A distributed advertising machine cooperative display method and system supporting cross-screen interaction

Abstract

The application discloses a kind of distributed advertising machine cooperative display method and system supporting cross-screen interaction, it is related to distributed advertising machine cooperative display technical field.This kind of distributed advertising machine cooperative display method and system supporting cross-screen interaction, current scene is identified and scene environment data is obtained, and then the type of distributed advertising machine is set by current scene type setting advertisement type, and scene environment data is used to adjust distributed advertising machine advertisement delivery parameter;Current scene flow data is monitored, and flow data is used to dynamically adjust distributed advertising machine advertisement cooperative display picture;Based on the interactive link in distributed advertising machine advertisement cooperative display picture and is obtained user interaction data, and then according to user interaction data, distributed advertising machine is periodically screened to the advertisement of being delivered.It is realized that more intelligent, personalized advertisement delivery management is carried out, so as to improve advertisement effect and user participation, while optimizing resource use and cost control.

Description

Technical Field

[0001] This invention relates to the field of distributed advertising machine collaborative display technology, specifically to a distributed advertising machine collaborative display method and system that supports cross-screen interaction. Background Technology

[0002] With the widespread adoption of mobile devices and smart display technologies, people's attention is scattered across multiple screens. A person may use a mobile phone, tablet, and TV simultaneously, which provides advertisers with more touchpoints and interaction opportunities. Traditional advertising methods can no longer meet consumers' increasingly personalized needs. Current research on cross-screen interaction is expected to enhance user experience and engagement through multi-screen collaborative display, thereby improving advertising effectiveness and brand relevance.

[0003] Current advertising display methods are static, such as playing the same advertising content in a fixed location or at a fixed time. This method cannot adjust the advertising content and parameters according to the real-time environment and audience characteristics, lacking personalization and targeting. Most advertising formats lack interactive mechanisms with the audience, who usually passively receive advertising content, making it difficult to stimulate active participation and feedback from the audience, thus limiting interactivity. Furthermore, current advertising displays lack the ability to perceive and adapt to the current scene environment, and cannot dynamically adjust advertising content and display format according to different locations, times, and pedestrian flow. They also cannot promptly filter and optimize advertising content based on user interactions and complaints.

[0004] Therefore, in order to address the above problems, there is an urgent need for a distributed advertising machine collaborative display method and system that supports cross-screen interaction. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a distributed advertising machine collaborative display method and system that supports cross-screen interaction. It solves the problems of advertising placement not being relevant to user interests and current scene characteristics, having low attractiveness, lacking dynamic adjustment of the display content and parameters of the advertising machine to effectively utilize advertising resources, improve the efficiency and cost-effectiveness of advertising placement, and the inability of advertising content to adapt to market changes and changes in audience interests in a timely manner.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a distributed advertising machine collaborative display method supporting cross-screen interaction, comprising the following steps: identifying the current scene and acquiring scene environment data, then setting the type of advertisement to be delivered by the distributed advertising machine based on the current scene type, and adjusting the advertisement delivery parameters of the distributed advertising machine using the scene environment data; monitoring the current scene pedestrian flow data, and dynamically adjusting the collaborative display screen of the distributed advertising machine using the pedestrian flow data; acquiring user interaction data based on the interactive links delivered in the collaborative display screen of the distributed advertising machine, and then periodically filtering the advertisements delivered by the distributed advertising machine based on the user interaction data.

[0007] Furthermore, the specific analysis of adjusting the advertising parameters of distributed advertising machines using scene environment data is as follows: The scene environment data specifically includes the current color temperature, current light intensity, and the position of each distributed advertising machine; the current color temperature is used to identify the corresponding white balance parameters in the database, and the white balance of the distributed advertising machines is adjusted accordingly; the brightness of the distributed advertising machines is adjusted by utilizing the inverse relationship between the current light intensity and the brightness of the distributed advertising machine's display screen; the position of each distributed advertising machine is used to identify the user's viewing angle, and then the corresponding screen projection angle and size in the database are identified, and the screen projection angle and size of each distributed advertising machine are adjusted accordingly.

[0008] Furthermore, the specific analysis of dynamically adjusting the collaborative display of advertisements on distributed advertising machines using pedestrian flow data is as follows: Pedestrian flow data specifically includes the main types of pedestrian flow, pedestrian density, average pedestrian movement speed, and pedestrian direction; advertisements corresponding to the current main types of pedestrian flow are placed in the database, and then the advertisements are divided into different advertisement slices according to the plot; the advertisement slices are displayed and sorted according to the pedestrian direction, and the display screen size of the distributed advertising machine is obtained; the switching rate and playback speed of the advertisement slices are obtained by combining the average pedestrian movement speed and the display screen size of the distributed advertising machine; pedestrian flow data is monitored in real time, and the placement, switching rate, and playback speed of advertisements are adjusted based on changes in pedestrian flow data.

[0009] Furthermore, the adjustment of the ad delivery, switching rate, and playback speed based on changes in pedestrian flow data also includes obtaining an ad screen-off time threshold when the pedestrian flow density is 0. When the time when the pedestrian flow density is continuously monitored exceeds the ad screen-off time threshold, the distributed advertising machine automatically triggers the screen-off state. When the monitored pedestrian flow density is greater than the pedestrian flow density threshold, the distributed advertising machine is triggered to display the ad and automatically delivers the ad.

[0010] Furthermore, the dynamic adjustment of the distributed advertising machine's collaborative display also includes recovering lost advertising segments when they are lost during switching. This is achieved by combining overlapping areas introduced by adjacent advertising segments and incorporating machine learning for repair. The specific analysis for recovering lost segments involves: acquiring all advertising segments of the advertising video, including complete and missing segments; determining the overlapping areas between adjacent segments of the lost segment, using these overlapping areas as input, extracting features using convolutional layers, and then generating the lost area through fully connected layers; and finally, stitching the generated lost area with adjacent segments to reconstruct the complete segment.

[0011] Furthermore, the specific analysis of periodically filtering advertisements delivered by the distributed advertising machine based on user interaction data includes: obtaining the periodic filtering period for the delivered advertisements; collecting user interaction data for each delivered advertisement within the periodic filtering period, specifically the conversion rate of users through interactive links; obtaining a user interaction data threshold, i.e., a conversion rate threshold; comparing the conversion rate of each delivered advertisement through interactive links with the conversion rate threshold, marking advertisements with conversion rates lower than the conversion rate threshold, and replacing the marked advertisements with the latest advertisements; for replacing marked advertisements with the latest advertisements, the analysis also includes real-time acquisition of user complaint data for each advertisement, specifically the number of complaints; when the number of complaints for an advertisement exceeds the complaint threshold, an advertisement filtering mechanism is automatically triggered, the advertisements that trigger the advertisement filtering mechanism are marked, and the latest advertisements are used to replace them.

[0012] Furthermore, the periodic screening also includes the periodic screening and adjustment of advertising machines. Specifically, the analysis is as follows: obtain the periodic screening and adjustment cycle of advertising machines, then collect the total number of user interactions for each advertising machine within the periodic screening and adjustment cycle, compare the total number of user interactions with the user interaction threshold, mark advertising machines whose total number of user interactions is lower than the user interaction threshold, and use an alarm mechanism to send a reminder SMS to remind the operation and maintenance personnel to adjust the location of the advertising machine.

[0013] A distributed advertising machine collaborative display system supporting cross-screen interaction, applying the aforementioned distributed advertising machine collaborative display method supporting cross-screen interaction, includes: an advertising type confirmation module, used to identify the current scene and obtain scene environment data, then set the advertising type of the distributed advertising machine according to the current scene type, and adjust the advertising parameters of the distributed advertising machine using the scene environment data; an advertising collaborative display screen adjustment module, used to monitor the current scene's pedestrian flow data, and dynamically adjust the advertising collaborative display screen of the distributed advertising machine using the pedestrian flow data; and an optimization filtering module, used to obtain user interaction data based on the interactive links placed in the advertising collaborative display screen of the distributed advertising machine, and then periodically filter the advertisements placed by the distributed advertising machine according to the user interaction data.

[0014] The present invention has the following beneficial effects:

[0015] This invention relates to a distributed advertising machine collaborative display method and system that supports cross-screen interaction. By identifying the current scene and acquiring scene environment data, it can intelligently set the advertising type, making the advertising content closer to the actual needs and scenarios of the audience, improving the personalization of the advertising, and enhancing the audience's interest and participation. By dynamically adjusting the display screen and delivery parameters of the advertising machine using scene environment data and pedestrian flow data, it can achieve real-time optimization and adjustment of advertising content, which not only improves advertising effectiveness and audience experience, but also effectively utilizes resources to ensure the best performance of the advertising. By placing interactive links in the collaborative display screen of the advertising and collecting user interaction data, it can effectively improve the audience's interactive experience, not only strengthening the connection between the audience and the advertising, but also providing more highly participatory advertising formats, increasing user engagement and loyalty. Based on user interaction data, it can regularly screen and adjust the advertising, promptly identify and improve the weaknesses of the advertising, thereby improving the conversion rate and effectiveness of the advertising, ensuring the effectiveness and long-term profitability of the advertising campaign. Through automated and intelligent advertising management, it can save human and material resources, improve the efficiency and cost-effectiveness of advertising, and more accurately reach the target audience, thereby improving the overall marketing effect and brand awareness. Attached Figure Description

[0016] Figure 1 This is a flowchart of a distributed advertising machine collaborative display method that supports cross-screen interaction according to the present invention.

[0017] Figure 2 This is a structural diagram of a distributed advertising machine collaborative display system that supports cross-screen interaction according to the present invention. Detailed Implementation

[0018] This application embodiment provides a distributed advertising machine collaborative display method and system that supports cross-screen interaction, thereby achieving more intelligent and personalized advertising delivery management, improving advertising effectiveness and user engagement, while optimizing resource utilization and cost control.

[0019] The problem addressed in this application's embodiments can be summarized as follows:

[0020] First, various sensors and cameras are used to identify the current scene type and environmental data. Based on the identified scene type and environmental data, the type of advertisement to be displayed on the distributed advertising machines is intelligently set. Using real-time acquired scene and environmental data, the advertising parameters of the distributed advertising machines, such as brightness and color, are dynamically adjusted to ensure the best performance of the advertisements in the current environment. The system monitors the current scene's pedestrian flow data and dynamically adjusts the collaborative display of advertisements based on pedestrian density and behavior patterns. Interactive links, such as scanning QR codes to participate in activities or visiting specific web pages, are placed in the collaborative display of advertisements to collect user interaction data. Based on the regularly collected user interaction data, the advertising is periodically filtered and optimized to achieve higher return on investment and user engagement.

[0021] Please see Figure 1 This invention provides a technical solution: a method for collaborative display of distributed advertising machines that supports cross-screen interaction, comprising the following steps: identifying the current scene and acquiring scene environment data, then setting the type of advertisement to be delivered by the distributed advertising machine based on the current scene type, and adjusting the advertising delivery parameters of the distributed advertising machine using the scene environment data; monitoring the current scene pedestrian flow data, and dynamically adjusting the collaborative display screen of the distributed advertising machine using the pedestrian flow data; acquiring user interaction data based on the interactive links delivered in the collaborative display screen of the distributed advertising machine, and then periodically filtering the advertisements delivered by the distributed advertising machine based on the user interaction data.

[0022] Specifically, examples of setting the advertising type for distributed advertising machines based on the current scenario type are as follows: For shopping mall scenarios, where users are mostly shoppers, the corresponding advertising type is to display special offers and promotions related to stores within the mall to guide consumers into the stores, while simultaneously displaying brand image advertisements related to fashion, beauty, and home furnishings to enhance brand awareness; For transportation hub scenarios, where users typically make short stays, the corresponding advertising type is to display service advertisements related to travel, such as ride-hailing apps and travel agency promotions, as well as fast-moving consumer goods advertisements for food, beverages, or mobile applications, to trigger immediate impulse purchases; For sports stadium scenarios, where users gather and their focus is on the competition, the corresponding advertising type is to display advertisements related to sports... The advertising categories include: sports event sponsorships, such as sports equipment and apparel; beverages, snacks, and transportation related to audience needs, allowing audiences to make purchases while experiencing the event; entertainment venues, where users typically have a fixed amount of time spent, where advertising is targeted at cinemas related to currently showing movies, as well as products related to family entertainment and dining, attracting customers to make purchases while enjoying entertainment; and outdoor public spaces, where users typically rest, socialize, or engage in activities, where advertising is targeted at leisure products related to outdoor activities, such as outdoor gear and concert tickets, as well as products related to seasons and weather, such as summer drinks and winter heating products.

[0023] The specific analysis of adjusting the advertising parameters of distributed advertising machines using scene environment data is as follows: Scene environment data specifically includes the current color temperature, current light intensity, and the position of each distributed advertising machine; the current color temperature is used to identify the corresponding white balance parameters in the database, and the white balance of the distributed advertising machines is adjusted accordingly; the inverse relationship between the current light intensity and the brightness of the distributed advertising machine's display is used to adjust the brightness of the distributed advertising machine; the position of each distributed advertising machine is used to identify the user's viewing angle, and then the corresponding image projection angle and size in the database are identified, and the image projection on each distributed advertising machine is adjusted accordingly.

[0024] In this implementation scheme, the current color temperature is obtained by directly measuring the color temperature of the current environment using a color temperature sensor. Color temperature sensors can identify the color characteristics of light and are typically used in devices such as digital cameras and smartphones. Alternatively, images captured by a camera can be analyzed using image processing algorithms to extract the color temperature information of the environment. The current light intensity is obtained by measuring the light intensity in the environment using a light sensor (photoresistor, photodiode, etc.). These sensors convert the light intensity into an electrical signal for system analysis. Alternatively, a camera can capture changes in brightness in the environment, and image processing can be used to estimate the light intensity. The location of the distributed advertising machine is obtained by using the correlation between Wi-Fi signal strength and location to infer the location of the advertising machine relative to the Wi-Fi access point. Alternatively, a camera or other visual sensors can be used to determine the location of the advertising machine by identifying surrounding environmental features (such as signs and buildings).

[0025] The specific methods for identifying user viewing angles are as follows: Install cameras on the advertising machine, and use real-time image processing and computer vision algorithms to detect and track the position of the audience and the angle facing the advertising machine. Facial recognition technology can also be used to identify the facial features of the audience and determine their position and viewing angle accordingly. Alternatively, infrared sensors can be used to detect the presence and position of the audience. Microphone arrays or other sound direction recognition technologies can also be used to determine the direction of sound source, thereby inferring the position and possible viewing angle of the audience.

[0026] Adjusting the white balance based on the current color temperature ensures that the advertising image maintains natural colors in different environments, avoiding visual discomfort caused by color temperature mismatch. For example, in a shopping mall, where ceiling lights have a warm color temperature, the white balance of the advertising machine can be adjusted using current environmental data to make the advertising image more harmonious with the surrounding environment. Adjusting the brightness of the advertising machine based on the current light intensity reduces brightness during the day or in strong light, saving energy and extending equipment life. For example, in an outdoor plaza, when the light intensity is strong, the advertising machine can automatically adjust its brightness to save energy and ensure clear visibility. Adjusting the projection angle and size of the image based on the location of each advertising machine and the user's viewing angle ensures that viewers can clearly see the advertising content from different angles, enhancing visual appeal and information delivery. For example, in a subway station, the angle of the image projected on the advertising machine can be adjusted according to the viewing angle of users at different locations on the platform, ensuring that all passengers on the platform can easily see the advertising content.

[0027] Specifically, the analysis of dynamically adjusting the collaborative display of advertisements on distributed advertising machines using pedestrian flow data is as follows: Pedestrian flow data includes the main types of pedestrian flow, pedestrian density, average pedestrian movement speed, and pedestrian direction; advertisements corresponding to the current main types of pedestrian flow are placed in the database, and then the advertisements are divided into different advertisement slices according to the plot; the advertisement slices are displayed and sorted according to the pedestrian flow direction, and the screen size of the distributed advertising machine is obtained; the switching rate and playback speed of the advertisement slices are obtained by combining the average pedestrian movement speed and the screen size of the distributed advertising machine, where the switching rate is specifically the ratio of the average pedestrian movement speed to the screen size of the distributed advertising machine, and the playback speed is specifically the ratio of the average pedestrian movement speed to the preset average playback speed; pedestrian flow data is monitored in real time, and the placement, switching rate, and playback speed of advertisements are adjusted based on changes in pedestrian flow data.

[0028] In this implementation plan, the main types of pedestrian flow refer to different types of people, specifically including adults, children, and the elderly. Based on these different groups, advertising content can be tailored to enhance the attractiveness and effectiveness of the advertisements. The number of people passing through a unit area or unit of time reflects the size of the advertising audience. In areas with high pedestrian density, it's advisable to increase the frequency of advertising or select more attractive advertising content. The average pedestrian movement speed represents the average speed at which people move within a specific area. Based on this speed, the frequency and speed of advertisement switching can be adjusted to ensure that the advertisement transitions are not too fast or too slow, thus better attracting viewer attention. The direction of pedestrian flow indicates the direction of pedestrian movement, which helps determine the display order and layout of the advertisements. Arranging advertisements according to the direction of pedestrian flow maximizes visual contact with the audience and enhances advertising effectiveness.

[0029] Arranging ad segments according to the direction of audience flow ensures that viewers can see the complete ad content sequentially as they move. For example, in a corridor where people typically move from left to right, ad segments should be played in this direction to prevent content from being missed by fast-moving viewers. Combining the average speed of pedestrian movement with the size of the advertising display screen allows for determining the switching rate and playback speed of ad segments, ensuring each segment receives sufficient dwell time in the viewer's line of sight, thus improving ad reach and appeal. Real-time monitoring of pedestrian flow data and adjusting ad delivery strategies, switching rates, and playback speeds based on these changes is also crucial. For instance, if pedestrian density suddenly increases in a certain area, the ad display frequency can be automatically adjusted or the rotation of related ad content can be increased to better attract and cover the target audience.

[0030] Specifically, adjusting the ad delivery, switching rate, and playback speed based on changes in pedestrian flow data also includes obtaining an ad screen-off time threshold when the pedestrian flow density is 0. When the time when the pedestrian flow density is continuously monitored exceeds the ad screen-off time threshold, the distributed advertising machine automatically triggers the screen-off state. When the monitored pedestrian flow density is greater than the pedestrian flow density threshold, the distributed advertising machine is triggered to display the ad and automatically delivers the ad.

[0031] In this implementation plan, the specific method for setting the ad screen-off time threshold is as follows: by analyzing past pedestrian flow data and ad playback records, the average continuous playback time of the ad is determined when the pedestrian flow density is 0 in the past. It is also possible to monitor the current pedestrian flow density and ad playback status in real time, and adjust the ad screen-off time threshold according to real-time feedback. For example, the screen-off time threshold can be dynamically adjusted based on the current audience reaction time and the average attention span of the ad.

[0032] The specific method for setting the crowd density threshold is as follows: By analyzing past crowd flow data, the average crowd density can be understood in different time periods or under specific conditions. The crowd density threshold is a dynamically adjustable parameter that can be adjusted according to different time periods or locations. A specific example of the function of the crowd density threshold is: a basic threshold is set based on the average crowd density. When the crowd density is lower than this basic threshold, the system considers the crowd density to be 0.

[0033] By automatically switching to screen-off mode when pedestrian density is zero, energy can be saved and the lifespan of the advertising machine can be extended, eliminating the need for continuous power consumption to play advertisements. Ensuring that advertisements are only displayed when there are viewers increases their exposure and viewing rate, as advertisements are only played when there are viewers, avoiding wasted time playing in empty spaces where no one sees them. The automatic triggering function reduces the need for human intervention, improving operational efficiency and management convenience. Combined with real-time monitoring and historical analysis of pedestrian flow data, the efficiency of advertisement playback and resource utilization are effectively optimized, thereby improving the effectiveness and cost-effectiveness of advertising.

[0034] Specifically, dynamically adjusting the collaborative display of distributed advertising machines also includes recovering lost advertising segments when they are lost during switching. This is achieved by combining overlapping areas introduced by adjacent advertising segments and incorporating machine learning for repair. The specific analysis for recovering lost segments involves: acquiring all advertising segments of the advertising video, including complete and missing segments; determining the overlapping areas between adjacent segments of the lost segment, using these overlapping areas as input, extracting features using convolutional layers, and then generating the lost area through fully connected layers; and finally, stitching the generated lost area with adjacent segments to reconstruct the complete segment.

[0035] In this implementation scheme, when an ad slice is lost during switching, the overlapping area introduced by adjacent ad slices is used for combination and repair, which can reduce the sense of discontinuity and incoherence in the picture, thereby improving the overall user experience when watching ads. Complete and coherent ad display can effectively convey ad content, increase audience attention and acceptance of ad information, and improve the effectiveness and conversion rate of ad placement. By using machine learning technology, especially convolutional neural networks (CNN) to extract features and generate lost regions, and using fully connected layers for repair, not only is the accuracy of repairing lost regions improved, but the efficiency and stability of the system in real-time processing of ad slices are also guaranteed.

[0036] Specifically, the analysis of periodically filtering ads delivered by distributed advertising machines based on user interaction data includes: obtaining the periodic filtering cycle for the ads; collecting user interaction data for each ad within the periodic filtering cycle, specifically the conversion rate of users through interactive links; obtaining a user interaction data threshold, i.e., a conversion rate threshold; comparing the conversion rate of each ad through interactive links with the conversion rate threshold, marking ads with conversion rates lower than the threshold, and replacing the marked ads with the latest ads; for ads replaced with the latest ads, the analysis also includes real-time acquisition of user complaint data for each ad, specifically the number of complaints. When the number of complaints for an ad exceeds the complaint threshold, the ad filtering mechanism is automatically triggered, the ads that trigger the filtering mechanism are marked, and the latest ads are used to replace them.

[0037] In this implementation plan, the periodic screening cycle for ads can be determined based on the characteristics of the ad campaign, the speed of user feedback, and the system's responsiveness. Screening can be done daily, weekly, or monthly, with the cycle length adjusted according to actual circumstances to ensure timely detection and handling of ineffective ads. The conversion rate through interactive links refers to the percentage of users who, after interacting with an ad, ultimately complete a desired action, such as purchasing, registering, or submitting information after clicking the ad link. This can be obtained through the ad machine's internal data collection and analysis system, recording user click behavior and analyzing the final conversion rate. The conversion rate threshold is determined based on the expected advertising effect and industry standards. A reasonable conversion rate target can be set based on historical data analysis or experimental results. If the actual conversion rate of an ad is lower than the set threshold, it may be necessary to consider replacing the ad to improve overall advertising effectiveness. The complaint threshold can be determined based on the frequency of ad campaigns and overall complaint data. When the number of complaints against an ad exceeds the set threshold, it can be considered that the ad may have problems or be unpopular with users, requiring screening and replacement. The complaint threshold setting needs to comprehensively consider the ad machine's tolerance, the frequency of user feedback, and the severity of complaints.

[0038] Regular screening ensures that ads remain efficient and effective. By monitoring user interaction data, ads with low conversion rates or those that generate complaints can be identified and replaced promptly, thereby improving click-through rates and conversion rates. Replacing ads that generate complaints reduces user dissatisfaction, increases user acceptance and experience with the ad console, and helps maintain good user relationships. Automated screening and replacement mechanisms reduce the need for manual intervention, lowering management and operational costs.

[0039] Specifically, periodic screening also includes periodic screening and adjustment of advertising machines. The specific analysis is as follows: obtain the periodic screening and adjustment cycle of advertising machines, then collect the total number of user interactions for each advertising machine within the periodic screening and adjustment cycle, compare the total number of user interactions with the user interaction threshold, mark advertising machines whose total number of user interactions is lower than the user interaction threshold, and use an alarm mechanism to send a reminder SMS to remind the operation and maintenance personnel to adjust the location of the advertising machine.

[0040] In this implementation plan, the periodic screening and adjustment cycle for advertising machines is specifically set based on the location characteristics of the advertising machines. Advertising machines in different locations receive varying levels of user traffic and attention, requiring the adjustment cycle to be determined according to the specific characteristics of each location. For example, high-traffic areas require more frequent adjustments. Based on historical data analysis of user interaction patterns and trends, the periodic screening cycle can be adjusted to ensure adjustments are made before a significant drop in interaction rates. Generally, the periodic screening and adjustment cycle for advertising machines can be initially assessed every quarter or semi-annually, with the specific cycle adjusted flexibly based on the aforementioned factors. The user interaction threshold refers to the threshold number of interactions that indicates the need for adjustment of the advertising machine. The specific setting method is as follows: by analyzing past advertising machine interaction data, an average value or trend is found as a reference for setting the threshold. Alternatively, an interaction threshold that achieves the advertiser's expected results and campaign goals can be set.

[0041] Regular screening and adjustments ensure that advertising machines are always operating efficiently, preventing reduced display effectiveness due to lack of updates or unsuitable placement. This helps improve advertising effectiveness and enhances user interaction and perception of the advertising content. Timely adjustments to the location of advertising machines or updates to advertising content avoid unnecessary resource waste and cost expenditures. By marking and adjusting advertising machines with low interaction, the efficiency of advertising resource utilization can be optimized. Introducing alarm mechanisms and SMS notifications allows for timely notification of advertising machine locations that need adjustment to maintenance personnel, reducing inspection and response time and improving management efficiency and response speed.

[0042] A distributed advertising machine collaborative display system supporting cross-screen interaction, applying the aforementioned distributed advertising machine collaborative display method supporting cross-screen interaction, includes: an advertising type confirmation module, used to identify the current scene and obtain scene environment data, then set the advertising type of the distributed advertising machine according to the current scene type, and adjust the advertising parameters of the distributed advertising machine using the scene environment data; an advertising collaborative display screen adjustment module, used to monitor the current scene's pedestrian flow data, and dynamically adjust the advertising collaborative display screen of the distributed advertising machine using the pedestrian flow data; and an optimization filtering module, used to obtain user interaction data based on the interactive links placed in the advertising collaborative display screen of the distributed advertising machine, and then periodically filter the advertisements placed by the distributed advertising machine according to the user interaction data.

[0043] In summary, this application has at least the following effects:

[0044] By identifying the current scene and monitoring pedestrian flow data, advertising content and parameters can be selected and adjusted based on the real-time environment and audience characteristics. Personalized targeting enhances ad relevance and attractiveness, increasing user attention and engagement. Adjusting ad delivery parameters based on scene environment data makes ad content more aligned with the atmosphere and needs of the current scene, thereby improving ad delivery effectiveness and efficiency. Through interactive links and the collection of user interaction data, direct interaction between users and ad content can be achieved, enriching the user experience, enhancing user recall and engagement, and ultimately increasing conversion rates. Based on user interaction data and feedback, ads can be regularly screened and optimized, allowing for timely adjustments to ad content and delivery strategies to improve ad effectiveness and ROI. Dynamically adjusting ad display and delivery parameters allows for more effective use of ad resources, avoiding waste and duplicate exposure. Adjusting ad strategies based on real-time data effectively controls costs, making ad delivery more economical and efficient.

[0045] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or systems. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This invention is described with reference to flowchart illustrations and structural diagrams of methods and systems according to embodiments of the invention. It should be understood that the combination of each process and module in the flowchart and structural diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 One or more processes and structures Figure 1 A device for a function specified in one or more modules.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and structures Figure 1 The function specified in one or more modules.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and structures Figure 1 The steps of a specified function in one or more modules.

[0049] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0050] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A distributed advertising machine cooperative display method supporting cross-screen interaction, characterized in that, The method comprises the following steps: identifying a current scene and obtaining scene environment data, and then setting the type of advertisement to be distributed by the distributed advertising machine according to the type of the current scene, and adjusting the advertisement distribution parameters of the distributed advertising machine by using the scene environment data; monitoring current scene flow data, and dynamically adjusting the advertisement cooperative display screen of the distributed advertising machine by using the flow data; obtaining user interaction data based on the interactive link distributed in the advertisement cooperative display screen of the distributed advertising machine, and then periodically screening the advertisement distributed by the distributed advertising machine according to the user interaction data; the specific analysis of dynamically adjusting the advertisement cooperative display screen of the distributed advertising machine by using the flow data is as follows: the flow data specifically includes the main type of flow, the flow density, the average moving speed of flow, and the flow direction; the database is used to distribute the corresponding advertisement according to the main type of current flow, and then the advertisement is divided into different advertisement slices according to the plot; the advertisement slices are displayed and sorted according to the flow direction, and the size of the display screen of the distributed advertising machine is obtained; the switching rate and the playing speed of the advertisement slices are obtained by combining the average moving speed of flow and the size of the display screen of the distributed advertising machine; the flow data is monitored in real time, and the distribution, switching rate and playing speed of the advertisement are adjusted based on the change of the flow data; the dynamic adjustment of the advertisement cooperative display screen of the distributed advertising machine also includes that when there is a loss in the switching process of the advertisement slices, the adjacent advertisement slices are combined by using the overlapping area introduced by the adjacent advertisement slices, and machine learning is used for repair to recover the lost area; the specific analysis of recovering the lost area is as follows: all advertisement slices of the advertisement video are obtained, including complete and lost parts of the advertisement slices; the overlapping area between the adjacent slices of the lost advertisement slices is determined, and the overlapping area is used as input to extract features by using a convolution layer, and then a full connection layer is used to generate the lost area; the generated lost area is spliced with the adjacent slices to reconstruct the complete slice.

2. The method of claim 1, wherein the method further comprises: The specific analysis of adjusting the advertisement distribution parameters of the distributed advertising machine by using the scene environment data is as follows: the scene environment data specifically includes the current color temperature, the current light intensity, and the positions of the distributed advertising machines; the corresponding white balance parameters in the database are identified by using the current color temperature, and the distributed advertising machines are adjusted by using the white balance parameters; the distributed advertising machines are adjusted by using the inverse proportional relationship between the current light intensity and the display screen brightness; the positions of the distributed advertising machines are used to identify the user viewing angle, and then the corresponding screen distribution angle and size in the database are identified, and the distributed advertising machines are adjusted by using the screen distribution angle and size.

3. The method of claim 1, wherein the method further comprises: The adjustment of the distribution, switching rate and playing speed of the advertisement based on the change of the flow data also includes that for the case that the flow density is 0, an advertisement off-screen time threshold value is obtained, when the time of continuously monitoring that the flow density is 0 exceeds the advertisement off-screen time threshold value, the distributed advertising machine automatically triggers the off-screen state, and when the flow density monitoring is greater than the flow density threshold value, the distributed advertising machine displays the state and automatically distributes and displays the advertisement.

4. The method of claim 1, wherein the method further comprises: The specific analysis of periodically screening the advertisement distributed by the distributed advertising machine according to the user interaction data is as follows: Obtaining the periodic screening period of the advertising; Collecting user interaction data of each advertising in the periodic screening period of the advertising, and the user interaction data is specifically the conversion rate of the user through the interactive link; Obtaining the user interaction data threshold, that is, the conversion rate threshold; Comparing the conversion rate of each advertising with the conversion rate threshold, marking the advertising with a conversion rate lower than the conversion rate threshold, and replacing the marked advertising with the latest advertising; For replacing the marked advertising with the latest advertising, it further includes real-time obtaining of user complaint data of each advertising, and the user complaint data is specifically the number of complaints. When the number of complaints of the advertising exceeds the complaint threshold, the advertising screening mechanism is automatically triggered, the advertising triggering the advertising screening mechanism is marked, and the latest advertising is replaced.

5. The method of claim 4, wherein the method further comprises: The periodic screening further includes periodic screening adjustment of the advertising machine, and the specific analysis is as follows: obtaining the periodic screening adjustment period of the advertising machine, and then collecting the total number of user interactions of each advertising machine in the periodic screening adjustment period of the advertising machine, comparing the total number of user interactions with the user interaction threshold, marking the advertising machine with a total number of user interactions lower than the user interaction threshold, and sending a prompt message to the operation and maintenance management personnel through the alarm mechanism to prompt the adjustment of the position of the advertising machine.

6. A distributed advertising machine cooperative display system supporting cross-screen interaction, applying the distributed advertising machine cooperative display method supporting cross-screen interaction according to any one of claims 1-5, characterized in that, It includes: An advertising type confirmation module is used to identify the current scene and obtain scene environment data, and then set the type of advertising distributed by the distributed advertising machine through the current scene type, and adjust the advertising distribution parameters of the distributed advertising machine through the scene environment data; An advertising cooperative display picture adjustment module is used to monitor the current scene flow data, and dynamically adjust the advertising cooperative display picture of the distributed advertising machine through the flow data; An optimization screening module is used to obtain user interaction data based on the interactive link distributed in the advertising cooperative display picture of the distributed advertising machine, and then perform periodic screening on the advertising distributed by the distributed advertising machine according to the user interaction data.

Images