A method of analyzing positioning data and related devices

Abstract

This application discloses a method and related equipment for analyzing positioning data. The method includes: acquiring at least two sets of raw driving data for a target vehicle, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle; performing an index character input operation on each set of the raw driving data to obtain driving data to be verified; performing missing data detection and repair operations, time synchronization operations, timestamp conversion operations, and coordinate transformation operations on the driving data to be verified in sequence to obtain driving data to be analyzed; and generating result text and driving curves based on the driving data to be analyzed for display on a data analysis interface.

Description

Technical Field

[0001] This specification relates to the field of data analysis, and more specifically, this application relates to a method and related equipment for analyzing location data. Background Technology

[0002] Currently, integrated navigation technology is widely used in scenarios such as smart mobility. Developers frequently review and analyze positioning data, comparing the performance of different sensors. However, displaying positioning data on maps currently requires adaptation to a fixed data format mandated by the system, which doesn't support a direct correspondence between data and display functions. When developers use multiple software programs to analyze data, the results must be manually compiled into reports, resulting in very low efficiency. Summary of the Invention

[0003] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0004] Firstly, this application proposes a method for analyzing location data, the method comprising:

[0005] At least two sets of raw driving data for the target vehicle are acquired, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle.

[0006] For each set of the above original driving data, enter the index character operation to obtain the driving data to be verified;

[0007] The above-mentioned driving data to be verified is subjected to missing data detection and repair, time synchronization, timestamp conversion and coordinate transformation operations in sequence to obtain the driving data to be analyzed.

[0008] Based on the driving data to be analyzed, generate result text and driving curves to be displayed on the data analysis interface.

[0009] Optionally, the above missing data detection and repair operation includes data missing detection and data repair operation. The data missing data detection is determined based on the continuity of index characters. After the missing data detection is completed, a record of the amount of missing data is generated. The data repair operation is determined based on the average value of the data associated with the missing data before and after the missing data.

[0010] Optionally, the above time synchronization operations include reference frequency determination, low-frequency interpolation, and high-frequency thinning.

[0011] The above methods also include:

[0012] For each group of the above raw driving data, a reference frequency determination operation is performed to determine the minimum sampling frequency of the raw driving data in all groups as the initial reference frequency;

[0013] If the ratio of the sampling frequency corresponding to each currently processed original driving data to the aforementioned initial reference frequency is less than or equal to a preset threshold, a high-frequency thinning operation is performed on the currently processed original driving data so that the currently processed original driving data corresponds to the aforementioned initial reference frequency.

[0014] If the ratio of the sampling frequency to the initial reference frequency corresponding to any of the above-mentioned current raw driving data is greater than the preset threshold, the average value of the sampling frequency and the initial reference frequency shall be used as the correction reference frequency.

[0015] The original driving data currently being processed is subjected to high-frequency thinning or low-frequency interpolation to make the original driving data currently being processed correspond to the correction reference frequency.

[0016] Optionally, the above timestamp conversion operation includes converting the 13-bit timestamp in the above original driving data into local time, and the above coordinate conversion operation includes converting the above original driving data from the WGS-84 coordinate system to the GCJ-02 coordinate system.

[0017] Optionally, the above-mentioned generation of result text and driving curves based on the driving data to be analyzed and displayed on the data analysis interface includes:

[0018] Obtain the statistical values ​​of the driving data to be analyzed, wherein the statistical values ​​include at least one of the following: maximum value, minimum value, variance, and error;

[0019] Generate a report text based on the above statistical values ​​using a specific document template;

[0020] Store the above report text to the specified path and display it in the above data analysis interface;

[0021] Calculate the driving coordinates for each group based on the driving data to be analyzed in each group.

[0022] For each set of driving coordinates mentioned above, multiple driving trajectories are generated based on the folium library and Gaode WMTS tile map service and displayed on the data analysis interface.

[0023] Optionally, the above methods also include:

[0024] Calculate the coordinate error of each set of driving coordinates at the same time. If the coordinate error exceeds the first preset difference, the road segment corresponding to the driving trajectory that exceeds the first preset difference will be flashed.

[0025] If the difference is less than the second preset error, the road segment corresponding to the driving trajectory that is less than the second preset error will be displayed with the same trajectory.

[0026] Optionally, the above methods also include:

[0027] The above statistical values ​​and the view information corresponding to the above driving trajectory are stored in the same document to generate a test report.

[0028] Secondly, this application also proposes a positioning data analysis device, comprising:

[0029] The acquisition unit is used to acquire at least two sets of raw driving data of the target vehicle, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle.

[0030] The input unit is used to input index characters for each set of the above-mentioned original driving data to obtain the driving data to be verified.

[0031] The processing unit is used to perform missing data detection and repair, time synchronization, timestamp conversion and coordinate transformation operations on the above-mentioned driving data to be verified in sequence to obtain driving data to be analyzed.

[0032] The analysis unit is used to generate result text and driving curves based on the driving data to be analyzed, which are then displayed on the data analysis interface.

[0033] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the positioning data analysis method as described in any of the first aspects above.

[0034] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for analyzing positioning data according to any one of the preceding claims in the first aspect.

[0035] In summary, the location data analysis method of this application embodiment includes: acquiring at least two sets of raw driving data of the target vehicle, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle; performing an index character input operation on each set of the raw driving data to obtain driving data to be verified; performing missing data detection and repair operations, time synchronization operations, timestamp conversion operations, and coordinate transformation operations on the driving data to be verified in sequence to obtain driving data to be analyzed; and generating result text and driving curves based on the driving data to be analyzed and displaying them on the data analysis interface. The method provided in this application can be used for performance testing of integrated navigation sensors during vehicle testing. It acquires different raw driving data from different types of integrated navigation sensors mounted on the same vehicle. The raw driving data is processed by entering index characters to obtain driving data to be verified. Then, the driving data to be verified undergoes sequential operations including missing data detection and repair, time synchronization, timestamp conversion, and coordinate transformation to obtain driving data to be analyzed. The analyzed driving data generates result text and driving curves to be displayed on the data analysis interface. Developers can simultaneously observe the results acquired by different sensors on the same analysis interface, facilitating understanding of the differences in data collected by various sensors, enabling developers to evaluate sensor performance, shortening the development cycle, and reducing the workload for developers.

[0036] The method for analyzing positioning data proposed in this application, as well as other advantages, objectives, and features of this application, will be partly apparent from the following description and partly understood by those skilled in the art through study and practice of this application. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 This is a schematic flowchart of a method for analyzing location data provided in an embodiment of this application;

[0039] Figure 2 This application provides a schematic diagram of the workflow of a location data analysis system according to an embodiment of the present application.

[0040] Figure 3 This is a schematic diagram of a positioning data analysis device provided in an embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the structure of an electronic device for analyzing positioning data, provided in an embodiment of this application. Detailed Implementation

[0042] The method provided in this application can be used for performance testing of integrated navigation sensors during vehicle testing. It acquires different raw driving data from different types of integrated navigation sensors mounted on the same vehicle. The raw driving data is processed by entering index characters to obtain driving data to be verified. Then, the driving data to be verified undergoes sequential operations including missing data detection and repair, time synchronization, timestamp conversion, and coordinate transformation to obtain driving data to be analyzed. The analyzed driving data generates result text and driving curves to be displayed on the data analysis interface. Developers can simultaneously observe the results acquired by different sensors on the same analysis interface, facilitating understanding of the differences in data collected by various sensors, enabling developers to evaluate sensor performance, shortening the development cycle, and reducing the workload for developers.

[0043] The terms "first," "second," "third," "fourth," etc. (if present) 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 described herein can be implemented in a sequence other than that 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. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0044] Please see Figure 1 This is a schematic flowchart of a location data analysis method provided in an embodiment of this application, which may specifically include:

[0045] S110. Obtain at least two sets of raw driving data for the target vehicle, wherein each set of raw driving data is obtained through different combined navigation sensors on the target vehicle.

[0046] For example, the target vehicle could be a test vehicle during the development phase, equipped with various integrated navigation sensors. This allows for analysis of performance differences between different integrated navigation sensors to select the best-performing sensor for installation in vehicles intended for sale. Raw driving data may be stored in the vehicle or transmitted to the cloud via remote data transfer.

[0047] S120. Enter the index character operation for each group of the above original driving data to obtain the driving data to be verified.

[0048] For example, the raw driving data acquired by each type of combined navigation sensor is processed, and the data analysis variables supported by the system are presented in a table format. By entering the position of the variable in the imported data, the system can perform retrieval, and the table data is saved in .xlsx format with the user choosing the save location. This facilitates the repeated creation of configuration files for the same type of data, achieving the goal of creating once and using multiple times.

[0049] S130. Perform missing data detection and repair, time synchronization, timestamp conversion and coordinate transformation on the above-mentioned driving data to be verified to obtain driving data to be analyzed.

[0050] For example, the driving data to be verified undergoes data preprocessing, which includes missing data detection and repair, time synchronization, timestamp conversion, and coordinate transformation. Missing data detection and repair checks the integrity of different groups of driving data and repairs any missing data. Time synchronization synchronizes data acquired by different sensors within the same time frame. Since different sensors may correspond to different sampling frequencies, the raw driving data needs to be processed to ensure that the frequencies of the data from different sensors are consistent. Timestamp conversion converts timestamps to a year-month-day-hour-minute-second format for easier analysis. Coordinate transformation converts the corresponding coordinates from the sensors to the coordinate format corresponding to the data processing API. After performing these operations sequentially, the driving data to be analyzed can be obtained.

[0051] S140. Generate result text and driving curves based on the driving data to be analyzed and display them on the data analysis interface.

[0052] For example, data statistics are performed on the data to be analyzed, and driving curves are fitted. The result texts corresponding to different sensors and the driving curves are displayed on the same data analysis interface. At the same time, the generated result texts can be saved to a specified directory for easy analysis later.

[0053] In summary, the method provided in this application can be used for performance testing of integrated navigation sensors during the vehicle testing phase. By acquiring different raw driving data from different types of integrated navigation sensors mounted on the same vehicle, index characters are entered into the raw driving data to obtain driving data to be verified. Then, the driving data to be verified is subjected to missing data detection and repair, time synchronization, timestamp conversion, and coordinate transformation operations in sequence to obtain driving data to be analyzed. The driving data to be analyzed generates result text and driving curves to be displayed on the data analysis interface. Developers can simultaneously observe the results acquired by different sensors on the same analysis interface, which facilitates developers' understanding of the differences in data collected by various sensors, makes it easier for developers to evaluate the performance of the sensors, shortens the development cycle, and reduces the workload of developers.

[0054] In some examples, the missing data detection and repair operation described above includes missing data detection and data repair operations. The missing data detection is determined based on the continuity of index characters. After the missing data detection is completed, a record of the amount of missing data is generated. The data repair operation is determined based on the average value of the data associated with the missing data.

[0055] For example, the detection and repair operation does include data missing detection and data repair operations. This includes filtering the original data according to the configuration file; checking for missing values ​​in the filtered data by traversing the data, which can be determined by whether the index characters are consecutive. If data is missing, it is filled using the average value method, and the number of missing values ​​is recorded. The standard for detecting outliers is that the timestamps must be sequentially increasing, and other data are judged using the Laida criterion.

[0056] In some examples, the time synchronization operations described above include a reference frequency determination operation, a low-frequency interpolation operation, and a high-frequency thinning operation.

[0057] The above methods also include:

[0058] For each group of the above raw driving data, a reference frequency determination operation is performed to determine the minimum sampling frequency of the raw driving data in all groups as the initial reference frequency;

[0059] If the ratio of the sampling frequency corresponding to each currently processed original driving data to the aforementioned initial reference frequency is less than or equal to a preset threshold, a high-frequency thinning operation is performed on the currently processed original driving data so that the currently processed original driving data corresponds to the aforementioned initial reference frequency.

[0060] If the ratio of the sampling frequency to the initial reference frequency corresponding to any of the above-mentioned current raw driving data is greater than the preset threshold, the average value of the sampling frequency and the initial reference frequency shall be used as the correction reference frequency.

[0061] The original driving data currently being processed is subjected to high-frequency thinning or low-frequency interpolation to make the original driving data currently being processed correspond to the correction reference frequency.

[0062] For example, time synchronization employs interpolation and extrapolation to process the data, avoiding the adverse effects on subsequent data analysis caused by asynchronous sampling data due to different clock references of various sensors. Assume that n sensors are used in a combined navigation application scenario. Data acquisition is performed, and the sensor sampling frequencies are respectively ,and ;

[0063] The minimum sampling frequency is determined as the initial reference frequency by statistically analyzing the sampling frequencies of all sensors. If the ratio between the sampling frequency of other sensors and the initial reference frequency is less than or equal to a preset threshold N (N can be 5), then the initial reference frequency is considered to be appropriate. In this case, all raw driving data collected by the sensors are thinned according to the initial reference frequency to make the frequency correspond to the initial reference frequency.

[0064] For example, if there exists at least one instance where the ratio of the sampling frequency corresponding to the currently processed raw driving data to the aforementioned initial reference frequency is greater than the aforementioned preset threshold N, then the initial base frequency needs to be adjusted according to... The correction reference frequency F is determined. If the current raw driving data is greater than F, a thinning operation is performed to make its frequency equal to F. If the current raw driving data is less than F, an interpolation operation is performed. After interpolation and thinning, the timestamp matching and recording are performed so that the data acquired by different sensors correspond in time and frequency.

[0065] In some examples, the timestamp conversion operation includes converting the 13-bit timestamp in the original driving data into the local time, and the coordinate conversion operation includes converting the original driving data from the WGS-84 coordinate system to the GCJ-02 coordinate system.

[0066] In some examples, the above-mentioned results text and driving curves generated based on the driving data to be analyzed are displayed on the data analysis interface, including:

[0067] Obtain the statistical values ​​of the driving data to be analyzed, wherein the statistical values ​​include at least one of the following: maximum value, minimum value, variance, and error;

[0068] Generate a report text based on the above statistical values ​​using a specific document template;

[0069] Store the above report text to the specified path and display it in the above data analysis interface;

[0070] Calculate the driving coordinates for each group based on the driving data to be analyzed in each group.

[0071] For each set of driving coordinates mentioned above, multiple driving trajectories are generated based on the folium library and Gaode WMTS tile map service and displayed on the data analysis interface.

[0072] For example, statistical analysis is performed on the processed data, including the calculation of the maximum, minimum, average, variance, and error values, as well as the travel distance and data collection time. For distance calculation, if vehicle speed data is imported, the distance is calculated by multiplying the speed by the single sampling time; if no vehicle speed data is imported, the distance is calculated based on latitude and longitude changes. The first and last timestamps of the imported data are subtracted, and then converted to local time using `datetime::datetime` for direct reading. The statistical results, including text and curves, are displayed in a visualization interface.

[0073] The map visualization unit includes data display, interaction between tables and maps, and trajectory visualization.

[0074] Data can be displayed using the QtWidgets::QTableWidget component, showing the latitude and longitude coordinates and timestamps of the N sets of data being compared. The interaction between the table and the map is implemented using the QTableWidget's cellPressed signal, used for generating, saving, and retrieving static maps. The specific steps are as follows:

[0075] S210. Clicking a cell in a QtableWidget returns the row and column index of that cell;

[0076] S220. Using row and column indices as parameters, obtain the latitude and longitude coordinates of N sets of data, call the Gaode static map API to generate a static image and save it. The generated static image contains N markers, each marker representing a coordinate.

[0077] The S230 uses the OpenCV library to read generated images, calling QtGui::QImage, QtGui::QPixmap, and Qt::QLabel to read and display the images.

[0078] Track visualization can be achieved by generating interactive web-based maps using the folium library and Amap WMTS tile map service. These maps support zoom functionality, display of latitude and longitude information for selected points, and simple distance measurement.

[0079] In some examples, the above method also includes:

[0080] Calculate the coordinate error of each set of driving coordinates at the same time. If the coordinate error exceeds the first preset difference, the road segment corresponding to the driving trajectory that exceeds the first preset difference will be flashed.

[0081] If the difference is less than the second preset error, the road segment corresponding to the driving trajectory that is less than the second preset error will be displayed with the same trajectory.

[0082] For example, because the analysis interface contains data from multiple sensors, although the interface has zoom in and out functions, it cannot quickly locate the source of data discrepancies when the deviation between each set of data is small. If the coordinate error exceeds a first preset difference, it is considered that the data from different sensors differs significantly, and the driving trajectory of that road segment is displayed in a flashing manner to attract the analyst's attention. If the coordinate error is less than a second preset error, it is considered that the difference between the data from different sensors is small and there is no abnormality. To simplify the display, the trajectories obtained from different sensors are displayed as a single trajectory for that road segment.

[0083] In summary, by comparing coordinate errors and adjusting the display method, the differences between the collected data can be clearly reflected visually, improving the flexibility of the analysis method and reducing the difficulty of analysis for the data analyzer.

[0084] In some examples, the above method also includes:

[0085] The above statistical values ​​and the view information corresponding to the above driving trajectory are stored in the same document to generate a test report.

[0086] For example, a test report can be generated after data analysis. Test report generation mainly involves setting a document template and importing data, which can specifically include:

[0087] S310. Generate a docx document by calling docx.Document.

[0088] S320. Set the header, footer and margin settings of the document using sections[0].header_distance and sections[0].footer_distance.

[0089] S330. Add text using the add_run function and set the alignment using the alignment function. The alignment is determined by the parameter of WD_PARAGRAPH_ALIGNMENT: LEFT for left alignment, CENTER for center alignment, and RIGHT for right alignment.

[0090] S340. Set the font, size, alignment, and color of the document's title and body text. The font.name function sets the font, font.size sets the font size, font.color.rgb function sets the font color, and the alignment function sets the alignment. See S330 for specific settings.

[0091] S350: Read the data saved by statistical calculation results using pandas.read_excel, distinguish the data source according to the sub-table name, call the add_table function in docx to import the table into docx, and add table headers to the table. The method of setting the text in the table is the same as in S340.

[0092] The S360, statistical analysis, and map views are all saved in JPG format to a folder. `os.listdir` is called to retrieve all files, `cv2.imread` is used to read the image data, and `endswith` is used to filter files with the JPG extension. Furthermore, different filenames are used to distinguish between statistical view and map view images. Images are added to the report by calling `docx.add_picture`.

[0093] S370. Save the test report to the specified folder.

[0094] like Figure 2 As shown, this application also proposes a positioning data analysis system, including:

[0095] The data import module is used to obtain raw data and distinguish different groups of data through different sub-tables.

[0096] The configuration file selection module allows for custom data filtering by creating and importing configuration files.

[0097] The data processing module filters data using configuration files, performs missing value detection, outlier detection, timestamp conversion, and coordinate transformation, and synchronizes multiple imported datasets based on their timestamps. Statistical analysis is then performed on the processed data.

[0098] The visualization module is divided into a main interface, a statistical view, and a map view. The main interface displays the raw data, filtered data, and operation prompts; the statistical view displays the statistical analysis results and data change curves; the map view displays the running trajectory, its starting point, ending point, and selected point information.

[0099] The report generation module combines the outlier statistics, statistical and map visualization results after data processing, as well as some information after data processing, such as the data driving distance statistics, data start time and end time used in this example, and outputs a document report.

[0100] The control module is used for function selection, including importing data, creating configuration files, switching views, map interaction, and exporting analysis reports; map interaction includes zooming in and out of the map view, viewing information of selected points on the map, and displaying the location of data in the selected data table.

[0101] Please see Figure 3 One embodiment of the location data analysis device in this application may include:

[0102] The acquisition unit 21 is used to acquire at least two sets of raw driving data of the target vehicle, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle.

[0103] The input unit 22 is used to input index characters for each set of the above-mentioned original driving data to obtain the driving data to be verified.

[0104] Processing unit 23 is used to perform missing data detection and repair operations, time synchronization operations, timestamp conversion operations and coordinate conversion operations on the above-mentioned driving data to be verified in sequence to obtain driving data to be analyzed;

[0105] Analysis unit 24 is used to generate result text and driving curves based on the driving data to be analyzed, and to display them on the data analysis interface.

[0106] like Figure 4 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the methods for analyzing the location data described above.

[0107] Since the electronic device described in this embodiment is a device used to implement a positioning data analysis device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0108] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0109] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0110] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] This application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device performs the analysis process of the positioning data in the corresponding embodiment, including:

[0115] At least two sets of raw driving data for the target vehicle are acquired, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle.

[0116] For each set of the above original driving data, enter the index character operation to obtain the driving data to be verified;

[0117] The above-mentioned driving data to be verified is subjected to missing data detection and repair, time synchronization, timestamp conversion and coordinate transformation operations in sequence to obtain the driving data to be analyzed.

[0118] Based on the driving data to be analyzed, generate result text and driving curves to be displayed on the data analysis interface.

[0119] In one feasible implementation, the above-mentioned missing data detection and repair operation includes data missing data detection and data repair operation. The data missing data detection is determined based on the continuity of index characters. After the missing data detection is completed, a record of the amount of missing data is generated. The data repair operation is determined based on the average value of the data associated with the missing data before and after the missing data.

[0120] In one feasible implementation, the time synchronization operation includes a reference frequency determination operation, a low-frequency interpolation operation, and a high-frequency thinning operation.

[0121] The above methods also include:

[0122] For each group of the above raw driving data, a reference frequency determination operation is performed to determine the minimum sampling frequency of the raw driving data in all groups as the initial reference frequency;

[0123] If the ratio of the sampling frequency corresponding to each currently processed original driving data to the aforementioned initial reference frequency is less than or equal to a preset threshold, a high-frequency thinning operation is performed on the currently processed original driving data so that the currently processed original driving data corresponds to the aforementioned initial reference frequency.

[0124] If the ratio of the sampling frequency to the initial reference frequency corresponding to any of the above-mentioned current raw driving data is greater than the preset threshold, the average value of the sampling frequency and the initial reference frequency shall be used as the correction reference frequency.

[0125] The original driving data currently being processed is subjected to high-frequency thinning or low-frequency interpolation to make the original driving data currently being processed correspond to the correction reference frequency.

[0126] In one feasible implementation, the timestamp conversion operation includes converting the 13-bit timestamp in the original driving data into the local time, and the coordinate conversion operation includes converting the original driving data from the WGS-84 coordinate system to the GCJ-02 coordinate system.

[0127] In one feasible implementation, the above-mentioned generation of result text and driving curves based on the driving data to be analyzed and displayed on the data analysis interface includes:

[0128] Obtain the statistical values ​​of the driving data to be analyzed, wherein the statistical values ​​include at least one of the following: maximum value, minimum value, variance, and error;

[0129] Generate a report text based on the above statistical values ​​using a specific document template;

[0130] Store the above report text to the specified path and display it in the above data analysis interface;

[0131] Calculate the driving coordinates for each group based on the driving data to be analyzed in each group.

[0132] For each set of driving coordinates mentioned above, multiple driving trajectories are generated based on the folium library and Gaode WMTS tile map service and displayed on the data analysis interface.

[0133] In one feasible implementation, the above method further includes:

[0134] Calculate the coordinate error of each set of driving coordinates at the same time. If the coordinate error exceeds the first preset difference, the road segment corresponding to the driving trajectory that exceeds the first preset difference will be flashed.

[0135] If the difference is less than the second preset error, the road segment corresponding to the driving trajectory that is less than the second preset error will be displayed with the same trajectory.

[0136] In one feasible implementation, the above method further includes:

[0137] The above statistical values ​​and the view information corresponding to the above driving trajectory are stored in the same document to generate a test report.

[0138] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for analyzing location data, characterized in that, include: At least two sets of raw driving data for the target vehicle are acquired, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle; For each set of original driving data, enter the index character to obtain the driving data to be verified; The driving data to be verified is sequentially subjected to missing data detection and repair, time synchronization, timestamp conversion, and coordinate transformation to obtain the driving data to be analyzed. The result text and driving curve are generated based on the driving data to be analyzed and displayed on the data analysis interface; The time synchronization operation includes a reference frequency determination operation, a low-frequency interpolation operation, and a high-frequency thinning operation. The method further includes: A reference frequency determination operation is performed on each group of raw driving data to determine the minimum sampling frequency of the raw driving data in all groups as the initial reference frequency; If the ratio of the sampling frequency corresponding to each currently processed original driving data to the initial reference frequency is less than or equal to a preset threshold, a high-frequency thinning operation is performed on the currently processed original driving data so that the currently processed original driving data corresponds to the initial reference frequency. If the ratio of the sampling frequency to the initial reference frequency corresponding to any of the currently processed raw driving data is greater than the preset threshold, the average value of the sampling frequency and the initial reference frequency shall be used as the correction reference frequency. The raw driving data being processed is subjected to high-frequency thinning or low-frequency interpolation to make the raw driving data being processed correspond to the correction reference frequency.

2. The method according to claim 1, characterized in that, The missing data detection and repair operation includes missing data detection and data repair operation. The missing data detection is determined based on the continuity of index characters. After the missing data detection is completed, a record of the amount of missing data is generated. The data repair operation is determined based on the average value of the data before and after the missing data.

3. The method according to claim 1, characterized in that, The timestamp conversion operation includes converting the 13-bit timestamp in the original driving data into the local time, and the coordinate conversion operation includes converting the original driving data from the WGS-84 coordinate system to the GCJ-02 coordinate system.

4. The method according to claim 1, characterized in that, The process of generating result text and driving curves based on the driving data to be analyzed and displaying them on the data analysis interface includes: Obtain statistical values ​​of the driving data to be analyzed, wherein the statistical values ​​include at least one of the following: maximum value, minimum value, variance, and error; The statistical values ​​are used to generate a report text based on the document template; The report text is stored in a specified path and displayed in the data analysis interface; Calculate the driving coordinates for each group based on the driving data to be analyzed in each group. Multiple driving trajectories are generated for each set of driving coordinates based on the folium library and Gaode WMTS tile map service and displayed on the data analysis interface.

5. The method according to claim 4, characterized in that, Also includes: Calculate the coordinate error of each set of driving coordinates at the same time. If the coordinate error exceeds a first preset difference, the road segment corresponding to the driving trajectory that exceeds the first preset difference will be flashed. If the difference is less than the second preset error, the road segment corresponding to the driving trajectory that is less than the second preset error will be displayed with the same trajectory.

6. The method according to claim 4, characterized in that, Also includes: The statistical values ​​and the view information corresponding to the driving trajectory are stored in the same document to generate a test report.

7. A device for analyzing positioning data, characterized in that, include: The acquisition unit is used to acquire at least two sets of raw driving data of the target vehicle, wherein each set of raw driving data is acquired through different combined navigation sensors on the target vehicle; The input unit is used to input index characters for each group of the original driving data to obtain the driving data to be verified. The processing unit is used to sequentially perform missing detection and repair operations, time synchronization operations, timestamp conversion operations, and coordinate conversion operations on the driving data to be verified to obtain the driving data to be analyzed. The time synchronization operation includes a reference frequency determination operation, a low-frequency interpolation operation, and a high-frequency thinning operation. The processing unit is also used to perform a reference frequency determination operation on each group of the original driving data, so as to determine the minimum sampling frequency of the original driving data in all groups as the initial reference frequency. If the ratio of the sampling frequency corresponding to each currently processed original driving data to the initial reference frequency is less than or equal to a preset threshold, a high-frequency thinning operation is performed on the currently processed original driving data so that the currently processed original driving data corresponds to the initial reference frequency. If the ratio of the sampling frequency to the initial reference frequency corresponding to any of the currently processed raw driving data is greater than the preset threshold, the average value of the sampling frequency and the initial reference frequency shall be used as the correction reference frequency. The raw driving data being processed is subjected to either high-frequency thinning or low-frequency interpolation to ensure that the raw driving data corresponds to the corrected reference frequency. The analysis unit is used to generate result text and driving curves based on the driving data to be analyzed, and to display them on the data analysis interface.

8. An electronic device, comprising: A memory and a processor, characterized in that the processor is used to execute a computer program stored in the memory to implement the steps of the method for analyzing positioning data as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the method for analyzing positioning data as described in any one of claims 1-6.

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