Video data statistical method and device and computer equipment

Abstract

The invention provides a video data statistical method and device and computer equipment, and the method comprises the steps: obtaining a playing link for playing a video, and obtaining the number of times that the playing link is clicked in a preset time period; obtaining the number of times of playing the video after the user enters the playing link, and the time when the video is played each time and the data caching amount; inputting the number of times of clicking the playing link, the number of times of playing the video, the time of playing the video each time and the data caching amount into a preset popularity model to predict popularity statistics of the video; based on big data analysis, acquiring a popularity statistical sequence of online videos of the same type as the video; and counting the current popularity sequence of the video based on the popularity statistics of the video and the popularity statistical sequence. According to the method, the popularity of the video is analyzed based on multiple dimensions, and the accuracy of video popularity analysis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a statistical method, device and computer equipment for video data. Background Art

[0002] With the popularization of Internet technology and smart terminals, more and more users are watching online videos. For video providers, it is very important to understand the viewing habits, preferences and interests of viewers in order to better meet their needs and increase viewing rates and revenue.

[0003] In the era of big data, the data base is huge, and the huge amount of data can be used for various data analysis and comparison. At present, when analyzing the popularity of videos based on big data, it is usually a simple heat analysis based on the number of video views. Although this method can perform heat analysis from a single dimension, the reference dimension is not comprehensive and cannot fully represent the popularity of the video. Summary of the invention

[0004] The main purpose of the present invention is to provide a statistical method, device and computer equipment for video data, aiming to overcome the defect of incomplete reference dimensions when performing thermal analysis on videos.

[0005] To achieve the above object, the present invention provides a video data statistics method, comprising the following steps: Obtain a playback link for playing a video, and obtain the number of times the playback link is clicked within a preset time period; Obtain the number of times the user plays the video after entering the play link, as well as the time and data cache amount of each video play; Input the number of times the play link is clicked, the number of times the video is played, the time each time the video is played, and the amount of data cache into a preset heat model to predict heat statistics of the video; Based on big data analysis, the popularity statistics and rankings of online videos of the same type as the video are obtained; based on the popularity statistics of the video and the popularity statistics and rankings, the current popularity ranking of the video is counted.

[0006] Furthermore, before the step of inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict the heat statistics of the video, the step includes: Obtain the playback platform to which the playback link belongs; Based on the playback platform to which the playback link belongs, a corresponding model parameter set is matched in a database; wherein the database pre-stores a mapping relationship between the playback platform and the model parameter set, and the model parameter set includes a plurality of model parameters; A preset heat model is obtained, and model parameters of the preset heat model are updated based on the model parameter set; wherein the model parameters in the model parameter set are optimal model parameters obtained through pre-training.

[0007] Furthermore, the step of calculating the current heat ranking of the video based on the heat statistics of the video and the heat statistics ranking includes: Calculate a first heat value of the video based on heat statistics of the video; Obtaining a second heat value of each video in the heat statistical sorting; Based on the first heat value and the second heat value, counting the current heat ranking of the video; The heat statistics of the video include multiple heat levels and the probability corresponding to each heat level; The step of calculating the first heat value of the video based on the heat statistics of the video includes: Obtaining the heat characters corresponding to each of the heat levels; the heat characters are the pinyin characters corresponding to the heat levels; Matching the heat scores corresponding to the heat characters in a database; wherein the database stores a mapping relationship between each heat character and the heat score; Based on the heat scores corresponding to the heat levels and the probabilities corresponding to the heat levels, a weighted calculation is performed to obtain a first heat value of the video.

[0008] Furthermore, after the step of inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict the heat statistics of the video, it also includes: Based on a preset coding table, the number of times the play link is clicked and the number of times the video is played are encoded to obtain corresponding click count codes and play count codes. For each time the video is played and the data cache amount, create an identification character; add a separator to the identification character, add the time each time the video is played to the identification character and place it before the separator, add the data cache amount each time the video is played to the identification character and place it after the separator, and obtain an identification field corresponding to the time each time the video is played and the data cache amount; Concatenate the identification fields corresponding to the time when the video is played each time and the data cache amount in sequence to obtain a concatenated field; perform hash calculation on the concatenated field based on a hash algorithm to obtain a corresponding hash value; The number of times the play link is clicked and the number of times the video is played are added to a first list in the database; the time and data cache amount of each time the video is played are added to a second list in the database; Generate an encrypted password based on the click count code and the play count code; The second list is encrypted based on the encryption password, and the hash value is stored in a designated field position of the first list.

[0009] Furthermore, the step of generating an encrypted password based on the click count code and the play count code includes: Get the preset encoding-password character mapping table; Based on the code-password character mapping table, each code in the click count code and the play count code is mapped to a password character respectively; According to a preset character selection rule, a plurality of target password characters are selected from the password characters of each encoding mapping; The target password characters are concatenated in sequence, and the obtained concatenated password characters are used as the encrypted password.

[0010] Furthermore, the step of generating an encrypted password based on the click count code and the play count code includes: Obtaining a preset password table; wherein the password table includes a plurality of characters arranged in sequence; Obtain each of the click count code and the play count code in sequence, and remove characters identical to the codes in the click count code and the play count code in the password table; wherein the codes in the click count code and the play count code are all characters, and the characters include numbers and English; The remaining characters in the password table are combined according to the order, and the obtained combined characters are used as the encrypted password.

[0011] The present invention also provides a statistical device for video data, comprising: A first acquisition unit is used to acquire a playback link of a video, and acquire the number of times the playback link is clicked within a preset time period; A second acquisition unit is used to acquire the number of times the user plays the video after entering the play link, as well as the time and data cache amount of each video playback; A prediction unit, used for inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict heat statistics of the video; The statistical unit is used to obtain the heat statistics ranking of online videos of the same type as the video based on big data analysis; based on the heat statistics of the video and the heat statistics ranking, calculate the current heat ranking of the video.

[0012] Furthermore, it also includes: The encoding unit is used to encode the number of times the play link is clicked and the number of times the video is played based on a preset encoding table to obtain a corresponding click count code and a play count code. An identification unit is used to create an identification character for each time the video is played and the data cache amount; add a separator to the identification character, add the time each time the video is played to the identification character and place it before the separator, add the data cache amount each time the video is played to the identification character and place it after the separator, and obtain an identification field corresponding to the time each time the video is played and the data cache amount; A hash unit, used to sequentially concatenate the identification fields corresponding to the time when the video is played each time and the data cache amount to obtain a concatenated field; and perform a hash calculation on the concatenated field based on a hash algorithm to obtain a corresponding hash value; An adding unit, used for adding the number of times the play link is clicked and the number of times the video is played to a first list in the database; and adding the time and data cache amount of each time the video is played to a second list in the database; A generating unit, used for generating an encrypted password based on the click count code and the play count code; An encryption unit is used to encrypt the second list based on the encryption password, and store the hash value in a specified field position of the first list.

[0013] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0014] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0015] The statistical method, device and computer equipment of video data provided by the present invention include: obtaining a play link for playing a video, obtaining the number of times the play link is clicked within a preset time period; obtaining the number of times the video is played after the user enters the play link, as well as the time and data cache amount of each time the video is played; inputting the number of times the play link is clicked, the number of times the video is played, the time and data cache amount of each time the video is played into a preset heat model to predict the heat statistics of the video; based on big data analysis, obtaining the heat statistics ranking of online videos of the same type as the video; based on the heat statistics of the video and the heat statistics ranking, counting the current heat ranking of the video. The present invention analyzes the heat of the video based on multiple dimensions such as the number of times the play link is clicked, the number of times the video is played, the time and data cache amount of each time the video is played, which is beneficial to improving the accuracy of video heat analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of steps of a statistical method for video data in one embodiment of the present invention; Figure 2 is a structural block diagram of a statistical device for video data in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Reference Figure 1 In one embodiment of the present invention, a video data statistics method is provided, comprising the following steps: Step S1, obtaining a play link for playing a video, and obtaining the number of times the play link is clicked within a preset time period; Step S2, obtaining the number of times the user plays the video after entering the play link, as well as the time and data cache amount of each video play; Step S3, inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict the heat statistics of the video; Step S4, based on big data analysis, obtain the heat statistics ranking of online videos of the same type as the video; based on the heat statistics of the video and the heat statistics ranking, calculate the current heat ranking of the video.

[0020] In this embodiment, the above scheme is used to perform heat statistics analysis on videos based on multi-dimensional data, so as to accurately obtain the heat ranking of the videos, and facilitate video recommendations to users based on the heat ranking.

[0021] As described in the above steps S1-S2, in order to perform heat statistics analysis on the video from multiple dimensions, it is necessary to obtain multi-dimensional data of the video, such as the number of times the play link is clicked, the number of times the video is played, the time each time the video is played, and the amount of data cached. Specifically, use a web crawler or API call technology to obtain the play link of the video played on the specified video platform. Then, within a preset time period, use an access log analysis tool to record and count the number of times the play link is clicked. In this embodiment, a unique identifier is added to the above play link to track the user's behavior of entering the play link. Use a monitoring tool to record information such as the start time, end time, number of plays, video size, and data cache amount each time a user watches a video.

[0022] As described in step S3 above, a deep learning network is pre-trained to obtain the preset heat model, and then the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache can be processed based on the preset heat model to predict the corresponding heat statistics, wherein the heat statistics include multiple heat levels and the probability corresponding to each heat level.

[0023] As described in step S4 above, a classification algorithm is used to classify all videos into different types. Other online video data of the same type is obtained, and the heat value statistics corresponding to the online video are obtained. Then, all videos are sorted according to the heat value to obtain the heat statistical ranking of online videos of the same type as the video, that is, the current heat ranking of the video. After obtaining the above current heat ranking, videos can be recommended to users based on the above ranking, so that users can watch videos easily.

[0024] In one embodiment, before step S3 of inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played, and the amount of data cache into a preset heat model to predict the heat statistics of the video, the method includes: Obtain the playback platform to which the playback link belongs; Based on the playback platform to which the playback link belongs, a corresponding model parameter set is matched in a database; wherein the database pre-stores a mapping relationship between the playback platform and the model parameter set, and the model parameter set includes a plurality of model parameters; A preset heat model is obtained, and model parameters of the preset heat model are updated based on the model parameter set; wherein the model parameters in the model parameter set are optimal model parameters obtained through pre-training.

[0025] In this embodiment, different playback platforms play videos in different ways and cache data in different ways. Therefore, for different playback platforms, the number of times the playback link is clicked, the number of times the video is played, the time each time the video is played, and the amount of data cached will have different effects on the video heat statistics. Therefore, for different playback platforms, the corresponding model parameter set is trained in advance; wherein, the model parameter set includes the optimal model parameters corresponding to the playback platform.

[0026] In one embodiment, a specific scheme for counting the current heat ranking is proposed. Specifically, the step S4 of counting the current heat ranking of the video based on the heat statistics of the video and the heat statistics ranking includes: Calculate a first heat value of the video based on heat statistics of the video; Obtaining a second heat value of each video in the heat statistical sorting; Based on the first heat value and the second heat value, counting the current heat ranking of the video; The heat statistics of the video include multiple heat levels and the probability corresponding to each heat level; The step of calculating the first heat value of the video based on the heat statistics of the video includes: Obtaining the heat characters corresponding to each of the heat levels; the heat characters are the pinyin characters corresponding to the heat levels; Matching the heat scores corresponding to the heat characters in a database; wherein the database stores a mapping relationship between each heat character and the heat score; Based on the heat scores corresponding to the heat levels and the probabilities corresponding to the heat levels, a weighted calculation is performed to obtain a first heat value of the video.

[0027] In one embodiment, after the step S3 of inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played, and the amount of data cache into a preset heat model to predict the heat statistics of the video, the step further includes: Based on a preset coding table, the number of times the play link is clicked and the number of times the video is played are encoded to obtain corresponding click count codes and play count codes; specifically, the above coding table can be a Base64 coding table or a custom coding table.

[0028] For each time the video is played and the amount of data cached, create an identification character; add a separator to the identification character, add the time each time the video is played to the identification character and place it before the separator, add the amount of data cached each time the video is played to the identification character and place it after the separator, and obtain an identification field corresponding to the time each time the video is played and the amount of data cached; the above identification characters can be [], {}, etc., and the above identification characters are used to separate and distinguish the time each time the video is played and the amount of data cached; it can be understood that a separator is also provided in the above identification character, and the above separator is used to distinguish the time and data cached amount of the same video being played.

[0029] Concatenate the identification fields corresponding to the time when the video is played each time and the data cache amount in sequence to obtain a concatenated field; perform hash calculation on the concatenated field based on a hash algorithm to obtain a corresponding hash value; The number of times the play link is clicked and the number of times the video is played are added to the first list in the database; the time and data cache amount of all the times the video is played are added to the second list in the database; the time and data cache amount of all the times the video is played refer to the total time and data cache amount of each time the video is played.

[0030] Generate an encrypted password based on the click count code and the play count code; The second list is encrypted based on the encryption password, and the hash value is stored in a designated field position of the first list.

[0031] In this embodiment, the second list is encrypted based on an encryption password, and the hash value is stored in a specified field position of the first list; data encryption can be achieved based on the above encryption password to prevent data leakage; and the above hash value is cross-stored in the first list, which can be used to verify the time when the video is played all times and the data cache amount.

[0032] Specifically, when it is necessary to verify whether the data has been tampered with later, the above encryption password can be obtained to decrypt the second list, and the time and data cache amount of each video played in the second list can be obtained; then, for the time and data cache amount of each video played, an identification character is created; a separator is added to the identification character, the time of each video played is added to the identification character and is located before the separator, and the data cache amount of each video played is added to the identification character and is located after the separator, and the identification field corresponding to the time and data cache amount of each video played is obtained; the identification fields corresponding to the time and data cache amount of each video played are sequentially concatenated to obtain the concatenated fields; the concatenated fields are hashed based on the hash algorithm to obtain the corresponding verification hash value. Then, the hash value stored at the specified field position of the first list is obtained, and the hash value is compared with the verification hash value to determine whether they are consistent. If they are consistent, it is determined that the data has not been tampered with, otherwise it has been tampered with.

[0033] In one embodiment, the step of generating an encrypted password based on the click count code and the play count code includes: Obtain a preset encoding-password character mapping table; the mapping relationship between different encodings and corresponding password characters expressed in the above preset encoding-password character mapping table, wherein both the encoding and the password characters are characters, and the characters include numbers and English.

[0034] Based on the code-password character mapping table, each code in the click count code and the play count code is mapped to a password character respectively; According to the preset character selection rules, multiple target password characters are selected from the password characters of each encoding mapping; the above character selection rules are predefined selection rules, such as selecting characters arranged in the first 6 positions, or selecting characters at specified positions, etc., which will not be repeated here.

[0035] The target password characters are concatenated in sequence, and the obtained concatenated password characters are used as the encrypted password.

[0036] In one embodiment, the step of generating an encrypted password based on the click count code and the play count code includes: Obtaining a preset password table; wherein the password table includes a plurality of characters arranged in sequence; Obtain each of the click count code and the play count code in sequence, and remove characters identical to the codes in the click count code and the play count code in the password table; wherein the codes in the click count code and the play count code are all characters, and the characters include numbers and English; The remaining characters in the password table are combined according to the order, and the obtained combined characters are used as the encrypted password.

[0037] In this embodiment, the password table is pre-set, wherein the password table includes a plurality of characters arranged in sequence, and the characters may be numbers or English characters. Then, characters identical to the codes in the click count code and the play count code are removed from the password table, and the remaining characters are combined in a pre-set sequence to obtain the encrypted password.

[0038] Reference Figure 2 In one embodiment of the present invention, a statistical device for video data is provided, comprising: A first acquisition unit is used to acquire a playback link of a video, and acquire the number of times the playback link is clicked within a preset time period; A second acquisition unit is used to acquire the number of times the user plays the video after entering the play link, as well as the time and data cache amount of each video playback; A prediction unit, used for inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict heat statistics of the video; The statistical unit is used to obtain the heat statistics ranking of online videos of the same type as the video based on big data analysis; based on the heat statistics of the video and the heat statistics ranking, calculate the current heat ranking of the video.

[0039] In one embodiment, it further includes: The encoding unit is used to encode the number of times the play link is clicked and the number of times the video is played based on a preset encoding table to obtain a corresponding click count code and a play count code. An identification unit is used to create an identification character for each time the video is played and the data cache amount; add a separator to the identification character, add the time each time the video is played to the identification character and place it before the separator, add the data cache amount each time the video is played to the identification character and place it after the separator, and obtain an identification field corresponding to the time each time the video is played and the data cache amount; A hash unit, used to sequentially concatenate the identification fields corresponding to the time when the video is played each time and the data cache amount to obtain a concatenated field; and perform a hash calculation on the concatenated field based on a hash algorithm to obtain a corresponding hash value; An adding unit, used for adding the number of times the play link is clicked and the number of times the video is played to a first list in the database; and adding the time and data cache amount of each time the video is played to a second list in the database; A generating unit, used for generating an encrypted password based on the click count code and the play count code; An encryption unit is used to encrypt the second list based on the encryption password, and store the hash value in a specified field position of the first list.

[0040] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0041] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a statistical method for video data is implemented.

[0042] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0043] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, a statistical method for video data is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0044] In summary, the statistical method, device and computer equipment for video data provided in the embodiments of the present invention include: obtaining a play link for playing a video, and obtaining the number of times the play link is clicked within a preset time period; obtaining the number of times the video is played after the user enters the play link, as well as the time and data cache amount of each time the video is played; inputting the number of times the play link is clicked, the number of times the video is played, the time and data cache amount of each time the video is played into a preset heat model to predict the heat statistics of the video; based on big data analysis, obtaining the heat statistics ranking of online videos of the same type as the video; based on the heat statistics of the video and the heat statistics ranking, counting the current heat ranking of the video. The present invention analyzes the heat of the video based on multiple dimensions such as the number of times the play link is clicked, the number of times the video is played, the time and data cache amount of each time the video is played, which is beneficial to improving the accuracy of video heat analysis.

[0045] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0046] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0047] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A statistical method for video data, characterized in that: The following steps are involved: Obtain a playback link for playing a video, and obtain the number of times the playback link is clicked within a preset time period; Obtain the number of times the user plays the video after entering the play link, as well as the time and data cache amount of each video play; Input the number of times the play link is clicked, the number of times the video is played, the time each time the video is played, and the amount of data cache into a preset heat model to predict heat statistics of the video; Based on big data analysis, obtain the popularity statistics and ranking of online videos of the same type as the video; Based on the heat statistics of the video and the heat statistics ranking, the current heat ranking of the video is counted.

2. The video data statistics method according to claim 1, characterized in that: Before the step of inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict the heat statistics of the video, the step includes: Obtain the playback platform to which the playback link belongs; Based on the playback platform to which the playback link belongs, a corresponding model parameter set is matched in a database; wherein the database pre-stores a mapping relationship between the playback platform and the model parameter set, and the model parameter set includes a plurality of model parameters; A preset heat model is obtained, and model parameters of the preset heat model are updated based on the model parameter set; wherein the model parameters in the model parameter set are optimal model parameters obtained through pre-training.

3. The video data statistics method according to claim 1, characterized in that: The step of calculating the current heat ranking of the video based on the heat statistics of the video and the heat statistics ranking includes: Calculate a first heat value of the video based on heat statistics of the video; Obtaining a second heat value of each video in the heat statistical sorting; Based on the first heat value and the second heat value, counting the current heat ranking of the video; The heat statistics of the video include multiple heat levels and the probability corresponding to each heat level; The step of calculating the first heat value of the video based on the heat statistics of the video includes: Obtaining the heat characters corresponding to each of the heat levels; the heat characters are the pinyin characters corresponding to the heat levels; Matching the heat scores corresponding to the heat characters in a database; wherein the database stores a mapping relationship between each heat character and the heat score; Based on the heat scores corresponding to the heat levels and the probabilities corresponding to the heat levels, a weighted calculation is performed to obtain a first heat value of the video.

4. The video data statistics method according to claim 1, characterized in that: After the step of inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict the heat statistics of the video, the step further includes: Based on a preset coding table, the number of times the play link is clicked and the number of times the video is played are encoded to obtain corresponding click count codes and play count codes. For each time the video is played and the data cache amount, create an identification character; add a separator to the identification character, add the time each time the video is played to the identification character and place it before the separator, add the data cache amount each time the video is played to the identification character and place it after the separator, and obtain an identification field corresponding to the time each time the video is played and the data cache amount; Concatenate the identification fields corresponding to the time when the video is played each time and the data cache amount in sequence to obtain a concatenated field; perform hash calculation on the concatenated field based on a hash algorithm to obtain a corresponding hash value; The number of times the play link is clicked and the number of times the video is played are added to a first list in the database; the time and data cache amount of each time the video is played are added to a second list in the database; Generate an encrypted password based on the click count code and the play count code; The second list is encrypted based on the encryption password, and the hash value is stored in a designated field position of the first list.

5. The video data statistics method according to claim 4, characterized in that: The step of generating an encrypted password based on the click count code and the play count code comprises: Get the preset encoding-password character mapping table; Based on the code-password character mapping table, each code in the click count code and the play count code is mapped to a password character respectively; According to a preset character selection rule, a plurality of target password characters are selected from the password characters of each encoding mapping; The target password characters are concatenated in sequence, and the obtained concatenated password characters are used as the encrypted password.

6. The video data statistics method according to claim 4, characterized in that: The step of generating an encrypted password based on the click count code and the play count code comprises: Obtaining a preset password table; wherein the password table includes a plurality of characters arranged in sequence; Obtain each of the click count code and the play count code in sequence, and remove characters identical to the codes in the click count code and the play count code in the password table; wherein the codes in the click count code and the play count code are all characters, and the characters include numbers and English; The remaining characters in the password table are combined in order, and the obtained combined characters are used as the encrypted password.

7. A statistical device for video data, characterized in that: include: A first acquisition unit is used to acquire a playback link of a video, and acquire the number of times the playback link is clicked within a preset time period; A second acquisition unit is used to acquire the number of times the user plays the video after entering the play link, as well as the time and data cache amount of each video play; A prediction unit, used for inputting the number of times the play link is clicked, the number of times the video is played, the time each time the video is played and the amount of data cache into a preset heat model to predict heat statistics of the video; A statistical unit, used for obtaining a popularity statistical ranking of online videos of the same type as the video based on big data analysis; Based on the heat statistics of the video and the heat statistics ranking, the current heat ranking of the video is counted.

8. The video data statistics device according to claim 7, characterized in that: Also includes: The encoding unit is used to encode the number of times the play link is clicked and the number of times the video is played based on a preset encoding table to obtain a corresponding click count code and a play count code. An identification unit is used to create an identification character for each time the video is played and the data cache amount; add a separator to the identification character, add the time each time the video is played to the identification character and place it before the separator, add the data cache amount each time the video is played to the identification character and place it after the separator, and obtain an identification field corresponding to the time each time the video is played and the data cache amount; A hash unit, used to sequentially concatenate the identification fields corresponding to the time when the video is played each time and the data cache amount to obtain a concatenated field; and perform a hash calculation on the concatenated field based on a hash algorithm to obtain a corresponding hash value; An adding unit, used for adding the number of times the play link is clicked and the number of times the video is played to the first list in the database; Add the time and data cache amount of each time the video is played to a second list in the database; A generating unit, used for generating an encrypted password based on the click count code and the play count code; An encryption unit is used to encrypt the second list based on the encryption password, and store the hash value in a specified field position of the first list.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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