A multifunctional coffee machine large screen interaction and content management system

Abstract

This invention discloses a multifunctional coffee machine large-screen interactive and content management system, belonging to the coffee machine field. The system includes a large-screen display module, a touch interaction module, a core control module, a cloud server, a content management module, and a data processing module. The content management module employs an adaptive caching algorithm to achieve intelligent caching management of local and cloud-based content data; the data processing module uses a user preference prediction algorithm to predict user taste preferences. This invention achieves a rich human-computer interaction experience through a smart large screen, efficient content management through advanced content management algorithms, and personalized recommendations through data analysis and machine learning. It effectively solves the problems of existing coffee machines, such as limited interactive functions, weak content management capabilities, and insufficient data utilization, providing users with an intelligent, personalized, and interconnected coffee-making experience.

Description

Technical Field

[0001] This invention relates to coffee machines, and more particularly to a multifunctional coffee machine large-screen interactive and content management system. Background Technology

[0002] With the rapid development of smart home and IoT technologies, traditional coffee machines are evolving towards intelligence, personalization, and connectivity. Traditional coffee machines are mainly operated through physical buttons, knobs, or simple LCD displays, offering limited functionality and interactive experiences that fail to meet modern users' demands for intelligent and personalized coffee experiences.

[0003] In the current technology, some high-end coffee machines have begun to use touch screens for operation, but they generally have the following problems:

[0004] Limited interactive functions: The interactive interface of existing touch screen coffee machines is mainly limited to beverage selection and parameter settings, lacking rich interactive content and multimedia display functions, resulting in a rather dull user experience.

[0005] Weak content management capabilities: The existing system lacks an effective content management mechanism, making it impossible to achieve unified management and synchronization of beverage recipes, multimedia resources, and user data, and making it difficult to provide a personalized service experience.

[0006] Insufficient data utilization: Although existing systems can record user operations and device operation data, they lack effective data analysis algorithms, making it impossible to extract user preferences from the data and achieve intelligent personalized recommendations.

[0007] Poor system scalability: Existing systems mostly adopt a closed architecture, making it difficult to expand new functions through software upgrades, and also unable to achieve deep integration with external systems (such as mobile applications and cloud services).

[0008] Therefore, a new type of coffee machine large-screen interactive and content management system is needed, which can provide a rich human-computer interaction experience, achieve efficient content management, and provide personalized services to users through intelligent data analysis. Summary of the Invention

[0009] Purpose of the invention: The purpose of this invention is to provide a multifunctional coffee machine large-screen interactive and content management system, which realizes rich human-computer interaction through a smart large screen, achieves efficient local and cloud content management through advanced content management algorithms, and realizes personalized recommendations through data analysis and machine learning, thereby solving the problems of poor interactive experience, weak content management capabilities, and insufficient data utilization in existing technologies.

[0010] Technical solution: A multi-functional coffee machine large-screen interactive and content management system, comprising:

[0011] The large screen display module is located on the back of the coffee machine and is used to display the interactive interface, beverage information and multimedia content;

[0012] A touch interaction module, connected to the large screen display module, is used to receive user touch operations and generate interaction commands;

[0013] The core control module, connected to the touch interaction module, is used to process interactive commands, control the beverage preparation process, and manage the system's operating status.

[0014] A cloud server, connected to the core control module via a wireless network, is used to store and manage cloud content data;

[0015] The content management module, deployed in the core control module and the cloud server, is used to manage local and cloud content data, including beverage recipes, multimedia files and user preference information;

[0016] The data processing module, deployed within the core control module, is used to collect, analyze, and process user behavior data and device operation data;

[0017] The content management module employs an adaptive caching algorithm for local caching management of content data. The cache value function V(i) of the adaptive caching algorithm is calculated as follows:

[0018]

[0019] Where i represents a content item, F(i) represents the access frequency of content item i, R(i) represents the recent access time decay factor of content item i, S(i) represents the file size normalization factor of content item i, and α, β, and γ are weight coefficients that satisfy α+β+γ=1. This algorithm can dynamically optimize the caching strategy according to actual usage. Compared with the traditional LRU (Least Recently Used) and LFU (Least Frequently Used) algorithms, it can achieve a higher cache hit rate and a better user experience.

[0020] The data processing module uses a user preference prediction algorithm to predict user taste preferences. The formula for calculating the predicted value P(u,c) of the user preference prediction algorithm is as follows:

[0021]

[0022] Where u represents the user, c represents the beverage category, J represents the feature set, j represents the feature item, S(u,j) represents the score of user u on feature item j, S(c,j) represents the matching degree of beverage category c on feature item j, and wj represents the weight of feature item j. This algorithm can accurately predict user preferences and provide a reliable basis for personalized recommendations. Compared with traditional simple statistical methods, it can provide more accurate personalized services.

[0023] Furthermore, the large-screen display module is a multi-touch capacitive display screen with a screen size of 8-15 inches and a resolution of 1920×1080 or higher, supporting simultaneous multi-touch and gesture recognition functions.

[0024] Furthermore, the touch interaction module includes a touch sensor array and a gesture recognition unit. The touch sensor array is used to detect touch position and pressure, and the gesture recognition unit is used to recognize gesture operations such as swiping, long pressing, and double-tapping, and convert the recognition results into corresponding interaction commands.

[0025] Furthermore, the core control module includes an embedded processor, a storage unit, and a network communication unit. The embedded processor is an ARM architecture multi-core processor with a main frequency of not less than 1.5GHz. The storage unit includes RAM and flash memory. The network communication unit supports Wi-Fi, Bluetooth, and mobile communication networks.

[0026] Furthermore, the content management module includes a local content management unit, a cloud synchronization unit, and a version control unit. The local content management unit is used to manage locally stored content data, the cloud synchronization unit is used to realize automatic synchronization between local and cloud content data, and the version control unit is used to track version changes and conflict resolution of content data.

[0027] Furthermore, the data processing module includes a data acquisition unit, a data analysis unit, and a data storage unit. The data acquisition unit is used to collect user operation logs, beverage preparation records, and equipment operating parameters. The data analysis unit uses machine learning algorithms to mine and analyze the collected data. The data storage unit uses a hierarchical storage strategy to store the raw data and analysis results.

[0028] Furthermore, the system also includes a personalized recommendation module, which connects the data processing module and the content management module, and is used to recommend personalized drinks, interactive content and customized services to users based on the output of the user preference prediction algorithm.

[0029] Furthermore, the system also includes a remote control module deployed on a mobile terminal device, which communicates with the core control module through the cloud server, enabling users to remotely view the coffee machine status, schedule beverage preparation, and manage personal preference settings.

[0030] Beneficial effects:

[0031] This invention provides users with an immersive coffee-making experience through a large-screen display and rich interactive content, changing the monotonous and tedious operation of traditional coffee machines. Through intelligent data analysis and user preference prediction, it offers personalized beverage recommendations and customized services to each user, improving user satisfaction and loyalty. Adaptive caching algorithms and cloud synchronization mechanisms enable efficient content management, ensuring fast system response and timely content updates. Through continuous data collection and analysis, the system can continuously learn and optimize, providing increasingly accurate personalized services. The system adopts a modular design, facilitating functional expansion and upgrades, and adapting to ever-changing user needs and technological advancements. Attached Figure Description

[0032] Figure 1 The system architecture diagram of the multifunctional coffee machine large-screen interactive and content management system provided in the embodiments of the present invention;

[0033] Figure 2 The system interaction flowchart provided for embodiments of the present invention. Detailed Implementation

[0034] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Example 1:

[0036] like Figure 1 As shown, the multi-functional coffee machine large-screen interactive and content management system provided in this embodiment includes a large-screen display module, a touch interaction module, a core control module, a cloud server, a content management module, and a data processing module.

[0037] The large display module is located on the back of the coffee machine and features a 10.1-inch multi-touch capacitive display with a resolution of 1920×1080, supporting simultaneous 10-point touch. The display uses fingerprint-resistant tempered glass, offering high light transmittance and good touch sensitivity. The display module connects to the core control module via a MIPI interface and supports a 60Hz refresh rate.

[0038] The touch interaction module includes a touch sensor array and a gesture recognition unit. The touch sensor array uses projected capacitive technology, capable of detecting the precise position and pressure information of 10 touch points. The gesture recognition unit uses machine learning algorithms to recognize various gesture operations such as clicks, swipes (up, down, left, right), long presses, double-clicks, and zooming, with an accuracy rate of over 98%.

[0039] The core control module includes an embedded processor, a storage unit, and a network communication unit. The embedded processor is a quad-core ARM Cortex-A53 processor with a clock speed of 1.8GHz, equipped with the NEON instruction set, enabling efficient processing of multimedia content and machine learning algorithms. The storage unit includes 3GB of LPDDR4 RAM and 32GB of eMMC flash memory for storing the operating system, applications, and user data. The network communication unit supports dual-band Wi-Fi (2.4GHz and 5GHz), Bluetooth 5.0, and 4G mobile communication, ensuring stable and high-speed network connectivity.

[0040] The cloud server adopts a distributed cloud computing architecture, equipped with a high-performance computing cluster and a massive storage system, capable of supporting concurrent access from millions of coffee machines. The cloud server provides a RESTful API interface, supporting functions such as content synchronization, user authentication, and data reporting.

[0041] The content management module includes a local content management unit, a cloud synchronization unit, and a version control unit. The local content management unit manages locally stored content data, using an SQLite database for metadata management and a file system for actual content storage. The cloud synchronization unit employs an incremental synchronization algorithm, synchronizing only changed content to reduce network bandwidth consumption. The version control unit uses a Git-style version management mechanism, recording every content change and supporting automatic conflict resolution and version rollback.

[0042] The data processing module comprises a data acquisition unit, a data analysis unit, and a data storage unit. The data acquisition unit collects user operation logs, beverage preparation records, and equipment operating parameters in an event-driven manner, with a acquisition frequency of 100Hz. The data analysis unit uses the lightweight machine learning framework TensorFlow Lite, running on the core control module, and can analyze user behavior and equipment status in real time. The data storage unit uses the time-series database InfluxDB to store raw data and an SQLite database to store analysis results.

[0043] Example 2:

[0044] like Figure 2 As shown, this embodiment describes the system's interaction flow, which includes three stages: user operation, system response, and feedback display.

[0045] User operation phase:

[0046] Step 1: As the user approaches the coffee machine, the large screen display module wakes up from standby mode and displays a welcome screen. The system detects the user's approach via a camera or infrared sensor, with a delay of no more than 500ms.

[0047] Step 2: Users select beverage categories via the touch interaction module. The interface uses a card-style layout, with each beverage category displaying a corresponding image and name, and supports swiping to switch between them.

[0048] Step 3: The user enters the beverage details page, selects a specific beverage, and customizes the parameters. Custom parameters include cup type, concentration, temperature, and sugar content, which are set using controls such as sliders and switches.

[0049] System response phase:

[0050] Step 4: The core control module receives the user's confirmation command and begins making the beverage. The system first checks if the ingredients are sufficient; if not, it prompts the user to replenish them.

[0051] Step 5: The core control module controls the various actuators of the coffee machine to complete the beverage preparation. During the preparation process, the large screen displays the preparation progress and estimated completion time.

[0052] Step 6: The data processing module records this operation and updates the user preference model. The recorded information includes the selected beverage, the set parameters, and the preparation time.

[0053] Feedback display phase:

[0054] Step 7: Once the beverage is prepared, a completion notification and beverage recommendations will be displayed on the large screen. These recommendations are based on a user preference prediction algorithm and will suggest 3-5 beverages that the user might be interested in.

[0055] Step 8: The user can continue operating or leave. If the user does not operate within 30 seconds, the system will automatically return to standby mode to save power.

[0056] Example 3:

[0057] This embodiment describes in detail the adaptive caching algorithm used by the content management module.

[0058] The core idea of ​​this algorithm is to dynamically adjust the caching strategy based on the value of content items, prioritizing the caching of high-value content items to improve the cache hit rate. The value of a content item is determined by a combination of three factors: access frequency, recent access time, and file size.

[0059] The specific implementation steps are as follows:

[0060] Step 1: Initialize the cache space and set the cache capacity limit C (e.g., 2GB).

[0061] Step 2: For each content item i, calculate its cache value V(i). The calculation formula is:

[0062]

[0063] in:

[0064] , represents the access frequency percentage of content item i, and count(i) represents the number of times content item i is accessed.

[0065] t represents the recent access time decay factor of content item i. current t represents the current time. last (i) represents the most recent access time of content item i, and T is a time constant (e.g., 24 hours).

[0066] , represents the file size normalization factor of content item i, and size(i) is the file size (in bytes) of content item i.

[0067] α, β, and γ are weighting coefficients that satisfy α + β + γ = 1, with default values ​​of α = 0.5, β = 0.3, and γ = 0.2, respectively.

[0068] Step 3: When a new content item `new` needs to be cached, if the cache is not full, cache it directly. If the cache is full, perform a cache replacement.

[0069] Step 4: Cache Replacement Strategy: Select the lowest-value item (min) in the cache for replacement, satisfying V(min) ≤ V(new) and min ≠ new. If multiple lowest-value items exist in the cache, select the item with the largest value to replace, thus freeing up more space.

[0070] Step 5: After each access to content item i, update its access count count(i) and last access time t. last (i), and recalculate its cache value V(i).

[0071] Step 6: Periodically (e.g., hourly) recalculate the value of all cached content items and adjust the caching strategy.

[0072] Actual tests show that, compared with the traditional LRU algorithm, this adaptive caching algorithm improves the cache hit rate by 15-20%, increases system response speed by 20-30%, and significantly improves user experience.

[0073] Example 4:

[0074] This embodiment describes in detail the user preference prediction algorithm used in the data processing module.

[0075] This algorithm is based on the idea of ​​collaborative filtering. By analyzing the user's historical behavioral characteristics and the feature matching degree of the beverage, it predicts the user's preference for different beverages.

[0076] The specific implementation steps are as follows:

[0077] Step 1: Establish a user feature model. For each user u, establish a feature vector U(u) = [S(u,j1), S(u,j2), ..., S(u,jn)], where j1, j2, ..., jn are n feature terms in the feature set J.

[0078] Feature set J includes the following feature terms:

[0079] Flavor intensity: The value ranges from 0 to 1, representing the user's preferred coffee strength.

[0080] Sweetness preference: The value ranges from 0 to 1, representing the user's preferred level of sweetness.

[0081] Cup size preference: The value ranges from 0 to 1, where 0 indicates a preference for small cups and 1 indicates a preference for large cups.

[0082] Price sensitivity: The value ranges from 0 to 1, where 0 indicates that the price is not sensitive and 1 indicates that the price is highly sensitive.

[0083] Production time preference: The value ranges from 0 to 1, where 0 indicates a preference for fast production and 1 indicates that you do not mind the waiting time.

[0084] Functional preference: The value ranges from 0 to 1, indicating the user's preference for multifunctional features.

[0085] The user's score S(u,j) on each feature item is calculated in the following way:

[0086] For users with a rich history, a weighted average method is used, with recent records having a higher weight than earlier records.

[0087] For new users or users with limited historical data, demographic characteristics are used for initialization.

[0088] Step 2: Establish a beverage feature model. For each beverage category c, establish a feature vector C(c) = [S(c,j1),S(c,j2), ..., S(c,jn)], where S(c,j) represents the matching degree of beverage category c on feature term j.

[0089] Beverage characteristics were determined through a combination of expert annotation and data analysis:

[0090] Flavor intensity: Determined based on the type of coffee beans and the degree of roasting.

[0091] Sweetness preference: determined based on the sugar content in the recipe.

[0092] Cup shape preference: Determined based on standard cup shape.

[0093] Price sensitivity: Determined based on the pricing of the beverage.

[0094] Production time preference: determined based on standard production time.

[0095] Functional preference: determined based on the functional characteristics of the beverage (such as containing milk, flavoring, etc.).

[0096] Step 3: Calculate the predicted preference value P(u,c) for user u regarding beverage category c. The calculation formula is:

[0097]

[0098] Where wj represents the weight of feature term j, learned from historical data using machine learning methods, and satisfies .

[0099] Step 4: Normalize the predicted value P(u,c) to obtain the final recommendation score R(u,c):

[0100]

[0101] Step 5: Sort the drinks according to the recommendation score R(u,c) and recommend the N drinks with the highest scores to the user (e.g., N=5).

[0102] Step 6: After the user selects a beverage, collect feedback data, update the user feature model and weight parameters, and achieve adaptive optimization of the model.

[0103] Actual testing shows that the algorithm achieves a recommendation accuracy of over 85%, and user satisfaction is more than 40% higher than that of traditional random recommendations.

[0104] Example 5:

[0105] The personalized recommendation module connects the data processing module and the content management module. It is used to recommend personalized drinks, interactive content, and customized services to users based on the output of the user preference prediction algorithm.

[0106] Recommended content includes:

[0107] Personalized beverage recommendations: Based on user preference predictions, recommend beverages that the user may be interested in on the homepage. Recommendations take time into account: energizing drinks in the morning, relaxing drinks in the afternoon, and low-caffeine drinks in the evening.

[0108] Interactive content recommendation: Based on users' usage habits and interests, recommend interactive content, such as coffee culture knowledge, coffee making technique videos, user reviews, etc.

[0109] Personalized service recommendations: Based on users' consumption history and preferences, we recommend personalized services such as membership discounts, new product tastings, and customized packaging.

[0110] The recommendation interface uses a sliding card layout, with each recommendation displaying an image, title, brief description, and reason for recommendation. Users can quickly browse and select, or swipe to ignore recommendations they are not interested in.

[0111] Example 6:

[0112] The remote control module is deployed on mobile terminal devices (such as smartphones and tablets) and communicates with the core control module through a cloud server, enabling users to remotely view the coffee machine status, schedule beverage preparation, and manage personal preference settings.

[0113] Remote control functions include:

[0114] Remote status monitoring: Users can view the coffee machine's working status (standby, brewing, maintenance), ingredient balance, and equipment health status in real time.

[0115] Beverage preparation by appointment: Users can schedule beverage preparation for a future time, and the system will automatically start preparation at the specified time to ensure that the beverage is ready when the user arrives.

[0116] Remote parameter settings: Users can remotely set beverage parameters, adjust system settings, and manage personal preferences.

[0117] Push notifications: The system pushes important notifications to users via the mobile application, such as when the beverage is ready, when ingredients are insufficient, or when equipment needs maintenance.

[0118] The remote control system employs a secure communication protocol, and all data transmissions are encrypted to ensure user privacy and system security. The mobile application supports both iOS and Android platforms, providing a consistent user experience.

[0119] Through the above embodiments, this invention realizes large-screen interaction and content management functions for coffee machines, providing users with an intelligent and personalized coffee experience. The system employs advanced algorithms and modular design, possessing excellent scalability and adaptability to meet ever-changing user needs.

[0120] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A multi-functional coffee machine large-screen interactive and content management system, characterized in that, include: The large screen display module is located on the back of the coffee machine and is used to display the interactive interface, beverage information and multimedia content; A touch interaction module, connected to the large screen display module, is used to receive user touch operations and generate interaction commands; The core control module, connected to the touch interaction module, is used to process interactive commands, control the beverage preparation process, and manage the system's operating status. A cloud server, connected to the core control module via a wireless network, is used to store and manage cloud content data; The content management module, deployed in the core control module and the cloud server, is used to manage local and cloud content data, including beverage recipes, multimedia files and user preference information; The data processing module, deployed within the core control module, is used to collect, analyze, and process user behavior data and device operation data; The content management module employs an adaptive caching algorithm for local caching management of content data. The cache value function V(i) of the adaptive caching algorithm is calculated as follows: ； Where i represents a content item, F(i) represents the access frequency of content item i, R(i) represents the recent access time decay factor of content item i, S(i) represents the file size normalization factor of content item i, and α, β, γ are weight coefficients that satisfy α+β+γ=1. The data processing module uses a user preference prediction algorithm to predict user taste preferences. The formula for calculating the predicted value P(u,c) of the user preference prediction algorithm is as follows: ； Where u represents the user, c represents the beverage category, J represents the feature set, j represents the feature item, S(u,j) represents the score of user u on feature item j, S(c,j) represents the matching degree of beverage category c on feature item j, and wj represents the weight of feature item j.

2. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The large screen display module is a multi-touch capacitive display screen with a screen size of 8-15 inches and a resolution of 1920×1080 or higher, supporting multi-touch and gesture recognition functions.

3. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The touch interaction module includes a touch sensor array and a gesture recognition unit. The touch sensor array is used to detect touch position and pressure, and the gesture recognition unit is used to recognize gesture operations and convert the recognition results into corresponding interaction commands.

4. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The core control module includes an embedded processor, a storage unit, and a network communication unit. The embedded processor is an ARM architecture multi-core processor, the storage unit includes RAM and flash memory, and the network communication unit supports Wi-Fi, Bluetooth, and mobile communication networks.

5. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The content management module includes a local content management unit, a cloud synchronization unit, and a version control unit. The local content management unit is used to manage locally stored content data, the cloud synchronization unit is used to realize automatic synchronization of local and cloud content data, and the version control unit is used to track version changes and conflict resolution of content data.

6. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The data processing module includes a data acquisition unit, a data analysis unit, and a data storage unit. The data acquisition unit is used to collect user operation logs, beverage preparation records, and equipment operating parameters. The data analysis unit uses machine learning algorithms to mine and analyze the collected data. The data storage unit uses a hierarchical storage strategy to store the raw data and analysis results.

7. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The system also includes a personalized recommendation module, which connects the data processing module and the content management module, and is used to recommend personalized drinks, interactive content and customized services to users based on the output of the user preference prediction algorithm.

8. The multi-functional coffee machine large-screen interactive and content management system according to claim 1, characterized in that, The system also includes a remote control module deployed on a mobile terminal device. It communicates with the core control module through the cloud server, enabling users to remotely view the coffee machine status, schedule beverage preparation, and manage personal preference settings.

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