NVH index formulating method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of vehicles, in particular to an NVH index making method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting NVH sensitivity data of a plurality of user groups, and constructing a user group feature database; determining a target user group, extracting a group feature model corresponding to the target user group from the user group feature database, and determining NVH sensitive characteristics of the target user group according to the group feature model; and based on the NVH sensitive characteristics of the target user group, determining the weight of each parameter in the preset NVH general index, and generating a final NVH index corresponding to the target user group according to the weight. Therefore, the problem that the general NVH index cannot adapt to the difference of different user groups on the NVH sensitive characteristics and cannot meet the personalized design requirement in the related technology is solved, the personalized requirement of the user is met, the product safety is improved, and meanwhile the product competitiveness is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to an NVH index formulation method and device, an electronic device and a storage medium. BACKGROUND

[0002] At present, noise, vibration and harshness (NVH) indexes are mostly general, and are formulated based on the average perception of the public or the general standards of the industry, such as unified noise sound pressure level limits and vibration acceleration thresholds.

[0003] However, the general NVH index in the related art does not take into account the physiological, psychological and usage habit differences of different user groups, and these differences can lead to significant differences in the sensitive characteristics of NVH for different user groups, making it impossible to accurately set the sensitive characteristics for a specific user group, resulting in products designed using general indexes being difficult to meet the usage needs of a specific user group, and also affecting the user experience and even bringing potential health risks. SUMMARY

[0004] The present application provides an NVH index formulation method, device, electronic device and storage medium to solve the problem that the general NVH index in the related art cannot adapt to the differences in the sensitive characteristics of NVH for different user groups and cannot meet the needs of individualized design, not only meeting the individualized needs of users, but also improving product safety and enhancing product competitiveness.

[0005] The first aspect of the present application provides an NVH index formulation method, comprising the following steps: Collecting NVH sensitivity data of multiple user groups, and constructing a user group feature database according to the NVH sensitivity data of the multiple user groups; Determining a target user group, extracting a group feature model corresponding to the target user group from the user group feature database, and determining the NVH sensitive characteristics of the target user group according to the group feature model corresponding to the target user group; Based on the NVH sensitive characteristics of the target user group, determining the weight of each parameter in the preset NVH general index, and generating the final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH general index.

[0006] Optionally, in some embodiments, the target user group is an infant group, and after generating the final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH general index, the method further comprises: Judging whether there is a safety seat evaluation requirement; If there is a safety seat evaluation requirement, a safety seat to be tested is determined based on the safety seat evaluation requirement, and noise signals and vibration signals of the safety seat to be tested during vehicle driving are collected, wherein the safety seat to be tested is installed on the vehicle, and the vehicle is manufactured based on the final NVH index; An evaluation result of the safety seat to be tested is obtained according to the noise signals and the vibration signals.

[0007] Optionally, in some embodiments, after obtaining the evaluation result of the safety seat to be tested according to the noise signals and the vibration signals, the method further comprises: generating an optimization suggestion for the safety seat to be tested according to the evaluation result; sending the optimization suggestion to a preset mobile terminal.

[0008] Optionally, in some embodiments, the user group feature database is constructed according to NVH sensitivity data of a plurality of user groups, comprising: preprocessing the NVH sensitivity data to obtain preprocessing data, and extracting typical sensitive features of the plurality of user groups from the preprocessing data; establishing a user group feature model according to the typical sensitive features of the plurality of user groups, and forming a user group feature database according to the user group feature model.

[0009] Optionally, in some embodiments, the preprocessing the NVH sensitivity data to obtain preprocessing data, and extracting typical sensitive features of the plurality of user groups from the preprocessing data, comprises: performing data cleaning on the NVH sensitivity data to obtain cleaned data, performing data screening on the cleaned data to obtain screened data, and performing data analysis on the screened data to obtain preprocessing data; extracting the typical sensitive features of the plurality of user groups from the preprocessing data based on a preset statistical method and a machine learning algorithm.

[0010] Optionally, in some embodiments, the NVH sensitivity data comprises at least one of an age group, a gender, a health condition, and an NVH sensitive feature of a user, wherein the NVH sensitive feature comprises a hearing threshold, a vibration perception threshold, an evaluation result of different frequency noises, and an evaluation result of different frequency vibrations.

[0011] The second aspect embodiment of the present application provides an NVH index formulation device, comprising: a construction module configured to collect NVH sensitivity data of a plurality of user groups, and construct a user group feature database according to the NVH sensitivity data of the plurality of user groups; The determining module is configured to determine a target user group, extract a group characteristic model corresponding to the target user group from a user group characteristic database, and determine NVH sensitive characteristics of the target user group according to the group characteristic model corresponding to the target user group. The calculating module is configured to determine weights of each parameter in a preset NVH universal index based on the NVH sensitive characteristics of the target user group, and generate a final NVH index corresponding to the target user group according to the weights of each parameter in the preset NVH universal index.

[0012] Optionally, in some embodiments, the target user group is an infant group, and after the final NVH index corresponding to the target user group is generated according to the weights of each parameter in the preset NVH universal index, the calculating module is further configured to: determine whether there is a safety seat evaluation requirement; if there is a safety seat evaluation requirement, determine a to-be-tested safety seat based on the safety seat evaluation requirement, and collect noise signals and vibration signals of the to-be-tested safety seat during vehicle driving, wherein the to-be-tested safety seat is installed on the vehicle, and the vehicle is manufactured based on the final NVH index; obtain an evaluation result of the to-be-tested safety seat according to the noise signals and the vibration signals.

[0013] Optionally, in some embodiments, after the evaluation result of the to-be-tested safety seat is obtained according to the noise signals and the vibration signals, the calculating module is further configured to: generate an optimization suggestion for the to-be-tested safety seat according to the evaluation result; send the optimization suggestion to a preset mobile terminal.

[0014] Optionally, in some embodiments, the constructing module is specifically configured to: preprocess NVH sensitivity data to obtain preprocessed data, and extract typical sensitive characteristics of a plurality of user groups from the preprocessed data; establish a user group characteristic model according to the typical sensitive characteristics of the plurality of user groups, and form a user group characteristic database according to the user group characteristic model.

[0015] Optionally, in some embodiments, the constructing module is further configured to: clean the NVH sensitivity data to obtain cleaned data, perform data screening on the cleaned data to obtain screened data, and perform data analysis on the screened data to obtain the preprocessed data; extract the typical sensitive characteristics of the plurality of user groups from the preprocessed data based on a preset statistical method and a machine learning algorithm.

[0016] Optionally, in some embodiments, the NVH sensitivity data includes at least one of an age group, a gender, a health condition, and an NVH sensitive feature of the user, wherein the NVH sensitive feature includes a hearing threshold, a vibration perception threshold, a result of evaluation on different frequency noises, and a result of evaluation on different frequency vibrations.

[0017] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for formulating the NVH index according to the first aspect of the present application.

[0018] The fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method for formulating the NVH index according to the first aspect of the present application.

[0019] Therefore, the embodiments of the present application have at least the following several aspects of innovation: first, a dynamic user group feature database is established to accurately capture the NVH sensitive features of different user groups; second, based on the sensitive features of the target user group, personalized NVH indexes are generated by adjusting the index weights to realize customization of the indexes; third, an NVH personalized test evaluation system including a user feature recognition module is constructed to realize an integrated process from user feature recognition to index generation and test evaluation. In addition, the method and system of the embodiments of the present application can be applied to the fields of smart home, medical devices, etc. Therefore, the method for formulating the NVH index of the embodiments of the present application has at least the following beneficial effects: (1) Meet individual needs: personalized NVH indexes can be generated for the sensitive features of different user groups, so that product design is more in line with the actual needs of specific user groups, and user experience is improved.

[0020] (2) Improve product safety: for special groups such as infants and patients, strict personalized index restrictions can reduce the potential harm of NVH to their health and improve the safety of products.

[0021] (3) Enhance product competitiveness: in the fields of smart home, medical devices, etc., products designed based on personalized NVH indexes can win the favor of target user groups to enhance the competitiveness of products in the market. (4) Promote the fine development of the industry: the embodiments of the present application provide a new way for formulating NVH indexes, promoting the development of the industry from general standards to individualization and refinement.

[0022] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for determining NVH (Noise, Vibration, and Harshness) indicators according to an embodiment of this application. Figure 2 This is a flowchart illustrating a method for customizing NVH (Noise, Vibration, and Harshness) indicators for a specific group, according to an embodiment of this application. Figure 3 This is a block diagram of an NVH index determination device according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] The following description, with reference to the accompanying drawings, illustrates a method, apparatus, electronic device, and storage medium for determining NVH (Noise, Vibration, and Harshness) indicators according to embodiments of this application. Addressing the problem mentioned in the background art that general NVH indicators cannot adapt to the differences in NVH sensitivity characteristics among different user groups and thus cannot meet personalized design needs, this application provides a method for determining NVH indicators. In this method, NVH sensitivity data from multiple user groups are first collected to construct a user group feature database; then, a target user group is identified, and a group feature model corresponding to the target user group is extracted to clarify its NVH sensitivity characteristics; finally, based on the NVH sensitivity characteristics of the target user group, the weight of each parameter in a preset general NVH indicator is determined, and the final NVH indicator corresponding to the target user group is generated. This solves the problem that general NVH indicators in related technologies cannot adapt to the differences in NVH sensitivity characteristics among different user groups and thus cannot meet personalized design needs. It not only meets users' personalized needs but also improves product safety and enhances product competitiveness.

[0026] Specifically, Figure 1 A flowchart illustrating a method for determining NVH (Noise, Vibration, and Harshness) indicators provided in this application embodiment.

[0027] like Figure 1 As shown, the method for determining this NVH indicator includes the following steps: In step S101, NVH sensitivity data of multiple user groups are collected, and a user group feature database is constructed based on the NVH sensitivity data of multiple user groups.

[0028] Among them, the user group refers to a group of users with common usage scenarios and demand characteristics; NVH sensitivity data refers to the user's perception and tolerance threshold of product noise, vibration and acoustic roughness; the user group characteristic database refers to the data set that stores the NVH sensitivity and core attributes of each user group.

[0029] In some embodiments, the NVH sensitivity data includes at least one of the user's age group, gender, health status, and NVH sensitivity characteristics, wherein the NVH sensitivity characteristics include hearing threshold, vibration perception threshold, evaluation results for noise at different frequencies, and evaluation results for vibration at different frequencies.

[0030] Among them, NVH sensitivity characteristics refer to the core attributes related to users' perception, tolerance, and preference for product noise, vibration, and acoustic roughness; hearing threshold refers to the lowest noise intensity that users can identify, which is the basic indicator for measuring noise perception sensitivity; vibration perception threshold refers to the lowest vibration intensity that users can perceive, reflecting the degree of sensitivity to vibration stimuli; the evaluation results for noise at different frequencies refer to users' subjective satisfaction or intolerance ratings for noise at different frequency bands; the evaluation results for vibration at different frequencies refer to users' subjective experience ratings for vibration at different frequency bands.

[0031] In some embodiments, a user group feature database is constructed based on NVH sensitivity data of multiple user groups, including: preprocessing the NVH sensitivity data to obtain preprocessed data, and extracting typical sensitivity features of multiple user groups from the preprocessed data; establishing a user group feature model based on the typical sensitivity features of multiple user groups, and forming a user group feature database based on the user group feature model.

[0032] Among them, preprocessed data refers to standardized NVH sensitivity data that meets the analysis requirements after cleaning and standardization; typical sensitivity features refer to core indicators that can represent the NVH perception characteristics of a certain user group; and user group feature model refers to a structured data model that is associated with user group attributes.

[0033] In some embodiments, NVH sensitivity data is preprocessed to obtain preprocessed data, and typical sensitivity features of multiple user groups are extracted from the preprocessed data. This includes: cleaning the NVH sensitivity data to obtain cleaned data, filtering the cleaned data to obtain filtered data, and performing data analysis on the filtered data to obtain preprocessed data; and extracting typical sensitivity features of multiple user groups from the preprocessed data based on preset statistical methods and machine learning algorithms.

[0034] Among them, cleaned data refers to the original NVH sensitivity data that meets the standards of data integrity and accuracy; filtered data refers to the NVH sensitivity data that meets the analysis requirements and focuses on the target user group; preprocessed data refers to standardized, high-quality data that can be directly used for feature extraction; preset statistical methods refer to the basic methods used for data feature analysis; and machine learning algorithms refer to the algorithms used to mine the potential sensitive features of the group.

[0035] It should be understood that different user groups have significantly different sensitivities to NVH (Noise, Vibration, and Harshness) due to physiological, psychological, and usage habits. For example, due to changes in physiological functions, the elderly are more sensitive to low-frequency vibrations, and even slight low-frequency vibrations can affect their rest and health; while young people have a relatively higher tolerance for high-frequency noise, focusing more on the functionality and appearance of products, and are less sensitive to high-frequency noise. In addition, infants and young children react strongly to high-frequency noise and sudden vibration stimuli, requiring lower high-frequency noise limits for baby and maternity appliances; users of medical devices may be more sensitive to vibration and noise due to their medical conditions, requiring more stringent NVH standards.

[0036] Specifically, this application embodiment can construct a user group characteristic database in the following ways: First, this application embodiment can collect relevant data from different user groups through various methods such as questionnaires, laboratory tests, and user feedback; the data content includes, but is not limited to, age group, gender, health status, usage habits, and NVH sensitivity characteristics; wherein, NVH sensitivity characteristics include, but are not limited to, hearing threshold, vibration perception threshold, and subjective evaluation of noise / vibration at different frequencies. Second, this application embodiment can clean, filter, and analyze the collected data, and use statistical methods and machine learning algorithms to extract typical sensitivity characteristics of each user group, establish a user group characteristic model, and form a user group characteristic database; wherein, the machine learning algorithm can be cluster analysis, regression analysis, etc. In addition, this database can be dynamically updated and optimized based on new collected data. For example, this application uses infants and toddlers as an example for illustration. Regarding data collection, embodiments of this application can collect data on infants and toddlers riding in child safety seats in vehicles through various methods: On the one hand, questionnaires are distributed to parents to understand their observations of children's reactions to in-vehicle noise and vibration during driving, such as under what road conditions and vehicle speeds children are more likely to cry, and whether they are sensitive to vibrations during vehicle start-up and braking; on the other hand, a professional laboratory is used to simulate the vehicle driving environment, testing infants and toddlers of different ages. Professional noise generators and vibration platforms are used to simulate common 200-800Hz noise and 5-50Hz vibrations encountered during vehicle operation. By monitoring the infants' physiological indicators and behavioral performance, their reactions under different NVH conditions are recorded. Physiological indicators include, but are not limited to, heart rate and respiratory rate; behavioral performance includes, but is not limited to, whether they fall asleep, whether they cry, and their limb movements. In terms of data processing and modeling, this application embodiment first cleans the large amount of collected data to remove invalid data and outliers; then, statistical methods are used to analyze the valid data to calculate the probability of infants exhibiting restlessness, crying, or other reactions under different frequencies of noise and vibration; next, clustering algorithms in machine learning are used to group infant data with similar sensitivity characteristics into one category and extract the typical sensitivity characteristics of this group. Analysis revealed that infants are particularly sensitive to noise in the 300-600Hz range and vibration in the 10-40Hz range when riding in vehicle safety seats; when noise in this frequency range exceeds a certain intensity or vibration acceleration is too high, infants are prone to irritability and incessant crying, seriously affecting their safety and comfort while riding. Based on these findings, this application embodiment can establish an NVH sensitivity characteristic model for infants riding in vehicle child safety seats within the mother-infant group and store it in a user group characteristic database.

[0037] In step S102, the target user group is determined, and the group feature model corresponding to the target user group is extracted from the user group feature database. The NVH sensitivity characteristics of the target user group are determined based on the group feature model corresponding to the target user group.

[0038] Among them, the target user group refers to the core service objects that focus on product research and development optimization; the group characteristic model refers to the structured data model that describes the core characteristics of the target user group's NVH sensitivity.

[0039] Specifically, embodiments of this application can determine the NVH sensitivity characteristics of a target user group in the following way: Embodiments of this application can first identify the target user group of the product, then retrieve the sensitivity characteristic model of the target user group from the user group characteristic database, and thereby determine key parameters such as the sensitive frequency range and sensitivity intensity of the target user group to NVH. The target user group can include mothers and infants, the elderly, and medical device users, etc.

[0040] For example, this application uses infants and toddlers as an example. First, the target user group is identified, and it is determined that the target user group of the vehicle child safety seat is the infants and toddlers who ride in the seat; then, sensitive feature matching is performed, and the sensitive feature model of the infant and toddler group is retrieved from the user group feature database to determine the sensitivity characteristics of this group to 300-600Hz noise and 10-40Hz vibration, as well as the sensitivity to sudden changes in noise and vibration.

[0041] In step S103, based on the NVH sensitivity characteristics of the target user group, the weight of each parameter in the preset NVH general index is determined, and the final NVH index corresponding to the target user group is generated according to the weight of each parameter in the preset NVH general index.

[0042] Among them, the preset NVH general index refers to the industry-standard noise and vibration related parameters that are based on the average perception of the general public; the weight of each parameter is a quantitative coefficient used to reflect the degree of influence of the parameter on the NVH comfort of the target user group; the final NVH index refers to the personalized NVH parameter requirements that can be directly used for product design or evaluation.

[0043] Specifically, the embodiments of this application can generate the final NVH index corresponding to the target user group in the following way: First, the index weights are adjusted. The embodiments of this application can adjust the weights of various parameters of the general NVH index according to the sensitivity characteristics of the target user group. These parameters can be noise limits, vibration acceleration limits, etc., for different frequency bands. For example, for the maternal and infant group, the allowable value for noise in the 300-500Hz range is reduced and the weight for limiting sudden vibrations is increased; for the elderly group, the weight for low-frequency vibrations is increased and their allowable value is reduced. Then, based on the adjusted weights, personalized NVH indexes adapted to the target user group are generated. Examples include "NVH safety index for maternal and infant scenarios," "vibration comfort index for elderly products," and "NVH friendliness index for medical equipment."

[0044] For example, this application uses infants and toddlers as an example. In the indicator weighting adjustment stage, this application embodiment can adjust the weights of various parameters of the general NVH indicators for vehicle child safety seats based on the actual conditions during vehicle operation. Specifically, the allowable noise value for 300-600Hz is reduced by 25%; the allowable vibration acceleration value for 10-40Hz is reduced by 35%; and the weighting of restrictions on sudden changes in noise and vibration is significantly increased. Scenarios that may cause sudden changes in noise and vibration include, but are not limited to, instantaneous noise and vibration generated by rapid vehicle acceleration or braking. In the personalized indicator generation stage, this application embodiment can generate a "Vehicle Child Safety Seat NVH Safety and Comfort Index" based on the adjusted weights. This index covers parameters such as the 300-600Hz noise limit, the 10-40Hz vibration acceleration limit, the noise or vibration abrupt change limit, and the duration limit of continuous noise.

[0045] Therefore, by constructing a user group characteristic database, the NVH sensitivity characteristics of different user groups can be accurately grasped, providing a reliable basis for the generation of personalized indicators and solving the problem that general indicators do not adequately consider the differences among user groups. During the generation of personalized indicators, the indicator weights are adjusted according to the sensitivity characteristics of the target user group, ensuring that the generated indicators accurately reflect the needs of that group, thereby guiding more targeted product design. The personalized NVH testing and evaluation method can test and evaluate products based on personalized indicators, ensuring that products meet the requirements of the target user group, ultimately achieving beneficial effects such as meeting personalized needs, improving product safety, and enhancing competitiveness.

[0046] Furthermore, after obtaining the final NVH indicators corresponding to the target user group, this application embodiment can also test the relevant products of the target user group, which will be described below with reference to specific embodiments.

[0047] Optionally, in some embodiments, the target user group is infants and young children. After generating the final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH general index, the method further includes: determining whether there is a need for safety seat evaluation; if there is a need for safety seat evaluation, determining the safety seat to be tested based on the safety seat evaluation need, and collecting noise and vibration signals of the safety seat to be tested during vehicle operation, wherein the safety seat to be tested is installed in the vehicle, and the vehicle is manufactured based on the final NVH index; and obtaining the evaluation result of the safety seat to be tested based on the noise and vibration signals.

[0048] Among them, the safety seat assessment requirement refers to the testing requirement to verify whether the seat meets the NVH sensitive characteristics requirements for infants and young children; the safety seat to be tested refers to the child safety seat in the vehicle that is intended to verify NVH performance and is suitable for use by infants and young children; noise signal refers to the acoustic data generated around the seat during vehicle operation, reflecting the intensity and frequency of noise; vibration signal refers to the mechanical data transmitted by the seat during vehicle operation, reflecting the acceleration and frequency of vibration; the assessment result refers to the conclusion drawn on whether the seat meets the safety and comfort requirements of infants and young children.

[0049] Specifically, embodiments of this application can construct a personalized NVH testing and evaluation system, which includes a user feature identification module, an indicator generation module, a data acquisition module, a testing and evaluation module, and a result output module. The user feature identification module is used to identify the target user group and retrieve its sensitive features; the indicator generation module is used to generate personalized indicators based on the sensitive features; the data acquisition module is used to collect the NVH signals of the product under test; the testing and evaluation module is used to test and evaluate the product under test based on the personalized indicators; and the result output module is used to output the testing and evaluation results.

[0050] For example, this application uses infants and toddlers as an example for illustration. The embodiments of this application can utilize a personalized NVH testing and evaluation system including a user feature recognition module to test a child safety seat in a vehicle of a certain brand. During vehicle operation, the data acquisition module collects noise and vibration signals around the child safety seat in real time using sensors installed near the seat. The testing and evaluation module uses the "Vehicle Child Safety Seat NVH Safety and Comfort Index" to analyze and evaluate the collected signals, determining whether each parameter is within the limit range. The results output module displays the evaluation results in intuitive charts and text.

[0051] Optionally, in some embodiments, after obtaining the evaluation results of the safety seat under test based on the noise signal and vibration signal, the method further includes: generating optimization suggestions for the safety seat under test based on the evaluation results; and sending the optimization suggestions to a preset mobile terminal.

[0052] Among them, the optimization suggestions refer to the specific improvement plans proposed for the NVH parameters of the safety seat under test that exceed the standard; the preset mobile terminal refers to the designated device that is pre-bound to receive the optimization suggestions.

[0053] Specifically, if one or more parameters in the evaluation results exceed the limit, this application embodiment will clearly indicate that the product needs to be optimized in the corresponding aspects. At the same time, optimization suggestions, including details of the parameters exceeding the limit, specific improvement directions and implementation suggestions, will be sent to the pre-bound preset mobile terminal to ensure that the relevant responsible persons receive them in a timely manner and promote the product optimization work. The preset mobile terminal can be a designated mobile phone or work terminal of the manufacturer's R&D manager or quality control personnel.

[0054] For example, this application uses infants and toddlers as an example for illustration. The improvement and optimization suggestions for the sound insulation materials of child safety seats based on the evaluation results in this application embodiment may include the following aspects: First, upgrading the seat sound insulation material by selecting more efficient sound insulation cotton to reduce noise in the 300-600Hz range; second, improving the seat's shock absorption structure by increasing the elastic coefficient of the buffer spring to reduce vibration acceleration in the 10-40Hz range; third, adding damping devices at the connection between the seat and the vehicle to reduce instantaneous vibration and noise generated during rapid acceleration and braking. After optimization and adjustment, all parameters of the child safety seat in this application embodiment meet the requirements of the "Vehicle Child Safety Seat NVH Safety Comfort Index" during subsequent testing.

[0055] Therefore, this application embodiment targets the infant and toddler target user group. After generating the appropriate final NVH index and using it for vehicle manufacturing, it can further respond to the needs of safety seat evaluation, collect the noise and vibration signals of the safety seat to be tested installed in the vehicle during driving, obtain the evaluation results, generate optimization suggestions and send them to the preset mobile terminal. Finally, by accurately matching the NVH sensitivity characteristics of the infant and toddler group and improving the safety seat evaluation and optimization process, the safety and competitiveness of the product are improved.

[0056] Furthermore, to enable those skilled in the art to better understand the method for formulating NVH indicators in the embodiments of this application, the following is combined with... Figure 2 Specific embodiments will be described in detail.

[0057] Figure 2 A flowchart illustrating a method for determining NVH (Noise, Vibration, and Harshness) indicators according to an embodiment of this application.

[0058] like Figure 2 As shown, the method for determining this NVH indicator includes the following steps: In step S201, the process begins.

[0059] In step S202, a user group characteristic database is established: data collection, data processing and modeling.

[0060] In step S203, personalized indicators are generated: the target user group is located, and the indicator weights are adjusted by matching sensitive features.

[0061] In step S204, a personalized NVH testing and evaluation system is constructed: system components and functions of each module.

[0062] In step S205, the process ends.

[0063] According to the NVH index formulation method proposed in this application, NVH sensitivity data of multiple user groups can be collected first to construct a user group feature database; then, a target user group can be identified, and a group feature model corresponding to the target user group can be extracted to clarify its NVH sensitivity characteristics; finally, based on the NVH sensitivity characteristics of the target user group, the weight of each parameter in the preset general NVH index is determined, and the final NVH index corresponding to the target user group is generated. This solves the problem in related technologies where general NVH indices cannot adapt to the differences in NVH sensitivity characteristics among different user groups and cannot meet personalized design needs. It not only meets users' personalized needs but also improves product safety and enhances product competitiveness.

[0064] Next, the apparatus for determining NVH indicators according to embodiments of this application is described with reference to the accompanying drawings.

[0065] Figure 3 This is a block diagram of an NVH index determination device according to an embodiment of this application.

[0066] like Figure 3 As shown, the NVH index setting device 10 includes: a construction module 100, a determination module 200, and a calculation module 300.

[0067] The system comprises the following modules: a construction module 100, which collects NVH sensitivity data from multiple user groups and constructs a user group feature database based on the NVH sensitivity data; a determination module 200, which determines the target user group, extracts the corresponding group feature model from the user group feature database, and determines the NVH sensitivity characteristics of the target user group based on the corresponding group feature model; and a calculation module 300, which determines the weight of each parameter in the preset NVH general indicators based on the NVH sensitivity characteristics of the target user group, and generates the final NVH indicator corresponding to the target user group based on the weight of each parameter in the preset NVH general indicators.

[0068] Optionally, in some embodiments, the target user group is infants and young children. After generating the final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH general index, the calculation module 300 is further used to: determine whether there is a need for safety seat evaluation; if there is a need for safety seat evaluation, determine the safety seat to be tested based on the safety seat evaluation need, and collect the noise signal and vibration signal of the safety seat to be tested during vehicle driving, wherein the safety seat to be tested is installed in the vehicle, and the vehicle is manufactured based on the final NVH index; and obtain the evaluation result of the safety seat to be tested based on the noise signal and vibration signal.

[0069] Optionally, in some embodiments, after obtaining the evaluation results of the safety seat under test based on the noise signal and vibration signal, the calculation module 300 is further configured to: generate optimization suggestions for the safety seat under test based on the evaluation results; and send the optimization suggestions to a preset mobile terminal.

[0070] Optionally, in some embodiments, the construction module 100 is specifically used for: preprocessing NVH sensitivity data to obtain preprocessed data, and extracting typical sensitivity features of multiple user groups from the preprocessed data; establishing a user group feature model based on the typical sensitivity features of multiple user groups, and forming a user group feature database based on the user group feature model.

[0071] Optionally, in some embodiments, the construction module 100 is further configured to: perform data cleaning on the NVH sensitivity data to obtain cleaned data, perform data filtering on the cleaned data to obtain filtered data, and perform data analysis on the filtered data to obtain preprocessed data; and extract typical sensitivity features of multiple user groups from the preprocessed data based on preset statistical methods and machine learning algorithms.

[0072] Optionally, in some embodiments, the NVH sensitivity data includes at least one of the user's age group, gender, health status, and NVH sensitivity characteristics, wherein the NVH sensitivity characteristics include hearing threshold, vibration perception threshold, evaluation results for noise at different frequencies, and evaluation results for vibration at different frequencies.

[0073] It should be noted that the explanation of the above-described method for determining NVH indicators also applies to the device for determining NVH indicators in this embodiment, and will not be repeated here.

[0074] The NVH index formulation apparatus proposed in this application first collects NVH sensitivity data from multiple user groups and constructs a user group feature database; then, it identifies the target user group, extracts the corresponding group feature model to clarify its NVH sensitivity characteristics; finally, based on the NVH sensitivity characteristics of the target user group, it determines the weight of each parameter in the preset general NVH index and generates the final NVH index corresponding to the target user group. This solves the problem in related technologies where general NVH indices cannot adapt to the differences in NVH sensitivity characteristics among different user groups and cannot meet personalized design needs. It not only meets users' personalized needs but also improves product safety and enhances product competitiveness.

[0075] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0076] When the processor 402 executes the program, it implements the method for determining NVH indicators provided in the above embodiments.

[0077] Furthermore, the electronic device also includes: Communication interface 403 is used for communication between memory 401 and processor 402.

[0078] The memory 401 is used to store computer programs that can run on the processor 402.

[0079] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0080] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0081] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0082] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0083] This application also provides a computer-readable storage medium having a computer program stored thereon, which is implemented when executed by a processor. Figure 1 An embodiment of a method for determining NVH (Noise, Vibration, and Harshness) indicators.

[0084] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0085] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0086] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0088] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0089] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0090] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0091] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method of formulating an NVH metric, characterized by, The method comprises the following steps: Collecting NVH sensitivity data of a plurality of user groups, and constructing a user group feature database according to the NVH sensitivity data of the plurality of user groups; Determining a target user group, extracting a group feature model corresponding to the target user group from the user group feature database, and determining NVH sensitive characteristics of the target user group according to the group feature model corresponding to the target user group; Based on the NVH sensitive characteristics of the target user group, determining the weight of each parameter in the preset NVH general index, and generating the final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH general index.

2. The method of claim 1, wherein, The target user group is an infant group, and after generating the final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH general index, the method further comprises: Judging whether there is a safety seat evaluation requirement; If the safety seat evaluation requirement exists, determining a to-be-tested safety seat based on the safety seat evaluation requirement, and collecting noise signals and vibration signals of the to-be-tested safety seat during vehicle driving, wherein the to-be-tested safety seat is installed on the vehicle, and the vehicle is manufactured based on the final NVH index; Obtaining an evaluation result of the to-be-tested safety seat according to the noise signals and the vibration signals.

3. The method of claim 2, wherein, After obtaining the evaluation result of the to-be-tested safety seat according to the noise signals and the vibration signals, the method further comprises: Generating an optimization suggestion for the to-be-tested safety seat according to the evaluation result; Sending the optimization suggestion to a preset mobile terminal.

4. The method of claim 1, wherein, The construction of the user group feature database according to the NVH sensitivity data of the plurality of user groups comprises: Preprocessing the NVH sensitivity data to obtain preprocessed data, and extracting typical sensitive features of the plurality of user groups from the preprocessed data; Establishing a user group feature model according to the typical sensitive features of the plurality of user groups, and forming the user group feature database according to the user group feature model.

5. The method of claim 4, wherein, The preprocessing of the NVH sensitivity data to obtain preprocessed data, and the extraction of typical sensitive features of the plurality of user groups from the preprocessed data comprises: Data cleaning of the NVH sensitivity data to obtain cleaned data, data screening of the cleaned data to obtain screened data, and data analysis of the screened data to obtain the preprocessed data; Based on a preset statistical method and a machine learning algorithm, the typical sensitive features of the plurality of user groups are extracted from the preprocessed data.

6. The method according to claim 4 or 5, characterized in that, The NVH sensitivity data includes at least one of the user's age, gender, health status, and NVH sensitive features, wherein the NVH sensitive features include hearing threshold, vibration perception threshold, evaluation results of different frequency noises, and evaluation results of different frequency vibrations.

7. An NVH index formulation device, comprising: The constructing module is configured to collect NVH sensitivity data of a plurality of user groups, and construct a user group feature database according to the NVH sensitivity data of the plurality of user groups; The determining module is configured to determine a target user group, extract a group feature model corresponding to the target user group from the user group feature database, and determine an NVH sensitive characteristic of the target user group according to the group feature model corresponding to the target user group; The computing module is configured to determine a weight of each parameter in a preset NVH universal index based on the NVH sensitive characteristic of the target user group, and generate a final NVH index corresponding to the target user group according to the weight of each parameter in the preset NVH universal index.

8. The apparatus of claim 7, wherein, The target user group is an infant group, and after the final NVH index corresponding to the target user group is generated according to the weight of each parameter in the preset NVH universal index, the computing module is further configured to: determine whether there is a safety seat evaluation requirement; if the safety seat evaluation requirement exists, determine a to-be-tested safety seat based on the safety seat evaluation requirement, and collect a noise signal and a vibration signal of the to-be-tested safety seat during vehicle driving, wherein the to-be-tested safety seat is installed on the vehicle, and the vehicle is manufactured based on the final NVH index; obtain an evaluation result of the to-be-tested safety seat according to the noise signal and the vibration signal.

9. An electronic device, comprising: The computer program is stored in the memory and executable on the processor, and the processor executes the program to implement the method for formulating the NVH index. The program is executed by the processor to implement the method for formulating the NVH index.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is stored in the memory and executable on the processor, and the processor executes the program to implement the method for formulating the NVH index.

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