Cloud video recording integrity detection method, device and computer equipment

Abstract

This application discloses a method, apparatus, and computer device for cloud video recording integrity detection. The method includes: acquiring video files for the period to be detected from cloud recordings; sequentially performing multiple levels of detection on the video files for the period to be detected, the multiple levels of detection including at least: the number of video files, the size of the video files, and the duration of the video files; and jointly determining whether the video recording to be detected is complete based on the results of the multiple levels of detection. This application solves the technical problem of low accuracy in video file integrity detection in related technologies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of video recording storage, in particular to a cloud video recording integrity detection method and device and computer equipment. BACKGROUND

[0002] With the rapid development of information technology, video monitoring systems have become an indispensable part of modern society. They are widely used in security protection, traffic management, public safety and other fields, providing great convenience and security for people's lives. As a key security tool, video monitoring systems not only serve real-time monitoring, but also serve post-tracing and analysis. At present, due to the rapid popularization of cloud technology, cloud video recording, i.e. video monitoring data stored through a cloud computing platform, has become an important means to ensure information security and improve post-tracing capabilities. However, in actual application, due to network fluctuations, system software defects and other problems, video recording data is damaged and the integrity of the video recording is missing, which may result in the loss of critical information.

[0003] There are two methods for detecting the integrity of video recording. One is to extract the target time information of multiple target videos, connect the multiple target time information in chronological order, and if the time information is connected continuously, determine that the video recording is complete. The other is to compare the start time and end time of the triggered video recording event with the start time and end time of the video file to determine the integrity of the video recording. However, the above two methods can only determine whether the video file is complete as a whole, and for network jitter, the generated video file cannot accurately determine whether the video file is complete. SUMMARY

[0004] The embodiments of the present application provide a cloud video recording integrity detection method, device and computer equipment to at least solve the technical problem of low accuracy of video file integrity detection in related technologies.

[0005] According to an aspect of an embodiment of the present application, a cloud video recording integrity detection method is provided, comprising: obtaining a video recording file in a to-be-detected period of cloud video recording; sequentially performing multiple levels of detection on the video recording file in the to-be-detected period, the multiple levels of detection at least including: the number of video recording files, the capacity of video recording files and the duration of video recording files; and determining whether the video recording file in the to-be-detected period is complete according to the detection results of the multiple levels.

[0006] Optionally, the video file of the to-be-detected time period is sequentially detected in multiple levels, including: obtaining video files of multiple sub-time periods in the video file of the to-be-detected time period; performing first-level detection on the video files of the multiple sub-time periods, the first-level detection being used to detect whether the number of the video files of the multiple sub-time periods is within a preset number range; in a case where the first-level detection result indicates that the video files of the multiple sub-time periods pass the detection, performing second-level detection on the video files of the multiple sub-time periods, the second-level detection being used to detect whether the capacity of the video files of the multiple sub-time periods is within a preset capacity range; in a case where the second-level detection result indicates that the video files of the multiple sub-time periods pass the detection, performing third-level detection on the video files of the multiple sub-time periods, the third-level detection being used to detect whether the time length of the video files of the multiple sub-time periods is within a preset time length range.

[0007] Optionally, the first-level detection on the video files of the multiple sub-time periods includes: dividing the video file of the to-be-detected time period into multiple standard video files according to a preset time length, and obtaining the number of the multiple standard video files; determining the product of the number of the multiple standard video files and a first coefficient as the minimum value of the preset number range; determining the product of the number of the multiple standard video files and a second coefficient as the maximum value of the preset number range, wherein the first coefficient is smaller than the second coefficient; in a case where the video files of the multiple sub-time periods are within the preset number range, it is determined that the video files of the multiple sub-time periods pass the first-level detection.

[0008] Optionally, the second-level detection on the video files of the multiple sub-time periods includes: obtaining the video segment time length and the camera transmission code rate of the video files of the multiple sub-time periods; determining a preset capacity range of each sub-time period based on the video segment time length and the camera transmission code rate of the video file of each sub-time period; obtaining the capacity of the video files of the multiple sub-time periods; sequentially comparing each sub-time period video file with its corresponding preset capacity range from the first sub-time period video file; in a case where the capacity of all sub-time period video files is within the corresponding preset capacity range, it is determined that the video files of the multiple sub-time periods pass the second-level detection.

[0009] Optionally, the third-level detection on the plurality of sub-period video files comprises: sequentially obtaining a start time and an end time of each sub-period video file in the video file of the to-be-detected period; sequentially splicing the start time and the end time of each sub-period video file in order of time to obtain a continuous time sequence; and determining that the plurality of sub-period video files pass the third-level detection in a case where the continuous time sequence can completely cover the to-be-detected period.

[0010] Optionally, the method further comprises: determining that the continuous time sequence satisfies a first preset condition in a case where a start time and an end time corresponding to the continuous time sequence are completely same as a start time and an end time of the to-be-detected period; determining that the continuous time sequence satisfies a second preset condition in a case where there is no discontinuity between the start time and the end time corresponding to the continuous time sequence; and determining that the continuous time sequence can completely cover the to-be-detected period in a case where the continuous time sequence satisfies both the first preset condition and the second preset condition.

[0011] Optionally, the preset capacity range of each sub-period video file is determined based on a video segment length of each sub-period video file and a camera transmission code rate, comprising: determining a product of the video segment length of each sub-period video file and the camera transmission code rate as a standard capacity; and determining products of the standard capacity and third and fourth coefficients as minimum and maximum values of the preset capacity range of each sub-period video file, wherein the third coefficient is less than the fourth coefficient.

[0012] According to another aspect of the embodiments of the present application, a cloud video integrity detection device is further provided, comprising: an acquisition module configured to acquire a video file of a to-be-detected period in a cloud video; a detection module configured to sequentially perform a plurality of levels of detection on the video file of the to-be-detected period, wherein the plurality of levels of detection at least comprise: a video file quantity, a video file capacity, and a video file length; and a determination module configured to determine, according to detection results of the plurality of levels of detection, whether the video file of the to-be-detected period is complete.

[0013] According to still another aspect of the embodiments of the present application, a computer device is further provided, comprising: a memory and a processor, wherein the memory is configured to store program instructions; and the processor, connected with the memory, is configured to execute the cloud video integrity detection method.

[0014] According to yet another aspect of the embodiments of the present application, a non-volatile storage medium is further provided, comprising a stored computer program, wherein a device where the non-volatile storage medium is located executes the cloud video integrity detection method by running the computer program.

[0015] According to still another aspect of the embodiments of the present application, a computer program product is provided, which comprises computer instructions, and the computer instructions, when executed by a processor, implement the cloud video integrity detection method.

[0016] In the embodiments of the present application, a video file of a to-be-detected time period in a cloud video is acquired; the video file of the to-be-detected time period is sequentially subjected to multiple levels of detection, and the multiple levels of detection at least include: a number of video files, a capacity of video files, and a time length of video files; and whether the video file of the to-be-detected time period is complete is determined according to the detection results of the multiple levels, so that the purpose of multiple levels of detection of the video file is achieved, and the technical effect of improving the accuracy of video file integrity detection is achieved, thereby solving the technical problem of low accuracy of video file integrity detection in the related art. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application, and the illustrative embodiments of the present application and their description serve to explain the present application, and do not limit the present application in any manner. In the drawings:

[0018] Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal for implementing a cloud video integrity detection method according to an embodiment of the present application;

[0019] Figure 2 FIG. 2 is a flowchart of a cloud video integrity detection method according to an embodiment of the present application;

[0020] Figure 3 FIG. 3 is a video file structure diagram according to an embodiment of the present application;

[0021] Figure 4 FIG. 4 is a hierarchical detection flowchart according to an embodiment of the present application;

[0022] Figure 5 FIG. 5 is another hierarchical detection flowchart according to an embodiment of the present application;

[0023] Figure 6 FIG. 6 is a first level detection flowchart according to an embodiment of the present application;

[0024] Figure 7 FIG. 7 is a second level detection flowchart according to an embodiment of the present application;

[0025] Figure 8 FIG. 8 is a third level detection flowchart according to an embodiment of the present application;

[0026] Figure 9 FIG. 9 is a structure diagram of a cloud video integrity detection device according to an embodiment of the present application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] The information collected in this application embodiment is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, and necessary confidentiality measures have been taken. It does not violate public order and good morals, and provides corresponding operation entry points for users to choose to authorize or reject the automated decision results. If the user chooses to reject, the process will proceed to the expert decision-making process.

[0030] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0031] Video recording integrity refers to the completeness and continuity of video recording data in a video surveillance system, ensuring that the recording accurately and completely records all events and activities within the monitored area. In the field of video surveillance, video recording integrity is one of the key indicators for measuring system performance and is of great significance for post-event tracing, evidence preservation, and security analysis.

[0032] Grading: Grading refers to the system performing step-by-step detection and judgment on issues of interest according to a preset standard process.

[0033] To address the problems existing in related technologies, this application provides a cloud video recording integrity detection method, which can be run on... Figure 1The computer terminal shown is explained below.

[0034] The cloud video recording integrity detection method provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a cloud video recording integrity detection method is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions connected via wired and / or wireless networks. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0035] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0036] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the cloud video recording integrity detection method in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned cloud video recording integrity detection method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0037] The transmission module 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission module 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0038] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0039] It should be noted here that, in some optional embodiments, the above... Figure 1 The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0040] In the above operating environment, this application provides an embodiment of a cloud video recording integrity detection method. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0041] Figure 2 This is a flowchart of a cloud video recording integrity detection method according to an embodiment of this application, such as... Figure 2As shown, the method includes the following steps:

[0042] Step S202: Obtain the video recording file for the time period to be detected from the cloud recording;

[0043] Step S204: Perform multiple levels of detection on the video files of the time period to be detected in sequence. The multiple levels of detection include at least: the number of video files, the size of the video files, and the duration of the video files.

[0044] Step S206: Determine whether the video file of the time period to be detected is complete based on the detection results of the multiple levels.

[0045] Through steps S202 to S206 above, video files for the period to be detected are obtained from the cloud recording; multiple levels of detection are sequentially performed on the video files for the period to be detected, including at least: the number of video files, the size of the video files, and the duration of the video files; based on the results of the multiple levels of detection, it is jointly determined whether the video files for the period to be detected are complete, thereby achieving the purpose of performing multi-level detection on the video files, thus achieving the technical effect of improving the accuracy of video file integrity detection, and solving the technical problem of low accuracy of video file integrity detection in related technologies. The following is a detailed explanation.

[0046] In practical applications, when saving video files, they are segmented into fixed time periods Δt. Therefore, under standard conditions, the number of video file segments within a time period ΔT is a fixed value ΔT / Δt; with a fixed camera transmission bitrate R, the size of each video file segment is a fixed value R×Δt; the start time S and end time E of each video file segment can be seamlessly covered by sequential splicing. In cases where network jitter causes recording interruption, the duration of a certain video segment may be non-standard Δt, and the number of video file segments within the time period ΔT will also change. However, as long as recording is resumed promptly, the recording can be considered complete. However, if there are large network fluctuations or software defects leading to prolonged recording loss or file corruption, this falls under the abnormal situation of video file loss or corruption that this application aims to detect. Figure 3 As shown, from top to bottom, the video files are displayed as standard video files, video files under network jitter conditions, and video files under abnormal conditions.

[0047] In some embodiments of this application, the video file of the period to be detected is subjected to multiple levels of detection sequentially, including: acquiring video files of multiple sub-periods within the video file of the period to be detected; performing a first-level detection on the video files of the multiple sub-periods, wherein the first-level detection is used to detect whether the number of video files of the multiple sub-periods is within a preset number range; if the first-level detection result indicates that the video files of the multiple sub-periods have passed the detection, performing a second-level detection on the video files of the multiple sub-periods, wherein the second-level detection is used to detect whether the size of the video files of the multiple sub-periods is within a preset size range; if the second-level detection result indicates that the video files of the multiple sub-periods have passed the detection, performing a third-level detection on the video files of the multiple sub-periods, wherein the third-level detection is used to detect whether the duration of the video files of the multiple sub-periods is within a preset duration range, such as... Figure 4 As shown, the first level of detection detects the number of video files, the second level detects the size (capacity) of the video files, and the third level detects the splicing of video files over time. Figure 5 A multi-level detection flowchart is shown, such as Figure 5 As shown, the video file is subjected to first-level, second-level, and third-level detection in sequence.

[0048] Specifically, the first-level detection of the video files in the multiple sub-time periods includes: dividing the video files of the time period to be detected into multiple standard video files according to a preset duration, and obtaining the number of the multiple standard video files; determining the minimum value of the preset number range by multiplying the number of the multiple standard video files by a first coefficient; determining the maximum value of the preset number range by multiplying the number of the multiple standard video files by a second coefficient, wherein the first coefficient is less than the second coefficient; and determining that the video files in the multiple sub-time periods pass the first-level detection if the video files in the multiple sub-time periods are within the preset number range.

[0049] Taking a first coefficient of 0.97 and a second coefficient of 1.03 as an example, the detection method for the first level is as follows: Figure 6 As shown, it includes: Data extraction: determining the time period ΔT to be detected; reading the number C of video files of all sub-time periods within this time period.

[0050] Standard document quantity calculation: The standard document quantity range is calculated according to the formula Count=(0.97~1.03)ΔT / Δt.

[0051] Data comparison: Compare the number of files C with the standard file count range Count to determine if there are any missing video files.

[0052] Result judgment: If C is not within the standard range, it is determined that the video recording is missing, the record is abnormal and the process jumps to the error handling process; if C is within the standard range, it continues to the second level of detection.

[0053] In one optional approach, a second-level detection is performed on the video files of the multiple sub-time periods, including: obtaining the video segment duration and camera transmission bitrate of the video files of the multiple sub-time periods; determining a preset capacity range for the video files of each sub-time period based on the video segment duration and camera transmission bitrate of the video files of each sub-time period; obtaining the capacity of the video files of the multiple sub-time periods; starting from the video file of the first sub-time period, comparing the video file of each sub-time period with its corresponding preset capacity range in turn; and determining that the video files of the multiple sub-time periods have passed the second-level detection if the capacity of the video files of all sub-time periods is within their corresponding preset capacity range.

[0054] The preset size range of the video file for each sub-time period is determined based on the video segment duration and camera transmission bitrate, including:

[0055] The standard capacity is determined by multiplying the recording segment duration of each sub-time period's recording file by the camera's transmission bitrate; the minimum and maximum values ​​of the preset capacity range for each sub-time period's recording file are determined by multiplying the standard capacity by the third coefficient and the fourth coefficient, respectively, wherein the third coefficient is less than the fourth coefficient.

[0056] Taking a third coefficient of 0.9 and a fourth coefficient of 1.1 as an example, the detection steps for the second level are as follows: Figure 7 As shown, it includes:

[0057] Data extraction: Traverse each video file within the time period ΔT to obtain the size D = [D1, D2, D3, ..., Dn] of each sub-time period video file, the duration T = [T1, T2, T3, ..., Tn] of the video segment, and the camera transmission bitrate R.

[0058] Standard file size calculation: Calculate the standard size of the video file for each sub-time period: DATE = [DATE1, DATE2, DATE3, ..., DATEn] = (0.9 ~ 1.1)R × T.

[0059] Data comparison: Compare the file size D with the standard file size DATE one by one to determine if there are any abnormalities in the size of the recorded files.

[0060] Result judgment: First, compare whether the size D1 of the first video file is within the standard file size DATE1. If D1 is within the standard range, the video file is determined to be undamaged, and the next video file is checked. The size of each file is compared one by one until the last video file. If the size of any video file is not within the standard range, the video file is determined to be damaged. The time period of the damaged video file is output, and the error handling process is jumped. If all are within the standard range, the third level of detection is continued.

[0061] In one optional approach, a third-level detection is performed on the video files of the multiple sub-time periods, including: obtaining the start and end times of the video files of each sub-time period in the video files of the period to be detected one by one; sequentially splicing the start and end times of the video files of each sub-time period in chronological order to obtain a continuous time sequence; and determining that the video files of the multiple sub-time periods pass the third-level detection if the continuous time sequence can completely cover the period to be detected.

[0062] Specifically, if the start and end times of the continuous time series are exactly the same as the start and end times of the period to be detected, the continuous time series is determined to satisfy a first preset condition; if there is no interruption between the start and end times of the continuous time series, the continuous time series is determined to satisfy a second preset condition; if the continuous time series satisfies both the first and second preset conditions, the continuous time series is determined to completely cover the period to be detected.

[0063] like Figure 8 As shown, the steps of the third-level detection include:

[0064] Data extraction: Obtain the start and end times [S1,E1], [S2,E2]……[Sn,En] of each video file within the time period ΔT.

[0065] Time splicing: The extracted start and end times are spliced ​​together in chronological order to form a continuous time series.

[0066] Integrity check: Check whether the spliced ​​time series can seamlessly cover the complete time period ΔT to determine if there are any missing recordings. Result judgment: If it can seamlessly cover the entire time period, the recording is considered complete.

[0067] Otherwise, determine that the video recording is incomplete and output the specific time period that is missing.

[0068] The video recording integrity detection method provided in this application significantly shortens the time required to detect damaged or missing video files through a hierarchical detection mechanism. The first level quickly screens the number of video files, the second level precisely checks the file size, and the third level verifies temporal continuity. This progressively deeper detection process quickly locates the problem, accelerates its resolution, and improves overall detection efficiency. By comprehensively checking the number, size, and temporal continuity of video files, damaged and missing video file segments can be located more accurately. Compared with traditional methods, its accuracy is significantly improved, ensuring data integrity and availability. The automated detection process reduces a significant amount of manual review and intervention, lowering long-term maintenance costs. Simultaneously, it can promptly detect and repair video data problems, avoiding repeated shooting or wasted storage resources due to missing or damaged recordings, further reducing operating costs. By implementing this solution, damaged and missing portions in video files can be quickly identified and processed, enhancing the stability and reliability of the video surveillance system and reducing system failures caused by data issues. Ensuring the integrity and continuity of video files provides users with accurate event playback, increasing user trust and satisfaction with the monitoring system. In scenarios requiring the retrieval of historical video recordings, this ensures the availability of the recordings and avoids the inconvenience and trouble caused by data corruption or loss. The software solution provided in this application features automation, can perform detection tasks periodically, and is compatible with multiple cloud platforms, supporting cross-platform operation. This not only simplifies the detection process but also expands the application scope, adapting to video surveillance needs in different environments. The introduction of dynamic thresholds and a reasonable fault tolerance ratio can adapt to different storage conditions and recording fluctuations, improving detection accuracy and reliability while reducing false alarms and ensuring the credibility of detection results.

[0069] Figure 9 This is a structural diagram of a cloud video recording integrity detection device according to an embodiment of this application, such as... Figure 9 As shown, the device includes:

[0070] The acquisition module 90 is used to acquire the recording files of the time period to be detected in the cloud recording;

[0071] The detection module 92 is used to perform multiple levels of detection on the video files of the time period to be detected in sequence. The multiple levels of detection include at least: the number of video files, the size of the video files, and the duration of the video files.

[0072] The determination module 94 is used to jointly determine whether the video file of the time period to be detected is complete based on the detection results of the multiple levels.

[0073] The detection module 92 includes a detection submodule, used to sequentially perform multiple levels of detection on the video file of the period to be detected, including: acquiring multiple sub-period video files in the video file of the period to be detected; performing a first level of detection on the multiple sub-period video files, wherein the first level of detection is used to detect whether the number of the multiple sub-period video files is within a preset number range; if the first level of detection result indicates that the multiple sub-period video files have passed the detection, performing a second level of detection on the multiple sub-period video files, wherein the second level of detection is used to detect whether the size of the multiple sub-period video files is within a preset size range; if the second level of detection result indicates that the multiple sub-period video files have passed the detection, performing a third level of detection on the multiple sub-period video files, wherein the third level of detection is used to detect whether the duration of the multiple sub-period video files is within a preset duration range.

[0074] The detection submodule includes a first detection unit, a second detection unit, and a third detection unit. The first detection unit is used to perform a first-level detection on the video files of the multiple sub-time periods, including: dividing the video files of the time period to be detected into multiple standard video files according to a preset duration, and obtaining the number of the multiple standard video files; determining the minimum value of the preset quantity range by multiplying the number of the multiple standard video files by a first coefficient; determining the maximum value of the preset quantity range by multiplying the number of the multiple standard video files by a second coefficient, wherein the first coefficient is less than the second coefficient; and determining that the video files of the multiple sub-time periods pass the first-level detection if the video files of the multiple sub-time periods are within the preset quantity range.

[0075] The second detection unit is used to perform a second-level detection on the video files of the multiple sub-time periods, including: obtaining the video segment duration and camera transmission bitrate of the video files of the multiple sub-time periods; determining a preset capacity range for the video files of each sub-time period based on the video segment duration and camera transmission bitrate of the video files of each sub-time period; obtaining the capacity of the video files of the multiple sub-time periods; starting from the video file of the first sub-time period, comparing the video file of each sub-time period with its corresponding preset capacity range in turn; and determining that the video files of the multiple sub-time periods have passed the second-level detection if the capacity of the video files of all sub-time periods is within their corresponding preset capacity range.

[0076] The third detection unit is used to perform third-level detection on the video files of the multiple sub-time periods, including: obtaining the start time and end time of the video file of each sub-time period in the video file of the period to be detected one by one; splicing the start time and end time of the video file of each sub-time period in chronological order to obtain a continuous time sequence; and determining that the video files of the multiple sub-time periods pass the third-level detection if the continuous time sequence can completely cover the period to be detected.

[0077] The third detection unit includes: a determination subunit, configured to: determine that the continuous time series satisfies a first preset condition when the start and end times of the continuous time series are exactly the same as the start and end times of the period to be detected; determine that the continuous time series satisfies a second preset condition when there is no interruption between the start and end times of the continuous time series; and determine that the continuous time series can completely cover the period to be detected when the continuous time series simultaneously satisfies the first preset condition and the second preset condition.

[0078] The second detection unit includes a range subunit, used to determine a preset capacity range for the video files of each sub-time period based on the video segment duration and camera transmission bitrate of the video files of each sub-time period. This includes: determining a standard capacity by multiplying the video segment duration and camera transmission bitrate of the video files of each sub-time period; and determining the minimum and maximum values ​​of the preset capacity range for the video files of each sub-time period by multiplying the standard capacity by a third coefficient and a fourth coefficient, respectively, wherein the third coefficient is less than the fourth coefficient.

[0079] The aforementioned cloud video recording integrity detection device acquires video files for the period to be detected from cloud recordings; performs multiple-level detections on the video files for the period to be detected, including at least the number of video files, the size of the video files, and the duration of the video files; and determines whether the video files for the period to be detected are complete based on the results of the multiple-level detections. This achieves the goal of performing multiple-level detections on the video files, thereby improving the accuracy of video file integrity detection and solving the technical problem of low accuracy in video file integrity detection in related technologies.

[0080] It should be noted that, Figure 9 The cloud video recording integrity detection device shown is used to perform... Figure 2 The cloud video recording integrity detection method shown above is also applicable to this cloud video recording integrity detection device, and will not be repeated here.

[0081] This application also provides a computer device, including: a memory and a processor, wherein the memory is used to store program instructions; and the processor, connected to the memory, is used to execute the above-described cloud video recording integrity detection method.

[0082] This application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device containing the non-volatile storage medium executes the above-described cloud video recording integrity detection method by running the computer program.

[0083] This application also provides a computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the cloud video recording integrity detection method of this application.

[0084] This application also provides a computer program product, including computer instructions that, when executed by a processor, implement the steps of the cloud video recording integrity detection method in this application.

[0085] This application also provides a computer program that, when executed by a processor, implements the steps of the cloud video recording integrity detection method in this application.

[0086] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0087] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0089] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] 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.

[0091] 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0092] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A cloud video recording integrity detection method, characterized in that, The method comprises the following steps: acquire a video file of a to-be-detected period in cloud video recording; perform a plurality of levels of detection on the video file of the to-be-detected period in sequence, wherein the plurality of levels of detection at least include: video file quantity, video file capacity, and video file duration; determine whether the video file of the to-be-detected period is complete according to the detection results of the plurality of levels; perform a plurality of levels of detection on the video file of the to-be-detected period in sequence, which comprises the following steps:

2. The method of claim 1, wherein, acquire a plurality of sub-period video files in the video file of the to-be-detected period; perform a first level of detection on the plurality of sub-period video files, wherein the first level of detection is used to detect whether the quantity of the plurality of sub-period video files is within a preset quantity range; in a case where the first level of detection result indicates that the plurality of sub-period video files pass the detection, perform a second level of detection on the plurality of sub-period video files, wherein the second level of detection is used to detect whether the capacity of the plurality of sub-period video files is within a preset capacity range; in a case where the second level of detection result indicates that the plurality of sub-period video files pass the detection, perform a third level of detection on the plurality of sub-period video files, wherein the third level of detection is used to detect whether the duration of the plurality of sub-period video files is within a preset duration range. perform the first level of detection on the plurality of sub-period video files, which comprises the following steps:

3. The method of claim 1, wherein, divide the video file of the to-be-detected period into a plurality of standard video files according to a preset duration, and acquire the quantity of the plurality of standard video files; determine the product of the quantity of the plurality of standard video files and a first coefficient as the minimum value of the preset quantity range; determine the product of the quantity of the plurality of standard video files and a second coefficient as the maximum value of the preset quantity range, wherein the first coefficient is smaller than the second coefficient; in a case where the plurality of sub-period video files are within the preset quantity range, determine that the plurality of sub-period video files pass the first level of detection. perform the second level of detection on the plurality of sub-period video files, which comprises the following steps: acquire the video segment duration and the camera transmission code rate of the plurality of sub-period video files; 4. The method of claim 1, wherein, determine the preset capacity range of each sub-period video file based on the video segment duration and the camera transmission code rate of each sub-period video file; acquire the capacity of the plurality of sub-period video files; compare each sub-period video file with its corresponding preset capacity range in sequence, starting from the first sub-period video file; in a case where the capacity of all sub-period video files is within the corresponding preset capacity range, determine that the plurality of sub-period video files pass the second level of detection. perform the third level of detection on the plurality of sub-period video files, which comprises the following steps: acquire the start time and the end time of each sub-period video file in the video file of the to-be-detected period one by one; splice the start time and the end time of each sub-period video file in sequence according to the chronological order to obtain a continuous time sequence; In a case that the continuous time sequence can completely cover the to-be-detected time period, it is determined that the video files of the plurality of sub time periods pass the third level detection.

5. The method of claim 4, wherein, The method further comprises: In a case that the start time and the end time corresponding to the continuous time sequence are completely same as the start time and the end time of the to-be-detected time period, it is determined that the continuous time sequence satisfies a first preset condition; In a case that there is no discontinuity between the start time and the end time corresponding to the continuous time sequence, it is determined that the continuous time sequence satisfies a second preset condition; In a case that the continuous time sequence satisfies both the first preset condition and the second preset condition, it is determined that the continuous time sequence can completely cover the to-be-detected time period.

6. The method of claim 3, wherein, The preset capacity range of each video file of each sub time period is determined based on the video segment length of the video file of each sub time period and the camera transmission code rate, comprising: The product of the video segment length of the video file of each sub time period and the camera transmission code rate is determined as a standard capacity; The product of the standard capacity and a third coefficient and a fourth coefficient is determined as the minimum value and the maximum value of the preset capacity range of the video file of each sub time period, wherein the third coefficient is less than the fourth coefficient.

7. A cloud recording integrity detection apparatus, characterized by, Comprise: The acquisition module is configured to acquire video files of a to-be-detected time period in cloud video; The detection module is configured to sequentially perform a plurality of levels of detection on the video files of the to-be-detected time period, the plurality of levels of detection at least comprising: video file quantity, video file capacity, and video file length; The determination module is configured to jointly determine, according to the detection results of the plurality of levels, whether the video files of the to-be-detected time period are complete. Sequentially performing a plurality of levels of detection on the video files of the to-be-detected time period comprises: acquiring video files of a plurality of sub time periods in the video files of the to-be-detected time period; performing first level detection on the video files of the plurality of sub time periods, the first level detection being configured to detect whether the quantity of the video files of the plurality of sub time periods is within a preset quantity range; in a case that the first level detection result indicates that the video files of the plurality of sub time periods pass the detection, performing second level detection on the video files of the plurality of sub time periods, the second level detection being configured to detect whether the capacity of the video files of the plurality of sub time periods is within a preset capacity range; in a case that the second level detection result indicates that the video files of the plurality of sub time periods pass the detection, performing third level detection on the video files of the plurality of sub time periods, the third level detection being configured to detect whether the length of the video files of the plurality of sub time periods is within a preset length range.

8. A computer device, comprising: Comprise: A memory and a processor, wherein the memory is configured to store program instructions; The processor, connected with the memory, is configured to execute the cloud video completeness detection method in any one of claims 1 to 6.

9. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the cloud video completeness detection method in any one of claims 1 to 6.

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