterizing the influence of the target component on the road noise inside the vehicle is obtained.
3. The component parameter optimization method according to claim 2, characterized in that: The target component includes at least two components, and the current parameter is adjusted according to the response sensitivity to obtain the adjusted parameter, including: Sort the response sensitivities of all components from large to small to obtain the corresponding sorting results; According to the sorting result, the current parameters of any component in the target components are adjusted in turn to obtain the adjusted parameters of the component.
4. The component parameter optimization method according to claim 3, characterized in that: According to the sorting result, adjusting the current parameters of any component in the target components in turn to obtain the adjusted parameters of the component includes: Obtaining a first response sensitivity in the sorting result, and determining a component to be adjusted corresponding to the first response sensitivity in the target component; Performing parameter adjustment on the current parameters of the component to be adjusted to obtain the adjusted parameters of the component to be adjusted, and updating the target component in the current development result by using the adjusted parameters of the component to be adjusted to obtain the updated target component; Returning to the step of obtaining the current parameters of the target component in the current development result for the updated target component, obtaining a corresponding new level result, and detecting whether the new level result meets the target level; If it is detected that the new level result does not meet the target level, the second response sensitivity in the sorting result is obtained, and the component to be adjusted corresponding to the second response sensitivity is determined in the updated target component, and the step of adjusting the current parameters of the component to be adjusted to obtain the adjusted parameters of the component to be adjusted is returned.
5. The component parameter optimization method according to any one of claims 2 to 4, characterized in that: The parameter adjustment method comprises: For any component, respectively obtain the standard vibration and noise transmission degree corresponding to the component under each other parameter, and for any standard vibration and noise transmission degree, calculate the similarity between the standard vibration and noise transmission degree and the corresponding adjusted vibration and noise transmission degree of the component; The maximum similarity among all similarities is obtained, and other parameters corresponding to the maximum similarity are used as adjusted parameters of the component.
6. The component parameter optimization method according to claim 1, characterized in that: The step of obtaining the trained in-vehicle road noise prediction model includes: Obtain historical parameters of target components of developed vehicles and historical level results of corresponding in-vehicle road noise; Based on the historical parameters, using the in-vehicle road noise prediction model to predict the in-vehicle road noise level of the developed vehicle to obtain a predicted sample level result, and using a mean square error loss function to calculate the loss between the sample level result and the historical level result; The model parameters of the in-vehicle road noise prediction model are reversely adjusted using a stochastic gradient descent algorithm until the loss converges, thereby obtaining a trained in-vehicle road noise prediction model.
7. A component parameter optimization device based on vehicle interior road noise prediction, characterized in that: The component parameter optimization device comprises: A noise prediction module is used to obtain current parameters of target components in the current development results during vehicle development, and to predict the in-vehicle road noise level of the vehicle using the trained in-vehicle road noise prediction model based on the current parameters to obtain a predicted level result; an impact analysis module, configured to detect whether the level result satisfies a preset target level, and if it is detected that the level result does not satisfy the target level, calculate a response sensitivity representing the impact of the target component on the in-vehicle road noise according to the current parameters and the level result; A parameter adjustment module, used for adjusting the current parameter according to the response sensitivity to obtain the adjusted parameter, and updating the target component in the current development result according to the adjusted parameter; A parameter determination module is used to return to the step of obtaining the current parameters of the target component in the current development result until it is detected that the level result meets the target level, and determine that the current parameters corresponding to the level result meeting the target level are the parameter optimization results of the target component.
8. The component parameter optimization system according to claim 7, characterized in that: The impact analysis module includes: a characteristic adjustment unit, used for obtaining a standard vibration-noise transfer degree corresponding to the target component under the current parameters, and adjusting the standard vibration-noise transfer degree using a preset adjustment coefficient to obtain an adjusted vibration-noise transfer degree; a change analysis unit, used to calculate a change in the vibration-noise transmission degree between the standard vibration-noise transmission degree and the adjusted vibration-noise transmission degree; The influence calculation unit is used to obtain the response sensitivity characterizing the influence of the target component on the road noise inside the vehicle according to the ratio between the change in the vibration noise transmission degree and the grade result.
9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the component parameter optimization method according to any one of claims 1 to 6 is implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the component parameter optimization method according to any one of claims 1 to 6 is implemented.