Movable buffet food selling system and method in hospital outpatient service area

Abstract

The invention provides a hospital outpatient service area movable buffet food selling system and method, which can enable a patient or family members to leave an outpatient service area to have meals during a doctor-seeing queuing period. The problems of crowded canteens and long queuing time in peak periods are solved. And a fast, convenient and sanitary meal scheme can be provided. The pressure of a hospital canteen in peak periods is reduced, the labor cost is reduced, and the service efficiency is improved. And healthy and safe meal choices are provided. A hospital can provide an efficient and convenient dining solution for outpatient patients and staff, and meanwhile the service level of the hospital and the satisfaction degree of the patients are improved. The user can obtain convenient and sanitary dining experience, and the queuing time is shortened. And healthy meals are selected through personalized recommendation. The hospital relieves the pressure of the canteen in the peak period and improves the service quality. The labor cost is reduced and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of catering platforms, and in particular to a movable self-service meal vending system, a vending machine and an application method thereof, an electronic device and a computer-readable storage medium in a hospital outpatient area. Background Art

[0002] Existing self-service food vending machines on the market allow users to select and purchase meals by themselves. Although this can reduce labor costs and overcome the drawbacks of manual food preparation (such as inconsistent portion sizes), they also have the following application disadvantages: The variety of food is limited, and the supply of hot food is insufficient, making it difficult to meet diverse demands; the equipment capacity is limited, and untimely replenishment during peak periods can easily lead to out-of-stock situations; there are hidden dangers in hygiene management, such as untimely cleaning or delayed replacement of expired food; the payment methods are not compatible enough, and are not user-friendly for some elderly users; the response to equipment failures is slow, affecting the user experience; the prices are high, and the cost-effectiveness is lower than that of traditional canteen meals.

[0003] Especially in the outpatient area of ​​the hospital, the disadvantages of the existing self-service food vending machines on the market are more obvious: First, patients and their families cannot leave the outpatient clinic to eat while waiting in line. During peak hours, cafeterias are crowded and queues are long, requiring a fast, convenient, and hygienic meal solution. However, existing self-service meal vending machines on the market are mostly fixed and therefore cannot meet these needs.

[0004] Secondly, hospitals need to alleviate peak cafeteria pressure, reduce labor costs, improve service efficiency, and provide healthy and safe meal options. However, existing self-service vending machines on the market are designed solely for vending and cannot process food. Therefore, manual labor is still required to prepare the food and place it in the self-service vending machine's grid, making it impossible to replace window displays and achieve integrated food preparation and vending services. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the present invention provides the following technical solutions: In one aspect, a movable self-service food vending system for a hospital outpatient area is provided, the system comprising: A smart app that recommends meals based on the user's health data and sends meal order information to a self-service meal vending machine, where the order information includes the user's location information; A self-service food vending machine, configured to parse the order information and generate corresponding meal configuration instructions, automatically prepare the meal to be delivered according to the instructions, and issue a delivery notification to the delivery robot; a delivery robot, configured to interact with the self-service food vending machine, retrieve the food to be delivered and deliver it to the user's location based on the user's location information in the notification, and send feedback to the self-service food vending machine after delivery is completed; Backend server, used to provide backend data processing and system management services; The smart APP, the self-service food vending machine and the delivery robot are respectively connected to the backend server for communication.

[0006] Preferably, the smart APP is also used to: Scan the ordering QR code on the self-service meal vending machine, access and log in to the backend management system of the self-service meal vending machine, browse the menu information and order meals: meal types, prices and corresponding nutritional ingredients; as well as, Scan the payment QR code generated by the backend management system based on the menu information selected by the user to order and pay; as well as, Log in and view the ordering progress information in the backend management system: the preparation progress of the meal to be delivered and / or the delivery progress of the meal to be delivered.

[0007] Preferably, the smart APP is also used to: Scan the QR code on the self-service food vending machine / delivery robot to verify the order information: When the backend successfully verifies the order information, a pick-up notification is issued; Otherwise, an alarm notification is issued.

[0008] Preferably, the backend server is further used for: Regularly send health survey notifications to users' smart apps to collect user health data, including: physical symptoms, allergy history, disease conditions, and dietary preferences; The machine learning algorithm model is used to analyze the data features in the user's health data, and the recommended meals corresponding to the data features are output to the smart APP.

[0009] Preferably, the self-service food vending machine includes: Processors, used to provide data processing services; Refrigeration / heating module, used to provide storage and heating services for cold / hot meals; An intelligent production module, configured to automatically prepare the meal to be delivered according to the meal configuration instructions, and to record and upload the production progress in real time; A delivery module, configured to send a delivery notification to the delivery robot when the production progress is completed; Interactive interface, used to provide touch screen interactive services, including: touch screen input, menu, ordering, progress and / or QR code display; Backend management system, used to provide menu management, order information management, payment management, progress management, delivery management services, food inventory services and data analysis services; A data communication module, used to provide data communication services with the backend server; Power supply, used for power supply; The refrigeration / heating module, intelligent production module, distribution module, interactive interface, background management system, data communication module and power supply are electrically connected to the processor respectively.

[0010] On the other hand, a method for selling mobile self-service meals in a hospital outpatient area is provided, which is implemented based on the above-mentioned mobile self-service meal selling system in a hospital outpatient area. The method comprises: Users use the smart app to scan the QR code on the self-service food vending machine, browse the menu information and place an order, and the self-service food vending machine generates the order information; The backend management system of the self-service food vending machine generates and displays a payment QR code based on the menu information selected by the user in the order information; The user scans the payment QR code through the smart app to order and pay; The backend management system verifies whether the payment is completed: If so, the meal order information is parsed and corresponding meal configuration instructions are generated, and the meal configuration instructions are pushed to the intelligent production module; Otherwise, a payment failure warning message is sent to the smart APP. If the failure warning exceeds the preset number of times, the order information is cancelled; The intelligent production module automatically prepares the meal to be delivered according to the meal configuration instructions, and records and uploads the production progress to the backend management system in real time; when the production is completed, the delivery module sends a delivery notification to the delivery robot; The delivery robot takes out the food to be delivered and delivers the food to the user's location according to the user's location information in the notification, and sends feedback to the self-service food vending machine after the delivery is completed; When the food is delivered, the user scans the QR code on the self-service food vending machine / delivery robot through the smart app to verify the order information: When the backend successfully verifies the order information, a pick-up notification is issued; Otherwise, an alarm notification is issued.

[0011] On the other hand, an electronic device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned movable self-service meal selling method in the hospital outpatient area is implemented.

[0012] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned movable self-service meal selling method in the outpatient area of ​​a hospital. The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: The mobile self-service meal vending system for hospital outpatient areas provided by this invention allows patients and their families to leave the outpatient area to eat while they wait in line for their appointments. This addresses the issues of cafeteria crowding and long queues during peak hours. It provides a fast, convenient, and hygienic meal solution, alleviating peak-hour pressure in hospital cafeterias, reducing labor costs, and improving service efficiency. It also offers healthy and safe meal options.

[0013] In addition, with mobile self-service meal vending machines and intelligent delivery systems, hospitals can provide outpatients and staff with efficient and convenient dining solutions, while improving the hospital's service level and patient satisfaction.

[0014] Users enjoy a convenient and hygienic dining experience, reducing waiting times. They can choose healthy meals through personalized recommendations. Hospitals: Alleviate cafeteria pressure during peak hours and improve service quality. Reduce labor costs and improve operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a structural diagram of a movable self-service food vending system for a hospital outpatient area provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of the hardware system structure of a self-service food vending machine provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a meal recommendation process provided by an embodiment of the present invention; Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0018] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0019] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0020] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0021] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0022] The embodiment of the present invention provides a movable self-service food vending system and method in the outpatient area of ​​a hospital. The method can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The architecture diagram of the mobile self-service food vending system in the outpatient area of ​​the hospital shown in the figure includes: A smart app that recommends meals based on the user's health data and sends meal order information to a self-service meal vending machine, where the order information includes the user's location information; A self-service food vending machine, configured to parse the order information and generate corresponding meal configuration instructions, automatically prepare the meal to be delivered according to the instructions, and issue a delivery notification to the delivery robot; a delivery robot, configured to interact with the self-service food vending machine, retrieve the food to be delivered and deliver it to the user's location based on the user's location information in the notification, and send feedback to the self-service food vending machine after delivery is completed; Backend server, used to provide backend data processing and system management services; The smart APP, the self-service food vending machine and the delivery robot are respectively connected to the backend server for communication.

[0023] Patients or their families can log in to the self-service meal vending machine's backend management system through a smart app, where they can order, pay, check progress, verify their identity, and receive meal recommendations. The backend records the patient's health data and historical dining history, using AI algorithms to analyze the patient's health status and recommend appropriate meals.

[0024] Delivery robots are mainly used for food delivery services. They can adopt the delivery robots used in the existing service industry, such as hotel delivery service robots. These are conventional technologies, so they will not be elaborated here.

[0025] The self-service food vending machine can automatically prepare meals according to the order information and report the production progress at the same time. When the production is completed, it will notify the robot to deliver the food to the address.

[0026] like Figure 2 As shown, preferably, the self-service food vending machine includes: Processors, used to provide data processing services; Refrigeration / heating module, used to provide storage and heating services for cold / hot meals; An intelligent production module, configured to automatically prepare the meal to be delivered according to the meal configuration instructions, and to record and upload the production progress in real time; A delivery module, configured to send a delivery notification to the delivery robot when the production progress is completed; Interactive interface, used to provide touch screen interactive services, including: touch screen input, menu, ordering, progress and / or QR code display; Backend management system, used to provide menu management, order information management, payment management, progress management, delivery management services, food inventory services and data analysis services; A data communication module, used to provide data communication services with the backend server; Power supply, used for power supply; The refrigeration / heating module, intelligent production module, distribution module, interactive interface, background management system, data communication module and power supply are electrically connected to the processor respectively.

[0027] The above-mentioned refrigeration and heating modules can refer to the hardware composition of existing catering refrigeration or heating modules, such as using semiconductor cooling or resistance wire heating. Other hardware can be configured by the user and refer to the following description.

[0028] Specific hardware design: 1. Smart app features are as follows: Menu browsing: Displays meal types, prices, and nutritional information. Scan-to-order: Users scan the QR code on the vending machine, select their meal, and pay. Order tracking: Displays real-time progress of meal preparation and delivery. Scan-to-pickup: Users scan the QR code on the delivery robot or vending machine to pick up their meal. Personalized recommendations: Recommended meals based on the user's health data (such as allergies and dietary preferences).

[0029] Smart Apps: Develop cross-platform apps using mobile development frameworks (such as React Native and Flutter). Integrate payment gateways (such as WeChat Pay and Alipay). Backend Systems: Deploy backend services using cloud computing platforms (such as AWS and Alibaba Cloud). Use databases (such as MySQL and MongoDB) to store order and user data.

[0030] Smart APP: The simple and intuitive interface allows users to quickly place orders. It provides pictures, prices, nutritional information, and other information about meals.

[0031] 2. Delivery Robot: Navigation System: Based on hospital maps and positioning technology, it enables precise navigation. Storage Compartment: Can accommodate multiple meals and supports heat preservation. Interactive Function: Supports voice prompts and QR code scanning to pick up meals.

[0032] Delivery robots: Use SLAM (Simultaneous Localization and Mapping) technology for precise navigation. Equipped with RFID or QR code recognition technology, they enable precise food delivery.

[0033] 3. Self-service food vending machine: Appearance design: Small and light, can be placed in the corner or corridor of the outpatient area.

[0034] The hardware functional modules are as follows: (1) Refrigeration / heating module: supports storage and heating of cold and hot meals.

[0035] (2) Intelligent production module: supports the automatic production of simple meals (such as sandwiches, salads, bento, etc.), such as the "improved automated food production equipment" mentioned in the public technology CN114747930A.

[0036] (3) Delivery module: connects with the delivery robot to realize automatic delivery of meals.

[0037] (4) Interactive interface: Equipped with a touch screen to display menu, price, meal pickup code and other information.

[0038] (5) Backend management system: Order management: Receive and process user orders and dispatch delivery robots. Inventory management: Monitor food inventory and replenish in a timely manner. Data analysis: Analyze user order data and optimize menus and operational strategies.

[0039] Other hardware such as processors, power supplies, and data communication modules can be configured in combination with existing vending machines and the connection solutions between the vending machines and the backend, such as using the Internet of Things to communicate with the backend.

[0040] Vending machines: Use Internet of Things (IoT) technology to enable real-time communication between vending machines and backend systems. Equipped with sensors to monitor food inventory and machine status.

[0041] The touch screen interface of the vending machine can also support voice prompts to facilitate operation by elderly users.

[0042] Ordering process: After the user scans the QR code, they will be automatically redirected to the order page. After successful payment, the pickup code and estimated pickup time will be displayed.

[0043] Meal pickup process: After the user scans the QR code, the delivery robot automatically opens the storage compartment.

[0044] The data analysis services of the backend management system are as follows: (1) Meal selection: Provide healthy and convenient meals (such as sandwiches, salads, and bento). Update the menu regularly to meet the diverse needs of users.

[0045] (2) Promotional activities: launch package discounts, points redemption and other activities to attract users.

[0046] (3) Maintenance management and equipment maintenance: Regularly check the operating status of vending machines and delivery robots. Replenish food inventory and clean equipment in a timely manner.

[0047] (4) User feedback: Collect user feedback and optimize meal types and service processes.

[0048] Preferably, the smart APP is also used to: Scan the ordering QR code on the self-service meal vending machine, access and log in to the backend management system of the self-service meal vending machine, browse the menu information and order meals: meal types, prices and corresponding nutritional ingredients; as well as, Scan the payment QR code generated by the backend management system based on the menu information selected by the user to order and pay; as well as, Log in and view the ordering progress information in the backend management system: the preparation progress of the meal to be delivered and / or the delivery progress of the meal to be delivered.

[0049] like Figure 3 As shown, preferably, the smart APP is also used to: Scan the QR code on the self-service food vending machine / delivery robot to verify the order information: When the backend successfully verifies the order information, a pick-up notification is issued; Otherwise, an alarm notification is issued.

[0050] The system background will track the progress of ordering and preparing meals and send real-time notifications to the smart app. Notifications can be sent through message subscription.

[0051] Workflow: 1. User places an order: The user opens the smart APP and scans the QR code on the vending machine.

[0052] Browse the menu, choose your meal and pay.

[0053] 2. Meal preparation: The vending machine receives orders and automatically prepares meals.

[0054] 3. Delivery: The delivery robot goes to the vending machine to pick up the food and delivers it to the user's designated location (such as the outpatient waiting area).

[0055] 4. User picks up their meal: The user receives a pick-up notification and scans the QR code on the delivery robot or vending machine to pick up the food.

[0056] Preferably, the backend server is further used for: Regularly send health survey notifications to users' smart apps to collect user health data, including: physical symptoms, allergy history, disease conditions, and dietary preferences; The machine learning algorithm model is used to analyze the data features in the user's health data, and the recommended meals corresponding to the data features are output to the smart APP.

[0057] The backend system regularly pushes health survey notifications to the user's smart app, collects user health data, and generates personalized meal recommendations after analyzing it through machine learning models. For details, please refer to the following technical implementation plan: 1. Core functions: Regular notifications: The backend server pushes health survey notifications to the user app once a week (frequency is configurable).

[0058] Data collection: User health data is collected through APP questionnaires, including: physical symptoms (such as headaches, fatigue, supporting multiple options or text input), allergy history (such as pollen, seafood, supporting multiple selection lists), disease conditions (such as diabetes, high blood pressure, supporting single selection or drop-down menus), and dietary preferences (such as vegetarian, low-carb, gluten-free, supporting multiple selections).

[0059] Machine learning analysis: Using historical data and real-time input, the model is trained to output meal recommendations (e.g., recommended recipes, nutritional information). Personalized meal recommendations are pushed to the app for display.

[0060] Non-functional requirements: Security: User data is encrypted, transmitted, and stored, and anonymized.

[0061] Performance: Response time < 2 seconds (model prediction phase), notification push latency < 5 minutes.

[0062] Availability: Supports 10,000+ users and can be expanded to high concurrency.

[0063] Compliance: Users are required to sign an informed consent form when the APP is first launched.

[0064] 2. System Design (Architecture and Components) Adopting a microservice architecture based on a cloud platform (AWS or Google Cloud is recommended) to ensure elastic scaling. The overall architecture is divided into three layers: Client (smart APP): Android / iOS native development, integrated push notifications and UI.

[0065] Server-side (backend server): handles notification scheduling, data storage, and machine learning.

[0066] Data layer and machine learning: Databases store data and models.

[0067] The detailed technical component design is as follows: Smart apps can use Firebase Cloud Messaging (FCM) for push notifications. The app receives the notification and displays a health survey. Users fill in their data and submit it to the server via the API. Notifications are triggered weekly, and users click to open the survey. Data can be saved offline (in local SQLite) and synchronized when the network is restored.

[0068] The backend server can use the framework: Python Flask or Node.js, and the scheduling service can use: Apache Airflow or AWS Lambda. Core services: 1. Notification Scheduler: Use a Cron job or AWS CloudWatch Events to trigger push notifications at a scheduled time (e.g., Monday at 9:00 AM). Personalize push notifications based on the user's time zone.

[0069] 2. API Gateway: RESTful API Design: - POST / survey / submit: Submit health data.

[0070] - GET / recommendation: Get meal recommendations.

[0071] 3. Data receiver: validates the data format (JSON schema) and filters invalid input.

[0072] The backend can use MongoDB as the database to store user health data (associated with anonymous IDs). Partition design: User table: user ID, time zone, survey history; health data table: symptoms, allergies, disease conditions, dietary preferences (stored as structured JSON).

[0073] Data retention policy: Automatically delete data older than 180 days (privacy compliance).

[0074] Machine learning system, using AWS SageMaker or Google AI Platform.

[0075] Machine Learning Process: 1) Data preprocessing: Input health data → Feature engineering (e.g., one-hot encoding of allergy history, text vectorization of symptoms).

[0076] 2). Model selection: Classification model: recommend recipe categories (e.g., low-salt recipes), using random forest (which handles multi-class features).

[0077] Collaborative filtering: Generate personalized recommendations (suitable for dietary preferences) based on similar user preferences.

[0078] 3) Training phase: Use public datasets (such as USDA FoodData or Kaggle health data) to pre-train the model, and the model output is a meal ID or nutritional label (e.g., calories < 500 calories).

[0079] 4) Inference phase: The model is called in real time (deployed as an API), user data features are input, and a list of recommended meals is output (JSON format, including recipe links and nutritional details).

[0080] 4. Recommendation output and integration: Recommend to smart apps via APIs; or subscribe to messages via message queues such as Kafka or AWS SQS.

[0081] After the server generates recommendations, it pushes them back to the app via FCM / APNS, where they are displayed as cards (e.g., meal images and details). User feedback (likes / dislikes) is supported for model iteration.

[0082] The machine learning model is trained and deployed as follows: (1) Data preparation: Use a public dataset to train the initial model (e.g., Kaggle's dietary health data).

[0083] (2) Model training: Scikit-learn example code: from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier() model.fit(X_train, y_train) # X_train: feature vector, y_train: meal label (3) Output: Meal recommendation ID (classification) (4) Feature mapping: Based on the input features, output recommendations (e.g., allergy history = "seafood" → recommend high-protein non-seafood meals).

[0084] (5) Deploy the model: TensorFlow Serving or AWS SageMaker hosting, exposing the API endpoint (POST / predict).

[0085] Step 5: Integration and Recommendation Output (Weeks 9-12) The server calls the model API and outputs the recommendation format: {recommendations: [{"id": 1, "name": "Vegetarian Salad", "calories": 350}, ...]}.

[0086] App display: ListView component rendering is recommended.

[0087] (5) Testing Testing strategy: Unit testing: PyTest tests API logic; Flutter Test is used for APP.

[0088] Integration testing: Postman tests the API link and simulates high-concurrency data submission (Load Testing with JMeter).

[0089] Model testing: validation set accuracy > 85%, ROC curve evaluation (using confusion matrix).

[0090] (6) Deployment Development environment: Docker container local testing.

[0091] Production environment: Kubernetes cluster management cloud deployment. Apps published to Google Play / App Store.

[0092] Monitoring: Prometheus / Grafana monitors API latency; Datadog records error logs.

[0093] (7) Operation, maintenance and optimization Regular maintenance: Server: Back up the database weekly and retrain the model monthly (with new data input).

[0094] APP: Update questionnaire content (feedback driven).

[0095] (8) Output example User APP interface: Notification pop-up: "This week's health survey: Take part and get personalized diet recommendations!" Recommended Display: "Based on your preferences and symptoms, try this low-calorie vegetarian platter! Learn more" Server logs: INFO: User anon123 submitted survey. Recommendation: meal_id=45 sent.

[0096] 5. Data flow timing diagram User app --1. Notification trigger ---> backend server (FCM / APNS push); User fills in data --2. Submits data ---> Server (POST / survey / submit); Server--3. Storage and pre-processing -> MongoDB; Server--4. Call the model--->ML engine (input feature vector); ML Engine --5. Output recommendations ---> Server (JSON recommendation list); Server--6. Push result---> User app (FCM / APNS notification display).

[0097] On the other hand, a method for selling mobile self-service meals in a hospital outpatient area is provided, which is implemented based on the above-mentioned mobile self-service meal selling system in a hospital outpatient area. The method comprises: Users use the smart app to scan the QR code on the self-service food vending machine, browse the menu information and place an order, and the self-service food vending machine generates the order information; The backend management system of the self-service food vending machine generates and displays a payment QR code based on the menu information selected by the user in the order information; The user scans the payment QR code through the smart app to order and pay; The backend management system verifies whether the payment is completed: If so, the meal order information is parsed and corresponding meal configuration instructions are generated, and the meal configuration instructions are pushed to the intelligent production module; Otherwise, a payment failure warning message is sent to the smart APP. If the failure warning exceeds the preset number of times, the order information is cancelled; The intelligent production module automatically prepares the meal to be delivered according to the meal configuration instructions, and records and uploads the production progress to the backend management system in real time; when the production is completed, the delivery module sends a delivery notification to the delivery robot; The delivery robot takes out the food to be delivered and delivers the food to the user's location according to the user's location information in the notification, and sends feedback to the self-service food vending machine after the delivery is completed; When the food is delivered, the user scans the QR code on the self-service food vending machine / delivery robot through the smart app to verify the order information: When the backend successfully verifies the order information, a pick-up notification is issued; Otherwise, an alarm notification is issued.

[0098] Please understand the above method in conjunction with the previous system description.

[0099] Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, optionally, the electronic device 410 may include a first processor 2001 .

[0100] Optionally, the electronic device 410 may further include a memory 2002 and a transceiver 2003 .

[0101] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0102] The following combination Figure 4 The components of the electronic device 410 are described in detail. The first processor 2001 is the control center of the electronic device 410 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0103] Optionally, the first processor 2001 can execute various functions of the electronic device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0104] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0105] In a specific implementation, as an embodiment, the electronic device 410 may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0106] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0107] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0108] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0109] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0110] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0111] It should be noted that Figure 4 The structure of the electronic device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0112] In addition, the technical effects of the electronic device 410 can refer to the technical effects of the movable self-service food vending system and method in the hospital outpatient area described in the above method embodiment, and will not be repeated here.

[0113] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0114] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0115] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0116] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0117] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0118] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0119] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0122] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0124] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0125] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A movable self-service food vending system for hospital outpatient areas, characterized by: The system comprises: A smart app that recommends meals based on the user's health data and sends meal order information to a self-service meal vending machine, where the order information includes the user's location information; A self-service food vending machine, configured to parse the order information and generate corresponding meal configuration instructions, automatically prepare the meal to be delivered according to the instructions, and issue a delivery notification to the delivery robot; a delivery robot, configured to interact with the self-service food vending machine, retrieve the food to be delivered and deliver it to the user's location based on the user's location information in the notification, and send feedback to the self-service food vending machine after delivery is completed; Backend server, used to provide backend data processing and system management services; The smart APP, the self-service food vending machine and the delivery robot are respectively connected to the backend server for communication.

2. The movable self-service food vending system for hospital outpatient areas according to claim 1 is characterized in that: The smart APP is also used to: Scan the ordering QR code on the self-service meal vending machine, access and log in to the backend management system of the self-service meal vending machine, browse the menu information and order meals: meal types, prices and corresponding nutritional ingredients; as well as, Scan the payment QR code generated by the backend management system based on the menu information selected by the user to order and pay; as well as, Log in and view the ordering progress information in the backend management system: the preparation progress of the meal to be delivered and / or the delivery progress of the meal to be delivered.

3. The movable self-service food vending system for hospital outpatient areas according to claim 1 is characterized in that: The smart APP is also used to: Scan the QR code on the self-service food vending machine / delivery robot to verify the order information: When the backend successfully verifies the order information, a pick-up notification is issued; Otherwise, an alarm notification is issued.

4. The movable self-service food vending system for hospital outpatient areas according to claim 1 is characterized in that: The backend server is further used for: Regularly send health survey notifications to users' smart apps to collect user health data, including: physical symptoms, allergy history, disease conditions, and dietary preferences; The machine learning algorithm model is used to analyze the data features in the user's health data, and the recommended meals corresponding to the data features are output to the smart APP.

5. The movable self-service food vending system for hospital outpatient areas according to claim 1 is characterized in that: The self-service food vending machine comprises: Processors, used to provide data processing services; Refrigeration / heating module, used to provide storage and heating services for cold / hot meals; An intelligent production module, configured to automatically prepare the meal to be delivered according to the meal configuration instructions, and to record and upload the production progress in real time; A delivery module, configured to send a delivery notification to the delivery robot when the production progress is completed; Interactive interface, used to provide touch screen interactive services, including: touch screen input, menu, ordering, progress and / or QR code display; Backend management system, used to provide menu management, order information management, payment management, progress management, delivery management services, food inventory services and data analysis services; A data communication module, used to provide data communication services with the backend server; Power supply, used for power supply; The refrigeration / heating module, intelligent production module, distribution module, interactive interface, background management system, data communication module and power supply are electrically connected to the processor respectively.

6. A method for selling mobile self-service meals in a hospital outpatient area, implemented based on the mobile self-service meal selling system in a hospital outpatient area according to any one of claims 1 to 5, characterized in that: The method comprises: Users use the smart app to scan the QR code on the self-service food vending machine, browse the menu information and place an order, and the self-service food vending machine generates the order information; The backend management system of the self-service food vending machine generates and displays a payment QR code based on the menu information selected by the user in the order information; The user scans the payment QR code through the smart app to order and pay; The backend management system verifies whether the payment is completed: If so, the meal order information is parsed and corresponding meal configuration instructions are generated, and the meal configuration instructions are pushed to the intelligent production module; Otherwise, a payment failure warning message is sent to the smart APP. If the failure warning exceeds the preset number of times, the order information is cancelled; The intelligent production module automatically prepares the meal to be delivered according to the meal configuration instructions, and records and uploads the production progress to the backend management system in real time; when the production is completed, the delivery module sends a delivery notification to the delivery robot; The delivery robot takes out the food to be delivered and delivers the food to the user's location according to the user's location information in the notification, and sends feedback to the self-service food vending machine after the delivery is completed; When the food is delivered, the user scans the QR code on the self-service food vending machine / delivery robot through the smart app to verify the order information: When the backend successfully verifies the order information, a pick-up notification is issued; Otherwise, an alarm notification is issued.

7. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to claim 6 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to claim 6.

Images