A video storage management system based on big data

Abstract

The invention discloses a video storage management system based on big data, and specifically relates to the field of video storage, comprising a storage area determination module: used for dividing storage areas according to keywords and numbering them sequentially as 1, 2, ..., i, ..., n; an information acquisition module for acquiring a first key data group according to video content features, and acquiring a second key data group according to video storage features; the invention improves retrieval accuracy by detecting the remaining space of a sub-storage space and reasonably allocating the capacity of the storage space, and simultaneously analyzing and processing the video basic information, video encoding information, and video content features.

Description

Technical Field

[0001] The present invention relates to the field of video storage technology, and more specifically, to a video storage management system based on big data. Background Art

[0002] With the rapid development of smart cities, intelligent transportation and other fields, video surveillance equipment is widely used, generating a large amount of video data; as the core component for processing large amounts of video data, the video storage management system has large-scale data storage capabilities, efficient data retrieval and access speed, and higher security and reliability; therefore, a video storage management system based on big data technology is needed to improve the processing capabilities and management efficiency of video data.

[0003] The traditional video storage management system includes a storage space division module, a feature extraction module, a management service module and a video storage module; the storage space division module automatically divides the storage space according to the characteristics of the video data to form multiple sub-storage units; each sub-storage unit focuses on storing video files with specific characteristics to improve the orderliness and retrieval efficiency of storage; the feature extraction module: deeply analyzes the video file, extracts key information such as keywords and timestamps, and calculates the comprehensive coefficient of each keyword; keywords with high comprehensive coefficients are regarded as the main features of the video file and are used to guide the division of storage space; the management server module is responsible for receiving information from the feature extraction module, counting the comprehensive coefficients of each keyword, and sending instructions to the storage space division module accordingly to guide the storage location of the video file; the video storage module is responsible for actually storing the video file. According to the instructions of the management server, the video file is stored in the corresponding sub-storage unit.

[0004] However, in actual use, it still has some shortcomings, such as incomplete data classification. Traditional video storage management systems usually only provide basic storage and retrieval functions and lack intelligent analysis and processing capabilities. Users need to rely on other tools or software to further analyze and process video data; the retrieval accuracy is low and there is a lack of intelligent analysis and processing capabilities. Only unilateral factors are retrieved, and the accuracy of data retrieval is not high enough; the retrieval efficiency is low. Traditional video storage management systems lack intelligent analysis capabilities and cannot perform deep mining and automatic processing of video data.

[0005] Therefore, there is an urgent need to provide a video storage management system based on big data to solve the problems of incomplete data classification, low retrieval accuracy and low retrieval efficiency in existing video storage management systems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a video storage management system based on big data, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: a video storage management system based on big data, comprising:

[0008] The system operation database, the system central processing module and the user information terminal are characterized in that they also include a storage area determination module, an information collection module, a data processing module, a comprehensive data analysis module, a video storage mode determination module and a data interaction transmission module;

[0009] The system operation database includes all data of the video storage management system and collects information output by each module in real time. The system central processing module is used to centrally control the information instructions output by each module. The user information terminal is an information output device for receiving the large computing power model optimization system.

[0010] The storage area determination module is used to divide the storage areas according to the keywords and number them in sequence as 1, 2, ..., i, ..., n;

[0011] The information collection module is used to collect a first key data group according to video content characteristics and to collect a second key data group according to video storage characteristics;

[0012] The data processing module is used to process the first key data group to obtain a third key data group, and process the second key data group to obtain a fourth key data group;

[0013] The data analysis module obtains a comprehensive storage mode output value based on the analysis of the second key data group and the fourth key data group;

[0014] The video storage mode determination module evaluates the rationality of the video storage mode of the target storage area according to the comprehensive data output value;

[0015] The data interactive transmission module outputs information to the user terminal based on the comprehensive data output value.

[0016] Preferably, the storage area determination module is used to obtain at least one storage range for the target storage system for the target video, divide the target video into detection sub-areas according to video keywords, and number the sub-areas in the target video in sequence as 1, 2, ..., i, ..., n.

[0017] Preferably, the information acquisition module acquires a first key data group of the target video according to the content characteristics of the target video; and acquires a second key data group of the target storage area according to the storage characteristics of the target video; the first key data group includes parameters affecting basic video information, parameters affecting video encoding information, and parameters affecting content characteristics; the second key data group includes parameters affecting storage performance and parameters affecting network transmission.

[0018] Preferably, the video basic information influencing parameters include video file size, denoted as F; video duration, denoted as T; video resolution, denoted as R; video frame rate, denoted as FPS; video encoding information influencing parameters include video duration, denoted as T; video bit rate, denoted as B; audio bit rate, denoted as b; audio sampling rate, denoted as S; number of audio channels, denoted as C; content feature influencing parameters include number of video key frames, denoted as K; number of video scene switching times, denoted as s; number of objects in the video, denoted as O; number of video text lines, denoted as t; storage performance influencing parameters include total storage space capacity, denoted as ; Used storage space, recorded as ; The remaining storage space is recorded as ; Storage speed, denoted as v; Network transmission influencing parameters include the I / O read and write speed of the storage space, denoted as V; Storage space access delay, denoted as D; Throughput, denoted as A; CPU utilization, denoted as U.

[0019] Preferably, the data processing module is used to process the first key data group to obtain a third key data group, and to process the second key data group to obtain a fourth key data group; wherein the third key data group includes a video basic information influence coefficient, a video coding information influence coefficient and a content feature influence coefficient; and the fourth key data group includes a storage performance influence coefficient and a network transmission influence coefficient.

[0020] Preferably, the video basic information coefficient calculation unit is used to import the video basic information parameters into the video basic information coefficient mathematical model to obtain the video basic information coefficient value; the video coding information influence coefficient calculation unit is used to import the video coding information influence parameters into the video coding information influence coefficient mathematical model to obtain the video coding information influence coefficient value; the content feature influence coefficient calculation unit is used to import the content feature influence parameters into the content feature influence coefficient mathematical model to obtain the content feature influence coefficient value; the storage performance influence coefficient calculation unit is used to import the storage performance influence parameters into the storage performance influence coefficient mathematical model to obtain the storage performance influence coefficient value; the network transmission influence coefficient calculation unit is used to import the network transmission influence parameters into the network transmission influence coefficient mathematical model to obtain the network transmission influence coefficient value.

[0021] Preferably, the mathematical model of the basic information coefficient of the video is specifically:

[0022] ,

[0023] The mathematical model of the video coding information influence coefficient is as follows:

[0024] ,

[0025] The mathematical model of content feature influence coefficient is as follows:

[0026] ,

[0027] The specific mathematical model of storage performance impact coefficient is:

[0028] ,

[0029] The mathematical model of network transmission influence coefficient is as follows:

[0030] ,

[0031] in Indicates the maximum video resolution. Indicates the maximum number of key frames; Indicates the maximum number of scene switching times; Indicates the maximum number of objects; Indicates the maximum number of text lines. Indicates the I / O read and write speed of the i-th storage space. represents the throughput of the i-th storage space, Indicates the minimum storage space throughput; Indicates the maximum storage space throughput; represents the access delay of the i-th storage space, Indicates the maximum storage space access latency, Indicates the minimum storage space access latency.

[0032] Preferably, the data analysis module is used to import the third key data group and the fourth key data group into the comprehensive storage mode output value mathematical model to obtain the comprehensive storage mode output value.

[0033] Preferably, the comprehensive storage mode output value mathematical model is specifically:

[0034] .

[0035] Technical effects and advantages of the present invention:

[0036] 1. The present invention not only provides basic storage and retrieval functions, but also integrates advanced intelligent analysis and processing capabilities, enabling users to perform in-depth analysis and processing of video data directly within the system without relying on external tools or software, thereby increasing the number of data classifications;

[0037] 2. The present invention improves the retrieval accuracy by detecting the remaining space of the sub-storage space and reasonably allocating the capacity of the storage space, and simultaneously analyzing and processing the video based on the basic information of the video, the video encoding information, and the video content features;

[0038] 3. The present invention facilitates rapid retrieval and calling of video files by storing video files in sub-storage units corresponding to keywords, and achieves orderly storage of video files and improves storage efficiency through keyword extraction and intelligent allocation of storage space. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0040] Figure 2 The structural diagram of the data processing module of the present invention is DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] As attached Figure 1 A video storage management system based on big data is shown, including a system operation database, a system central processing module and a user information terminal, and is characterized in that it also includes a storage area determination module, an information collection module, a data processing module, a comprehensive data analysis module, a video storage method judgment module and a data interaction transmission module.

[0043] The system operation database includes all data of the video storage management system and collects information output by each module in real time. The system central processing module is used to control the information instructions output by each module. The user information terminal is an information output device for receiving the large computing power model optimization system.

[0044] The output end of the storage area determination module is connected to the input end of the information acquisition module, the output end of the information acquisition module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the comprehensive data analysis module, and the output end of the comprehensive data analysis module is connected to the input end of the data interaction and transmission module.

[0045] The storage area determination module is used to divide the storage area into detection sub-areas according to the video keyword method, and number the sub-areas in the target storage area as 1, 2, ..., i, ..., n in sequence.

[0046] In this embodiment, it is specifically necessary to explain that the storage area determination module is used to obtain at least one storage range for the target storage system for the target video, and divide it into sub-areas according to keywords, and number each sub-area in sequence as 1, 2, ..., i, ..., n.

[0047] The information acquisition module is used to acquire a first key data group of the target video according to the content characteristics of the target video; acquire a second key data group of the target storage area according to the storage characteristics of the target video, and transmit it to the data processing module.

[0048] In this embodiment, it should be specifically explained that the first key data group includes video basic information influencing parameters, video encoding information influencing parameters and content feature influencing parameters; the second key data group includes storage performance influencing parameters and network transmission influencing parameters.

[0049] In this embodiment, it is specifically necessary to explain that the video basic information influencing parameters include the video file size, denoted as F; video duration, denoted as T; video resolution, denoted as R; video frame rate, denoted as FPS; video encoding information influencing parameters include video duration, denoted as T; video bit rate, denoted as B; audio bit rate, denoted as b; audio sampling rate, denoted as S; number of audio channels, denoted as C; content feature influencing parameters include the number of video key frames, denoted as K; number of video scene switching times, denoted as s; number of objects in the video, denoted as O; number of video text lines, denoted as t; storage performance influencing parameters include the total storage space capacity, denoted as ; Used storage space, recorded as ; The remaining storage space is recorded as ; Storage speed, denoted as v; Network transmission influencing parameters include the I / O read and write speed of the storage space, denoted as V; Storage space access delay, denoted as D; Throughput, denoted as A; CPU utilization, denoted as U.

[0050] The data processing module is used to process the first key data group obtained by the information collection module to obtain a third key data group, process the second key data group to obtain a fourth key data group, and transmit it to the data analysis module.

[0051] In this embodiment, it should be specifically explained that the third key data group includes the video basic information influence coefficient, the video encoding information influence coefficient and the content feature influence coefficient; the fourth key data group includes the storage performance influence coefficient and the network transmission influence coefficient.

[0052] In this embodiment, the data processing module that needs to be specifically explained includes a video basic information influence coefficient calculation unit, a video encoding information influence coefficient calculation unit, a content feature influence coefficient calculation unit, a storage performance influence coefficient calculation unit, and a network transmission influence coefficient calculation unit.

[0053] The video basic information coefficient calculation unit is used to import the video basic information parameters into the video basic information coefficient mathematical model to obtain the video basic information coefficient value; the video coding information influence coefficient calculation unit is used to import the video coding information influence parameters into the video coding information influence coefficient mathematical model to obtain the video coding information influence coefficient value; the content feature influence coefficient calculation unit is used to import the content feature influence parameters into the content feature influence coefficient mathematical model to obtain the content feature influence coefficient value; the storage performance influence coefficient calculation unit is used to import the storage performance influence parameters into the storage performance influence coefficient mathematical model to obtain the storage performance influence coefficient value; the network transmission influence coefficient calculation unit is used to import the network transmission influence parameters into the network transmission influence coefficient mathematical model to obtain the network transmission influence coefficient value.

[0054] In this embodiment, it should be specifically noted that the mathematical model of the basic information coefficient of the video is specifically:

[0055] ,

[0056] The mathematical model of the video coding information influence coefficient is as follows:

[0057] ,

[0058] The mathematical model of content feature influence coefficient is as follows:

[0059] ,

[0060] The specific mathematical model of storage performance impact coefficient is:

[0061] ,

[0062] The mathematical model of network transmission influence coefficient is as follows:

[0063] ,

[0064] in Indicates the maximum video resolution. Indicates the maximum number of key frames; Indicates the maximum number of scene switching times; Indicates the maximum number of objects; Indicates the maximum number of text lines, where Indicates the I / O read and write speed of the i-th storage space. represents the throughput of the i-th storage space, Indicates the minimum storage space throughput; Indicates the maximum storage space throughput; represents the access delay of the i-th storage space, Indicates the maximum storage space access latency, Indicates the minimum storage space access latency.

[0065] The data analysis module is used to import the third key data group and the fourth key data group obtained by the data processing module into the comprehensive storage mode output value mathematical model, obtain the comprehensive storage mode output value, and transmit it to the video storage mode judgment module.

[0066] In this embodiment, it should be specifically explained that the mathematical model of the output value of the comprehensive storage method is specifically: .

[0067] The video storage mode judgment module evaluates the rationality of the video storage mode of the target storage area according to the comprehensive data output value, compares the comprehensive storage mode output value with the preset comprehensive storage mode output value, calculates the difference between the comprehensive storage mode output value and the preset video safety storage rationality index value, and when the difference value is less than the preset difference value, transmits the difference value and the video storage data to the data interaction transmission module.

[0068] In this embodiment, it should be specifically explained that the preset comprehensive storage mode output value is the average of the historical video storage rationality index, and the preset difference value is the minimum difference between the historical comprehensive storage mode output value and the preset comprehensive storage mode output value.

[0069] The data interaction transmission module outputs information to the user terminal based on the comprehensive data output value, and is used to transmit the difference value calculated by the video storage mode judgment module and the video storage data to the web data terminal, and provide reference data for alerting the administrator to make adjustment measures.

[0070] In this embodiment, it should be specifically noted that the data terminal of the administrator is a common data receiver such as a mobile phone or a computer, so this embodiment does not specifically limit the data terminal.

[0071] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0072] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A video storage management system based on big data, including a system operation database, a system central processing module and a user information terminal, characterized in that: It also includes a storage area determination module, an information collection module, a data processing module, a comprehensive data analysis module, a video storage mode determination module, and a data interaction transmission module; The system operation database includes all data of the video storage management system and collects information output by each module in real time. The system central processing module is used to centrally control the information instructions output by each module. The user information terminal is an information output device for receiving the large computing power model optimization system. The storage area determination module is used to divide the storage areas according to the keywords and number them in sequence as 1, 2, ..., i, ..., n; The information collection module is used to collect the first key data group according to the video content characteristics and to collect the second key data group according to the video storage characteristics; The information acquisition module acquires a first key data group of the target video according to the content characteristics of the target video; acquires a second key data group of the target storage area according to the storage characteristics of the target video; the first key data group includes video basic information influencing parameters, video encoding information influencing parameters and content characteristic influencing parameters; The second key data group includes storage performance influencing parameters and network transmission influencing parameters; The video basic information influencing parameters include video file size, denoted as F; video duration, denoted as T; video resolution, denoted as R; video frame rate, denoted as FPS; video encoding information influencing parameters include video duration, denoted as T; video bit rate, denoted as B; audio bit rate, denoted as b; audio sampling rate, denoted as S; The number of audio channels, denoted by C; The parameters affecting content features include the number of video key frames, denoted as K; the number of video scene switching times, denoted as s; the number of objects in the video, denoted as O; the number of video text lines, denoted as t; the parameters affecting storage performance include the total storage space capacity, denoted as ; Used storage space, recorded as ; The remaining storage space is recorded as ; Storage speed, denoted as v; Network transmission influencing parameters include storage space I / O read and write speed, denoted as V; storage space access delay, denoted as D; throughput, denoted as A; CPU utilization, denoted as U; The data processing module is used to process the first key data group to obtain a third key data group, and process the second key data group to obtain a fourth key data group; The data processing module is used to process the first key data group to obtain a third key data group, and process the second key data group to obtain a fourth key data group; wherein the third key data group includes a video basic information influence coefficient, a video encoding information influence coefficient, and a content feature influence coefficient; the fourth key data group includes a storage performance influence coefficient and a network transmission influence coefficient; The data processing module includes a video basic information influence coefficient calculation unit, a video coding information influence coefficient calculation unit, a content feature influence coefficient calculation unit, a storage performance influence coefficient calculation unit and a network transmission influence coefficient calculation unit; The video basic information coefficient calculation unit is used to import the video basic information parameters into the video basic information coefficient mathematical model to obtain the video basic information coefficient value; the video coding information influence coefficient calculation unit is used to import the video coding information influence parameters into the video coding information influence coefficient mathematical model to obtain the video coding information influence coefficient value; the content feature influence coefficient calculation unit is used to import the content feature influence parameters into the content feature influence coefficient mathematical model to obtain the content feature influence coefficient value; the storage performance influence coefficient calculation unit is used to import the storage performance influence parameters into the storage performance influence coefficient mathematical model to obtain the storage performance influence coefficient value; The network transmission influence coefficient calculation unit is used to import the network transmission influence parameter into the network transmission influence coefficient mathematical model to obtain the network transmission influence coefficient value; The mathematical model of the basic information coefficient of the video is specifically: ， The mathematical model of the video coding information influence coefficient is as follows: , The mathematical model of content feature influence coefficient is as follows: ， The specific mathematical model of storage performance impact coefficient is: ， The mathematical model of network transmission influence coefficient is as follows: ， in Indicates the maximum video resolution. Indicates the maximum number of key frames; Indicates the maximum number of scene switching times; Indicates the maximum number of objects; Indicates the maximum number of text lines. Indicates the I / O read and write speed of the i-th storage space. represents the throughput of the i-th storage space, Indicates the minimum storage space throughput; Indicates the maximum storage space throughput; represents the access delay of the i-th storage space, Indicates the maximum storage space access latency, Indicates the minimum storage space access latency; The data analysis module obtains a comprehensive storage mode output value based on the analysis of the second key data group and the fourth key data group; The video storage mode determination module evaluates the rationality of the video storage mode of the target storage area according to the comprehensive data output value; The data interactive transmission module outputs information to the user terminal based on the comprehensive data output value.

2. The video storage management system based on big data according to claim 1, characterized in that: The storage area determination module is used to obtain at least one storage range for a target storage system for a target video, and divide it into sub-areas according to keywords, and number the sub-areas in sequence as 1, 2, ..., i, ..., n.

3. The video storage management system based on big data according to claim 1, characterized in that: The data analysis module is used to import the third key data group and the fourth key data group into the comprehensive storage mode output value mathematical model to obtain the comprehensive storage mode output value.

4. The video storage management system based on big data according to claim 3 is characterized in that: The output value mathematical model of the comprehensive storage method is specifically: 。

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