A vending machine intelligent interaction UI design method and system

Abstract

This application discloses a method and system for designing an intelligent interactive UI for vending machines. The method involves deploying a hardware sensing module to collect ambient light and user biometric data in real time, using a user profiling engine to identify user types and constructing dynamic profiles based on historical purchase records. It then performs three-dimensional scene adaptation decisions, including environment adaptation based on light intensity, accessibility adaptation based on user type, and layout adaptation based on the deployment scenario. The UI interface is dynamically reconstructed and rendered, intelligent content recommendation and multimodal interaction are implemented, and finally, closed-loop control is achieved through transaction process monitoring and emergency feedback mechanisms. This application achieves comprehensive UI optimization through the collaborative work of the hardware sensing module, user profiling engine, scene adaptation algorithm, and dynamic interaction module.

Description

Technical Field

[0001] This application relates to the field of intelligent terminal interaction design technology, and in particular to an intelligent interactive UI design method and system for vending machines. Background Technology

[0002] As a crucial component of intelligent terminal devices, the user interface (UI) design of vending machines directly impacts transaction efficiency and user experience. With the development of IoT technology and artificial intelligence algorithms, vending machines have evolved from traditional mechanical dispensing devices into intelligent terminals integrating touch displays, mobile payments, and data sensing functions. However, the UI interaction design of existing vending machines remains fixed and generic, failing to adapt to the diverse needs of various usage scenarios and differentiated user groups.

[0003] Specifically, the existing technology has the following main drawbacks:

[0004] The interaction logic is too simple: the existing UI mostly uses a fixed layout and has not optimized the operation process for different user groups (such as the elderly, children and people with disabilities), resulting in a high operating threshold for some users;

[0005] Lack of scene adaptation: The UI display parameters were not adjusted according to environmental factors (such as light intensity and usage scenario: subway station / school / hospital). The screen visibility is poor under strong light and the brightness is dazzling in low light environment.

[0006] Inefficient information presentation: Product categories are disorganized, payment method entry points are hidden, users need to jump multiple times to complete the purchase, the average operation time exceeds 30 seconds, and the transaction efficiency is low;

[0007] Lack of personalization: It is unable to push relevant products based on users' historical purchase records and preference tags, lacks proactive guidance, and results in a homogenized user experience;

[0008] Weak emergency response: In the event of emergencies such as payment errors or product shortages, the UI lacks clear fault prompts and solution guidance, which can easily lead to user complaints.

[0009] Existing technologies have failed to achieve dynamic adaptation of the UI to users, environment, and scenarios, resulting in low interaction efficiency and poor user experience, which restricts the intelligent upgrade of vending machines. Summary of the Invention

[0010] Based on this, this application provides a method and system for designing intelligent interactive UI for vending machines. Through the collaborative work of a hardware perception module, a user profile engine, a scene adaptation algorithm, and a dynamic interaction module, the UI is optimized in all dimensions.

[0011] Firstly, a method for designing an intelligent interactive UI for vending machines is provided, the method comprising:

[0012] The system collects environmental data and user biometric data in real time through hardware sensing modules deployed within the vending machine; the environmental data includes light intensity, and the user biometric data includes facial feature images and hand movement trajectories.

[0013] Based on the user's biometric data, the user's age type is identified and combined with historical purchase records to construct a dynamic user profile, generating a multi-dimensional feature vector containing user type tags and preference tags;

[0014] Based on the environmental data and the dynamic user profile, a 3D scene adaptation decision is made to generate an adaptation instruction set that includes interface layout parameters, visual style parameters, and function configuration parameters; wherein, the 3D scene adaptation includes environment adaptation based on light intensity, user adaptation based on user type tags, and scene adaptation based on deployment geographic location;

[0015] The UI interface is dynamically reconstructed and rendered according to the adapted instruction set, and intelligent content recommendation and interactive element configuration are performed.

[0016] Monitor user actions and execute transaction processes, provide status prompts through a multimodal feedback mechanism, and trigger emergency response plans when anomalies are detected.

[0017] Optionally, the hardware sensing module includes a light sensor, a distance sensor, a high-definition camera, and a touch feedback module;

[0018] The real-time acquisition of environmental data and user biometric data includes: monitoring ambient light intensity through a light sensor, detecting the user's operating distance from the screen through a distance sensor, capturing the user's facial images and hand movements through a camera, and performing noise filtering and feature extraction on the acquired raw data.

[0019] Optionally, the step of identifying user age type based on user biometric data and constructing a dynamic user profile by combining it with historical purchase records includes:

[0020] The facial feature images are analyzed using machine learning algorithms to identify user age ranges and classify them as elderly users, young users, or children, and to detect whether users have mobility impairments.

[0021] Retrieve the user's historical purchase records from the cloud or local storage, analyze product preferences using a collaborative filtering algorithm, and generate the user type tag and preference tag;

[0022] Establish a profile update mechanism to create initial profiles for new users and update preference weights for historical users.

[0023] Optionally, the environment adaptation based on light intensity includes:

[0024] When the ambient light intensity is detected to be greater than the first threshold, the screen brightness is automatically increased to the preset high brightness value and the contrast is enhanced.

[0025] When the ambient light intensity is detected to be less than the second threshold, the system automatically switches to night mode and reduces the screen brightness.

[0026] The display size of interface elements is dynamically adjusted based on the user's operating distance detected by the distance sensor.

[0027] Optionally, the user adaptation based on user type tags and the scenario adaptation based on deployment geographic location include:

[0028] When the user type is elderly, enlarge the interface font to the preset font size, expand the touch area of ​​the operation buttons, hide non-core function entrances, and enable voice guidance;

[0029] When the user type is a child, load the cartoon icon library and enable the anti-accidental touch mechanism;

[0030] When the deployment location is a subway station, switch to quick shopping mode and pin frequently used product categories to the top;

[0031] When the deployment location is a hospital, the health food category will be displayed first.

[0032] Optionally, the step of dynamically reconstructing and rendering the UI interface according to the adaptation instruction set, and performing intelligent content recommendation and interactive element configuration includes:

[0033] Based on the aforementioned preference tags, historically preferred products are displayed in the top area of ​​the homepage, with product information presented in the form of 3D icons. In response to user clicks, a details pop-up window containing the brand, capacity, and price appears.

[0034] The payment entry point is fixed at the bottom of the screen, and QR code payment, facial recognition payment and NFC payment interfaces are loaded;

[0035] The page architecture is dynamically adjusted based on the decision results of the scenario adaptation, hiding the member registration and promotional activity entrances that are irrelevant to the current scenario.

[0036] Optionally, the monitoring of user operations and execution of transaction processes, the provision of status alerts through a multimodal feedback mechanism, and the triggering of emergency response plans when an anomaly is detected include:

[0037] Monitor user touch operations, verify product inventory status, perform payment verification, and control shipment;

[0038] Upon successful payment, a dual shipment notification will be provided via screen animation and voice announcement.

[0039] When a payment anomaly is detected, a visual solution guide pops up in real time, providing quick access to rescan the code or contact customer service;

[0040] When a product is detected to be out of stock, the system automatically marks the out-of-stock status and recommends similar alternative products based on the preference tags.

[0041] Secondly, an intelligent interactive UI design system for vending machines is provided, the system comprising:

[0042] The hardware sensing module is used to collect environmental data and user biometric data in real time; wherein, the environmental data includes light intensity, and the user biometric data includes facial feature images and hand movement trajectories;

[0043] The user profiling engine is used to identify user age type based on the user's biometric data and build a dynamic user profile by combining historical purchase records, generating a multi-dimensional feature vector containing user type tags and preference tags.

[0044] The scene adaptation algorithm module is used to perform three-dimensional scene adaptation decisions based on the environmental data and the dynamic user profile, and generate an adaptation instruction set including interface layout parameters, visual style parameters and function configuration parameters; wherein, the three-dimensional scene adaptation includes environment adaptation based on light intensity, user adaptation based on user type tags and scene adaptation based on deployment geographical location;

[0045] The dynamic interaction module is used to dynamically reconstruct and render the UI interface according to the adaptation instruction set, and to perform intelligent content recommendation and interactive element configuration.

[0046] The feedback control module is used to monitor user operations and execute transaction processes, provide status prompts through a multimodal feedback mechanism, and trigger emergency response plans when abnormal situations are detected.

[0047] Thirdly, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the methods described in the first aspect above.

[0048] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0049] The beneficial effects of the technical solutions provided in this application include at least the following:

[0050] (1) Through the innovative environment-user-scenario three-in-one adaptation mechanism, the system can automatically enlarge the font and buttons for elderly users, enable cartoon icons and voice guidance for children, and enable voice control for disabled users, breaking through the limitations of the fixed layout of existing technologies. After implementation and verification, the success rate of operation for elderly users can be increased from 75% of the existing technology to 98%, effectively solving the problems of high operation threshold and heavy cognitive burden faced by some user groups.

[0051] (2) By simplifying the operation steps (fixing the core function entry and hiding unnecessary jumps), highlighting recommended products, and using 3D icons and detail pop-ups to reduce page layers, the system achieves a fast transaction loop of no more than 15 seconds. Compared with the average operation time of 32 seconds of existing technologies, the transaction efficiency is improved by 56%, which is especially suitable for dense crowd scenarios such as subway stations where fast passage is required.

[0052] (3) In response to emergencies such as payment errors, network interruptions, and product shortages, the system is designed with visual fault prompts and step-by-step solution guidance (such as real-time pop-up of a re-scan entry and automatic recommendation of alternative products), and adopts a dual status confirmation mechanism of animation and voice. This design eliminates users' anxiety and uncertainty when transactions are abnormal, resulting in an 80% decrease in the complaint rate for out-of-stock products and an increase in user satisfaction score from 3.2 to 4.7. Attached Figure Description

[0053] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0054] Figure 1 A flowchart illustrating the steps of an intelligent interactive UI design method for vending machines provided in this application embodiment;

[0055] Figure 2 A system block diagram of an intelligent interactive UI design method for vending machines provided in this application embodiment;

[0056] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] In the description of this application, the terms "comprising," "having," and any variations thereof are intended to cover non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those steps or units that are expressly listed, but may also include other steps or units that are not expressly listed but are inherent to these processes, methods, products, or apparatuses, or steps or units added based on further optimizations conceived in this application.

[0059] Please refer to Figure 1 The document illustrates a flowchart of a smart interactive UI design method for vending machines provided in an embodiment of this application. This method may include the following steps:

[0060] S1 collects environmental data and user biometric data in real time through hardware sensing modules deployed in the vending machine.

[0061] The environmental data includes light intensity, and the user biometric data includes facial feature images and hand movement trajectories. The hardware sensing module includes a light sensor, a distance sensor, a high-definition camera, and a touch feedback module. It collects environmental data and user biometric data in real time, including: monitoring ambient light intensity through the light sensor, detecting the user's operating distance from the screen through the distance sensor, capturing user facial images and hand movements through the camera, and performing noise filtering and feature extraction on the collected raw data.

[0062] S2 identifies user age types based on user biometric data and constructs dynamic user profiles by combining historical purchase records, generating multi-dimensional feature vectors containing user type labels and preference labels.

[0063] Among them, facial feature images are analyzed using machine learning algorithms to identify user age ranges and classify them into elderly users, young users or children, and to detect whether users have mobility impairment characteristics;

[0064] Retrieve the user's historical purchase records from the cloud or local storage, analyze product preferences using a collaborative filtering algorithm, and generate user type tags and preference tags;

[0065] Establish a profile update mechanism to create initial profiles for new users and update preference weights for historical users.

[0066] S3 performs 3D scene adaptation decisions based on environmental data and dynamic user profiles, generating an adaptation instruction set that includes interface layout parameters, visual style parameters, and function configuration parameters.

[0067] Among them, 3D scene adaptation includes environment adaptation based on light intensity, user adaptation based on user type tags, and scene adaptation based on deployment location.

[0068] In this embodiment, when the ambient light intensity is detected to be greater than a first threshold, the screen brightness is automatically increased to a preset high brightness value and the contrast is enhanced; when the ambient light intensity is detected to be less than a second threshold, the screen brightness is automatically switched to night mode and the screen brightness is reduced; the display size of the interface elements is dynamically adjusted according to the user operation distance detected by the distance sensor.

[0069] When the user type is an elderly user, enlarge the interface font to the preset font size, expand the touch area of ​​the operation buttons, hide non-core function entrances, and enable voice guidance; when the user type is a child user, load the cartoon icon library and enable the anti-accidental touch mechanism; when the deployment location is a subway station, switch to quick shopping mode and pin the high-frequency product category; when the deployment location is a hospital, prioritize displaying the health food category.

[0070] S4 dynamically reconstructs and renders the UI based on the adapted instruction set, and performs intelligent content recommendation and interactive element configuration.

[0071] Among them, historically preferred products are displayed in the top area of ​​the homepage based on preference tags, and product information is displayed in the form of 3D icons. A details pop-up window containing brand, capacity and price appears in response to user clicks.

[0072] The payment entry point is fixed at the bottom of the screen, and QR code payment, facial recognition payment and NFC payment interfaces are loaded;

[0073] The page architecture is dynamically adjusted based on the decision results of scenario adaptation, hiding the entry points for member registration and promotional activities that are irrelevant to the current scenario.

[0074] S5 monitors user operations and executes transaction processes, provides status prompts through a multimodal feedback mechanism, and triggers emergency response plans when anomalies are detected.

[0075] Among them, monitoring user touch operations, verifying product inventory status, performing payment verification and controlling shipment;

[0076] Upon successful payment, a dual shipment notification will be provided via screen animation and voice announcement.

[0077] When a payment anomaly is detected, a visual solution guide pops up in real time, providing quick access to rescan the code or contact customer service;

[0078] When a product is detected to be out of stock, it is automatically marked as out of stock and similar alternative products are recommended based on preference tags.

[0079] In summary, this invention provides an intelligent adaptive interactive UI design for vending machines. Through the collaborative work of a hardware perception module, a user profiling engine, a scene adaptation algorithm, and a dynamic interaction module, it achieves full-dimensional UI optimization. The specific solution is as follows:

[0080] Hardware sensing module deployment: Light sensors, distance sensors, cameras and touch feedback modules are integrated into the vending machine to collect environmental data (light intensity, ambient brightness) and user data (operation distance, hand movements, facial features) in real time.

[0081] User profile engine construction: Automatically classify user types (elderly users / young users / child users / disabled users) based on collected data, generate preference tags by combining historical purchase records, and build a dynamic user profile database;

[0082] Scene adaptation algorithm design:

[0083] Environmental adaptation: Automatically adjusts screen brightness and contrast according to light intensity, enhances text clarity in bright light, and switches to night mode in low light;

[0084] User adaptation: For elderly users, the font size is enlarged, the operation process is simplified, and unnecessary function entries are hidden; for children, cartoon icons and voice guidance are used; for users with disabilities, voice control and touch delay adjustment are supported.

[0085] Scenario adaptation: Switch UI layouts according to the deployment scenario (subway station: quick selection mode, highlighting popular products; hospital: health food category prioritized; school: quick access to snacks and beverages);

[0086] Dynamic interaction module optimization:

[0087] Product display: Uses 3D icons + details pop-up, supports category filtering and keyword search, and recommends products at the top based on preference tags;

[0088] Payment process: The payment entry is fixed at the bottom of the screen and supports multiple payment methods such as QR code payment, facial recognition payment, and NFC payment. In case of payment failure, a solution will pop up in real time (such as re-scanning the code or contacting customer service).

[0089] Feedback mechanism: Success / failure of operation and product shipment status are indicated by both animation and voice prompts. Out-of-stock items are automatically marked and alternative products are recommended.

[0090] As can be seen, the key points of this invention include:

[0091] Multi-dimensional adaptation mechanism: It innovatively proposes a three-in-one UI adaptation logic of "environment-user-scenario" to solve the pain point of the single design of existing technologies and realize personalized interaction in different scenarios;

[0092] Dynamic updates to user profiles: User tags are dynamically adjusted based on real-time data collection to ensure the accuracy of UI recommendations and operation optimizations, breaking through the limitations of traditional fixed layouts;

[0093] Efficient interactive process design: Improve transaction efficiency by simplifying operation steps (the entire process of selecting, paying, and picking up goods takes no more than 15 seconds) and optimizing information hierarchy (prioritizing the display of core functions);

[0094] Full-scenario emergency response: For emergencies such as payment errors, product shortages, and operational mistakes, we design visual and step-by-step solutions to guide users and lower the barrier to entry.

[0095] The following is a specific implementation process based on the above method:

[0096] In this embodiment, the vending machine is deployed in urban subway stations (during morning peak hours of 7:00-9:00 and evening peak hours of 17:00-19:00, when there is a high flow of people and users mainly need to make quick purchases). The hardware configuration includes: a 10-inch touch screen, a light sensor, a high-definition camera, a voice module, and an NFC reader.

[0097] Environmental perception and scene recognition: The light sensor detected that the natural light intensity during the morning rush hour was 800 lux. The system automatically switched to "Quick Selection Mode", adjusted the screen brightness to 500 cd / m², and increased the contrast by 30%.

[0098] User identification and profile matching: The camera captured a 55-year-old male user (facial features identified as elderly user), and combined with his historical purchase records (multiple purchases of bottled water and bread), the tag "elderly user + breakfast preference" was generated;

[0099] Dynamic UI adaptation:

[0100] Layout switch: Pin the "Breakfast Zone" (bread, milk, mineral water) to the top, and hide unnecessary entrances such as promotional ads and membership registration;

[0101] Interface optimization: The font size has been increased to 24 (default 16), the operation button size has been increased by 50%, and only the three core buttons "Select", "Pay" and "Cancel" have been retained;

[0102] Interaction process:

[0103] When a user clicks the "Mineral Water" icon, a 3D product detail page (brand, capacity, price) will pop up. After confirming the purchase, a payment option will automatically appear at the bottom of the screen (supports QR code scanning and facial recognition).

[0104] After the user selects to pay by scanning the code and the payment is successful, the screen displays the animation "Shipping in progress" and is accompanied by a voice prompt "Your goods have been dropped into the pickup slot, please check."

[0105] The entire process takes 12 seconds, allowing users to quickly complete their purchase without having to navigate through multiple pages.

[0106] This embodiment verifies the feasibility and superiority of the technical solution of the present invention, which can be widely applied to vending machines in different scenarios such as subway stations, schools, hospitals, and communities, and is suitable for the usage needs of various user groups.

[0107] like Figure 2 This application also provides an intelligent interactive UI design system for vending machines, which may include:

[0108] The hardware sensing module is used to collect environmental data and user biometric data in real time; wherein, the environmental data includes light intensity, and the user biometric data includes facial feature images and hand movement trajectories;

[0109] The user profiling engine is used to identify user age type based on the user's biometric data and build a dynamic user profile by combining historical purchase records, generating a multi-dimensional feature vector containing user type tags and preference tags.

[0110] The scene adaptation algorithm module is used to perform three-dimensional scene adaptation decisions based on the environmental data and the dynamic user profile, and generate an adaptation instruction set including interface layout parameters, visual style parameters and function configuration parameters; wherein, the three-dimensional scene adaptation includes environment adaptation based on light intensity, user adaptation based on user type tags and scene adaptation based on deployment geographical location;

[0111] The dynamic interaction module is used to dynamically reconstruct and render the UI interface according to the adaptation instruction set, and to perform intelligent content recommendation and interactive element configuration.

[0112] The feedback control module is used to monitor user operations and execute transaction processes, provide status prompts through a multimodal feedback mechanism, and trigger emergency response plans when abnormal situations are detected.

[0113] Specific limitations regarding the intelligent interactive UI design system for vending machines can be found in the limitations of the intelligent interactive UI design method for vending machines mentioned above, and will not be repeated here. Each module in the aforementioned intelligent interactive UI design system for vending machines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0114] In one embodiment, an electronic device is provided, which may be a computer, and its internal structure diagram may be as follows: Figure 3As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database of the computer device is used for UI design data. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a UI design methodology.

[0115] Those skilled in the art will understand that, Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described intelligent interactive UI design method for vending machines.

[0117] In one embodiment of this application, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the above-described intelligent interactive UI design method for vending machines.

[0118] The computer-readable storage medium and computer program product provided in this embodiment are similar in implementation principle and technical effect to the above method embodiments, and will not be repeated here.

[0119] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0120] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A vending machine intelligent interaction UI design method, characterized in that, The method includes: The system collects environmental data and user biometric data in real time through hardware sensing modules deployed within the vending machine; the environmental data includes light intensity, and the user biometric data includes facial feature images and hand movement trajectories. Based on the user's biometric data, the user's age type is identified and combined with historical purchase records to construct a dynamic user profile, generating a multi-dimensional feature vector containing user type tags and preference tags; Based on the environmental data and the dynamic user profile, a 3D scene adaptation decision is made to generate an adaptation instruction set that includes interface layout parameters, visual style parameters, and function configuration parameters; wherein, the 3D scene adaptation includes environment adaptation based on light intensity, user adaptation based on user type tags, and scene adaptation based on deployment geographic location; The UI interface is dynamically reconstructed and rendered according to the adapted instruction set, and intelligent content recommendation and interactive element configuration are performed. Monitor user actions and execute transaction processes, provide status prompts through a multimodal feedback mechanism, and trigger emergency response plans when anomalies are detected.

2. The method of claim 1, wherein, The hardware sensing module includes a light sensor, a distance sensor, a high-definition camera, and a touch feedback module; The real-time acquisition of environmental data and user biometric data includes: monitoring ambient light intensity through a light sensor, detecting the user's operating distance from the screen through a distance sensor, capturing the user's facial images and hand movements through a camera, and performing noise filtering and feature extraction on the acquired raw data.

3. The method of claim 1, wherein, The method of identifying user age type based on user biometric data and constructing dynamic user profiles by combining historical purchase records includes: The facial feature images are analyzed using machine learning algorithms to identify user age ranges and classify them as elderly users, young users, or children, and to detect whether users have mobility impairments. Retrieve the user's historical purchase records from the cloud or local storage, analyze product preferences using a collaborative filtering algorithm, and generate the user type tag and preference tag; Establish a profile update mechanism to create initial profiles for new users and update preference weights for historical users.

4. The method according to claim 1, characterized in that, The light intensity-based environment adaptation includes: When the ambient light intensity is detected to be greater than the first threshold, the screen brightness is automatically increased to the preset high brightness value and the contrast is enhanced. When the ambient light intensity is detected to be less than the second threshold, the system automatically switches to night mode and reduces the screen brightness. The display size of interface elements is dynamically adjusted based on the user's operating distance detected by the distance sensor.

5. The method according to claim 1, characterized in that, The user adaptation based on user type tags and the scenario adaptation based on deployment geolocation include: When the user type is elderly, enlarge the interface font to the preset font size, expand the touch area of ​​the operation buttons, hide non-core function entrances, and enable voice guidance; When the user type is a child, load the cartoon icon library and enable the anti-accidental touch mechanism; When the deployment location is a subway station, switch to quick shopping mode and pin frequently used product categories to the top; When the deployment location is a hospital, the health food category will be displayed first.

6. The method according to claim 1, characterized in that, The step of dynamically reconstructing and rendering the UI interface based on the adapted instruction set, and executing intelligent content recommendation and interactive element configuration includes: Based on the aforementioned preference tags, historically preferred products are displayed in the top area of ​​the homepage, with product information presented in the form of 3D icons. In response to user clicks, a details pop-up window containing the brand, capacity, and price appears. The payment entry point is fixed at the bottom of the screen, and QR code payment, facial recognition payment and NFC payment interfaces are loaded; The page architecture is dynamically adjusted based on the decision results of the scenario adaptation, hiding the member registration and promotional activity entrances that are irrelevant to the current scenario.

7. The method according to claim 1, characterized in that, The monitoring of user operations and execution of transaction processes, the provision of status alerts through a multimodal feedback mechanism, and the triggering of emergency response plans when anomalies are detected include: Monitor user touch operations, verify product inventory status, perform payment verification, and control shipment; Upon successful payment, a dual shipment notification will be provided via screen animation and voice announcement. When a payment anomaly is detected, a visual solution guide pops up in real time, providing quick access to rescan the code or contact customer service; When a product is detected to be out of stock, the system automatically marks the out-of-stock status and recommends similar alternative products based on the preference tags.

8. A smart interactive UI design system for vending machines, characterized in that, The system includes: The hardware sensing module is used to collect environmental data and user biometric data in real time; wherein, the environmental data includes light intensity, and the user biometric data includes facial feature images and hand movement trajectories; The user profiling engine is used to identify user age type based on the user's biometric data and build a dynamic user profile by combining historical purchase records, generating a multi-dimensional feature vector containing user type tags and preference tags. The scene adaptation algorithm module is used to perform three-dimensional scene adaptation decisions based on the environmental data and the dynamic user profile, and generate an adaptation instruction set including interface layout parameters, visual style parameters and function configuration parameters; wherein, the three-dimensional scene adaptation includes environment adaptation based on light intensity, user adaptation based on user type tags and scene adaptation based on deployment geographical location; The dynamic interaction module is used to dynamically reconstruct and render the UI interface according to the adaptation instruction set, and to perform intelligent content recommendation and interactive element configuration. The feedback control module is used to monitor user operations and execute transaction processes, provide status prompts through a multimodal feedback mechanism, and trigger emergency response plans when abnormal situations are detected.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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