A preview method, device and equipment of a data file and a storage medium

Abstract

The application provides a preview method, device and equipment of a data file and a storage medium, belonging to the technical field, which comprises: performing parallel processing on different data regions of a first data file to generate a spectrum data stream; updating newly added spectrum data to a corresponding position of a view data structure according to the time-frequency attribute of the newly added spectrum data in the spectrum data stream; the view data structure is used to represent the global time-frequency signal distribution of the first data file; generating a spectrum preview graph based on the view data structure, and in response to the update of the view data structure, supplementing first display content corresponding to the newly added spectrum data to the spectrum preview graph. The application performs data fusion on the asynchronously generated spectrum data and simultaneously visualizes the rendering, realizes gradual spectrum restoration through continuous superposition of data processing results, enables the user to obtain a global overview of the signal in the shortest possible time, improves the user experience, and improves the processing efficiency.

Description

Technical Field

[0001] This application relates to the field of signal analysis technology, and in particular to a method, apparatus, device and storage medium for previewing data files. Background Technology

[0002] In signal analysis applications, analysts typically need to find and extract target signals of interest from wideband, long-time-history data files containing a large amount of irrelevant signals. To meet this need, existing technologies usually employ commercial analysis tools or custom-developed software code. These tools or code generally use a sequential processing mode, that is, starting from the beginning of the data file, reading data segment by segment in sequence, performing spectral transformation operations, until the entire file is processed, and then presenting all the analysis results to the user at once.

[0003] However, the aforementioned existing technical solutions, when processing ultra-large data files ranging from hundreds of gigabytes to terabytes, suffer from long overall processing times and low efficiency due to their sequential reading and processing mode. Furthermore, users cannot see the final spectrum analysis results until the entire processing is complete, resulting in a very high initial latency. During this period, users cannot understand or grasp the signals within the data file, severely impacting the timeliness and interactive experience of signal analysis. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for previewing data files, aiming to solve the problems of poor timeliness and unsatisfactory interactive experience caused by the existing sequential reading and processing working mode.

[0005] Firstly, this application provides a method for previewing data files, including: Different data regions of the first data file are processed in parallel to generate a spectrum data stream; Based on the time-frequency attributes of the newly added spectrum data in the spectrum data stream, the newly added spectrum data is updated to the corresponding position in the view data structure; the view data structure is used to characterize the global time-frequency signal distribution of the first data file; A spectrum preview image is generated based on the view data structure, and in response to an update of the view data structure, the first display content corresponding to the newly added spectrum data is added to the spectrum preview image.

[0006] As one embodiment, after generating the spectrum preview based on the view data structure, the method further includes: In response to receiving a region selection instruction on the spectrum preview map, the target of processing is adjusted to the target data region corresponding to the region selection instruction; A second display content for the target data area is generated based on the processing result of the target data area, and the resolution of the second display content is higher than that of the first display content.

[0007] As one embodiment, after generating the spectrum preview based on the view data structure, the method further includes: Determine the first similarity between the spectral characteristics of each spectral data in the spectral data stream and the spectral characteristics of the target sample signal; Spectral data with a first similarity greater than a first threshold are used as target spectral data, and the regions on the spectrum preview map corresponding to the target spectral data are initially labeled to obtain the initial region where the target signal is located.

[0008] As one example, the data file preview method further includes: Multiple consecutive initial regions in the time domain are merged into a long-term region; Determine the second similarity between the long-time spectral data corresponding to the long-time region and the corresponding equal-length sample signal in the target sample signal, and if the second similarity is greater than the second threshold, mark the long-time region as the final region where the target signal is located.

[0009] As one embodiment, the parallel processing of different data regions of the first data file to generate a spectrum data stream includes: Multiple data processing threads are started, and different data regions are allocated to different data processing threads; For each data processing thread, multiple data frames are sequentially read from the data region corresponding to the data processing thread, and a fast Fourier transform is performed on each data frame to generate the spectrum data stream corresponding to the data processing thread.

[0010] As one embodiment, the step of supplementing the spectrum preview image with the first display content corresponding to the newly added spectrum data in response to the update of the view data structure includes: The multiple spectrum data at the positions corresponding to the newly added spectrum data in the view data structure are aggregated to obtain the aggregation result; The display attributes corresponding to the positions on the spectrum preview map are determined based on the aggregation results, and the spectrum preview map is updated based on the display attributes.

[0011] As one example, the data file preview method further includes: In response to receiving multiple box selection operations on the spectrum preview map, a target signal label is generated for each box selection operation. The target signal label records the time-frequency region selected by the box selection operation and at least one signal parameter within the time-frequency region.

[0012] As one example, the data file preview method further includes: In response to receiving a batch data extraction instruction on the spectrum preview map, the data segment corresponding to the time-frequency region in each of the target signal annotations is extracted from the first data file, and the data segment is stored as an independent second data file.

[0013] Secondly, this application also provides a data file preview device, comprising: The parallel processing module is used to perform parallel processing on different data regions of the first data file to generate a spectrum data stream; The view data structure update module is used to update the newly added spectrum data to the corresponding position in the view data structure according to the time-frequency attributes of the newly added spectrum data in the spectrum data stream; the view data structure is used to represent the global time-frequency signal distribution of the first data file; The spectrum preview generation module is used to generate a spectrum preview based on the view data structure, and in response to the update of the view data structure, to supplement the spectrum preview with the first display content corresponding to the newly added spectrum data.

[0014] Thirdly, this application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned methods for previewing data files.

[0015] Fourthly, this application also provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the above-described methods for previewing data files.

[0016] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements any of the above-described methods for previewing data files.

[0017] This application provides a method, apparatus, device, and storage medium for previewing data files. It abandons the traditional method of restoring the spectrum by processing data sequentially. Instead, it performs parallel processing of the data file, fuses asynchronously generated spectrum data, and performs visualization rendering while the data is being fused. By continuously overlaying the data processing results, more signal details are obtained and presented, achieving progressive spectrum restoration. This allows users to obtain a global overview of the signal in large or ultra-large data files in the shortest possible time, improving the user experience, while continuously refreshing and enriching signal details. The clarity and detail of the global overview are continuously enhanced as data processing progresses, thereby significantly improving processing efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in 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, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts illustrating the data file preview method provided in this application; Figure 2 This is a schematic diagram of the parallel processing flow provided in this application; Figure 3 This is the second flowchart illustrating the method for previewing data files provided in this application; Figure 4 This is an example of an initial spectrum preview provided in this application; Figure 5 This is an example of the display content obtained based on the region selection command provided in this application; Figure 6 The time resolution provided in this application is relatively high. Figure 5 Example of displaying higher content; Figure 7 This is a schematic diagram of the structure of the data file preview device provided in this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0022] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0023] In the field of signal analysis and processing, when the target signal is unknown, users often use acquisition and storage devices to collect all signals within the target frequency band and then return to the laboratory for analysis and processing. The amount of data collected in this way is often enormous: taking a 500MHz bandwidth, a 600MHz sampling rate, and two bytes per sampling point (IQ channels), the data volume reaches 2.4GB per second, and 144GB per minute. Finding the target signal from such a large amount of data is a prerequisite for analyzing the target signal.

[0024] To locate target signals, analysts typically use commercial tools such as CoolEdit to perform spectral reconstruction of data, then search for the target signal from the reconstructed results, or develop their own code for the search. However, commercial tools do not support processing data files that are hundreds of gigabytes or even terabytes in size, and their processing efficiency is low. Developing custom code is also inefficient and costly.

[0025] The following description, in conjunction with the accompanying drawings, describes the method, apparatus, device, and storage medium for previewing the data files provided in this application.

[0026] It should be noted that the data file preview method provided in this application embodiment is implemented based on a data file preview device. The data file preview method can complete the preliminary spectrum restoration and display of TB-level data files within a few seconds, and continuously extract and update detailed information, thereby improving the timeliness and interactive experience of signal analysis; at the same time, it supports spectrum fuzzy matching based on target sample data, thereby realizing automatic labeling of target signals.

[0027] This application describes a data file preview method using a data file preview device as the execution subject.

[0028] Figure 1 This is one of the flowcharts illustrating the data file preview method provided in this application. For example... Figure 1 As shown, the data file preview method provided in this application includes: S1010: Perform parallel processing on different data regions of the first data file to generate a spectrum data stream.

[0029] The first data file is the raw signal data recorded by the acquisition and storage device, and its data volume is huge, reaching hundreds of GB or even TB. In order to process the first data file quickly, the entire data space of the first data file is logically divided into multiple data regions, and multiple independent data processing threads are started. Each data processing thread is allocated one or more data regions to make full use of the computing power of the multi-core processor of the electronic device and the high-concurrency I / O capability of the storage device. Data reading and processing can start simultaneously from different data regions of the first data file, thereby achieving parallel processing.

[0030] Each data processing thread performs windowing and Fast Fourier Transform (FFT) processing on its assigned data region, converting the time-domain signal data into spectral data. It's important to note that each data processing thread reads and processes a portion of the data from its assigned region at a time; therefore, the data reading process is continuous, and spectral data is continuously generated, thus forming a spectral data stream.

[0031] In a preferred implementation, the computing power of the Graphics Processing Unit (GPU) can be used to perform FFT calculations in parallel to further improve efficiency.

[0032] S1020: Based on the time-frequency attributes of the newly added spectrum data in the spectrum data stream, update the newly added spectrum data to the corresponding position in the view data structure; the view data structure is used to characterize the global time-frequency signal distribution of the first data file.

[0033] In one possible implementation, such as Figure 2 As shown, the view data structure can logically be a two-dimensional numerical matrix, where one dimension (e.g., the horizontal axis) corresponds to the time interval and the other dimension (e.g., the vertical axis) corresponds to the frequency interval. The value stored at each coordinate point in the matrix represents the power intensity of the signal at that time and frequency point. Based on the data tags (such as data offset, data length, etc.) output by the data processing thread, the position of each spectrum data point output by the data processing thread on the two-dimensional matrix is ​​calculated. Therefore, the view data structure can completely represent the distribution of all signals contained in the first data file across the entire time and frequency span; in other words, the view data structure represents the global time-frequency signal distribution.

[0034] When any data processing thread completes the FFT operation of a data frame, it will obtain a set of newly added spectrum data. This set of newly added spectrum data itself contains its time-frequency attributes. For example, the data offset of its corresponding data frame in the first data file can be converted into a time attribute, and the index of each frequency point in its FFT operation result can be converted into a frequency attribute.

[0035] In one possible implementation, a separate data fusion thread asynchronously receives newly generated spectrum data from all data processing threads and accurately updates the corresponding positions in the view data structure based on their time-frequency attributes. For example, if a data point's time attribute corresponds to a column in a matrix and its frequency attribute corresponds to a row, then the power value of that data point is written to or updated in the cell where that row and column intersect. As long as the data processing threads are still running, new spectrum data will be continuously written to the view data structure, thus ensuring continuous updates to the view data structure.

[0036] S1030: Generate a spectrum preview image based on the view data structure, and in response to the update of the view data structure, supplement the spectrum preview image with the first display content corresponding to the newly added spectrum data.

[0037] In one possible implementation, a graphics rendering technique (such as OpenGL) is used to visualize the view data structure. Specifically, based on the current state of the view data structure, its numerical matrices are mapped into a two-dimensional image, i.e., a spectrum preview. In this spectrum preview, different colors or brightness are typically used to characterize different signal power levels.

[0038] It should be noted that the generation and display of the spectrum preview is a gradual process. In the initial stages of processing the first data file, the data processing thread only performs sparse sampling and processing on different regions of the entire data file. At this point, only a few locations in the view data structure are filled with data. Therefore, the initial view data structure is incomplete but covers the entire signal, and the initial spectrum preview is somewhat blurry. However, it is clear that users do not need to wait and can see a global overview of the signal within a few seconds.

[0039] As parallel processing continues, more and more new spectrum data is updated into the view data structure, filling previous data gaps or refining existing data points. Whenever the view data structure is updated, the new display information brought about by the new spectrum data, i.e., the first display content, is added to the spectrum preview graph. As a result, the spectrum preview graph observed by the user will dynamically change over time, with the global outline becoming clearer and the signal details becoming richer. Figure 4 , Figure 5 and Figure 6 This visualization shows the process, with the horizontal axis representing time in seconds. Figure 4 This is a preview of the spectrum generated quickly during the initial processing stage when previewing a 10-minute Bluetooth signal. Figure 5 and Figure 6 It displays a detailed image of the signal with further improved temporal resolution. This progressive presentation perfectly balances the dual requirements of fast response and rich detail.

[0040] This application's embodiments abandon the traditional method of restoring the spectrum by processing data sequentially. Instead, it processes the data file in parallel, fuses the asynchronously generated spectrum data, and performs visualization rendering simultaneously. By continuously overlaying the data processing results, more signal details are obtained and presented, achieving progressive spectrum restoration. This allows users to obtain a global overview of the signal in large or ultra-large data files in the shortest possible time, improving the user experience, while continuously refreshing and enriching signal details. The clarity and detail of the global overview are continuously enhanced as data processing progresses, thereby significantly improving processing efficiency.

[0041] In one possible implementation, such as Figure 2 As shown, in S1010, the parallel processing of different data regions of the first data file to generate a spectrum data stream includes: S1011: Start multiple data processing threads and allocate different data areas to different data processing threads.

[0042] The number of data processing threads can be configured based on hardware resources such as the number of processor cores and memory size of the electronic device to achieve high processing efficiency. The data area can be allocated in various ways; for example, the total byte length of the first data file can be evenly distributed among the data processing threads, with each thread responsible for processing a contiguous block of data.

[0043] S1012: For each data processing thread, multiple data frames are sequentially read from the data region corresponding to the data processing thread, and a fast Fourier transform is performed on each data frame to generate the spectrum data stream corresponding to the data processing thread.

[0044] Each data processing thread works independently within its assigned data region. It divides the data region into multiple data frames according to a preset unit data length and a step value, where the length of each data frame is typically related to the desired frequency resolution.

[0045] During processing, each data processing thread starts from the beginning of its assigned data region and sequentially reads data from the hard drive into memory, forming a data frame. Before performing the Fast Fourier Transform (FFT), the data frame is typically windowed to reduce spectral leakage. Subsequently, the FFT is executed to convert the time-domain data frame into frequency-domain spectral data.

[0046] After processing a data frame, the data processing thread moves the read pointer according to a set step value, reads the next data frame, and repeats the above process. After processing a data area, the data processing thread changes the starting data offset and continues to perform the same processing on another data area until all the data handled by the data processing thread has been read and processed, or the user command terminates the processing, at which point the thread stops working.

[0047] In this way, each data processing thread continuously and independently generates its own spectrum data stream. The spectrum data streams generated by all data processing threads are aggregated together to form the global spectrum data stream.

[0048] The embodiments of this application decompose macroscopic parallel tasks into microscopic data frame sequential processing to ensure stable and efficient concurrent execution of data processing tasks, thereby achieving rapid response.

[0049] In one possible implementation, in S1030, the step of supplementing the spectrum preview image with the first display content corresponding to the newly added spectrum data in response to the update of the view data structure includes: S1310: Perform aggregation operations on multiple spectrum data at the positions corresponding to the newly added spectrum data in the view data structure to obtain an aggregation result.

[0050] Because multiple data processing threads operate in parallel and independently, multiple different spectral data points are ultimately mapped to the same logical location in the view data structure. Therefore, aggregation operations are performed on the spectral data at the same logical location. These aggregation operations can be executed according to preset aggregation rules. For example, the average power of all spectral data falling at the same logical location can be calculated to smooth noise; the maximum power of all data can be taken to highlight the strongest signal that occurred at that time-frequency point; or the minimum value, root mean square value, etc., can be selected by the user according to their analysis needs.

[0051] S1320: Determine the display attribute corresponding to the position on the spectrum preview map based on the aggregation result, and update the spectrum preview map based on the display attribute.

[0052] The aggregation result is a numerical value, which is then mapped to a specific display attribute. In one possible implementation, the display attribute can be a color value. For example, a mapping table from numerical value to color can be established, mapping low power values ​​to cool tones (such as blue), high power values ​​to warm tones (such as red), and intermediate values ​​to a smooth transition. After obtaining the aggregation result, the color that the pixel at the corresponding position on the spectrum preview image should display is determined by querying this mapping table. Finally, the graphics rendering engine redraws or updates the pixel at that position based on this newly determined display attribute (color), thereby reflecting the information changes brought about by the added spectrum data in the final display screen.

[0053] In this embodiment, when new spectrum data appears, all spectrum data at its logical location are aggregated, and the display attribute of that location is determined based on the aggregation result. The display content of that location in the spectrum preview image is updated based on the display attribute, thereby supplementing the display content of the new spectrum data into the spectrum preview image. This solves the data write conflict problem that may occur in parallel processing. Furthermore, by fusing multiple data points at the same location, the final spectrum preview image can more accurately and stably reflect the true power distribution of the signal, effectively supporting a progressive display effect from a rough overview to rich details.

[0054] In one possible implementation, such as Figure 3 As shown, after generating the spectrum preview based on the view data structure, the process also includes: S1040: Determine the first similarity between the spectral characteristics of each spectral data in the spectral data stream and the spectral characteristics of the target sample signal.

[0055] The target sample signal is a known data file containing signal features of interest to the user. This target sample signal can be pre-processed, for example, by performing FFT processing at a certain time resolution to obtain several frames of spectral feature data, which can then be used as a benchmark for comparison.

[0056] When parallel data processing threads generate new spectral data, they extract the spectral features of the new spectral data and compare them with the spectral features of the pre-stored target sample signal to calculate the first similarity. Specifically, this can be measured by calculating the root mean square error (RMSE) between the two; the smaller the error, the higher the similarity.

[0057] S1050: Use the spectrum data with a first similarity greater than a first threshold as the target spectrum data, and perform preliminary labeling on the region labeling on the spectrum preview map corresponding to the target spectrum data to obtain the initial region where the target signal is located.

[0058] The first threshold is used to determine whether the match is successful. When the first similarity is greater than the first threshold (for example, the root mean square error is less than a certain set threshold, indicating that the first similarity between the two has reached the first threshold), the new spectral data is determined to match the target spectral features and is identified as the target spectral data. Subsequently, on the spectral preview image, the time-frequency region corresponding to the target spectral data is visually marked, for example, by circling it with a box of a specific color (e.g., ...). Figure 4 The yellow box in the diagram represents the initial region where the target signal is located.

[0059] This application embodiment quickly performs similarity judgment between the obtained spectrum data and the spectrum characteristics of the target sample, and realizes preliminary automatic target screening through fuzzy matching. This enables the rapid and automated discovery of all signal segments that may meet the target characteristics in massive data streams, freeing analysts from tedious visual searches and significantly improving the efficiency of target signal discovery.

[0060] In one possible implementation, such as Figure 3 As shown, the methods for previewing data files also include: S1060: Merge multiple consecutive initial regions in the time domain into a long-term region.

[0061] Specifically, the temporal relationships of all initial regions generated through preliminary labeling are determined. If multiple initial regions are continuous or adjacent on the time axis, these independent, short-term initial regions are merged into a longer-span continuous region, i.e., a long-term region. This merging process itself is a form of filtering, as it can eliminate isolated suspected signal points that lack temporal continuity.

[0062] S1070: Determine the second similarity between the long-time spectrum data corresponding to the long-time region and the corresponding equal-length sample signal in the target sample signal, and if the second similarity is greater than the second threshold, mark the long-time region as the final region where the target signal is located.

[0063] After obtaining the long-term region, the original data segment that corresponds perfectly to the long-term region in terms of time span can be extracted from the first data file, and its overall spectrum can be analyzed to obtain long-term spectrum data. Alternatively, all spectrum data corresponding to the long-term region obtained by the data processing thread can be combined to obtain long-term spectrum data. Simultaneously, a data segment of equal length is also extracted from the target sample signal for processing. Then, the similarity of these two long-term spectrum data segments of equal length and richer information is compared to obtain a second similarity score.

[0064] Only when this more comprehensive second similarity, based on a longer data segment, also meets the second threshold, will the long-term region be finally confirmed as the target signal, and it will be given a final, high-confidence label. If the second similarity does not meet the condition, the labeling of all initial regions within that area will be revoked.

[0065] Based on the initial region selected through preliminary rapid screening, this embodiment merges the initial regions that are continuous in the time domain and compares the long-term spectral data of the merged region with the corresponding part of the target sample signal to achieve higher-precision matching. While maintaining a high detection rate, it can effectively filter out false targets caused by noise or accidental interference, significantly reducing the false alarm rate of automatic labeling, thereby providing users with more reliable and accurate automatic target discovery results.

[0066] In one possible implementation, such as Figure 3 As shown, after generating the spectrum preview based on the view data structure, the process also includes: S1080: In response to receiving a region selection instruction on the spectrum preview map, adjust the target of processing to the target data region corresponding to the region selection instruction.

[0067] The region selection command can be triggered by user interaction. For example, a user might click and drag a rectangle on the spectrum preview image, defining a time-frequency range of interest. Upon receiving this operation, it is parsed as a region selection command. Then, all data processing threads currently executing a global scan of the entire first data file are paused or terminated. Based on the time-frequency range defined by the region selection command, the corresponding target data region (i.e., a smaller data segment) in the first data file is calculated. Finally, this target data region is allocated to all data processing threads, and parallel processing is restarted only for this target data region.

[0068] S1090: Generate a second display content for the target data area based on the processing result of the target data area, wherein the resolution of the second display content is higher than that of the first display content.

[0069] Because all computational resources are focused on processing the target data region, more optimized FFT parameters can be used to perform the calculations (e.g., longer data frame lengths for higher frequency resolution, or smaller step sizes for higher temporal resolution). The results of these refined calculations are then used to update the view data structure or a new view data structure dedicated to focusing on the target data region. The resulting second display content will exhibit significantly higher signal detail (i.e., resolution) than the first display content seen in the global preview. For example, Figure 5 To Figure 4 The second display content and spectrum overview generated after focusing analysis on a selected area in the image can clearly show fine structures such as signal frequency hopping patterns that are indistinguishable in the global view.

[0070] Understandably, after obtaining new display content based on region selection commands, it is also possible to obtain higher resolution display content on top of that new display content based on new region selection commands. Figure 6 It demonstrates a higher time resolution Figure 5 Higher display content, with more signal details Figure 5 More.

[0071] This application embodiment dynamically redirects computing resources based on region selection instructions to achieve refined analysis of the selected region, thereby enabling seamless switching from macroscopic overview to microscopic detailed examination. This allows users to perform rapid, high-resolution analysis of signal regions of interest, effectively improving the efficiency and depth of interactive analysis and helping users accurately identify and understand the fine features of target signals.

[0072] In one possible implementation, such as Figure 3 As shown, the methods for previewing data files also include: S1100: In response to receiving multiple box selection operations on the spectrum preview map, generate a target signal label for each box selection operation, the target signal label recording the time-frequency region selected by the box selection operation and at least one signal parameter within the time-frequency region.

[0073] In one possible implementation, the user can freely drag and select multiple target signals on the spectrum preview using the mouse. Each time a selection is completed, the system considers it a selection operation. The system maintains a list to store the multiple target signal labels generated by these operations.

[0074] The target signal annotation is a structured data record. The time-frequency region is determined by mapping the pixel coordinates of the user-selected rectangle back to the time and frequency axes of the spectrum preview image. It typically includes information such as the signal's start time, end time (or duration), center frequency, and bandwidth. The at least one signal parameter is a quantized indicator calculated based on the signal data within the time-frequency region. For example, by traversing all data points falling within the time-frequency region in the view data structure, the average power of all data points is calculated to obtain the signal's average power parameter. The at least one signal parameter may also include other parameters such as peak power and signal-to-noise ratio.

[0075] This application embodiment allows users to simultaneously mark multiple target signals on a visual interface and automatically calculate the key physical parameters of these signals. This not only simplifies the recording work of signal analysis but also provides direct data support for the quantitative evaluation and comparison of signals, improving the accuracy and efficiency of manual analysis.

[0076] In one possible implementation, such as Figure 3 As shown, the methods for previewing data files also include: S1110: In response to receiving a batch data extraction instruction on the spectrum preview map, extract the data segment corresponding to the time-frequency region from the first data file according to the time-frequency region marked in each of the target signals, and store the data segment as an independent second data file.

[0077] In one possible implementation, the batch data extraction command can be triggered by the user clicking the "Extract All Labels" button on the interface.

[0078] Upon receiving a batch data extraction instruction, the system extracts the data segment corresponding to the time-frequency region from the first data file based on the time-frequency region in each of the target signal annotations. Specifically, the system iterates through all stored target signal annotations. For each target signal annotation, using the start time information in its recorded time-frequency region, combined with the sampling rate and data format of the first data file (e.g., the number of bytes per sampling point), the system accurately calculates the starting byte offset of the signal segment in the first data file. Then, based on the duration information, the total byte length of the data segment to be read is calculated.

[0079] After determining the start position and length of the data segment, a file I / O operation is performed to read the data segment directly from the first data file and store it as an independent second data file. This process is repeated for each target signal in the list, ultimately generating multiple independent second data files, each much smaller than the original first data file. To facilitate subsequent use, these newly generated files can be automatically named or tagged based on annotation information (such as start time, center frequency, etc.) during storage.

[0080] This application embodiment achieves automated batch extraction of multiple target signal data segments by accurately mapping the visualized annotation information to the data address of the original file. It solves the pain points of tedious, time-consuming and error-prone manual data extraction in the traditional signal analysis process, opens up the key link from signal discovery to data acquisition, and significantly improves the efficiency of the overall workflow.

[0081] Based on the above, this application also provides a data file previewing device. The data file previewing device and the data file previewing method described above can be referred to in correspondence.

[0082] As an example, such as Figure 7 As shown, the data file preview device includes: The parallel processing module 710 is used to perform parallel processing on different data regions of the first data file to generate a spectrum data stream; The view data structure update module 720 is used to update the newly added spectrum data to the corresponding position of the view data structure according to the time-frequency attributes of the newly added spectrum data in the spectrum data stream; the view data structure is used to represent the global time-frequency signal distribution of the first data file; The spectrum preview generation module 730 is used to generate a spectrum preview based on the view data structure, and in response to an update of the view data structure, to supplement the spectrum preview with the first display content corresponding to the newly added spectrum data.

[0083] This application's embodiments abandon the traditional method of restoring the spectrum by processing data sequentially. Instead, it processes data files in parallel, fuses asynchronously generated spectrum data, and performs visualization rendering simultaneously. By continuously overlaying the data processing results, more signal details are obtained and presented, achieving progressive spectrum restoration. This allows users to obtain a global overview of the signal in large or ultra-large data files in the shortest possible time, improving the user experience. Furthermore, it continuously refreshes and enriches signal details, making the clarity and detail of the global overview continuously enhanced as data processing progresses, thereby significantly improving processing efficiency.

[0084] Figure 8 This is a schematic diagram of the structure of the electronic device provided in this application, such as... Figure 8 As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a data file preview method, which includes: Different data regions of the first data file are processed in parallel to generate a spectrum data stream; Based on the time-frequency attributes of the newly added spectrum data in the spectrum data stream, the newly added spectrum data is updated to the corresponding position in the view data structure; the view data structure is used to characterize the global time-frequency signal distribution of the first data file; A spectrum preview image is generated based on the view data structure, and in response to an update of the view data structure, the first display content corresponding to the newly added spectrum data is added to the spectrum preview image.

[0085] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, 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 a 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 described in 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.

[0086] On the other hand, this application also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to execute the data file preview method provided in the above embodiments, the method including: Different data regions of the first data file are processed in parallel to generate a spectrum data stream; Based on the time-frequency attributes of the newly added spectrum data in the spectrum data stream, the newly added spectrum data is updated to the corresponding position in the view data structure; the view data structure is used to characterize the global time-frequency signal distribution of the first data file; A spectrum preview image is generated based on the view data structure, and in response to an update of the view data structure, the first display content corresponding to the newly added spectrum data is added to the spectrum preview image.

[0087] In another aspect, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the preview method for data files provided in the above embodiments, the method comprising: Different data regions of the first data file are processed in parallel to generate a spectrum data stream; Based on the time-frequency attributes of the newly added spectrum data in the spectrum data stream, the newly added spectrum data is updated to the corresponding position in the view data structure; the view data structure is used to characterize the global time-frequency signal distribution of the first data file; A spectrum preview image is generated based on the view data structure, and in response to an update of the view data structure, the first display content corresponding to the newly added spectrum data is added to the spectrum preview image.

[0088] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, 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 can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0090] Finally, it should be noted that 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 previewing data files, characterized in that, include: Different data regions of the first data file are processed in parallel to generate a spectrum data stream; Based on the time-frequency attributes of the newly added spectrum data in the spectrum data stream, the newly added spectrum data is updated to the corresponding position in the view data structure; the view data structure is used to characterize the global time-frequency signal distribution of the first data file; A spectrum preview image is generated based on the view data structure, and in response to an update of the view data structure, the first display content corresponding to the newly added spectrum data is added to the spectrum preview image.

2. The data file preview method according to claim 1, characterized in that, After generating the spectrum preview based on the view data structure, the following is also included: In response to receiving a region selection instruction on the spectrum preview map, the target of processing is adjusted to the target data region corresponding to the region selection instruction; A second display content for the target data area is generated based on the processing result of the target data area, and the resolution of the second display content is higher than that of the first display content.

3. The data file preview method according to claim 1, characterized in that, After generating the spectrum preview based on the view data structure, the following is also included: Determine the first similarity between the spectral characteristics of each spectral data in the spectral data stream and the spectral characteristics of the target sample signal; Spectral data with a first similarity greater than a first threshold are used as target spectral data, and the regions on the spectrum preview map corresponding to the target spectral data are initially labeled to obtain the initial region where the target signal is located.

4. The data file preview method according to claim 3, characterized in that, Also includes: Multiple consecutive initial regions in the time domain are merged into a long-term region; Determine the second similarity between the long-time spectral data corresponding to the long-time region and the corresponding equal-length sample signal in the target sample signal, and if the second similarity is greater than the second threshold, mark the long-time region as the final region where the target signal is located.

5. The data file preview method according to claim 1, characterized in that, The parallel processing of different data regions of the first data file to generate a spectrum data stream includes: Multiple data processing threads are started, and different data regions are allocated to different data processing threads; For each data processing thread, multiple data frames are sequentially read from the data region corresponding to the data processing thread, and a fast Fourier transform is performed on each data frame to generate the spectrum data stream corresponding to the data processing thread.

6. The method for previewing data files according to claim 1, characterized in that, The step of supplementing the spectrum preview image with the first display content corresponding to the newly added spectrum data in response to the update of the view data structure includes: The multiple spectrum data at the positions corresponding to the newly added spectrum data in the view data structure are aggregated to obtain the aggregation result; The display attributes corresponding to the positions on the spectrum preview map are determined based on the aggregation results, and the spectrum preview map is updated based on the display attributes.

7. The method for previewing data files according to claim 1, characterized in that, Also includes: In response to receiving multiple box selection operations on the spectrum preview map, a target signal label is generated for each box selection operation. The target signal label records the time-frequency region selected by the box selection operation and at least one signal parameter within the time-frequency region.

8. The method for previewing data files according to claim 7, characterized in that, Also includes: In response to receiving a batch data extraction instruction on the spectrum preview map, the data segment corresponding to the time-frequency region in each of the target signal annotations is extracted from the first data file, and the data segment is stored as an independent second data file.

9. A data file preview device, characterized in that, include: The parallel processing module is used to perform parallel processing on different data regions of the first data file to generate a spectrum data stream; The view data structure update module is used to update the newly added spectrum data to the corresponding position in the view data structure according to the time-frequency attributes of the newly added spectrum data in the spectrum data stream; the view data structure is used to represent the global time-frequency signal distribution of the first data file; The spectrum preview generation module is used to generate a spectrum preview based on the view data structure, and in response to the update of the view data structure, to supplement the spectrum preview with the first display content corresponding to the newly added spectrum data.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the data file preview method as described in any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium, wherein a computer program is stored on the non-transitory computer-readable storage medium, characterized in that, When the computer program is executed by the processor, it implements the data file preview method as described in any one of claims 1 to 8.

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