Data processing method and device, storage medium, electronic equipment and vehicle

Abstract

The invention relates to a data processing method and device, a storage medium, electronic equipment and a vehicle, and relates to the technical field of data processing.The method comprises the steps that a server side receives an NVH noise level obtaining request sent by the vehicle, and the obtaining request carries the vehicle type, the real-time working condition and driving road information of the vehicle; obtaining a target mapping functional expression in a noise mapping model according to the vehicle type, the real-time working condition and the driving road information; then, the vehicle speed, the vehicle model, the driving road information and the environmental noise serve as input parameters of a target mapping functional expression, and the target NVH noise level corresponding to the environmental noise is calculated through the target mapping functional expression; and the target NVH noise level is sent to the vehicle. According to the technical scheme, the NVH noise level can be obtained through calculation by means of the noise mapping model of the server side, effective NVH information is provided for applications such as intelligent mode planning of the vehicle, effective noise control can be conveniently carried out in real time, and therefore the intelligent control effect of the vehicle is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a data processing method, apparatus, storage medium, electronic device, and vehicle. Background Technology

[0002] With the development of intelligent vehicle navigation technology, people have increasingly higher requirements for the quality and efficiency of road traffic management and intelligent travel services.

[0003] Currently, the relevant technology involves acquiring noise audio samples of different vehicle types at different speeds on actual roads through audio acquisition, and storing them on the backend server of the noise map. The backend server uses a random principle to digitally mix the noise audio samples and converts the digital audio mixing signal into a mixed audio file. Then, the frontend map can retrieve and parse the mixed audio file of the vehicle's travel segment to play the traffic noise of that vehicle's travel segment.

[0004] However, this method can only obtain the effect of road traffic noise playback at the user's perception level, and cannot obtain the road condition noise, vibration, and harshness (NVH) noise levels that can evaluate the road noise situation, thus making it impossible to carry out effective noise control. Summary of the Invention

[0005] In view of this, this application provides a data processing method, a storage medium, an electronic device, and a vehicle, the main purpose of which is to improve the technical problem in the prior art that it is impossible to obtain the NVH noise level that can be used to evaluate the noise situation of road sections, and thus impossible to carry out effective noise control.

[0006] Firstly, this application provides a data processing method that can be applied to server-side execution, including:

[0007] The system receives a request from a vehicle to obtain noise, vibration, and harshness (NVH) levels. The request includes the vehicle's model, real-time operating conditions, and road information. The real-time operating conditions include the vehicle's speed and ambient noise after removing noise from onboard devices. The road information includes road grade and road conditions.

[0008] Based on the vehicle model, the real-time operating conditions, and the driving road information, the target mapping function in the noise mapping model is obtained. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information.

[0009] The vehicle speed, vehicle type, road information, and ambient noise are used as input parameters of the target mapping function, and the target NVH noise level corresponding to the ambient noise is calculated through the target mapping function.

[0010] The target NVH noise level is sent to the vehicle.

[0011] Secondly, this application provides a data processing method that can be applied to vehicle-side execution, including:

[0012] A request to obtain NVH noise level is sent to the server. The request carries the vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The driving road information includes road grade and road conditions. The server is used to calculate the target NVH noise level corresponding to the ambient noise using a target mapping function in a noise mapping model that corresponds to the vehicle model, real-time operating conditions, and driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information.

[0013] Receive the target NVH noise level sent by the server.

[0014] Thirdly, this application provides a data processing apparatus that can be applied to server-side execution, including:

[0015] The receiving module is configured to receive a request from the vehicle to obtain the NVH noise level. The request carries the vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and the ambient noise after removing noise from onboard devices. The driving road information includes the road grade and road conditions.

[0016] The acquisition module is configured to acquire the target mapping function in the noise mapping model based on the vehicle model, the real-time operating conditions and the driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions and road information.

[0017] The input module is configured to take the vehicle speed, the vehicle type, the driving road information and the ambient noise as input parameters of the target mapping function, and calculate the target NVH noise level corresponding to the ambient noise through the target mapping function.

[0018] The transmitting module is configured to transmit the target NVH noise level to the vehicle.

[0019] Fourthly, this application provides a data processing apparatus that can be applied to vehicle-side execution, including:

[0020] The sending module is configured to send an NVH noise level acquisition request to the server. The acquisition request carries the vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The driving road information includes road grade and road conditions. The server is used to calculate the target NVH noise level corresponding to the ambient noise using a target mapping function in a noise mapping model that corresponds to the vehicle model, real-time operating conditions, and driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information.

[0021] The receiving module is configured to receive the target NVH noise level sent by the server.

[0022] Fifthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or the method described in the second aspect.

[0023] In a sixth aspect, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect or the method described in the second aspect.

[0024] In a seventh aspect, this application provides a vehicle including the apparatus described in the fourth aspect.

[0025] Using the above technical solution, this application provides a data processing method, apparatus, storage medium, electronic device, and vehicle. First, the server receives a request from the vehicle to obtain its NVH noise level. The request includes the vehicle model, real-time operating conditions, and road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The road information includes road grade and road conditions. Based on the vehicle model, real-time operating conditions, and road information, a target mapping function in a noise mapping model is obtained. The noise mapping model includes multiple mapping functions used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information. Then, the vehicle speed, vehicle model, road information, and ambient noise are used as input parameters to the target mapping function, and the target NVH noise level corresponding to the ambient noise is calculated using the target mapping function. Finally, the target NVH noise level is sent to the vehicle. By applying the technical solution of this embodiment, during the vehicle's operation on a road segment, the NVH noise level corresponding to the vehicle's model, real-time operating conditions, and driving road information can be calculated online in real time using the noise mapping model on the server side. This allows for the real-time acquisition of NVH noise levels that can evaluate the road segment's noise situation, providing effective NVH information for applications such as intelligent mode planning of the vehicle. This facilitates effective noise control in real time, thereby improving the effectiveness of intelligent vehicle control.

[0026] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating a data processing method provided in an embodiment of this application is shown;

[0030] Figure 2 A flowchart illustrating an application example provided in an embodiment of this application is shown;

[0031] Figure 3A flowchart illustrating another data processing method provided in an embodiment of this application is shown;

[0032] Figure 4 This paper shows a schematic diagram of the structure of a data processing apparatus provided in an embodiment of the present application;

[0033] Figure 5 A schematic diagram of another data processing apparatus provided in an embodiment of this application is shown. Detailed Implementation

[0034] To better understand the above-mentioned objectives, features, and advantages of this application, the solution of this application will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0035] To address the technical problem in existing technologies where sufficient data to evaluate road noise levels is unavailable, thus hindering effective planning, this embodiment provides a data processing method applicable to server-side execution, such as... Figure 1 As shown, the method includes:

[0036] Step 101: Receive the NVH noise level acquisition request sent by the vehicle.

[0037] Among them, NVH noise level is a comprehensive evaluation index of the noise, vibration and acoustic roughness generated by a vehicle during operation, which can reflect the sound quality and driving comfort of the vehicle under different operating conditions.

[0038] In some examples, the request includes the vehicle type, real-time operating conditions, and road information. The vehicle type can be categorized by powertrain, such as gasoline cars, diesel cars, hybrid cars, and electric cars. Real-time operating conditions include vehicle speed and ambient noise after removing onboard device noise. Road information can include road grade, road conditions, traffic flow, geographical environment, and other information that can affect vehicle operation, which can be obtained by combining navigation and route planning information.

[0039] Road classification can be a standard for categorizing roads based on factors such as their position in the transportation network, technical standards, service level, and design speed. For example, roads can be classified according to the areas they connect and their design speed: Class I highways connect important cities and regions, possessing high traffic capacity and good service facilities; Class II highways connect county towns, important townships, and industrial and mining bases; Class III highways primarily serve traffic connections between county-level towns; and Class IV highways primarily connect county towns and rural areas. Generally, road classification reflects a road's service function and traffic carrying capacity.

[0040] Step 102: Based on vehicle type, real-time operating conditions, and driving road information, obtain the target mapping function in the noise mapping model.

[0041] The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information.

[0042] For example, by learning and fitting the training set composed of feature data and label data from the dataset corresponding to different vehicle models, real-time operating conditions and driving road information, the target mapping function in the corresponding noise mapping model can be obtained. Multiple mapping functions can jointly constitute a complete noise mapping model to predict or simulate the NVH performance of the vehicle under specific conditions.

[0043] Step 103: Using vehicle speed, vehicle type, road information, and ambient noise as input parameters for the target mapping function, calculate the target NVH noise level corresponding to the ambient noise using the target mapping function.

[0044] For example, the noise mapping model composed of target mapping functions can be used for intelligent vehicles to dynamically adapt to changes in road conditions, and to actively control NVH noise levels through the controller to improve the in-vehicle NVH environment.

[0045] Step 104: Send the target NVH noise level to the vehicle.

[0046] In some examples, the vehicle needs to obtain the NVH noise level corresponding to the future driving segment in the navigation. After obtaining the target NVH noise level, the energy mode planning for the future driving segment of the vehicle can be carried out.

[0047] Compared with existing technologies, this embodiment can use the noise mapping model on the server to calculate the NVH noise level corresponding to the vehicle model, real-time operating conditions and driving road information in real time during the vehicle's driving on the road. This allows for the real-time acquisition of NVH noise levels that can evaluate the road noise situation, providing effective NVH information for applications such as intelligent mode planning of the vehicle, facilitating effective noise control in real time, and thus improving the effect of intelligent vehicle control.

[0048] Further optionally, the method in this embodiment may also include: obtaining vehicle model range, operating condition range, and road information corresponding to multiple mapping functions respectively; and obtaining a target mapping function in the noise mapping model that corresponds to the vehicle model, the real-time operating condition, and the driving road information, according to the vehicle model range, operating condition range, and road information corresponding to the multiple mapping functions respectively.

[0049] For example, each mapping function corresponds to a vehicle model range, operating condition range, and road information. After obtaining the above information for the target vehicle, the corresponding target mapping function can be found in the noise mapping model so as to accurately predict the NVH noise level of the vehicle under specific conditions.

[0050] Optionally, the method in this embodiment may further include: receiving historical sample data collected from the sample vehicle, the historical sample data including the vehicle type, road information of the route the sample vehicle traveled, and driving conditions corresponding to the route the sample vehicle traveled, the driving conditions including the vehicle speed, operating mode, remaining fuel, operating power of the range extender, and ambient noise after removing noise from onboard devices; dividing the historical sample data according to the road information of the route the sample vehicle traveled to obtain multiple datasets; clustering the datasets according to the vehicle type and driving conditions to obtain multiple sub-datasets corresponding to different vehicle type ranges, operating condition ranges, and road information, and analyzing the distribution of NVH noise levels corresponding to the multiple sub-datasets; constructing mapping functions corresponding to the multiple sub-datasets based on the distribution of NVH noise levels; and creating a noise mapping model using the constructed multiple mapping functions.

[0051] For example, such as Figure 2 As shown, after a period of data collection from vehicles, a large amount of historical vehicle sample data will accumulate in the server database. Once a certain amount has been accumulated, data can be extracted from the database and divided according to the road information of the sample vehicles' travel segments to obtain datasets of NVH noise levels corresponding to different road information. These datasets are then divided into multiple sub-datasets, and mapping functions and noise mapping models are constructed based on the NVH noise levels corresponding to the sub-datasets.

[0052] Optionally, the historical sample data is further divided based on road information of the sample vehicle's travel segments to obtain multiple datasets, including: dividing the historical sample data according to the road level of the sample vehicle's travel segments to obtain a dataset corresponding to each road level. Correspondingly, the datasets are clustered according to the sample vehicle's model and driving conditions to obtain multiple sub-datasets corresponding to different vehicle model ranges, driving condition ranges, and road information, including: clustering the datasets according to the road conditions of the sample vehicle's travel segments, the sample vehicle's model, and driving conditions to obtain multiple sub-datasets corresponding to different vehicle model ranges, driving condition ranges, and road information.

[0053] For example, clustering algorithms can be used to automatically divide the data in the dataset into different groups according to different vehicle types, operating conditions, and road information, and outlier data can be removed. The majority of valid data remaining are the subsets corresponding to different vehicle types, operating conditions, and road information. Subsets are defined based on data density, without needing to pre-specify the number of subsets. The dataset corresponding to road level is divided into multiple subsets through clustering. For example, subsets could include: vehicle types (gasoline and / or diesel), speed range (60km / h-80km / h), operating mode (economy mode), fuel remaining (35%-55%), range extender power range (5kW-8kW), ambient noise level (40dB-50dB after removing vehicle device noise), and no road congestion. Each subset obtained in this way has a similar data density.

[0054] Further optionally, based on the distribution of NVH noise levels, the above-mentioned construction of mapping functions corresponding to multiple subsets of data includes: based on the subsets and the distribution of NVH noise levels corresponding to the subsets, constructing mapping functions corresponding to the subsets through linear regression fitting and / or neural network algorithms.

[0055] In this embodiment, a corresponding mapping function can be trained based on each subset of data. The NVH noise level calculated by the mapping function can be used to evaluate the comfort and stability of the vehicle when driving on different road sections, providing a comfort reference for the driver and considering NVH factors when planning routes to provide more comfortable driving route planning. Furthermore, it is also of great significance for automakers to optimize vehicle NVH performance and for improving road construction and maintenance.

[0056] To further illustrate the vehicle-side processing, this embodiment also provides a data processing method applicable to the vehicle side, such as... Figure 3 As shown, the method includes:

[0057] Step 201: Send a request to the server to obtain the NVH noise level.

[0058] In some examples, the request may include vehicle model, real-time operating conditions, and road information. Real-time operating conditions include vehicle speed and ambient noise after removing noise from onboard devices. Road information includes road grade and road conditions. The server is used to calculate the target NVH noise level corresponding to the ambient noise using the target mapping function in the noise mapping model that corresponds to the vehicle model, real-time operating conditions, and road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information.

[0059] For example, such as Figure 2 As shown, environmental noise in real-time operating conditions can be collected using an onboard microphone during vehicle operation. A fixed-frame-length audio signal is then extracted from the collected NVH information, and a Fast Fourier Transform (FFT) is performed on this fixed-frame-length audio signal to obtain a frequency spectrum. Based on the vehicle's operating state, the speed and power information (CAN signal) of the main noise sources (such as the engine, generator, and drive motor) are obtained, and the noise order corresponding to different devices is calculated. Based on the noise order of different devices in the vehicle, the corresponding order of noise can be separated from the frequency spectrum, and the remaining portion is used as the environmental noise segment. Through time-frequency domain conversion and noise separation of the audio signal, the actual environmental noise level of the vehicle can be calculated in real time for data collection and analysis. The environmental noise is then combined with the corresponding vehicle speed information, vehicle model, and road information, and after timestamp matching, it can be included in the acquisition request for server processing. The specific implementation method for the server to calculate the corresponding NVH noise level using the noise mapping model can be found in step 103, and will not be elaborated here.

[0060] Step 202: Receive the target NVH noise level sent by the server.

[0061] This embodiment can receive NVH noise levels of multiple vehicle models in real time for road condition analysis and vehicle self-diagnosis, providing basic data for subsequent intelligent planning functions.

[0062] Compared to existing technologies, this embodiment utilizes a server-side noise mapping model to calculate the NVH noise level corresponding to the vehicle's model, real-time operating conditions, and road information in real time during vehicle operation. This allows for the real-time assessment of road noise levels, providing effective NVH information for applications such as intelligent vehicle mode planning. This facilitates effective real-time noise control and improves the overall effectiveness of intelligent vehicle control.

[0063] Further optionally, the method of this embodiment may also include: controlling the operating point of each device in the vehicle based on the target NVH noise level.

[0064] In some examples, when a vehicle is planning its next driving energy mode, it can obtain the planned road segment information and the planned vehicle speed information corresponding to the planned road segment information in the navigation while obtaining the navigation information. Then, it can obtain the NVH noise level corresponding to this information through the server and control the operating point of each device in the vehicle according to the NVH noise level to reduce the energy loss generated by the vehicle during driving.

[0065] Optionally, the above-mentioned control of the operating points of various devices in the vehicle based on the target NVH noise level may specifically include: referring to the corresponding operating table of each device, obtaining at least one operating point of each device that can reduce the target NVH noise level; determining the target operating point of each device from the at least one operating point; and when the vehicle is traveling on the planned road segment in the navigation, the control device operates according to its corresponding target operating point.

[0066] In some examples, energy pattern planning is primarily used to plan the energy patterns used for driving on different road sections and to reduce the operating power of noisy components. Each key component (such as the engine, suspension system, sound insulation materials, aerodynamic components, etc.) has a corresponding worksheet, which mainly records the performance data of the component under different operating conditions (such as speed, load, stiffness, thickness, etc.). The energy consumption varies at different operating points in the worksheet, considering factors such as vehicle performance, energy consumption, and cost. Controlling components such as the engine to operate at points that minimize NVH noise levels without affecting other performance indicators ensures that these components operate within a higher power range without increasing the overall vehicle noise level.

[0067] For example, if the target NVH noise level is a noise value K, multiple operating points below noise value K can be found for the engine. Similarly, multiple operating points below noise value K can also be found for the generator. Based on these obtained operating points, it is necessary to further determine the target operating point for each device, so that when the device operates at the target operating point, the noise level is as low as possible, and / or energy consumption is as low as possible, thereby effectively improving the comfort of the vehicle on future road trips and reducing the energy consumption required for travel.

[0068] Furthermore, as Figure 1 The specific implementation of the method shown in this embodiment provides a data processing device that can be applied to the server side, such as... Figure 4 As shown, the device includes: a receiving module 31, an acquisition module 32, a calculation module 33, and a sending module 34.

[0069] The receiving module 31 is configured to receive a request from the vehicle to obtain the NVH noise level. The request carries the vehicle model, real-time operating conditions and driving road information. The real-time operating conditions include the vehicle speed and the ambient noise after removing the noise of the vehicle-mounted devices. The driving road information includes the road grade and road conditions.

[0070] The acquisition module 32 is configured to acquire the target mapping function in the noise mapping model based on the vehicle model, the real-time operating conditions and the driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions and road information.

[0071] The calculation module 33 is configured to take the vehicle speed, the vehicle type, the driving road information and the environmental noise as input parameters of the target mapping function, and calculate the target NVH noise level corresponding to the environmental noise through the target mapping function;

[0072] The transmitting module 34 is configured to transmit the target NVH noise level to the vehicle.

[0073] In some examples, the acquisition module 32 is specifically configured to acquire the vehicle model range, operating condition range, and road information corresponding to the plurality of mapping functions respectively; and according to the vehicle model range, operating condition range, and road information corresponding to the plurality of mapping functions respectively, acquire the target mapping function in the noise mapping model that corresponds to the vehicle model, the real-time operating condition, and the driving road information.

[0074] In some examples, the real-time operating conditions also include: operating mode, remaining fuel level, and range extender operating power. Correspondingly, the acquisition module 32 is further configured to receive historical sample data collected by the sample vehicle. The historical sample data includes the vehicle model, road information of the vehicle's travel segment, and the corresponding operating conditions. The operating conditions include the vehicle speed, operating mode, remaining fuel level, range extender operating power, and ambient noise after removing onboard device noise. The historical sample data is divided according to the road information of the vehicle's travel segment to obtain multiple datasets. The datasets are then clustered according to the vehicle model and operating conditions to obtain multiple sub-datasets corresponding to different vehicle model ranges, operating condition ranges, and road information. The distribution of NVH noise levels corresponding to each of the multiple sub-datasets is analyzed. Based on the distribution of NVH noise levels, mapping functions corresponding to each of the multiple sub-datasets are constructed. The noise mapping model is created using the constructed mapping functions.

[0075] In some examples, the acquisition module 32 is further configured to divide the historical sample data according to the road level of the sample vehicle's travel segment to obtain a dataset corresponding to each road level; and to cluster the dataset according to the road conditions of the sample vehicle's travel segment, the vehicle type of the sample vehicle, and the driving conditions to obtain multiple sub-datasets corresponding to different vehicle type ranges, driving condition ranges, and road information.

[0076] In some examples, the acquisition module 32 is specifically configured to construct a mapping function corresponding to the subset based on the subset and the distribution of NVH noise levels corresponding to the subset, through linear regression fitting and / or neural network algorithms.

[0077] It should be noted that other corresponding descriptions of the functional units involved in the data processing apparatus provided in this embodiment can be found in [reference needed]. Figure 1 The corresponding descriptions in [the document] will not be repeated here.

[0078] Furthermore, as Figure 3 The specific implementation of the method shown in this embodiment provides a data processing device that can be applied to the vehicle end, such as... Figure 5 As shown, the device includes: a transmitting module 41 and a receiving module 42.

[0079] Sending module 41 is configured to send an NVH noise level acquisition request to the server. The acquisition request carries vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The driving road information includes road grade and road conditions. The server is used to calculate the target NVH noise level corresponding to the ambient noise using a target mapping function in a noise mapping model that corresponds to the vehicle model, real-time operating conditions, and driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information.

[0080] The receiving module 42 is configured to receive the target NVH noise level sent by the server.

[0081] In some examples, the receiving module 42 is specifically configured to control the operating point of each device in the vehicle based on the target NVH noise level.

[0082] In some examples, the receiving module 42 is further configured to refer to the working table corresponding to each device to obtain at least one working point of each device that can reduce the target NVH noise level; determine the target working point corresponding to each device from the at least one working point; and control the devices to work according to their respective target working points while the vehicle is traveling on the planned road segment in the navigation.

[0083] It should be noted that other corresponding descriptions of the functional units involved in the data processing apparatus provided in this embodiment can be found in [reference needed]. Figure 3 The corresponding descriptions in [the document] will not be repeated here.

[0084] Based on the above, Figure 1 or Figure 3 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 or Figure 3 The method shown.

[0085] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0086] Based on the above, Figure 1 or Figure 3 The method shown, and Figure 4 or Figure 5 To achieve the above objectives, this application also provides an electronic device, which can be configured on the end side of a vehicle (such as a new energy vehicle), as shown in the virtual device embodiment. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-described... Figure 1 or Figure 3 The method shown.

[0087] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0088] Furthermore, this embodiment also provides a vehicle, which may include, for example: Figure 5 The apparatus shown.

[0089] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0090] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented using hardware. By applying the solution of this embodiment, during the vehicle's operation on a road segment, the NVH noise level corresponding to the vehicle's model, real-time operating conditions, and driving road information can be calculated online in real time using the noise mapping model on the server side. This allows for the real-time acquisition of NVH noise levels that can evaluate the road segment's noise situation, providing effective NVH information for applications such as intelligent mode planning of the vehicle, facilitating effective noise control in real time, and thereby improving the effect of intelligent vehicle control.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0093] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A data processing method, characterized in that, include: Receive a request from a vehicle to obtain its NVH noise level. The request includes the vehicle model, real-time operating conditions, and road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The road information includes road grade and road conditions. Based on the vehicle model, the real-time operating conditions, and the driving road information, the target mapping function in the noise mapping model is obtained. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information. The vehicle speed, vehicle type, road information, and ambient noise are used as input parameters of the target mapping function, and the target NVH noise level corresponding to the ambient noise is calculated through the target mapping function. The target NVH noise level is sent to the vehicle.

2. The method according to claim 1, characterized in that, The step of obtaining the target mapping function in the noise mapping model based on the vehicle model, the real-time operating conditions, and the driving road information includes: Obtain the vehicle model range, operating condition range, and road information corresponding to the multiple mapping functions, respectively; Based on the vehicle model range, operating condition range, and road information corresponding to the multiple mapping functions, a target mapping function corresponding to the vehicle model, the real-time operating condition, and the driving road information is obtained in the noise mapping model.

3. The method according to claim 1, characterized in that, The real-time operating conditions also include: operating mode, remaining fuel level, and range extender operating power; the method further includes: Receive historical sample data collected from the sample vehicle. The historical sample data includes the model of the sample vehicle, the road information of the sample vehicle's driving section, and the driving conditions corresponding to the sample vehicle's driving section. The driving conditions include the sample vehicle's speed, operating mode, remaining fuel, range extender's operating power, and environmental noise after removing onboard device noise. The historical sample data is divided into multiple datasets based on the road information of the sample vehicles' travel segments; The dataset is clustered and divided according to the vehicle type and driving conditions of the sample vehicles to obtain multiple sub-datasets corresponding to different vehicle type ranges, driving condition ranges and road information, and the distribution of NVH noise levels corresponding to the multiple sub-datasets is analyzed. Based on the distribution of the NVH noise levels, mapping functions are constructed for each of the multiple subset datasets. The noise mapping model is created using the multiple mapping functions obtained from the construction.

4. The method according to claim 3, characterized in that, The historical sample data is divided according to the road information of the sample vehicle's travel segment to obtain multiple datasets, including: The historical sample data is divided according to the road level of the road segment traveled by the sample vehicles to obtain the dataset corresponding to each road level; The dataset is clustered and divided according to the vehicle type and driving conditions of the sample vehicles to obtain multiple sub-datasets corresponding to different vehicle type ranges, driving condition ranges, and road information, including: The dataset is clustered and divided according to the road conditions of the sample vehicle's travel route, the vehicle type, and the driving conditions to obtain multiple sub-datasets corresponding to different vehicle type ranges, driving condition ranges, and road information.

5. The method according to claim 3, characterized in that, The step of constructing mapping functions corresponding to the multiple subsets of data based on the distribution of NVH noise levels includes: Based on the subset dataset and the distribution of NVH noise levels corresponding to the subset dataset, a mapping function corresponding to the subset dataset is constructed through linear regression fitting and / or neural network algorithms.

6. A data processing method, characterized in that, include: A request to obtain NVH noise level is sent to the server. The request carries the vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The driving road information includes road grade and road conditions. The server is used to calculate the target NVH noise level corresponding to the ambient noise using a target mapping function in a noise mapping model that corresponds to the vehicle model, real-time operating conditions, and driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information. Receive the target NVH noise level sent by the server.

7. The method according to claim 6, characterized in that, The method further includes: The operating point control of each component in the vehicle is performed based on the target NVH noise level.

8. The method according to claim 7, characterized in that, The control of the operating points of various devices in the vehicle based on the target NVH noise level includes: Referring to the worksheets corresponding to each device, obtain at least one operating point for each device that can reduce the target NVH noise level; Determine the target operating point corresponding to each device from the at least one operating point; While the vehicle is traveling on the planned route in the navigation system, the control devices operate according to their respective target working points.

9. A data processing apparatus, characterized in that, include: The receiving module is configured to receive a request from the vehicle to obtain the NVH noise level. The request carries the vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and the ambient noise after removing noise from onboard devices. The driving road information includes the road grade and road conditions. The acquisition module is configured to acquire the target mapping function in the noise mapping model based on the vehicle model, the real-time operating conditions and the driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions and road information. The calculation module is configured to take the vehicle speed, the vehicle type, the driving road information and the ambient noise as input parameters of the target mapping function, and calculate the target NVH noise level corresponding to the ambient noise through the target mapping function; The transmitting module is configured to transmit the target NVH noise level to the vehicle.

10. A data processing apparatus, characterized in that, include: The sending module is configured to send an NVH noise level acquisition request to the server. The acquisition request carries the vehicle model, real-time operating conditions, and driving road information. The real-time operating conditions include the vehicle speed and ambient noise after removing noise from onboard devices. The driving road information includes road grade and road conditions. The server is used to calculate the target NVH noise level corresponding to the ambient noise using a target mapping function in a noise mapping model that corresponds to the vehicle model, real-time operating conditions, and driving road information. The noise mapping model includes multiple mapping functions, which are used to calculate the NVH noise levels corresponding to different vehicle models, operating conditions, and road information. The receiving module is configured to receive the target NVH noise level sent by the server.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

12. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 8.

13. A vehicle, characterized in that, include: The apparatus as described in claim 10.

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