CN109033167B - Movie classification method and system - Google Patents
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- CN109033167B CN109033167B CN201810637800.2A CN201810637800A CN109033167B CN 109033167 B CN109033167 B CN 109033167B CN 201810637800 A CN201810637800 A CN 201810637800A CN 109033167 B CN109033167 B CN 109033167B
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Abstract
The invention provides a movie grading method and a movie grading system, wherein the method comprises the following steps: in the movie playing process, acquiring real-time physiological response signals of different types of user groups; determining the real-time concentration degree of each type of user group according to the real-time physiological response signals of each type of user group; comparing the real-time concentration degree of each type of user group with a preset threshold value, and determining a target type of user group with the real-time concentration degree larger than the threshold value; and extracting group labels corresponding to the target type user groups, and setting classification labels corresponding to the movies according to the group labels. Therefore, the real-time physiological response signals which are difficult to change by the user are used as the basis, the film is accurately graded, and the method has important significance for ensuring the box office of the film and the like.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a method and a system for classifying movies.
Background
At present, the film has other artistic characteristics in artistic expressive force, and the artistic leaping film assembling skill of Mengtie can be used, so that it has the expression means superior to all other artists, and can be extensively copied and projected. With the popularization of movies, movie-related industries have also been developed, wherein movie boxes are widely used by movie-related industries as a main reference for movie revenues.
In the related art, the box office is pulled through vigorous propaganda, for example, advertisements are published on a social network site, however, the propaganda method using audiences as all people has poor effect and poor movie propaganda effect.
Disclosure of Invention
The invention provides a movie classification method and a movie classification system, which aim to solve the technical problem that the effect of movie publicity and the like is not good in the prior art.
The embodiment of the invention provides a movie classification method, which comprises the following steps: in the movie playing process, acquiring real-time physiological response signals of different types of user groups; determining the real-time concentration degree of each type of user group according to the real-time physiological response signals of each type of user group; comparing the real-time concentration degree of each type of user group with a preset threshold value, and determining a target type of user group with the real-time concentration degree larger than the threshold value; and extracting a group label corresponding to the target type user group, and setting a classification label corresponding to the movie according to the group label.
Another embodiment of the present invention provides a movie classification system, including: the system comprises physiological response signal acquisition equipment and a processor, wherein the physiological response signal acquisition equipment is connected with the processor, and the physiological response signal acquisition equipment is used for acquiring real-time physiological response signals of different types of user groups in the movie playing process; the processor is configured to determine a real-time concentration degree of each type of user group according to the real-time physiological response signal of each type of user group, compare the real-time concentration degree of each type of user group with a preset threshold, determine a target type of user group of which the real-time concentration degree is greater than the threshold, extract a group tag corresponding to the target type of user group, and set a classification tag corresponding to the movie according to the group tag.
Yet another embodiment of the present invention provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the movie classification method as described in the above embodiments.
Yet another embodiment of the present invention provides a computer program product, wherein when the instructions of the computer program product are executed by a processor, the movie classification method according to the above embodiment is performed.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in the movie playing process, real-time physiological response signals of different types of user groups are obtained, the real-time concentration degree of each type of user group is determined according to the real-time physiological response signals of each type of user group, the real-time concentration degree of each type of user group is compared with a preset threshold value, a target type of user group with the real-time concentration degree larger than the threshold value is determined, then, a group label corresponding to the target type of user group is extracted, and a classification label corresponding to the movie is set according to the group label. Therefore, the method and the device have the advantages that the real-time physiological response signals which are difficult to change by the user are used as the basis, the movies are accurately classified, and the important significance is achieved for guaranteeing the box office of the movies and the like.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a movie classification method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a movie classification method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a movie classification method according to a third embodiment of the present invention;
fig. 4 is a flowchart of a movie classification method according to a fourth embodiment of the present invention;
fig. 5(a) is a schematic view of an application scenario of the movie classification method according to an embodiment of the present invention;
fig. 5(b) is a schematic diagram of an application scenario of a movie classification method according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of the structure of a movie classification system according to one embodiment of the invention; and
fig. 7 is a schematic structural diagram of a movie classification system according to another embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The movie classification method and system according to the embodiment of the present invention will be described with reference to the drawings.
Fig. 1 is a flowchart of a movie classification method according to a first embodiment of the present invention. As shown in fig. 1, the method includes:
It can be understood that, in order to solve the technical problem of inaccurate movie publicity effect in the prior art, in the embodiment of the present invention, a plurality of types of user groups are preset, and movies are classified by detecting physiological response signals of the different types of user groups, so that, since the real-time physiological response signals of the users directly reflect the interest degree of the movies, when the users are interested in the movies, the users obviously see the related movies, and therefore, the classification of the movies based on the physiological response signals of the users has an important meaning for improving movie box rooms.
The different types of user groups can be set according to application needs, the setting of the different types of user groups is convenient for further prediction of interested groups of the movie, the different types of user groups can be divided according to ages and comprise adolescent groups, youth groups, middle-aged groups, old-aged groups and the like, and the different types of user groups can also be divided according to professions and comprise education industry groups, entertainment industry groups, art industry groups and the like.
The physiological response signal may include one or a combination of more of a skin conductance signal, a heart rate signal, an electrocardiogram signal, an eye movement signal, and an electroencephalogram signal, which is not limited herein.
Specifically, in the embodiment of the present invention, real-time physiological response signals of different types of user groups can be collected through the skin sensor device, wherein the skin sensor device can be different devices according to different application scenarios, for example, a device capable of directly contacting with a wrist band, a hat, a glove, a necklace, a face sticker, etc. that contains the skin sensor to view the skin of a user can be provided.
And 102, determining the real-time concentration degree of each type of user group according to the real-time physiological response signals of each type of user group.
In different application scenarios, the concentration degree of each type of user group may be represented by a specific numerical value, for example, a number in a percentile system, where a larger numerical value represents that the user in each type of user group is more concentrated, for example, a rank in a rank system represents that the user in each type of user group is more concentrated, and for example, the larger rank represents that the user in each type of user group is more concentrated, and the larger number of symbols represents that the user in each type of user group is more concentrated, for example, the number of specific symbol signs (star sign, flower sign, heart sign, etc.) is represented.
It can be understood that the physiological response signals of the various types of user groups truly reflect the real-time attentiveness of the various types of user groups, for example, when the physiological response signals include skin conductance signals, the skin presents a certain resistance to current or voltage, and the magnitude of the resistance changes with mood changes, generally, in a relaxed state, the user corresponding to the various types of user groups may be currently in the nerve, the attentiveness for watching is not very high, the current movie does not arouse the interest of the user, the resistance of the human skin is relatively high, so that the skin conductance signals are relatively low, in a mental stress, the user corresponding to the various types of user groups may be currently in the attention for watching the current performance, the resistance of the human skin is relatively low, so that the skin conductance signals of the human body are relatively high, because the sympathetic and parasympathetic nerves perform antagonistic adjustment according to the change of the cognitive state of the brain, whereas sympathetic and parasympathetic activity affects skin resistance.
For another example, when the physiological response signal includes a heart rate signal, if the heart rate fluctuation of the user corresponding to each type of user group is small, it indicates that the user corresponding to each type of user group is more attentive to the current movie, and if the heart rate variation of the user corresponding to each type of user group is large, it indicates that the user is more inattentive to the current movie.
It should be noted that, under different application scenarios, the determination of the real-time concentration degree of each type of user group for watching the movie according to the real-time physiological response signal of each type of user group can be implemented in different manners, which is illustrated as follows:
the first example:
in this example, the corresponding relationship between the attentiveness and the physiological response signal is obtained and stored in advance according to a large amount of experimental data, so that after the physiological response signal is obtained, the corresponding relationship is queried to obtain the real-time attentiveness for watching the movie of each type of matched user group.
The second example is:
and constructing a deep network model of the physiological response signals in advance according to a large amount of experimental data, wherein the model inputs the physiological response signals and outputs the concentration degrees for watching by each type of user group, so that the acquired physiological response signals for watching by each type of user group are input into the deep network model to obtain the real-time concentration degrees for watching by each type of user group.
The third example:
in this example, as shown in fig. 2, the step 102 includes:
It should be understood that, in different application scenarios, the preset strategy for analyzing the real-time physiological response signals of each type of user group is different, so that the extracted concentration characteristic information of each type of user group is different:
as a possible implementation manner, the concentration feature information is the concentration times, and the concentration times can be extracted by detecting the times that the physiological response signal is greater than the preset threshold value.
For example, when the physiological response signal is a skin conductance signal, since in practical applications, the concentration of each type of user group is higher, the brain nerve activity of the user is richer, the influence of the stimulation of the movie content and the sympathetic nerve activity changes the conductivity of the skin surface (the cause of which is related to the activity of the sympathetic nerve and sweat gland), the concentration of the mental state is higher, the skin conductance is higher, and the skin conductance signal obtained by detection is higher according to the principle.
For example, when the physiological response signal is an eye movement signal, the more attentive the group of users of each type is watching, the more the eyes of the watching user move with the performer in the movie, so that the change rate of the detected eye movement signal is high, and the less attentive the group of users of each type is watching, the less the eyes of the watching user move with the performer or the sight line is not focused on the screen, so that the change rate of the detected eye movement signal is low or the point of regard is beyond the screen.
Thus, in this example, a preset threshold corresponding to the physiological response signal is set in advance according to a large amount of experimental data, and the concentration times are extracted for the times when the extracted physiological response signal is greater than the preset threshold.
As another possible implementation, the concentration time is extracted when the real-time physiological response signal is detected to be greater than a preset threshold.
In practical applications, the more attentive the watching of each type of user group is, the more abundant the cranial nerve activity of the watching user is, for example, when the physiological response signal is a skin conductance signal, the stimulation of the movie content and the influence of sympathetic nerve activity may change the conductivity of the skin surface (the cause of which is related to the activity of sympathetic nerves and sweat glands), the more attentive the mental state is, the larger the skin conductance is, and the larger the skin conductance signal obtained by the detection according to the principle is.
Thus, in this example, the concentration time may also be extracted at a time when the real-time physiological response signal is greater than a preset threshold.
As yet another possible implementation, the concentration intensity may be extracted by detecting an amplitude of the real-time physiological response signal that is greater than a preset threshold.
Since the concentration intensity of each type of user group viewing can be reflected in the physiological response signal in practical application, the concentration intensity of each type of user group viewing can be extracted according to the magnitude of the physiological response signal, for example, when the physiological response signal includes a skin conduction signal, the richer the cranial nerve activity of the viewing user is, the more the brain nerve activity is affected by the stimulation of the movie content and the sympathetic nerve activity, the conductivity of the skin surface is changed (the cause of which is related to the activity of the sympathetic nerve and the sweat gland), the more the mental state is concentrated, the larger the skin conductance is, and the larger the skin conductance signal obtained by detection according to this principle is.
Thus, in this example, the amplitude extraction concentration intensity of the physiological response signal greater than the preset threshold may also be detected, for example, the amplitude extraction concentration intensity of the skin conductance signal greater than the preset threshold is detected, for example, if the preset threshold is a and the current skin conductance signal is B greater than a, then B-a may be used as the concentration intensity.
In different application scenarios, the concentration characteristic information collected by the three examples and viewed by each type of user group may be used as an individual reference factor for further determining the concentration degree viewed by each type of user group, or a combination of any two of the concentration characteristic information collected by the three examples and viewed by each type of user group may be used as a reference factor for further determining the concentration degree viewed by each type of user group, or the concentration characteristic information collected by the three examples and viewed by each type of user group may be used as a reference factor for further determining the concentration degree viewed by each type of user group.
In addition, in order to ensure the accuracy of further determining the concentration degree of each type of user group, in an embodiment of the present invention, the preset threshold value compared with the physiological response signal may be set according to the type of the constitution viewed by each type of user group, for example, when the physiological response signal includes a skin conductance signal, the cutin and dryness of the skin surface are different for a female type of user group and a male user group, or for user groups of different ages, so that the skin conductance signal measured at the same concentration degree is different, and in order to compensate for the difference in constitution of the user groups, the difference data may be obtained according to a large amount of experimental data, and different preset threshold values are set for different constitution user groups, for example, since the sweat glands of males are more developed, the skin is more moist, and the electrical conductivity is stronger, the preset threshold thus set is relatively high, etc.
Specifically, in order to judge the concentration degree of each type of user group in watching the movie, a preset algorithm is applied to calculate the real-time concentration characteristic information, and the real-time concentration degree of each type of user group is obtained.
Specifically, according to different application scenarios, the preset algorithm is applied to calculate the concentration characteristic information, and the manner of obtaining the real-time concentration degree of each type of user group is different, and the following example is performed by combining different application scenarios:
scene one:
in this scenario, the real-time concentration feature information of each type of user group is single feature information, such as only concentration times, or concentration time, or concentration intensity.
Since the larger the data value corresponding to the concentration characteristic information of each type of user group is, for example, the larger the concentration frequency is, it indicates that each type of user group concentrates on the current movie, the preset algorithm in this scenario may be a linear operation algorithm corresponding to the concentration characteristic information, for example, the algorithm may be Y ═ a × X, where Y is the concentration degree of the viewing user, X is the data value corresponding to the concentration characteristic information, and a may be any number greater than 0.
For example, when the concentration degree of each type of user group is determined, the concentration time of each type of user group is usually more meaningful than the reference of the concentration times, because each type of user group is sometimes considered to be concentrated on the current movie for a plurality of times but has a shorter duration, or is considered to be not concentrated on the current movie, the a may correspond to different weight values of the concentration characteristic information, for example, when the concentration characteristic information is the concentration times, the corresponding a is 0.6, and when the concentration characteristic information is the concentration time, the corresponding a is 0.8.
Scene two:
in this scenario, the concentration feature information of each type of user group is a plurality of feature information, such as concentration times and concentration time, or concentration time and concentration intensity, or concentration times, concentration time and concentration intensity, and the like.
Since the larger the data value corresponding to the concentration characteristic information of each type of user group is, for example, the larger the concentration frequency is, the more concentrated the user group is on the current movie, the corresponding preset algorithm is positively correlated with the data value corresponding to the concentration characteristic information of each type of user group, for example, Y ═ a1 × X1+ … + an Xn, where n is a positive integer greater than or equal to 2, a1 to an are positive numbers, a1 to an may be equal to or may be unequal, when a1 to an are unequal, the preset algorithm may be used to represent weighted values of different reference meanings of the concentration characteristic information of different types of user groups to the concentration degree, and X1 to Xn represent data values corresponding to the concentration characteristic information of different types of user groups.
Of course, in the actual operation process, the preset algorithm in the scene may also be any algorithm expression that embodies positive correlation of the data values corresponding to the concentration characteristic information of each type of user group, which is not listed here.
And 103, comparing the real-time concentration degree of each type of user group with a preset threshold value, and determining the target type of user group with the real-time concentration degree larger than the threshold value.
And 104, extracting the group label corresponding to the target type user group, and setting a classification label corresponding to the movie according to the group label.
It should be emphasized that the more various user groups of each type are, the more comprehensive the user groups are covered with the actual watching users, the more accurate the predicted movie box office is, and it can be understood that in the embodiment of the present invention, the user corresponding to each user group of each type can relatively completely cover the type of the actual watching user in the corresponding dimension no matter what group classification manner is adopted, so that the degree of interest of the user in the movie of the predicted box office is inferred according to the real-time concentration degree of the user group watching the movie, and therefore, the most interested user type of the movie can be predicted according to the real-time concentration degree of the user group watching the movie.
Specifically, a preset threshold corresponding to the real-time concentration degree is preset, when the real-time concentration degree is greater than the corresponding preset threshold, it is indicated that the corresponding type user group is interested in the current movie, otherwise, when the real-time concentration degree is less than or equal to the corresponding preset threshold, it is indicated that the corresponding type user group is not interested in the current movie, therefore, the real-time concentration degree of each type user group is compared with the preset threshold, and a target type user group with the real-time concentration degree greater than the threshold is determined, so that the target type user group is a group with interest in the current movie, a group label corresponding to the target type user group is extracted, and a classification label corresponding to the movie is set according to the group label, so that classification of the movie in the type dimension of the interested user is realized, and the targeted propaganda of the movie is facilitated, thereby improving the movie box house.
In an embodiment of the present invention, as shown in fig. 3, after step 104, the method further includes:
and 301, formulating a propaganda strategy matched with the target type user group according to the classification label.
It should be understood that, for example, if the interested target type user group is teenagers, the promotion strategy achieving a better promotion effect should be matched with the preferences of the teenagers, and advertisements can be put in the mini-games on the social network site, or if the interested target type user group is old, the promotion strategy achieving a better promotion effect should be matched with the preferences of the old, and advertisements can be put in newspapers.
Specifically, according to the classification labels, propaganda strategies matched with the target type user groups are formulated, and the movies are advertised to the target type user groups according to the propaganda strategies, so that a good advertising effect is achieved.
In an embodiment of the present invention, as shown in fig. 4, after the step 104, the method further includes:
It should be understood that, for example, the interested target type user group is teenagers, the movie arrangement place and the movie arrangement time for achieving the better publicity effect should be consistent with the living habits of teenagers, and the movie arrangement can be performed near schools and at vacation and school time, or for example, the interested target type user group is old people, the movie arrangement place and the movie arrangement time for achieving the better publicity effect should be consistent with the living habits of old people, and the movie can be taken in the daytime of the activity center of old people.
Specifically, the activity area and the idle time of the target type user group are obtained, wherein the activity area and the idle time of the target type user group are obtained by acquiring the life data of the corresponding type user, and the film arrangement place and the film arrangement time of the movie are determined according to the activity area and the idle time under different application scenes, including but not limited to user registration, or the like through the analysis of the collected life data of the corresponding type user, so that better box office income is achieved.
Of course, management of the movies may be performed according to the set classification tags corresponding to the movies, for example, when the classification tags corresponding to the movies are teenagers, the corresponding management standards are strict, so as to contribute to physical and mental health of the teenagers.
In order to more clearly illustrate the implementation process of the movie classification method of the present invention, the following example is performed in conjunction with specific application scenarios, and the following description is given:
in this example, different types of user groups 1, 2, and 3 are preset, where the user type corresponding to the user group 1 is a teenager user, the user type corresponding to the user group 2 is a middle-aged user, the user type corresponding to the user group 3 is an elderly user, the user groups 1, 2, and 3 basically cover all types of viewing users who may watch movies, and a movie manufacturer needs to release a movie a and a movie B.
In the embodiment of the present invention, as shown in fig. 5(a) and 5(B), in the process of playing a movie a and a movie B respectively, real-time physiological response signals of the user groups 1, 2, and 3 are obtained, real-time attentiveness of the user groups 1, 2, and 3 is determined according to the real-time physiological response signals of the user groups 1, 2, and 3, the real-time attentiveness of the user groups 1, 2, and 3 is compared with a preset threshold, and a target type user group with the real-time attentiveness greater than the threshold is determined, wherein the target type user group 1 of the target type user group of the movie a is determined, the target type user group 2 of the target type user group of the movie B is determined, a group tag corresponding to the target type user group is extracted, a classification tag corresponding to the movie is set according to the group tag, as shown in fig. 5(a), the movie a is set for juvenile classification, as shown in fig. 5(B), the middle-age category is set for movie B so as to promote mainly teenagers for movie a and the like, and promote mainly middle-ages for movie B and the like, to realize higher box rooms for movie a and movie B.
In summary, in the movie classification method according to the embodiment of the present invention, in the movie playing process, real-time physiological response signals of different types of user groups are obtained, the real-time concentration degree of each type of user group is determined according to the real-time physiological response signals of each type of user group, the real-time concentration degree of each type of user group is compared with a preset threshold, a target type of user group with the real-time concentration degree greater than the threshold is determined, further, a group tag corresponding to the target type of user group is extracted, and a classification tag corresponding to the movie is set according to the group tag. Therefore, the method and the device have the advantages that the real-time physiological response signals which are difficult to change by the user are used as the basis, the movies are accurately classified, and the important significance is achieved for guaranteeing the box office of the movies and the like.
In order to implement the foregoing embodiment, the present invention further provides a movie classification system, and fig. 6 is a schematic structural diagram of the movie classification system according to an embodiment of the present invention, as shown in fig. 6, the system includes: a physiological response signal acquisition device and a processor 200, wherein the physiological response signal acquisition device 100 is connected with the processor 200.
The physiological response signal acquisition device 100 is configured to acquire real-time physiological response signals of different types of user groups during a movie playing process.
The processor 200 is configured to determine a real-time concentration degree of each type of user group according to the real-time physiological response signal of each type of user group, compare the real-time concentration degree of each type of user group with a preset threshold, determine a target type of user group with the real-time concentration degree greater than the threshold, extract a group tag corresponding to the target type of user group, and set a classification tag corresponding to the movie according to the group tag.
In one embodiment of the invention, as shown in fig. 7, the processor 200 includes an extraction unit 210 and an acquisition unit 220.
The extracting unit 210 is configured to analyze the real-time physiological response signals of the user groups of the various types according to a preset policy, and extract the real-time concentration feature information of the user groups of the various types.
The obtaining unit 220 is configured to calculate the real-time concentration feature information of each type of user group by applying a preset algorithm, and obtain the real-time concentration degree of each type of user group.
In one embodiment of the present invention, the extracting unit 210 extracts the concentration times by detecting the times that the real-time physiological response signal is greater than the preset threshold.
In one embodiment of the present invention, the extraction unit 210 extracts the concentration time when the real-time physiological response signal is greater than the preset threshold.
In one embodiment of the present invention, the extraction unit 210 detects the amplitude of the real-time physiological response signal greater than the preset threshold value to extract concentration intensity.
It should be noted that the foregoing explanation of the embodiment of the movie classification method is also applicable to the movie classification system of this embodiment, and details not disclosed in the embodiment of the movie classification system of the present invention are not repeated herein.
In summary, in the movie classification system according to the embodiment of the present invention, in the movie playing process, the real-time physiological response signals of different types of user groups are obtained, the real-time concentration degree of each type of user group is determined according to the real-time physiological response signals of each type of user group, the real-time concentration degree of each type of user group is compared with the preset threshold, the target type of user group with the real-time concentration degree greater than the threshold is determined, then, the group tag corresponding to the target type of user group is extracted, and the classification tag corresponding to the movie is set according to the group tag. Therefore, the method and the device have the advantages that the real-time physiological response signals which are difficult to change by the user are used as the basis, the movies are accurately classified, and the important significance is achieved for guaranteeing the box office of the movies and the like.
To achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium, in which instructions, when executed by a processor, enable execution of the movie classification method according to the above embodiments.
In order to implement the above embodiments, the present invention further provides a computer program product, which when executed by an instruction processor in the computer program product, executes the movie classification method according to the above embodiments.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (5)
1. A method for classifying a motion picture, comprising the steps of:
in the movie playing process, acquiring real-time physiological response signals of different types of user groups;
analyzing real-time physiological response signals of various types of user groups according to a preset strategy, extracting real-time concentration characteristic information of the various types of user groups, calculating the real-time concentration characteristic information of the various types of user groups by applying a preset algorithm, and acquiring real-time concentration degree of the various types of user groups, wherein the real-time concentration characteristic information comprises at least one of concentration times and concentration time, and the extraction of the real-time concentration characteristic information of the various types of user groups comprises the following steps: detecting the times that the real-time physiological response signal is greater than a first preset threshold value, extracting concentration times, and/or detecting the time that the real-time physiological response signal is greater than the first preset threshold value, extracting concentration time, wherein the first preset threshold value is set according to the physique types of all types of user groups;
comparing the real-time concentration degree of each type of user group with a second preset threshold value, and determining a target type of user group with the real-time concentration degree larger than the second preset threshold value;
extracting a group label corresponding to the target type user group, and setting a classification label corresponding to the movie according to the group label;
formulating a propaganda strategy matched with the target type user group according to the classification label;
and advertising the movie to the target type user group according to the advertising strategy.
2. The method of claim 1, wherein the physiological response signal comprises:
one or more of skin conductance signal, heart rate signal, electrocardiosignal, eye movement signal and electroencephalogram signal.
3. The method of claim 1, wherein after the setting of the category label corresponding to the movie according to the group label, further comprising:
acquiring an active area and idle time of the target type user group;
and determining the film arrangement place and the film arrangement time of the film according to the active area and the idle time.
4. A motion picture classification system, comprising: a physiological response signal acquisition device and a processor, wherein the physiological response signal acquisition device is connected with the processor, wherein,
the physiological response signal acquisition equipment is used for acquiring real-time physiological response signals of different types of user groups in the movie playing process;
the processor is used for determining the real-time concentration degree of each type of user group according to the real-time physiological response signals of each type of user group, comparing the real-time concentration degree of each type of user group with a second preset threshold value, determining a target type of user group with the real-time concentration degree larger than the second preset threshold value, extracting a group label corresponding to the target type of user group, and setting a classification label corresponding to the movie according to the group label; formulating a propaganda strategy matched with the target type user group according to the classification label, and propagandizing the movie for the target type user group according to the propaganda strategy;
the processor includes: the extraction unit is used for analyzing the real-time physiological response signals of the user groups of all types according to a preset strategy and extracting real-time concentration characteristic information of the user groups of all types, wherein the real-time concentration characteristic information comprises at least one of concentration times and concentration time; the extraction unit is specifically configured to: detecting the times that the real-time physiological response signal is greater than a first preset threshold value, extracting concentration times, and/or detecting the time that the real-time physiological response signal is greater than the first preset threshold value, extracting concentration time, wherein the first preset threshold value is set according to the physique types of all types of user groups;
and the acquisition unit is used for calculating the real-time concentration characteristic information of each type of user group by applying a preset algorithm and acquiring the real-time concentration degree of each type of user group.
5. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the movie classification method according to any one of claims 1-3.
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