Product intelligent display method and system based on interactive shelf advertising machine

Abstract

The present invention relates to the field of human-computer interaction, and proposes a product intelligent display method based on an interactive shelf advertising machine, including: first obtaining the interactive advertising machine shelf to be displayed and its product information and 3D model, constructing product attributes and display layout, then real-time monitoring of the interaction state, extracting interaction data, mining mapping parameters to generate user preference curves and data, calculating product recommendation values, and then combining the display layout with the recommendation value to construct an intelligent display target, divide the ladder target and calculate the display priority, thereby determining the display point, display material and material effect, and generating effect dynamic instructions. Then, display adjustment is performed according to the instruction and feedback optimization mechanism, an interactive adjustment log is formed, key adjustment factors are extracted, and finally a display report is generated. The present invention can improve the effect and efficiency of product display.

Description

Technical Field

[0001] The present invention relates to the field of human-computer interaction, and in particular to a product intelligent display method and system based on an interactive shelf advertising machine. Background Art

[0002] In today's human-computer interaction field, with the increasing prosperity and intensified competition in the consumer market, product display has become a key link to attract consumer attention and promote sales. As an emerging display medium, interactive shelf advertising machines integrate multiple advantages such as multimedia display and interactive functions, and have been increasingly widely used in retail stores, supermarkets, exhibition halls and many other places.

[0003] At present, traditional product display methods mainly include static display, paper poster promotion and other methods. On the one hand, static display methods lack appeal and it is difficult to fully display the characteristics and advantages of products. Although paper poster promotion can convey certain information, it is in a single form and cannot realize dynamic display and interactive functions. On the other hand, some existing electronic display devices, although they have certain dynamic display capabilities, lack intelligence and cannot realize personalized display, resulting in obvious deficiencies in the effect of product display. Therefore, a product intelligent display method based on interactive shelf advertising machines is needed to improve the effect and efficiency of product display and better meet the needs of commercial marketing. Summary of the invention

[0004] The present invention provides a product intelligent display method and system based on an interactive shelf advertising machine, the main purpose of which is to improve the effect and efficiency of product display.

[0005] To achieve the above-mentioned purpose, the present invention provides a product intelligent display method based on an interactive shelf advertising machine, comprising:

[0006] Obtain a shelf to be displayed corresponding to the interactive advertising machine, query product information in the shelf to be displayed, determine the display requirements corresponding to the product information, and simultaneously obtain 3D model information corresponding to the product information, construct product attributes corresponding to the product information based on the display requirements and the 3D model information, and construct a display layout corresponding to the interactive advertising machine based on the product attributes;

[0007] Monitor the interactive state corresponding to the interactive advertising machine in real time, extract the interactive data corresponding to the interactive state, mine the mapping parameters in the interactive data, generate a user preference curve corresponding to the interactive advertising machine based on the mapping parameters, extract the user preference data in the user preference curve, and calculate the product recommendation value corresponding to the interactive advertising machine based on the user preference data;

[0008] Based on the display layout and the product recommendation value, construct an intelligent display target corresponding to the interactive advertising machine, divide the intelligent display target into levels to obtain level targets, and calculate the display priority corresponding to the interactive advertising machine based on the level targets;

[0009] Based on the display priority, determine the display point of the product display in the interactive advertising machine, identify the display material corresponding to the display point, analyze the material effect corresponding to the display material, and generate the effect dynamic instruction corresponding to the interactive advertising machine based on the material effect;

[0010] Based on the effect dynamic instruction and the preset feedback optimization mechanism, the interactive advertising machine is displayed and adjusted to obtain an interactive adjustment log, key adjustment factors in the interactive adjustment log are extracted, and based on the key adjustment factors, a display report corresponding to the interactive advertising machine is generated.

[0011] Optionally, constructing product attributes corresponding to the product information based on the display requirements and the 3D model information includes:

[0012] Classify the display requirements into categories to obtain classified requirements;

[0013] Analyze the 3D model information to obtain 3D analysis data;

[0014] Respectively extracting the demand points and analysis points in the classified demand and the 3D analysis data;

[0015] Associating and integrating the demand point with the analysis point to obtain an associated data set;

[0016] Extracting attribute clusters corresponding to the data in the associated data set;

[0017] Based on the attribute cluster, a product attribute corresponding to the product information is constructed.

[0018] Optionally, constructing a display layout corresponding to the interactive advertising machine based on the product attributes includes:

[0019] Analyze the functional characteristics corresponding to the product attributes;

[0020] Based on the functional characteristics, determining a functional display area in the interactive advertising machine;

[0021] Identify the basic architecture corresponding to the functional display area;

[0022] Determine an architecture optimization direction corresponding to the basic architecture;

[0023] Extracting potential optimization points corresponding to the architecture optimization direction;

[0024] Based on the potential optimization points, a display layout corresponding to the interactive advertising machine is constructed.

[0025] Optionally, generating a user preference curve corresponding to the interactive advertising machine based on the mapping parameters includes:

[0026] Extracting key mapping indicators from the mapping parameters;

[0027] Quantifying the key mapping indicators to obtain a quantified indicator set;

[0028] Determining the preferred tendency elements in the quantitative indicator set;

[0029] Calculating the preference weight value corresponding to the preference tendency element;

[0030] Based on the preference weight value, a user preference curve corresponding to the interactive advertising machine is generated.

[0031] Optionally, the calculating the product recommendation value corresponding to the interactive advertising machine based on the user preference data includes:

[0032] The product recommendation value corresponding to the interactive advertising machine is calculated using the following formula:

[0033]

[0034] in, Indicates the product recommendation value corresponding to the interactive advertising machine, represents the total number of preference dimensions corresponding to the user preference data, represents the number index of the preference dimension, Indicated in The preference value corresponding to the preference dimension is Indicated in The matching weights of the preference dimensions and the user preference data, Indicates the number of product attribute features, Indicates the quantity index of product attribute characteristics, Indicates The quantitative value corresponding to each attribute feature.

[0035] Optionally, constructing the intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value includes:

[0036] Decomposing the display layout into elements to obtain layout sub-elements;

[0037] Determine the advantageous display area corresponding to the layout sub-element;

[0038] Analyze the regional display focus corresponding to the advantageous display areas;

[0039] Combine the regional display focus with the product recommendation value to query the product display characteristics corresponding to the product in the interactive advertising machine;

[0040] Based on the product display characteristics, an intelligent display target corresponding to the interactive advertising machine is constructed.

[0041] Optionally, the calculating the display priority corresponding to the interactive advertising machine based on the ladder target includes:

[0042] The display priority corresponding to the interactive advertising machine is calculated using the following formula:

[0043]

[0044] in, Indicates the display priority corresponding to the interactive advertising machine. Indicates the total number of corresponding ladder targets, Indicates the quantity index corresponding to the step target, Indicates The importance score of each level goal, Indicates The urgency weight of each level goal, Indicates The feasibility coefficient of each step target, represents the total number of external influencing factors, Represents the quantitative index of external influencing factors, Indicates The factor adjustment value of external influencing factors, Represents the normalization constant.

[0045] Optionally, generating an effect dynamic instruction corresponding to the interactive advertising machine based on the material effect includes:

[0046] Querying effect features in the material effect;

[0047] Identify the visual dominant factor corresponding to the effect feature;

[0048] Performing extreme value analysis on the visual dominant factor to obtain a dominant extreme value;

[0049] Analyzing the visual weight corresponding to the dominant extreme value;

[0050] Based on the visual weight, an effect dynamic instruction corresponding to the interactive advertising machine is generated.

[0051] Optionally, the interactive advertising machine is displayed and adjusted based on the effect dynamic instruction and a preset feedback optimization mechanism to obtain an interactive adjustment log, including:

[0052] Obtaining instruction parameters corresponding to the effect dynamic instruction;

[0053] Determine the feedback collection dimensions in the preset feedback optimization mechanism;

[0054] Based on the instruction parameters and the feedback collection dimensions, collecting the interactive feedback data corresponding to the interactive advertising machine;

[0055] Analyzing the preference characteristics of the interactive advertising machine for display effects according to the interactive feedback data;

[0056] Based on the preference feature, the interactive advertising machine is displayed and adjusted to obtain an interactive adjustment log.

[0057] Optionally, in order to solve the above problem, the present invention provides a product intelligent display system based on an interactive shelf advertising machine, the system comprising:

[0058] A layout construction module is used to obtain a shelf to be displayed corresponding to the interactive advertising machine, query product information in the shelf to be displayed, determine the display requirements corresponding to the product information, and simultaneously obtain 3D model information corresponding to the product information, construct product attributes corresponding to the product information based on the display requirements and the 3D model information, and construct a display layout corresponding to the interactive advertising machine based on the product attributes;

[0059] A recommendation value calculation module, used to monitor the interaction state corresponding to the interactive advertising machine in real time, extract the interaction data corresponding to the interaction state, mine the mapping parameters in the interaction data, generate a user preference curve corresponding to the interactive advertising machine based on the mapping parameters, extract the user preference data in the user preference curve, and calculate the product recommendation value corresponding to the interactive advertising machine based on the user preference data;

[0060] A priority calculation module, for constructing an intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value, dividing the intelligent display target into levels to obtain level targets, and calculating the display priority corresponding to the interactive advertising machine based on the level targets;

[0061] An instruction generation tool, for determining, based on the display priority, a display point for product display in the interactive advertising machine, identifying a display material corresponding to the display point, analyzing a material effect corresponding to the display material, and generating an effect dynamic instruction corresponding to the interactive advertising machine based on the material effect;

[0062] A report generation tool is used to adjust the display of the interactive advertising machine based on the effect dynamic instructions and the preset feedback optimization mechanism, obtain an interactive adjustment log, extract key adjustment factors in the interactive adjustment log, and generate a display report corresponding to the interactive advertising machine based on the key adjustment factors.

[0063] Firstly, the present invention obtains the shelf to be displayed corresponding to the interactive advertising machine, queries the product information in the shelf to be displayed, has precise control over the display of goods, clarifies the category, brand and specific style of goods carried by each shelf, thereby achieving a perfect match between the display content and the shelf position, making the display more orderly and reasonable. At the same time, the present invention monitors the interaction state corresponding to the interactive advertising machine in real time, extracts the interaction data corresponding to the interaction state, and mines the mapping parameters in the interaction data, which helps merchants to grasp the user's interest in different content and provide a basis for subsequent display optimization. It can accurately analyze the logic behind user behavior, such as inferring the user's exploration path and focus on product functions from the parameters of click sequence and dwell time, and then discovering the user's real needs. Based on the display layout and the product recommendation value, the present invention constructs the intelligent display target corresponding to the interactive advertising machine, which can achieve precise customization of display content, present products to users in the most optimized way, and ensure that users are in the first place. The user can see the content that best meets his needs and interests at the right time, which greatly improves the efficiency and pertinence of information transmission. The display content can be dynamically adjusted according to the preferences of different users to create personalized display scenes for users. The present invention determines the display points of the product display in the interactive advertising machine based on the display priority, and can allocate limited advertising machine display resources (such as screen space, playback time, etc.) to the display of products with high priority, so as to avoid waste and scattered use of resources. For example, high-priority products can obtain more prominent display positions and more exquisite display effects, thereby improving the utilization efficiency of resources. Furthermore, the present invention adjusts the display of the interactive advertising machine based on the effect dynamic instruction and the preset feedback optimization mechanism, and obtains an interactive adjustment log, which can continuously adapt to the needs and preferences of different users, always maintain the best display state, and enhance the attractiveness of the display to users. Secondly, the interactive adjustment log records the detailed information of the entire adjustment process, and provides data support for subsequent analysis and improvement, thereby enhancing the user experience. Therefore, the product intelligent display method and system based on the interactive shelf advertising machine proposed in the present invention can improve the effect and efficiency of product display. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of a process for intelligent product display based on an interactive shelf advertising machine provided by an embodiment of the present invention;

[0065] Figure 2A schematic diagram of modules for implementing the product intelligent display system based on the interactive shelf advertising machine provided in one embodiment of the present invention.

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0068] The embodiment of the present application provides a product intelligent display method based on an interactive shelf advertising machine. The execution subject of the product intelligent display method based on the interactive shelf advertising machine includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the product intelligent display method based on the interactive shelf advertising machine can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0069] Embodiment 1:

[0070] Reference Figure 1 FIG. 1 is a flow chart of a method for intelligently displaying products based on an interactive shelf advertising machine according to an embodiment of the present invention. In this embodiment, the method for intelligently displaying products based on an interactive shelf advertising machine includes:

[0071] S1. Obtain a shelf to be displayed corresponding to an interactive advertising machine, query product information in the shelf to be displayed, determine display requirements corresponding to the product information, and simultaneously obtain 3D model information corresponding to the product information, construct product attributes corresponding to the product information based on the display requirements and the 3D model information, and construct a display layout corresponding to the interactive advertising machine based on the product attributes.

[0072] The present invention obtains the shelf to be displayed corresponding to the interactive advertising machine, queries the product information in the shelf to be displayed, accurately controls the display of goods, clarifies the category, brand and specific style of goods carried by each shelf, thereby achieving a perfect match between the display content and the shelf position, making the display more orderly and reasonable.

[0073] Among them, the interactive advertising machine refers to a new type of display equipment that integrates multimedia display and interactive functions. It is usually placed in commercial places such as retail stores, supermarkets, and exhibition halls. It can display rich and diverse content to consumers through screens, touch interactions, sensing and other technologies, including but not limited to product images, videos, 3D models, etc., and can receive consumers' operating instructions and make corresponding feedback. For example, consumers click to view product details, conduct virtual trials, etc., to achieve interaction between humans and machines; the display shelf refers to a physical structure specially used to display goods for display and sale in a commercial environment, which can be an ordinary shelf in a supermarket, a display rack in a boutique, a large storage rack in a warehouse-style store, etc.; the product information refers to information covering all aspects of the product, including It includes basic attributes of the product, such as name, model, specification, color, material, etc.; functional characteristics, such as performance parameters of electronic products, working principles of mechanical products, and efficacy of daily necessities; and market-related information of the product, such as price, brand story, target customer group, sales data, market positioning, etc. Optionally, the acquisition of the shelf to be displayed corresponding to the interactive advertising machine can be achieved through wireless radio frequency identification technology, such as: installing RFID tags on the shelf, each tag represents a product, when the reader is close to the shelf, it can automatically identify and obtain the information of the shelf to be displayed; the query of product information in the shelf to be displayed can be achieved through OCR technology, such as: when using OCR tools to scan product labels or information codes on the shelf, the product information on it can be extracted and identified.

[0074] Furthermore, the present invention can make the display direction clear and accurate by determining the display requirements corresponding to the product information and simultaneously obtaining the 3D model information corresponding to the product information, and can make the display more targeted, thereby presenting the product in an all-round and three-dimensional manner.

[0075] The display demand refers to the requirements for product display content, display form, display focus and display style, which are determined based on factors such as product characteristics, target audience characteristics, marketing purposes and display scenarios. It covers multiple levels. From the perspective of display content, it includes product attributes that need to be highlighted, such as innovative technical parameters of high-tech products, brand stories and unique craftsmanship of luxury goods, etc.; in terms of display form, it is to choose dynamic video display, interactive experience or static picture display, etc.; in terms of display focus, it is clear whether to emphasize product functions, appearance design, cost performance or other factors; the display style involves style tendencies such as simple and modern, fashionable and gorgeous, professional and rigorous; The 3D model information refers to data about the three-dimensional model of the product, which includes the geometric shape information of the product in three-dimensional space, such as the vertex coordinates of the model, the composition of the surface, the connection relationship of the edges, etc. This information accurately depicts the appearance outline of the product. Optionally, the determination of the display requirements corresponding to the product information can be achieved through a hierarchical analysis method, such as: decomposing complex decision-making problems into multiple levels and factors, and determining the comprehensive display requirements by comparing and quantifying the relative importance of each factor; the acquisition of the 3D model information corresponding to the product information can be achieved through CAD tools, such as: SolidWorks, AutoCAD and other tools.

[0076] Furthermore, the present invention constructs product attributes corresponding to the product information based on the display requirements and the 3D model information, guides the construction of product attributes in a direction that meets the expectations of the target audience and marketing purposes, and provides an intuitive visual basis for the construction of product attributes. It can concretize abstract attributes so that each attribute of the product can be vividly presented in three-dimensional space.

[0077] Among them, the product attributes refer to a series of attribute descriptions that comprehensively reflect the characteristics, advantages and value of the product, which cover all aspects of the product, including but not limited to physical attributes (such as size, weight, material, etc.), functional attributes (such as product functions, performance parameters, etc.), appearance attributes (such as shape, color, design style, etc.), and emotional attributes (such as the brand image conveyed by the product, the emotional experience it brings to consumers, etc.).

[0078] As an embodiment of the present invention, constructing the product attributes corresponding to the product information based on the display requirements and the 3D model information includes: classifying the display requirements into types to obtain classified requirements; parsing the 3D model information to obtain 3D parsed data; respectively extracting the demand points and parsing points from the classified requirements and the 3D parsed data; associating and integrating the demand points with the parsing points to obtain an associated data set; extracting the attribute clusters corresponding to the data in the associated data set; and constructing the product attributes corresponding to the product information based on the attribute clusters.

[0079] Among them, the classified demand refers to the category obtained by dividing the display demand according to different dimensions or standards. For example, it can be divided into demand from consumers, market competition demand, brand promotion demand, etc. according to the source of the demand; it can also be divided into functional display demand, emotional communication demand, visual presentation demand, etc. according to the nature of the demand; the 3D analysis data refers to the data obtained after in-depth analysis of the 3D model information, including the geometric structure analysis data of the model, such as the shape parameters of each face, vertex coordinates, edge connection relationship, etc.; material attribute analysis data, such as material type (metal, plastic, wood, etc.), color, texture, transparency, glossiness and other parameters; and model animation analysis data, such as animation key frame information, motion trajectory, time parameters and the linkage relationship of each component in the animation; the demand point refers to the specific and key demand elements contained in the classified demand. For example, in the functional display demand category, how a specific function is displayed and to what extent is the demand point; the analysis point refers to the 3D The key data points in the parsed data that can be linked to the demand points may be a unique modeling structure point in the geometric structure parsing data; in the material attribute parsing data, it may be a material parameter point that matches the target emotional color; in the animation parsing data, it may be an animation key frame point that can reflect the core functions or features of the product, etc.; the associated data set refers to a data set formed by matching and integrating the demand points with the parsing points according to certain logic and rules. This data set contains information determined after association analysis and used to construct product attributes; the attribute cluster refers to a group of data sets with similar characteristics or functions that are further extracted from the associated data set, which jointly describe the attributes of a certain aspect of the product. For example, a group of associated data related to the geometric shape, material texture and color matching of the product appearance design can form an appearance attribute cluster; another group of associated data related to the operating components, internal structure and animation demonstration of the product function realization constitutes a functional attribute cluster.

[0080] Furthermore, the classification of the display requirements can be achieved through a decision tree model, such as: by building a decision tree model, the display requirements are classified according to different characteristics; the parsing and processing of the 3D model information can be achieved through a 3D modeling tool, such as: using the model analysis module provided by the 3D modeling tool to parse the model information; the extraction of the classification requirements and the demand points and parsing points in the 3D parsing data can be achieved through a word vector model, such as: Word2Vec, BERT model, etc.; the association and integration of the demand points and the parsing points can be achieved through an association method based on a knowledge graph, such as: building a knowledge graph, taking the demand points and the parsing points as nodes, and establishing edges through the semantic relationship between them to achieve association integration; the extraction of the attribute clusters corresponding to the data in the associated data set can be achieved through a density-based spatial clustering algorithm, such as: dividing the clusters according to the density of the data points, and if the density of the data points in a region exceeds a certain threshold, these points are divided into a cluster; the construction of the product attributes corresponding to the product information can be achieved through an attribute weighted fusion method, such as: assigning weights to each attribute according to the importance and relevance of the attribute cluster, and then merging the attributes into product attributes.

[0081] The present invention constructs a display layout corresponding to the interactive advertising machine based on the product attributes, so that the product can be presented in the best state on the advertising machine. A simple and elegant design can be adopted to avoid interference from too many complicated elements, so that the product can be fully demonstrated. Through reasonable zoning and display order, consumers can clearly understand the product, thereby better understanding and accepting the product.

[0082] Among them, the display layout refers to the overall planning and design of presenting product information on the interactive advertising machine after comprehensively considering various attributes of the product, including the layout arrangement of various parts such as the function display area, appearance display area, emotional atmosphere creation area, as well as the relationship between the various parts and the overall visual effect.

[0083] As an embodiment of the present invention, constructing a display layout corresponding to the interactive advertising machine based on the product attributes includes: analyzing functional characteristics corresponding to the product attributes; determining a functional display area in the interactive advertising machine based on the functional characteristics; identifying a basic architecture corresponding to the functional display area; determining an architecture optimization direction corresponding to the basic architecture; extracting potential optimization points corresponding to the architecture optimization direction; and constructing a display layout corresponding to the interactive advertising machine based on the potential optimization points.

[0084] Among them, the functional characteristics refer to the part of product attributes related to product functions, including the main functions, auxiliary functions, unique functions of the product, as well as the working principles, implementation methods, operation processes, functional parameters and other contents of these functions; the functional display area refers to a specific area in the display interface of the interactive advertising machine that is specially used to display product function-related information to users. This area is planned according to the functional characteristics of the product and is the main visual window for users to understand the product functions; the basic architecture refers to the initial design framework of the functional display area, including the distribution form of the display content, the arrangement order of the display modules, the display level of different functional information, etc. For example, in the functional display area, a top-down layered structure is adopted. The architecture can be either to highlight the main functions on the upper layer, or to adopt a left-right column architecture, with the function operation demonstration on the left and the function parameters on the right; or to distribute the functions in a radial pattern according to their importance, etc. These structural forms constitute the basic architecture; the architecture optimization direction refers to the direction for improving and perfecting the basic architecture based on factors such as the product's functional characteristics, the characteristics of the target audience, and the market competition situation; the potential optimization points refer to the specific and actionable improvement points extracted from the architecture optimization direction. For example, if the architecture optimization direction is to improve the interactivity of the function display, then the optimization points may include adding touch operation function demonstrations, setting up interactive modules for users to ask and answer questions, adding interactive charts for function comparisons, etc.

[0085] Furthermore, the analysis of the functional characteristics corresponding to the product attributes can be achieved through a functional decomposition method, such as: regarding the product as a system, decomposing it into various sub-functions according to certain rules, and clarifying the specific content and mutual relationship of each sub-function; the determination of the functional display area in the interactive advertising machine can be achieved through a method based on user line of sight tracking, such as: by tracking the user's line of sight movement path on the advertising machine, determining the areas that the user is most likely to pay attention to, and displaying important functions in these areas; the identification of the basic architecture corresponding to the functional display area can be achieved through a layout template matching method, such as: matching the existing common display layout templates with the requirements of the functional display area, and selecting Select a suitable template as the basic architecture; the determination of the architecture optimization direction corresponding to the basic architecture can be achieved through a competitive product analysis method, such as: studying the advantages and disadvantages of competitor products in the market in similar functional display architectures to determine the optimization direction; the extraction of potential optimization points corresponding to the architecture optimization direction can be achieved through a key factor extraction algorithm, such as: extracting key, quantifiable or actionable factors from the description of the architecture optimization direction as potential optimization points; the construction of the display layout corresponding to the interactive advertising machine can be achieved through a visual design tool, such as: using a visual design tool to integrate various functional display areas, optimization points and other elements into a complete display layout.

[0086] S2. Monitor the interaction status corresponding to the interactive advertising machine in real time, extract the interaction data corresponding to the interaction status, mine the mapping parameters in the interaction data, generate a user preference curve corresponding to the interactive advertising machine based on the mapping parameters, extract the user preference data in the user preference curve, and calculate the product recommendation value corresponding to the interactive advertising machine based on the user preference data.

[0087] The present invention monitors the interactive state corresponding to the interactive advertising machine in real time, extracts the interactive data corresponding to the interactive state, and mines the mapping parameters in the interactive data, so as to help merchants grasp the user's interest in different contents and provide a basis for subsequent display optimization. It can accurately analyze the logic behind user behavior, such as inferring the user's exploration path and focus on product functions from the parameters of click sequence and dwell time, and then discover the user's real needs.

[0088] Among them, the interactive state refers to a comprehensive description of the various instant situations of the interactive advertising machine during the interaction between the user and the interactive advertising machine, including whether the user is operating (such as touching the screen, clicking a button, etc.), the status of the currently displayed content interface (is it a product introduction interface, a function demonstration interface or other types of interfaces), the system response status (such as loading status, information feedback status, etc.), and factors such as the time point and duration of the interaction; the interactive data refers to all relevant data information generated during the interaction between the user and the interactive advertising machine. These data can be divided into different types, such as user operation data, covering the user's touch position, click frequency, operation sequence, etc.; user browsing data, including the dwell time on each interface, the sequence of browsed pages, the page scrolling distance, etc.; it also includes the feedback data of the advertising machine, such as the response time to user operations, information push status, etc.; the mapping parameters refer to the key quantitative indicators or qualitative descriptions mined from the interactive data that can reflect the relationship between user behavior and psychological characteristics and the content displayed by the advertising machine, for example For example, mapping parameters between the dwell time on a product display interface and the user's interest in the product; mapping parameters between the order and frequency of users operating specific function buttons and their understanding of product functions and demand tendencies; or mapping parameters of behavioral patterns of different types of users (based on age, gender, etc.) when browsing different styles of display content, etc. Optionally, the real-time monitoring of the interactive state corresponding to the interactive advertising machine can be achieved through a sensor monitoring method, such as: using various sensors installed on the interactive advertising machine to obtain information related to the interactive state; the extraction of interactive data corresponding to the interactive state can be achieved through a log recording and analysis method, such as: setting a log recording function in the interactive advertising machine system to record relevant information during the interaction process, and then analyzing the log file to extract data; the mining of mapping parameters in the interactive data can be achieved through an association rule mining algorithm, such as: by analyzing the frequent occurrence patterns between different variables in the interactive data, mining the associations between them, and thus determining the mapping parameters.

[0089] Furthermore, the present invention generates a user preference curve corresponding to the interactive advertising machine based on the mapping parameters, which can intuitively present the user's preference tendencies for different products, functions or display contents, help to implement personalized display strategies, and can tailor display content for different user groups, thereby adjusting the display focus and promotion methods of the advertising machine.

[0090] Among them, the user preference curve refers to a visual curve that presents the user's preference for different products, functions or display content. The horizontal axis of the curve can be different product types, function categories or display content themes, etc., and the vertical axis is the preference degree determined based on the preference weight value.

[0091] As an embodiment of the present invention, the generation of a user preference curve corresponding to the interactive advertising machine based on the mapping parameters includes: extracting key mapping indicators in the mapping parameters; quantifying the key mapping indicators to obtain a quantized indicator set; determining preference tendency elements in the quantized indicator set; calculating preference weight values ​​corresponding to the preference tendency elements; and generating a user preference curve corresponding to the interactive advertising machine based on the preference weight values.

[0092] Among them, the key mapping indicators refer to parameter indicators that are screened out from the mapping parameters and are of key significance to reflecting user preferences, such as the number of clicks in the mapping relationship between the number of clicks on the display of different product categories and the purchase behavior, or the length of stay in the mapping relationship between the length of time a user stays on a specific function demonstration interface and the frequency of subsequent questions about the function; the quantitative indicator set refers to the key mapping indicators converted into a measurable and comparable data set through a specific quantitative method, for example, the number of clicks on the product display by the user is directly recorded in digital form, the length of stay is converted from a time format into a numerical value in seconds, and the sequence of user operations is recorded in the quantitative indicator set. The sequence is converted into an ordered digital sequence by setting coding rules, etc.; the preference tendency element refers to a specific element in the quantitative indicator set that can reflect the user's preference direction for a certain product, function or display method. For example, in the quantitative indicator set, if it is found that the user's click count on the display of a certain type of fashion product is high and the stay time is long, then these elements related to high click counts and long stay time constitute the preference tendency element for fashion products; the preference weight value refers to the weight value assigned to each preference tendency element according to its importance in the user's overall interactive behavior and the degree of influence on the final decision (such as purchase, further understanding of the product, etc.).

[0093] Furthermore, the extraction of key mapping indicators from the mapping parameters can be achieved through a principal component analysis algorithm, such as: converting the original mapping parameter data into a set of linearly independent representations of each dimension through a linear transformation, sorting these new dimensions according to the size of the variance, and selecting the parameters corresponding to the dimensions with larger variance as the key mapping indicators; the quantification of the key mapping indicators can be achieved through a discretization method, such as: for some key mapping indicators with continuous values ​​(such as residence time, operation time interval, etc.), discretizing them into different categories by setting a certain interval; the determination of the preference tendency elements in the quantitative indicator set can be achieved through a clustering analysis method, such as: quantification indicators with similar characteristics are grouped into different categories. The indicators are clustered together, and each cluster represents a preference tendency element; the calculation of the preference weight value corresponding to the preference tendency element can be achieved through a logistic regression algorithm, such as: establishing a logistic regression model between the preference tendency element and the user preference result (such as purchase, high evaluation, etc.), and the coefficient in the model can reflect the influence of each preference tendency element on the result, which is used as the preference weight value; the generation of the user preference curve corresponding to the interactive advertising machine can be achieved through business intelligence tools, such as: Tableau, PowerBI and other tools, importing data containing preference weight values ​​and related category information into these tools, and generating a visual curve through simple operations (such as dragging and dropping variables to the coordinate axis).

[0094] By extracting user preference data from the user preference curve, the present invention can, based on these data, deeply understand the user's preferences for various aspects of the product, whether it is the product's functional characteristics, appearance design, or price range, thereby tailoring exclusive recommendation content for the user, avoiding excessive investment in functions that the user is not interested in, and achieving precise improvement of the product.

[0095] Among them, the user preference data refers to the specific data information obtained from the user preference curve, which can accurately reflect the user's preference tendencies for different product attributes, functional characteristics, display content, brand elements, etc. during the interaction with the interactive advertising machine. For example, it contains the user's preference value for various products (such as electronic products, household items, etc.), and through different values ​​in the curve, it intuitively shows whether the user is highly fond of a certain type of product, generally concerned, or has little interest; for specific functions of the product, such as the camera function of a mobile phone, the sports tracking function of a smart watch, etc., the preference data can reflect the user's emphasis on these functions, whether they are the core focus or dispensable; it also involves the user's preference for different brand styles and brand images, as well as the acceptance and preference for specific display forms (such as video demonstrations, graphic introductions, etc.). Optionally, the extraction of user preference data in the user preference curve can be achieved through data reading tools, such as: CSV, JSON and other tools.

[0096] Furthermore, the present invention calculates the product recommendation value corresponding to the interactive advertising machine based on the user preference data, which can greatly improve the accuracy of the recommendation, help improve user experience and satisfaction, and effectively improve sales conversion rate. In addition, accurate recommendations can guide users to pay attention to and purchase products that they are truly interested in, reduce the time cost of user decision-making, and achieve reasonable allocation of resources.

[0097] Among them, the product recommendation value refers to a value obtained after comprehensively considering user preference data and product attribute characteristics, which reflects the degree of recommendation of the product to a specific user or user group. The higher the value, the more worthy the product is to be recommended to the user.

[0098] As an embodiment of the present invention, the calculating the product recommendation value corresponding to the interactive advertising machine based on the user preference data includes:

[0099] The product recommendation value corresponding to the interactive advertising machine is calculated using the following formula:

[0100]

[0101] in, Indicates the product recommendation value corresponding to the interactive advertising machine, represents the total number of preference dimensions corresponding to the user preference data, represents the number index of the preference dimension, Indicated in The preference value corresponding to the preference dimension is Indicated in The matching weights of the preference dimensions and the user preference data, Indicates the number of product attribute features, Indicates the quantity index of product attribute characteristics, Indicates The quantitative value corresponding to each attribute feature.

[0102] Furthermore, the preference dimension refers to the classification of user preference for different aspects of the product, for example, it may include multiple aspects such as product function, appearance, price, brand, etc., each of which constitutes a preference dimension; the preference degree value refers to the quantitative expression of the user's preference for each preference dimension, and the value range is usually within a certain interval, such as [0,1]. The closer the value is to 1, the higher the user's preference for the dimension, and vice versa; the matching weight refers to the quantitative weight of the matching degree between the product's characteristics on the dimension and the user's preference on each preference dimension; the product attribute characteristics refer to the various characteristics or attributes of the product, which can be used to describe and distinguish different products. For example, for electronic products, product attribute characteristics may include processor performance, screen resolution, battery life, etc.; for clothing products, they may include material, style, color, etc.; the quantitative value refers to the numerical expression of the product attribute characteristics after quantification. For example, processor performance can be quantified by the processor's main frequency, core number, etc.; screen resolution can be quantified by the number of pixels. The selection and calculation method of the quantitative value are usually determined according to the type of product and the characteristics of the attribute characteristics.

[0103] S3. Based on the display layout and the product recommendation value, construct an intelligent display target corresponding to the interactive advertising machine, divide the intelligent display target into levels to obtain level targets, and calculate the display priority corresponding to the interactive advertising machine based on the level targets.

[0104] The present invention constructs an intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value, which can realize accurate customization of display content, present products to users in the most optimized way, and ensure that users see the content that best meets their needs and interests at the first time, greatly improving the efficiency and pertinence of information transmission, and can dynamically adjust the display content according to the preferences of different users to create a personalized display scene for users.

[0105] Among them, the intelligent display goal refers to the specific display purpose and expected effect set for the interactive advertising machine by comprehensively considering factors such as display layout, product recommendation value and product display characteristics. For example, the intelligent display goal can be to increase the attention and sales of a certain product within a specific time, or to improve the user's satisfaction and participation in the overall display content of the advertising machine.

[0106] As an embodiment of the present invention, constructing an intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value includes: decomposing the display layout into elements to obtain layout sub-elements; determining advantageous display areas corresponding to the layout sub-elements; analyzing regional display focuses corresponding to the advantageous display areas; combining the regional display focuses with the product recommendation values ​​to query product display characteristics corresponding to the products in the interactive advertising machine; and constructing an intelligent display target corresponding to the interactive advertising machine based on the product display characteristics.

[0107] Among them, the layout sub-elements refer to the specific elements that make up the display layout after disassembling it. For example, for the display layout of an interactive advertising machine, the layout sub-elements may include the size, shape, position of the display area, the division of different display modules, color matching, text layout, etc.; the advantageous display area refers to those specific areas in the display layout that have outstanding display effects and can attract more user attention, such as the center position of the advertising machine screen, the display module area with a larger size, or the area with special design (such as dynamic effects, high-brightness display, etc.); the regional display focus refers to the content or characteristics that each advantageous display area focuses on displaying, for example, a certain advantageous display area may focus on displaying the innovative functions of the product, another area may focus on highlighting the fashionable appearance design of the product, or some areas may focus on emphasizing the high cost performance of the product, etc.; the product display characteristics refer to the unique attributes and characteristics of the product when it is displayed on the interactive advertising machine, which include the product's picture quality, video demonstration effect, the detail and attractiveness of the text description, user interaction methods (such as touch operation, animation feedback, etc.), etc.

[0108] Further, the element decomposition of the display layout can be achieved through a visual hierarchy analysis method, such as: by analyzing the visual hierarchy structure in the display layout, decomposing it into elements of different levels; the determination of the advantageous display area corresponding to the layout sub-element can be achieved through an attention heat map analysis method, such as: by collecting the user's sight data when viewing the display layout, generating an attention heat map, so as to determine which areas attract more user attention, namely, the advantageous display area; the analysis of the regional display focus corresponding to the advantageous display area can be achieved through a user demand analysis method, such as: by investigating the user's needs for different product features and functions, determining the content that the advantageous display area should focus on displaying; the analysis of the regional display focus corresponding to the advantageous display area can be achieved through a content theme clustering method, such as: performing theme analysis and clustering on the content in the advantageous display area, and determining the regional display focus according to the clustering results; the construction of the intelligent display target corresponding to the interactive advertising machine can be achieved through a target setting and decomposition method, such as: clarifying the overall display target of the interactive advertising machine, such as increasing product awareness, increasing sales, etc., and then decomposing it into specific sub-targets, and determining the implementation strategy of each sub-target according to the display layout and product recommendation value, and finally constructing an intelligent display target.

[0109] The present invention divides the intelligent display target into levels to obtain level targets, which can make the target clearer and more operational, and help to better evaluate and adjust the strategy. By monitoring and analyzing each level target, the stage where the problem is located can be discovered in time, and targeted adjustments and optimizations can be made. Corresponding resources can be allocated according to their importance and difficulty, avoiding waste or unreasonable allocation of resources.

[0110] Among them, the ladder goal refers to a series of sub-goals obtained by decomposing the intelligent display goal corresponding to the interactive advertising machine according to a certain logic and level. For example, if the intelligent display goal is to increase the popularity and sales of a certain product within a certain period of time, the ladder goals may include: the first level goal is to attract users' attention to the product on the advertising machine, such as through a prominent display position and eye-catching visual effects; the second level goal is to guide users to have an in-depth understanding of the characteristics and advantages of the product, such as providing detailed product introductions and demonstrations; the third level goal is to encourage users to have a willingness to buy, such as setting up promotional activities and user evaluation displays, etc. Optionally, the ladder division of the intelligent display goals can be achieved through a goal decomposition method, such as: decomposing the intelligent display goals according to different dimensions and levels to form ladder goals with logical relationships.

[0111] Furthermore, the present invention calculates the display priority corresponding to the interactive advertising machine based on the tiered goals. By clarifying the importance of different tiered goals, limited display resources (such as screen space, playback time, etc.) can be reasonably allocated to the content corresponding to each goal. A scientific and reasonable display strategy can be formulated to ensure that each tiered goal is achieved in sequence according to its importance, thereby gradually promoting the achievement of the overall goal.

[0112] The display priority refers to an indicator used to measure the order and prominence of each part when the interactive advertising machine displays content.

[0113] As an embodiment of the present invention, the step of calculating the display priority corresponding to the interactive advertising machine based on the step target includes:

[0114] The display priority corresponding to the interactive advertising machine is calculated using the following formula:

[0115]

[0116] in, Indicates the display priority corresponding to the interactive advertising machine. Indicates the total number of corresponding ladder targets, Indicates the quantity index corresponding to the step target, Indicates The importance score of each level goal, Indicates The urgency weight of each level goal, Indicates The feasibility coefficient of each step target, represents the total number of external influencing factors, Represents the quantitative index of external influencing factors, Indicates The factor adjustment value of external influencing factors, Represents the normalization constant.

[0117] Furthermore, the importance score refers to a numerical value that quantifies the importance of each ladder goal in achieving the overall goal. For example, if a ladder goal is to promote a high-profit product, it will be given a higher importance score; the urgency weight refers to a quantitative indicator that reflects how quickly the ladder goal needs to be paid attention to and executed. For example, when a competitor launches a similar product, the counterattack ladder goal against the competitor may have a higher urgency weight; the feasibility coefficient refers to a numerical value that evaluates the possibility of each ladder goal being achieved under current resources and conditions. The feasibility coefficient takes into account factors such as technical feasibility, resource availability, and time constraints; the external influencing factors refer to various factors from the external environment of the advertising machine, which may affect the display priority of the interactive advertising machine. External influencing factors may include market trends, competitors' actions, changes in laws and regulations, social hot events, etc.; the factor adjustment value refers to a specific value for adjusting the display priority according to the external influencing factors. For example, if the external influencing factors are conducive to improving the display priority of a certain ladder goal, then the corresponding factor adjustment value can be a positive number; if the external influencing factors have a negative impact on the display priority, then the factor adjustment value can be a negative number.

[0118] S4. Based on the display priority, determine the display point of the product display in the interactive advertising machine, identify the display material corresponding to the display point, analyze the material effect corresponding to the display material, and generate the effect dynamic instruction corresponding to the interactive advertising machine based on the material effect.

[0119] The present invention determines the display points of the products in the interactive advertising machine based on the display priority, and can centrally allocate limited display resources (such as screen space, playback time, etc.) of the advertising machine to the display of products with high priority, thereby avoiding waste and dispersed use of resources. For example, high-priority products can obtain more prominent display positions and more exquisite display effects, thereby improving resource utilization efficiency.

[0120] The display point refers to a specific position, area or method for displaying products in an interactive advertising machine. It can be a specific area on the screen of the advertising machine, such as a center position, a corner position, etc.; it can also be a specific display method, such as dynamic display, static display, video playback, etc. Optionally, the display point for displaying products in the interactive advertising machine can be determined by a method based on data analysis, such as: by collecting and analyzing user behavior data, product sales data and other information, determining products with high user attention and great sales potential, and displaying these products at key display points.

[0121] Furthermore, the present invention can create a visual effect that best meets the product characteristics and the preferences of the target audience by identifying the display material corresponding to the display point and analyzing the material effect corresponding to the display material. For example, for the display point of high-end electronic products, selecting a textured metal material or a high-definition glass material can highlight the technological sense and quality of the product. Secondly, it helps to improve the user experience.

[0122] Among them, the display material refers to the various material materials used in the product display of the interactive advertising machine, which may include the material of the screen (such as LCD screen, OLED screen, etc.), the material of the display frame (such as metal, plastic, wood, etc.), the material of the display props (such as cloth, paper, acrylic, etc.) and various auxiliary materials used to decorate and highlight the product; the material effect refers to the specific visual, tactile and psychological feelings of the display material on the interactive advertising machine. For example, metal material may bring a solid, high-end visual effect and a cold tactile feeling; wooden material may create a natural and warm atmosphere; transparent material can increase the transparency and technological sense of the product display. The material effect also includes the reflection, refraction, absorption and other characteristics of the display material to light, as well as the ability to present product color and details. Optionally, the identification of the display material corresponding to the display point can be achieved through visual recognition technology, such as: using image recognition technology to analyze the appearance characteristics of the display point to determine the display material that may be used, for example, by identifying color, texture, gloss and other characteristics to infer the material type; the analysis of the material effect corresponding to the display material can be achieved through simulation experiments, such as: by simulating different environmental conditions and usage scenarios, testing the display material, observing its performance in different situations, and analyzing the material effect.

[0123] Furthermore, the present invention generates effect dynamic instructions corresponding to the interactive advertising machine based on the material effects, so that specific materials can show unique charm under different dynamic effects. For example, the metal material can highlight its texture and sense of technology under the dynamic instructions of light and shadow changes, while the wooden material can create a warm and natural atmosphere under the soft dynamic effects, which helps to enhance the interactive experience between users and the advertising machine.

[0124] Among them, the effect dynamic instructions refer to a series of instructions for controlling the display dynamic effects generated for the interactive advertising machine based on the analysis results related to the material effects, such as the flashing frequency of light, the gradient method of color, the dynamic change of texture (such as simulating the flow effect of water on the texture), the movement trajectory of the display elements, etc.

[0125] As an embodiment of the present invention, the effect dynamic instructions corresponding to the interactive advertising machine are generated based on the material effect, including: querying the effect characteristics in the material effect; identifying the visual dominant factor corresponding to the effect characteristic; performing extreme value analysis on the visual dominant factor to obtain the dominant extreme value; analyzing the visual weight corresponding to the dominant extreme value; and generating the effect dynamic instructions corresponding to the interactive advertising machine based on the visual weight.

[0126] Among them, the effect characteristics refer to the various properties and forms of expression of the material effect, which cover the characteristics of the material in many aspects such as vision, touch, physics, etc., such as the gloss of the material (such as mirror gloss, matte, etc.), transparency (different degrees from completely transparent to opaque), texture (smooth, rough, regular or irregular patterns, etc.), color rendering ability (color vividness, contrast, etc.) and the optical properties of the material such as reflection, refraction and absorption of light; the visual dominant factor refers to the factor that plays a major guiding and influencing role in the user's visual perception among many effect characteristics. For example, in a material with high gloss and complex texture, gloss can be a visual dominant factor, which can better attract the user's attention and occupy a dominant position in the visual perception process. ; or for transparent materials, transparency and the refraction effect caused by transparency can become the dominant visual factors; the dominant extreme value refers to the limit value or key value reached by its related parameters when analyzing the visual dominant factor. Taking glossiness as an example, if it is analyzed by quantifying the parameters of glossiness (such as reflectivity), its maximum value (such as the highest reflectivity) or minimum value (such as the lowest diffuse reflectivity) within a specific range and other special values ​​are the dominant extreme values; the visual weight refers to the relative importance of each dominant extreme value in the overall visual perception. For example, in a material with high gloss and high transparency, the dominant extreme value of glossiness accounts for 60% of the visual weight, which is more attractive and visually impactful to users, while the dominant extreme value of transparency accounts for 40%.

[0127] Furthermore, the query of the effect features in the material effect can be achieved through a database query method, such as: establishing a database containing various common material properties and effect features, and querying and obtaining the corresponding effect features by inputting relevant information such as material type; the identification of the visual dominant factor corresponding to the effect feature can be achieved through a visual perception experiment method, such as: designing a visual perception experiment, allowing a large number of testers to observe the material display, collecting the testers' visual focus data, and analyzing the visual dominant factor; the extreme value analysis of the visual dominant factor can be achieved through an extreme value solution method, such as: expressing the relevant parameters of the visual dominant factor with a mathematical function, and then using a mathematical method for finding the extreme value (such as the derivative method, the quadratic function vertex formula, etc.) to find the extreme value; the analysis of the visual weight corresponding to the dominant extreme value can be achieved through a hierarchical analysis method, such as: taking different dominant extreme values ​​as elements in a hierarchical structure, constructing a judgment matrix through pairwise comparison, and calculating the weight of each element, that is, the visual weight; the generation of the effect dynamic instruction corresponding to the interactive advertising machine can be achieved through an instruction generation tool, such as: Adobe Animate, Unity 3D and other tools.

[0128] S5. Based on the effect dynamic instruction and the preset feedback optimization mechanism, the interactive advertising machine is displayed and adjusted to obtain an interactive adjustment log, key adjustment factors in the interactive adjustment log are extracted, and a display report corresponding to the interactive advertising machine is generated based on the key adjustment factors.

[0129] The present invention adjusts the display of the interactive advertising machine based on the effect dynamic instructions and the preset feedback optimization mechanism to obtain an interactive adjustment log, which can continuously adapt to the needs and preferences of different users, always maintain the best display state, and enhance the attractiveness of the display to users. Secondly, the interactive adjustment log records the detailed information of the entire adjustment process, providing data support for subsequent analysis and improvement, thereby enhancing the user experience.

[0130] Among them, the preset feedback optimization mechanism refers to a pre-designed and set systematic strategy and method for continuously improving the display effect of interactive advertising machines, which includes the following key parts: the first is the feedback collection plan, that is, clarifying the feedback collection dimensions, which is the basis of the entire mechanism. Starting from multiple dimensions, it comprehensively considers all aspects of the interaction between users and advertising machines, including but not limited to behavior, vision, emotions and physiological reactions, to ensure that various feedback information of users in the process of interacting with the advertising machine can be fully and accurately obtained; the interactive adjustment log refers to the interactive adjustment log refers to the detailed record of each adjustment operation and the related interactive feedback data in the process of display adjustment of the interactive advertising machine based on preference characteristics, which records the time of each display adjustment, the specific instruction parameters of the adjustment (such as adjusting the movement speed of a dynamic element from how much to how much, the specific modification value of the color parameter, etc.), and the changes in the interactive feedback data before and after the adjustment (such as the number of user clicks and dwell time before the adjustment and the corresponding data comparison after the adjustment).

[0131] As an embodiment of the present invention, the interactive advertising machine is displayed and adjusted based on the effect dynamic instruction and the preset feedback optimization mechanism to obtain an interactive adjustment log, including: obtaining instruction parameters corresponding to the effect dynamic instruction; determining the feedback collection dimension in the preset feedback optimization mechanism; based on the instruction parameters and the feedback collection dimension, collecting interactive feedback data corresponding to the interactive advertising machine; according to the interactive feedback data, analyzing the preference characteristics of the interactive advertising machine for the display effect; based on the preference characteristics, the interactive advertising machine is displayed and adjusted to obtain an interactive adjustment log.

[0132] Among them, the instruction parameters refer to various parameter information contained in the effect dynamic instructions for controlling the display behavior of the interactive advertising machine. These parameters specify the specific details of the advertising machine display, including but not limited to the movement speed, direction, acceleration of the dynamic display elements, the color change mode (such as the gradient color range, gradient speed, color contrast adjustment parameters), the intensity, angle, color temperature, flickering frequency of the lighting effect, and the time interval and switching method of the display content switching (such as fade in and fade out, sliding switching, etc.); the feedback collection dimension refers to the aspects and angles from which to collect feedback information during the interaction between the user and the interactive advertising machine in the preset feedback optimization mechanism, which covers multiple levels. From the user's behavioral actions, it includes click behavior (such as the location, frequency, and time interval of the click), touch operation (such as the strength of the touch, the direction and length of the slide), and dwell time (the length of time stayed on a certain display area or element); from the user's visual attention perspective, there is a line of sight focus (determining the area and order of the user's gaze through eye tracking technology); it can also include the user's emotional feedback level, For example, facial expression recognition technology can be used to obtain information about the user's interest, surprise, boredom, and other facial expressions, as well as physiological reactions such as heart rate changes (monitored by wearable devices or special sensors). The interactive feedback data refers to the actual data collected from the interaction between the user and the interactive advertising machine in the manner and path determined by the feedback collection dimension. For example, based on the user's click behavior, specific data such as the number of clicks and click time on different display contents and different display areas can be obtained. Eye tracking technology can be used to obtain data such as the trajectory of the user's line of sight moving on the advertising machine screen, the time point and duration of stay in each area, etc. The preference characteristics refer to the preference for specific display effects shown by users when watching the content displayed by the interactive advertising machine after in-depth analysis of the interactive feedback data. These characteristics can be manifested as a preference for a certain color combination (such as liking warm colors or high-contrast colors), a preference for specific dynamic effects (such as being more interested in slowly rotating display elements), and a preference for the layout of display content (such as paying more attention to the content in the center area of ​​the screen).

[0133] Furthermore, the acquisition of instruction parameters corresponding to the effect dynamic instruction can be achieved through an instruction parsing algorithm, such as: using an instruction parsing algorithm to parse the effect dynamic instruction, extracting the parameter information contained therein, and converting it into a readable and processable parameter form; the determination of the feedback collection dimensions in the preset feedback optimization mechanism can be achieved through a demand analysis method, such as: combining the display objectives of the advertising machine and the characteristics of the user group, and determining the feedback dimensions that need to be collected through analysis and discussion; the collection of interactive feedback data corresponding to the interactive advertising machine can be achieved through sensor fusion technology, such as: comprehensively using multiple types of sensors (such as touch sensors, eye tracking sensors, cameras, microphones, etc.), and fusing the data collected by these sensors to obtain comprehensive interactive feedback data; the analysis of the preference characteristics of the interactive advertising machine for display effects can be achieved through machine learning algorithms, such as: clustering algorithms, association rule mining algorithms, classification algorithms, etc.; the display adjustment of the interactive advertising machine can be achieved through dynamic display management tools, such as: Scala, Navori and other tools.

[0134] By extracting the key adjustment factors in the interactive adjustment log, the present invention can quickly make targeted adjustments to these key factors, avoid wasting time on unnecessary parameter adjustments, enable the advertising machine display to reach an ideal state faster, help to deeply understand user needs, avoid dispersing resources on aspects that have little impact on the display effect, and improve the effectiveness of resource utilization.

[0135] Among them, the key adjustment factors refer to those factors or parameters in the interactive adjustment log that have an important impact on the display effect of the interactive advertising machine, and may include parameters related to the visual presentation of the displayed content, such as color-related factors, like color hue, saturation, contrast, etc. Optionally, the extraction of the key adjustment factors in the interactive adjustment log can be achieved through a decision tree algorithm, such as: using the adjustment parameters in the interactive adjustment log as features and user feedback as labels to build a decision tree model, and the features (adjustment parameters) that play a key role in dividing the nodes are the key adjustment factors.

[0136] Furthermore, the present invention generates a display report corresponding to the interactive advertising machine based on the key adjustment factors, thereby clarifying which display parameters and strategies are effective and which need to be improved, so as to reasonably arrange resources and more accurately optimize and adjust the display content and method of the advertising machine, ensure the optimal operating state of the advertising machine, make the advertisement more compatible with the display environment of the advertising machine, and improve the efficiency of advertising delivery.

[0137] Among them, the display report refers to a document that comprehensively and detailed analyzes and presents the display of the interactive advertising machine based on key adjustment factors, which contains many aspects: from the perspective of display effect, the report presents how the key adjustment factors affect the user's visual perception of the display content of the advertising machine, for example, it details the use of different hues and saturations in the key adjustment factor of color in attracting user attention, such as the improvement of user click-through rate in a certain display scene by warm colors, and how the change of color contrast affects the user's acquisition efficiency of display information; for the dynamic display part, the report covers the role of the motion parameters of the dynamic elements in the key adjustment factors on the user experience, including the relationship analysis between the speed, direction, trajectory and other factors of the element movement and the user's stay time and participation in interactive behavior. For example, the report points out that when a specific dynamic element moves in a curve at a moderate speed, the user is more willing to stay and further explore the display content. Optionally, the generation of the display report corresponding to the interactive advertising machine can be achieved through a report generation tool, such as FineReport, Crystal Reports and other tools.

[0138] Firstly, the present invention obtains the shelf to be displayed corresponding to the interactive advertising machine, queries the product information in the shelf to be displayed, has precise control over the display of goods, clarifies the category, brand and specific style of goods carried by each shelf, thereby achieving a perfect match between the display content and the shelf position, making the display more orderly and reasonable. At the same time, the present invention monitors the interaction state corresponding to the interactive advertising machine in real time, extracts the interaction data corresponding to the interaction state, and mines the mapping parameters in the interaction data, which helps merchants to grasp the user's interest in different content and provide a basis for subsequent display optimization. It can accurately analyze the logic behind user behavior, such as inferring the user's exploration path and focus on product functions from the parameters of click sequence and dwell time, and then discovering the user's real needs. Based on the display layout and the product recommendation value, the present invention constructs the intelligent display target corresponding to the interactive advertising machine, which can achieve precise customization of display content, present products to users in the most optimized way, and ensure that users are in the first place. The user can see the content that best suits his needs and interests at the right time, which greatly improves the efficiency and pertinence of information transmission. The display content can be dynamically adjusted according to the preferences of different users to create personalized display scenes for users. The present invention determines the display points of the product display in the interactive advertising machine based on the display priority, and can allocate limited advertising machine display resources (such as screen space, playback time, etc.) to the display of products with high priority to avoid waste and scattered use of resources. For example, high-priority products can obtain more prominent display positions and more exquisite display effects, thereby improving the utilization efficiency of resources. Furthermore, the present invention adjusts the display of the interactive advertising machine based on the effect dynamic instructions and the preset feedback optimization mechanism to obtain an interactive adjustment log, which can continuously adapt to the needs and preferences of different users, always maintain the best display state, and enhance the attractiveness of the display to users. Secondly, the interactive adjustment log records the detailed information of the entire adjustment process, and provides data support for subsequent analysis and improvement, thereby enhancing the user experience. Therefore, the product intelligent display method and system based on the interactive shelf advertising machine proposed in the present invention can improve the effect and efficiency of product display. Example 2:

[0139] like Figure 2 FIG. 1 is a schematic diagram of a module of a product intelligent display system based on an interactive shelf advertising machine provided by an embodiment of the present invention.

[0140] The product intelligent display system 200 based on the interactive shelf advertising machine of the present invention can be installed in an electronic device. According to the functions to be implemented, the product intelligent display system 200 based on the interactive shelf advertising machine may include a layout construction module 201, a recommendation value calculation module 202, a priority calculation module 203, an instruction generation tool 204, and a report generation tool 205. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0141] In this embodiment, the functions of each module / unit are as follows:

[0142] The layout construction module 201 is used to obtain the shelf to be displayed corresponding to the interactive advertising machine, query the product information in the shelf to be displayed, determine the display requirements corresponding to the product information, and simultaneously obtain the 3D model information corresponding to the product information, and construct the product attributes corresponding to the product information based on the display requirements and the 3D model information, and construct the display layout corresponding to the interactive advertising machine based on the product attributes;

[0143] The recommendation value calculation module 202 is used to monitor the interaction state corresponding to the interactive advertising machine in real time, extract the interaction data corresponding to the interaction state, mine the mapping parameters in the interaction data, generate the user preference curve corresponding to the interactive advertising machine based on the mapping parameters, extract the user preference data in the user preference curve, and calculate the product recommendation value corresponding to the interactive advertising machine based on the user preference data;

[0144] The priority calculation module 203 is used to construct an intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value, divide the intelligent display target into levels to obtain level targets, and calculate the display priority corresponding to the interactive advertising machine based on the level targets;

[0145] The instruction generation tool 204 is used to determine the display point of the product display in the interactive advertising machine based on the display priority, identify the display material corresponding to the display point, analyze the material effect corresponding to the display material, and generate the effect dynamic instruction corresponding to the interactive advertising machine based on the material effect;

[0146] The report generation tool 205 is used to adjust the display of the interactive advertising machine based on the effect dynamic instruction and the preset feedback optimization mechanism, obtain the interactive adjustment log, extract the key adjustment factors in the interactive adjustment log, and generate a display report corresponding to the interactive advertising machine based on the key adjustment factors.

[0147] In detail, the modules described in the product intelligent display system 200 based on the interactive shelf advertising machine in the embodiment of the present invention adopt the same technical means as the product intelligent display method based on the interactive shelf advertising machine described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0148] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A product intelligent display method based on an interactive shelf advertising machine, characterized in that: The method comprises: Obtain a shelf to be displayed corresponding to the interactive advertising machine, query product information in the shelf to be displayed, determine the display requirements corresponding to the product information, and simultaneously obtain 3D model information corresponding to the product information, construct product attributes corresponding to the product information based on the display requirements and the 3D model information, and construct a display layout corresponding to the interactive advertising machine based on the product attributes; Monitor the interactive state corresponding to the interactive advertising machine in real time, extract the interactive data corresponding to the interactive state, mine the mapping parameters in the interactive data, generate a user preference curve corresponding to the interactive advertising machine based on the mapping parameters, extract the user preference data in the user preference curve, and calculate the product recommendation value corresponding to the interactive advertising machine based on the user preference data; Based on the display layout and the product recommendation value, construct the intelligent display target corresponding to the interactive advertising machine, and divide the intelligent display target into levels to obtain level targets, wherein the intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value is constructed, including: Decomposing the display layout into elements to obtain layout sub-elements; Determine the advantageous display area corresponding to the layout sub-element; Analyze the regional display focus corresponding to the advantageous display areas; Combine the regional display focus with the product recommendation value to query the product display characteristics corresponding to the product in the interactive advertising machine; Based on the product display characteristics, construct an intelligent display target corresponding to the interactive advertising machine, and based on the ladder target, calculate the display priority corresponding to the interactive advertising machine, wherein the calculation of the display priority corresponding to the interactive advertising machine based on the ladder target includes: The display priority corresponding to the interactive advertising machine is calculated using the following formula: in, Indicates the display priority corresponding to the interactive advertising machine. Indicates the total number of corresponding ladder targets, Indicates the quantity index corresponding to the step target, Indicates The importance score of each level goal, Indicates The urgency weight of each level goal, Indicates The feasibility coefficient of each ladder target, represents the total number of external influencing factors, Represents the quantitative index of external influencing factors, Indicates The factor adjustment value of external influencing factors, represents the normalization constant; Based on the display priority, determine the display point of the product display in the interactive advertising machine, identify the display material corresponding to the display point, analyze the material effect corresponding to the display material, and generate the effect dynamic instruction corresponding to the interactive advertising machine based on the material effect; Based on the effect dynamic instruction and the preset feedback optimization mechanism, the interactive advertising machine is displayed and adjusted to obtain an interactive adjustment log, key adjustment factors in the interactive adjustment log are extracted, and based on the key adjustment factors, a display report corresponding to the interactive advertising machine is generated.

2. The product intelligent display method based on the interactive shelf advertising machine according to claim 1, characterized in that: The constructing the product attributes corresponding to the product information based on the display requirements and the 3D model information includes: Classify the display requirements into categories to obtain classified requirements; Analyze the 3D model information to obtain 3D analysis data; Respectively extracting the demand points and analysis points in the classified demand and the 3D analysis data; Associating and integrating the demand point with the analysis point to obtain an associated data set; Extracting attribute clusters corresponding to the data in the associated data set; Based on the attribute cluster, a product attribute corresponding to the product information is constructed.

3. The product intelligent display method based on the interactive shelf advertising machine according to claim 1, characterized in that: The step of constructing a display layout corresponding to the interactive advertising machine based on the product attributes includes: Analyze the functional characteristics corresponding to the product attributes; Based on the functional characteristics, determining a functional display area in the interactive advertising machine; Identify the basic architecture corresponding to the functional display area; Determine an architecture optimization direction corresponding to the basic architecture; Extracting potential optimization points corresponding to the architecture optimization direction; Based on the potential optimization points, a display layout corresponding to the interactive advertising machine is constructed.

4. The product intelligent display method based on the interactive shelf advertising machine according to claim 1, characterized in that: The step of generating a user preference curve corresponding to the interactive advertising machine based on the mapping parameters includes: Extracting key mapping indicators from the mapping parameters; Quantifying the key mapping indicators to obtain a quantified indicator set; Determining the preferred tendency elements in the quantitative indicator set; Calculating the preference weight value corresponding to the preference tendency element; Based on the preference weight value, a user preference curve corresponding to the interactive advertising machine is generated.

5. The product intelligent display method based on the interactive shelf advertising machine according to claim 1, characterized in that: The calculating the product recommendation value corresponding to the interactive advertising machine based on the user preference data includes: The product recommendation value corresponding to the interactive advertising machine is calculated using the following formula: in, Indicates the product recommendation value corresponding to the interactive advertising machine, represents the total number of preference dimensions corresponding to the user preference data, represents the number index of the preference dimension, Indicated in The preference value corresponding to the preference dimension is Indicated in The matching weights of the preference dimensions and the user preference data, Indicates the number of product attribute features, Indicates the quantity index of product attribute characteristics, Indicates The quantitative value corresponding to each attribute feature.

6. The product intelligent display method based on the interactive shelf advertising machine according to claim 1, characterized in that: The generating of the effect dynamic instruction corresponding to the interactive advertising machine based on the material effect includes: Querying effect features in the material effect; Identify the visual dominant factor corresponding to the effect feature; Performing extreme value analysis on the visual dominant factor to obtain a dominant extreme value; Analyzing the visual weight corresponding to the dominant extreme value; Based on the visual weight, an effect dynamic instruction corresponding to the interactive advertising machine is generated.

7. The product intelligent display method based on the interactive shelf advertising machine according to claim 1, characterized in that: The interactive advertising machine is displayed and adjusted based on the effect dynamic instruction and the preset feedback optimization mechanism to obtain an interactive adjustment log, including: Obtaining instruction parameters corresponding to the effect dynamic instruction; Determine the feedback collection dimensions in the preset feedback optimization mechanism; Based on the instruction parameters and the feedback collection dimensions, collecting the interactive feedback data corresponding to the interactive advertising machine; Analyzing the preference characteristics of the interactive advertising machine for display effects according to the interactive feedback data; Based on the preference feature, the interactive advertising machine is displayed and adjusted to obtain an interactive adjustment log.

8. The product intelligent display system based on interactive shelf advertising machine is characterized by: The system is used to execute the product intelligent display method based on the interactive shelf advertising machine according to any one of claims 1 to 7, and comprises: A layout construction module is used to obtain a shelf to be displayed corresponding to the interactive advertising machine, query product information in the shelf to be displayed, determine the display requirements corresponding to the product information, and simultaneously obtain 3D model information corresponding to the product information, construct product attributes corresponding to the product information based on the display requirements and the 3D model information, and construct a display layout corresponding to the interactive advertising machine based on the product attributes; A recommendation value calculation module, used to monitor the interaction state corresponding to the interactive advertising machine in real time, extract the interaction data corresponding to the interaction state, mine the mapping parameters in the interaction data, generate a user preference curve corresponding to the interactive advertising machine based on the mapping parameters, extract the user preference data in the user preference curve, and calculate the product recommendation value corresponding to the interactive advertising machine based on the user preference data; A priority calculation module, for constructing an intelligent display target corresponding to the interactive advertising machine based on the display layout and the product recommendation value, dividing the intelligent display target into levels to obtain level targets, and calculating the display priority corresponding to the interactive advertising machine based on the level targets; An instruction generation tool, for determining, based on the display priority, a display point for product display in the interactive advertising machine, identifying a display material corresponding to the display point, analyzing a material effect corresponding to the display material, and generating an effect dynamic instruction corresponding to the interactive advertising machine based on the material effect; A report generation tool is used to adjust the display of the interactive advertising machine based on the effect dynamic instructions and the preset feedback optimization mechanism, obtain an interactive adjustment log, extract key adjustment factors in the interactive adjustment log, and generate a display report corresponding to the interactive advertising machine based on the key adjustment factors.

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