A method, device and equipment for analyzing frame skipping reasons of a video frame skipping file

Abstract

This application discloses a method, apparatus, and device for root cause analysis of video frame skipping files. The method includes: extracting video file information from a target video file, the video file information including video duration, file size, and bitrate, and the target video file including the corresponding source device; performing frame skipping anomaly analysis on the target video file based on the video file information using a preset analysis model, and determining the video frame skipping device, the video frame skipping device being determined based on the number of video frame skipping files; performing network correlation analysis, device model analysis, and resource pool analysis on the video frame skipping device, and performing multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping. This application can solve the technical problem in the prior art where the causes of video frame skipping are complex and diverse, and not easily determined.

Description

Technical Field

[0001] This application relates to the field of video analysis technology, and in particular to a method, apparatus and device for root cause analysis of video frame skipping files. Background Technology

[0002] With the increasing penetration of home security technologies and equipment, installing surveillance cameras at home has become commonplace, even a necessity. From public places in cities to rural roads and private courtyards, surveillance cameras are ubiquitous. In the field of video surveillance, recorded videos are often uploaded to the cloud in real time. Operators provide video cloud storage services and offer cloud-based video playback functions for users to retrieve and view historical videos. Video surveillance greatly enriches our lives and makes us feel safer. However, when a technology or product becomes widespread, some accompanying problems inevitably arise, such as video frame skipping.

[0003] From the user's perspective, video frame skipping causes screen tearing, unclear images, and missing video segments; moreover, it can even lead to missing crucial footage during important cases, indicating a problem with the surveillance system. From the operator's perspective, this is a critical factor affecting product quality, as customers will consider recording more stable, high-quality content less prone to frame skipping, jeopardizing the product's survival. Most importantly, the causes of frame skipping are diverse and cannot be completely resolved by simply improving a single aspect; furthermore, current technology lacks a comprehensive method for analyzing the causes of video frame skipping and offers effective solutions. Summary of the Invention

[0004] This application provides a method, apparatus, and device for root cause analysis of video frame skipping files, which is used to solve the technical problem that the causes of video frame skipping are complex and diverse and not easy to determine in the prior art.

[0005] In view of this, the first aspect of this application provides a root cause analysis method for video frame skipping files, including:

[0006] Extract video file information from the target video file, the video file information including video duration, file size and bitrate, the target video file including the corresponding source device;

[0007] A preset analysis model is used to perform frame skipping anomaly analysis on the target video file based on the video file information, and the video frame skipping device is determined. The video frame skipping device is determined based on the number of video frame skipping files.

[0008] The video frame skipping devices are subjected to network-related analysis, device model analysis, and resource pool analysis, and multi-factor weighted analysis is performed based on preset weights to obtain the root cause of video frame skipping.

[0009] Preferably, the step of extracting video file information from the target video file includes video duration, file size, and bitrate. The target video file includes the corresponding source device. This is further supported by:

[0010] The target video file is obtained by retrieving the recorded video file containing skipped frames of a customer complaint video from the cloud storage recording files.

[0011] Preferably, the step of using a preset analysis model to perform frame skipping anomaly analysis on the target video file based on the video file information, and determining the video frame skipping device, wherein the video frame skipping device is determined based on the number of video frame skipping files, includes:

[0012] A preset analysis model is used to determine whether the video duration, file size, and bitrate of the video file information are within a preset abnormal range. If so, the corresponding target video file is marked as a video skipping file, and the video skipping file includes the corresponding skipping source device.

[0013] If the number of video frame files corresponding to a certain frame skipping source device exceeds a certain threshold within a preset time period, then the frame skipping source device is marked as a video frame skipping device.

[0014] Preferably, if the number of video skipped frame files corresponding to a certain skipped frame source device exceeds a number threshold within a preset time period, the step of marking the skipped frame source device as a video skipped frame device further includes:

[0015] The frame skipping ratio is calculated based on the number of video skipping files and the number of video skipping devices to obtain the frame skipping ratio result.

[0016] Preferably, the step of performing network correlation analysis, device model analysis, and resource pool analysis on the video frame skipping device, and then performing multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping, further includes:

[0017] Based on the root cause of the video frame skipping, repair suggestions are provided to the operators, including resource pool expansion and device firmware upgrades.

[0018] A second aspect of this application provides a root cause analysis apparatus for video frame skipping files, comprising:

[0019] An information extraction unit is used to extract video file information from a target video file, wherein the video file information includes video duration, file size and bitrate, and the target video file includes a corresponding source device;

[0020] The frame skipping analysis unit is used to perform frame skipping anomaly analysis on the target video file based on the video file information using a preset analysis model, and to determine the video frame skipping device, wherein the video frame skipping device is determined according to the number of video frame skipping files;

[0021] The root cause analysis unit is used to perform network-related analysis, device model analysis, and resource pool analysis on the video frame skipping device, and perform multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping.

[0022] Preferably, it further includes:

[0023] The video acquisition unit is used to obtain the recorded video file containing skipped frames of customer complaint videos from the cloud storage recording files, and thus obtain the target video file.

[0024] Preferably, the frame skipping analysis unit is specifically used for:

[0025] A preset analysis model is used to determine whether the video duration, file size, and bitrate of the video file information are within a preset abnormal range. If so, the corresponding target video file is marked as a video skipping file, and the video skipping file includes the corresponding skipping source device.

[0026] If the number of video frame files corresponding to a certain frame skipping source device exceeds a certain threshold within a preset time period, then the frame skipping source device is marked as a video frame skipping device.

[0027] Preferably, it further includes:

[0028] The repair suggestion unit is used to provide repair suggestions to operators based on the root cause of the video frame skipping. The modification suggestions include expanding the resource pool and upgrading the device firmware.

[0029] A third aspect of this application provides a root cause analysis device for video frame skipping files, including: a processor and a memory;

[0030] The memory is used to store program code and transmit the program code to the processor;

[0031] The processor is used to execute the root cause analysis method for video frame skipping files as described in the first aspect, according to the instructions in the program code.

[0032] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0033] This application provides a root cause analysis method for video frame skipping files, comprising: extracting video file information from a target video file, the video file information including video duration, file size, and bitrate, and the target video file including the corresponding source device; performing frame skipping anomaly analysis on the target video file based on the video file information using a preset analysis model, and determining the video frame skipping device, the video frame skipping device being determined based on the number of video frame skipping files; performing network-related analysis, device model analysis, and resource pool analysis on the video frame skipping device, and performing multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping.

[0034] The root cause analysis method for video frame skipping provided in this application identifies the specific video frame skipping device by extracting video file information related to the video and performing frame skipping analysis. Then, by performing multi-level information analysis and weighted calculations on the video frame skipping device, the root cause of the video frame skipping can be clearly identified. The entire process is simple to execute, consumes few resources, and has a short computation time. Most importantly, by analyzing the network, model, and resource pool associated with the device, a more comprehensive understanding of the cause of video frame skipping can be obtained, leading to more accurate and reliable results. Therefore, this application can solve the technical problem in the prior art where the causes of video frame skipping are complex, diverse, and difficult to determine. Attached Figure Description

[0035] Figure 1 A flowchart illustrating a root cause analysis method for video frame skipping files provided in this application embodiment;

[0036] Figure 2 This is a schematic diagram of the structure of a root cause analysis device for video frame skipping files provided in an embodiment of this application. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0038] For easier understanding, please refer to Figure 1 An embodiment of a root cause analysis method for video frame skipping files provided in this application includes:

[0039] Step 101: Extract video file information from the target video file. The video file information includes video duration, file size, and bitrate. The target video file includes the corresponding source device.

[0040] The target video files are pre-prepared recordings to be analyzed, typically obtained from recordings on a cloud platform, and may contain frame skipping anomalies. Each target video file is captured by a specific camera device and stored on the cloud platform, so each target video file records its corresponding source device or source camera, facilitating tracing during subsequent frame skipping root cause analysis. In addition to video duration, file size, and bitrate, other relevant parameters can be configured for the video file information, which are not limited here.

[0041] Furthermore, step 101, preceding the following, also includes:

[0042] The target video file is obtained by retrieving the video file containing skipped frames from the cloud storage recording files of the customer complaint. To improve analysis efficiency, this embodiment retrieves the video file that has been the subject of a customer complaint, i.e., the video file in which the customer complains that there are skipped frames, from the cloud storage recording files on the cloud platform; such a recorded video file is selected as the target video file to improve analysis efficiency.

[0043] Step 102: Use a preset analysis model to perform frame skipping anomaly analysis on the target video file based on the video file information, and determine the video frame skipping device. The video frame skipping device is determined based on the number of video frame skipping files.

[0044] Further, step 102 includes:

[0045] A preset analysis model is used to determine whether the video duration, file size and bitrate of the video file information are within a preset abnormal range. If so, the corresponding target video file is marked as a video skipping frame file, and the video skipping frame file includes the corresponding skipping frame source device.

[0046] If the number of video frame files corresponding to a certain frame skipping source device exceeds the number threshold within a preset duration, the frame skipping source device will be marked as a video frame skipping device.

[0047] The preset analysis model is built based on video file information analysis and can be directly applied to specific engineering practices. The specific analysis mechanism is parameter judgment, that is, judging whether the video duration, file size, and bitrate exceed a normal range, also known as the preset abnormal range. For example, it judges whether the video duration exceeds the normal value by 15%, whether the file size exceeds the normal value by 50%, and whether the bitrate is lower than the normal value by 50%. If all are true, then the target video file corresponding to this video file information is judged as a skipped frame file, denoted as a skipped frame file. The above is only an example; the specific normal values ​​and exceedance limits can be configured according to actual conditions and are not limited here.

[0048] Furthermore, since each target video file includes a corresponding source device, the video frame skipping file similarly includes a corresponding frame skipping source device. Finding a video frame skipping file does not necessarily mean that its source device is a video frame skipping device, because the reasons for frame skipping are complex and varied; other issues may have caused the video file to skip frames, unrelated to the device. Therefore, to avoid such problems, this embodiment uses a preset duration and a quantity threshold to determine the frame skipping device. That is, if the cumulative number of video frame skipping files corresponding to each frame skipping source device exceeds a certain quantity threshold within a certain time period, then this device is determined to be a video frame skipping device because the video frame skipping frequency recorded by this device is too high. The specific preset duration and quantity threshold are configured according to actual conditions and are not limited here.

[0049] Furthermore, if the number of video skipped frame files corresponding to a certain skipped frame source device exceeds a certain threshold within a preset duration, the skipped frame source device is marked as a video skipped frame device, and the process further includes:

[0050] The frame skipping percentage is calculated based on the number of video skipping files and the number of video skipping devices, thus obtaining the frame skipping percentage result.

[0051] The percentage of video frames skipped can be calculated by comparing the number of skipped video files with the total number of video files; similarly, the percentage of skipped video devices can be calculated by comparing the number of skipped video devices with the total number of video recording devices. There is a correlation between skipped video files and skipped video devices, so the percentage of skipped frames can provide a preliminary analysis of which devices skip frames more frequently. Generally speaking, high-definition (HD) devices may skip frames slightly more than standard-definition (SD) devices because HD devices have higher requirements for network conditions and resource pools, and when these requirements are not met, anomalies such as frame skipping are more likely to occur.

[0052] Step 103: Perform network-related analysis, device model analysis, and resource pool analysis on the video frame skipping devices, and perform multi-factor weighted analysis based on preset weights to obtain the root cause of video frame skipping.

[0053] Network-related analysis includes network access method analysis and network strength analysis. Network access method analysis determines how each video frame-skipping device connects to the cloud platform, such as wired or wireless connections, which is fundamental to information transmission. Under normal network conditions, it's also necessary to analyze whether the network strength is sufficient to upload a specific video. For example, if the network connection is via Wi-Fi, the network strength can be categorized into five levels: 80-100, 60-79, 40-59, 20-39, and below 19. A network strength below 40-59 will cause frame skipping during video recording due to insufficient network strength, resulting in video anomalies.

[0054] Each video frame skipping file has a specific device origin, and each video frame skipping device has its own model number. Different models of devices produce different video recording results. For example, high-definition cameras typically have 8 megapixels or higher, recording clear video, but they consume more resources, have higher network transmission requirements, and produce larger video files. Standard-definition cameras, on the other hand, have less than 8 megapixels, recording less clear video, but they have lower requirements for network conditions and resources, producing smaller video files. When the device model is a high-definition camera, the video is more prone to anomalies, i.e., frame skipping; conversely, frame skipping is less likely to occur with standard-definition cameras.

[0055] The resource pool is used to store recorded video files. When there are an unusually large number of videos stored in the resource pool, the storage resources become quite strained, which can easily lead to frame skipping. The preset weights in this embodiment are used to weigh the impact of different factors on video frame skipping. For example, the preset weight for network-related factors is 35%, the weight for device signal factors is 20%, and the weight for resource pool factors is 45%.

[0056] Specifically, regarding the network aspect, the ratio of the number of skipped frame files per network strength for each video skipping device to the total number of skipped frame files within each network strength range is analyzed. This ratio is then multiplied by a preset weight to obtain the network impact score. Similarly, regarding device models, for ease of calculation, high-definition devices are labeled as 1, and standard-definition devices as 0. The ratio of the number of high-definition devices to the number of skipped high-definition devices is calculated, and then multiplied by a preset weight to obtain the skipping impact score for each device model. Regarding the resource pool, the ratio of the number of skipped frame files uploaded to the resource pool by each device to the total number of skipped frames in the resource pool is calculated, and then multiplied by the corresponding preset weight to obtain the skipping impact score for the resource pool. Finally, the highest score is selected as the root cause of video skipping.

[0057] Further step 103, followed by:

[0058] Based on the root cause of video frame skipping, repair suggestions are provided to operators, including resource pool expansion and device firmware upgrades.

[0059] If the root cause is network-related, such as difficulty in data transmission via wireless connection, it is recommended to change the network connection method. If the network strength during transmission is low, it is recommended to improve the network strength. If the root cause is related to the device model, such as a particular device model being prone to frame skipping, it is recommended to replace the device with a different model or upgrade the relevant firmware. If the root cause is related to the resource pool, it is recommended to expand the resource pool to provide redundant capacity.

[0060] The root cause analysis method for video frame skipping provided in this application can identify the specific video frame skipping device by extracting video file information related to the video and performing frame skipping analysis. Then, by performing multi-level information analysis and weighted calculation on the video frame skipping device, the root cause of the video frame skipping can be clearly identified. The entire process is simple and easy to execute, consumes few resources, and has a short computation time. Most importantly, by analyzing the network, model, and resource pool associated with the device, the cause of video frame skipping can be more comprehensively grasped, and the results obtained are more accurate and reliable. Therefore, this application embodiment can solve the technical problem in the prior art where the causes of video frame skipping are complex and diverse, and difficult to determine.

[0061] For easier understanding, please refer to Figure 2 This application provides an embodiment of a root cause analysis device for video frame skipping files, comprising:

[0062] The information extraction unit 201 is used to extract video file information from the target video file. The video file information includes video duration, file size and bitrate. The target video file includes the corresponding source device.

[0063] The frame skipping analysis unit 202 is used to perform frame skipping anomaly analysis on the target video file based on the video file information using a preset analysis model, and to determine the video frame skipping device. The video frame skipping device is determined based on the number of video frame skipping files.

[0064] The root cause analysis unit 203 is used to perform network-related analysis, device model analysis and resource pool analysis on the video frame skipping device, and perform multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping.

[0065] Furthermore, it also includes:

[0066] The video acquisition unit 204 is used to acquire the recorded video file containing skipped frames of a customer complaint video from the cloud storage recording file, and obtain the target video file.

[0067] Furthermore, the frame skipping analysis unit 202 is specifically used for:

[0068] A preset analysis model is used to determine whether the video duration, file size and bitrate of the video file information are within a preset abnormal range. If so, the corresponding target video file is marked as a video skipping frame file, and the video skipping frame file includes the corresponding skipping frame source device.

[0069] If the number of video frame files corresponding to a certain frame skipping source device exceeds the number threshold within a preset duration, the frame skipping source device will be marked as a video frame skipping device.

[0070] Furthermore, it also includes:

[0071] The repair suggestion unit 205 is used to provide repair suggestions to operators based on the root cause of video frame skipping. The modification suggestions include expanding the resource pool and upgrading the device firmware.

[0072] This application also provides a root cause analysis device for video frame skipping files, including: a processor and a memory;

[0073] The memory is used to store program code and transfer the program code to the processor;

[0074] The processor is used to execute the root cause analysis method for video frame skipping files in the above method embodiment according to the instructions in the program code.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed apparatus 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; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] 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.

[0077] 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.

[0078] 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 for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0079] The above-described 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 the root causes of frame skipping in video files, characterized in that, include: Extract video file information from the target video file, the video file information including video duration, file size and bitrate, the target video file including the corresponding source device; A preset analysis model is used to perform frame skipping anomaly analysis on the target video file based on the video file information to determine the video frame skipping device, which is determined based on the number of video frame skipping files. The video frame skipping devices are subjected to network correlation analysis, device model analysis, and resource pool analysis, and multi-factor weighted analysis is performed based on preset weights to obtain the root cause of video frame skipping. The step of using a preset analysis model to perform frame skipping anomaly analysis on the target video file based on the video file information, and determining the video frame skipping device, wherein the video frame skipping device is determined based on the number of video frame skipping files, includes: A preset analysis model is used to determine whether the video duration, file size, and bitrate of the video file information are within a preset abnormal range. If so, the corresponding target video file is marked as a video skipping file, and the video skipping file includes the corresponding skipping source device. If the number of video frame files corresponding to a certain frame skipping source device exceeds a certain threshold within a preset time period, then the frame skipping source device is marked as a video frame skipping device.

2. The method for analyzing the root causes of frame skipping in video files according to claim 1, characterized in that, The step involves extracting video file information from the target video file. This video file information includes video duration, file size, and bitrate. The target video file includes the corresponding source device. Prior to this, the method also includes: The target video file is obtained by retrieving the recorded video file containing skipped frames of a customer complaint video from the cloud storage recording files.

3. The method for analyzing the root cause of frame skipping in video files according to claim 1, characterized in that, If the number of video frame files corresponding to a certain frame skipping source device exceeds a certain threshold within a preset time period, the frame skipping source device is marked as a video frame skipping device. The method further includes: The frame skipping ratio is calculated based on the number of video skipping files and the number of video skipping devices to obtain the frame skipping ratio result.

4. The method for analyzing the root causes of frame skipping in video files according to claim 1, characterized in that, The process involves performing network-related analysis, device model analysis, and resource pool analysis on the video frame-skipping device, followed by multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping. This process further includes: Based on the root cause of the video frame skipping, repair suggestions are provided to the operator, including expanding the resource pool and upgrading the device firmware.

5. A device for analyzing the root cause of frame skipping in video skipped files, characterized in that, include: An information extraction unit is used to extract video file information from a target video file, wherein the video file information includes video duration, file size and bitrate, and the target video file includes a corresponding source device; The frame skipping analysis unit is used to perform frame skipping anomaly analysis on the target video file based on the video file information using a preset analysis model, and to determine the video frame skipping device, wherein the video frame skipping device is determined according to the number of video frame skipping files; The root cause analysis unit is used to perform network-related analysis, device model analysis, and resource pool analysis on the video frame skipping device, and perform multi-factor weighted analysis based on preset weights to obtain the root cause of the video frame skipping. The frame skipping analysis unit is specifically used for: A preset analysis model is used to determine whether the video duration, file size, and bitrate of the video file information are within a preset abnormal range. If so, the corresponding target video file is marked as a video skipping file, and the video skipping file includes the corresponding skipping source device. If the number of video frame files corresponding to a certain frame skipping source device exceeds a certain threshold within a preset time period, then the frame skipping source device is marked as a video frame skipping device.

6. The frame skipping root cause analysis device for video frame skipping files according to claim 5, characterized in that, Also includes: The video acquisition unit is used to obtain the recorded video file containing skipped frames of customer complaint videos from the cloud storage recording files, and thus obtain the target video file.

7. The frame skipping root cause analysis device for video frame skipping files according to claim 5, characterized in that, Also includes: The repair suggestion unit is used to provide repair suggestions to operators based on the root cause of the video frame skipping. The repair suggestions include expanding the resource pool and upgrading the device firmware.

8. A root cause analysis device for video frame skipping files, characterized in that, include: Processor and memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the frame skipping root cause analysis method for video frame skipping files according to any one of claims 1-4, based on the instructions in the program code.

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