Data file processing system, data file analysis method, and data file transmission method

Abstract

The invention discloses a data file processing system, a data file analysis method and a data file sending method, and relates to the field of vehicles, and the data file processing system comprises a target vehicle which is used for collecting sensor signals of m acceleration sensors, generating a data file in a target format according to p sensor signals, the data file is sent to a server, the m acceleration sensors are arranged at m power assembly suspension positions of a target vehicle respectively, the target vehicle comprises t power assembly suspension parts, each power assembly suspension part corresponds to n power assembly suspension positions, m, n, t and p are positive integers, and p is a multiple of m; the server is used for receiving the data file, reading vehicle data from the data file and carrying out durability analysis on the t power assembly suspension parts according to the vehicle data, and the vehicle data at least comprises p sensor signals.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more specifically, to a data file processing system, a data file analysis method, and a data file transmission method. Background Technology

[0002] In the automotive industry, newly developed powertrain mount projects require endurance acceleration testing at designated proving grounds to verify the durability and reliability of the vehicle's powertrain mounts. Currently, the common approach is to modify the tooling of the mount components to integrate force sensors, thereby enabling the acquisition of mount force data and subsequent durability assessment. However, this modification alters the inherent structure of the mount components, limiting their application in long-term, high-intensity endurance testing and preventing the achievement of full data coverage over approximately four months. Therefore, this method has significant errors in powertrain mount reliability assessment, making it difficult to provide consistent and accurate evaluation results. Currently, no effective solution has been proposed to address the problem that existing powertrain endurance acceleration tests require the integration of force sensors into the mount components, altering the vehicle's physical structure and leading to inaccurate powertrain mount durability and reliability assessments with significant errors. Summary of the Invention

[0003] This application provides a data file processing system, a data file analysis method, and a data file sending method to at least solve the problem that existing powertrain durability acceleration tests require integrating force sensors into suspension components, which alters the vehicle's physical structure and leads to inaccurate assessment results of the vehicle's powertrain suspension durability and reliability, resulting in significant errors.

[0004] According to one aspect of the embodiments of this application, a data file processing system is provided, comprising: a target vehicle, configured to collect sensor signals from m acceleration sensors, generate a target format data file based on p sensor signals, and send the data file to a server, wherein the m acceleration sensors are respectively disposed at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount components, each powertrain mount component corresponds to n powertrain mount positions, m, n, t, and p are all positive integers, and p is a multiple of m; and a server, configured to receive the data file, read vehicle data from the data file, and perform durability analysis on the t powertrain mount components based on the vehicle data, wherein the vehicle data includes at least p sensor signals.

[0005] In an exemplary embodiment, the server includes: a data monitoring module, configured to monitor the server's disk storage center and, upon determining that the disk storage center has received the data file, send a prompt signal to a data processing module; the data processing module, connected to the data monitoring module, configured to, upon receiving the prompt signal, read the vehicle data from the data file, process the vehicle data to obtain a durability index, and store the durability index in a database; and a data analysis module, connected to the database, configured to read the durability index from the database and perform durability analysis on the t powertrain mounting components based on the durability index.

[0006] In an exemplary embodiment, the data processing module is further configured to: perform data cleaning on the vehicle data to obtain cleaned vehicle data; process the cleaned vehicle data using a target model to obtain t powertrain mounting force signals; merge multiple cleaned vehicle data sets according to different data dimensions to obtain merged data, wherein the merged data includes at least multiple sets of powertrain mounting force signals, each set of powertrain mounting force signals including t powertrain mounting force signals, and the multiple cleaned vehicle data sets, the multiple sets of powertrain mounting force signals, and multiple data files correspond one-to-one; determine the durability index based on the merged data, and store the durability index in the database, wherein the durability index includes at least the powertrain mounting force signals and the load fatigue damage of the target vehicle.

[0007] In an exemplary embodiment, the data analysis module is further configured to: statistically analyze the powertrain mounting force signal to obtain evaluation parameters, wherein the evaluation parameters include: extreme values, mean values, standard deviations, and RMS values; determine multiple consecutive load fatigue damages of the target vehicle in multiple data dimensions, perform linear prediction based on the multiple load fatigue losses, and obtain linear prediction results; and perform durability prediction on the t powertrain mounting components based on the evaluation parameters and the linear prediction results, and obtain prediction results, wherein the prediction results are used to indicate whether there is a target powertrain mounting component among the t powertrain mounting components, and the durability of the target powertrain mounting component reaches a preset threshold within a preset time period.

[0008] In an exemplary embodiment, the data analysis module is further configured to: generate visualization data based on the durability index and display the visualization data on the target page when the prediction result indicates that the target powertrain mounting part does not exist; and send the part anomaly information of the target powertrain mounting part to the target object when the prediction result indicates that the target powertrain mounting part exists.

[0009] In an exemplary embodiment, the target vehicle includes: a data acquisition device, configured to, when the target vehicle is powered on, acquire sensor signals from the m acceleration sensors at a first frequency; generate the data file based on the p sensor signals acquired within the current time period at a second frequency; and send the data file to the disk storage center of the server via a communication module, wherein the second frequency corresponds to the length of the current time period.

[0010] According to another aspect of the embodiments of this application, a data file analysis method is also provided, applied to a data file processing system, comprising: receiving a data file in a target format sent by a target vehicle; reading vehicle data from the data file, wherein the vehicle data includes at least p sensor signals from m acceleration sensors, the m acceleration sensors being respectively disposed at m powertrain mounting positions of the target vehicle, the target vehicle including t powertrain mounting parts, each powertrain mounting part corresponding to n powertrain mounting positions, m, n, t, and p being positive integers, and p being a multiple of m; and performing durability analysis on the t powertrain mounting parts based on the vehicle data.

[0011] In an exemplary embodiment, performing durability analysis on the t powertrain mounting components based on the vehicle data includes: processing the vehicle data to obtain a durability index and storing the durability index in a database; and performing durability analysis on the t powertrain mounting components based on the durability index.

[0012] In an exemplary embodiment, processing the vehicle data to obtain a durability index and storing the durability index in a database includes: cleaning the vehicle data to obtain cleaned vehicle data; processing the cleaned vehicle data using a target model to obtain t powertrain mounting force signals; merging multiple cleaned vehicle data sets according to different data dimensions to obtain merged data, wherein the merged data includes at least multiple sets of powertrain mounting force signals, each set of powertrain mounting force signals including t powertrain mounting force signals, and the multiple cleaned vehicle data sets, the multiple sets of powertrain mounting force signals, and multiple data files corresponding one-to-one; determining the durability index based on the merged data and storing the durability index in the database, wherein the durability index includes at least: the powertrain mounting force signals and the load fatigue damage of the target vehicle.

[0013] In an exemplary embodiment, durability analysis of the t powertrain mounting components based on the durability index includes: statistically analyzing the powertrain mounting force signals to obtain evaluation parameters, wherein the evaluation parameters include: extreme values, mean values, standard deviations, and RMS values; determining multiple consecutive load fatigue damages of the target vehicle in multiple data dimensions, performing linear prediction based on the multiple load fatigue losses to obtain a linear prediction result; and performing durability prediction on the t powertrain mounting components based on the evaluation parameters and the linear prediction result to obtain a prediction result, wherein the prediction result is used to indicate whether there is a target powertrain mounting component among the t powertrain mounting components, and the durability of the target powertrain mounting component reaches a preset threshold within a preset time period.

[0014] In an exemplary embodiment, after performing durability analysis on the t powertrain mounting parts according to the durability index, the method further includes: if the prediction result indicates that the target powertrain mounting part does not exist, generating visualization data according to the durability index and displaying the visualization data on a target page; if the prediction result indicates that the target powertrain mounting part exists, sending the part anomaly information of the target powertrain mounting part to a target object.

[0015] According to another aspect of the embodiments of this application, a method for sending a data file is also provided, applied to a data file processing system, comprising: acquiring sensor signals from m acceleration sensors, wherein the m acceleration sensors are respectively disposed at m powertrain mounting positions of a target vehicle, the target vehicle includes t powertrain mounting parts, each powertrain mounting part corresponding to n powertrain mounting positions, and m, n, and t are all positive integers; generating a data file in a target format based on p sensor signals, wherein p is a multiple of m; and sending the data file to a server to instruct the server to perform a durability analysis on the t powertrain mounting parts based on the data file.

[0016] In an exemplary embodiment, generating a target format data file based on m sensor signals includes: acquiring sensor signals from the m acceleration sensors at a first frequency when the target vehicle is powered on; and generating the data file based on p sensor signals acquired within the current time period at a second frequency, wherein the second frequency corresponds to the length of the current time period.

[0017] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-mentioned data file analysis method or data file transmission method when running.

[0018] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the data file analysis method or the data file transmission method described above through the computer program.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0020] In this embodiment, only m acceleration sensors are installed in the target vehicle. The target vehicle periodically collects sensor signals from the acceleration sensors to generate a data file in the target format, and sends the data file to the server for analysis and processing. The m acceleration sensors are respectively installed at m powertrain mount positions on the target vehicle, and one powertrain mount component corresponds to n powertrain mount positions. After receiving the data file, the server extracts vehicle data from it and performs durability analysis on t powertrain mount components of the target vehicle based on the vehicle data. By adopting the above scheme, acceleration sensors are only installed on the powertrain mounts, which does not damage the vehicle's main structure. This allows for continuous and uninterrupted evaluation of the durability and reliability of the powertrain mounts of the test vehicle (i.e., the target vehicle). This solves the problem in related technologies where existing powertrain durability acceleration tests require integrating force sensors into the mount components, which changes the vehicle's physical structure and leads to inaccurate evaluation results of the vehicle's powertrain mount durability and reliability, resulting in large errors. Attached Figure Description

[0021] 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.

[0022] 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.

[0023] Figure 1 This is a structural block diagram of an optional data file processing system according to an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of the overall framework of an optional data file processing system according to an embodiment of this application;

[0025] Figure 3 This is a schematic diagram illustrating the working principle of an optional data processing module according to an embodiment of this application;

[0026] Figure 4 This is a flowchart illustrating a data file processing method according to an embodiment of this application;

[0027] Figure 5 This is a flowchart of a data file analysis method according to an embodiment of this application;

[0028] Figure 6 This is a flowchart of a data file sending method according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] To address the technical problems existing in related technologies, this embodiment provides a data file processing system. Figure 1 This is a structural block diagram of an optional data file processing system according to an embodiment of this application. The system includes:

[0032] The target vehicle 12 is used to collect sensor signals from m acceleration sensors 122, generate a target format data file based on p sensor signals, and send the data file to a server. The m acceleration sensors 122 are respectively installed at m powertrain mount positions of the target vehicle. The target vehicle includes t powertrain mount parts 124, and each powertrain mount part 124 corresponds to n powertrain mount positions. m, n, t, and p are all positive integers, and p is a multiple of m.

[0033] It should be noted that the target format mentioned above can be understood as CSV format. Optionally, other data file formats are also possible, and this application does not impose any restrictions on them.

[0034] It should be noted that the number n of the n powertrain mount positions corresponding to different powertrain mount parts can be the same or different. In one optional embodiment, the m powertrain mount positions are: engine body, engine mount, transmission body, transmission mount, lower transmission body, and lower transmission mount.

[0035] It should be noted that the data collected by the target vehicle includes, but is not limited to, the sensor signals mentioned above, and may also include GPS signals and signals from the vehicle's CAN bus.

[0036] Server 14 is used to receive the data file, read vehicle data from the data file, and perform durability analysis on the t powertrain mounting parts 124 based on the vehicle data, wherein the vehicle data includes at least p sensor signals.

[0037] Only m acceleration sensors are installed in the target vehicle. The target vehicle periodically collects sensor signals from the acceleration sensors to generate data files in the target format, and sends the data files to the server for analysis and processing. The m acceleration sensors are respectively installed at m powertrain mount positions on the target vehicle, with one powertrain mount component corresponding to n powertrain mount positions. After receiving the data file, the server extracts vehicle data from it and performs durability analysis on t powertrain mount components of the target vehicle based on the vehicle data. Using the above scheme, acceleration sensors are only deployed on the powertrain mounts, without damaging the vehicle's main structure. This allows for continuous and uninterrupted evaluation of the durability and reliability of the powertrain mounts of the test vehicle (i.e., the target vehicle). This solves the problem in related technologies where existing powertrain durability acceleration tests require integrating force sensors into the mount components, which alters the vehicle's physical structure and leads to inaccurate evaluation results of the vehicle's powertrain mount durability and reliability, resulting in large errors.

[0038] In an exemplary embodiment, the server 14 includes: a data monitoring module, configured to monitor the server's disk storage center and, upon determining that the disk storage center has received the data file, send a prompt signal to a data processing module; the data processing module, connected to the data monitoring module, configured to, upon receiving the prompt signal, read the vehicle data from the data file, process the vehicle data to obtain a durability index, and store the durability index in a database; and a data analysis module, connected to the database, configured to read the durability index from the database and perform durability analysis on the t powertrain mounting components based on the durability index.

[0039] In an optional embodiment, the overall framework of the data file processing system described above is as follows: Figure 2As shown, the system includes an on-board unit 22 (i.e., the target vehicle mentioned above) and a server unit 24 (i.e., the server mentioned above). Specifically, the server unit 24 includes a data monitoring module 242, a data processing module 244, and a data analysis module 246. The data monitoring module 242 is used to monitor the signals of the on-board unit in real time to understand the vehicle's operating status. This module synchronizes the data files of the on-board unit to the disk storage center NAS of the server unit in real time through a third-party file synchronization service. Once a new file (i.e., the data file mentioned above) is synchronized to the disk storage center, a prompt signal is sent to the data processing module to trigger the subsequent processing flow. The data processing module reads all vehicle data from the data file and processes the vehicle data to obtain durability indicators, which are then stored in the database. The data analysis module reads these durability indicators from the database to perform durability analysis on t powertrain mounting parts.

[0040] Furthermore, the data processing module 244 is also configured to: perform data cleaning on the vehicle data to obtain cleaned vehicle data; process the cleaned vehicle data through a target model to obtain t powertrain mounting force signals; merge multiple cleaned vehicle data according to different data dimensions to obtain merged data, wherein the merged data includes at least multiple sets of powertrain mounting force signals, each set of powertrain mounting force signals includes t powertrain mounting force signals, and the multiple cleaned vehicle data, the multiple sets of powertrain mounting force signals, and multiple data files correspond one-to-one; determine the durability index based on the merged data, and store the durability index in the database, wherein the durability index includes at least: the powertrain mounting force signals and the load fatigue damage of the target vehicle.

[0041] The data processing module's process for processing vehicle data is as follows: Figure 3 As shown, it includes the following steps:

[0042] 3.1 The data processing module will perform in-depth processing on the CSV data. First, it will remove all outliers and spikes (i.e., perform data cleaning) to ensure the accuracy of the original data.

[0043] 3.2 Then, the FIR model (a finite impulse response model, i.e. the target model mentioned above) is used to generate the powertrain mounting force signal to obtain the mounting force data;

[0044] 3.3 After obtaining the required suspension force signal, the CSV data is merged. After statistical analysis and damage calculation, the merged data is used to obtain the calculation results (i.e., the above-mentioned durability index).

[0045] It should be noted that since the data acquisition device generates a CSV file every 5 seconds, tens of thousands of CSV files will be generated every day. Therefore, it is necessary to merge these data in a logical manner to increase the information content of individual data points. Data merging can be performed on multiple data dimensions, such as merging data based on each parking cycle of the target vehicle, or merging data at fixed time intervals (e.g., half an hour). Alternatively, a unit mileage can be set, and data merging can be performed after the target vehicle travels each unit mile. Other methods or data dimensions can also be used for data merging, and this application does not limit this.

[0046] 3.4. Import the calculation results into a MySQL database for structured storage. The subsequent data analysis module can access the data in the database to perform various analyses and comparisons.

[0047] Furthermore, the aforementioned data analysis module 246 is also used to statistically analyze the powertrain mounting force signal to obtain evaluation parameters, wherein the evaluation parameters include: extreme values, mean values, standard deviations, and RMS values; determine multiple consecutive load fatigue damages of the target vehicle in multiple data dimensions, perform linear prediction based on the multiple load fatigue losses, and obtain linear prediction results; and perform durability prediction on the t powertrain mounting parts based on the evaluation parameters and the linear prediction results to obtain prediction results, wherein the prediction results are used to indicate whether there is a target powertrain mounting part among the t powertrain mounting parts, and the durability of the target powertrain mounting part reaches a preset threshold within a preset time period.

[0048] The data analysis module performs statistical analysis and comparison of data. It conducts various queries on the structured data in the database, then performs necessary calculations and structural processing on the retrieved data to meet preset data structure requirements. The module primarily performs statistical analysis on the suspension force (i.e., the powertrain suspension force indicated by the aforementioned powertrain suspension force signal) to obtain evaluation parameters for the suspension force. Then, based on the load fatigue loss from the merged data sets, it performs linear prediction of the total loss over a future period, obtaining the linear prediction result. Finally, based on the evaluation parameters and the linear prediction result, the module predicts the durability of these powertrain suspension components over a future period, determining whether there are any target powertrain suspension components whose durability has deteriorated below a preset threshold.

[0049] It should be noted that the data analysis module provides a damage calculation and prediction function. By performing damage calculation and linear fitting on the predicted powertrain mounting force, the cumulative total damage at a future point in time can be predicted, thereby determining whether there is a high probability of failure during this period (i.e., performing durability prediction).

[0050] It should be noted that RMS is used to describe the magnitude of a signal amplitude, reflecting the signal's strength or amplitude.

[0051] Based on the above steps, the data analysis module 246 is further configured to: generate visualization data according to the durability index and display the visualization data on the target page when the prediction result indicates that the target powertrain mounting part does not exist; and send the part abnormality information of the target powertrain mounting part to the target object when the prediction result indicates that the target powertrain mounting part exists.

[0052] It should be noted that, in the embodiments of this application, the above-mentioned durability indicators include the target vehicle's motion trajectory (latitude and longitude data), speed and mileage data, suspension force, and load fatigue damage; based on these data, the data analysis module performs a series of processes to obtain data structure requirements that meet the requirements of various charts, thereby generating the visualization data, which is finally displayed on the front-end page in the form of charts.

[0053] Optionally, the target vehicle 12 (i.e., the vehicle-mounted terminal 22) includes: a data acquisition device 222, such as... Figure 2 As shown, when the target vehicle is powered on, the system collects sensor signals from the m acceleration sensors at a first frequency; generates a data file based on the p sensor signals collected within the current time period at a second frequency; and sends the data file to the disk storage center of the server via a communication module. The second frequency corresponds to the length of the current time period.

[0054] Specifically, after the target vehicle is powered on, the data acquisition device starts up and collects sensor signals from multiple acceleration sensors installed on the vehicle at a certain sampling frequency (i.e., the first frequency mentioned above). The sampling frequency can reach 512Hz. Then, every 5 seconds (i.e., the second frequency mentioned above), it can generate a 5-second CSV data file locally. Unless the vehicle is powered off, the data acquisition device can continuously collect and generate files. Then, through the communication module on the data acquisition device and the file synchronization service function of a third party, the data files collected by the data acquisition device are synchronized to the disk storage center NAS on the server side. This realizes high-frequency data acquisition and synchronous data transmission at the vehicle end.

[0055] In this embodiment, an edge computing data collector (i.e., the aforementioned collector device) is installed on the vehicle. This collector can send CSV data collected on the vehicle back to the server in real time. The server monitors the vehicle data by reading the CSV data, and also cleans, merges, calculates, and fits the data to ultimately obtain the required powertrain mounting force data. Through the system architecture established in this application, continuous and uninterrupted evaluation of the durability and reliability of the powertrain mounting components of the test vehicle can be achieved.

[0056] Optionally, embodiments of this application provide a method for processing data files, the process of which is as follows: Figure 4 As shown, it includes the following steps:

[0057] 4.1 The data monitoring module synchronizes data files from the vehicle to the NAS disk storage center on the server in real time through a third-party file synchronization service. Once a new file is synchronized to the disk storage center, subsequent steps will be triggered.

[0058] 4.2 File Reading: Read all data rows from a CSV file;

[0059] 4.3 Data Processing: The read data rows are processed and abnormal data is monitored. If abnormal data is detected, the email service function is triggered to remind the user. The email contains specific information about the abnormal data (e.g., abnormal vehicle status, abnormal sensor, etc.) and the data is displayed on the front-end page (i.e., the target page mentioned above). If no abnormal data is detected, only the data is displayed. The following content is displayed on the front-end page: dynamic map trajectory, real-time vehicle speed, real-time mileage, vehicle CAN data, and acceleration sensor data (i.e., the visualization data mentioned above).

[0060] According to another aspect of the embodiments of this application, a data file analysis method is also provided, applied to the aforementioned data file processing system, the process of which is as follows: Figure 5 As shown, it includes the following steps:

[0061] Step S502: Receive the target format data file sent by the target vehicle;

[0062] Step S504: Read vehicle data from the data file, wherein the vehicle data includes at least p sensor signals from m acceleration sensors, the m acceleration sensors are respectively set at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, m, n, t, and p are all positive integers, and p is a multiple of m;

[0063] Step S506: Perform durability analysis on the t powertrain mounting components based on the vehicle data.

[0064] Through the above steps, after receiving the target format data file sent by the target vehicle, the server reads the vehicle data from the data file. The vehicle data includes at least p sensor signals collected by m acceleration sensors over a period of time. These m acceleration sensors are respectively set at m powertrain mount positions on the target vehicle. The target vehicle includes t powertrain mount components, and each powertrain mount component corresponds to n powertrain mount positions. Finally, the server performs durability analysis on these t powertrain mount components based on the vehicle data. Using the above scheme, acceleration sensors are only placed on the powertrain mounts, without damaging the vehicle's main structure. This allows for continuous and uninterrupted evaluation of the durability and reliability of the powertrain mounts of the test vehicle (i.e., the target vehicle). This solves the problem in related technologies where existing powertrain durability acceleration tests require integrating force sensors into the mount components, altering the vehicle's physical structure and resulting in inaccurate evaluation results of the vehicle's powertrain mount durability and reliability, leading to significant errors.

[0065] Optionally, a durability analysis is performed on the t powertrain mounting components based on the vehicle data, including: processing the vehicle data to obtain a durability index and storing the durability index in a database; and performing a durability analysis on the t powertrain mounting components based on the durability index.

[0066] Optionally, the vehicle data is processed to obtain a durability index, and the durability index is stored in a database. This includes: cleaning the vehicle data to obtain cleaned vehicle data; processing the cleaned vehicle data using a target model to obtain t powertrain mounting force signals; merging multiple cleaned vehicle data sets according to different data dimensions to obtain merged data, wherein the merged data includes at least multiple sets of powertrain mounting force signals, each set of powertrain mounting force signals including t powertrain mounting force signals, and the multiple cleaned vehicle data sets, the multiple sets of powertrain mounting force signals, and multiple data files corresponding one-to-one; determining the durability index based on the merged data, and storing the durability index in the database, wherein the durability index includes at least: the powertrain mounting force signals and the load fatigue damage of the target vehicle.

[0067] Optionally, durability analysis is performed on the t powertrain mounting components based on the durability index, including: statistically analyzing the powertrain mounting force signals to obtain evaluation parameters, wherein the evaluation parameters include: extreme values, mean values, standard deviations, and RMS values; determining multiple consecutive load fatigue damages of the target vehicle in multiple data dimensions, performing linear prediction based on the multiple load fatigue losses to obtain linear prediction results; and performing durability prediction on the t powertrain mounting components based on the evaluation parameters and the linear prediction results to obtain prediction results, wherein the prediction results are used to indicate whether there is a target powertrain mounting component among the t powertrain mounting components, and the durability of the target powertrain mounting component reaches a preset threshold within a preset time period.

[0068] Based on the above steps, after performing durability analysis on the t powertrain mounting parts according to the durability index, the method further includes: if the prediction result indicates that the target powertrain mounting part does not exist, generating visualization data according to the durability index and displaying the visualization data on the target page; if the prediction result indicates that the target powertrain mounting part exists, sending the part anomaly information of the target powertrain mounting part to the target object.

[0069] According to another aspect of this application, embodiments of this application provide a method for sending data files, applied to the aforementioned data file processing system, the process of which is as follows: Figure 6 As shown, it includes:

[0070] Step S602: Collect sensor signals from m acceleration sensors, wherein the m acceleration sensors are respectively installed at m powertrain mounting positions of the target vehicle, the target vehicle includes t powertrain mounting parts, each powertrain mounting part corresponds to n powertrain mounting positions, and m, n, and t are all positive integers;

[0071] Step S604: Generate a data file in the target format based on p sensor signals, where p is a multiple of m;

[0072] Step S606: Send the data file to the server to instruct the server to perform a durability analysis on the t powertrain mounting components based on the data file.

[0073] Through the above steps, the target vehicle collects sensor signals from m acceleration sensors, which are respectively installed at m powertrain mount positions on the target vehicle. The target vehicle includes t powertrain mount components, each powertrain mount component corresponding to n powertrain mount positions. Then, a target format data file is generated based on p sensor signals and sent to the server, instructing the server to perform durability analysis on these t powertrain mount components based on the data file. Using the above scheme, acceleration sensors are only deployed on the powertrain mounts, without damaging the vehicle's main structure, thus enabling continuous and uninterrupted evaluation of the durability and reliability of the powertrain mounts of the test vehicle (i.e., the target vehicle). This solves the problem in related technologies where existing powertrain durability acceleration tests require integrating force sensors into the mount components, altering the vehicle's physical structure and resulting in inaccurate evaluation results of the vehicle's powertrain mount durability and reliability, leading to significant errors.

[0074] Optionally, generating a target format data file based on the m sensor signals includes: when the target vehicle is powered on, collecting sensor signals from the m acceleration sensors at a first frequency; and generating the data file based on the p sensor signals collected within the current time period at a second frequency, wherein the second frequency corresponds to the length of the current time period.

[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0076] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0077] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0078] S1, Receives a data file in the target format sent by the target vehicle;

[0079] S2, read vehicle data from the data file, wherein the vehicle data includes at least p sensor signals from m acceleration sensors, the m acceleration sensors are respectively set at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, m, n, t, and p are all positive integers, and p is a multiple of m;

[0080] S3, perform durability analysis on the t powertrain mounting components based on the vehicle data.

[0081] Optionally, in this embodiment, the storage medium may also be configured to store program code for performing the following steps:

[0082] S4, collect sensor signals from m acceleration sensors, wherein the m acceleration sensors are respectively installed at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, and m, n, and t are all positive integers;

[0083] S5, Generate a data file in the target format based on p sensor signals, where p is a multiple of m;

[0084] S6, the data file is sent to the server to instruct the server to perform a durability analysis on the t powertrain mounting components based on the data file.

[0085] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0086] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0087] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0088] S1, Receives a data file in the target format sent by the target vehicle;

[0089] S2, read vehicle data from the data file, wherein the vehicle data includes at least p sensor signals from m acceleration sensors, the m acceleration sensors are respectively set at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, m, n, t, and p are all positive integers, and p is a multiple of m;

[0090] S3, perform durability analysis on the t powertrain mounting components based on the vehicle data.

[0091] Optionally, in this embodiment, the processor may also be configured to perform the following steps via a computer program:

[0092] S4, collect sensor signals from m acceleration sensors, wherein the m acceleration sensors are respectively installed at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, and m, n, and t are all positive integers;

[0093] S5, Generate a data file in the target format based on p sensor signals, where p is a multiple of m;

[0094] S6, the data file is sent to the server to instruct the server to perform a durability analysis on the t powertrain mounting components based on the data file.

[0095] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0096] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0097] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by a processor:

[0098] S1, Receives a data file in the target format sent by the target vehicle;

[0099] S2, read vehicle data from the data file, wherein the vehicle data includes at least p sensor signals from m acceleration sensors, the m acceleration sensors are respectively set at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, m, n, t, and p are all positive integers, and p is a multiple of m;

[0100] S3, perform durability analysis on the t powertrain mounting components based on the vehicle data.

[0101] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by the processor:

[0102] S4, collect sensor signals from m acceleration sensors, wherein the m acceleration sensors are respectively installed at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, and m, n, and t are all positive integers;

[0103] S5, Generate a data file in the target format based on p sensor signals, where p is a multiple of m;

[0104] S6, the data file is sent to the server to instruct the server to perform a durability analysis on the t powertrain mounting components based on the data file.

[0105] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0106] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0107] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A data file processing system, characterized in that, include: The target vehicle is used to collect sensor signals from m acceleration sensors, generate a target format data file based on p sensor signals, and send the data file to a server. The m acceleration sensors are respectively installed at m powertrain mount positions of the target vehicle. The target vehicle includes t powertrain mount parts, and each powertrain mount part corresponds to n powertrain mount positions. m, n, t, and p are all positive integers, and p is a multiple of m. A server is configured to receive the data file, read vehicle data from the data file, and perform durability analysis on the t powertrain mounting components based on the vehicle data, wherein the vehicle data includes at least p sensor signals.

2. The data file processing system according to claim 1, characterized in that, The server includes: The data monitoring module is used to monitor the disk storage center of the server. When it is determined that the disk storage center has received the data file, it sends a prompt signal to the data processing module. The data processing module is connected to the data monitoring module and is used to read the vehicle data from the data file when it receives the prompt signal, process the vehicle data to obtain a durability index, and store the durability index in the database. The data analysis module is connected to the database and is used to read the durability index from the database and perform durability analysis on the t powertrain mounting parts based on the durability index.

3. The data file processing system according to claim 2, characterized in that, The data processing module is also used for: The vehicle data is cleaned to obtain cleaned vehicle data; The cleaned vehicle data is processed using the target model to obtain t powertrain mounting force signals; Multiple cleaned vehicle data are merged according to different data dimensions to obtain merged data. The merged data includes at least multiple sets of powertrain mounting force signals. Each set of powertrain mounting force signals includes t powertrain mounting force signals. The multiple cleaned vehicle data, the multiple sets of powertrain mounting force signals and the multiple data files correspond one-to-one. The durability index is determined based on the merged data and stored in the database. The durability index includes at least the powertrain mounting force signal and the load fatigue damage of the target vehicle.

4. The data file processing system according to claim 3, characterized in that, The data analysis module is also used for: The powertrain mounting force signal is statistically analyzed to obtain evaluation parameters, which include: extreme values, mean values, standard deviations, and RMS values. The target vehicle is identified as having multiple consecutive load fatigue damages across multiple data dimensions. Linear prediction is then performed based on these multiple load fatigue losses to obtain a linear prediction result. Based on the evaluation parameters and the linear prediction results, the durability of the t powertrain mounting parts is predicted to obtain the prediction results. The prediction results are used to indicate whether there is a target powertrain mounting part among the t powertrain mounting parts, and the durability of the target powertrain mounting part reaches a preset threshold within a preset time period.

5. The data file processing system according to claim 4, characterized in that, The data analysis module is also used for: If the prediction result indicates that the target powertrain mounting component does not exist, visualization data is generated based on the durability index and displayed on the target page. If the prediction result indicates the presence of the target powertrain mounting component, the abnormality information of the target powertrain mounting component is sent to the target object.

6. The data file processing system according to claim 1, characterized in that, The target vehicle includes: The data acquisition device is used to acquire sensor signals from the m acceleration sensors at a first frequency when the target vehicle is powered on; generate the data file based on the p sensor signals acquired in the current time period at a second frequency; and send the data file to the disk storage center of the server through a communication module, wherein the second frequency corresponds to the length of the current time period.

7. A method for analyzing data files, characterized in that, The data file processing system according to any one of claims 1 to 6 includes: Receive data files in the target format sent by the target vehicle; Vehicle data is read from the data file, wherein the vehicle data includes at least p sensor signals from m acceleration sensors, the m acceleration sensors are respectively set at m powertrain mount positions of the target vehicle, the target vehicle includes t powertrain mount parts, each powertrain mount part corresponds to n powertrain mount positions, m, n, t, and p are all positive integers, and p is a multiple of m; Durability analysis is performed on the t powertrain mounting components based on the vehicle data.

8. The data file analysis method according to claim 7, characterized in that, Based on the vehicle data, a durability analysis is performed on the t powertrain mounting components, including: The vehicle data is processed to obtain a durability index, and the durability index is stored in a database. Durability analysis was performed on the t powertrain mounting components based on the aforementioned durability index.

9. The data file analysis method according to claim 8, characterized in that, The vehicle data is processed to obtain durability indicators, and the durability indicators are stored in a database, including: The vehicle data is cleaned to obtain cleaned vehicle data; The cleaned vehicle data is processed using the target model to obtain t powertrain mounting force signals; Multiple cleaned vehicle data are merged according to different data dimensions to obtain merged data. The merged data includes at least multiple sets of powertrain mounting force signals. Each set of powertrain mounting force signals includes t powertrain mounting force signals. The multiple cleaned vehicle data, the multiple sets of powertrain mounting force signals and the multiple data files correspond one-to-one. The durability index is determined based on the merged data and stored in the database. The durability index includes at least the powertrain mounting force signal and the load fatigue damage of the target vehicle.

10. The data file analysis method according to claim 9, characterized in that, Durability analysis is performed on the t powertrain mounting components based on the aforementioned durability index, including: The powertrain mounting force signal is statistically analyzed to obtain evaluation parameters, which include: extreme values, mean values, standard deviations, and RMS values. The target vehicle is identified as having multiple consecutive load fatigue damages across multiple data dimensions. Linear prediction is then performed based on these multiple load fatigue losses to obtain a linear prediction result. Based on the evaluation parameters and the linear prediction results, the durability of the t powertrain mounting parts is predicted to obtain the prediction results. The prediction results are used to indicate whether there is a target powertrain mounting part among the t powertrain mounting parts, and the durability of the target powertrain mounting part reaches a preset threshold within a preset time period.

11. The data file analysis method according to claim 10, characterized in that, After performing durability analysis on the t powertrain mounting components based on the aforementioned durability index, the method further includes: If the prediction result indicates that the target powertrain mounting component does not exist, visualization data is generated based on the durability index and displayed on the target page. If the prediction result indicates the presence of the target powertrain mounting component, the abnormality information of the target powertrain mounting component is sent to the target object.

12. A method for sending a data file, characterized in that, The data file processing system applied to any one of claims 1 to 6 includes: The sensor signals of m acceleration sensors are collected. The m acceleration sensors are respectively set at m powertrain mount positions of the target vehicle. The target vehicle includes t powertrain mount parts. Each powertrain mount part corresponds to n powertrain mount positions. m, n and t are all positive integers. A target format data file is generated based on p sensor signals, where p is a multiple of m; the data file is sent to a server to instruct the server to perform durability analysis on t powertrain mounting components based on the data file.

13. The method for sending data files according to claim 12, characterized in that, Generate a data file in the target format based on the m sensor signals, including: When the target vehicle is powered on, the sensor signals of the m acceleration sensors are collected at a first frequency; The data file is generated according to a second frequency based on the p sensor signals collected within the current time period, wherein the second frequency corresponds to the length of the current time period.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 7 to 11, or performs the method of any one of claims 12 to 13.

15. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute, via the computer program, the method described in any one of claims 7 to 11, or the method described in any one of claims 12 to 13.

16. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the methods of claims 7 to 11, or performs the method of any one of claims 12 to 13.

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