CN113239231B - Advertisement putting management system based on big data - Google Patents
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Abstract
The invention discloses an advertisement putting management system based on big data, which comprises a putting video selecting module, a putting place selecting module, an image information collecting module and an image information analyzing module, wherein the putting video selecting module is used for selecting a plurality of suspected interest paragraphs from a complete advertisement video to serve as advertisement putting paragraphs, the putting place selecting module is used for selecting advertisement putting places for putting the advertisement putting paragraphs, the image information collecting module is used for collecting image information of viewers when the advertisement putting places put the advertisement putting paragraphs, the image information analyzing module is used for analyzing the image information of the viewers of each advertisement putting paragraph and obtaining the optimal putting paragraphs, and the management system further comprises a two-dimension code setting module which is used for setting two-dimension codes related to the complete advertisement video on the end video image of each suspected interest paragraph.
Description
Technical Field
The invention relates to the field of big data, in particular to an advertisement delivery management system based on big data.
Background
Advertisement is the most common propaganda marketing means in the information age today, and the merchant puts advertisements to be capable of propaganda of own products and attracting consumers to know the products, so that the consumers generate purchasing behavior, and therefore, putting advertisements is an indispensable propaganda behavior of the merchant. The advertisement is put in various forms, and the advertisement can be put in broadcast television media and internet multimedia. Among them, outdoor multimedia delivery advertisements are deeply favored by people. However, when the advertisement is put in outdoor multimedia, the longer the put advertisement is played, the more cost is required to put the advertisement.
Disclosure of Invention
The invention aims to provide an advertisement delivery management system and method based on big data, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the advertisement putting management system based on big data comprises a putting video selecting module, a putting place selecting module, an image information collecting module and an image information analyzing module, wherein the putting video selecting module is used for selecting a plurality of suspected interest paragraphs from a complete advertisement video to serve as advertisement putting paragraphs, the putting place selecting module is used for selecting advertisement putting places for putting the advertisement putting paragraphs, the image information collecting module is used for collecting image information of viewers when the advertisement putting paragraphs are put in the advertisement putting places, and the image information analyzing module is used for analyzing the image information of the viewers of each advertisement putting paragraph and obtaining optimal putting paragraphs.
More optimally, the management system further comprises a two-dimension code setting module, wherein the two-dimension code setting module is used for setting a two-dimension code associated with a complete advertisement video on an end video image of each suspected interest paragraph, the image information acquisition module comprises a face tracking module and an action tracking module, the face tracking module is used for tracking a watching time interval of a face of a person when the suspected interest paragraph is played, and the action tracking module is used for tracking whether the person lifts the two-dimension code on a mobile phone scanning end video image at a certain time point.
More optimally, the image information analysis module comprises a face tracking statistics module, an action tracking statistics module, an interestingness calculation module, a complete advertisement playing statistics module, a conversion rate calculation module, a comprehensive conversion rate calculation module, a value calculation module and an optimal delivery paragraph selection module, wherein the face tracking statistics module comprises a viewer classification module and a viewing time length statistics module, the viewer classification module is divided into different viewer types according to the difference of the viewing intervals tracked by the face tracking module and counts the number of the viewers of each viewer type, the viewing time length statistics module is used for counting the viewing time length of each viewer in each viewer type, the action tracking statistics module counts the number of the viewers lifting the two-dimensional code on the mobile phone scanning end video image according to the tracking result of the action tracking module, the interest degree calculation module calculates the interest degree of the watched suspected interest segments according to the viewers of each viewer category and the watching time length of each viewer, the complete advertisement playing statistics module is used for counting the playing times of the complete advertisement pointed by the two-dimension code of each suspected interest segment, the conversion rate calculation module calculates the first watching conversion rate and the second watching conversion rate according to the number of the viewers counted by the viewer classification module and the number of the viewers counted by the action tracking statistics module, calculates the third watching conversion rate according to the playing times counted by the complete advertisement playing statistics module and the number of the viewers counted by the action tracking statistics module, the comprehensive conversion rate calculation module calculates the comprehensive conversion rate according to the first watching conversion rate, the second watching conversion rate and the third watching conversion rate, the value calculating module calculates the value of the suspected interest paragraphs according to the interest level and the comprehensive conversion rate, and the optimal delivery paragraph selecting module is used for sequencing the value of each suspected interest paragraph according to the sequence from big to small and selecting the suspected interest paragraph sequenced first as the optimal delivery paragraph.
An advertisement delivery management method based on big data, the management method comprising the following steps:
step S1: selecting an advertisement putting paragraph and an advertisement putting place, and putting the advertisement putting paragraph at the advertisement putting place;
step S2: and collecting the image information of the viewers when the advertisement putting section is put in the advertisement putting place, and analyzing the image information of the viewers of each advertisement putting section to obtain the optimal putting section.
More preferably, the step S1 further includes:
selecting n suspected interest paragraphs from a complete advertisement video as advertisement delivery paragraphs, wherein each suspected interest paragraph is not overlapped, each suspected interest paragraph comprises a start point and an end point of each paragraph, a unique two-dimensional code is arranged on an end point video image of each suspected interest paragraph, each two-dimensional code is associated with the complete advertisement video, n advertisement delivery places with the same people flow are selected, and the n suspected interest paragraphs are delivered to the n advertisement delivery places for a h.
More preferably, the step S2 further includes:
step S21: acquiring image information of each advertisement putting place in the a hour, and performing face tracking on the image information in the a hour, wherein the face tracking comprises tracking a watching time interval of a face of a person when the suspected interesting paragraph is played, and if the watching time interval of the person comprises an end point of the suspected interesting paragraph, performing action tracking on the person, wherein the action tracking comprises whether the person lifts a two-dimensional code on a mobile phone scanning end point video image when the suspected interesting paragraph is played to the end point of the paragraph;
step S22: counting the number m1 of the viewers of the first category, the watching time length t1 of each viewer of the first category, the number m2 of the viewers of the second category and the watching time length t2 of each viewer of the first category according to the face tracking, and calculating the interestingness U of the suspected interest section according to the watching time length, wherein the first category is the viewer of which the watching time interval does not comprise the end point of the suspected interest section, and the second category is the viewer of which the watching time interval comprises the end point of the suspected interest section;
step S23: counting the number q of viewers of the two-dimensional code on the mobile phone scanning end video image lifted by the second category of viewers according to the action tracking, and respectively calculating a first viewing conversion rate H1=q/m 2 and a second viewing conversion rate H2=q/(m1+m2);
step S24: counting the number p of complete playing of the complete advertisement pointed by the two-dimensional code of each suspected interest paragraph, calculating a third viewing conversion rate H3=p/q,
the overall conversion v=0.3×h1+0.4×h2+0.3×h3;
step S24: calculating the value Z=0.3+0.7×V of each suspected interest paragraph, and selecting the best delivery paragraph according to the value.
More preferably, the step S22 further includes: .
Interest level of the suspected interest sectionWherein T is the paragraph duration of the suspected interest paragraph, j represents the j first class viewer, T1 j Represents the viewing duration of the j-th first category viewer, b represents the b-th second category viewer, t2 b Representing the viewing duration of the b second category of viewers.
More preferably, the step S24 further includes: and sequencing the value of each suspected interest paragraph according to the sequence from big to small, and selecting the first suspected interest paragraph as the best putting paragraph, wherein the best putting paragraph is used for being put on outdoor multimedia as an advertisement.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the optimal advertisement putting paragraph is selected from the complete advertisement video and is used as the advertisement to be put on the outdoor multimedia, and the two-dimensional code capable of viewing the complete advertisement is arranged on the terminal video image of the optimal advertisement putting paragraph, so that the cost required for putting the advertisement is reduced, and the audience can be attracted to watch the advertisement.
Drawings
FIG. 1 is a schematic diagram of a big data based advertising management system;
FIG. 2 is a flow chart of an advertisement delivery management method based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, in an embodiment of the present invention, an advertisement delivery management system based on big data includes a delivery video selection module, a delivery location selection module, an image information collection module, and an image information analysis module, where the delivery video selection module is configured to select a plurality of suspected interest paragraphs from a complete advertisement video as advertisement delivery paragraphs, the delivery location selection module is configured to select an advertisement delivery location where the advertisement delivery paragraphs are delivered, the image information collection module is configured to collect image information of viewers when the advertisement delivery paragraphs are delivered at the advertisement delivery location, and the image information analysis module is configured to analyze the image information of viewers of each advertisement delivery paragraph and obtain an optimal delivery paragraph.
The management system further comprises a two-dimension code setting module, wherein the two-dimension code setting module is used for setting a two-dimension code associated with a complete advertisement video on an end video image of each suspected interest paragraph, the image information acquisition module comprises a face tracking module and an action tracking module, the face tracking module is used for tracking a watching time interval when a face of a person is played in the suspected interest paragraph, and the action tracking module is used for tracking whether the person lifts the two-dimension code on the mobile phone scanning end video image at a certain time point.
The image information analysis module comprises a face tracking statistics module, an action tracking statistics module, an interestingness calculation module, a complete advertisement playing statistics module, a conversion rate calculation module, a comprehensive conversion rate calculation module, a value calculation module and an optimal delivery paragraph selection module, wherein the face tracking statistics module comprises a viewer classification module and a viewing time length statistics module, the viewer classification module is divided into different viewer types according to different viewing intervals tracked by the face tracking module and counts the number of viewers of each viewer type, the viewing time length statistics module is used for counting the viewing time length of each viewer in each viewer type, the action tracking statistics module counts the number of viewers lifting the two-dimensional code on the mobile phone scanning end point video image according to the tracking result of the action tracking module, the interest degree calculation module calculates the interest degree of the watched suspected interest paragraphs according to the viewers of each viewer category and the watching time length of each viewer, the complete advertisement playing statistics module is used for counting the playing times of the complete advertisement pointed by the two-dimensional code of each suspected interest paragraph, the conversion rate calculation module calculates the first watching conversion rate and the second watching conversion rate according to the number of viewers counted by the viewer classification module and the number of viewers counted by the action tracking statistics module, calculates the third watching conversion rate according to the playing times counted by the complete advertisement playing statistics module and the number of viewers counted by the action tracking statistics module, the comprehensive conversion rate calculation module calculates the comprehensive conversion rate according to the first watching conversion rate, the second watching conversion rate and the third watching conversion rate, the value calculation module calculates the value degree of the suspected interest paragraphs according to the interest degree and the comprehensive conversion rate, the optimal delivery paragraph selection module is used for sequencing the value degree of each suspected interest paragraph according to the sequence from big to small, and selecting the suspected interest paragraph with the first sequencing as the optimal delivery paragraph.
An advertisement delivery management method based on big data, the management method comprising the following steps:
step S1: selecting an advertisement putting paragraph and an advertisement putting place, and putting the advertisement putting paragraph in the advertisement putting place:
selecting n suspected interest paragraphs from a complete advertisement video as advertisement delivery paragraphs, wherein each suspected interest paragraph is not overlapped, each suspected interest paragraph comprises a starting point and an end point of each paragraph, a unique two-dimensional code is arranged on an end point video image of each suspected interest paragraph, each two-dimensional code is associated with the complete advertisement video, n advertisement delivery places with the same people flow are selected, and n suspected interest paragraphs are delivered to n advertisement delivery places for a h respectively; the advertisement expert selects a plurality of paragraphs capable of inducing the watching interests of the audience from a complete advertisement video as suspected interest paragraphs, puts the suspected interest paragraphs in different advertisement putting places, and selects the paragraphs capable of inducing the interests of the audience to put according to the reaction of the watching of the suspected interest paragraphs by the audience, thereby achieving the effects of improving the playing efficiency and attracting the audience; meanwhile, some paragraphs in the advertisement are played, so that suspense can be made, and a spectator is further attracted to watch the complete advertisement; the two-dimension code is used for the audience to scan, and the audience can watch the complete advertisement video by scanning the two-dimension code, so that advertisements are further known, in the embodiment, the advertisements pointed by the two-dimension code on each suspected interest paragraph are unique, the positions of the pointed advertisements are different, and the subsequent statistical analysis of the conversion rate of each suspected advertisement is facilitated; the selection of the advertisement delivery places should meet the condition that the people flow is the same as much as possible, so that the situation that the selection of the optimal delivery paragraphs is influenced because the people flow is too different is prevented, meanwhile, the adjacent advertisement delivery places should be as far away as possible, the situation that the advertisement delivery places are too far away from each other is prevented, and the advertisement delivery places of some viewers at one place lose the watching interests of other advertisement delivery places because the suspected interest paragraphs delivered have watched the complete advertisement videos;
step S2: collecting viewer image information when advertisement putting paragraphs are put in the advertisement putting place, and analyzing the viewer image information of each advertisement putting paragraph to obtain the optimal putting paragraph:
step S21: acquiring image information of each advertisement putting place in the a hour, and performing face tracking on the image information in the a hour, wherein the face tracking comprises tracking a watching time interval of a face of a person when the suspected interesting paragraph is played, and if the watching time interval of the person comprises an end point of the suspected interesting paragraph, performing action tracking on the person, wherein the action tracking comprises whether the person lifts a two-dimensional code on a mobile phone scanning end point video image when the suspected interesting paragraph is played to the end point of the paragraph;
step S22: counting the number m1 of the viewers in the first category and the watching time period t1 of each viewer in the first category according to the face tracking, counting the number m2 of the viewers in the second category and the watching time period t2 of each viewer in the first category, and calculating the interestingness of the suspected interest segment according to the watching time periodWherein T is the paragraph duration of the suspected interest paragraph, j represents the j first class viewer, T1 j Represents the viewing duration of the jth first category viewer, b represents the b-th first category viewerTwo kinds of viewers, t2 b Indicating the viewing duration of the b second category of viewers,
the first category is a viewer whose viewing time interval does not include the end point of the suspected interest paragraph, and the second category is a viewer whose viewing time interval includes the end point of the suspected interest paragraph; the longer the viewer's viewing time, the higher the degree to which this suspected interest passage is of interest to the viewer; the first category of viewers viewing time period and the second category of viewers viewing time period are counted separately because the first category of viewers does not see the end point of the suspected interest paragraph, so that the first category of viewers cannot be converted into viewers viewing the complete video, and the influence degree of the first category of viewers viewing the interest degree of the suspected interest paragraph is lower than that of the second category of viewers;
step S23: counting the number q of viewers of the two-dimensional code on the mobile phone scanning end video image lifted by the second category of viewers according to the action tracking, and respectively calculating a first viewing conversion rate H1=q/m 2 and a second viewing conversion rate H2=q/(m1+m2); when a viewer lifts the mobile phone to scan the two-dimension code, the content of the suspected interest paragraph is shown to arouse the interest of the viewer, the viewer has a wish to see the complete advertisement, and the more viewers of the two-dimension code are scanned, the more people converted from the suspected interest paragraph are shown to watch the complete advertisement;
step S24: counting the number p of complete playing of the complete advertisement pointed by the two-dimensional code of each suspected interest paragraph, calculating a third viewing conversion rate H3=p/q,
the overall conversion v=0.3×h1+0.4×h2+0.3×h3;
step S24: calculating the value Z=0.3+0.7×V of each suspected interest paragraph, selecting the best putting paragraph according to the value, sequencing the value of each suspected interest paragraph according to the sequence from big to small, and selecting the suspected interest paragraph with the first sequencing as the best putting paragraph, wherein the best putting paragraph is used for being put on outdoor multimedia as an advertisement.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (3)
1. A management method of an advertisement delivery management system based on big data is characterized in that: the management system comprises a putting video selecting module, a putting place selecting module, an image information collecting module and an image information analyzing module, wherein the putting video selecting module is used for selecting a plurality of suspected interest paragraphs from a complete advertisement video to serve as advertisement putting paragraphs, the putting place selecting module is used for selecting advertisement putting places for putting the advertisement putting paragraphs, the image information collecting module is used for collecting image information of viewers when the advertisement putting paragraphs are put in the advertisement putting places, and the image information analyzing module is used for analyzing the image information of the viewers of each advertisement putting paragraph and obtaining the optimal putting paragraphs;
the management method comprises the following steps:
step S1: selecting an advertisement putting paragraph and an advertisement putting place, and putting the advertisement putting paragraph at the advertisement putting place;
step S2: collecting image information of viewers when advertising paragraphs are placed in the advertising places, and analyzing the image information of the viewers of each advertising paragraph to obtain the optimal advertising paragraph;
the step S1 further includes:
selecting n suspected interest paragraphs from a complete advertisement video as advertisement delivery paragraphs, wherein each suspected interest paragraph is not overlapped, each suspected interest paragraph comprises a starting point and an end point of each paragraph, a unique two-dimensional code is arranged on an end point video image of each suspected interest paragraph, each two-dimensional code is associated with the complete advertisement video, n advertisement delivery places with the same people flow are selected, and n suspected interest paragraphs are delivered to n advertisement delivery places for a h respectively;
the step S2 further includes:
step S21: acquiring image information of each advertisement putting place in the a hour, and performing face tracking on the image information in the a hour, wherein the face tracking comprises tracking a watching time interval of a face of a person when the suspected interesting paragraph is played, and if the watching time interval of the person comprises an end point of the suspected interesting paragraph, performing action tracking on the person, wherein the action tracking comprises whether the person lifts a two-dimensional code on a mobile phone scanning end point video image when the suspected interesting paragraph is played to the end point of the paragraph;
step S22: counting the number m1 of the viewers of the first category, the watching time length t1 of each viewer of the first category, the number m2 of the viewers of the second category and the watching time length t2 of each viewer of the first category according to the face tracking, and calculating the interestingness U of the suspected interest section according to the watching time length, wherein the first category is the viewer of which the watching time interval does not comprise the end point of the suspected interest section, and the second category is the viewer of which the watching time interval comprises the end point of the suspected interest section;
step S23: counting the number q of viewers of the two-dimensional code on the mobile phone scanning end video image lifted by the second category of viewers according to the action tracking, and respectively calculating a first viewing conversion rate H1=q/m 2 and a second viewing conversion rate H2=q/(m1+m2);
step S24: counting the number p of complete playing of the complete advertisement pointed by the two-dimensional code of each suspected interest paragraph, calculating a third viewing conversion rate H3=p/q,
the overall conversion v=0.3×h1+0.4×h2+0.3×h3;
step S24: calculating the value Z=0.3+0.7×V of each suspected interest paragraph, and selecting the best delivery paragraph according to the value.
2. The method for managing a big data based advertisement delivery management system according to claim 1, wherein: the step S22 further includes:
interest level of the suspected interest sectionWherein T is the paragraph duration of the suspected interest paragraph, j represents the j first class viewer, T1 j Represents the viewing duration of the j-th first category viewer, b represents the b-th second category viewer, t2 b Representing the viewing duration of the b second category of viewers.
3. The method for managing a big data based advertisement delivery management system according to claim 1, wherein: the step S24 further includes: and sequencing the value of each suspected interest paragraph according to the sequence from big to small, and selecting the first suspected interest paragraph as the best putting paragraph, wherein the best putting paragraph is used for being put on outdoor multimedia as an advertisement.
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| CN202110421253.6A CN113239231B (en) | 2020-04-27 | 2020-04-27 | Advertisement putting management system based on big data |
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| CN202010344442.3A CN111553731B (en) | 2020-04-27 | 2020-04-27 | Advertisement putting management system and method based on big data |
| CN202110421253.6A CN113239231B (en) | 2020-04-27 | 2020-04-27 | Advertisement putting management system based on big data |
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| CN102222306A (en) * | 2011-06-23 | 2011-10-19 | 迈普通信技术股份有限公司 | Method and system for feeding back putting effect of unidirectional video advertisement |
| CN106709765A (en) * | 2017-01-11 | 2017-05-24 | 北京图知天下科技有限责任公司 | Advertisement delivery management method |
| CN109344726A (en) * | 2018-09-05 | 2019-02-15 | 顺丰科技有限公司 | A kind of advertisement placement method and device |
| CN110751502A (en) * | 2019-09-10 | 2020-02-04 | 深圳市铂骏科技开发有限公司 | Advertisement putting tracking method and device and terminal equipment |
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| US10319046B2 (en) * | 2012-07-20 | 2019-06-11 | Salesforce.Com, Inc. | System and method for aggregating social network feed information |
| CN110611710A (en) * | 2019-09-11 | 2019-12-24 | 王英敏 | Outdoor intelligent advertisement promotion system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222306A (en) * | 2011-06-23 | 2011-10-19 | 迈普通信技术股份有限公司 | Method and system for feeding back putting effect of unidirectional video advertisement |
| CN106709765A (en) * | 2017-01-11 | 2017-05-24 | 北京图知天下科技有限责任公司 | Advertisement delivery management method |
| CN109344726A (en) * | 2018-09-05 | 2019-02-15 | 顺丰科技有限公司 | A kind of advertisement placement method and device |
| CN110751502A (en) * | 2019-09-10 | 2020-02-04 | 深圳市铂骏科技开发有限公司 | Advertisement putting tracking method and device and terminal equipment |
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| CN111553731A (en) | 2020-08-18 |
| CN113239231A (en) | 2021-08-10 |
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