Server and method for processing NVH data, storage medium and program product

Abstract

The embodiment of the invention provides a server and method for processing NVH data, a storage medium, a program product and a data storage module, and the data storage module is provided with a plurality of storage nodes to receive the NVH data of a vehicle and store the NVH data in the corresponding storage nodes in a distributed manner; the task scheduling module is used for identifying a task type of a data analysis task of a user, decomposing the data analysis task into a plurality of sub-tasks based on the data distribution information and the task type of the plurality of storage nodes, and distributing the plurality of sub-tasks to the corresponding computing nodes; the parallel computing module is provided with a plurality of computing nodes, executes a plurality of subtasks in parallel and obtains a processing result, distributed storage is adopted, the storage capacity and expansibility can be improved, the parallelism of data access is improved, the tasks are decomposed into the subtasks, load balancing can be achieved, and then the resource utilization rate is increased; the subtasks are executed in parallel, so that the processing time is remarkably shortened, and especially for large-scale NVH data, the processing speed can be remarkably increased.

Description

Technical Field

[0001] This application relates to the field of NVH (Noise, Vibration and Harshness) signal analysis technology, and in particular to a server, method, storage medium and program product for processing NVH data. Background Technology

[0002] In related technologies, commonly used NVH data processing methods include stand-alone computing and local area network (LAN) distributed computing. The workflow of stand-alone computing is as follows: read NVH data, preprocess the data to improve data quality, and then analyze the preprocessed NVH data to obtain the corresponding processing results. LAN distributed computing, on the other hand, uses multiple computers working collaboratively to decompose large NVH data processing tasks into multiple sub-tasks, and assigns these sub-tasks to appropriate slave nodes based on node resource availability, thereby obtaining the final calculation results.

[0003] However, related technologies suffer from low computational efficiency, limited storage capacity, poor scalability, and inflexible resource scheduling when processing large-scale NVH test time-domain data. Furthermore, their reliance on specific hardware or local area network environments makes multi-team collaboration and cross-regional data sharing difficult, necessitating improvements. Summary of the Invention

[0004] This application provides a server, method, storage medium, and program product for processing NVH data, aiming to improve upon the shortcomings of related technologies, such as low computational efficiency, limited storage capacity, poor scalability, and inflexible resource scheduling. Furthermore, it addresses issues such as reliance on specific hardware or local area network environments, making it difficult to achieve multi-team collaboration and cross-regional data sharing.

[0005] A first aspect of this application provides a server for processing NVH data, comprising: a data storage module having multiple storage nodes to receive NVH data from at least one vehicle and distribute the NVH data among the corresponding storage nodes; a task scheduling module for identifying the task type of a user's data analysis task, and decomposing the data analysis task into multiple sub-tasks based on the data distribution information of the multiple storage nodes and the task type, and allocating the multiple sub-tasks to corresponding computing nodes; and a parallel computing module having multiple computing nodes to execute the multiple sub-tasks in parallel based on the NVH data stored in the multiple storage nodes to obtain the processing result of the NVH data.

[0006] Through the above technical solutions, the data storage module adopts a distributed storage architecture, which can distribute NVH data across multiple storage nodes, thereby improving storage capacity and scalability. In addition, distributed storage can also improve the parallelism of data access, allowing multiple users or tasks to access data on different nodes simultaneously without creating bottlenecks. The task scheduling module identifies the user's data analysis task, decomposes the task into multiple sub-tasks, and assigns them to the corresponding computing nodes to achieve load balancing and improve resource utilization. The parallel computing module utilizes multiple computing nodes to execute sub-tasks in parallel, significantly shortening processing time. Especially for large-scale NVH data, the processing speed is improved by computing on multiple nodes simultaneously.

[0007] Optionally, in one embodiment of this application, the task scheduling module includes: a retrieval unit, used to retrieve NVH data corresponding to different storage nodes based on the task type; and a decomposition unit, used to decompose the data analysis task into multiple sub-tasks based on the NVH data corresponding to the different storage nodes.

[0008] The above technical solution can be used to obtain NVH data corresponding to different storage nodes through the retrieval unit, and then the decomposition unit can be used to decompose the data analysis task into multiple sub-tasks. This ensures that the required data can be found quickly during task scheduling, reduces data transmission latency, splits large tasks into multiple sub-tasks, improves task parallelism, optimizes resource utilization, and supports complex task types.

[0009] Optionally, in one embodiment of this application, the parallel computing module includes: a first determining unit, configured to determine a computing method for processing the NVH data based on the task type; and a generating unit, configured to execute the multiple sub-tasks in parallel according to the computing method based on the NVH data stored in the multiple storage nodes, so as to generate the processing result.

[0010] Through the above technical solution, the first determining unit can determine the calculation method for processing NVH data, and the generating unit can execute multiple sub-tasks in parallel according to the calculation method to generate corresponding processing results. This achieves intelligent adaptation of the calculation method, optimizes the performance driven by task type, enables parallel execution, reduces time costs, and improves computing efficiency.

[0011] Optionally, in one embodiment of this application, it further includes: an analysis module, used to perform visualization analysis on the processing results to obtain corresponding visualization analysis results; and a visualization module, used to obtain the user's display requirements and, based on the display requirements and the visualization analysis results, obtain visualization results of the NVH data.

[0012] The above technical solution allows for the visualization and analysis of processing results using the analysis module, and the visualization module to display NVH data visually, helping users understand the data more intuitively and making it easier for them to identify patterns and anomalies. Furthermore, customized visualization based on user needs can enhance user experience, meet diverse requirements, break geographical limitations, and enable real-time collaboration and remote cooperation.

[0013] Optionally, in one embodiment of this application, the task scheduling module includes: an acquisition unit for acquiring load information of the computing node; a judgment unit for judging whether the load information meets a preset load condition; and a second determination unit for re-determining the corresponding computing node based on the load information when the load information does not meet the preset load condition.

[0014] The above technical solution allows the acquisition unit to obtain the load information of the computing nodes, and the judgment unit to determine whether the current node meets certain load conditions. If the conditions are not met, the second determination unit can be used to redistribute the computing nodes to achieve dynamic load balancing, reduce manual intervention, lower costs, and avoid some nodes being overloaded while others are idle, thereby improving resource utilization and task processing speed and enhancing overall processing efficiency.

[0015] A second aspect of this application provides a method for processing NVH data, comprising the following steps: receiving NVH data from at least one vehicle and distributing the NVH data to corresponding storage nodes; identifying the task type of a user's data analysis task, and decomposing the data analysis task into multiple sub-tasks based on the data distribution information of the multiple storage nodes and the task type, and allocating the multiple sub-tasks to corresponding computing nodes; and executing the multiple sub-tasks in parallel based on the NVH data stored in the multiple storage nodes to obtain the processing result of the NVH data.

[0016] The above technical solutions allow for the distributed storage architecture to disperse NVH data across multiple storage nodes, thereby improving storage capacity and scalability. Furthermore, distributed storage enhances the parallelism of data access, enabling multiple users or tasks to access data on different nodes simultaneously without creating bottlenecks. By identifying user data analysis tasks, tasks are decomposed into multiple subtasks and assigned to corresponding computing nodes, achieving load balancing and improving resource utilization. This allows for the parallel execution of subtasks across multiple computing nodes, significantly shortening processing time. This is particularly effective for large-scale NVH data, where simultaneous computation across multiple nodes significantly improves processing speed.

[0017] Optionally, in one embodiment of this application, the step of decomposing the data analysis task into multiple sub-tasks includes: retrieving NVH data corresponding to different storage nodes based on the task type; and decomposing the data analysis task into multiple sub-tasks based on the NVH data corresponding to the different storage nodes.

[0018] The above technical solution allows for the NVH data of different storage nodes to be retrieved, thereby decomposing the data analysis task into multiple sub-tasks. This ensures that the required data can be found quickly during task scheduling, reduces data transmission latency, breaks down large tasks into multiple sub-tasks, improves task parallelism, optimizes resource utilization, and supports complex task types.

[0019] Optionally, in one embodiment of this application, the step of executing the multiple sub-tasks in parallel based on the NVH data stored by the multiple storage nodes to obtain the processing result of the NVH data includes: determining the calculation method for processing the NVH data based on the task type; and executing the multiple sub-tasks in parallel according to the calculation method based on the NVH data stored by the multiple storage nodes to generate the processing result.

[0020] The above technical solution can first determine the calculation method for processing NVH data, and then execute multiple sub-tasks in parallel according to different calculation methods to generate corresponding processing results. This achieves intelligent adaptation of calculation methods, optimizes the performance driven by task type, enables parallel execution, reduces time costs, and improves computational efficiency.

[0021] Optionally, in one embodiment of this application, the method further includes: performing visualization analysis on the processing results to obtain corresponding visualization analysis results; obtaining the user's display requirements, and obtaining visualization results of the NVH data based on the display requirements and the visualization analysis results.

[0022] The above technical solutions enable visual analysis and presentation of processing results, helping users understand the data more intuitively and making it easier for them to identify patterns and anomalies. Furthermore, customized visualizations based on user needs can enhance user experience, meet diverse requirements, break geographical limitations, and facilitate real-time and remote collaboration.

[0023] Optionally, in one embodiment of this application, the step of assigning the plurality of subtasks to corresponding computing nodes includes: obtaining the load information of the computing nodes; determining whether the load information meets preset load conditions; and when the load information does not meet the preset load conditions, re-determining the corresponding computing nodes based on the load information.

[0024] The above technical solution can determine whether the current node meets certain load conditions based on the obtained load information of the computing nodes. If not, the computing nodes can be reallocated to achieve dynamic load balancing, reduce manual intervention, lower costs, and avoid some nodes being overloaded while others are idle, thereby improving resource utilization and task processing speed and enhancing overall processing efficiency.

[0025] A third aspect of this application provides a server, including: 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 processing NVH data as described in the above embodiments.

[0026] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for processing NVH data.

[0027] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the method for processing NVH data as described above.

[0028] The data storage module in this embodiment adopts a distributed storage architecture, which can distribute NVH data across multiple storage nodes, thereby improving storage capacity and scalability. Furthermore, distributed storage enhances the parallelism of data access, allowing multiple users or tasks to access data on different nodes simultaneously without creating bottlenecks. The task scheduling module identifies user data analysis tasks, decomposes them into multiple sub-tasks, and allocates them to corresponding computing nodes to achieve load balancing and improve resource utilization. The parallel computing module utilizes multiple computing nodes to execute sub-tasks in parallel, significantly shortening processing time, especially for large-scale NVH data, by simultaneously computing across multiple nodes, thus improving processing speed. This solves the problems of low computational efficiency, limited storage capacity, poor scalability, and inflexible resource scheduling in related technologies. Additionally, it addresses the difficulties in achieving multi-team collaboration and cross-regional data sharing due to reliance on specific hardware or local area network environments.

[0029] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0030] 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:

[0031] Figure 1 This is a block diagram of a server for processing NVH data according to an embodiment of this application;

[0032] Figure 2 This is a block diagram illustrating a simplified architecture of a data storage module according to an embodiment of this application;

[0033] Figure 3 This is a schematic diagram of a simplified architecture of a retrieval unit according to an embodiment of this application;

[0034] Figure 4 This is a simplified schematic diagram of the processing result of a parallel computing module according to an embodiment of this application;

[0035] Figure 5 This is a schematic diagram of a visualization result provided according to an embodiment of this application;

[0036] Figure 6 This is a flowchart of a method for processing NVH data according to an embodiment of this application;

[0037] Figure 7 This is a schematic diagram of the structure of a server provided according to an embodiment of this application.

[0038] Figure label:

[0039] Among them, 10 is a server for processing NVH data; 100 is a data storage module; 200 is a task scheduling module; 300 is a parallel computing module; 701 is a processor; and 702 is a memory. Detailed Implementation

[0040] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] Example 1

[0042] This application provides a server for processing NVH data. Please refer to... Figure 1 The server 10 that processes NVH data includes: a data storage module 100, a task scheduling module 200, and a parallel computing module 300.

[0043] The data storage module 100 has multiple storage nodes to receive NVH data from at least one vehicle and distribute the NVH data among the corresponding storage nodes.

[0044] It is understood that, in the embodiments of this application, the data storage module 100 may include, but is not limited to, multiple storage nodes such as storage node 1, storage node 2, storage node 3, ..., storage node n, where n is an integer greater than or equal to 2.

[0045] Furthermore, embodiments of this application can simultaneously receive NVH data from at least one vehicle, such as vehicle A, vehicle B, and vehicle C, and distribute and store the NVH data to different storage nodes.

[0046] In some embodiments of this application, users can upload NVH data to the data storage module 100 via a client. The data storage module 100 supports multiple data formats (such as CSV (Comma-Separated Values), MAT (MATLAB Matrix), binary, etc., which are not specifically limited in this application), and provides data compression and encryption functions to ensure data security and transmission efficiency.

[0047] Furthermore, in this embodiment, the received NVH data from different vehicles are distributed and stored in corresponding storage nodes. In this embodiment, the storage system may employ redundancy backup and sharding techniques to ensure high data availability and fault tolerance.

[0048] For example, embodiments of this application can be combined with Figure 2 As shown, the NVH data is stored using the data storage module 100. Among them, Figure 2 This is a simplified structural diagram of the data storage module 100 provided in the embodiments of this application, with sensitive content in the diagram blurred.

[0049] The task scheduling module 200 is used to identify the task type of the user's data analysis task, and based on the data distribution information of multiple storage nodes and the task type, decompose the data analysis task into multiple sub-tasks, and allocate the multiple sub-tasks to the corresponding computing nodes.

[0050] It is understood that, in the embodiments of this application, the data analysis tasks of NVH data may include, but are not limited to, time domain analysis, frequency domain analysis, transmission path analysis, modal analysis, order analysis, etc., and this application does not impose specific limitations.

[0051] Furthermore, in this embodiment, the corresponding task type can be determined according to different data analysis tasks, and then the data analysis task can be decomposed into multiple sub-tasks, such as sub-task 1, sub-task 2, ..., sub-task i, etc., based on the data distribution information of different storage nodes, where i is an integer greater than or equal to 2. This application does not impose specific restrictions to ensure the balance and efficiency of task allocation.

[0052] In addition, it should be noted that in the embodiments of this application, different subtasks correspond to their respective computing nodes, which can be executed independently to improve computing efficiency.

[0053] For example, in this embodiment of the application, the task scheduling module 200 identifies the task type of the user's data analysis task, and then decomposes the data analysis task into multiple sub-tasks according to the data distribution information and the task type, and different sub-tasks are executed by corresponding computing nodes.

[0054] Optionally, in one embodiment of this application, the task scheduling module 200 includes: a retrieval unit, used to retrieve NVH data corresponding to different storage nodes based on task type; and a decomposition unit, used to decompose the data analysis task into multiple sub-tasks based on the NVH data corresponding to different storage nodes.

[0055] It is understood that, in the embodiments of this application, the retrieval unit, as one of the core components of the task scheduling module 200, can quickly locate and retrieve relevant NVH data stored on different storage nodes according to different task types, such as time domain analysis tasks, frequency domain analysis tasks, etc. This application does not impose specific limitations.

[0056] For example, in this embodiment of the application, for time-domain analysis tasks, the retrieval unit can quickly locate and retrieve relevant time-domain NVH data stored on different storage nodes; for frequency-domain analysis tasks, the retrieval unit can quickly locate and retrieve relevant frequency-domain NVH data stored on different storage nodes. A simplified structural diagram of the retrieval unit is shown below. Figure 3 As shown, sensitive content within it has been blurred.

[0057] The decomposition unit, another core component of the task scheduling module 200, can decompose the data analysis task into multiple sub-tasks based on the distribution of the retrieved NVH data. Specifically, the decomposition unit can select an appropriate data sharding strategy to decompose the data analysis task into multiple sub-tasks.

[0058] For example, in the embodiments of this application, for time-domain analysis tasks, the decomposition unit can decompose the data analysis task into multiple sub-tasks according to time windows; for frequency-domain analysis tasks, the decomposition unit can decompose the data analysis task into multiple sub-tasks according to frequency ranges. The specific settings can be made by those skilled in the art according to the actual situation, and this application does not impose specific limitations.

[0059] Optionally, in one embodiment of this application, the task scheduling module 200 includes: an acquisition unit for acquiring load information of computing nodes; a judgment unit for judging whether the load information meets preset load conditions; and a second determination unit for re-determining the corresponding computing node based on the load information when the load information does not meet the preset load conditions.

[0060] It is understood that, in the embodiments of this application, the load information may include, but is not limited to, CPU (Central Processing Unit) utilization, memory occupancy, I / O (Input / Output) latency, etc., and this application does not impose specific limitations.

[0061] In some embodiments, the present application can acquire load information of different computing nodes through an acquisition unit, and then use a judgment unit to determine whether the load information on different computing nodes meets certain load conditions. If the certain load conditions are not met, a second determination unit can re-determine the corresponding computing node based on the load information. The certain load conditions can be set by those skilled in the art according to actual conditions, and the present application does not impose specific limitations.

[0062] For example, in the embodiments of this application, when the CPU utilization rate is ≥95% and the memory utilization rate is ≥80%, it can be determined that the corresponding computing node 1 does not meet certain load conditions, and computing node 2 is re-determined.

[0063] The parallel computing module 300 has multiple computing nodes to execute multiple subtasks in parallel based on NVH data stored on multiple storage nodes, in order to obtain the processing results of NVH data.

[0064] It is understood that, in the embodiments of this application, the parallel computing module 300 may include, but is not limited to, multiple computing nodes such as computing node 1, computing node 2, computing node 3, ..., computing node m, where m is an integer greater than or equal to 2.

[0065] In some embodiments, the parallel computing module 300 of this application has multiple computing nodes. Different computing nodes can perform parallel processing on assigned subtasks to obtain the processing results of NVH data. The processing results are synchronized in real time between nodes to ensure data consistency. The processing results are as follows: Figure 4 As shown, sensitive content has been obfuscated. The processing results may include, but are not limited to, the publisher, working conditions, master node, child nodes, task duration, publication time, and task progress. This application does not impose specific restrictions.

[0066] For example, for subtasks A, B, and C, this application embodiment can call the corresponding computing nodes 1, 2, and 3 to execute in parallel based on the NVH data stored on multiple storage nodes, thereby obtaining the corresponding processing results.

[0067] Optionally, in one embodiment of this application, the parallel computing module 300 includes: a first determining unit, used to determine the computing method for processing NVH data based on the task type; and a generating unit, used to execute multiple sub-tasks in parallel according to the computing method based on NVH data stored in multiple storage nodes to generate processing results.

[0068] It is understood that, in the embodiments of this application, the calculation method may include, but is not limited to, intensive algorithms, accelerated processing algorithms, and deep learning framework algorithms. The specific method can be set by those skilled in the art according to the actual situation, and this application does not impose any specific restrictions.

[0069] In some embodiments, the calculation method for processing NVH data can be determined by the first determining unit based on the task type.

[0070] For example, in the embodiments of this application, for time-domain analysis tasks, the first determining unit may select a CPU-intensive algorithm for computation; for frequency-domain analysis tasks, the first determining unit may select a GPU (Graphics Processing Unit) accelerated Fast Fourier Transform algorithm for computation; this application does not impose specific limitations.

[0071] In some embodiments, the generation unit of this application can generate processing results by performing multiple subtasks in parallel based on NVH data stored on multiple storage nodes and according to a calculation method.

[0072] For example, in the embodiments of this application, for the time domain analysis task, subtask 1 and subtask 2 are executed in parallel according to the NVH data of storage node 1 using a CPU-intensive algorithm to generate the processing result; for the frequency domain analysis task, subtask 3 and subtask 5 are executed in parallel according to the fast Fourier transform algorithm based on the NVH data of storage nodes 3 and 4 to generate the processing result. The specific settings can be configured by those skilled in the art according to the actual situation, and this application does not impose any specific limitations.

[0073] Optionally, in one embodiment of this application, it further includes: an analysis module for performing visualization analysis on the processing results to obtain corresponding visualization analysis results; and a visualization module for obtaining the user's display requirements and obtaining visualization results of NVH data based on the display requirements and visualization analysis results.

[0074] It is understood that the results of visualization analysis may include, but are not limited to, chart results, such as time-domain waveforms, spectrum graphs, and feature distribution graphs; interactive visualization results, such as 3D data displays and model displays, can be set by those skilled in the art according to the actual situation, and this application does not impose specific restrictions.

[0075] In some embodiments, after obtaining the processing result, the analysis module of this application can present the processing result to the user in a visual form, thereby obtaining a visual analysis result.

[0076] For example, embodiments of this application can perform visual analysis on the processing results to obtain the corresponding time-domain waveform s.

[0077] In some embodiments, the visualization module of this application can display the visualization analysis results according to the display needs of different users, thereby obtaining the corresponding visualization results.

[0078] For example, in this embodiment of the application, user A's display requirement is a gray, unfilled data chart, while user B's display requirement is that different colors can represent different value ranges, and important data is bolded. For the time-domain waveform s, this embodiment of the application can generate corresponding visualization results according to the display requirements of user A and user B. Its schematic diagram is shown below. Figure 5 As shown.

[0079] The server for processing NVH data proposed in this application employs a distributed storage architecture in its data storage module. This architecture disperses NVH data across multiple storage nodes, thereby improving storage capacity and scalability. Furthermore, distributed storage enhances the parallelism of data access, allowing multiple users or tasks to access data on different nodes simultaneously without bottlenecks. The task scheduling module identifies user data analysis tasks, decomposes them into multiple sub-tasks, and allocates them to corresponding computing nodes, achieving load balancing and improving resource utilization. The parallel computing module utilizes multiple computing nodes to execute sub-tasks in parallel, significantly shortening processing time. This is particularly effective for large-scale NVH data, where simultaneous computation across multiple nodes significantly improves processing speed. Thus, this addresses the problems of low computational efficiency, limited storage capacity, poor scalability, and inflexible resource scheduling found in related technologies. Additionally, it addresses the limitations of relying on specific hardware or local area network environments, making multi-team collaboration and cross-regional data sharing difficult.

[0080] Example 2

[0081] The following is combined with Figure 6 As shown, an embodiment of the method for processing NVH data proposed in this application will be described.

[0082] in, Figure 6 This is a flowchart of a method for processing NVH data according to an embodiment of this application.

[0083] like Figure 6 As shown, the method for processing NVH data includes the following steps:

[0084] In step S601, NVH data of at least one vehicle is received and distributed and stored in the corresponding storage nodes.

[0085] In step S602, the task type of the user's data analysis task is identified, and based on the data distribution information of multiple storage nodes and the task type, the data analysis task is decomposed into multiple sub-tasks, and the multiple sub-tasks are assigned to the corresponding computing nodes.

[0086] In step S603, multiple subtasks are executed in parallel based on the NVH data stored on multiple storage nodes to obtain the processing results of the NVH data.

[0087] Optionally, in one embodiment of this application, the data analysis task is decomposed into multiple sub-tasks, including: retrieving NVH data corresponding to different storage nodes based on task type; and decomposing the data analysis task into multiple sub-tasks based on the NVH data corresponding to different storage nodes.

[0088] Optionally, in one embodiment of this application, multiple subtasks are executed in parallel based on NVH data stored on multiple storage nodes to obtain NVH data processing results, including: determining the calculation method for processing NVH data based on the task type; and executing multiple subtasks in parallel according to the calculation method based on NVH data stored on multiple storage nodes to generate processing results.

[0089] Optionally, in one embodiment of this application, the method further includes: performing visualization analysis on the processing results to obtain corresponding visualization analysis results; obtaining the user's display requirements, and obtaining visualization results of NVH data based on the display requirements and visualization analysis results.

[0090] Optionally, in one embodiment of this application, assigning multiple subtasks to corresponding computing nodes includes: obtaining load information of the computing nodes; determining whether the load information meets preset load conditions; and when the load information does not meet the preset load conditions, re-determining the corresponding computing nodes based on the load information.

[0091] It should be noted that the foregoing explanation of the server embodiment for processing NVH data also applies to the method for processing NVH data in this embodiment, and will not be repeated here.

[0092] The method for processing NVH data proposed in this application can employ a distributed storage architecture to disperse NVH data across multiple storage nodes, thereby improving storage capacity and scalability. Furthermore, distributed storage enhances the parallelism of data access, allowing multiple users or tasks to access data on different nodes simultaneously without bottlenecks. By identifying user data analysis tasks, tasks are decomposed into multiple sub-tasks and allocated to corresponding computing nodes, achieving load balancing and improving resource utilization. This allows for parallel execution of sub-tasks across multiple computing nodes, significantly shortening processing time, especially for large-scale NVH data, where simultaneous computation across multiple nodes improves processing speed. This solves the problems of low computational efficiency, limited storage capacity, poor scalability, and inflexible resource scheduling in related technologies. Additionally, it addresses the limitations of specific hardware or local area network environments, hindering multi-team collaboration and cross-regional data sharing.

[0093] This application also provides a server 20, please refer to... Figure 7 It includes a processor 701 and a memory 702, wherein the memory 701 is used to store computer programs; the processor 702 is used to execute the programs stored in the memory 701 to implement the method for processing NVH data described in any embodiment of this application.

[0094] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for processing NVH data described in any embodiment of this application.

[0095] This application also provides a computer program product, including a computer program that, when executed, implements the method for processing NVH data described in any embodiment of this application.

[0096] In this application, "multiple" refers to two or more.

[0097] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0098] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0099] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0100] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0101] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A server for processing NVH data, characterized in that, include: The data storage module has multiple storage nodes to receive noise, vibration, and acoustic roughness (NVH) data of at least one vehicle and to distribute and store the NVH data in the corresponding storage nodes. The task scheduling module is used to identify the task type of the user's data analysis task, and based on the data distribution information of the multiple storage nodes and the task type, decompose the data analysis task into multiple sub-tasks, and allocate the multiple sub-tasks to the corresponding computing nodes. A parallel computing module has multiple computing nodes to execute multiple subtasks in parallel based on the NVH data stored on the multiple storage nodes, so as to obtain the processing results of the NVH data.

2. The server for processing NVH data according to claim 1, characterized in that, The task scheduling module includes: The retrieval unit is used to retrieve NVH data corresponding to different storage nodes based on the task type. The decomposition unit is used to decompose the data analysis task into multiple sub-tasks based on the NVH data corresponding to the different storage nodes.

3. The server for processing NVH data according to claim 1, characterized in that, The parallel computing module includes: The first determining unit is used to determine the calculation method for processing the NVH data based on the task type. The generation unit is used to execute the multiple subtasks in parallel according to the calculation method based on the NVH data stored in the multiple storage nodes to generate the processing result.

4. The server for processing NVH data according to claim 1, characterized in that, Also includes: The analysis module is used to perform visual analysis on the processing results to obtain corresponding visual analysis results; The visualization module is used to obtain the user's display requirements and, based on the display requirements and the visualization analysis results, obtain the visualization results of the NVH data.

5. The server for processing NVH data according to claim 1, characterized in that, The task scheduling module includes: An acquisition unit is used to acquire the load information of the computing node; The judgment unit is used to determine whether the load information meets the preset load conditions; The second determining unit is used to redetermine the corresponding computing node based on the load information when the load information does not meet the preset load conditions.

6. A method for processing NVH data, characterized in that, The method employs a server for processing NVH data as described in any one of claims 1-5, wherein the method includes the following steps: Receive NVH data from at least one vehicle and distribute the NVH data to the corresponding storage nodes; Identify the task type of the user's data analysis task, and based on the data distribution information of the multiple storage nodes and the task type, decompose the data analysis task into multiple sub-tasks, and assign the multiple sub-tasks to the corresponding computing nodes; The multiple subtasks are executed in parallel based on the NVH data stored on the multiple storage nodes to obtain the processing results of the NVH data.

7. The method for processing NVH data according to claim 6, characterized in that, The process of decomposing the data analysis task into multiple sub-tasks includes: Based on the task type, NVH data corresponding to different storage nodes are retrieved; Based on the NVH data corresponding to the different storage nodes, the data analysis task is decomposed into multiple sub-tasks.

8. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for processing NVH data as described in any one of claims 6-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for processing NVH data as described in any one of claims 6-7.

10. A computer program product, characterized in that, Includes a computer program, which, when executed, is used to implement the method for processing NVH data as described in any one of claims 6-7.

Images