Digital exhibition hall multimedia equipment intelligent interaction control method and system

Abstract

The invention relates to the technical field of intelligent interaction control, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment. According to the method, the number, position and behavior data of visitors are collected through multiple sensors in an exhibition hall, characteristics such as moving tracks and staying time of the visitors are analyzed, and a behavior analysis result is generated; the system calculates attention indexes and emotional response degrees of visitors, a display effect quantitative evaluation system is established, and attraction levels and participation degree scores of different display schemes are evaluated; based on the evaluation result, the system automatically adjusts the display content and mode of the multimedia equipment to realize dynamic optimization of the display effect; meanwhile, a worker can send a manual adjustment instruction for correction through the mobile terminal; according to the method, automatic adjustment and manual intervention are fused, automatic operation of the system is ensured, and the reliability and adaptability of an adjustment result are ensured.

Description

Technical Field

[0001] This application relates to the technical field of intelligent interactive control, and in particular to intelligent interactive control methods and systems for multimedia equipment in digital exhibition halls. Background Technology

[0002] With the deep integration of technology and culture, digital exhibition halls have become an important platform for showcasing cultural content and technological innovation. Through multimedia equipment and interactive technologies, digital exhibition halls provide visitors with an immersive experience, greatly enhancing the display effect and the efficiency of cultural dissemination.

[0003] In existing technologies, digital exhibition halls mostly employ preset display schemes and fixed display sequences, collecting visitor information in real time through sensor networks and automatically switching display content based on preset rules. Simultaneously, the exhibition hall management system can adjust the operating status of multimedia equipment according to visitor density and visitor paths.

[0004] However, the pre-set display schemes of existing technologies lack in-depth analysis of visitors' real-time reactions and cannot accurately assess the display effect; the switching of display content mainly relies on simple trigger conditions and fails to fully consider changes in visitors' attention and emotions, which needs to be further improved. Summary of the Invention

[0005] To address the shortcomings of existing pre-designed display schemes, such as a lack of in-depth analysis of visitors' real-time reactions, inability to accurately evaluate display effectiveness, and failure to fully consider changes in visitors' attention and emotions, this application provides an intelligent interactive control method and system for multimedia equipment in digital exhibition halls, employing the following technical solution: In a first aspect, this application provides an intelligent interactive control method for multimedia equipment in a digital exhibition hall, comprising the following steps: Visitor information is collected through multiple sensors deployed within the digital exhibition hall, including the number of visitors, their location, and behavioral data. Based on the visitor quantity, location, and behavioral data, analyze visitor behavior patterns and generate behavioral analysis results; Based on the behavioral analysis results, the visitor's attention index and emotional response level to the current display content are calculated. Based on the attention index and emotional response level, the attractiveness level and engagement score of different presentation schemes are evaluated; Based on the attractiveness level and engagement score, the content and display method of multimedia devices are automatically adjusted. Receive manual adjustment instructions sent by the mobile terminal, and correct the automatic adjustment results according to the manual adjustment instructions.

[0006] By adopting the above technical solution, this application first collects comprehensive data on the number, location, and behavior of visitors through multiple sensors deployed within the exhibition hall; the system analyzes the visitors' movement trajectories, dwell time, and other behavioral characteristics to generate behavioral analysis results; by deeply calculating the visitors' attention index and emotional response to the exhibition content, a quantitative evaluation system for the exhibition effect is established; based on this, the system evaluates the attractiveness level and participation score of different exhibition schemes, thereby providing a scientific basis for adjusting the exhibition strategy; on this basis, the system can automatically adjust the display content and display method of multimedia equipment to achieve dynamic optimization of the exhibition effect; at the same time, staff can send manual adjustment commands through mobile terminals to make necessary corrections to the automatic adjustment results, ensuring that the exhibition effect is always kept at its best; this application integrates two modes of automatic adjustment and manual intervention, ensuring both the automation level of system operation and the reliability and adaptability of the adjustment results.

[0007] Optionally, based on the number, location, and behavioral data of the visitors, the behavioral patterns of the visitors are analyzed to generate behavioral analysis results, specifically including the following steps: Based on the number and location of the visitors, obtain the spatial distribution density of the visitors; Based on the spatial distribution density and the behavioral data, obtain the movement trajectory parameters, dwell time parameters, and interaction behavior parameters; The spatial distribution density, movement trajectory parameters, dwell time parameters, and interaction behavior parameters are correlated to generate the behavior analysis results.

[0008] By adopting the above technical solution, in order to accurately grasp the real-time status and behavioral tendencies of visitors, it is necessary to conduct a comprehensive analysis of visitors' activities in the exhibition hall. This application first uses a density calculation model based on the number and location information of visitors to obtain the spatial distribution density of visitors in the exhibition area, thereby achieving a quantitative description of the distribution status of the visitor group. The system combines the obtained spatial distribution density and real-time collected behavioral data to obtain the movement trajectory parameters of visitors through a trajectory extraction algorithm, including features such as movement speed and direction changes. At the same time, it calculates the dwell time parameters of visitors in each exhibition area to reflect their attention to the exhibits. It also analyzes the interaction behavior parameters between visitors and the exhibits to reflect the degree of visitor participation. Finally, the system uses a multi-dimensional parameter correlation analysis method to systematically correlate the spatial distribution density, movement trajectory parameters, dwell time parameters, and interaction behavior parameters to generate analysis results that can comprehensively reflect the behavioral characteristics of visitors. This not only enables a macroscopic grasp of the distribution pattern of the visitor group but also accurately describes the microscopic characteristics of individual visitor behavior, providing reliable data support for the evaluation and optimization of exhibition effects.

[0009] Optionally, the dwell time parameter includes a dwell time coefficient and a browsing time coefficient, and the interaction behavior parameter includes an interaction frequency parameter and a group aggregation parameter. Based on the spatial distribution density and the behavior data, the movement trajectory parameter, dwell time parameter, and interaction behavior parameter are obtained, specifically including the following steps: Based on the spatial distribution density and the behavioral data, obtain the dwell time coefficient, the browsing time coefficient, the interaction frequency parameter, and the group aggregation parameter; The dwell time parameter is obtained based on the dwell time coefficient and the browsing time coefficient.

[0010] By adopting the above technical solution, in order to accurately assess visitors' attention to and engagement with the exhibits, it is necessary to deeply distinguish different types of dwelling behavior and interaction patterns. Since visitor dwelling behavior includes various forms such as simple passing through, brief pausing, and in-depth browsing, and interaction behavior involves different scenarios such as individual interaction and group gathering, traditional single-duration statistics and interaction counting methods are difficult to accurately reflect the true experience of visitors. This application first uses spatial distribution density and behavioral data to subdivide visitor dwelling behavior into two modes: pausing and browsing, based on a behavior recognition algorithm, and calculates pausing duration coefficients and browsing duration coefficients respectively. The pausing duration coefficient reflects visitors' brief pausing and observation behavior. The browsing duration coefficient reflects visitors' in-depth observation and learning behaviors. The system classifies and analyzes visitors' interactive behaviors, extracts interaction frequency parameters to quantify the degree of interaction between individuals and display devices, and describes the intensity of social interaction among visitors through group aggregation parameters. After obtaining these subdivided parameters, a weighted fusion method is used to generate a dwell time parameter that can comprehensively reflect the quality of visitor behavior based on the combined characteristics of dwell time coefficient and browsing duration coefficient. Through the refined division of dwell behavior and interactive behavior, a multi-level behavioral characteristic analysis system is established. It can not only distinguish different types of dwell behavior, but also quantify the impact of individual interaction and group interaction, making the behavioral analysis results more objective and accurate.

[0011] Optionally, the behavioral analysis results include attention feature vectors and emotion feature matrices. Based on the behavioral analysis results, the visitor's attention index and emotional response level to the current display content are calculated, specifically including the following steps: Obtain the spatial and temporal behavioral sequences of visitors; The attention feature vector is constructed based on the dwell time parameter, the movement trajectory parameter, and the spatial behavior sequence; The emotion feature matrix is ​​constructed based on the interaction behavior parameters, group aggregation parameters, and time behavior sequence.

[0012] By adopting the above technical solution, this application first extracts the spatial and temporal behavioral sequences of visitors in the exhibition hall, and establishes a spatiotemporal mapping relationship of behavioral data. In the spatial dimension, the system integrates dwell time parameters, movement trajectory parameters, and spatial behavioral sequences, and constructs an attention feature vector through feature extraction algorithms. This vector can reflect the distribution and changing trends of visitors' attention in different spatial locations. In the temporal dimension, the system combines interactive behavior parameters, group aggregation parameters, and temporal behavioral sequences, and uses a matrix construction method to generate an emotion feature matrix. This matrix can describe the changes in visitors' emotions and group interaction effects in different time periods. Through feature modeling in both spatiotemporal dimensions, the system achieves accurate quantification of visitors' attention and emotional states. It can not only capture the spatial distribution patterns of visitors' behavioral characteristics, but also track their evolution in the temporal sequence, providing a more comprehensive and in-depth analytical basis for evaluating the exhibition effect.

[0013] Optionally, based on the attractiveness level and engagement score, the display content and display method of the multimedia device can be automatically adjusted, specifically including the following steps: Based on the attractiveness level and engagement score, obtain information on changes in display effectiveness; Obtain abnormal effect trigger information, and trigger a development zone environment detection command based on the abnormal effect trigger information; Obtain the environmental monitoring results of the exhibition area, and based on the environmental monitoring results of the exhibition area, combined with the information on changes in the display effect, obtain the current display status information; Based on the current display status information and the display scheme parameters, obtain adjustment strategy information.

[0014] By adopting the above technical solution, this application first obtains information on changes in display effects based on attractiveness level and participation score through trend analysis algorithm, and monitors the fluctuations of display effects in real time. When the system detects an abnormality in display effects, it automatically generates anomaly trigger information and triggers an environmental detection command for the exhibition area, initiating a comprehensive detection of the exhibition area environment. After obtaining the environmental detection results, the system correlates and analyzes them with the information on changes in display effects to comprehensively generate current display status information, which can fully reflect the causes and degree of impact of abnormal display effects. Finally, based on the current display status information and combined with preset display scheme parameters, the system generates targeted adjustment strategy information through decision-making algorithm. This achieves intelligent adjustment of display effects, which can not only detect abnormal changes in display effects in a timely manner, but also accurately locate the source of the problem, providing a reliable basis for the formulation of adjustment strategies.

[0015] Optionally, based on the exhibition area environment detection results and the display effect change information, the current display status information can be obtained, specifically including the following steps: The environmental monitoring results of the exhibition area are classified and processed to obtain the noise level, lighting intensity and crowd density of the exhibition area; Extract attention change trends and emotional fluctuation amplitudes from the information on changes in the display effects; The degree of environmental impact on the display effect is determined based on the noise level, lighting intensity, and pedestrian density of the exhibition area. The degree of environmental influence is combined with the trend of attention changes and the magnitude of emotional fluctuations to form the current display status information.

[0016] By adopting the above technical solutions, it is possible to reveal the essential characteristics of the exhibition state due to the complex interaction between environmental factors and visitor reactions, which is difficult to achieve using traditional individual analysis methods. For example, a decline in visitor attention may stem from the exhibit itself or from environmental noise interference; emotional fluctuations may arise from resonance with the exhibit or from discomfort caused by overcrowding. This application first performs multi-dimensional classification processing on the environmental monitoring results of the exhibition area, extracting three key indicators—noise level, lighting intensity, and crowd density—through an environmental parameter analysis algorithm to construct a quantitative description of the exhibition area's environmental status. The system extracts attention change trends and emotional fluctuation amplitudes from information on changes in exhibition effects, establishing a dynamic characteristic model of visitor reactions. Based on this, the system uses an environmental impact assessment algorithm to analyze the comprehensive impact of noise level, lighting intensity, and crowd density on the exhibition effect, quantifying the interference of environmental factors on the visitor experience. Finally, the system integrates the degree of environmental impact with attention change trends and emotional fluctuation amplitudes to generate status information that comprehensively reflects the current state of the exhibition. Through the collaborative analysis of environmental factors and visitor reactions, a complete exhibition status assessment system is established. This system can not only identify the impact mechanism of environmental factors on exhibition effects but also accurately track the changing patterns of visitor reactions, providing a more comprehensive and reliable basis for optimizing exhibition strategies.

[0017] Optionally, before receiving a manual adjustment instruction sent by a mobile terminal and correcting the automatic adjustment result according to the manual adjustment instruction, the method further includes the following steps: Calculate the thematic relevance and display format relevance between the currently displayed content and the next displayed content, and generate content switching correlation analysis results; Generate a multi-dimensional display mode comparison table for the currently displayed content, the display mode comparison table including sound and light combination mode, playback rhythm and device layout; The system analyzes the coverage of currently displayed content with key content in the display library and generates a report on the distribution of key content. Prioritize the content not displayed in the display library, create a display method adaptation list for each piece of content to be displayed, and generate a recommended list of content to be displayed and multi-dimensional display methods; The results of the content switching correlation analysis, the comparison table of display methods, the report on the distribution of key content, and the recommended list of content to be displayed and multi-dimensional display methods will be sent to the mobile terminal as a reference for manual adjustment.

[0018] By adopting the above technical solution, this application first analyzes the degree of correlation between the currently displayed content and the next displayed content in terms of both thematic connotation and display form using a relevance calculation algorithm, generating content switching correlation analysis results to provide a basis for content connection. The system constructs a multi-dimensional display mode comparison table including elements such as sound and light combination, playback rhythm, and device layout to achieve a systematic presentation of display modes. In addition, the system also performs coverage statistics on key content in the display library, generates a key content distribution report, and classifies undisplayed content using a priority ranking algorithm. Combined with display mode adaptation analysis, it generates a list of content to be displayed and its multi-dimensional display mode recommendation list. Finally, the system uniformly sends these analysis results and recommendation information to mobile terminals, providing a comprehensive reference for managers' adjustment decisions. By establishing a complete information support system of content correlation analysis, display mode comparison, key content statistics, and content to be displayed recommendation, the system achieves the scientific and systematic nature of manual adjustment. It not only ensures the orderly connection of displayed content but also provides multi-dimensional display mode references, providing reliable decision support for optimizing display effects.

[0019] Secondly, this application provides an intelligent interactive control system for multimedia equipment in digital exhibition halls, comprising: The visitor information collection module is used to collect visitor information through multiple sensors deployed in the digital exhibition hall. The visitor information includes the number of visitors, their location, and behavioral data. The behavior analysis module is used to analyze the behavior patterns of visitors based on the number, location, and behavior data of the visitors, and generate behavior analysis results. The calculation module is used to calculate the visitor's attention index and emotional response level to the current display content based on the behavioral analysis results. The scheme evaluation module is used to evaluate the attractiveness level and engagement score of different presentation schemes based on the attention index and emotional response level. The content display adjustment module is used to automatically adjust the display content and display method of the multimedia device based on the attractiveness level and engagement score; The adjustment and correction module is used to receive manual adjustment instructions sent by the mobile terminal and correct the automatic adjustment results according to the manual adjustment instructions.

[0020] In summary, this application includes at least one of the following beneficial technical effects: 1. This application first comprehensively collects visitor quantity, location, and behavioral data through multiple sensors deployed within the exhibition hall; the system analyzes visitor movement trajectories, dwell time, and other behavioral characteristics to generate behavioral analysis results; by deeply calculating visitors' attention index and emotional response to the exhibits, a quantitative evaluation system for exhibit effectiveness is established; based on this, the system evaluates the attractiveness level and participation score of different exhibit schemes, thus providing a scientific basis for adjusting exhibit strategies; on this basis, the system can automatically adjust the exhibit content and display method of multimedia equipment to achieve dynamic optimization of exhibit effectiveness; simultaneously, staff can send manual adjustment commands via mobile terminals to make necessary corrections to the automatic adjustment results, ensuring that the exhibit effectiveness is always maintained at its best; this application integrates both automatic adjustment and manual intervention modes, ensuring both the automation level of system operation and the reliability and adaptability of adjustment results; 2. This application first uses a density calculation model to obtain the spatial distribution density of visitors within the exhibition area based on the number and location information of visitors, thus achieving a quantitative description of the distribution status of the visitor group. The system combines the obtained spatial distribution density with real-time collected behavioral data, and uses a trajectory extraction algorithm to obtain the movement trajectory parameters of visitors, including features such as movement speed and direction changes. Simultaneously, it calculates the dwell time parameters of visitors in each exhibition area, reflecting their level of attention to the exhibited content; and analyzes the interaction behavior parameters between visitors and the exhibits, reflecting their participation level. Finally, the system uses a multi-dimensional parameter correlation analysis method to systematically correlate the spatial distribution density, movement trajectory parameters, dwell time parameters, and interaction behavior parameters, generating analysis results that comprehensively reflect the behavioral characteristics of visitors. This not only enables a macroscopic understanding of the distribution patterns of visitor groups but also accurately describes the microscopic characteristics of individual visitor behavior, providing reliable data support for the evaluation and optimization of exhibition effects. 3. This application first uses a trend analysis algorithm based on attractiveness level and participation score to obtain information on changes in display effects and monitor fluctuations in display effects in real time. When the system detects an anomaly in display effects, it automatically generates anomaly trigger information and triggers an environmental detection command for the exhibition area, initiating a comprehensive inspection of the exhibition area environment. After obtaining the environmental detection results, the system correlates and analyzes them with the information on changes in display effects to comprehensively generate current display status information. This information can fully reflect the causes and degree of impact of abnormal display effects. Finally, based on the current display status information and the preset display scheme parameters, the system generates targeted adjustment strategy information through a decision-making algorithm. This achieves intelligent adjustment of display effects, which can not only detect abnormal changes in display effects in a timely manner but also accurately locate the source of the problem, providing a reliable basis for the formulation of adjustment strategies. Attached Figure Description

[0021] Figure 1This is a flowchart illustrating an intelligent interactive control method for multimedia equipment in a digital exhibition hall, according to an embodiment of this application. Figure 2 This is a flowchart illustrating step S200 in an intelligent interactive control method for multimedia equipment in a digital exhibition hall, according to an embodiment of this application. Figure 3 This is a flowchart illustrating step S220 in an intelligent interactive control method for multimedia equipment in a digital exhibition hall according to an embodiment of this application. Figure 4 This is a flowchart illustrating step S300 in an intelligent interactive control method for multimedia equipment in a digital exhibition hall according to an embodiment of this application. Figure 5 This is a flowchart illustrating step S500 in a method for intelligent interactive control of multimedia equipment in a digital exhibition hall according to an embodiment of this application. Figure 6 This is a flowchart illustrating step S530 in an intelligent interactive control method for multimedia equipment in a digital exhibition hall according to an embodiment of this application. Figure 7 This is a flowchart illustrating step S600 in a method for intelligent interactive control of multimedia equipment in a digital exhibition hall according to an embodiment of this application. Figure 8 This is a schematic diagram of a module of an intelligent interactive control system for multimedia equipment in a digital exhibition hall, according to an embodiment of this application. Figure 9 This is an internal structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0023] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0024] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0025] Firstly, this application provides an intelligent interactive control method for multimedia equipment in digital exhibition halls, referring to... Figure 1 It includes the following steps: S100 collects visitor information through multiple sensors deployed within the digital exhibition hall. The visitor information includes the number of visitors, their location, and behavioral data.

[0026] In this embodiment, the sensors include a camera, an infrared sensor, and an audio acquisition device. The camera is used to capture images of the visitor's location, number, and behavior, and a visual algorithm is used to track the visitor's movement trajectory in real time. The infrared sensor is used to assist in detecting densely populated areas and improve statistical accuracy. The audio acquisition device is used to collect the volume of visitors' conversations and voice information. Visitor behavior data includes movement speed, movement direction, dwell time, gestures, and voice loudness.

[0027] Specifically, the system deploys cameras within the exhibition hall according to spatial coverage requirements, installs infrared sensors at key points of visitor flow, and sets up audio collectors around the exhibits. The system timestamps the data collected by the sensors, establishes a visitor information database, and enables continuous monitoring and recording of visitor behavior.

[0028] S200. Analyze visitor behavior patterns based on visitor number, location, and behavior data, and generate behavior analysis results.

[0029] In this embodiment, behavioral pattern analysis includes spatial distribution analysis, movement trajectory analysis, dwelling characteristic analysis, and interactive behavior analysis. Spatial distribution analysis reflects the density distribution of visitors within the exhibition area; movement trajectory analysis describes the movement patterns of visitors; dwelling characteristic analysis reflects the attractiveness of the content; and interactive behavior analysis reflects the level of participation.

[0030] Specifically, the system divides crowded areas using crowd density thresholds, extracts movement trajectory features based on changes in movement speed and direction, determines visitor interest by considering dwell time, and analyzes gesture frequency to assess interaction levels. The system then performs a comprehensive analysis of these features to generate an analytical report reflecting visitor behavior patterns.

[0031] S300. Based on the behavioral analysis results, calculate the visitor's attention index and emotional response level to the current display content.

[0032] In this embodiment, the attention index is derived through analysis of dwell time parameters, movement trajectory parameters, and spatial behavior sequence. The degree of emotional response is composed of interaction behavior parameters, group aggregation parameters, and temporal behavior sequence.

[0033] Specifically, the system first extracts dwell time parameters, including single dwell time and cumulative viewing time; it extracts movement trajectory parameters, including viewing distance and line of sight orientation; and it extracts spatial behavior sequences, recording changes in the spatial location of visitors at different time points. Simultaneously, the system extracts interaction behavior parameters, including operation frequency and response time; it extracts group aggregation parameters, including aggregation size and duration; and it extracts temporal behavior sequences, recording the patterns of visitor behavior changes over time.

[0034] S400: Evaluate the attractiveness and engagement scores of different presentation schemes based on attention index and emotional response level.

[0035] In this embodiment, the attractiveness level reflects the degree to which the displayed content is appealing to visitors, calculated using the cumulative value of the attention index over time. The engagement score reflects visitors' willingness to interact, based on a comprehensive assessment of emotional response and interactive behavior.

[0036] Specifically, the system records attention index and emotional response data for different presentation schemes. By setting scoring rules, attention persistence is converted into an attractiveness score, and emotional activity and interaction frequency are converted into an engagement score, generating an effectiveness evaluation report for the presentation schemes.

[0037] S500 automatically adjusts the content and presentation style of multimedia devices based on attractiveness level and engagement score.

[0038] In this embodiment, the displayed content includes images, videos, audio, and text information. Display methods include playback speed, volume, brightness contrast, and display order. The system automatically selects the optimal content combination and display parameters based on the evaluation score.

[0039] Specifically, the system presets display scheme adjustment rules and establishes a correspondence between score ranges and adjustment strategies. When the evaluation score falls below a preset threshold, automatic adjustment is triggered. The system selects appropriate display content and parameter combinations from the preset scheme library to achieve dynamic optimization of the display effect.

[0040] S600: Receives manual adjustment instructions sent by the mobile terminal and corrects the automatic adjustment results according to the manual adjustment instructions.

[0041] In this embodiment, the mobile terminal is a management device provided to staff, used to display the current display status and adjustment suggestions. Manual adjustment commands include content switching commands, parameter adjustment commands, and display pause commands.

[0042] Specifically, the system displays the automatic adjustment results and key evaluation data on mobile terminals, and staff can send adjustment instructions based on the on-site situation. After receiving the instructions, the system prioritizes manual adjustments and records the adjustment results in the system log for subsequent optimization and improvement of the demonstration solution.

[0043] In one embodiment, refer to Figure 2 In step S200, the behavioral patterns of visitors are analyzed based on the number, location, and behavioral data of visitors to generate behavioral analysis results. This specifically includes the following steps: S210. Obtain the spatial distribution density of visitors based on the number and location of visitors.

[0044] In this embodiment, spatial distribution density refers to the number of visitors in each cell of the exhibition hall's spatial grid. The system divides the exhibition hall's floor plan into several grid cells, maps the visitor location coordinates obtained through visual recognition to the corresponding grid cells, and calculates the number of visitors per unit area.

[0045] Specifically, the system pre-establishes a spatial density calculation mapping table, which includes three fields: grid number, location coordinate range, and density threshold. The system performs a density calculation every 10 seconds, substituting visitor location data into the mapping table to obtain the real-time density value for each grid cell. When the density value of a certain area exceeds the preset threshold, the system marks that area as a densely populated area.

[0046] S220. Based on spatial distribution density and behavioral data, obtain movement trajectory parameters, dwell time parameters, and interaction behavior parameters.

[0047] In this embodiment, the movement trajectory parameters include movement speed, direction angle, and path length; the dwell time parameters include single dwell time, cumulative dwell time, and dwell frequency; and the interaction behavior parameters include the number of gesture actions, voice interaction duration, and number of operation responses. The system extracts these behavioral feature parameters using a visual recognition algorithm.

[0048] S230. Correlate spatial distribution density, movement trajectory parameters, dwell time parameters, and interaction behavior parameters to generate behavior analysis results.

[0049] Specifically, the system establishes a parameter correlation analysis database, aligns various parameters by timestamp, and constructs a parameter correlation graph. By setting parameter weights, the correlation strength of different parameter combinations is calculated.

[0050] In one embodiment, the dwell time parameter includes a dwell time coefficient and a browsing time coefficient, and the interaction behavior parameter includes an interaction frequency parameter and a group aggregation parameter, referring to... Figure 3 In step S220, based on spatial distribution density and behavioral data, the movement trajectory parameters, dwell time parameters, and interaction behavior parameters are obtained, specifically including the following steps: S221. Based on spatial distribution density and behavioral data, obtain the dwell time coefficient, browsing time coefficient, interaction frequency parameter, and group aggregation parameter.

[0051] In this embodiment, the dwell time coefficient refers to the percentage of time visitors spend remaining stationary in front of exhibits, while the browsing time coefficient refers to the percentage of time visitors spend moving slowly within the exhibition area. The interaction frequency parameter reflects the number of times visitors interact with exhibits, and the group aggregation parameter describes the size and duration of the visitor group that remains in the exhibit. The system obtains these parameter values ​​by calculating the time distribution of visitors under different movement states and combining this with spatial location change characteristics.

[0052] Specifically, the system pre-establishes a time-based coefficient calculation table, categorizing visitor movement into three types: stationary, slow-moving, and fast-moving. When the detected change in a visitor's position coordinates is less than a threshold, it is determined to be in a stationary state and counted as dwell time; when the position coordinates change slowly, it is determined to be in a browsing state and counted as browsing time. Simultaneously, an interaction behavior recognition table is established to record the number of visitor gestures and touch operations, and to statistically analyze gatherings of more than three people.

[0053] S222. Obtain the dwell time parameter based on the dwell time coefficient and the browsing time coefficient.

[0054] In this embodiment, the dwell time parameter is a weighted combination of the dwell time coefficient and the browsing time coefficient. The system sets dwell time weight and browsing weight for different display content to reflect the viewing characteristics of the display content. For example, interactive exhibits emphasize dwell time, while display content emphasizes browsing time.

[0055] In one embodiment, the behavioral analysis results include an attention feature vector and an emotion feature matrix, referencing... Figure 4 In step S300, based on the behavioral analysis results, the visitor's attention index and emotional response level to the current display content are calculated, specifically including the following steps: S310. Obtain the spatial and temporal behavioral sequences of visitors.

[0056] In this embodiment, the spatial behavior sequence records the changes in visitors' behavior within the exhibition space, including three basic dimensions: location coordinates, movement state, and orientation angle. The temporal behavior sequence records the changes in visitors' behavior over time, including three basic dimensions: behavior type, duration, and time of occurrence.

[0057] Specifically, the system establishes a behavior sequence collection table, recording visitors' real-time behavior data with timestamps. Spatial behavior sequences are stored in quadruplets (x, y, s, θ), where (x, y) are position coordinates, s is the motion state code, and θ is the orientation angle. Temporal behavior sequences are stored in triplets (b, t, m), where b is the behavior code, t is the duration, and m is the time marker. The system collects behavior data once per second, forming a continuous sequence record.

[0058] S320. Construct an attention feature vector based on the dwell time parameter, movement trajectory parameter, and spatial behavior sequence.

[0059] Specifically, the system pre-establishes attention feature mapping rules, mapping dwell time parameters to dwell degree components, converting movement trajectory parameters into movement feature components, and converting orientation information extracted from spatial behavior sequences into spatial attention components. The final generated attention feature vector reflects the visitor's overall attention state.

[0060] S330. Construct an emotion feature matrix based on interaction behavior parameters, group aggregation parameters, and time behavior sequences.

[0061] In this embodiment, the emotion feature matrix is ​​an m×n dimensional matrix used to characterize changes in the emotional state of visitors. The rows of the matrix represent different time points, and the columns represent different dimensions of emotion features, including three main dimensions: interaction activity, group interaction intensity, and behavioral continuity.

[0062] Specifically, the system establishes an emotion feature calculation database, transforming interactive behavior parameters into interaction activity feature columns, group aggregation parameters into group interaction intensity feature columns, and behavioral change patterns in time-series behavior into behavioral continuity feature columns. The system sets quantification standards for each feature dimension, generating standardized feature values. By aggregating feature values ​​from different time points, a complete emotion feature matrix is ​​formed for subsequent emotion response analysis.

[0063] In one embodiment, refer to Figure 5 In step S500, based on the attractiveness level and engagement score, the display content and display method of the multimedia device are automatically adjusted, specifically including the following steps: S510. Obtain information on changes in display effectiveness based on attractiveness level and engagement score.

[0064] In this embodiment, the information on changes in display effects includes three dimensions: effect fluctuation trend, magnitude of change, and duration. The effect fluctuation trend reflects the upward or downward direction of the display effect, the magnitude of change indicates the drastic degree of change, and the duration describes the time span of the effect change. The system identifies the changing characteristics of the display effect through time-series analysis of attractiveness level and participation score.

[0065] Specifically, the system establishes an effectiveness change evaluation table to record the baseline value and change threshold of the display effect. When a change in the attractiveness level or participation score is detected to exceed the preset range, the system calculates the rate of change and judges the trend of change.

[0066] S520. Obtain abnormal effect trigger information, and trigger the development zone environmental detection command based on the abnormal effect trigger information.

[0067] In this embodiment, the effect anomaly trigger information refers to a situation where the display effect deviates significantly from expectations, and includes three attributes: anomaly type, anomaly level, and location of occurrence. The exhibition area environment detection command is used to initiate the detection of physical environment parameters of the exhibition area, including brightness detection command, volume detection command, and temperature detection command.

[0068] Specifically, the system has a pre-defined list of exception trigger rules, defining different criteria for different types of exceptions. When changes in the display effect meet the exception trigger conditions, the system generates a corresponding sequence of detection instructions. For example, if the system detects that the participation rate in a certain exhibition area is consistently low, it will send environmental parameter detection instructions in a pre-defined order, checking the lighting conditions, background noise, and ambient temperature of the exhibition area in turn.

[0069] S530. Obtain the environmental monitoring results of the exhibition area. Based on the environmental monitoring results of the exhibition area and the information on changes in the display effect, obtain the current display status information.

[0070] In this embodiment, the environmental monitoring results of the exhibition area are a combination of data from multiple environmental parameters, including illumination, sound pressure, and temperature. The current exhibition status information is a comprehensive assessment of the exhibition effect and environmental conditions, including the status of the exhibition content, the operating status of the equipment, and an environmental adaptability score.

[0071] Specifically, the system establishes a status assessment database and correlates environmental monitoring results with information on changes in display effects. By setting parameter combination rules, it identifies key factors affecting the display effect. The system then organizes the analysis results into standardized status descriptions for use in formulating subsequent adjustment strategies.

[0072] S540. Obtain adjustment strategy information based on the current display status information and display scheme parameters.

[0073] Specifically, the system establishes an adjustment strategy mapping table, pre-setting adjustment schemes for different display states. Based on the current state information, the system queries the mapping table and selects the most suitable adjustment strategy. The system then translates the adjustment strategy into specific execution instructions, including content replacement instructions, parameter adjustment instructions, and device control instructions.

[0074] In one embodiment, refer to Figure 6 In step S530, based on the results of the exhibition area environment detection and the information on changes in the display effect, the current display status information is obtained, specifically including the following steps: S531. Classify and process the environmental monitoring results of the exhibition area to obtain the noise level, lighting intensity and pedestrian density of the exhibition area.

[0075] In this embodiment, the environmental monitoring results of the exhibition area are a collection of raw data collected by multiple sensors. Noise level refers to the background sound intensity within the exhibition area, lighting intensity refers to the ambient light level, and crowd density refers to the number of visitors per unit area. The system obtains quantified values ​​for these three environmental indicators by classifying and standardizing the raw data.

[0076] S532. Extract the trend of attention change and the magnitude of emotional fluctuation from the information on changes in display effects.

[0077] Specifically, the system establishes a feature extraction table for effect changes, recording historical data on attention index and emotional response values. By calculating the difference between adjacent time points, the trend of change is determined; by calculating the range of fluctuation, the amplitude of fluctuation is determined.

[0078] S533. Determine the degree of influence of the environment on the display effect based on the noise level, lighting intensity and pedestrian density of the exhibition area.

[0079] Specifically, the system pre-establishes an environmental impact assessment matrix, defining the impact levels corresponding to different combinations of environmental indicators. When an environmental indicator is detected to exceed the appropriate range, the system queries the assessment matrix and calculates the comprehensive impact score. For example, when both high noise and high pedestrian density occur simultaneously in the exhibition area, the system determines it as a serious impact; when only a single indicator is abnormal, it is determined as a minor impact.

[0080] S534. Combine the degree of environmental influence with the trend of attention changes and the amplitude of emotional fluctuations to form the current display status information.

[0081] Specifically, the system establishes a rule base for combining state information, defining the combination methods and weight allocation of information from different dimensions. Using state coding, the degree of environmental influence, the trend of attention changes, and the amplitude of emotional fluctuations are converted into standardized state descriptions. For example, when the environmental influence is moderate, attention is declining, and emotional fluctuations are significant, the system generates a state description of "environmental interference - declining effect - significant fluctuations," providing a basis for demonstration and adjustment.

[0082] In one embodiment, refer to Figure 7 In step S600, before receiving the manual adjustment instruction sent by the mobile terminal and correcting the automatic adjustment result according to the manual adjustment instruction, the method further includes the following steps: S610. Calculate the thematic relevance and display format relevance between the currently displayed content and the next displayed content, and generate content switching association analysis results.

[0083] Specifically, the system establishes a topic relevance calculation table, pre-sets a topic tag system and keyword thesaurus, and calculates topic relevance by calculating tag overlap rate and keyword similarity. Simultaneously, it establishes a display format feature table to record the format attributes of different display content, and calculates format relevance through attribute matching.

[0084] S620. Generate a multi-dimensional display mode comparison table for the currently displayed content. The display mode comparison table includes the sound and light combination mode, playback rhythm, and device layout.

[0085] S630. Calculate the coverage of the currently displayed content with the key displayed content in the display library, and generate a key content distribution report.

[0086] S640. Prioritize the content not displayed in the display library, establish a display method adaptation list for each content to be displayed, and generate a recommended list of content to be displayed and multi-dimensional display methods.

[0087] In this embodiment, the priority of the content to be displayed is determined based on three factors: content importance, display timeliness, and audience preference. The display method adaptation list contains multiple display schemes that match the content to be displayed, and each scheme has undergone adaptation evaluation. The recommendation list is a systematic organization of the content to be displayed and the display methods.

[0088] Specifically, the system establishes a content priority scoring table, calculating the priority score for each piece of content not yet displayed through weighted averages. Simultaneously, it establishes a display method adaptation rule base, matching a suitable display scheme to each piece of content to be displayed. The system combines the priority ranking results with the adaptation schemes to generate a structured recommendation list.

[0089] S650. Send the content switching correlation analysis results, display mode comparison table, key content distribution report, and recommended list of content to be displayed and multi-dimensional display modes to the mobile terminal as a reference for manual adjustment.

[0090] In this embodiment, the four types of analysis results generated by the system constitute a complete display status reference system. This information is transmitted to the mobile terminal via a data interface, providing staff with comprehensive decision support. The mobile terminal displays this information through a visual interface, facilitating quick understanding and operation by staff.

[0091] Specifically, the system establishes a mobile terminal data push mechanism to send the four types of analysis results to mobile terminals. The system features an intuitive interactive interface on the mobile terminals, allowing staff to view detailed data, conduct comparative analysis, and ultimately make adjustment decisions.

[0092] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0093] Secondly, this application provides an intelligent interactive control system for multimedia equipment in digital exhibition halls. The intelligent interactive control system for multimedia equipment in digital exhibition halls of this application will be described below in conjunction with the aforementioned intelligent interactive control method for multimedia equipment in digital exhibition halls.

[0094] Reference Figure 8 A smart interactive control system for multimedia equipment in a digital exhibition hall, comprising: The visitor information collection module is used to collect visitor information through multiple sensors deployed in the digital exhibition hall. The visitor information includes the number of visitors, their location, and behavioral data. The behavior analysis module is used to analyze visitor behavior patterns based on the number, location, and behavior data of visitors, and generate behavior analysis results. The calculation module is used to calculate the visitor's attention index and emotional response level to the current display content based on the behavioral analysis results. The program evaluation module is used to assess the attractiveness and engagement scores of different presentation programs based on attention index and emotional response level. The content display adjustment module is used to automatically adjust the display content and display method of multimedia devices based on the level of attractiveness and engagement score; The adjustment and correction module is used to receive manual adjustment instructions sent by the mobile terminal and correct the automatic adjustment results according to the manual adjustment instructions.

[0095] In one embodiment, this application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent interactive control method for multimedia equipment in a digital exhibition hall.

[0096] Those skilled in the art will understand that Figure 9The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0097] In one embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0098] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0099] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for intelligent interactive control of multimedia equipment in a digital exhibition hall, characterized in that, Includes the following steps: Visitor information is collected through multiple sensors deployed within the digital exhibition hall, including the number of visitors, their location, and behavioral data. Based on the visitor quantity, location, and behavioral data, analyze visitor behavior patterns and generate behavioral analysis results; Based on the behavioral analysis results, the visitor's attention index and emotional response level to the current display content are calculated. Based on the attention index and emotional response level, the attractiveness level and engagement score of different presentation schemes are evaluated; Based on the attractiveness level and engagement score, the content and display method of multimedia devices are automatically adjusted. Receive manual adjustment instructions sent by the mobile terminal, and correct the automatic adjustment results according to the manual adjustment instructions.

2. The intelligent interactive control method for multimedia equipment in digital exhibition halls according to claim 1, characterized in that, Based on the visitor quantity, location, and behavioral data, analyze visitor behavior patterns to generate behavioral analysis results, specifically including the following steps: Based on the number and location of the visitors, obtain the spatial distribution density of the visitors; Based on the spatial distribution density and the behavioral data, obtain the movement trajectory parameters, dwell time parameters, and interaction behavior parameters; The spatial distribution density, movement trajectory parameters, dwell time parameters, and interaction behavior parameters are correlated to generate the behavior analysis results.

3. The intelligent interactive control method for multimedia equipment in digital exhibition halls according to claim 2, characterized in that, The dwell time parameter includes a dwell time coefficient and a browsing time coefficient; the interaction behavior parameter includes an interaction frequency parameter and a group aggregation parameter. Based on the spatial distribution density and the behavior data, the movement trajectory parameter, dwell time parameter, and interaction behavior parameter are obtained, specifically including the following steps: Based on the spatial distribution density and the behavioral data, obtain the dwell time coefficient, the browsing time coefficient, the interaction frequency parameter, and the group aggregation parameter; The dwell time parameter is obtained based on the dwell time coefficient and the browsing time coefficient.

4. The intelligent interactive control method for multimedia equipment in digital exhibition halls according to claim 3, characterized in that, The behavioral analysis results include attention feature vectors and emotion feature matrices. Based on these results, the visitor's attention index and emotional response to the current display content are calculated, specifically including the following steps: Obtain the spatial and temporal behavioral sequences of visitors; The attention feature vector is constructed based on the dwell time parameter, the movement trajectory parameter, and the spatial behavior sequence; The emotion feature matrix is ​​constructed based on the interaction behavior parameters, group aggregation parameters, and time behavior sequence.

5. The intelligent interactive control method for multimedia equipment in a digital exhibition hall according to claim 1, characterized in that, Based on the attractiveness level and engagement score, the display content and display method of multimedia devices are automatically adjusted, specifically including the following steps: Based on the attractiveness level and engagement score, obtain information on changes in display effectiveness; Obtain abnormal effect trigger information, and trigger a development zone environment detection command based on the abnormal effect trigger information; Obtain the environmental monitoring results of the exhibition area, and based on the environmental monitoring results of the exhibition area, combined with the information on changes in the display effect, obtain the current display status information; Based on the current display status information and the display scheme parameters, obtain adjustment strategy information.

6. The intelligent interactive control method for multimedia equipment in a digital exhibition hall according to claim 5, characterized in that, Based on the environmental monitoring results of the exhibition area and the information on changes in the display effect, the current display status information is obtained, specifically including the following steps: The environmental monitoring results of the exhibition area are classified and processed to obtain the noise level, lighting intensity and crowd density of the exhibition area; Extract attention change trends and emotional fluctuation amplitudes from the information on changes in the display effects; The degree of environmental impact on the display effect is determined based on the noise level, lighting intensity, and pedestrian density of the exhibition area. The degree of environmental influence is combined with the trend of attention changes and the magnitude of emotional fluctuations to form the current display status information.

7. The intelligent interactive control method for multimedia equipment in digital exhibition halls according to claim 1, characterized in that, Before receiving a manual adjustment instruction sent by a mobile terminal and correcting the automatic adjustment result according to the manual adjustment instruction, the method further includes the following steps: Calculate the thematic relevance and display format relevance between the currently displayed content and the next displayed content, and generate content switching correlation analysis results; Generate a multi-dimensional display mode comparison table for the currently displayed content, the display mode comparison table including sound and light combination mode, playback rhythm and device layout; The system analyzes the coverage of currently displayed content with key content in the display library and generates a report on the distribution of key content. Prioritize the content not displayed in the display library, create a display method adaptation list for each piece of content to be displayed, and generate a recommended list of content to be displayed and multi-dimensional display methods; The results of the content switching correlation analysis, the comparison table of display methods, the report on the distribution of key content, and the recommended list of content to be displayed and multi-dimensional display methods will be sent to the mobile terminal as a reference for manual adjustment.

8. A smart interactive control system for multimedia equipment in a digital exhibition hall, characterized in that, include: The visitor information collection module is used to collect visitor information through multiple sensors deployed in the digital exhibition hall. The visitor information includes the number of visitors, their location, and behavioral data. The behavior analysis module is used to analyze the behavior patterns of visitors based on the number, location, and behavior data of the visitors, and generate behavior analysis results. The calculation module is used to calculate the visitor's attention index and emotional response level to the current display content based on the behavioral analysis results. The scheme evaluation module is used to evaluate the attractiveness level and engagement score of different presentation schemes based on the attention index and emotional response level. The content display adjustment module is used to automatically adjust the display content and display method of the multimedia device based on the attractiveness level and engagement score; The adjustment and correction module is used to receive manual adjustment instructions sent by the mobile terminal and correct the automatic adjustment results according to the manual adjustment instructions.

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