Cloud video integrity detection method and device and computer equipment

Abstract

The invention discloses a cloud video integrity detection method and device and computer equipment. The method comprises the following steps: acquiring a video file of a to-be-detected time period in a cloud video; sequentially carrying out multi-level detection on the video files in the to-be-detected time period, wherein the multi-level detection at least comprises the number of the video files, the capacity of the video files and the duration of the video files; and jointly determining whether the to-be-detected video is complete or not according to the detection results of the multiple levels. The technical problem that the integrity detection accuracy of the video file is low in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of video storage technology, and more specifically, to a cloud video integrity detection method, device, and computer equipment. Background Art

[0002] With the rapid development of information technology, video surveillance systems have become an indispensable component of modern society. They are widely used in a variety of fields, including security, traffic management, and public safety, providing significant convenience and security for people's lives. As a critical security tool, video surveillance systems are not limited to real-time monitoring but also provide comprehensive retrospective analysis. With the rapid adoption of cloud technology, cloud-based video storage—video surveillance data stored on cloud computing platforms—has become a crucial means of ensuring information security and improving retrospective capabilities. However, in practice, video data corruption and loss of integrity are common due to network fluctuations, system software defects, and other issues, leading to the loss of critical information.

[0003] There are currently two methods for detecting video integrity. One is to extract the target time information of multiple target videos and connect them in chronological order. If the time information is connected continuously, the video is considered complete. The other is to determine the integrity of the video by comparing the start and end times of the triggering event with the start and end times of the video file. However, both of these methods can only determine the integrity of the video file as a whole. In the case of network jitter, the generated video files cannot accurately determine the integrity of the video file. Summary of the Invention

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

[0005] According to one aspect of an embodiment of the present application, a cloud video integrity detection method is provided, including: obtaining video files of a time period to be detected in the cloud video; performing multiple levels of detection on the video files of the time period to be detected in sequence, and the multiple levels of detection include at least: the number of video files, the capacity of the video files and the length of the video files; and jointly determining whether the video files of the time period to be detected are complete based on the detection results of the multiple levels.

[0006] Optionally, multiple levels of detection are performed on the video files of the time period to be detected in sequence, including: obtaining video files of multiple sub-time periods in the video files of the time period to be detected; performing a first level of detection on the video files of the multiple sub-time periods, the first level of detection is used to detect whether the number of video files of the multiple sub-time periods is within a preset number range; when the detection result of the first level indicates that the video files of the multiple sub-time periods have passed the detection, performing a second level of detection on the video files of the multiple sub-time periods, the second level of detection is used to detect whether the capacity of the video files of the multiple sub-time periods is within a preset capacity range; when the detection result of the second level indicates that the video files of the multiple sub-time periods have passed the detection, performing a third level of detection on the video files of the multiple sub-time periods, the third level of detection is used to detect whether the duration of the video files of the multiple sub-time periods is within a preset duration range.

[0007] Optionally, the first-level detection is performed 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 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 less than the second coefficient; when the video files of the multiple sub-time periods are within the preset number range, determining that the video files of the multiple sub-time periods have passed the first-level detection.

[0008] Optionally, the video files of the multiple sub-periods are subjected to a second-level detection, including: obtaining the video clip duration and camera transmission bit rate of the video files of the multiple sub-periods; determining the preset capacity range of the video files of each sub-period based on the video clip duration and camera transmission bit rate of the video files of each sub-period; obtaining the capacity of the video files of the multiple sub-periods; starting from the video file of the first sub-period, comparing the video files of each sub-period with its corresponding preset capacity range in turn; when the capacity of the video files of all sub-periods is within their corresponding preset capacity range, determining that the video files of the multiple sub-periods have passed the second-level detection.

[0009] Optionally, the video files of the multiple sub-periods are subjected to a third-level detection, including: obtaining the start time and end time of the video files of each sub-period in the video files of the time period to be detected one by one; splicing the start time and end time of the video files of each sub-period in sequence according to the chronological order of the times to obtain a continuous time series; and when the continuous time series can completely cover the time period to be detected, determining that the video files of the multiple sub-periods have passed the third-level detection.

[0010] Optionally, the method also includes: when the start moment and end moment corresponding to the continuous time series are exactly the same as the start moment and end moment of the time period to be detected, determining that the continuous time series meets the first preset condition; when there is no interruption between the start moment and the end moment corresponding to the continuous time series, determining that the continuous time series meets the second preset condition; when the continuous time series satisfies both the first preset condition and the second preset condition, determining that the continuous time series can completely cover the time period to be detected.

[0011] Optionally, the preset capacity range of the video file of each sub-period is determined based on the video clip length and the camera transmission bit rate of the video file of each sub-period, including: determining the product of the video clip length and the camera transmission bit rate of the video file of each sub-period as the standard capacity; and determining the product of the standard capacity with the third coefficient and the fourth coefficient respectively as the minimum and maximum values ​​of the preset capacity range of the video file of each sub-period, wherein the third coefficient is smaller than the fourth coefficient.

[0012] According to another aspect of an embodiment of the present application, a cloud video integrity detection device is also provided, including: an acquisition module for acquiring video files of a time period to be detected in the cloud video; a detection module for performing multiple levels of detection on the video files of the time period to be detected in sequence, and the multiple levels of detection include at least: the number of video files, the capacity of the video files and the length of the video files; a determination module for jointly determining whether the video files of the time period to be detected are complete based on the detection results of the multiple levels.

[0013] According to another aspect of the embodiment of the present application, a computer device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned cloud video integrity detection method.

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

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

[0016] In an embodiment of the present application, by obtaining the video files of the time period to be detected in the cloud video; performing multiple levels of detection on the video files of the time period to be detected in sequence, and the multiple levels of detection include at least: the number of video files, the capacity of the video files and the length of the video files; based on the detection results of the multiple levels, jointly determining whether the video files of the time period to be detected are complete, thereby achieving the purpose of performing multiple levels of detection on the video files, thereby achieving the technical effect of improving the accuracy of video file integrity detection, and further solving the technical problem of low accuracy of video file integrity detection in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 This 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 This is a flow chart of a cloud video integrity detection method according to an embodiment of the present application;

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

[0021] Figure 4 is a hierarchical detection flow chart according to an embodiment of the present application;

[0022] Figure 5 is another hierarchical detection flow chart according to an embodiment of the present application;

[0023] Figure 6 is a first-level detection flow chart according to an embodiment of the present application;

[0024] Figure 7 is a second-level detection flow chart according to an embodiment of the present application;

[0025] Figure 8 is a third-level detection flow chart according to an embodiment of the present application;

[0026] Figure 9 This is a structural diagram of a cloud video integrity detection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] The information collected in the embodiments of the present application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or reject the automated decision results; if the user chooses to reject, the expert decision-making process will be entered.

[0030] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0031] Video integrity refers to the completeness and continuity of video data within a video surveillance system, ensuring that the footage accurately and completely records all events and activities within the surveillance area. In the video surveillance field, video integrity is a key indicator of system performance and is crucial for post-event tracing, evidence preservation, and security analysis.

[0032] Grading: Grading means that the system detects and judges issues of concern step by step according to preset standard processes.

[0033] In order to solve the problems existing in the related art, the embodiment of the present application provides a cloud video integrity detection method, which can be run on Figure 1In the computer terminal shown, the computer terminal is explained below.

[0034] The cloud video integrity detection method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The following is a hardware block diagram of a computer terminal for implementing a cloud video integrity detection method. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated by 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. 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. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0035] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal 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 integrity detection method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned cloud video integrity detection method. The memory 104 may include a high-speed random access memory, and may also include a 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 a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0037] The transmission module 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission module 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission module 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

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

[0039] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

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

[0041] Figure 2 This is a flow chart of a cloud video integrity detection method according to an embodiment of the present application. Figure 2As shown, the method includes the following steps:

[0042] Step S202: Obtain the video file of the time period to be detected in the cloud video;

[0043] Step S204: performing multiple-level detection on the video files of the time period to be detected, wherein the multiple-level detection includes at least: the number of video files, the capacity of the video files, and the length of the video files;

[0044] Step S206 : determining 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 described above, by obtaining video files from the cloud video recording for the time period to be tested, sequentially performing multiple-level tests on the video files in the time period to be tested, wherein the multiple-level tests include at least the number of video files, the video file capacity, and the video file duration; and jointly determining whether the video files in the time period to be tested are complete based on the test results of the multiple levels, thereby achieving the purpose of performing multiple-level tests on the video files, thereby achieving the technical effect of improving the accuracy of video file integrity testing, and further solving the technical problem of low accuracy of video file integrity testing in related technologies. Detailed description is given below.

[0046] In actual application scenarios, when saving video files, the video files are segmented and saved with a fixed time period Δt as a segment. Therefore, under standard circumstances, the number of video file segments within the time period ΔT is a fixed value ΔT / Δt; when the camera transmission bit rate is fixed at R, the size of each video file segment is a fixed value R×Δt; the video start time S and end time E of each video file segment can be seamlessly covered by sequential splicing to cover the time ΔT. In the case of video interruption due to instantaneous network jitter, the length of a certain video segment may be a non-standard length Δt, and the number of video file segments within the time period ΔT will also change. However, as long as the video recording is resumed in time, the video can be considered to be still complete. However, if the network fluctuations are large or there are defects in the software, resulting in long-term loss of video or damage to the video file, this is an abnormal situation of video file loss or damage to be detected by this application, such as Figure 3 As shown in the figure, from top to bottom, standard video files, video files under network jitter conditions, and video files under abnormal conditions are displayed.

[0047] In some embodiments of the present application, multiple levels of detection are performed on the video files of the time period to be detected in sequence, including: obtaining video files of multiple sub-time periods in the video files of the time period to be detected; performing a first level of detection on the video files of the multiple sub-time periods, the first level of detection is used to detect whether the number of video files of the multiple sub-time periods is within a preset number range; when the detection result of the first level indicates that the video files of the multiple sub-time periods have passed the detection, performing a second level of detection on the video files of the multiple sub-time periods, the second level of detection is used to detect whether the capacity of the video files of the multiple sub-time periods is within a preset capacity range; when the detection result of the second level indicates that the video files of the multiple sub-time periods have passed the detection, performing a third level of detection on the video files of the multiple sub-time periods, the third level of detection is used to detect whether the duration of the video files of the multiple sub-time periods is within a preset duration range, such as Figure 4 As shown, the first level detection detects the number of video files, the second level detection detects the size (capacity) of the video files, and the third level detection detects the time splicing of the video files. Figure 5 A multi-level detection flow chart is shown, such as Figure 5 As shown, the video files are sequentially tested at the first level, the second level, and the third level.

[0048] Specifically, the first-level detection is performed 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 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 less than the second coefficient; when the video files of the multiple sub-time periods are within the preset number range, determining that the video files of the multiple sub-time periods have passed the first-level detection.

[0049] Taking the first coefficient as 0.97 and the second coefficient as 1.03 as an example, the first-level detection method 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-periods within the time period.

[0050] Calculation of the number of standard files: Calculate the range of the number of standard files according to the formula Count = (0.97-1.03)ΔT / Δt.

[0051] Data comparison: Compare the file quantity C with the standard file quantity range Count to determine whether there is any missing video file quantity.

[0052] Result judgment: If C is not within the standard range, it is determined that the video is missing, the abnormality is recorded 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 an optional manner, the video files of the multiple sub-periods are subjected to a second-level detection, including: obtaining the video clip duration and camera transmission bit rate of the video files of the multiple sub-periods; determining the preset capacity range of the video files of each sub-period based on the video clip duration and camera transmission bit rate of the video files of each sub-period; obtaining the capacity of the video files of the multiple sub-periods; starting from the video file of the first sub-period, comparing the video files of each sub-period with its corresponding preset capacity range in turn; when the capacity of the video files of all sub-periods is within their corresponding preset capacity range, determining that the video files of the multiple sub-periods have passed the second-level detection.

[0054] The preset capacity range of the video file of each sub-period is determined based on the video clip length and camera transmission bit rate of the video file of each sub-period, including:

[0055] The product of the length of the video clip of the video file of each sub-period and the camera transmission bit rate is determined as the standard capacity; the product of the standard capacity and the third coefficient and the fourth coefficient are respectively determined as the minimum and maximum values ​​of the preset capacity range of the video file of each sub-period, wherein the third coefficient is smaller than the fourth coefficient.

[0056] Taking the third coefficient as 0.9 and the fourth coefficient as 1.1 as an example, the second-level detection steps are as follows: Figure 7 Shown, including:

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

[0058] Standard file size calculation: Calculate the standard size of the video file in the sub-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 whether the video file size is abnormal.

[0060] Result judgment: First, compare whether the size of the first video file D1 is within the standard file size range DATE1. If D1 is within the standard range, it is determined that the video file is not damaged and continue to detect the next video file; compare the size of each file one by one until the last video file. If the size of a video file is not within the standard range, it is determined that the video file is damaged, the video file damage time period is output, and jump to the error handling process. If all are within the standard range, continue to the third level of detection.

[0061] In an optional manner, the video files of the multiple sub-periods are subjected to a third-level detection, including: obtaining the start time and end time of the video files of each sub-period in the video files of the time period to be detected one by one; splicing the start time and end time of the video files of each sub-period in sequence according to the chronological order of the times to obtain a continuous time series; and when the continuous time series can completely cover the time period to be detected, determining that the video files of the multiple sub-periods have passed the third-level detection.

[0062] Among them, when the start moment and end moment corresponding to the continuous time series are exactly the same as the start moment and end moment of the time period to be detected, it is determined that the continuous time series meets the first preset condition; when there is no interruption between the start moment and the end moment corresponding to the continuous time series, it is determined that the continuous time series meets the second preset condition; when the continuous time series satisfies both the first preset condition and the second preset condition, it is determined that the continuous time series can completely cover the time period to be detected.

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

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

[0065] Time splicing: Splice the extracted start time and end time 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 whether there are any missing videos. Result judgment: If it can be seamlessly covered, the video is considered complete;

[0067] Otherwise, the video is determined to be missing and the specific missing time period is output.

[0068] The video integrity detection method provided in the present application utilizes a hierarchical detection mechanism to significantly shorten the time required to detect damaged and missing video files. The first level rapidly screens the number of video files, the second level accurately checks file size, and the third level verifies temporal continuity. This progressively more in-depth detection process quickly locates issues, accelerates problem resolution, and improves overall detection efficiency. By comprehensively examining the number, size, and temporal continuity of video files, it can more accurately locate damaged and missing video file segments. Compared to traditional methods, this method significantly improves accuracy, ensuring data integrity and availability. The automated detection process reduces manual review and intervention, lowering long-term operational and maintenance costs. Furthermore, it promptly detects and fixes video data issues, avoiding the need for duplicate recording or storage resources wasted due to missing or corrupted video, further reducing operational costs. By implementing this solution, damaged and missing sections in video files can be quickly identified and addressed, enhancing the stability and reliability of the video surveillance system and reducing system failures caused by data issues. This ensures the integrity and continuity of video files, providing users with accurate event playback and increasing their trust and satisfaction with the surveillance system. In scenarios where historical recordings need to be retrieved, the availability of recordings is ensured, avoiding the inconvenience and distress caused by data corruption or loss. The software solution provided in this application has automated features, can perform detection tasks regularly, and is compatible with multiple cloud platforms and supports cross-platform operations. This not only simplifies the detection process, but also expands the scope of application to meet the needs of video surveillance in different environments. The introduction of dynamic thresholds and a reasonable fault tolerance ratio can adapt to different storage conditions and recording fluctuations, improve 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 integrity detection device according to an embodiment of the present application. Figure 9 As shown, the device includes:

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

[0071] A detection module 92 is configured to perform multiple levels of detection on the video files of the time period to be detected, wherein the multiple levels of detection include at least: the number of video files, the capacity of the video files, and the duration of the video files;

[0072] The determination module 94 is configured to 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, which is used to perform multiple levels of detection on the video files of the time period to be detected in sequence, including: obtaining video files of multiple sub-time periods in the video files of the time period to be detected; performing a first-level detection on the video files of the multiple sub-time periods, and the first-level detection is used to detect whether the number of video files of the multiple sub-time periods is within a preset number range; when the detection result of the first level indicates that the video files of the multiple sub-time periods have passed the detection, performing a second-level detection on the video files of the multiple sub-time periods, and the second-level detection is used to detect whether the capacity of the video files of the multiple sub-time periods is within a preset capacity range; when the detection result of the second level indicates that the video files of the multiple sub-time periods have passed the detection, performing a third-level detection on the video files of the multiple sub-time periods, and the third-level detection is used to detect whether the duration of the video files of the multiple sub-time periods is within a preset duration range.

[0074] The detection submodule includes: a first detection unit, a second detection unit and a third detection unit, wherein 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 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 less than the second coefficient; when the video files of the multiple sub-time periods are within the preset number range, determining that the video files of the multiple sub-time periods have passed the first-level detection.

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

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

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

[0078] The second detection unit includes: a range subunit, which is used to determine the preset capacity range of the video file of each sub-period based on the video clip length and the camera transmission bit rate of the video file of each sub-period, including: multiplying the product of the video clip length and the camera transmission bit rate of the video file of each sub-period as the standard capacity; and multiplying the standard capacity by the third coefficient and the fourth coefficient respectively as the minimum value and maximum value of the preset capacity range of the video file of each sub-period, wherein the third coefficient is less than the fourth coefficient.

[0079] The cloud video integrity detection device described above obtains video files of a time period to be detected in the cloud video; performs multiple-level detection on the video files of the time period to be detected in sequence, and the multiple-level detection includes at least: the number of video files, the capacity of video files, and the length of video files; and jointly determines whether the video files of the time period to be detected are complete based on the detection results of the multiple levels, thereby achieving the purpose of performing multiple-level detection on the video files, thereby realizing the technical effect of improving the accuracy of video file integrity detection, and further solving the technical problem of low accuracy of video file integrity detection in related technologies.

[0080] It should be noted that Figure 9 The cloud video integrity detection device shown is used to perform Figure 2 The cloud video integrity detection method shown in the figure, therefore the relevant explanations in the above cloud video integrity detection method are also applicable to the cloud video integrity detection device, and will not be repeated here.

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

[0082] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned cloud video integrity detection method by running the computer program.

[0083] An embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the cloud video integrity detection method in the present application are implemented.

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

[0085] An embodiment of the present application also provides a computer program, which, when executed by a processor, implements the steps of the cloud video recording integrity detection method in the present application.

[0086] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0087] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description 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. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0089] The units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the present embodiment.

[0090] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0091] If the integrated unit is implemented in the form of 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 the present application is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0092] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A cloud video integrity detection method, characterized in that: include: Obtain the video files of the time period to be detected in the cloud video; Performing multiple levels of detection on the video files of the time period to be detected in sequence, wherein the multiple levels of detection at least include: the number of video files, the capacity of video files and the duration of video files; Whether the video file of the time period to be detected is complete is determined based on the detection results of the multiple levels.

2. The method according to claim 1, characterized in that The video files of the time period to be detected are sequentially tested at multiple levels, including: Obtaining video files of multiple sub-periods in the video file of the time period to be detected; Performing a first level detection on the video files of the multiple sub-periods, where the first level detection is used to detect whether the number of the video files of the multiple sub-periods is within a preset number range; When the detection result of the first level indicates that the detection of the video files of the multiple sub-periods is passed, 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 capacity of the video files of the multiple sub-periods is within a preset capacity range; When the detection result of the second level indicates that the video files of the multiple sub-periods have passed the detection, the video files of the multiple sub-periods are subjected to a third level detection, and 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.

3. The method according to claim 2, characterized in that Performing a first level detection on the video files of the multiple sub-periods includes: Dividing the video file of the time period to be detected into a plurality of standard video files according to a preset duration, and obtaining the number of the plurality of standard video files; Determine the product of the number of the plurality of standard video files and the first coefficient as the minimum value of the preset number range; Determine the product of the number of the plurality of 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; When 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.

4. The method according to claim 2, characterized in that: Performing a second level detection on the video files of the multiple sub-periods includes: Obtaining the video clip duration and camera transmission bit rate of the video files of the multiple sub-periods; Determine a preset capacity range of the video file of each sub-period based on the video clip length of the video file of each sub-period and the camera transmission bit rate; Obtaining the capacity of the video files of the multiple sub-periods; Starting from the video file of the first sub-period, the video file of each sub-period is compared with its corresponding preset capacity range in turn; When the capacity of the video files in all sub-periods is within the corresponding preset capacity range, it is determined that the video files in the sub-periods pass the second level detection.

5. The method according to claim 2, characterized in that: Performing a third level of detection on the video files of the multiple sub-periods includes: Obtaining the start time and end time of each sub-period of the video file in the video file of the time period to be detected one by one; The start time and end time of the video files of each sub-period are sequentially spliced ​​in the order of time to obtain a continuous time series; In the case that the continuous time sequence can completely cover the time period to be detected, it is determined that the video files of the multiple sub-time periods pass the third level of detection.

6. The method according to claim 5, characterized in that The method further comprises: In a case where the start time and the end time corresponding to the continuous time series are exactly the same as the start time and the end time of the time period to be detected, determining that the continuous time series satisfies a first preset condition; In a case where there is no interruption between the start time and the end time corresponding to the continuous time series, determining that the continuous time series satisfies a second preset condition; When the continuous time series satisfies both the first preset condition and the second preset condition, it is determined that the continuous time series can completely cover the time period to be detected.

7. The method according to claim 4, characterized in that The preset capacity range of the video files in each sub-period is determined based on the video clip length and the camera transmission bit rate of the video files in each sub-period, including: The product of the length of the video clip of the video file of each sub-period and the camera transmission bit rate is determined as the standard capacity; The products of the standard capacity, the third coefficient and the fourth coefficient are respectively determined as the minimum and maximum values ​​of the preset capacity range of the video file of each sub-period, wherein the third coefficient is smaller than the fourth coefficient.

8. A cloud video integrity detection device, characterized in that: include: The acquisition module is used to obtain the video files of the time period to be detected in the cloud video; A detection module, used to perform multiple levels of detection on the video files in the time period to be detected in sequence, wherein the multiple levels of detection at least include: the number of video files, the capacity of video files and the duration of video files; The determination module is used to jointly determine whether the video file of the time period to be detected is complete according to the detection results of the multiple levels.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory and is used to execute the cloud video integrity detection method described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the cloud video recording integrity detection method described in any one of claims 1 to 7 is implemented.

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