CN112511901B - Method and system for predicting comprehensive drama playing amount, computer device and storage medium - Google Patents
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Abstract
The application relates to a method, a system, a computer device and a computer readable storage medium for predicting the playing amount of a comprehensive art drama, wherein the method comprises the following steps: a data acquisition step, which is used for acquiring the variety scenario to be predicted; a historical data acquisition step, which is used for acquiring the historical data of the integrated art drama and taking one or any combination of the popularity, the word of mouth score, the plot value and the topic degree in the historical data as the influence factor of the playing amount; an influence coefficient obtaining step, which is used for dividing the playing period of the comprehensive drama into different time periods and constructing a calculation model according to the playing period to obtain the influence coefficient of the influence factor; and a playing amount prediction step, which is used for establishing a prediction model according to the influence factors and the influence coefficients thereof, and calculating the playing amounts of different time periods based on the prediction model. The method and the device can accurately predict the playing amount of the comprehensive drama in different time periods, and further effectively guide brand merchants to carry out advertisement putting.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method, a system, a computer device, and a computer-readable storage medium for predicting a play amount of a variety of art shows.
Background
At present, various comprehensive arts and programs emerge endlessly, and the trend of how to predict the playing amount in the broadcasting process before broadcasting of one comprehensive art or program and in the large ending is very important for advertising to each brand party.
At present, the broadcast volume prediction of the comprehensive art drama is generally judged manually, various influence factors of the drama are comprehensively considered manually and compared with the comprehensive art drama of the historical similar subject, and the broadcast volume prediction is obtained by using a unified calculation model in the whole period. On one hand, the manually sampled data volume is small, and the accuracy of big data analysis is not achieved; on the other hand, the calculation model based on the full period cannot adapt to the dynamic change of the comprehensive drama playing period, so that the predicted playing amount is inaccurate.
On the premise of inaccurate prediction of the playing amount, a brand merchant cannot be effectively guided to carry out effective advertisement putting.
Disclosure of Invention
The embodiment of the application provides a method, a system, computer equipment and a computer readable storage medium for predicting the playing amount of an integrated art drama, wherein a calculation model and a prediction model are designed based on a large amount of historical data, and the playing amount of the integrated art drama in different time periods is accurately predicted by calculating coefficients of various influence factors, so that a brand manufacturer is effectively instructed to carry out advertisement putting.
In a first aspect, an embodiment of the present application provides a method for predicting a play amount of a hedging program, including:
a data acquisition step, which is used for acquiring the variety scenario to be predicted;
a history data obtaining step, configured to obtain history data of the synthesis drama, where the history data includes one or any combination of popularity, word-of-mouth value, scenario value, topicality, and playing volume, and the popularity of the one or any combination of popularity, word-of-mouth value, scenario value, and topicality is used as an influence factor of the playing volume, and specifically, the popularity of the synthesis drama is calculated based on the number of fans of actors starring the synthesis drama; the public praise value is obtained from the internet, and optionally, the public praise value is set by referring to the bean petal score of the heddles; the plot value and the topicality are also obtained from an internet platform; optionally, the topic degree is calculated according to the microblog public opinion popularity degree.
An influence coefficient obtaining step, configured to divide a playing period of the integrated art scenario into different time periods, establish a corresponding relationship between an influence factor in the historical data and the playing amount according to the playing period to construct a calculation model, and solve the calculation model to obtain an influence coefficient of the influence factor on the playing amount of the integrated art scenario in the different time periods;
and a playing amount prediction step, which is used for establishing a prediction model according to the influence factors and the influence coefficients thereof, and calculating the playing amount of the comprehensive art drama in different time periods based on the prediction model.
Through the steps, the calculation model and the prediction model are established by analyzing the influence factors and the playing periods of the comprehensive art drama playing quantity, different playing periods, different factors and influence degrees of the different playing periods and the different factors are quantized, the accurate and rapid prediction of the playing quantity of the comprehensive art drama is realized, the targeted selection of the comprehensive art drama with higher commercial value for advertisement putting is facilitated, and the advertisement brand propaganda effect is maximized.
In some of these embodiments, the different time periods include at least: the playing initial stage, the playing middle stage and the playing later stage; optionally, the time period may be divided according to the number of episodes in the actual hedging program or other factors.
In some embodiments, assuming that the playback volume is denoted by B, the expression of the prediction model is as follows:
B n ＝Y*x n +K*y n +D*z n +H*w n
y is the known name degree of the time period to be predicted, K is the word-of-mouth value of the time period to be predicted, D is the plot value of the time period to be predicted, H is the topicality of the time period to be predicted, the value range of n is {1,2,3}, n =1 is used for representing the early stage of playing, n =2 is used for representing the middle stage of playing, n =3 is used for representing the later stage of playing, and x =3 is used for representing the later stage of playing n Influence coefficient, y, of degree of awareness of corresponding time periods n Influence coefficient, z, being the public praise value of the corresponding time segment n Influence coefficient, w, of plot value for corresponding time segment n Influence coefficient of topic degree corresponding to time period, and x n +y n +z n +w n ＝1。
In some embodiments, the computational model is a system of linear equations with the following expression:
wherein m represents the number of historical data sets solved by the model, m is a finite integer greater than 1, a m As degree of awareness in the historical data, b m Is a public praise value, c, in said historical data m For plot values in said historical data, d m Is topicality in the historical data, e m Is the playing amount in the history data.
In a second aspect, an embodiment of the present application provides a system for predicting a play amount of a variety scenario, including:
the data acquisition module is used for acquiring the comprehensive art drama to be predicted;
a history data obtaining module, configured to obtain history data of the synthesis drama, where the history data includes one or any combination of popularity, word-of-mouth value, scenario value, topicality, and playing volume, and the popularity of the one or any combination of popularity, word-of-mouth value, scenario value, and topicality is used as an influence factor of the playing volume, and specifically, the popularity of the synthesis drama is calculated based on the number of fans of actors starring the synthesis drama; the public praise value is obtained from the internet, and optionally, the public praise value is set by referring to the bean petal score of the heddles; the plot value and the topicality are also obtained from an Internet platform; optionally, the topic degree is calculated according to the popularity degree of the microblog;
the influence coefficient acquisition module is used for dividing the playing period of the variety scenario into different time periods, establishing a corresponding relation between the influence factors in the historical data and the playing amount according to the playing period to construct a calculation model, and solving the calculation model to obtain the influence coefficients of the influence factors on the playing amount of the variety scenario in the different time periods; by way of example and not limitation, the influence factors of the embodiments of the present application are combinations of the popularity, the word-of-mouth value, the plot value and the topicality.
And the playing amount prediction module is used for establishing a prediction model according to the influence factors and the influence coefficients thereof, and calculating the playing amount of the comprehensive art drama in different time periods based on the prediction model.
Through the structure, the system establishes the calculation model and the prediction model by analyzing the influence factors and the play periods of the comprehensive art dramas, quantifies different play periods, different factors and influence degrees thereof, realizes accurate and rapid prediction of the play amount of the comprehensive art dramas, is beneficial to directionally selecting the comprehensive art dramas with higher commercial value for advertisement putting, and maximizes the advertising effect of the advertisement brand.
In some of these embodiments, the different time periods include at least: the playing initial stage, the playing middle stage and the playing later stage; optionally, the time period may be divided according to the number of episodes in the actual hedging program or other factors.
In some embodiments, assuming that the playback volume is represented as B, the expression of the prediction model is as follows:
B n ＝Y*x n +K*y n +D*z n +H*w n
y is the known name degree of the time period to be predicted, K is the word-of-mouth value of the time period to be predicted, D is the plot value of the time period to be predicted, H is the topicality of the time period to be predicted, the value range of n is {1,2,3}, n =1 is used for representing the early stage of playing, n =2 is used for representing the middle stage of playing, n =3 is used for representing the later stage of playing, and x =3 is used for representing the later stage of playing n Influence coefficient, y, of degree of awareness of corresponding time periods n Influence coefficient, z, of public praise value for corresponding time segment n Influence coefficient, w, of plot value for corresponding time segment n Influence coefficient of topic degree of corresponding time period, and x n +y n +z n +w n ＝1。
In some embodiments, the computational model is a system of equations of the first order form, the expression of which is as follows:
wherein m represents the number of historical data sets solved by the model, m is a finite integer greater than 1, a m As degree of awareness in the historical data, b m Is a public praise value, c, in said historical data m For plot values in said historical data, d m Is the topicality in the historical data, e m Is the playing amount in the history data.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for predicting the playing amount of the variety scenario as described in the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for predicting the amount of shows of a variety of art as described in the first aspect above.
Compared with the prior art, the method, the system, the computer equipment and the computer readable storage medium for predicting the playing amount of the variety drama provided by the embodiment of the application can be used for accurately predicting the playing amount of the variety drama in different time periods and further effectively guiding the brand trader to put advertisements by dividing different time periods of the playing period and considering the influence degree of each influence factor on the playing amount in different time periods.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a method for predicting the play amount of a variety scenario according to an embodiment of the present application;
fig. 2 is a block diagram of a structure of a comprehensive drama play amount prediction system according to an embodiment of the present application.
Description of the drawings:
101. a data acquisition module;
102. a historical data acquisition module;
103. an influence coefficient acquisition module;
104. and a play amount prediction module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The embodiment provides a method for predicting the playing amount of a comprehensive art drama. Fig. 1 is a schematic flowchart of a method for predicting the play amount of a variety scenario according to an embodiment of the present application, where as shown in fig. 1, the flowchart includes the following steps:
a data acquisition step S101, which is used for acquiring the variety scenario to be predicted;
a history data obtaining step S102, configured to obtain history data of the synthesis scenario, where the history data includes one or any combination of popularity, word-of-mouth value, scenario value, topicality, and playing volume, and the popularity of the one or any combination of popularity, word-of-mouth value, scenario value, and topicality is used as an influence factor of the playing volume, and specifically, the popularity of the synthesis scenario is calculated based on the number of fans of actors starring the synthesis scenario; the public praise value is obtained from the Internet, and optionally, the public praise value is set by referring to the bean petal score of the anarchism drama; the plot value and the topic degree are also obtained from the Internet platform; optionally, the topic degree is calculated according to the popularity degree of the microblog; notably, the historical data is updatable in real time.
An influence coefficient obtaining step S103 is configured to divide the playing period of the variety scenario into different time periods, and establish a corresponding relationship between the influence factor and the playing amount in the history data according to the playing period to construct a calculation model, where the calculation model is a multivariate linear equation set, and an expression of the multivariate linear equation set is as follows:
wherein m represents the number of historical data sets solved by the model, m is a finite integer greater than 1, a m As degree of awareness in historical data, b m Is a public praise value in the historical data, c m For plot values in historical data, d m As topical degree in historical data, e m Is the amount of play in the history data.
In the embodiment of the present application, 4 sets of data in a time period are selected, and then the multivariate linear equation of the expression (1) is a quaternion linear equation set, as follows:
importing historical data to solve the formula (2) to obtain influence coefficients of the influence factors on the comprehensive drama playing quantity in different time periods; by way of example and not limitation, the influence factors of the embodiments of the present application are combinations of unknown degree, word-of-mouth value, plot value and topicality. Specifically, the different time periods at least include: the initial playing stage, the middle playing stage and the later playing stage; optionally, the division of the time period may be divided according to the number of episodes of the actual integrated program episode or other factors, and in the embodiment of the present application, the playing period is divided into an initial playing period, a middle playing period, and a later playing period, but is not limited to the above division manner.
And a play amount prediction step S104, which is used for establishing a prediction model according to the influence factors and the influence coefficients thereof, and calculating the play amount of the comprehensive art drama in different time periods based on the prediction model. Specifically, assuming that the play amount is represented as B, the expression of the prediction model is as follows:
B n ＝Y*x n +K*y n +D*z n +H*w n (3)
wherein Y is the degree of awareness of the time period to be predicted, K is the public praise value of the time period to be predicted, and D is the time to be predictedThe plot value of the section, H is the topicality of the time period to be predicted, the value range of n is {1,2,3}, the value range of n is used for representing the early stage of playing when n =1, the value range of n is used for representing the middle stage of playing when n =2, the value range of n is used for representing the late stage of playing when n =3, and x is used for representing the late stage of playing when n =3 n Influence coefficient, y, of degree of awareness of corresponding time segment n Influence coefficient, z, being the public praise value of the corresponding time segment n Influence coefficient, w, of plot value for corresponding time segment n Influence coefficient of topic degree of corresponding time period, and x n +y n +z n +w n And =1. It should be noted that n is not limited to the foregoing value range, and may also be adaptively adjusted according to the division of the playing period, for example, if the playing period is divided into four time segments, the value range of n may be {1,2,3,4}.
It should be noted that, the above formulas (1) and (3) may also be added with more detailed influence factors for comprehensive evaluation, such as early promotion of artistic drama, etc., to improve the accuracy of the whole formula.
Taking the "thirty days of TV plays", the most fierce TV plays in this year as an example, the TV plays have good performance after being played, the performances of word-of-mouth, topicality, scenario and the like are very good, the playing volume is flush red all the way, but the later-stage scenario leads to disappointing of audiences, the word-of-mouth also slides down, certain influence is caused to the playing volume of the big end, and the playing volume of the big end slides down. If the unified calculation model is used in the whole period of the prior art, the play amount of the integrated art drama can not be accurately predicted obviously, and the comprehensive art drama play amount prediction method provided by the embodiment of the application is applied, the calculation model and the prediction model are established by analyzing the influence factors and the play periods on the play amount of the comprehensive art drama, so that different play periods, different factors and influence degrees of the different factors are quantized, the play amount of the comprehensive art drama is accurately and quickly predicted, the comprehensive art drama with high commercial value can be directionally selected for advertising, and the advertising brand propaganda effect is maximized.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The embodiment also provides a system for predicting the playing amount of the comprehensive art drama. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 2 is a block diagram of a configuration of a heald drama play amount prediction system according to an embodiment of the present application, and as shown in fig. 2, the system includes:
the data acquisition module 101 is used for acquiring the variety scenario to be predicted;
the history data acquisition module 102 is configured to acquire history data of the integrated art scenario, where the history data includes one or any combination of popularity, word-of-mouth value, scenario value, topicality, and playing volume, and the popularity, word-of-mouth value, scenario value, and topicality is used as an influence factor of the playing volume, and specifically, the popularity of the integrated art scenario is calculated based on the number of fans of a main actor of the integrated art scenario; the public praise value is obtained from the internet, and optionally, the public praise value is set by referring to the bean petal score of the heddles; the plot value and the topic degree are also obtained from the Internet platform; optionally, the topic degree is calculated according to the microblog public opinion popularity degree; notably, the historical data is updatable in real time.
The influence coefficient acquisition module 103 is configured to divide the playing period of the variety scenario into different time periods, establish a corresponding relationship between an influence factor and a playing amount in the history data according to the playing period to construct a calculation model, where the specific calculation model is as shown in the formula (2) in the foregoing embodiment, and solve the calculation model to obtain influence coefficients of the influence factor on the playing amount of the variety scenario in different time periods; by way of example and not limitation, the influence factors of the embodiments of the present application are combinations of degree of awareness, word of mouth value, plot value and topicality. Specifically, the different time periods at least include: the playing initial stage, the playing middle stage and the playing later stage; optionally, the division of the time period may be divided according to the number of episodes of the actual integrated program episode or other factors, and in the embodiment of the present application, the playing period is divided into an initial playing period, a middle playing period, and a later playing period, but is not limited to the above division manner.
And the play amount prediction module 104 is used for establishing a prediction model according to the influence factors and the influence coefficients thereof, and calculating the play amount of the comprehensive art drama in different time periods based on the prediction model. Specifically, the prediction model is shown in formula (3) in the above embodiment.
Through the structure, the system establishes the calculation model and the prediction model by analyzing the influence factors and the play periods of the comprehensive art dramas, quantifies different play periods, different factors and influence degrees thereof, realizes accurate and rapid prediction of the play amount of the comprehensive art dramas, is beneficial to directionally selecting the comprehensive art dramas with higher commercial value for advertisement putting, and maximizes the advertising effect of the advertisement brand.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the above modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the method for predicting the playing amount of the comprehensive art drama in the embodiment of the present application described in conjunction with fig. 1 may be implemented by a computer device. The computer device may include a processor and a memory storing computer program instructions. In particular, the processor may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory may include a Hard Disk Drive (Hard Disk Drive, abbreviated HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical disc, a magneto-optical disc, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. The memory may include removable or non-removable (or fixed) media, where appropriate. The memory may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory is a Non-Volatile (Non-Volatile) memory. In particular embodiments, the Memory includes Read-Only Memory (ROM) and Random Access Memory (RAM). Where appropriate, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically Alterable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended Data Out Dynamic Random Access Memory (EDODRAM), a Synchronous Dynamic Random Access Memory (SDRAM), and the like.
The memory may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by the processor.
The processor reads and executes the computer program instructions stored in the memory to realize the method for predicting the playing amount of the variety drama in any one of the above embodiments.
In addition, in combination with the method for predicting the playing amount of the variety drama in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement the method. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the methods for predicting an amount of shows in an integrated program in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (4)
1. A method for predicting the playing amount of a comprehensive art drama is characterized by comprising the following steps:
a data acquisition step, which is used for acquiring the heddles to be predicted;
a historical data acquisition step, which is used for acquiring historical data of the comprehensive art drama, wherein the historical data comprises one or any combination of popularity, word-of-mouth value, scenario value, topicality and playing amount, and the influence factor of the playing amount is one or any combination of the popularity, the word-of-mouth value, the scenario value and the topicality;
an influence coefficient obtaining step, configured to divide different time periods into the playing period of the integrated art scenario, establish a corresponding relationship between an influence factor in the historical data and the playing amount according to the playing period to construct a calculation model, and solve the calculation model to obtain an influence coefficient of the influence factor on the playing amount of the integrated art scenario in the different time periods, where the different time periods at least include: at the initial stage of playing, the middle stage of playing and the later stage of playing, the calculation model is a multivariate linear equation set, and the expression is as follows:
wherein m represents the number of historical data sets solved by the model, m is a finite integer greater than 1, a m As degree of awareness in the historical data, b m Is a public praise value, c, in said historical data m For plot values in said historical data, d m Is topicality in the historical data, e m The playing amount in the historical data is;
and a playing amount prediction step, namely establishing a prediction model according to the influence factors and the influence coefficients thereof, calculating the playing amount of the variety scenario in different time periods based on the prediction model, and if the playing amount is represented as B, the expression of the prediction model is as follows:
B n ＝Y*x n +K*y n +D*z n +H*w n
y is the known name degree of the time period to be predicted, K is the word of mouth value of the time period to be predicted, D is the plot value of the time period to be predicted, H is the topic degree of the time period to be predicted, the value range of n is {1,2,3}, n =1 is used for representing the early stage of playing, n =2 is used for representing the middle stage of playing, n =3 is used for representing the later stage of playing, and x is used for representing the late stage of playing n Influence coefficient, y, of degree of awareness of corresponding time periods n Influence coefficient, z, being the public praise value of the corresponding time segment n Influence coefficient, w, of plot value for corresponding time segment n Influence coefficient of topic degree corresponding to time period, and x n +y n +z n +w n ＝1。
2. A system for predicting the playing amount of a comprehensive art scenario is characterized by comprising:
the data acquisition module is used for acquiring the variety scenario to be predicted;
the historical data acquisition module is used for acquiring historical data of the comprehensive art drama, wherein the historical data comprises one or any combination of popularity, word-of-mouth value, plot value, topicality and playing amount, and the influence factor of the playing amount is one or any combination of the popularity, the word-of-mouth value, the plot value and the topicality;
an influence coefficient obtaining module, configured to divide different time periods for a play cycle of the integrated art scenario, establish a corresponding relationship between an influence factor in the historical data and the play amount according to the play cycle to construct a calculation model, and solve the calculation model to obtain an influence coefficient of the influence factor on the play amount of the integrated art scenario in the different time periods, where the different time periods at least include: the calculation model is a multivariate linear equation set at the initial playing stage, the middle playing stage and the later playing stage, and the expression is as follows:
wherein m represents the number of historical data sets solved by the model, m is a finite integer greater than 1, a m As degree of awareness in the historical data, b m Is a public praise value, c, in said historical data m For plot values in said historical data, d m Is the topicality in the historical data, e m The playing amount in the historical data is;
the playing amount prediction module is used for establishing a prediction model according to the influence factors and the influence coefficients thereof, calculating the playing amount of the comprehensive art drama in different time periods based on the prediction model, and if the playing amount is represented as B, the expression of the prediction model is as follows:
B n ＝Y*x n +K*y n +D*z n +H*w n
y is the known name degree of the time period to be predicted, K is the word-of-mouth value of the time period to be predicted, D is the plot value of the time period to be predicted, H is the topicality of the time period to be predicted, the value range of n is {1,2,3}, n =1 is used for representing the early stage of playing, n =2 is used for representing the middle stage of playing, n =3 is used for representing the later stage of playing, and x =3 is used for representing the later stage of playing n Influence coefficient, y, of degree of awareness of corresponding time segment n As ports for corresponding time periodsInfluence coefficient of tombstone value, z n Influence coefficient, w, of plot value for corresponding time segment n Influence coefficient of topic degree of corresponding time period, and x n +y n +z n +w n ＝1。
3. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the synthesis scenario play amount prediction method of claim 1 when executing the computer program.
4. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the integrated drama playback amount prediction method according to claim 1.
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