A smart signage management system based on data analysis

Abstract

The present invention relates to the field of smart city information release technology, and specifically to a smart signage management system based on data analysis, comprising a data acquisition module, a feature fusion module, a behavior analysis module, a strategy generation module, and an execution control module; wherein: the data acquisition module is used to collect user dwell time and interaction frequency data within the target area covered by the signage; the feature fusion module is used to generate a user attention index; the behavior analysis module is used to identify high-attention user groups and calculate the effective service range boundary of the signage; and the strategy generation module is used to generate an optimized strategy set. The present invention, through precise perception of user behavior data and hierarchical strategy control, achieves targeted, hierarchical, and coordinated delivery of smart signage information, effectively improving the system's response efficiency and delivery accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city information release technology, and in particular to a smart signage management system based on data analysis. Background Art

[0002] With the rapid development of smart cities and information visualization technologies, digital signage systems deployed in public places are gradually evolving from traditional static displays to intelligent interaction and dynamic perception. Currently, most smart signage systems rely mainly on preset time carousels or regional push mechanisms to release information. Although some systems have touch response functions, they still lack the ability to actively perceive user behavior characteristics and provide data-based strategic feedback. Especially in transportation hubs, supermarket advertising areas and indoor guidance environments, where users stay for short periods of time, interactions are unstable, and behavior patterns are scattered, traditional signage systems based on static configurations find it difficult to effectively adapt to real-time information needs and user response behaviors in different scenarios.

[0003] Existing technologies generally suffer from the following problems: First, they lack a real-time fusion mechanism for multimodal user behavior data, making it impossible to accurately capture user attention behavior characteristics; second, in terms of behavior analysis and regional strategy generation, they cannot implement placement priority division based on spatial aggregation patterns, resulting in low information coverage efficiency; third, they lack a closed-loop execution control mechanism, and the information placement process lacks the ability to dynamically adjust. Therefore, there is an urgent need to build a smart signage management system based on data analysis to solve the above problems. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a smart signage management system based on data analysis.

[0005] A smart signage management system based on data analysis includes a data acquisition module, a feature fusion module, a behavior analysis module, a strategy generation module, and an execution control module; wherein:

[0006] Data collection module: used to collect real-time data on users' stay time and interaction frequency in the target area covered by the sign through the infrared thermal imaging sensor array and touch interactive panel deployed on the sign body;

[0007] Feature fusion module: used to receive dwell time data and interaction frequency data, generate user behavior feature packages through spatiotemporal correlation matching, and calculate the corresponding weight coefficient based on the scene type to which the sign belongs, and then generate the user attention index through weighted fusion;

[0008] Behavior analysis module: used to identify high-attention user groups based on the comparison results of user attention index and preset threshold, and calculate the effective service range boundary of the signage using grid hot zone division method based on their spatial cluster density;

[0009] Strategy generation module: Based on the effective service range boundary coordinates and the real-time distribution coordinates of high-attention user groups, it generates an optimization strategy set including multi-level information push priority areas, optimal signage orientation angles, and multi-signage collaborative control parameters;

[0010] Execution control module: used to analyze and optimize the strategy set, and control the sign content update, posture adjustment and information collaboration through the Internet of Things terminal to achieve information delivery.

[0011] Optionally, the data acquisition module includes an infrared image acquisition unit, a trajectory recognition unit, a touch event recording unit, and a data synchronization unit; wherein:

[0012] Infrared image acquisition unit: used to continuously acquire thermal image frame data within the signage coverage area through the infrared thermal imaging sensor array, and capture the two-dimensional coordinates of the user's body heat source in real time based on the set frame rate;

[0013] Trajectory recognition unit: used to track the heat source points in the infrared image frame in time series, by calculating the continuous displacement vector of each user in the image coordinate system, to determine whether the user is in a stationary state, and to convert the user's stay time at the specified location based on the number of stay frames and the image frame rate ;

[0014] Touch event recording unit: used to monitor each touch event on the touch interaction panel and record the corresponding timestamp to count the interaction frequency within a given time window ;

[0015] Data synchronization unit: used to synchronize the dwell time Corresponding touch frequency Perform timestamp alignment and package to generate structured original interaction behavior records.

[0016] Optionally, the feature fusion module includes a spatial aggregation unit, a feature package assembly unit, a weight coefficient calculation unit, and an attention generation unit; wherein:

[0017] Spatial aggregation unit: divides the grid units based on the user coordinate information in the original interaction behavior record, and aggregates the behavior data in the same grid and with overlapping time periods into the same user instance;

[0018] Feature package assembly unit: used to extract the dwell time, interaction frequency and time period labels from each user instance and encapsulate them into a user behavior feature package;

[0019] Weight coefficient calculation unit: used to call the preset scene weight table according to the scene type to which the sign belongs, and assign a weight coefficient of a certain value to the dwell time, interaction frequency and time period label respectively. The scene types include public transportation station scenes, supermarket advertising scenes and indoor guide scenes;

[0020] Attention generation unit: performs weighted fusion of user behavior feature packages based on corresponding weight coefficients to generate the corresponding user attention index.

[0021] Optionally, the spatial aggregation unit includes:

[0022] Grid division subunit: used to divide the coverage area into equal intervals according to the fixed grid side length based on the reference origin of the sign, and calculate the grid index of any user coordinate ;

[0023] Time overlap determination subunit: used to determine the time overlap of the same grid index Compare the two behavioral data of the recording time interval and , when satisfied When , it is determined that the two behavioral data have effective time overlap; These are the start and end timestamps of the corresponding behavior data;

[0024] User instance aggregation subunit: used to merge behavioral data in the same grid index and meeting the time overlap judgment condition into a single user instance.

[0025] Optionally, the attention generation unit includes:

[0026] Feature extraction subunit: used to extract feature parameters from user behavior feature packages, including user stay time , user interaction frequency and the time period label for the behavior ;

[0027] Weight loading subunit: used to read the corresponding weight coefficient of each feature from the preset weight configuration table according to the scene type of the sign, namely, the residence time weight , interaction frequency weight , time period weight ,satisfy ;

[0028] Weighted calculation subunit: used to perform linear weighted operation on the extracted feature parameters and the loaded weight coefficients to generate the final user attention index. The calculation formula is: ,in, Indicates the user attention index.

[0029] Optionally, the behavior analysis module includes a threshold comparison unit, a group identification unit, a hot zone division unit, and a service boundary calculation unit; wherein:

[0030] Threshold comparison unit: used to receive the user attention index output by the feature fusion module, compare it with the attention threshold built into the behavior analysis module one by one, and generate a judgment label. The judgment label distinguishes between high attention and normal attention.

[0031] Group identification unit: This unit is used to scan the connectivity of user instances in the same attention state and adjacent to each other in space based on the judgment labels output by the threshold comparison unit, and aggregate high-attention users in a continuous area into a high-attention user group;

[0032] Hot zone division unit: used to calculate the spatial cluster density of high-attention user groups, divide the coverage area into multi-level hot zone grids, and classify the grids according to the number of high-attention users in each grid to obtain a hot zone grid set;

[0033] Service boundary calculation unit: used to fit the circumscribed polygon of the hotspot grid set and output the vertex coordinates of the fitted polygon as the effective service range boundary of the sign.

[0034] Optionally, the hot zone division unit includes:

[0035] Density calculation subunit: used to traverse and count the user coordinates in each high-attention user group. Calculate spatial cluster density within the base grid ;

[0036] Grid hierarchical sub-units: based on grid density Perform multi-level grid subdivision on the coverage area and The basic grid continues to use the binary refinement method to refine the edge length. Divide dense sub-grids to form a multi-level hot zone grid system;

[0037] Heat calibration subunit: used to calibrate the heat level of all grids according to the number of high-attention users. The heat level is divided into three levels: first level heat, second level heat, and third level heat according to the threshold range. The specific expression is:

[0038] ;

[0039] in, is the threshold number of high-attention users; is the heat level, value ;

[0040] Hotspot extraction subunit: used to collect heat levels The grids are combined to obtain the hotspot grid set.

[0041] Optionally, the strategy generation module includes a region priority division unit, a direction optimization unit, and a collaborative control unit; wherein:

[0042] Regional Priority Division Unit: This unit receives the coordinates of the vertex points on the service boundary and the real-time distribution coordinates of the high-attention user group, divides the boundary area into multiple levels of information push priority areas based on the minimum distance between the user location and the service boundary, and assigns a corresponding priority identifier to each area.

[0043] Orientation optimization unit: Based on the coordinates of all high-attention users in each priority area, the coordinates of the area's center of gravity are calculated. With the signage installation center point as the rotation center, the optimal orientation angle for each area is determined according to the following formula: ,in, For the The optimal orientation angle corresponding to the level push area; and For the The horizontal and vertical coordinates of the center of gravity of high-attention users in the priority area of ​​the highest push, in meters; The coordinates of the location where the sign will be installed; is the two-variable inverse tangent function;

[0044] Collaborative control subunit: used to generate signage content update timing and communication relay strategies in multi-signage deployment scenarios based on the overlapping relationship of service boundaries of each signage and the push area priority.

[0045] Optionally, the area priority division unit includes:

[0046] Distance calculation subunit: used to calculate the spatial coordinates of each high-attention user and the set of all edges of the service range boundary polygon The minimum Euclidean distance is calculated as follows:

[0047] ,in, is the user coordinate; The coordinates of the point on the boundary polygon closest to the user; is the minimum distance from the user to the service boundary;

[0048] Ring area division subunit: used to calculate the output of the distance calculation subunit Distance threshold By comparison, the service area is divided into three levels of concentric rings from the boundary inward. Specifically,

[0049] The first level information push area meets ;

[0050] The second level information push area meets ;

[0051] The third level information push area meets .

[0052] Optionally, the execution control module includes a strategy analysis unit, a display control unit, a posture driving unit, and an information synchronization unit; wherein:

[0053] Strategy parsing unit: used to receive the optimization strategy set output by the strategy generation module, extract the information push priority area data, sign optimal orientation angle instructions and collaborative control parameters, perform structured analysis in the execution order, and output to the corresponding control unit;

[0054] Display control unit: used to filter the content push list issued by the signage master control system according to the priority area identifier contained in the policy analysis unit, and push the corresponding content to the display screen in descending order of priority;

[0055] Attitude drive unit: used to generate rotation control instructions according to the analyzed optimal orientation angle, and drive the built-in attitude adjustment motor to rotate the sign body to the specified direction angle;

[0056] Information synchronization unit: used to establish a master-slave signage communication link based on collaborative control parameters, and dispatch communication repeaters to synchronously forward content update and posture adjustment instructions according to a predetermined timing.

[0057] Beneficial effects of the present invention:

[0058] The present invention deploys an infrared thermal imaging sensor array and a touch interactive panel to achieve multimodal real-time collection of user dwell and interaction behaviors; combined with spatiotemporal correlation and hierarchical aggregation technology, it can accurately extract user behavior characteristics and quantify the generation of an attention index, greatly improving the accuracy of user behavior capture and data consistency.

[0059] The present invention, through the aggregation pattern of high-attention users, adopts algorithms such as threshold comparison, hot zone division and ring zone priority division to automatically generate hierarchical information push areas and targeted delivery strategies, and completes content updates, posture adjustments and multi-signage collaborative control through Internet of Things terminals, realizing a closed-loop linkage between strategy and execution; thereby significantly improving the accuracy of information delivery, coverage efficiency and the system's adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 This is a schematic diagram of a smart signage management system according to an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of a behavior analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0064] like Figure 1-Figure 2 As shown, a smart signage management system based on data analysis includes a data acquisition module, a feature fusion module, a behavior analysis module, a strategy generation module and an execution control module; wherein:

[0065] Data collection module: used to collect real-time data on users' stay time and interaction frequency in the target area covered by the sign through the infrared thermal imaging sensor array and touch interactive panel deployed on the sign body;

[0066] Feature fusion module: used to receive dwell time data and interaction frequency data, generate user behavior feature packages through spatiotemporal correlation matching, and calculate the corresponding weight coefficient based on the scene type to which the sign belongs, and then generate the user attention index through weighted fusion;

[0067] Behavior analysis module: used to identify high-attention user groups based on the comparison results of user attention index and preset threshold, and calculate the effective service range boundary of the signage using grid hot zone division method based on their spatial cluster density;

[0068] Strategy generation module: Based on the effective service range boundary coordinates and the real-time distribution coordinates of high-attention user groups, it generates an optimization strategy set including multi-level information push priority areas, optimal signage orientation angles, and multi-signage collaborative control parameters;

[0069] Execution control module: used to analyze and optimize the strategy set, and control the sign content update, posture adjustment and information collaboration through the Internet of Things terminal to achieve information delivery.

[0070] The data acquisition module includes an infrared image acquisition unit, a trajectory recognition unit, a touch event recording unit, and a data synchronization unit; wherein:

[0071] Infrared image acquisition unit: used to continuously acquire thermal image frame data within the signage coverage area through the infrared thermal imaging sensor array, and capture the two-dimensional coordinates of the user's body heat source in real time based on the set frame rate;

[0072] Trajectory recognition unit: used to track the heat source points in the infrared image frame in time series, by calculating the continuous displacement vector of each user in the image coordinate system, to determine whether the user is in a stationary state, and to convert the user's stay time at the specified location based on the number of stay frames and the image frame rate ; Its calculation formula is: ,in, Indicates the user's stay time in seconds; Indicates the number of frames in which the user appears consecutively in the same spatial grid; Indicates the infrared image frame rate in frames per second;

[0073] Touch event recording unit: used to monitor each touch event on the touch interaction panel and record the corresponding timestamp to count the interaction frequency within a given time window ;

[0074] Data synchronization unit: used to synchronize the dwell time Corresponding touch frequency Perform timestamp alignment and package to generate structured original interaction behavior records as data input for the feature fusion module. By setting the infrared image acquisition unit and the trajectory recognition unit to work together, the user's stay behavior in the area around the sign can be captured contactlessly. Combined with the interaction behavior recorded by the touch event recording unit, the spatiotemporal fusion of thermal imaging and touch data is effectively realized, improving the accuracy and real-time performance of the data acquisition module in restoring the user's actual behavior.

[0075] The feature fusion module includes a spatial aggregation unit, a feature package assembly unit, a weight coefficient calculation unit, and an attention generation unit; wherein:

[0076] Spatial aggregation unit: divides the grid units based on the user coordinate information in the original interaction behavior record, and aggregates the behavior data in the same grid and with overlapping time periods into the same user instance;

[0077] Feature package assembly unit: used to extract the dwell time, interaction frequency and time period labels from each user instance and encapsulate them into a user behavior feature package;

[0078] Weight coefficient calculation unit: used to call the preset scene weight table according to the scene type to which the sign belongs, and assign weight coefficients of determined values ​​to the dwell time, interaction frequency and time period tags respectively. The scene types include public transportation station scenes, supermarket advertising scenes and indoor guide scenes;

[0079] Table 1 Example of preset scene weights

[0080]

[0081] In Table 1 above, the dwell time weight is used to reflect the importance of the length of time a user stays in front of a sign to their attention; the interaction frequency weight is used to reflect the evaluation weight of the user's touch or operation behavior in this scenario; and the time period weight is used to reflect the intensity of the impact of the time period when the user's behavior occurs on their attention in this scenario.

[0082] Attention generation unit: Performs weighted fusion of user behavior feature packages based on corresponding weight coefficients to generate the corresponding user attention index, which is then output to the behavior analysis module. This unit achieves precise spatiotemporal pairing of dwell and interaction behaviors, scenario-based weight allocation, and indexed representation. This allows for rapid generation of the user attention index while ensuring data consistency, improving the feature fusion module's ability to identify differences in user behavior across scenarios and providing a reliable basis for subsequent analysis.

[0083] Spatial aggregation units include:

[0084] Grid division subunit: used to divide the coverage area into equal intervals according to the fixed grid side length based on the reference origin of the sign, and calculate the grid index of any user coordinate , the specific calculation formula is: ,in, and Represents the grid numbers in the horizontal and vertical directions respectively (dimensionless); and The real-time coordinate value of the user in the signage coordinate system (unit: meter); The coordinates of the sign installation location (unit: meter); is the grid side length (unit: meter);

[0085] Time overlap determination subunit: used to determine the time overlap of the same grid index Compare the two behavioral data of the recording time interval and , when satisfied When , it is determined that the two behavioral data have effective time overlap; These are the start and end timestamps of the corresponding behavior data (unit: seconds);

[0086] User instance aggregation subunit: It is used to merge behavioral data in the same grid index and meeting the time overlap judgment conditions into a single user instance, and assign a unique number to the instance for reference by the subsequent feature package assembly subunit; by setting the grid division subunit to achieve unified spatial quantitative subdivision of the coverage area, combined with the second-level interval comparison of the time overlap judgment subunit, it can accurately aggregate user data with the same location and intersecting behavioral time periods. The user instance aggregation subunit further ensures the consistency and integrity of the behavioral data, thereby significantly improving the subsequent feature fusion accuracy and computational efficiency.

[0087] The attention generation unit includes:

[0088] Feature extraction subunit: used to extract feature parameters from user behavior feature packages, including user stay time , user interaction frequency and the time period label for the behavior ,in The unit is seconds, is the number of touches per unit time (times), A hierarchical code indicating the time period in which the behavior occurred;

[0089] Weight loading subunit: used to read the corresponding weight coefficient of each feature from the preset weight configuration table according to the scene type of the sign, namely, the residence time weight , interaction frequency weight , time period weight , where each weight coefficient is a dimensionless positive real number, satisfying ;

[0090] Weighted calculation subunit: used to perform linear weighted operation on the extracted feature parameters and the loaded weight coefficients to generate the final user attention index. The calculation formula is: ,in, Indicates the user attention index, in seconds (keep the same as The above sub-units combine different feature dimensions (stay, interaction, time period) with preset scenario weight strategies and use a linear weighting method to generate a unified attention index. This not only achieves a unified evaluation of multiple behavioral indicators, but also has good scenario adaptability, providing quantitative support for subsequent user group screening and signage strategy optimization.

[0091] The behavior analysis module includes a threshold comparison unit, a group identification unit, a hot zone division unit, and a service boundary calculation unit; wherein:

[0092] Threshold comparison unit: used to receive the user attention index output by the feature fusion module, compare it with the attention threshold built into the behavior analysis module one by one, and generate a judgment label. The judgment label distinguishes between high attention and normal attention.

[0093] The specific judgment formula is: ;

[0094] in, is the user attention index; is the attention threshold, in seconds; Determine the label for the user, 1 indicates high attention, 0 indicates normal attention;

[0095] Group identification unit: This unit is used to scan the connectivity of user instances in the same attention state and adjacent to each other in space based on the judgment labels output by the threshold comparison unit, and aggregate high-attention users in a continuous area into a high-attention user group;

[0096] Hot zone division unit: used to calculate the spatial cluster density of high-attention user groups, divide the coverage area into multi-level hot zone grids, and classify the grids according to the number of high-attention users in each grid to obtain a hot zone grid set;

[0097] Service boundary calculation unit: used to fit the circumscribed polygon of the hotspot grid set, output the vertex coordinates of the fitted polygon as the effective service range boundary of the sign, and send the boundary coordinates to the strategy generation module; through the above unit, high-attention users can be quickly screened and the service boundary can be determined based on their aggregation density, which not only improves the accuracy of identifying high-attention user groups, but also accurately defines the effective service range of the sign, providing a reliable spatial basis for subsequent strategy optimization.

[0098] The thermal zone division unit includes:

[0099] Density calculation subunit: used to traverse and count the user coordinates in each high-attention user group. Calculate spatial cluster density within the base grid , and its calculation formula is: ,in, Indicates the grid number The number of high-attention users in the (dimensionless); Represents the area of ​​a single basic grid in square meters; is the basic grid space aggregation density, in people / square meter; is the grid side length;

[0100] Grid hierarchical sub-units: based on grid density Perform multi-level grid subdivision on the coverage area and The basic grid continues to use the binary refinement method to refine the edge length. Divide dense sub-grids to form a multi-level hot zone grid system;

[0101] Heat calibration sub-unit: used to calibrate the heat level of all grids (basic grid and sub-grids) according to the number of high-attention users within the grid. The heat level is divided into three levels: first level heat, second level heat, and third level heat according to the threshold range. The specific expression is:

[0102] ;

[0103] in, is the threshold number of high-attention users, both of which are dimensionless positive integers; is the heat level, value ;

[0104] Hotspot extraction subunit: used to collect heat levels The grids are combined to obtain a hotspot grid set, and the set index is passed to the service boundary calculation subunit; the above subunit adopts a four-level processing flow of density calculation, hierarchical refinement, heat calibration and hotspot extraction. It can accurately characterize the degree of user aggregation under the premise of ensuring dimensional consistency, realize the multi-scale heat distribution visualization of the coverage area, and provide high-resolution hotspot data support for subsequent service boundary fitting.

[0105] The strategy generation module includes a region priority division unit, a direction optimization unit, and a collaborative control unit; wherein:

[0106] Regional Priority Division Unit: This unit receives the coordinates of the vertex points on the service boundary and the real-time distribution coordinates of the high-attention user group, divides the boundary area into multiple levels of information push priority areas based on the minimum distance between the user location and the service boundary, and assigns a corresponding priority identifier to each area.

[0107] Orientation optimization unit: Based on the coordinates of all high-attention users in each priority area, the coordinates of the area's center of gravity are calculated. With the signage installation center point as the rotation center, the optimal orientation angle for each area is determined according to the following formula: ,in, For the The optimal orientation angle corresponding to the level push area; and For the The horizontal and vertical coordinates of the center of gravity of high-attention users in the priority area of ​​the highest push, in meters; The coordinates of the location where the sign will be installed; is the two-variable inverse tangent function;

[0108] Collaborative control subunit: used to generate the signage content update sequence and communication relay strategy based on the overlapping service boundaries and push area priorities of each signage in a multi-signage deployment scenario, including:

[0109] Determine the order of content push based on regional priority;

[0110] Assigning communication relay parameters for mutual backup to adjacent signage to ensure that key area information is transmitted in sequence between the main signage and the backup signage;

[0111] Output multi-signage collaborative control parameter set for execution control module drive.

[0112] Regional priority division units include:

[0113] Distance calculation subunit: used to calculate the spatial coordinates of each high-attention user and the set of all edges of the service range boundary polygon The minimum Euclidean distance is calculated as follows:

[0114] ,in, is the user coordinate, unit is m; The coordinates of the point on the boundary polygon closest to the user, in meters; The minimum distance from the user to the service boundary, in meters;

[0115] Ring area division subunit: used to calculate the output of the distance calculation subunit Distance threshold By comparison, the service area is divided into three levels of concentric rings from the boundary inward. Specifically,

[0116] The first level information push area meets ;

[0117] The second level information push area meets ;

[0118] The third level information push area meets ;

[0119] The above sub-units can quickly generate hierarchical push areas based on the spatial distance between the user and the boundary, achieve fine control of information delivery priority, and improve the efficiency of signage resource allocation and user reception experience.

[0120] The execution control module includes a strategy analysis unit, a display control unit, a posture drive unit, and an information synchronization unit; wherein:

[0121] Strategy parsing unit: used to receive the optimization strategy set output by the strategy generation module, extract the information push priority area data, sign optimal orientation angle instructions and collaborative control parameters, perform structured analysis in the execution order, and output to the corresponding control unit;

[0122] Display control unit: used to filter the content push list issued by the signage master control system according to the priority area identifier contained in the policy analysis unit, and push the corresponding content to the display screen in descending order of priority, ensuring that users closest to the inner circle of the boundary receive the target information content first;

[0123] Attitude drive unit: used to generate rotation control instructions based on the analyzed optimal orientation angle, and drive the built-in attitude adjustment motor to rotate the sign body to the specified direction angle, so as to achieve directional placement towards the target user group;

[0124] Information synchronization unit: used to establish a master-slave signage communication link based on collaborative control parameters, and dispatch communication repeaters to synchronously forward content update and posture adjustment instructions according to a predetermined sequence to achieve multi-signage information collaboration; the above unit can realize the complete link conversion from strategy to action, so that the signage can achieve accurate content release, intelligent posture adjustment and synchronous collaboration of multiple devices after receiving the optimization strategy, thereby ensuring the unified effectiveness of information delivery in space, time and user dimensions.

[0125] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0126] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A smart signage management system based on data analysis, characterized in that: It includes data acquisition module, feature fusion module, behavior analysis module, strategy generation module and execution control module; among which: Data collection module: used to collect real-time data on users' stay time and interaction frequency in the target area covered by the sign through the infrared thermal imaging sensor array and touch interactive panel deployed on the sign body; Feature fusion module: used to receive dwell time data and interaction frequency data, generate user behavior feature packages through spatiotemporal correlation matching, and calculate the corresponding weight coefficient based on the scene type to which the sign belongs, and then generate the user attention index through weighted fusion; Behavior analysis module: used to identify high-attention user groups based on the comparison results of user attention index and preset threshold, and calculate the effective service range boundary of the signage using grid hot zone division method based on their spatial cluster density; The behavior analysis module includes a threshold comparison unit, a group identification unit, a hot zone division unit, and a service boundary calculation unit; wherein: Threshold comparison unit: used to receive the user attention index output by the feature fusion module, compare it with the attention threshold built into the behavior analysis module one by one, and generate a judgment label. The judgment label distinguishes between high attention and normal attention. Group identification unit: This unit is used to scan the connectivity of user instances in the same attention state and adjacent to each other in space based on the judgment labels output by the threshold comparison unit, and aggregate high-attention users in a continuous area into a high-attention user group; Hot zone division unit: used to calculate the spatial cluster density of high-attention user groups, divide the coverage area into multi-level hot zone grids, and classify the grids according to the number of high-attention users in each grid to obtain a hot zone grid set; Service boundary calculation unit: used to fit the circumscribed polygon of the hotspot grid set and output the vertex coordinates of the fitted polygon as the effective service range boundary of the sign; Strategy generation module: Based on the effective service range boundary coordinates and the real-time distribution coordinates of high-attention user groups, it generates an optimization strategy set including multi-level information push priority areas, optimal signage orientation angles, and multi-signage collaborative control parameters; Execution control module: used to analyze and optimize the strategy set, and control the sign content update, posture adjustment and information collaboration through the Internet of Things terminal to achieve information delivery.

2. The smart signage management system based on data analysis according to claim 1 is characterized in that: The data acquisition module includes an infrared image acquisition unit, a trajectory recognition unit, a touch event recording unit, and a data synchronization unit; wherein: Infrared image acquisition unit: used to continuously acquire thermal image frame data within the signage coverage area through the infrared thermal imaging sensor array, and capture the two-dimensional coordinates of the user's body heat source in real time based on the set frame rate; Trajectory recognition unit: used to track the heat source points in the infrared image frame in time series, by calculating the continuous displacement vector of each user in the image coordinate system, to determine whether the user is in a stationary state, and to convert the user's stay time at the specified location based on the number of stay frames and the image frame rate ; Touch event recording unit: used to monitor each touch event on the touch interaction panel and record the corresponding timestamp to count the interaction frequency within a given time window ; Data synchronization unit: used to synchronize the dwell time Corresponding touch frequency Perform timestamp alignment and package to generate structured original interaction behavior records.

3. The smart signage management system based on data analysis according to claim 2 is characterized in that: The feature fusion module includes a spatial aggregation unit, a feature package assembly unit, a weight coefficient calculation unit, and an attention generation unit; wherein: Spatial aggregation unit: divides the grid units based on the user coordinate information in the original interaction behavior record, and aggregates the behavior data in the same grid and with overlapping time periods into the same user instance; Feature package assembly unit: used to extract the dwell time, interaction frequency and time period labels from each user instance and encapsulate them into a user behavior feature package; Weight coefficient calculation unit: used to call the preset scene weight table according to the scene type to which the sign belongs, and assign a weight coefficient of a certain value to the dwell time, interaction frequency and time period label respectively. The scene types include public transportation station scenes, supermarket advertising scenes and indoor guide scenes; Attention generation unit: performs weighted fusion of user behavior feature packages based on corresponding weight coefficients to generate the corresponding user attention index.

4. The smart signage management system based on data analysis according to claim 3 is characterized in that: The spatial aggregation unit includes: Grid division subunit: used to divide the coverage area into equal intervals according to the fixed grid side length based on the reference origin of the sign, and calculate the grid index for any user coordinate ; Time overlap determination subunit: used to determine the time overlap of the same grid index Compare the two behavioral data of the recording time interval and , when satisfied When , it is determined that the two behavioral data have effective time overlap; These are the start and end timestamps of the corresponding behavior data; User instance aggregation subunit: used to merge behavioral data in the same grid index and meeting the time overlap judgment condition into a single user instance.

5. The data analysis-based smart signage management system according to claim 4 is characterized in that: The attention generation unit includes: Feature extraction subunit: used to extract feature parameters from user behavior feature packages, including user stay time , user interaction frequency and the time period label for the behavior ; Weight loading subunit: used to read the corresponding weight coefficient of each feature from the preset weight configuration table according to the scene type of the sign, namely, the residence time weight , interaction frequency weight , time period weight ,satisfy ; Weighted calculation subunit: used to perform linear weighted operation on the extracted feature parameters and the loaded weight coefficients to generate the final user attention index. The calculation formula is: ,in, Indicates the user attention index.

6. The smart signage management system based on data analysis according to claim 1 is characterized in that: The thermal zone division unit includes: Density calculation subunit: used to traverse and count the user coordinates in each high-attention user group. Calculate spatial cluster density within the base grid ; Grid hierarchical sub-units: based on grid density Perform multi-level grid subdivision on the coverage area and The basic grid continues to use the binary refinement method to refine the edge length. Divide dense sub-grids to form a multi-level hot zone grid system; Heat calibration subunit: used to calibrate the heat level of all grids according to the number of high-attention users. The heat level is divided into three levels: first level heat, second level heat, and third level heat according to the threshold range. The specific expression is: ； in, is the threshold number of high-attention users; is the heat level, value , Indicates the grid number The number of high-attention users within Hotspot extraction subunit: used to collect heat levels The grids are combined to obtain the hotspot grid set.

7. The smart signage management system based on data analysis according to claim 1 is characterized in that: The strategy generation module includes an area priority division unit, a direction optimization unit and a collaborative control unit; wherein: Regional Priority Division Unit: This unit receives the coordinates of the vertex points on the service boundary and the real-time distribution coordinates of the high-attention user group, divides the boundary area into multiple levels of information push priority areas based on the minimum distance between the user location and the service boundary, and assigns a corresponding priority identifier to each area. Orientation optimization unit: Based on the coordinates of all high-attention users in each priority area, the coordinates of the area's center of gravity are calculated. With the signage installation center point as the rotation center, the optimal orientation angle for each area is determined according to the following formula: ,in, For the The optimal orientation angle corresponding to the level push area; and For the The horizontal and vertical coordinates of the center of gravity of high-attention users in the priority area of ​​the highest push, in meters; The coordinates of the location where the sign will be installed; is the two-variable inverse tangent function; Collaborative control subunit: used to generate signage content update timing and communication relay strategies in multi-signage deployment scenarios based on the overlapping relationship of service boundaries of each signage and the push area priority.

8. The smart signage management system based on data analysis according to claim 7 is characterized in that: The regional priority division unit includes: Distance calculation subunit: used to calculate the spatial coordinates of each high-attention user and the set of all edges of the service range boundary polygon The minimum Euclidean distance is calculated as follows: ,in, is the user coordinate; The coordinates of the point on the boundary polygon closest to the user; is the minimum distance from the user to the service boundary; Ring area division subunit: used to calculate the output of the distance calculation subunit Distance threshold By comparison, the service area is divided into three levels of concentric rings from the boundary inward. Specifically, The first level information push area meets ; The second level information push area meets ; The third level information push area meets .

9. The smart signage management system based on data analysis according to claim 1, characterized in that: The execution control module includes a strategy analysis unit, a display control unit, a posture driving unit and an information synchronization unit; wherein: Strategy parsing unit: used to receive the optimization strategy set output by the strategy generation module, extract the information push priority area data, sign optimal orientation angle instructions and collaborative control parameters, perform structured analysis in the execution order, and output to the corresponding control unit; Display control unit: used to filter the content push list issued by the signage master control system according to the priority area identifier contained in the policy analysis unit, and push the corresponding content to the display screen in descending order of priority; Attitude drive unit: used to generate rotation control instructions according to the analyzed optimal orientation angle, and drive the built-in attitude adjustment motor to rotate the sign body to the specified direction angle; Information synchronization unit: used to establish a master-slave signage communication link based on collaborative control parameters, and dispatch communication repeaters to synchronously forward content update and posture adjustment instructions according to a predetermined timing.

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