CN111246294A - Method, device, equipment and storage medium for processing audience rating index data - Google Patents

Method, device, equipment and storage medium for processing audience rating index data Download PDF

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CN111246294A
CN111246294A CN202010010941.9A CN202010010941A CN111246294A CN 111246294 A CN111246294 A CN 111246294A CN 202010010941 A CN202010010941 A CN 202010010941A CN 111246294 A CN111246294 A CN 111246294A
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curve
user
optimized
viewing
program
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郑冠雯
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Planning Institute Of Radio And Television Of State Administration Of Radio And Television
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Planning Institute Of Radio And Television Of State Administration Of Radio And Television
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/44213—Monitoring of end-user related data
    • H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
    • H04N21/25866—Management of end-user data
    • H04N21/25891—Management of end-user data being end-user preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Social Psychology (AREA)
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  • Computer Networks & Wireless Communication (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)

Abstract

The invention discloses a method, a device, equipment and a storage medium for processing audience rating index data. The method comprises the following steps: acquiring the number of effective behaviors to the object to be processed acquired at each acquisition time in a target time period and the number of watching users to the object to be processed corresponding to the acquisition time; the object to be processed comprises a target channel or a target geographical area; determining a curve of the average effective behavior number of the users according to the effective behavior number and the number of watching users; normalizing the number of the user average effective behaviors corresponding to the curve of the number of the user average effective behaviors to obtain a curve of an activity coefficient; acquiring an initial audience rating index curve for an object to be processed in a target time period; the audience rating index curve is a curve reflecting the mapping relation between the initial audience rating index value and the acquisition time; and obtaining an optimized viewing index curve according to the curve of the activity coefficient and the initial viewing index curve.

Description

Method, device, equipment and storage medium for processing audience rating index data
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method for processing audience rating data, an apparatus for processing audience rating data, an electronic device, and a computer-readable storage medium.
Background
Currently, a television set top box stb (settopbox) can broadcast a television program and simultaneously transmit back viewing behavior data of a user, such as a channel being watched by the user, through a bidirectional cable television network. The television operator utilizes the big data platform to perform statistical analysis on the returned viewing behavior data of the user, so as to obtain a viewing index curve and the like, such as a rating curve, of the television program provided by the television operator.
However, since the tv set-top box and the tv set are usually two independent devices, it is a common phenomenon that the tv set-top box is not turned off after the tv is turned off, and the tv set-top box cannot sense the on/off state of the tv set, which results in that the tv set is turned off, that is, the user does not watch programs actually any more, and the tv set-top box still transmits back the viewing behavior data of the user, such as the channel being watched by the user. In this case, the returned viewing behavior data of the user is still included in the calculation of the viewing index curve and the like, so that the viewing index curve and the like obtained by statistical analysis of the big data platform are inaccurate.
Disclosure of Invention
It is an object of the present invention to provide a new solution for processing viewership indicator data.
According to a first aspect of the present invention, there is provided a method of processing viewership indicator data, comprising:
acquiring the number of effective behaviors to the object to be processed acquired at each acquisition time in a target time period and the number of watching users to the object to be processed corresponding to the acquisition time; the object to be processed comprises a target channel or a target geographical area;
determining a curve of the average effective behavior number of the users according to the effective behavior number and the number of the watching users;
normalizing the number of the user average effective behaviors corresponding to the curve of the number of the user average effective behaviors to obtain a curve of an activity coefficient;
acquiring an initial audience rating index curve for the object to be processed in the target time period; the audience rating index curve is a curve reflecting the mapping relation between the initial audience rating index value and the acquisition time;
and obtaining an optimized viewing index curve according to the curve of the activity coefficient and the initial viewing index curve.
Optionally, after determining a curve of the average effective behavior number of users according to the effective behavior number and the number of viewing users, the method further includes:
smoothing the curve of the average number of the user behaviors to obtain a smoothed curve of the average number of the user behaviors;
the normalizing the number of the average user behavior corresponding to the curve of the number of the average user behavior to obtain the curve of the activity coefficient includes:
and normalizing the average number of the effective behaviors of the user corresponding to the smoothed average number of the effective behaviors of the user to obtain a curve of the activity coefficient.
Optionally, in a case that the object to be processed is a target channel and the optimized viewing index curve is a first optimized viewing index curve based on viewing time, the method further includes:
determining the playing time period of each program according to the program list of the target channel;
acquiring a first target optimized viewing index curve corresponding to the playing time period of the corresponding program from the first optimized viewing index curve;
and taking the average index value of all the audience rating index values corresponding to the audience rating curve after the first target optimization as the optimized audience rating index value of the corresponding program.
Optionally, in a case that the object to be processed is a target channel and the optimized viewing index curve is a second optimized viewing index curve based on the number of viewing users, the method further includes:
determining the playing time period of each program according to the program list of the target channel;
acquiring a second target optimized viewing index curve corresponding to the playing time period of the corresponding program from the second optimized viewing index curve;
acquiring initial watching time length corresponding to each acquisition time, optimized watching time length corresponding to the acquisition time, the number of initial watching users corresponding to the acquisition time and the time length of the corresponding program in the playing time period of the corresponding program;
determining the optimized number of the watching users corresponding to the acquisition time according to the initial watching time corresponding to each acquisition time, the optimized watching time corresponding to the corresponding acquisition time, the number of the initial watching users corresponding to the corresponding acquisition time and the time of the corresponding program;
and determining the optimized audience rating index value of the corresponding program according to the optimized number of the watching users at the corresponding acquisition time and the optimized audience rating index curve of the second target.
Optionally, the normalizing the number of user average effective behaviors corresponding to the curve of the number of user average effective behaviors to obtain the curve of the activity coefficient includes:
and normalizing the user average effective behavior number corresponding to the curve of the user average effective behavior number through a set normalization function to obtain a curve of the activity coefficient.
Optionally, the normalization function is: linear piecewise function, piecewise function corresponding to trigonometric function, logarithmic function, and growth curve function.
Optionally, the method further includes: and outputting an analysis result according to the optimized viewing index curve.
Optionally, the audience rating index curve is any one of a viewing duration curve, a viewing user number curve, an audience rating curve, an audience share curve, and an arrival rate curve.
According to a second aspect of the present invention, there is provided an apparatus for processing audience measurement data, comprising:
the first acquisition module is used for acquiring the effective behavior number of the object to be processed acquired at each acquisition time in the target time period and the number of watching users of the object to be processed corresponding to the acquisition time; the object to be processed comprises a target channel or a target geographical area;
the determining module is used for determining a curve of the average effective behavior number of the users according to the effective behavior number and the number of the watching users;
the normalization module is used for performing normalization processing on the user average effective behavior number corresponding to the curve of the user average effective behavior number to obtain a curve of the activity coefficient;
the second acquisition module is used for acquiring an initial audience rating index curve for the object to be processed in the target time period; the audience rating index curve is a curve reflecting the mapping relation between the initial audience rating index value and the acquisition time;
and the optimization module is used for obtaining an optimized viewing index curve according to the curve of the activity coefficient and the initial viewing index curve.
According to a third aspect of the present invention, there is provided an electronic device comprising the apparatus of the second aspect; alternatively, the first and second electrodes may be,
comprising a memory for storing computer instructions and a processor for invoking the computer instructions from the memory for performing the method according to any of the first aspects.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to any one of the first aspects.
In this embodiment, a curve of the number of user-average effective behaviors is determined according to the number of acquired effective behaviors to the object to be processed at each acquisition time in the target time period and the number of users watching the object to be processed at the corresponding acquisition time. Since the average number of user effective behaviors can reflect the activity coefficient representing the activity degree of the user, the activity coefficient curve is obtained based on the average number of user effective behaviors. Therefore, according to the curve of the activity coefficient and the initial viewing index curve, the obtained optimized viewing index curve can screen active users from the initial viewing index curve, namely screening viewing indexes generated by users enabling the television on-off state and the set top box on-off state to be consistent. Namely, the optimized viewing index curve is an accurate viewing index curve.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a block diagram of a hardware configuration of an electronic device implementing a method for processing viewing indicator data according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for processing audience index data according to this embodiment;
FIG. 3 is a schematic diagram of various normalization functions provided by embodiments of the present invention;
FIG. 4 is a schematic diagram of an initial rating curve for an object to be processed during a target time period according to an embodiment of the present invention;
fig. 5 is a diagram illustrating a curve of the number of switching channels according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a curve for adjusting the volume level according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a curve of the number of other human-computer interaction actions according to an embodiment of the present invention;
FIG. 8 is a graphical illustration of a graph of the number of effective actions provided by an embodiment of the present invention;
FIG. 9 is a diagram illustrating a graph of the number of user-averaged effective actions according to an embodiment of the present invention;
FIG. 10 is a graph illustrating a smoothed number of user average effective actions according to an embodiment of the present invention;
FIG. 11 is a graphical illustration of an activity factor curve provided by an embodiment of the present invention;
FIG. 12 is a schematic diagram of an optimized rating curve and an initial rating curve provided by an embodiment of the present invention;
fig. 13 is a schematic structural diagram of an apparatus for processing audience measurement data according to an embodiment of the present invention;
fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
Fig. 1 is a block diagram of a hardware configuration of an electronic device that provides a method for processing viewership indicator data according to an embodiment of the present invention.
The electronic device 1000 is a big data platform generally set up by a television operator, and the big data platform may be a terminal or a server. Further, the server may be a cloud server or the like.
The electronic device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and so forth. The processor 1100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a headphone interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 1600 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 1700 and the microphone 1800.
Although a plurality of devices are shown in fig. 1 for each of the electronic devices 1000, the present invention may relate to only some of the devices, for example, the electronic device 1000 may relate to only the memory 1200 and the processor 2100.
In an embodiment of the present invention, the memory 1200 of the electronic device 1000 is used for storing instructions for controlling the processor 1100 to execute the method for processing the audience measurement data according to the embodiment of the present invention.
In the above description, the skilled person will be able to design instructions in accordance with the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
< method examples >
As shown in fig. 2, the present embodiment provides a method for processing audience rating data, which includes the following steps S2100 to S2500:
s2100, acquiring the number of effective behaviors to the object to be processed acquired at each acquisition time in a target time period and the number of watching users to the object to be processed corresponding to the acquisition time; the object to be processed comprises a target channel or a target geographical area.
In one embodiment, the target time period may be a specific day, such as 1/2019. Alternatively, the target time period may be a specific month, for example, 1/2019-31/2019. Of course, the target time period may be other time periods, which is not limited in this embodiment.
In this embodiment, the above-mentioned collection time is the time of collecting data of a probe built in the television set-top box for collecting viewing behavior data of the user. Typically, the probe collects the viewing behavior data of the user every 1s, or every 1 min. Wherein, the viewing behavior data of the user comprises: at least one of a channel that the user is watching, a switching time when the user switches the channel, an adjusting time when the user adjusts the volume, another man-machine interaction time when the user performs another man-machine interaction action (e.g., adjusting the brightness), and the like. In addition, it should be noted that the probe acquires a set of data at each acquisition time, and the set of data at least includes: the device information of the probe, the acquisition time and the viewing behavior data of the user corresponding to the acquisition time.
In this embodiment, the effective behavior refers to viewing behavior corresponding to viewing behavior data related to human-computer interaction in the viewing behavior data of the user. Based on the above description of the viewing behavior data of the user, the effective behavior may specifically be: switching channels, adjusting volume, or other human interaction (e.g., adjusting brightness). It is understood that the effective behavior count in S2100 described above may be determined based on the viewing behavior data of the human-computer interaction action involved in the viewing behavior count of the user.
In one example, for an object to be processed, if a total of 1000 probes acquire data at one acquisition time in a target time period, a total of 1000 sets of data are obtained. And if the corresponding effective behavior in the viewing behavior data of the user corresponding to the 1000 groups of data is 300 groups of switching channels, the number of the switching channels corresponding to the acquisition time is 300. If the effective behavior corresponding to the viewing behavior data of the user in the 100 groups of data is 400 groups of adjusted volume, the number of adjusted volume corresponding to the collection time is 400. If the effective behavior corresponding to the viewing behavior data of the user in the 1000 groups of data is 200 groups corresponding to the acquisition time of other human-computer interaction, the channel number is switched to 200. Further, in the 1000 groups of data, 900 groups of data correspond to effective behaviors in the viewing behavior data of the corresponding user. Then the number of valid actions for the corresponding acquisition instant referred to in S2100 above is 900.
Based on the above, in one example, the effective behavior is: in the case of switching channels, adjusting volume, or other human-computer interaction, the number of effective actions in S2100 may be specifically expressed as:
Actt＝ΣActt,sw+∑Actt,vol+∑Actt,other
wherein, ActtThe effective behavior number of the object to be processed is acquired at an acquisition time t in a target time period;
∑Actt,swthe number of switching channels for the object to be processed, which is acquired at the acquisition time t;
∑Actt,voladjusting the volume number of the object to be processed acquired at the acquisition time t;
∑Actt,otherand acquiring the number of other human-computer interaction actions for the object to be processed acquired at the acquisition time t.
It should be noted that the number of switching channels, the number of adjusting tones, or the number of other interactive actions at each acquisition time can be represented by corresponding curves. The specific examples herein may be: the curve of the number of switching channels at each acquisition time in the target time period, the curve of the number of adjusting tones at each acquisition time in the target time period and the curve of the number of other interactive actions at each acquisition time in the target time period are reflected. Based on the curve here, a curve reflecting the number of effective behaviors at each acquisition time within the target time period can be obtained. By using the curve of the effective behavior number, the effective behavior number of each acquisition time in the target time period can be obtained.
In this embodiment, the number of users viewing the object to be processed corresponding to the acquisition time is specifically: the number of probes corresponding to the returned data at the time of acquisition. Continuing with the above example, for the object to be processed, when 1000 probes are collecting data at one collection time in the target time period, the number of viewing users is 1000.
In addition, taking the object to be processed as the target geographic area as an example, the probe based on which the number of effective behaviors and the number of viewing users in S2100 is determined as the probe in the target geographic area.
The number of effective behaviors and the number of viewing users obtained in S2100 may be obtained by other methods, for example, by using a sample probe.
S2200, determining an average user effective behavior number curve according to the effective behavior number and the number of watching users.
In this embodiment, the horizontal axis of the user average effective behavior number curve is time, and the vertical axis is the user average effective behavior number.
In this embodiment, the foregoing S2200 is specifically implemented as: taking the ratio of the number of the effective behaviors corresponding to the same acquisition time and the number of the watching users obtained based on the S2100 as the number of the average effective behaviors of the users corresponding to the acquisition time; then, a curve formed by the average number of user valid behaviors corresponding to each acquisition time in the target time period is used as the average number of user valid behaviors curve in S2200.
In one example, for the user average effective behavior number curve, the effective behavior number of the object to be processed, which is acquired at an acquisition time t in the target time period, is ActtWhen the average number of the user effective behaviors at the corresponding acquisition time t is expressed, the average number of the user effective behaviors at the corresponding acquisition time t can be expressed as the following formula:
Figure BDA0002357138550000091
wherein, ApUtCollecting the number of the average effective behaviors of the user at the moment t;
VNtthe number of users watching at the acquisition time t.
It can be understood that, since the active user (the user who makes the on/off state of the television and the on/off state of the set-top box consistent) exists, the active user only generates the effective behaviors, and therefore, from the perspective of big data, when a user of a certain scale turns on the television to watch a television program, the effective behaviors of a matched scale are correspondingly generated. Based on the principle, the activity degree of the user can be reflected through the number of the effective behaviors of the user. Further, the number of user-average effective behaviors can be used to reflect an activity coefficient representing the activity degree of the user.
And S2300, normalizing the number of the user average effective behaviors corresponding to the curve of the number of the user average effective behaviors to obtain a curve of the activity coefficient.
In this embodiment, since the range of the average user behavior number is usually between 0.001 and 0.1, and the range of the activity coefficient reflecting the user activity level is 0 to 100%, after the average user behavior number at an acquisition time is obtained, the average user behavior number may be normalized to obtain the activity coefficient corresponding to the acquisition time. And then taking a curve formed by the activity coefficient of each acquisition time in the target time period as the activity curve in the S2300.
It is understood that the horizontal axis of the activity curve in S2400 represents time, and the vertical axis represents an activity coefficient.
In an embodiment, the specific implementation of S2300 may be S2310 as follows:
and S2310, normalizing the user average effective behavior number corresponding to the curve of the user average effective behavior number through a set normalization function to obtain a curve of the activity coefficient.
In the present embodiment, the set normalization function refers to a normalization function obtained from a preset inflection point value and a preset unevenness.
The inflection point refers to the smallest number of user-average effective behaviors when the value of the normalization function is 1. It can be understood that, when the number of the average user behaviors exceeds a certain value, that is, the minimum number of the average user behaviors is, the number of the viewing users at the corresponding acquisition time is considered to be approximately the real number of the viewing users, that is, the number of the viewing users at the corresponding acquisition time is approximately the same as the number of the television boots at the corresponding acquisition time.
It should be noted that. In this embodiment, the inflection values are based on empirical values, or are obtained through a large number of experiments. For example, since the number of viewing users corresponding to the collection time in the golden time period is approximately equal to the number of viewing users, at this time, for example, the average value of the number of effective behaviors of the user corresponding to the collection time in the golden time period may be used as the above-mentioned inflection point value. And, it can be understood that the larger the inflection point of the normalization function, the more the irregularity of the curve of the activity coefficient.
The unevenness refers to an uneven state that appears during the rise of the normalization function. And the concavity and convexity can be set according to the parameter values of the parameters corresponding to the normalization function.
It should be noted that, in this embodiment, the unevenness of the normalization function can be obtained by an experimental value or a large number of experiments. It can be understood that when a segment function in the normalization function is convex, the normalization function can amplify the activity coefficient. When a certain segment function of the normalization function presents a concave shape, the normalization function can compress the activity coefficient.
In one example, there are many inactive users (users that make the on/off state of the television and the on/off state of the set-top box inconsistent) during the midnight time period, and therefore, the number of user-averaged effective behaviors corresponding to the midnight time period is generally low, and therefore, the normalization function may be set to appear concave down during the midnight time period.
In one embodiment, the normalization function is any one of a linear piecewise function, a piecewise function corresponding to a trigonometric function, a logarithmic function, and a growing curve function. Wherein:
the linear piecewise function may be expressed as:
Figure BDA0002357138550000101
the piecewise function corresponding to the trigonometric function may be expressed as:
Figure BDA0002357138550000102
the logarithmic function can be expressed as:
fnor(x)＝logabx+k,x∈(0,+∞)
the growth curve function can be expressed as:
Figure BDA0002357138550000111
where x refers to the number of average user activity. f. ofnor(x) And corresponding to a normalized value for the number x of the user-average effective behaviors. a. b, w and k are parameters corresponding to the normalization functions. It is understood that the specific values of a, b, w and k can be determined according to a preset inflection point value and a preset concavity and convexity.
In one example, the linear piecewise function, the piecewise function corresponding to the trigonometric function, the logarithmic function, and the growing curve function are exemplarily shown in fig. 3.
S2400, acquiring an initial audience rating index curve for the object to be processed in the target time period.
The initial audience rating index curve is a curve reflecting the mapping relation between the initial audience rating index value and the acquisition time.
In this embodiment, the initial viewing index curve is a viewing index curve statistically analyzed according to a conventional technique. The viewing index curve here is any one of a viewing time length curve, a viewing user number curve, a rating curve, a market share curve, and an arrival rate curve. It is understood that the viewing index profile herein may also be used with other viewing index profiles.
And S2500, obtaining an optimized audience rating index curve according to the curve of the liveness coefficient and the initial audience rating index curve.
In this embodiment, the specific implementation of S2500 is: and multiplying the corresponding liveness coefficient at the same acquisition moment by the corresponding viewing index in the initial viewing index curve, and taking the curve obtained after multiplication as the optimized viewing index curve.
In one example, for the optimized audience rating curve, the optimized audience rating value corresponding to the collection time t may be represented as:
Rtg_Acvt＝Rtgt×Acvt
wherein Rtg _ AcvtFor acquiring optimized audience rating index value at time t；
RtgtAcquiring an initial audience rating index value at a time t;
Acvtis the activity coefficient of the acquisition time t.
In this embodiment, a curve of the number of user-average effective behaviors is determined according to the number of acquired effective behaviors to the object to be processed at each acquisition time in the target time period and the number of users watching the object to be processed at the corresponding acquisition time. Since the average number of user effective behaviors can reflect the activity coefficient representing the activity degree of the user, the activity coefficient curve is obtained based on the average number of user effective behaviors. Therefore, according to the curve of the activity coefficient and the initial viewing index curve, the obtained optimized viewing index curve can screen active users from the initial viewing index curve, namely screening viewing indexes generated by users enabling the television on-off state and the set top box on-off state to be consistent. Namely, the optimized viewing index curve is an accurate viewing index curve.
On the basis of any of the foregoing embodiments, the method for processing audience rating index provided in this embodiment further includes the following step S2510:
and S2510, outputting an analysis result according to the optimized viewing index curve.
In one embodiment, the analysis results may be programming recommendations based on optimized viewership metric curves. For example, when the integrated value of the audience rating values corresponding to the audience rating curve is lower than a preset rating value, the analysis result may be: it is proposed to introduce popular programs. The comprehensive value may be an average value of the audience rating values in the golden time period, or a median value of the whole time period corresponding to the audience rating curve, or the like. This embodiment is not limited to this.
For example, the analysis result may be: in the case that the maximum value of the obtained audience rating value of a certain program in the corresponding time slot is lower than another preset rating value within, for example, 08:00 to 22:30, the analysis result may be: it is recommended that the program be scheduled within 22: 30-the next day 08: 00.
In another embodiment, the analysis result may also be popular programs counted according to the optimized audience rating index. In this way, the user may be recommended relevant programs based on the analysis results.
On the basis of any of the foregoing embodiments, the method for processing the audience rating index further includes, after the foregoing S2200, the following S2210:
s2210, smoothing the curve of the average number of the user behaviors to obtain the smoothed curve of the average number of the user behaviors.
In this embodiment, since a large number of valid behaviors are usually generated at the starting or ending time of a certain program, the user-average valid behavior data has a large value at this time. Reflected on the curve of the number of user-averaged effective behaviors, a "spike" occurs at that time. This "spike" is due to programming and does not represent a user that is much more active than the adjacent time. Therefore, it is more reasonable to consider the number of effective behaviors over a period of time. Based on this, after obtaining a curve of the number of user-averaged effective behaviors, the curve may be smoothed. Therefore, the obtained curve of the average effective behavior number of the user can reflect the activity degree of the user more accurately.
In one embodiment, the average or convolution operation may be performed on the number of effective behaviors per household within a certain time window by using a moving average method or a low-pass filtering method. It should be noted that the size of the time window can be determined empirically, and the time window is usually a time window greater than 30 min.
Based on this, the implementation of S2300 is specifically S2310 as follows:
and S2310, normalizing the average user behavior number corresponding to the smoothed average user behavior number curve to obtain an activity coefficient curve.
On the basis of the foregoing embodiment, the activity coefficient corresponding to the acquisition time t obtained in this embodiment may be represented as:
Figure BDA0002357138550000131
wherein, ApUtCollecting the number of the average effective behaviors of the user at the moment t;
Acvtthe activity coefficient at the acquisition time t;
fwnd(ApUt) Smoothing the number of the effective behaviors of the user at the acquisition time t;
fnor(fwnd(ApUt) Normalized value for the smoothed number of average user behaviors at the acquisition time t);
VNtthe number of users watching at the acquisition time t.
On the basis of any of the foregoing embodiments, in the method for processing audience rating data provided in this embodiment, when the object to be processed is the target channel and the optimized audience rating curve is the first optimized audience rating curve based on the audience rating time, the method further includes the following steps S2610 to S2630:
s2610, according to the program list of the target channel, the playing time quantum of each program is determined.
In this embodiment, the first optimized index curve based on the viewing time may be: an optimized audience rating curve, an optimized viewing duration curve and an optimized audience share curve.
In this embodiment, when the object to be processed is a target channel, a program list (also referred to as an electronic program guide EPG, electronic program guide) of the target channel may be obtained, and then a playing time period of each program is determined from the program list.
S2620, obtaining a first target optimized viewing indicator curve corresponding to the playing time period of the corresponding program from the first optimized viewing indicator curve.
In this embodiment, after the playing time period of each program is obtained, a curve corresponding to the same time period as the playing time period of the corresponding program in the first optimized viewing curve is used as the viewing index curve after the first target optimization.
S2630, using the average index value of all the viewing index values corresponding to the viewing index curve after the first target optimization as the optimized viewing index value of the corresponding program.
In one example, the principle of the present embodiment is described by taking the audience rating as an example.
Figure BDA0002357138550000141
Wherein, the Rtg _ Acv is the optimized audience rating index value of the program;
vt _ Acv is the optimized viewing duration of the program;
dur is the duration of the program (the time length of the playing time period of the program);
s is the number of watching users;
n is the number of acquisition moments contained in the program duration;
t is the time interval between adjacent acquisition moments;
Acvtthe activity coefficient at the acquisition time t;
Vttis the viewing duration at the acquisition time t.
Based on the above formula, the optimized viewing index value of the program based on the viewing time can be obtained based on the first optimized viewing curve of the viewing time, and the optimized viewing index value of the program based on the viewing time is the average index value of all the corresponding viewing index values in the first target optimized viewing index curve.
In this embodiment, the method provided in this embodiment may optimize the audience rating value of the program based on time.
On the basis of any of the above embodiments, the method for processing audience rating index data provided in this embodiment further includes, when the object to be processed is a target channel and the optimized audience rating index curve is a second optimized audience rating index curve based on the number of viewing users, the following steps S2710 to S275:
s2710, determining the playing time period of each program according to the program list of the target channel.
And S2720, obtaining a second target optimized audience rating index curve corresponding to the playing time period of the corresponding program from the second optimized audience rating index curve.
In this embodiment, the above S2710 is the same as the above S2610, and the above S2720 is the same as the above S2620, so that the detailed description thereof is omitted here. In this embodiment, the second optimized audience rating index curve based on the number of viewing users is: any one of the optimized viewing user number curve and the optimized arrival rate curve.
S2730, acquiring an initial watching time length corresponding to each acquisition time, an optimized watching time length corresponding to the acquisition time, an initial watching user number corresponding to the acquisition time and a time length corresponding to the program in the playing time period of the corresponding program;
in this embodiment, the initial viewing duration corresponding to each acquisition time is the viewing duration corresponding to the acquisition time in the viewing duration curve statistically analyzed by using the conventional statistical technique.
The optimized viewing duration corresponding to the acquisition time is the viewing duration corresponding to the acquisition time, which is obtained by executing the method provided by this embodiment, when the initial viewing index curve is the initial viewing duration curve.
The number of the initial watching users corresponding to the acquisition time is: and in the user watching number curve counted by using the traditional statistical mode, the number of the user watching at the acquisition moment corresponds to the user watching number.
The duration of the corresponding program is: the time length of the playing time period of the corresponding program.
S2740, determining the number of the optimized watching users corresponding to the acquisition time according to the initial watching time corresponding to each acquisition time, the optimized watching time corresponding to the corresponding acquisition time, the number of the initial watching users corresponding to the corresponding acquisition time and the time of the corresponding program;
in this embodiment, the inactive user does not have viewing behavior after the television is turned off, but for the inactive user, the television set-top box still can return viewing behavior data, that is, for the inactive user, after the television is turned off, the television set-top box still returns data reflecting that the user "completely views" the program played at the time before the television is turned off. It will be appreciated that the viewing duration at this time is the same as the duration of the program. Therefore, the difference between the optimized program viewing time period and the initial program viewing time period contributes to the inactive user. Based on this, the number of viewing users after optimization is:
Figure BDA0002357138550000161
wherein, Rch _ Acv is the number of optimized watching users of the program;
rch is the number of initial viewing users of the program;
vt _ Acv is the optimized viewing duration of the program;
dur is the duration of the corresponding program.
S2740, determining the optimized audience rating index value of the corresponding program according to the optimized number of the watching users at the corresponding acquisition time and the optimized audience rating index curve of the second target.
In this embodiment, taking the viewing index curve after the second objective optimization as an example of the number of viewing users, the specific implementation of S2740 is as follows: for the number of viewing users corresponding to each collection time in the viewing index curve after the second target optimization, the optimized number of viewing users of the program corresponding to the collection time is obtained by using the above S2730, and the optimized number of viewing users of the program corresponding to each collection time is used as the optimized viewing index value of the corresponding program.
In this embodiment, the method provided in this embodiment may optimize an audience rating value of a program based on the number of viewing users.
Based on the method for processing the audience rating data provided in the embodiment, in an embodiment, the object to be processed is used as the target channel, the target time period is three consecutive days (the first day 0:00:00 to the third day 23:59:59)), the audience rating curve is the audience rating curve, and the effective behaviors are as follows: the method for processing the audience rating index data provided in this embodiment is described by taking channel switching, volume adjustment, and other human-computer interaction behaviors as examples.
An initial rating curve for the object to be processed in the target time period obtained based on the above S2400 is shown in fig. 4;
a curve of the number of switching channels acquired based on the above S2200 is shown in fig. 5, a curve of the number of adjusting tones acquired is shown in fig. 6, and a curve of the number of other interactive actions is shown in fig. 7; based on FIGS. 5-7, the resulting curves for the number of effective behaviors are shown in FIG. 8; further, based on fig. 8, the obtained curve of the number of user-averaged effective behaviors is shown in fig. 9.
Based on S2210, smoothing the number of user average effective behaviors shown in fig. 9, and obtaining a curve of the number of user average effective behaviors after smoothing as shown in fig. 10;
based on S2310, the curve of the number of user average effective behaviors after the smoothing processing as shown in fig. 10 is normalized, and the obtained curve of the activity coefficient is shown in fig. 11.
Based on S2500, the optimized rating curve obtained using the curve of the activity coefficient shown in fig. 11 and the initial rating curve shown in fig. 4 is shown in fig. 12.
Based on the above method for processing audience rating data provided in this embodiment, taking the optimized audience rating of a program as an example, the optimized audience rating of the program will be described as follows:
the program list of the target channel in the three consecutive days is the same, wherein the program list is specifically shown in the following table:
program and method for providing a program Starting time End time Initial rating
Program
1 7:00 7:30 0.268％
Program
2 12:00 12:30 0.670％
Program
3 15:00 15:30 0.547％
Program
4 18:00 18:30 0.652％
Program
5 20:00 20:30 0.853％
Program
6 23:00 23:30 0.558％
Program
7 0:00 0:05 0.487% (midnight)
Program 8 1:00 1:30 0.362% (midnight)
Program 9 3:00 3:30 0.221% (midnight)
Program 10 5:00 5:30 0.140％
TABLE 1
Based on the above steps, the optimized rating of each program is obtained as shown in table 2 below:
Figure BDA0002357138550000171
Figure BDA0002357138550000181
TABLE 2
As can be seen from the table, the program audience ratings in the morning hours (program 1), the noon hours (program 2), and the evening prime hours (program 4, program 5) are hardly affected; in the nighttime (program 6, program 7), midnight (program 8, program 9) and afternoon (program 3), the corresponding audience rating is falsely high due to the presence of a large number of inactive users. By the method for processing the audience rating index data, the inactive users in the periods can be effectively processed, and the audience rating result of the program is more real and reliable.
< apparatus embodiment >
As shown in fig. 13, the present embodiment provides an apparatus 130 for processing audience measurement data, the apparatus 130 comprising: a first obtaining module 131, a determining module 132, a normalizing module 133, a second obtaining module 134, and an optimizing module 135. Wherein:
the first obtaining module 131 obtains the number of effective behaviors to the object to be processed collected at each collection time in the target time period and the number of users to watch the object to be processed corresponding to the collection time; the object to be processed comprises a target channel or a target geographical area.
The determining module 132 is configured to determine a curve of the average effective behavior number of the users according to the effective behavior number and the number of the users watching the television program.
The normalization module 133 is configured to perform normalization processing on the user average effective behavior number corresponding to the curve of the user average effective behavior number to obtain a curve of the activity coefficient.
A second obtaining module 134, configured to obtain an initial audience rating curve for the object to be processed in the target time period; the viewing index curve is a curve reflecting the mapping relation between the initial viewing index value and the acquisition time.
And the optimizing module 135 is configured to obtain an optimized viewing index curve according to the curve of the activity coefficient and the initial viewing index curve.
In one embodiment, the apparatus 130 provided in this embodiment further includes a smoothing module 136. Wherein the smoothing module 136 is configured to:
and smoothing the curve of the average number of the user behaviors to obtain the smoothed curve of the average number of the user behaviors.
In this embodiment, the normalization module 133 is specifically configured to:
and normalizing the average number of the effective behaviors of the user corresponding to the smoothed average number of the effective behaviors of the user to obtain a curve of the activity coefficient.
In one embodiment, in the case that the object to be processed is a target channel and the optimized viewing index curve is a first optimized viewing index curve based on viewing time, the optimization module 135 is further configured to:
determining the playing time period of each program according to the program list of the target channel;
acquiring a first target optimized viewing index curve corresponding to the playing time period of the corresponding program from the first optimized viewing index curve;
and taking the average index value of all the audience rating index values corresponding to the audience rating curve after the first target optimization as the optimized audience rating index value of the corresponding program.
In this embodiment, in the case that the object to be processed is a target channel and the optimized viewing index curve is a second optimized viewing index curve based on the number of viewing users, the optimizing module 135 is further specifically configured to:
determining the playing time period of each program according to the program list of the target channel;
acquiring a second target optimized viewing index curve corresponding to the playing time period of the corresponding program from the second optimized viewing index curve;
acquiring initial watching time length corresponding to each acquisition time, optimized watching time length corresponding to the acquisition time, the number of initial watching users corresponding to the acquisition time and the time length of the corresponding program in the playing time period of the corresponding program;
determining the optimized number of the watching users corresponding to the acquisition time according to the initial watching time corresponding to each acquisition time, the optimized watching time corresponding to the corresponding acquisition time, the number of the initial watching users corresponding to the corresponding acquisition time and the time of the corresponding program;
and determining the optimized audience rating index value of the corresponding program according to the optimized number of the watching users at the corresponding acquisition time and the optimized audience rating index curve of the second target.
In one embodiment, the normalization module 133 is specifically configured to:
and normalizing the user average effective behavior number corresponding to the curve of the user average effective behavior number through a set normalization function to obtain a curve of the activity coefficient.
In one embodiment, the normalization function is: linear piecewise function, piecewise function corresponding to trigonometric function, logarithmic function, and growth curve function.
In one embodiment, the means for processing 130 the viewership metric data further comprises an output module. Wherein, the output module is used for: and outputting an analysis result according to the optimized viewing index curve.
In one embodiment, the viewing index curve is any one of a viewing duration curve, a number of viewing users curve, a rating curve, a share of viewing curve, and an arrival rate curve.
< apparatus embodiment >
As shown in fig. 14, the present embodiment provides an electronic device 140, and the electronic device 140 includes an apparatus 140 for processing audience rating data as provided in the above apparatus embodiments. Alternatively, it comprises: a processor 141 and a memory 142. The memory 142 is configured to store executable instructions for controlling the processor 141 to perform the method according to any of the above method embodiments.
Alternatively, the electronic device 140 may also be the electronic device 1000 shown in fig. 1.
< storage Medium embodiment >
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method according to any of the above.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims (10)

1. A method of processing viewership indicator data, comprising:
acquiring the number of effective behaviors to the object to be processed acquired at each acquisition time in a target time period and the number of watching users to the object to be processed corresponding to the acquisition time; the object to be processed comprises a target channel or a target geographical area;
determining a curve of the average effective behavior number of the users according to the effective behavior number and the number of the watching users;
normalizing the number of the user average effective behaviors corresponding to the curve of the number of the user average effective behaviors to obtain a curve of an activity coefficient;
acquiring an initial audience rating index curve for the object to be processed in the target time period; the audience rating index curve is a curve reflecting the mapping relation between the initial audience rating index value and the acquisition time;
and obtaining an optimized viewing index curve according to the curve of the activity coefficient and the initial viewing index curve.
2. The method of claim 1, after determining a curve of the number of user average effective behaviors from the number of effective behaviors and the number of viewing users, further comprising:
smoothing the curve of the average number of the user behaviors to obtain a smoothed curve of the average number of the user behaviors;
the normalizing the number of the average user behavior corresponding to the curve of the number of the average user behavior to obtain the curve of the activity coefficient includes:
and normalizing the average number of the effective behaviors of the user corresponding to the smoothed average number of the effective behaviors of the user to obtain a curve of the activity coefficient.
3. The method according to claim 1, wherein in a case where the object to be processed is a target channel and the optimized viewing index curve is a first optimized viewing index curve based on viewing time, the method further comprises:
determining the playing time period of each program according to the program list of the target channel;
acquiring a first target optimized viewing index curve corresponding to the playing time period of the corresponding program from the first optimized viewing index curve;
and taking the average index value of all the audience rating index values corresponding to the audience rating curve after the first target optimization as the optimized audience rating index value of the corresponding program.
4. The method of claim 1, wherein in the case that the object to be processed is a target channel and the optimized viewing index curve is a second optimized viewing index curve based on the number of viewing users, the method further comprises:
determining the playing time period of each program according to the program list of the target channel;
acquiring a second target optimized viewing index curve corresponding to the playing time period of the corresponding program from the second optimized viewing index curve;
acquiring initial watching time length corresponding to each acquisition time, optimized watching time length corresponding to the acquisition time, the number of initial watching users corresponding to the acquisition time and the time length of the corresponding program in the playing time period of the corresponding program;
determining the optimized number of the watching users corresponding to the acquisition time according to the initial watching time corresponding to each acquisition time, the optimized watching time corresponding to the corresponding acquisition time, the number of the initial watching users corresponding to the corresponding acquisition time and the time of the corresponding program;
and determining the optimized audience rating index value of the corresponding program according to the optimized number of the watching users at the corresponding acquisition time and the optimized audience rating index curve of the second target.
5. The method according to claim 1, wherein the normalizing the user average effective behavior number corresponding to the curve of the user average effective behavior number to obtain the curve of the activity coefficient comprises:
and normalizing the user average effective behavior number corresponding to the curve of the user average effective behavior number through a set normalization function to obtain a curve of the activity coefficient.
6. The method of claim 5, wherein the normalization function is: linear piecewise function, piecewise function corresponding to trigonometric function, logarithmic function, and growth curve function.
7. The method according to any one of claims 1-6, further comprising:
and outputting an analysis result according to the optimized viewing index curve.
8. An apparatus for processing viewership metric data, comprising:
the first acquisition module is used for acquiring the effective behavior number of the object to be processed acquired at each acquisition time in the target time period and the number of watching users of the object to be processed corresponding to the acquisition time; the object to be processed comprises a target channel or a target geographical area;
the determining module is used for determining a curve of the average effective behavior number of the users according to the effective behavior number and the number of the watching users;
the normalization module is used for performing normalization processing on the user average effective behavior number corresponding to the curve of the user average effective behavior number to obtain a curve of the activity coefficient;
the second acquisition module is used for acquiring an initial audience rating index curve for the object to be processed in the target time period; the audience rating index curve is a curve reflecting the mapping relation between the initial audience rating index value and the acquisition time;
and the optimization module is used for obtaining an optimized viewing index curve according to the curve of the activity coefficient and the initial viewing index curve.
9. An electronic device, characterized in that the electronic device comprises the apparatus of claim 8; alternatively, the first and second electrodes may be,
comprising a memory for storing computer instructions and a processor for invoking the computer instructions from the memory to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the method according to any one of the claims 1-7.
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CN113286179A (en) * 2021-04-21 2021-08-20 北京爱奇艺科技有限公司 Processing method and device for jumping-out curve, electronic equipment and storage medium
CN113129302A (en) * 2021-05-13 2021-07-16 北京爱奇艺科技有限公司 Method and device for optimizing jump-out curve, electronic equipment and storage medium
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CN116095409B (en) * 2023-04-07 2023-08-04 国家广播电视总局广播影视信息网络中心 Audience data analysis method and electronic equipment
CN116781984A (en) * 2023-08-21 2023-09-19 深圳市华星数字有限公司 Set top box data optimized storage method
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