Methods, apparatus and electronic equipment for determining the impact data of video files

Abstract

This application discloses a method, apparatus, and electronic device for determining the impact data of a video file. The method includes: acquiring a client group of the video file and multiple tag data of the client group; determining multiple tag indices based on the multiple tag data, wherein the tag indices characterize the degree of impact of the video file's usage frequency; analyzing the multiple tag indices to obtain analysis results, wherein the analysis results characterize whether the tag indices are normal tag indices; in response to the analysis result indicating that the tag indices are normal tag indices, determining the account percentage of the multiple tag data based on the number of accounts in the client group and the number of accounts for the multiple tag data; and determining the impact data of the video file based on the multiple account percentages and the multiple tag indices. This application solves the technical problem of the inability to effectively determine the impact data of video files.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of video files, in particular, to a video file influence data determination method and device and electronic equipment. BACKGROUND

[0002] In the process of watching a video file, the watching on-demand in the operation behavior of the user remote controller can be analyzed, and then the watching habits and interests of the user can be specifically analyzed, but this way does not analyze the user activity, resulting in the technical problem that the influence data of the video file cannot be effectively determined.

[0003] In view of the above technical problem that the influence data of the video file cannot be effectively determined, no effective solution has been proposed so far. SUMMARY

[0004] The embodiments of the present application provide a video file influence data determination method, device and electronic equipment to at least solve the technical problem that the influence data of the video file cannot be effectively determined.

[0005] According to an aspect of an embodiment of the present application, a video file influence data determination method is provided. The method can include: obtaining a client group of a video file and a plurality of label data of the client group, wherein the label data is data affecting the frequency of use of the video file; determining a plurality of label indices of the plurality of label data based on the plurality of label data, wherein the label index is used to represent the degree of influence of the frequency of use of the video file; analyzing the plurality of label indices to obtain an analysis result of the plurality of label indices, wherein the analysis result is used to represent whether the label index is a normal label index; in response to the analysis result being that the label index is a normal label index, determining an account proportion value of the plurality of label data based on the number of accounts of the client group and the number of accounts of the plurality of label data; and determining the influence data of the video file based on the plurality of account proportion values and the plurality of label indices.

[0006] Optionally, determining the plurality of label indices of the plurality of label data based on the plurality of label data includes: calling a label index function of the label data based on the plurality of label data to obtain the plurality of label indices.

[0007] Optionally, analyzing the plurality of label indices to obtain the analysis result of the plurality of label indices includes: calling a density function of the label index based on a target time to obtain a target mean and a target variance of the plurality of label indices; determining a first target value and a second target value of the plurality of label indices based on the target mean and the target variance, wherein the first target value is less than the second target value; and analyzing the plurality of label indices based on the first target value and the second target value to obtain the analysis result.

[0008] Optionally, based on the first target value and the second target value, the plurality of label indexes are analyzed to obtain an analysis result, including: in response to the label index being greater than or equal to the first target value and less than or equal to the second target value, determining that the analysis result is that the label index is a normal label index; and in response to the label index being less than the first target value or the label index being greater than the second target value, determining that the analysis result is that the label index is an abnormal label index.

[0009] Optionally, based on the plurality of account proportion values and the plurality of label indexes, the influence data of the video file is determined, including: based on the first baseline value of the label index, the plurality of account proportion values and the plurality of label indexes, calling a frequency function of the client group to obtain a target array matrix of the client group; filtering data in the target array matrix to obtain target data of the target array matrix; and based on the target data, determining the influence data of the video file.

[0010] Optionally, based on the target data, the influence data of the video file is determined, including: based on the target account proportion value corresponding to the target data, the target label index corresponding to the target data, the first baseline value and the second baseline value of the account proportion value, determining the influence data.

[0011] Optionally, based on the target account proportion value corresponding to the target data, the target label index corresponding to the target data, the first baseline value and the second baseline value of the account proportion value, the influence data is determined, including: in response to the target account proportion value being greater than the second baseline value and the target label index being greater than the first baseline value, obtaining the influence data.

[0012] Optionally, the method further includes: verifying the influence data to obtain a verification result, wherein the verification result is used to represent whether the influence data is accurate.

[0013] According to another aspect of the embodiments of the present application, a video file influence data determination apparatus is also provided, including: a first obtaining unit, configured to obtain a client group of a video file and a plurality of label data of the client group, wherein the label data is data affecting the frequency of use of the video file; a first determining unit, configured to determine a plurality of label indexes of the plurality of label data based on the plurality of label data, wherein the label index is used to represent the influence degree of the frequency of use of the video file; an analysis unit, configured to analyze the plurality of label indexes to obtain an analysis result of the plurality of label indexes, wherein the analysis result is used to represent whether the label index is a normal label index; a second determining unit, configured to determine an account proportion value of the plurality of label data based on the number of accounts of the client group and the number of accounts of the plurality of label data in response to the analysis result being that the label index is a normal label index; and a third determining unit, configured to determine the influence data of the video file based on the plurality of account proportion values and the plurality of label indexes.

[0014] According to a further aspect of the embodiments of the present application, a non-transitory storage medium is also provided, including a storage medium including a stored program, wherein the program, when executed, controls a device in which the storage medium is located to perform any of the video file influence data determination methods.

[0015] According to a further aspect of the embodiments of the present application, an electronic device is also provided, including a processor, a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement any of the video file influence data determination methods.

[0016] In the embodiments of the present application, a client group of a video file is obtained, and a plurality of tag data of the client group is obtained, wherein the tag data is data influencing the frequency of use of the video file; based on the plurality of tag data, a plurality of tag indexes of the plurality of tag data is determined, wherein the tag index is used to represent the degree of influence of the frequency of use of the video file; the plurality of tag indexes is analyzed to obtain an analysis result of the plurality of tag indexes, wherein the analysis result is used to represent whether the tag index is a normal tag index; in response to the analysis result being that the tag index is a normal tag index, an account proportion value of the plurality of tag data is determined based on the number of accounts of the client group and the number of accounts of the plurality of tag data; and the influence data of the video file is determined based on the plurality of account proportion values and the plurality of tag indexes. That is, in the embodiments, the client group of the video file and the plurality of tag data of the client group can be obtained first, and then based on the plurality of tag data, the plurality of tag indexes of the plurality of tag data can be determined, and then the plurality of tag indexes is analyzed to obtain an analysis result of the plurality of tag indexes, at this time, if the analysis result is that the tag index is a normal tag index, the account proportion value of the plurality of tag data can be determined based on the number of accounts of the client group and the number of accounts of the plurality of tag data, and finally the influence data of the video file can be determined based on the plurality of account proportion values and the plurality of tag indexes obtained above. Since it is considered that after the obtained tag index is analyzed to obtain an analysis result of the plurality of tag indexes, when the analysis result at this time is that the tag index is a normal tag index, the account proportion value of the plurality of tag data can be determined based on the number of accounts of the client group and the number of accounts of the plurality of tag data, so that the influence data of the video file can be determined, thereby solving the technical problem that the influence data of the video file cannot be effectively determined, and achieving the technical effect that the influence data of the video file can be effectively determined. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and serve to explain the present application. In the drawings:

[0018] Figure 1This is a flowchart of a method for determining the impact data of a video file according to an embodiment of this application;

[0019] Figure 2 This is a flowchart of a method for analyzing the causes of low user activity in online videos according to an embodiment of this application;

[0020] Figure 3 This is a flowchart illustrating the generation of an analysis report according to an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of a video file impact data determination device according to an embodiment of this application;

[0022] Figure 5 This is a schematic diagram of an example electronic device used to implement embodiments of this application, according to an embodiment of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] To facilitate a better understanding by those skilled in the art, the technical terms or some nouns that may be involved in this application are explained below in the relevant embodiments:

[0026] The Group and Time Network Video User Activity Model (GT-NVAM) analyzes the multi-attribute label index of different customer groups to identify those whose label index exceeds the warning baseline and whose number of users with that characteristic attribute also exceeds the warning baseline. This model then outputs the factors influencing low network video user activity.

[0027] Customer groups: User tags are used to generate customer groups through combinations of AND, OR, and NOT.

[0028] According to an embodiment of this application, an embodiment of a method for determining the impact data of a video file is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a method for determining the impact data of a video file according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Obtain the client group of the video file and multiple tag data of the client group.

[0031] In the technical solution provided in step S102 of this application, the client group of the video file and multiple tag data of the client group can be obtained. The tag data refers to data that affects the frequency of use of the video file.

[0032] Optionally, the client group can be referred to as the user group or user customer base, and the video file can be referred to as online video or simply video. Tag data can be referred to as user tag data or user characteristic attribute tags.

[0033] Optionally, a client-side analysis system can be used to obtain data on the client-side users and multiple tags associated with those users for the video file. This analysis system can be referred to as a user experience analysis system. For example, the user-side analysis system's interface can be used to obtain data on the user-side users and multiple user tags associated with them.

[0034] Furthermore, user groups can be pre-categorized, such as low-activity customers, high-activity customers, customers with poor service experience, and high-star customer groups. Tag data can be used to characterize the reasons for low activity in different customer groups. For example, it can be categorized and listed as user characteristic attribute tags such as basic customer information, terminal information, behavioral preferences, and user experience.

[0035] For example, through the interface of the user experience analysis system, the user group of online video can be obtained. If the user group of online video is a low-activity customer group, the tag data of the user group can be obtained, including basic customer information, terminal information, behavioral preferences, and user experience data.

[0036] It should be noted that this is only a preferred implementation of obtaining the client group of video files and multiple tag data of the client group. The process and method of obtaining the client group of video files and multiple tag data of the client group are not specifically limited. As long as the process and method of obtaining the client group of video files and multiple tag data of the client group are achieved through the user's analysis system, they are all within the protection scope of this application and will not be listed here.

[0037] Step S104: Based on multiple label data, determine multiple label indices for the multiple label data.

[0038] In the technical solution provided by step S104 of this application, after obtaining multiple tag data, it is possible to determine multiple tag indices of the multiple tag data, wherein the tag index is used to characterize the degree of influence of the frequency of use of video files.

[0039] Optionally, based on the obtained multiple tag data, a tag index algorithm is used to obtain multiple tag indices for the multiple tag data. Among them, the tag index (TargetIndex, abbreviated as TIndex) can be formed by user tags such as customer basic information, terminal information, behavioral preferences, and customer experience, to form the preference for a certain tag in a certain customer group.

[0040] It is understood that this is only a preferred embodiment for determining multiple label indices of multiple label data, and the process and method for determining multiple label indices of multiple label data are not specifically limited. As long as the process and method for determining multiple label indices of multiple label data are based on multiple label data, they are within the protection scope of this application, and will not be elaborated here.

[0041] Step S106: Analyze multiple label indices to obtain the analysis results of multiple label indices.

[0042] In the technical solution provided in step S106 of this application, based on the multiple label indices obtained in the above steps, a relative density algorithm can be used to analyze the multiple label indices in order to obtain the analysis results of the multiple label indices. The analysis results are used to characterize whether the label indices are normal label indices. The relative density algorithm is implemented using statistical methods.

[0043] Furthermore, when using the relative density algorithm to analyze multiple label indices, the distribution of label indices under each customer group can be used to select relevant data that are considered normal label indices based on the analysis results.

[0044] Step S108: In response to the analysis result that the tag index is a normal tag index, determine the account ratio of multiple tag data based on the number of accounts in the client group and the number of accounts for multiple tag data.

[0045] In the technical solution provided in step S108 of this application, after obtaining the analysis results of multiple tag indices, if the analysis result indicates that the tag index is a normal tag index, the account ratio of multiple tag data can be determined based on the number of accounts in the client group and the number of accounts for multiple tag data. The number of accounts in the client group can be the number of users in the client group, for example, 1000. The number of accounts for the tag data can be the number of users categorized under that tag, for example, 160.

[0046] Optionally, the ratio of the number of accounts in the client group to the number of accounts in the tag data can be determined as the account percentage of that tag data. For example, when the client group is a low-activity customer group with 1000 users, and the tag data is "likes educational programs" among video type preference tags, then the number of users who like educational programs among video type preference tags is 160, and the account percentage of that tag data is 160 / 1000. Therefore, based on this low-activity customer group, the calculation method for other tag data is the same as described above, and will not be exemplified here.

[0047] It should be noted that this is only a preferred implementation method for determining the account proportion of multiple tag data. The process and method for determining the account proportion of multiple tag data are not specifically limited. As long as the analysis result is that the tag index is a normal tag index, the process and method for determining the account proportion of multiple tag data based on the number of accounts in the client group and the number of accounts in multiple tag data are within the protection scope of this application, and will not be elaborated here.

[0048] Step S110: Determine the impact data of the video file based on multiple account percentages and multiple tag indices.

[0049] In the technical solution provided by step S110 of this application, after obtaining multiple account percentage values ​​and multiple tag indices, it is possible to determine the impact data of the video file. This impact data can be referred to as the influencing factor leading to low activity levels among the client group, the low activity factor, or the key factor.

[0050] For example, for a customer group with poor service experience, based on the proportion of multiple accounts and multiple tag indices of this customer group, it can be determined that the impact data of online video is the customer experience rating of poor customer experience in customer experience, which leads to low user activity in this customer group, and the impact data of poor customer experience is the main influencing factor of this customer group.

[0051] In this embodiment, the client group of the video file and multiple tag data of the client group can be obtained first. Then, based on the multiple tag data, multiple tag indices can be determined. These multiple tag indices are then analyzed to obtain analysis results. If the analysis result indicates that the tag index is a normal tag index, the account percentage of each tag can be determined based on the number of accounts in the client group and the number of accounts in the multiple tag data. Finally, based on the obtained account percentages and multiple tag indices, the impact data of the video file can be determined. Considering that after analyzing the obtained tag indices and obtaining the analysis results, if the analysis result indicates that the tag index is a normal tag index, the account percentage of each tag can be determined based on the number of accounts in the client group and the number of accounts in the multiple tag data, thereby determining the impact data of the video file. This solves the technical problem of not being able to effectively determine the impact data of video files and achieves the technical effect of effectively determining the impact data of video files.

[0052] In some embodiments of this application, determining multiple label indices based on multiple label data includes: calling a label index function for the label data to obtain multiple label indices.

[0053] In this embodiment, after obtaining multiple tag data, the tag index function of the tag data can be called to obtain multiple tag indices. The tag index function can be a function constructed by the tag index algorithm.

[0054] Optionally, the tag index algorithm is executed as follows: If the number of customers in a customer group is P, then the number of users with multiple tag feature attributes in that customer group is A1...Ai; the number of viewers in the entire province is T, and the number of users with the same tag and feature attribute among the viewers in the entire province is B1...Bi. Then the tag index can be expressed by the following formula:

[0055] TIndex=[card(A 1...∩Ai) / card(P)] / [card(B 1...∩Bi) / card(T)]×100.

[0056] Here, `card()` represents a predefined data processing function. For example, if there are 1000 low-activity users, and 160 users in the film type preference tag like "educational programs," and the total number of viewers in the province is 200,000, with 31,000 users in the province liking "educational programs," then the tag index can be calculated using the following formula:

[0057] TIndex=(160 / 1000) / (31000 / 200000)*100=103.25%.

[0058] In some optional embodiments of this application, multiple label indices are analyzed to obtain analysis results for the multiple label indices, including: based on a target time, calling the density function of the label indices to obtain the target mean and target variance of the multiple label indices; based on the target mean and target variance, determining a first target value and a second target value for the multiple label indices, wherein the first target value is less than the second target value; and based on the first target value and the second target value, analyzing the multiple label indices to obtain analysis results.

[0059] In this embodiment, the density function of the tag index can be called first according to the target time to obtain the target mean and target variance of multiple tag indices. Then, based on the target mean and target variance obtained above, the first target value and the second target value of multiple tag indices can be determined. Finally, based on the first target value and the second target value, the multiple tag indices can be analyzed to obtain the analysis results.

[0060] Optionally, the target time can be a preset time period, such as a time interval n. The target mean can be simply referred to as the mean and is represented by u. The target variance can be called the standard deviation and is represented by σ. The first target value can be the minimum value, which can be represented by Mi, and Mi = u - 3σ. The second target value can be the maximum value, which can be represented by Ma, and Ma = u + 3σ.

[0061] Optionally, the density function can be a function established using a relative density algorithm. The relative density algorithm identifies abnormal and normal label indices based on the distribution of label indices for each customer group within a time frame.

[0062] For example, within a time interval n, the label index for a customer group is x. i Call the density function, and the density function is as follows:

[0063]

[0064] And according to the following formula:

[0065]

[0066] The mean u and standard deviation σ of the customer group time samples can be calculated. Then, a function is called to iterate through the customer group time samples to obtain the minimum value Mi = u - 3σ and the maximum value Ma = u + 3σ. Based on the obtained maximum and minimum values, multiple label indices can be analyzed to obtain the analysis results.

[0067] In some optional embodiments of this application, multiple label indices are analyzed based on a first target value and a second target value to obtain analysis results, including: in response to a label index being greater than or equal to the first target value and less than or equal to the second target value, determining the analysis result as a normal label index; in response to a label index being less than the first target value or a label index being greater than the second target value, determining the analysis result as an abnormal label index.

[0068] In this embodiment, after obtaining the first target value and the second target value, if the label index is greater than or equal to the first target value and less than or equal to the second target value, the analysis result at this time can be determined as a normal label index; if the label index is less than the first target value or the label index is greater than the second target value, the analysis result at this time can be determined as an abnormal label index.

[0069] For example, after obtaining the label index, as well as its minimum and maximum values, a data range can be formed by the minimum and maximum values. Then, it can be determined whether the obtained label index falls within this range. If it does, it indicates that the label index is a normal label index; if it does not fall within the range, it is determined that it is not an abnormal label index.

[0070] In some embodiments of this application, the impact data of a video file is determined based on multiple account percentage values ​​and multiple tag indices, including: calling a frequency function of the client group based on a first baseline value of the tag indices, multiple account percentage values, and multiple tag indices to obtain a target array matrix of the client group; filtering the data in the target array matrix to obtain target data of the target array matrix; and determining the impact data of the video file based on the target data.

[0071] In this embodiment, based on the first baseline value of the tag index, multiple account percentage values, and multiple tag indices, the frequency function of the client group is first invoked, and a two-dimensional analysis algorithm of the client group and time is used to obtain the target array matrix of the client group. Then, the data in the target array matrix is ​​filtered to obtain the target data of the target array matrix. Finally, based on the target data obtained above, the purpose of determining the impact data of the video file is achieved. The target array matrix can be called the tag three-dimensional array matrix. The frequency function can be called the network video user activity function.

[0072] Table 1. Three-dimensional array matrix of tags for popular movies and TV shows among highly active customer groups in April.

[0073]

[0074] Optionally, a first baseline value for the label index is determined based on the target mean, target variance, first target value, and second target value. The first baseline value for the label index can be referred to as the label index baseline value, which can be represented by β. That is, for customer time sample data from multiple time periods, multiple sets of density data are collected, and the label index baseline value β = max(Mi / avg(u), Ma / avg(u)) is calculated.

[0075] Optionally, the tag indices of customer groups such as low-activity customers, high-activity customers, high-star customers, and customers with poor service experience are analyzed over a period of time to identify the causes of low activity. Based on the tag indices, a two-dimensional analysis algorithm of customer groups and time is introduced to analyze the two-dimensional tag indices of customer groups and time. The time dimension t = 1, 2, 3, 4, 5… (t>30), the customer group dimension k1, k2, k3…, the tags corresponding to the customer group n1, n2, n3…, the percentage of users p = p1, p2, p3…, and the corresponding tag index g = g1, g2, g3….

[0076] Furthermore, the three-dimensional array matrix of tags whose absolute error exceeds the tag index baseline value β is calculated using the following formula:

[0077]

[0078] Table 1 is a three-dimensional array matrix of tags for popular movies and TV shows among highly active customer groups in April. As shown in Table 1, the user percentage (p) and tag index (g) for each day in April can be obtained. For example, the tag index for April 1st is 106.71%, and the user percentage is 12.78%.

[0079] In some optional embodiments of this application, the impact data of the video file is determined based on the target data, including: determining the impact data based on the target account percentage value corresponding to the target data, the target tag index corresponding to the target data, the first baseline value, and the second baseline value of the account percentage value.

[0080] In this embodiment, the influencing data can be determined based on the target account percentage value corresponding to the obtained target data, the target tag index corresponding to the target data, the first baseline value, and the second baseline value of the account percentage value.

[0081] Optionally, the second baseline value of the account percentage can be called the second baseline value of the user percentage, which can be obtained through the tag index algorithm and can be represented by α, i.e.: α = min(A i ÷P i )∩A i ≥100.

[0082] Optionally, for the three-dimensional array matrix of tags, if the percentage of users with the same tag is lower than the baseline value α of the percentage of users with the same tag for the total number of days, the tag is considered a non-critical factor.

[0083] In some optional embodiments of this application, the impact data is determined based on the target account percentage value corresponding to the target data, the target tag index corresponding to the target data, the first baseline value, and the second baseline value of the account percentage value, including: obtaining the impact data in response to the target account percentage value being greater than the second baseline value and the target tag index being greater than the first baseline value.

[0084] In this embodiment, after obtaining the target account percentage value corresponding to the target data, the target tag index corresponding to the target data, the first baseline value, and the second baseline value of the account percentage value, if the target account percentage value is greater than the second baseline value and the target tag index is greater than the first baseline value, then the influencing data can be obtained. The influencing data can also be referred to as influencing factors.

[0085] For example, if the percentage of users in the selected tag data is greater than the baseline α, and the corresponding tag index is greater than the warning baseline β, then the tag data at this time can be determined as the final influencing factor. In other words, tags that meet the above conditions are influencing factors.

[0086] For example, Table 2 shows the influencing factors for different customer groups on April 1st. As shown in Table 2, the user percentage and tag index of the influencing factors for different customer groups are obtained. For the low-activity customer group, the tag data is "poor customer experience," with a tag index of 132.71% and a user percentage of 12.78%. For the high-activity customer group, the tag data is "popular drama enthusiast," with a tag index of 112.32% and a user percentage of 11.56%. For the customer group with poor service experience, the tag data is "live stream buffering," with a tag index of 113.43% and a user percentage of 64.68%. For the high-star user group, the tag data is "popular drama enthusiast," with a tag index of 111.12% and a user percentage of 10.12%.

[0087] Table 2. Factors influencing user groups

[0088]

[0089] In some optional embodiments of this application, the method further includes: verifying the impact data to obtain a verification result, wherein the verification result is used to characterize whether the impact data is accurate.

[0090] In this embodiment, after obtaining the impact data of the video file, the impact data can be verified to obtain a verification result that characterizes whether the impact data is accurate.

[0091] For example, on April 1st and 2nd, 100 users were randomly selected each day for telephone follow-up verification. The actual influencing factors were identified from customer service experience, popular movie updates, video buffering, etc. The actual influencing factors were compared with the impact data obtained from this solution. If they are consistent, it means that the impact data obtained is accurate; if they are inconsistent, it means that the impact data obtained is inaccurate.

[0092] Furthermore, Table 3 is a comparison table of actual influencing factors and influencing data. As shown in Table 3, for the tag data: poor customer service experience, the number of failed follow-up visits is 4, the number of low activity factors that meet the criteria is 55, and the number of low activity factors that do not meet the criteria is 4. For the tag data: untimely updates of popular movies, the number of failed follow-up visits is 3, the number of low activity factors that meet the criteria is 79, and the number of low activity factors that do not meet the criteria is 4. For the tag data: video buffering, the number of failed follow-up visits is 3, the number of low activity factors that meet the criteria is 46, and the number of low activity factors that do not meet the criteria is 2.

[0093] Table 3 Comparison of Actual Impact Factors and Impact Data

[0094]

[0095] In this embodiment, the client group of the video file and multiple tag data of the client group can be obtained first. Then, based on the multiple tag data, multiple tag indices can be determined. These multiple tag indices are then analyzed to obtain analysis results. If the analysis result indicates that the tag index is a normal tag index, the account percentage of each tag can be determined based on the number of accounts in the client group and the number of accounts in the multiple tag data. Finally, based on the obtained account percentages and multiple tag indices, the impact data of the video file can be determined. Considering that after analyzing the obtained tag indices and obtaining the analysis results, if the analysis result indicates that the tag index is a normal tag index, the account percentage of each tag can be determined based on the number of accounts in the client group and the number of accounts in the multiple tag data, thereby determining the impact data of the video file. This solves the technical problem of not being able to effectively determine the impact data of video files and achieves the technical effect of effectively determining the impact data of video files.

[0096] Currently, during the viewing of video files, it is possible to analyze the user's remote control operation behavior in the viewing on-demand mode, and then specifically analyze the user's viewing habits and interests. However, this method does not analyze user activity, resulting in a technical problem that the impact data of video files cannot be effectively determined.

[0097] To address the aforementioned issues, this application proposes a method for analyzing the factors contributing to low user activity in online videos. This method utilizes a two-dimensional time-based analysis model of online video user activity, GT-NVAM, which allows for the customization of different time periods (such as holidays and periods of trending events). By combining different user groups for combined analysis, the method identifies the differences in the factors contributing to low activity across different user groups. Based on this time-related factor, the influence of disturbances such as holidays and trending events is eliminated, revealing the key factors contributing to low activity. This addresses the technical problem of being unable to effectively determine the impact data of video files, achieving the technical effect of effectively determining the impact data of video files.

[0098] In the embodiments of this application, time and multiple customer groups can be selected, and based on tag data, a low-activity multi-tag index analysis model can be constructed to output tags with significant low-activity impact and their corresponding characteristic attributes, as well as an analysis report, thereby improving user stickiness and product value. This solves the technical problem of being unable to effectively determine the impact data of video files and achieves the technical effect of being able to effectively determine the impact data of video files.

[0099] To facilitate a better understanding of the technical solutions of this application by those skilled in the art, a specific embodiment will now be described.

[0100] In the embodiments of this application, Figure 2 This is a flowchart of a method for analyzing the causes of low user activity in online videos according to an embodiment of this application. The method includes the following steps:

[0101] Step S201: Obtain user tag data and user customer groups.

[0102] In this embodiment, user tag data can be acquired, such as basic customer information, terminal information, behavioral preferences, and user characteristic attribute tags such as user experience. User groups can be acquired, such as low-activity groups, high-activity groups, groups with poor service experience, and high-star user groups.

[0103] Step S202: Obtain the user percentage and tag index of the tag data.

[0104] In this embodiment, based on the obtained multiple tag data, a tag index algorithm is used to obtain multiple tag indices for the multiple tag data, and then the user proportion value of the tag data can be determined based on the tag index.

[0105] Optionally, the tag index algorithm is executed as follows: If the number of customers in a customer group is P, then the number of users with multiple tag feature attributes in that customer group is A1...Ai; the number of viewers in the entire province is T, and the number of users with the same tag and feature attribute among the viewers in the entire province is B1...Bi. Then the tag index can be expressed by the following formula:

[0106] TIndex=[card(A 1...∩Ai) / card(P)] / [card(B 1...∩Bi) / card(T)]×100.

[0107] Here, `card()` represents a predefined data processing function. For example, if there are 1000 low-activity users, and 160 users in the film type preference tag like "educational programs," and the total number of viewers in the province is 200,000, with 31,000 users in the province liking "educational programs," then the tag index can be calculated using the following formula:

[0108] TIndex=(160 / 1000) / (31000 / 200000)*100=103.25%.

[0109] Optionally, based on the above formula, a tag index can be obtained, which can then be used to determine the user proportion of the tag data.

[0110] Step S203: Determine whether the label index is a normal label index.

[0111] In this embodiment, the relative density algorithm is used to analyze multiple label indices to determine whether the label index is a normal label index. If the label index is a normal label index, then step S204 is executed; if the label index is an abnormal label index, then the label index needs to be obtained again, i.e., step S202 is executed.

[0112] Optionally, the relative density algorithm can be used to identify abnormal and normal label indices based on the distribution of label indices for each customer group within a time frame.

[0113] For example, within a time interval n, the label index for a customer group is x. i Call the density function, and the density function is as follows:

[0114]

[0115] And according to the following formula:

[0116]

[0117] The mean u and standard deviation σ of the customer group's time samples can be calculated. Then, a function is called to iterate through the customer group's time samples to obtain the minimum value Mi = u - 3σ and the maximum value Ma = u + 3σ. It is then determined whether the obtained label index falls within the interval [Mi, Ma]. If it does not exist, it is judged as an abnormal label index; if it exists, it is judged as a normal label index.

[0118] Optionally, multiple sets of density data are collected, and the baseline value of the tag index β = max(Mi / avg(u), Ma / avg(u)) is calculated. For tag numbers within a reasonable range, the baseline value of the user proportion α = min(A) is obtained through the tag index algorithm. i ÷P i )∩A i ≥100.

[0119] Step S204: Determine the influencing factors of the video.

[0120] In this embodiment, after obtaining a label index that is a normal label index, the influencing factors of online videos can be determined.

[0121] Optionally, the tag indices of customer groups such as low-activity customers, high-activity customers, high-star customers, and customers with poor service experience are analyzed over a period of time to identify the causes of low activity. Based on the tag indices, a two-dimensional analysis algorithm of customer groups and time is introduced to analyze the two-dimensional tag indices of customer groups and time. The time dimension t = 1, 2, 3, 4, 5… (t>30), the customer group dimension k1, k2, k3…, the tags corresponding to the customer group n1, n2, n3…, the percentage of users p = p1, p2, p3…, and the corresponding tag index g = g1, g2, g3….

[0122] Furthermore, the three-dimensional array matrix of tags whose absolute error exceeds the tag index baseline value β is calculated using the following formula:

[0123]

[0124] Optionally, based on the above formula, the user percentage and tag index of a certain customer group can be obtained, as shown in Table 1, which will not be elaborated here.

[0125] Optionally, for the three-dimensional array matrix of tags, if the percentage of users with the same tag is lower than the baseline value α of the percentage of users with the same tag for the total number of days, the tag is considered a non-critical factor.

[0126] Optionally, for example, if the user percentage of the selected tag data is greater than the baseline α, and its corresponding tag index is greater than the warning baseline β, then the tag data at this time can be determined as the final influencing factor. That is to say, the tags that meet the above conditions are the influencing factors, and the influencing factors of different customer groups are shown in Table 2, which will not be listed here.

[0127] Step S205: Verify the influencing factors.

[0128] In this embodiment, after obtaining the influencing factors, these factors can be verified. For example, 100 users are randomly selected each day from April 1st to 2nd for telephone follow-up verification. Actual influencing factors are identified from aspects such as customer service experience, trending movie updates, and video buffering. These actual influencing factors are then compared with the influence data obtained in this solution. If they match, the obtained influence data is accurate; if they do not match, the obtained influence data is inaccurate. It should be noted that Table 3 shows the comparison results between the actual influencing factors and the influence data, which will not be elaborated upon here.

[0129] In the embodiments of this application, Figure 3 This is a flowchart of generating an analysis report according to an embodiment of this application. The process mainly includes the following steps:

[0130] Step S301, data acquisition.

[0131] Step S302: Obtain user group and multiple tag data.

[0132] Step S303: Calculate the label index.

[0133] Step S304: Determine the two-dimensional analysis algorithm for time-based customer groups.

[0134] Step S305: Generate an analysis report.

[0135] In this embodiment, the group information, parameter information, and process information of the task can be obtained first. Then, based on the obtained group information, parameter information, and process information, the task dispatch mode can be determined. Then, based on the dispatch mode, the target database of the task can be used to determine the target execution status of the task. Furthermore, the target execution status of the task can be displayed in the task management system to solve the technical problem of not being able to manage tasks in a unified manner and achieve the technical effect of being able to manage tasks in a unified manner.

[0136] Figure 4 This is a schematic diagram of a video file impact data determination device according to an embodiment of this application, such as... Figure 4 As shown, the impact data determination device 400 for the video file includes: a first acquisition unit 401, a first determination unit 402, an analysis unit 403, a second determination unit 404, and a third determination unit 405.

[0137] The first acquisition unit 401 is used to acquire the client group of the video file and multiple tag data of the client group, wherein the tag data is data that affects the frequency of use of the video file.

[0138] The first determining unit 402 is used to determine multiple label indices of multiple label data based on multiple label data, wherein the label index is used to characterize the degree of influence of the frequency of use of video files.

[0139] Analysis unit 403 is used to analyze multiple label indices and obtain analysis results for multiple label indices. The analysis results are used to characterize whether the label indices are normal label indices.

[0140] The second determining unit 404 is used to determine the account ratio of multiple tag data based on the number of accounts in the client group and the number of accounts for multiple tag data in response to the analysis result that the tag index is a normal tag index.

[0141] The third determining unit 405 is used to determine the impact data of the video file based on multiple account percentage values ​​and multiple tag indices.

[0142] Optionally, the first determining unit 402 may include: a first obtaining module, used to call the label index function of the label data based on multiple label data to obtain multiple label indices.

[0143] Optionally, the analysis unit 403 may include: a calling module, used to call the density function of the label index based on the target time to obtain the target mean and target variance of multiple label indices; a first determining module, used to determine a first target value and a second target value of multiple label indices based on the target mean and target variance, wherein the first target value is less than the second target value; and an analysis module, used to analyze the multiple label indices based on the first target value and the second target value to obtain analysis results.

[0144] Optionally, the analysis module may include: a first determining submodule, used to determine that the analysis result is a normal label index in response to the label index being greater than or equal to a first target value and less than or equal to a second target value; and a second determining submodule, used to determine that the analysis result is an abnormal label index in response to the label index being less than the first target value or greater than the second target value.

[0145] Optionally, the third determining unit 405 may include: a second obtaining module, used to call the frequency function of the client group based on the first baseline value of the tag index, multiple account percentage values ​​and multiple tag indices to obtain the target array matrix of the client group; a third obtaining module, used to filter the data in the target array matrix to obtain the target data of the target array matrix; and a second determining module, used to determine the impact data of the video file based on the target data.

[0146] Optionally, the second determining module may include: a third determining submodule, used to determine the influencing data based on the target account percentage value corresponding to the target data, the target tag index corresponding to the target data, the first baseline value, and the second baseline value of the account percentage value.

[0147] Optionally, the third determining submodule is also used to obtain impact data in response to the target account percentage value being greater than the second baseline value and the target tag index being greater than the first baseline value.

[0148] Optionally, the device further includes: a verification unit for verifying the impact data and obtaining a verification result, wherein the verification result is used to characterize whether the impact data is accurate.

[0149] In this device, the first acquisition unit 401 acquires the client group of the video file and multiple tag data of the client group, wherein the tag data is data affecting the usage frequency of the video file; the first determination unit 402 determines multiple tag indices of the multiple tag data based on the multiple tag data, wherein the tag indices are used to characterize the degree of influence of the usage frequency of the video file; the analysis unit 403 analyzes the multiple tag indices to obtain the analysis results of the multiple tag indices, wherein the analysis results are used to characterize whether the tag indices are normal tag indices; the second determination unit 404, in response to the analysis result that the tag indices are normal tag indices, determines the account ratio of the multiple tag data based on the number of accounts in the client group and the number of accounts in the multiple tag data; the third determination unit 405 determines the impact data of the video file based on the multiple account ratios and the multiple tag indices, thereby solving the technical problem of not being able to effectively determine the impact data of the video file and achieving the technical effect of being able to effectively determine the impact data of the video file.

[0150] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute a method for determining the impact data of any video file.

[0151] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0152] The process involves acquiring the client group associated with the video file and multiple tag data for that group, where the tag data represents the frequency of video file usage. Based on this tag data, multiple tag indices are determined, where each tag index characterizes the degree of influence of the video file usage frequency. These tag indices are then analyzed to determine whether they represent normal indices. If the analysis indicates that the tag indices are normal, the account percentages for each tag are determined based on the number of accounts in the client group and the number of accounts associated with each tag. Finally, based on these account percentages and the multiple tag indices, the impact data of the video file is determined.

[0153] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0154] In an exemplary embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the method for determining the impact data of any of the aforementioned video files.

[0155] Optionally, when executed by a processor, the computer program may perform the following steps:

[0156] The process involves acquiring the client group associated with the video file and multiple tag data for that group, where the tag data represents the frequency of video file usage. Based on this tag data, multiple tag indices are determined, where each tag index characterizes the degree of influence of the video file usage frequency. These tag indices are then analyzed to determine whether they represent normal indices. If the analysis indicates that the tag indices are normal, the account percentages for each tag are determined based on the number of accounts in the client group and the number of accounts associated with each tag. Finally, based on these account percentages and the multiple tag indices, the impact data of the video file is determined.

[0157] An electronic device is provided according to an embodiment of this application, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described methods for determining the impact data of a video file.

[0158] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0159] Figure 5This is a schematic diagram of an example electronic device for implementing embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0160] like Figure 5 As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0161] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0162] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the method for processing chained data. For example, in some embodiments, the method for processing chained data may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method for processing chained data described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform a method for processing call chain data by any other suitable means (e.g., by means of firmware).

[0163] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0164] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0165] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0166] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0167] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0168] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0169] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0170] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0175] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining the impact data of a video file, characterized in that, The method comprises the following steps: obtaining a client group of a video file and a plurality of label data of the client group, wherein the label data is data affecting the frequency of use of the video file; determining a plurality of label indexes of the plurality of label data based on the plurality of label data, wherein the label index is used to represent the degree of influence of the frequency of use of the video file; analyzing the plurality of label indexes to obtain an analysis result of the plurality of label indexes, wherein the analysis result is used to represent whether the label index is a normal label index; in response to the analysis result being that the label index is the normal label index, determining an account proportion value of the plurality of label data based on the number of accounts of the client group and the number of accounts of the plurality of label data; determining influence data of the video file based on the plurality of account proportion values and the plurality of label indexes.

2. The method of claim 1, wherein, Determining a plurality of label indexes of a plurality of label data based on the plurality of label data comprises: calling a label index function of the label data based on the plurality of label data to obtain the plurality of label indexes.

3. The method of claim 1, wherein, Analyzing the plurality of label indexes to obtain an analysis result of the plurality of label indexes comprises: calling a density function of the label index based on a target time to obtain a target mean and a target variance of the plurality of label indexes; determining a first target value and a second target value of the plurality of label indexes based on the target mean and the target variance, wherein the first target value is less than the second target value; analyzing the plurality of label indexes based on the first target value and the second target value to obtain the analysis result.

4. The method of claim 3, wherein, Analyzing the plurality of label indexes based on the first target value and the second target value to obtain the analysis result comprises: in response to the label index being greater than or equal to the first target value and less than or equal to the second target value, determining that the analysis result is that the label index is the normal label index; in response to the label index being less than the first target value or the label index being greater than the second target value, determining that the analysis result is that the label index is an abnormal label index.

5. The method of claim 1, wherein, Determining influence data of the video file based on the plurality of account proportion values and the plurality of label indexes comprises: calling a frequency function of the client group based on a first baseline value of the label index, the plurality of account proportion values and the plurality of label indexes to obtain a target array matrix of the client group; filtering data in the target array matrix to obtain target data of the target array matrix; determining the influence data of the video file based on the target data.

6. The method of claim 5, wherein, Determining the influence data of the video file based on the target data comprises: determining the influence data based on a target account proportion value corresponding to the target data, a target label index corresponding to the target data, a first baseline value and a second baseline value of the account proportion value.

7. The method of claim 6, wherein, Determine the influence data based on the target account proportion value corresponding to the target data, the target label index corresponding to the target data, the first baseline value, and a second baseline value of the account proportion value, including: In response to the target account proportion value being greater than the second baseline value and the target label index being greater than the first baseline value, obtain the influence data.

8. The method of claim 1, wherein, The method further includes: Verify the influence data to obtain a verification result, wherein the verification result is used to represent whether the influence data is accurate.

9. A device for determining the impact data of a video file, characterized in that, Including: A first obtaining unit is configured to obtain a client group of a video file and a plurality of label data of the client group, wherein the label data is data that affects the use frequency of the video file; A first determining unit is configured to determine a plurality of label indices of the plurality of label data based on the plurality of label data, wherein the label index is used to represent the influence degree of the use frequency of the video file; An analysis unit is configured to analyze the plurality of label indices to obtain an analysis result of the plurality of label indices, wherein the analysis result is used to represent whether the label index is a normal label index; A second determining unit is configured to determine an account proportion value of the plurality of label data based on the number of accounts of the client group and the number of accounts of the plurality of label data in response to the analysis result being that the label index is the normal label index; A third determining unit is configured to determine the influence data of the video file based on the plurality of account proportion values and the plurality of label indices.

10. An electronic device, comprising: Including: A processor; A memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method for determining the influence data of the video file according to any one of claims 1 to 8.

Images