Cloud data analysis method, storage medium, electronic equipment and vehicle

Abstract

The invention discloses a cloud data analysis method, a storage medium, electronic equipment and a vehicle, and relates to the technical field of vehicles. The method comprises the following steps: determining a to-be-queried target data file from a cloud data source; extracting all signals of the target data file, and displaying a signal list; in response to a first selection instruction of a user, selecting a to-be-analyzed target signal from the signal list; extracting to-be-analyzed data corresponding to the target signal from the target data file; and based on a preset data analysis configuration file, converting the to-be-analyzed data to obtain a conversion result. Therefore, automatic conversion from the cloud file to the local readable data file can be realized, and the efficiency of data viewing and problem analysis is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a cloud data analysis method, a storage medium, an electronic device and a vehicle. BACKGROUND

[0002] In recent years, with the deep integration of Internet of Vehicles technology and intelligent networked vehicles, the paradigm of driving data has been transformed from local storage to real-time transmission in the cloud. As a "digital mirror" of the vehicle's operating state, the efficient collection, transmission and analysis of driving data have become a key factor in realizing remote safety monitoring, optimizing product design and driving service innovation. Making full use of driving data uploaded to the big data cloud platform has an important impact on improving research and development efficiency and data value release, and provides theoretical support and practical path for innovation of intelligent networked vehicle research and development mode and reconstruction of industrial ecology.

[0003] The current cloud platform compression data analysis technology has the following deficiencies: 1) The method of manual decoding relying on instruction sequence is not easy to operate, has low efficiency and low fault tolerance; 2) Manual splicing of data is required to avoid insufficient analysis continuity caused by small capacity data files, and the conventional splicing method is inefficient, which is difficult to meet the demand of efficient analysis; 3) Manual signal screening is required, and the search time is high; 4) Manual formula processing of data is required, which relies on experience, has low reuse rate of calculation formula and high maintenance cost; 5) Manual alignment of data is required to reduce the interference caused by the time difference of signal tasks, and the calibration time is high.

[0004] These deficiencies together cause the "three high and one low" characteristics of cloud data processing: high degree of manual intervention, high error rate, high operation and maintenance cost accounting for the total analysis cost, and low overall analysis efficiency. Therefore, an automatic analysis method with intelligent instruction generation, adaptive encapsulation analysis and automatic feature extraction capability needs to be constructed. SUMMARY

[0005] The present application aims to at least solve one of the technical problems in the related art. To this end, the purpose of the present application is to propose a cloud data analysis method, a storage medium, an electronic device and a vehicle to realize automatic conversion from cloud files to local readable data files, and improve the efficiency of data viewing and problem analysis.

[0006] In a first aspect, embodiments of the present invention propose a method for parsing cloud data, comprising the following steps: determining a target data file to be queried from a cloud data source; extracting all signals from the target data file and displaying a signal list; in response to a user's first selection instruction, selecting a target signal to be parsed from the signal list; extracting the data to be parsed corresponding to the target signal from the target data file; and converting the data to be parsed based on a preset data parsing configuration file to obtain a conversion result.

[0007] In some embodiments, determining the target data file to be queried includes: responding to a user's data loading instruction, determining the type of the data loading instruction; if the type is a direct loading instruction, then determining all data files in the cloud data source as the target data file.

[0008] In some embodiments, determining the target data file to be queried further includes: if the type is a filter loading instruction, receiving the filter conditions input by the user, wherein the filter conditions include vehicle identity information and / or a target time range; based on the filter conditions, querying and displaying a list of matching data files from the cloud data source; in response to a second selection instruction from the user, selecting a data file to be decompressed from the list of data files; decompressing the data file to be decompressed, and using the decompressed file as the target data file.

[0009] In some embodiments, the method further includes: saving the conversion result in response to the user's save instruction.

[0010] In some embodiments, the method further includes: in response to the user's drawing instruction, drawing the conversion result into a visual chart.

[0011] In some embodiments, the number of data to be parsed is multiple; the conversion of the data to be parsed based on a preset data parsing configuration file includes: concatenating multiple data to be parsed according to time order to form an integrated data file with a longer time sequence; and converting the embedded data in the integrated data file into standard format data based on the preset data parsing configuration file.

[0012] In some embodiments, the method further includes: using a forward padding algorithm to complete the data of the conversion result so that each frame timestamp has a corresponding data value; and dividing the completed data into different data channels according to signal type and timing.

[0013] Secondly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the cloud data parsing method described in the first aspect embodiment.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory, and when the computer program is executed by the processor, the cloud data analysis method in the first aspect is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a vehicle, comprising the electronic device in the third aspect.

[0016] The cloud data analysis method, storage medium, electronic device and vehicle of the embodiments of the present application first determine a target data file to be queried from a cloud data source; then extract all signals of the target data file and display a signal list; then, in response to a first selection instruction of a user, select a target signal to be analyzed from the signal list and extract the target signal corresponding to the data to be analyzed from the target data file; and finally, based on a preconfigured data analysis configuration file, convert the data to be analyzed to obtain a conversion result. Thus, automatic conversion from a cloud file to a local readable data file can be realized, and the efficiency of data viewing and problem analysis can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the cloud data analysis method of an embodiment of the present application; Figure 2 is a flowchart of the cloud data analysis method of a specific embodiment of the present application. DETAILED DESCRIPTION

[0018] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0019] The cloud data analysis method, storage medium, electronic device and vehicle of the embodiments of the present application are described below with reference to the accompanying drawings.

[0020] Figure 1 is a flowchart of the cloud data analysis method of an embodiment of the present application;

[0021] As shown in Figure 1 , the cloud data analysis method comprises the following steps: S1, determining a target data file to be queried from a cloud data source.

[0022] In one implementation, determining the target data file to be queried includes: responding to a user's data loading instruction, determining the type of the data loading instruction; if the type is a direct loading instruction, then determining all data files in the cloud data source as the target data file.

[0023] For example, determining the target data file to be queried further includes: if the type is a filter loading instruction, receiving filter conditions input by the user, wherein the filter conditions include vehicle identity information (such as VIN code) and / or target time range; querying and displaying a list of matching data files from a cloud data source based on the filter conditions; in response to the user's second selection instruction, selecting a data file to be decompressed from the list of data files; decompressing the data file to be decompressed, and using the decompressed file as the target data file.

[0024] S2 extracts all signals from the target data file and displays the signal list.

[0025] S3, in response to the user's first selection command, selects the target signal to be parsed from the signal list.

[0026] S4, extract the data to be parsed corresponding to the target signal from the target data file.

[0027] S5, based on a pre-set data parsing configuration file, transforms the data to be parsed and obtains the transformation result.

[0028] In one implementation, there are multiple data to be parsed; based on a preset data parsing configuration file, the data to be parsed is transformed, including: splicing multiple data to be parsed according to time order to form an integrated data file with a longer time sequence; based on the preset data parsing configuration file, converting the embedded data in the integrated data file into standard format data.

[0029] In this context, "embedded data" refers to data collected from "data collection points" pre-set at key locations in the program code for subsequent analysis and understanding of user behavior, system status, or product performance. In the context of vehicle data, embedded data can refer to event and status data automatically recorded by vehicle systems (such as electronic control units, sensors, and infotainment systems) during operation. This may include, but is not limited to, user interaction events (such as clicking the "navigation" icon on the central control screen, pressing the "voice assistant" button on the steering wheel, or adjusting the air conditioning temperature), system function events (such as triggering automatic emergency braking, activating adaptive cruise control, turning on lane keeping assist, or experiencing a system malfunction), and application events (such as an application launching, crashing, or receiving a push notification).

[0030] In some embodiments of the present application, the cloud data analysis method further comprises: in response to a save instruction from the user, saving the conversion result.

[0031] In some embodiments of the present application, the cloud data analysis method further comprises: in response to a drawing instruction from the user, drawing the conversion result into a visual chart.

[0032] In some embodiments of the present application, the cloud data analysis method further comprises: using a forward fill algorithm to complete the data of the conversion result so that each time stamp has a corresponding data value; and dividing the completed data into different data channels according to signal type and time sequence.

[0033] The cloud data analysis method of one specific embodiment of the present application is described below. Figure 2

[0034] As shown in Figure 2 , the cloud data analysis method comprises: Step 1: When the program is running, the user can choose whether to load the data file. If yes, go to Step 2; otherwise, go to Step 3.

[0035] In this step, the user inputs the basic information such as vehicle VIN code and time, and the program searches and filters the data file information required by the user.

[0036] Step 2: After the user inputs the above basic information, the program filters the data files that meet the conditions and displays them for the user to select. If there is no data file that meets the conditions, a prompt is given.

[0037] Step 3: After the user selects the file, the program performs decompression operation on it, and then extracts all the signals contained in the decompressed file.

[0038] In this step, the user can download and decompress the data file required by the user by checking.

[0039] Step 4: The program displays the signal list in a dialog box for the user to select.

[0040] Step 5: After the user completes the signal selection, the program extracts all the data corresponding to the selected signals from the data file.

[0041] Step 6: The program converts the data of each signal based on the pre-configured data analysis file.

[0042] ​In this step, after the user selects the desired signal, the program can automatically splice the data according to the time sequence, increasing the size of a single file from more than ten minutes to one hour. The data points are converted into the required format according to the corresponding configuration file.

[0043] Step 7: If the user clicks "Save Data", the program will save the data in a file according to a specific format.

[0044] Step 8: If the user selects to draw, the program will plot the data of all signals on the graphical interface with time as the horizontal axis.

[0045] In steps 7 and 8, the program can supplement data using a forward-filling algorithm, filling in the time data for each frame, and then dividing the organized file into different channels according to signal and time, generating charts or storing them in a file according to user needs.

[0046] In summary, the cloud data parsing method of this invention can automatically convert compressed files in the cloud into visualized data files, thereby improving the efficiency of data viewing and problem analysis.

[0047] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cloud data parsing method of the above embodiments.

[0048] The present invention also proposes an electronic device, 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 cloud data parsing method of the above embodiments.

[0049] The present invention also proposes a vehicle comprising: the electronic equipment described in the above embodiments.

[0050] It is to be appreciated that the above description and the examples that follow are intended to be illustrative only and that changes can be made to the description and examples without departing from the scope of the application. Note also that the use of particular brand names in the description is solely for illustration and should not be construed as an endorsement of such brands.

[0051] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, can be used: a hybrid of the technologies mentioned above, discrete logic circuitry having logic gates for implementing logic functions upon data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth.

[0052] In the description of the present application, reference has been made to the use of terms such as "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" means that a particular feature, structure, material or characteristic is included in at least one embodiment or example of the present application. The illustrative examples given are not necessarily to be construed as preferred or advantageous or with the exclusion of other equally valid examples that can be particularly adapted to a given application. Furthermore, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the application to the exact construction and operation described. Accordingly, all suitable modifications and equivalents should be included within the scope of the present application.

[0053] In the description of the application, it should be understood that the orientation or positional relationship indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the purpose of facilitating the description of the application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the application.

[0054] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise explicitly specified and limited.

[0055] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements, unless otherwise explicitly limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0056] In the present application, unless otherwise explicitly specified and limited, the first feature is "on" or "under" the second feature, which can be direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. Moreover, the first feature "above", "above" and "above" the second feature can be directly above or obliquely above the first feature, or only indicate that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "below" and "below" the second feature can be directly below or obliquely below the first feature, or only indicate that the horizontal height of the first feature is less than that of the second feature.

[0057] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for parsing cloud data, characterized in that, Includes the following steps: Identify the target data file to be queried from the cloud data source; Extract all signals from the target data file and display the signal list; In response to the user's first selection instruction, the target signal to be parsed is selected from the signal list; Extract the data to be parsed corresponding to the target signal from the target data file; Based on a pre-set data parsing configuration file, the data to be parsed is transformed to obtain the transformation result. 2.The cloud data analysis method of claim 1, wherein, The process of determining the target data file to be queried includes: In response to a user's data loading instruction, determine the type of the data loading instruction; If the type is a direct load instruction, then all data files in the cloud data source are determined to be the target data file. 3.The cloud data analysis method of claim 2, wherein, The process of determining the target data file to be queried also includes: If the type is a filter loading instruction, then the filter conditions input by the user are received, wherein the filter conditions include vehicle identity information and / or target time range; Based on the filtering criteria, a list of matching data files is queried from the cloud data source and displayed; In response to the user's second selection instruction, a data file to be decompressed is selected from the list of data files; The data file to be decompressed is decompressed, and the decompressed file is used as the target data file. 4.The cloud data analysis method of claim 1, wherein, The method further includes: In response to the user's save command, the conversion result is saved.

5. The method of claim 1, wherein, The method further includes: In response to the user's drawing command, the transformation result is drawn into a visual chart.

6. The cloud data analysis method of claim 1, wherein, The number of data to be parsed is multiple; the transformation of the data to be parsed based on a preset data parsing configuration file includes: Multiple data sets to be parsed are concatenated according to time sequence to form an integrated data file with a longer time sequence; Based on the preset data parsing configuration file, the embedded data in the integrated data file is converted into standard format data.

7. The cloud data parsing method according to any one of claims 1-6, characterized in that, The method further includes: The transformation result is padded with a forward padding algorithm to ensure that each frame timestamp has a corresponding data value. The completed data is divided into different data channels according to signal type and timing.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the cloud data parsing method as described in any one of claims 1-7.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the cloud data parsing method as described in any one of claims 1-7.

10. A vehicle characterized by comprising: include: The electronic device as described in claim 9.

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