Video data editing and analysis system based on artificial intelligence and big data

Abstract

The present invention discloses a video data editing and analysis system based on artificial intelligence and big data, which specifically relates to the field of artificial intelligence, including a system operation database, a system central processing module and a user information terminal, and also includes a video frame division module, a video data acquisition module, a video data preprocessing module, a video data analysis module, a video data comprehensive analysis module, a video data judgment module, and a human-computer interaction module. The video frame division module of the present invention is used to filter out feature text data in a data text, and can realize a refined analysis of a video, select a first key text based on the feature text data, perform preprocessing analysis on the first key text to obtain a second key text, and obtain any text output value based on the analysis of the second key text, thereby providing an important reference basis for system comprehensive analysis and early warning.

Description

Technical Field

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

[0002] With the rapid development of short videos and live broadcast platforms, the market demand for high-quality, fast-paced video content continues to increase. Video content is no longer limited to traditional platforms such as televisions and computers. Mobile phones, tablets, and smart watches have become important carriers of video dissemination. Therefore, the analysis of video data editing has prompted the video data editing industry to develop in a more efficient and professional direction.

[0003] The traditional video data editing and analysis system mainly includes an image acquisition module, an image recognition module, a data transmission module and a data analysis module. The image acquisition module acquires video images through a high-definition network camera at a certain acquisition frequency and stores the acquired images locally; the image recognition module uses an image recognition algorithm to temporarily store the video acquired by the camera locally according to the specific meaning represented by the data; the data transmission module organizes the identified and analyzed data into a message format and transmits it to the data analysis module according to the data transmission protocol, so that the data analysis module can perform data editing and subsequent processing; the data analysis module processes and analyzes the data in the data transmission module, providing important support for video data editing.

[0004] However, it still has some shortcomings in actual use. The data transmission module in the traditional video data editing and analysis system mainly transmits data including the bit rate, frame rate and resolution of the video, and transmits it to the data analysis module. The data analysis module analyzes the three parameters, but only focusing on the bit rate, frame rate and resolution of the video can no longer meet the needs of society. For example, the RGB value of the video also has an important impact on video data editing and analysis. Therefore, multi-dimensional analysis of video data is essential. Artificial intelligence and big data will further optimize the video data editing and analysis process and improve analysis efficiency and accuracy. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a video data editing and analysis system based on artificial intelligence and big data, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The system operation database includes all data texts of the video data editing and analysis system, and collects the information texts output by each module in real time. The system central processing module is used to centrally control the information text instructions output by each module. The user information terminal is an information output device for receiving the video data editing and analysis system;

[0008] The video frame division module is used to filter out feature text data from the data text, store the filtered feature text data in a database, and mark the feature texts as 1, 2, ..., i, ..., n in sequence;

[0009] The video data acquisition module selects the first key text based on the feature text data in response to the video data editing;

[0010] The video data preprocessing module is used to perform preprocessing analysis on the key text to obtain a second key text;

[0011] The video data analysis module obtains any text output value based on the second key text analysis;

[0012] The video data comprehensive analysis module obtains a comprehensive text output value based on multiple text output values ​​in the video data analysis module;

[0013] The video data judgment module makes a good judgment on the video based on the comprehensive text output value and issues an early warning on the judgment result;

[0014] The human-computer interaction module outputs information to the user information terminal based on the judgment result.

[0015] Preferably, the data text of the video frame division module is the target video, the target video is screened and stored, and the screened video frames are the feature text data.

[0016] Preferably, the user information terminal is bound to a mobile phone or any output-capable electronic device based on the administrator's work information.

[0017] Preferably, the first key text is video quality data and video stability data, the video quality data includes video picture data and video color data, the video picture data includes the bit rate, horizontal pixels and vertical pixels of each video frame, respectively denoted as B, H, V, and the total video duration is denoted as T; the video color data includes the number of grayscale bytes, the number of chromaticity bytes and the number of concentration bytes of each video frame, respectively denoted as Cy, Cb and Cr.

[0018] Preferably, the video stabilization data includes optical flow data and feature point data, the optical flow data includes the speed of the optical flow on the x-axis and the speed of the optical flow on the y-axis, denoted as u and v respectively, the feature point data includes the feature point extraction distance and the feature point motion loss rate, denoted as Wd and Wl respectively, the video picture data, video color data, optical flow data and feature point data are integrated into the first key text and imported into the video data preprocessing model.

[0019] Preferably, the second key text is preprocessed based on the first key text, by establishing a video data preprocessing model, importing the first key text into the video data preprocessing model, and obtaining a bit rate fluctuation value, a frame rate stability value, and a resolution stability value through video picture data. The calculation formula of the bit rate fluctuation value is:

[0020] ,

[0021] Among them B i represents the bit rate of the i-th video frame, k represents the total number of video frames except the i-th video frame, and n represents the total number of divided video frames; the calculation formula for the frame rate stability value is:

[0022] ,

[0023] Where n represents the total number of divided video frames, and the ratio of the total number of video frames to the total length of the video represents the frame rate of the video. 标 represents the standard video frame rate, T represents the total video duration, and e represents a natural constant; the calculation formula for the resolution stability value is:

[0024] ,

[0025] Among them, H i Represents the horizontal pixels of the i-th video frame, V i Represents the vertical pixels of the i-th video frame. The product of the horizontal pixels and vertical pixels of the i-th video frame represents the resolution of the i-th video frame. 标 Indicates standard video resolution.

[0026] Preferably, the video quality preprocessing unit is used to establish a video quality preprocessing model, import the video color data transmitted by the video data acquisition module into the video quality preprocessing model, and obtain the color relative value through the video color data. The calculation formula of the color relative value is:

[0027] ,

[0028] Among them, Cy i Indicates the number of grayscale bytes of the i-th video frame, Cb i Indicates the number of chroma bytes of the i-th video frame, Cr irepresents the number of bytes of the concentration of the i-th video frame, ζ i Represents the relative color value of the i-th video frame; the optical flow velocity fluctuation value and the relative change value of the feature point are obtained through the video optical flow data and feature point data. The calculation formula of the optical flow velocity fluctuation value is:

[0029] ,

[0030] Where k represents the total number of video frames except the i-th video frame, n represents the total number of divided video frames, and u i represents the speed of the optical flow of the i-th video frame on the x-axis, v i represents the speed of the optical flow of the i-th video frame on the y-axis, γ 1i Represents the optical flow velocity fluctuation value of the i-th video frame; the calculation formula for the relative change value of the feature point is:

[0031] ,

[0032] Where Wd i represents the feature point extraction distance of the i-th video frame, Wl i represents the motion loss rate of the feature points of the i-th video frame, Wl 预 Represents the motion loss rate of the preset feature points of the video frame, γ 2i Represents the relative change value of the feature points of the i-th video frame.

[0033] Preferably, the video data analysis module establishes a video data analysis model, imports the second key text transmitted by the video data preprocessing module into the video data analysis model, and obtains any text output value, the text output value includes a video quality coefficient and a video stability coefficient, and the specific analysis formula of the video quality coefficient is:

[0034] ,

[0035] Where P' represents the stable frame rate of the video, R' i represents the stable value of the resolution of the i-th video frame, B´ represents the bit rate fluctuation value of the video, ζ i represents the relative color value of the i-th video frame, ω i represents the video quality coefficient of the i-th video frame; the specific analysis formula of the video stability coefficient is:

[0036] ,

[0037] where γ 1i represents the optical flow velocity fluctuation value of the i-th video frame, γ 2i represents the relative change value of the feature point of the i-th video frame, γ iRepresents the video stability coefficient of the i-th video frame. Multiple text values ​​are integrated and imported into the video data comprehensive analysis module.

[0038] Preferably, the video data comprehensive analysis module is used to receive multiple text output values ​​transmitted by the video data analysis module, and by establishing a video data comprehensive analysis model, the multiple text output values ​​are imported into the model to obtain a comprehensive text output value, and the specific analysis formula of the comprehensive text output value is:

[0039] ,

[0040] Where n represents the total number of divided video frames, ω i represents the video quality coefficient of the i-th video frame, γ i represents the video stabilization coefficient of the i-th video frame, λ1 and λ2 represent the influencing factors of the video quality coefficient and the video stabilization coefficient, respectively.

[0041] Technical effects and advantages of the present invention:

[0042] 1. The present invention can filter out characteristic text data from data texts through a video frame division module, thereby realizing refined analysis of the video, so that the system can more accurately capture subtle changes in the video in different video frames, and provide more accurate data support for subsequent comprehensive analysis and early warning; the characteristic text data is acquired to select a first key text; the selected first key text is preprocessed by a video data preprocessing module to obtain a second key text, so that by establishing a mathematical model, any text output value is obtained through the second key text, and a comprehensive analysis of video data editing can be performed according to multiple text output values. The system can grasp the playback status of the video, and provide data support for subsequent data processing and analysis to make the text output value more in-depth and accurate;

[0043] 2. Import multiple text output values ​​of the video data analysis module into the video data comprehensive analysis model through the video data comprehensive analysis module to obtain a comprehensive text output value, which can comprehensively reflect the overall playback status and quality status of the video, and provide a decision-making basis for the video data judgment module. Through comprehensive analysis, it can comprehensively reflect the quality of video data editing; set a preset value for the comprehensive text output value through the video data judgment module. When the video editing comprehensive analysis index exceeds the preset value, the system will automatically issue a warning signal to remind relevant personnel to deal with it in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0045] Figure 2 It is an operation flow chart of the present invention.

[0046] Figure 3 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] As attached Figure 1 The video data editing and analysis system based on artificial intelligence and big data shown in the figure includes a system operation database, a system central processing module and a user information terminal, and also includes a video frame division module, a video data acquisition module, a video data preprocessing module, a video data analysis module, a video data comprehensive analysis module, a video data judgment module, and a human-computer interaction module.

[0049] The output end of the video frame division module is connected to the input end of the video data acquisition module, the output end of the video data acquisition module is connected to the input end of the video data preprocessing module and the system operation database respectively, the output end of the video data preprocessing module is connected to the input end of the video data analysis module, the output end of the video data analysis module is connected to the input end of the video data comprehensive analysis module, the output end of the video data comprehensive analysis module is connected to the input end of the video data judgment module, the output end of the video data judgment module is connected to the input end of the human-computer interaction module, and the output end of the human-computer interaction module is connected to the input end of the system central processing module and the user information end respectively.

[0050] The video frame division module is used to filter out feature text data from the data text, store the filtered feature text data in a database, and mark the feature texts as 1, 2, ..., i, ..., n in sequence.

[0051] In this embodiment, it should be specifically explained that the data text of the video frame division module is the target video, the target video is screened and stored, and the screened video frames are feature text data, which contain complete image data and are used to define the beginning and end of key scenes or actions in the video sequence.

[0052] The video data acquisition module selects the first key text based on the characteristic text data in response to the video data editing.

[0053] In this embodiment, it should be specifically explained that the first key text is the video quality data and the video stability data. The video quality data includes the video picture data and the video color data. The video picture data is captured and read by Wireshark and Adobe Premiere Pro to obtain the bit rate, horizontal pixels and vertical pixels of each video frame, which are respectively recorded as B, H and V, and the total video duration is recorded as T; the video color data is collected by the YUV color coding model to obtain the grayscale bytes, chroma bytes and concentration bytes of the video frame, which are respectively recorded as Cy, Cb and Cr.

[0054] In this embodiment, it should be specifically explained that the video stabilization data includes optical flow data and feature point data. The optical flow of the video is calculated through artificial intelligence and big data. The powerful feature extraction and learning capabilities of artificial intelligence and big data, such as FlowNet and SpyNet models, are utilized to realize the optical flow calculation of each pixel in the image sequence through end-to-end training. The optical flow data includes the speed of the optical flow on the x-axis and the speed of the optical flow on the y-axis, which are denoted as u and v respectively. The feature point data includes the feature point extraction distance and the feature point motion loss rate, which are denoted as Wd and Wl respectively.

[0055] The video data preprocessing module is used to perform preprocessing analysis on the first key text to obtain the second key text.

[0056] In this embodiment, it should be specifically explained that the second key text is preprocessed based on the first key text, by establishing a video data preprocessing model, importing the first key text into the video data preprocessing model, and obtaining the bit rate fluctuation value, the frame rate stability value and the resolution stability value through the video picture data. The calculation formula of the bit rate fluctuation value is:

[0057] ,

[0058] Among them B i represents the bit rate of the i-th video frame, k represents the total number of video frames except the i-th video frame, and n represents the total number of divided video frames; the calculation formula for the frame rate stability value is:

[0059] ,

[0060] Where n represents the total number of divided video frames, and the ratio of the total number of video frames to the total length of the video represents the frame rate of the video. 标 represents the standard video frame rate, T represents the total video duration, e represents the natural constant, and the calculation formula for the resolution stability value is:

[0061] ,

[0062] Among them, H iRepresents the horizontal pixels of the i-th video frame, V i Represents the vertical pixels of the i-th video frame. The product of the horizontal pixels and vertical pixels of the i-th video frame represents the resolution of the i-th video frame. 标 Indicates the standard video resolution; the color relative value is obtained through the video color data, and the calculation formula of the color relative value is:

[0063] ,

[0064] Among them, Cy i Indicates the number of grayscale bytes of the i-th video frame, Cb i Indicates the number of chroma bytes of the i-th video frame, Cr i represents the number of bytes of the concentration of the i-th video frame, ζ i Represents the relative color value of the i-th video frame; the optical flow velocity fluctuation value and the relative change value of the feature point are obtained through the video optical flow data and feature point data. The calculation formula of the optical flow velocity fluctuation value is:

[0065] ,

[0066] Where k represents the total number of video frames except the i-th video frame, n represents the total number of divided video frames, and u i represents the speed of the optical flow of the i-th video frame on the x-axis, v i represents the speed of the optical flow of the i-th video frame on the y-axis, γ 1i Represents the optical flow velocity fluctuation value of the i-th video frame; the calculation formula for the relative change value of the feature point is:

[0067] ,

[0068] Where Wd i represents the feature point extraction distance of the i-th video frame, Wl i represents the motion loss rate of the feature points of the i-th video frame, Wl 预 Represents the motion loss rate of the preset feature points of the video frame, γ 2i Represents the relative change value of the feature points of the i-th video frame.

[0069] The video data analysis module obtains any text output value based on the second key text analysis.

[0070] In this embodiment, it should be specifically explained that the video data analysis module imports the second key text transmitted by the video data preprocessing module into the video data analysis model by establishing a video data analysis model, and obtains any text output value, the text output value includes a video quality coefficient and a video stability coefficient, and the specific analysis formula of the video quality coefficient is:

[0071] ,

[0072] Where P' represents the stable frame rate of the video, R' i represents the stable value of the resolution of the i-th video frame, B´ represents the bit rate fluctuation value of the video, ζ i represents the relative color value of the i-th video frame, ω i represents the video quality coefficient of the i-th video frame; the specific analysis formula of the video stability coefficient is:

[0073] ,

[0074] where γ 1i represents the optical flow velocity fluctuation value of the i-th video frame, γ 2i represents the relative change value of the feature point of the i-th video frame, γ i Represents the video stability coefficient of the i-th video frame. Multiple text values ​​are integrated and imported into the video data comprehensive analysis module.

[0075] The video data comprehensive analysis module obtains a comprehensive text output value based on multiple text output values ​​in the video data analysis module.

[0076] In this embodiment, it should be specifically explained that the video data comprehensive analysis module is used to receive multiple text output values ​​transmitted by the video data analysis module, and by establishing a video data comprehensive analysis model, the multiple text output values ​​are imported into the model to obtain a comprehensive text output value. The specific analysis formula of the comprehensive text output value is:

[0077] ,

[0078] Where n represents the total number of divided video frames, ω i represents the video quality coefficient of the i-th video frame, γ i represents the video stabilization coefficient of the i-th video frame, λ1 and λ2 represent the influencing factors of the video quality coefficient and the video stabilization coefficient, such as video encoding format, video compression algorithm and video noise, respectively.

[0079] The video data judgment module makes an excellent judgment on the video based on the comprehensive text output value, warns the judgment result, and issues a warning signal according to the judgment result.

[0080] In this embodiment, it should be specifically explained that the video data judgment module sets a preset value of the comprehensive text output value based on the comprehensive text output value pair, and the preset value of the comprehensive text output value is marked as n. D , when η D <η, it means that the comprehensive text output value of the target video is greater than the preset value of the comprehensive text output value, indicating that the target video is a well-edited video. D>η, it means that the comprehensive text output value of the target video is less than the preset value of the comprehensive text output value, indicating that the target video is a poorly edited video, and a warning signal is sent to the human-computer interaction module.

[0081] The human-computer interaction module outputs information to the user information terminal based on the judgment results.

[0082] The present invention can screen out characteristic text data from data texts through a video frame division module, realize refined analysis of videos, enable the system to more accurately capture subtle changes of videos in different video frames, and provide more accurate data support for subsequent comprehensive analysis and early warning; obtain characteristic text data to select a first key text; perform preprocessing operation on the selected first key text through a video data preprocessing module to obtain a second key text, thereby establishing a mathematical model, obtaining any text output value through the second key text, and comprehensively analyzing video data editing according to multiple text output values, so that the system can grasp the playback status of the video, provide data support for subsequent data processing and analysis, and make the text output value more in-depth and accurate; import multiple text output values ​​of the video data analysis module into the video data comprehensive analysis model through the video data comprehensive analysis module, obtain a comprehensive text output value, and comprehensively reflect the overall playback status and quality status of the video, provide a decision basis for the video data judgment module, and comprehensively reflect the goodness of video data editing through comprehensive analysis; set a preset value of the comprehensive text output value through the video data judgment module, and when the video editing comprehensive analysis index exceeds the preset value, the system will automatically send a warning signal to remind relevant personnel to handle it in time.

[0083] The system operation database includes all data texts of the video data editing and analysis system, and collects the information texts output by each module in real time. The system central processing module is used to centrally control the information text instructions output by each module. The user information terminal is the information output device that receives the video data editing and analysis system.

[0084] like Figure 2 This embodiment provides an operation flow of a video data editing and analysis system based on artificial intelligence and big data, including the following steps:

[0085] S1: filter out feature text data from the data text, store the filtered feature text data in a database, and mark the feature texts as 1, 2, ..., i, ..., n in sequence;

[0086] S2: In response to video data editing, selecting a first key text based on feature text data;

[0087] S3: preprocessing and analyzing the first key text to obtain a second key text;

[0088] S4: Obtaining any text output value based on the second key text analysis;

[0089] S5: obtaining a comprehensive text output value based on the multiple text output values;

[0090] S6: Make a good judgment on the video based on the comprehensive text output value, and issue an early warning for the judgment result;

[0091] S7: Output information to the user information terminal based on the judgment result.

[0092] 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;

[0093] 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 data editing and analysis system based on artificial intelligence and big data, including a system operation database, a system central processing module and a user information terminal, characterized in that: It also includes a video frame division module, a video data acquisition module, a video data preprocessing module, a video data analysis module, a video data comprehensive analysis module, a video data judgment module, and a human-computer interaction module; The system operation database includes all data texts of the video data editing and analysis system, and collects the information texts output by each module in real time. The system central processing module is used to centrally control the information text instructions output by each module. The user information terminal is an information output device for receiving the video data editing and analysis system; The video frame division module is used to filter out feature text data from the data text, store the filtered feature text data in a database, and mark the feature texts as 1, 2, ..., i, ..., n in sequence; The video data acquisition module selects the first key text based on the feature text data in response to the video data editing; The video data preprocessing module is used to perform preprocessing analysis on the first key text to obtain the second key text; The second key text is preprocessed based on the first key text, and a video data preprocessing model is established, the first key text is imported into the video data preprocessing model, and a bit rate fluctuation value, a frame rate stability value, and a resolution stability value are obtained through video picture data. The calculation formula of the bit rate fluctuation value is: ， Among them B i represents the bit rate of the i-th video frame, k represents the total number of video frames except the i-th video frame, and n represents the total number of divided video frames; the calculation formula for the frame rate stability value is: ， Where n represents the total number of divided video frames, and the ratio of the total number of video frames to the total length of the video represents the frame rate of the video. 标 represents the standard video frame rate, T represents the total video duration, and e represents a natural constant; the calculation formula for the resolution stability value is: ， Among them, H i Represents the horizontal pixels of the i-th video frame, V i Represents the vertical pixels of the ith video frame. The product of the horizontal pixels and vertical pixels of the ith video frame represents the resolution of the ith video frame. 标 Indicates the standard video resolution; the color relative value is obtained through the video color data, and the calculation formula of the color relative value is: ， Among them, Cy i Indicates the number of grayscale bytes of the i-th video frame, Cb i Indicates the number of chroma bytes of the i-th video frame, Cr i represents the number of bytes of the concentration of the i-th video frame, ζ i Represents the relative color value of the i-th video frame; the optical flow velocity fluctuation value and the relative change value of the feature point are obtained through the video optical flow data and feature point data. The calculation formula of the optical flow velocity fluctuation value is: ， Where k represents the total number of video frames except the i-th video frame, n represents the total number of divided video frames, and u i represents the speed of the optical flow of the i-th video frame on the x-axis, v i represents the speed of the optical flow of the i-th video frame on the y-axis, γ 1i Represents the optical flow velocity fluctuation value of the i-th video frame; the calculation formula for the relative change value of the feature point is: ， Where Wd i represents the feature point extraction distance of the i-th video frame, Wl i represents the motion loss rate of the feature points of the i-th video frame, Wl 预 Represents the motion loss rate of the preset feature points of the video frame, γ 2i Represents the relative change value of the feature points of the i-th video frame; The video data analysis module obtains any text output value based on the second key text analysis; The video data comprehensive analysis module obtains a comprehensive text output value based on multiple text output values ​​in the video data analysis module; The video data comprehensive analysis module is used to receive multiple text output values ​​transmitted by the video data analysis module, and by establishing a video data comprehensive analysis model, the multiple text output values ​​are imported into the model to obtain a comprehensive text output value. The specific analysis formula of the comprehensive text output value is: ， Where n represents the total number of divided video frames, ω i represents the video quality coefficient of the i-th video frame, γ i represents the video stabilization coefficient of the i-th video frame, λ1 and λ2 represent the influencing factors of the video quality coefficient and the video stabilization coefficient, respectively; The video data judgment module makes a good judgment on the video based on the comprehensive text output value and issues an early warning on the judgment result; The human-computer interaction module outputs information to the user information terminal based on the judgment result.

2. The video data editing and analysis system based on artificial intelligence and big data according to claim 1, characterized in that: The data text of the video frame division module is the target video. The target video is screened and stored, and the screened video frames are feature text data, which contain complete image data and are used to define the beginning and end of key scenes or actions in the video sequence.

3. The video data editing and analysis system based on artificial intelligence and big data according to claim 1, characterized in that: The user information terminal is bound to a mobile phone or any output-capable electronic device based on the administrator's work information.

4. The video data editing and analysis system based on artificial intelligence and big data according to claim 1, characterized in that: The first key text is video quality data and video stability data. The video quality data includes video picture data and video color data. The video picture data includes the bit rate, horizontal pixels and vertical pixels of each video frame, which are denoted as B, H and V respectively, and the total video duration is denoted as T; the video color data includes the number of grayscale bytes, the number of chromaticity bytes and the number of concentration bytes of each video frame, which are denoted as Cy, Cb and Cr respectively.

5. The video data editing and analysis system based on artificial intelligence and big data according to claim 4 is characterized in that: The video stabilization data includes optical flow data and feature point data. The optical flow data includes the speed of the optical flow on the x-axis and the speed of the optical flow on the y-axis, which are denoted as u and v respectively. The feature point data includes the feature point extraction distance and the feature point motion loss rate, which are denoted as Wd and Wl respectively. The video picture data, video color data, optical flow data and feature point data are integrated into the first key text and imported into the video data preprocessing model.

6. The video data editing and analysis system based on artificial intelligence and big data according to claim 1, characterized in that: The video data analysis module establishes a video data analysis model, imports the second key text transmitted by the video data preprocessing module into the video data analysis model, and obtains any text output value, the text output value includes a video quality coefficient and a video stability coefficient, and the specific analysis formula of the video quality coefficient is: ， Where P' represents the stable frame rate of the video, R' i represents the stable value of the resolution of the i-th video frame, B´ represents the bit rate fluctuation value of the video, ζ i represents the relative color value of the i-th video frame, ω i represents the video quality coefficient of the i-th video frame; the specific analysis formula of the video stability coefficient is: ， where γ 1i represents the optical flow velocity fluctuation value of the i-th video frame, γ 2i represents the relative change value of the feature point of the i-th video frame, γ i Represents the video stability coefficient of the i-th video frame. Multiple text values ​​are integrated and imported into the video data comprehensive analysis module.

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