Making management method and device of soldable beverage and electronic equipment

Abstract

The invention discloses a manufacturing management method and device of a soldable beverage and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring a user request input by a user; according to the beverage type and the beverage quantity, calculating a corresponding required raw material quantity; the inventory of each raw material is obtained, and the size relation between the inventory and the required raw material amount is judged; if it is determined that the stock is larger than or equal to the required raw material amount, it is determined that the beverage can be made according to the user request; acquiring current environment data; the beverage type and the current environment data are integrated, beverage preparation parameters are generated, the preparation process for the beverage is started according to the beverage preparation parameters, and the beverage preparation parameters comprise the ice block amount of the ice beverage and the temperature of the hot beverage; and if it is determined that the preparation state of the preparation process is preparation completion, the beverage is pushed to a discharge port. By implementing the technical scheme provided by the invention, the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, device and electronic equipment for managing the production of salable beverages. Background Art

[0002] As modern consumers demand fast, convenient, and personalized beverage service, the beverage industry faces the challenge of providing efficient, reliable, and diverse beverage options. Consumers not only expect to receive their desired beverage quickly, but also want it customized to their personal preferences and environmental conditions. How to ensure service speed and quality while achieving efficient use of raw materials and environmental adaptability has become a key requirement for the industry's development.

[0003] Current beverage production technologies rely heavily on traditional manual labor or semi-automated equipment. While these methods can effectively meet consumer demand on a small scale or during off-peak hours, they are often inefficient when faced with large numbers of customized orders. Furthermore, these technologies often fail to monitor and adapt to environmental changes (such as temperature and humidity) in real time, impacting the final quality of the beverage and the consumer experience, resulting in a subpar user experience.

[0004] Therefore, there is an urgent need for a production management method, device and electronic equipment for selling beverages. Summary of the Invention

[0005] The present application provides a production and management method, device and electronic equipment for vendable beverages, which enhances the user experience.

[0006] In a first aspect of the present application, a method for managing the production of salable beverages is provided, the method comprising: obtaining a user request input by a user, the user request including a beverage and the beverage type and quantity corresponding to the beverage; calculating the corresponding required amount of raw materials based on the beverage type and the beverage quantity; obtaining the inventory of each of the raw materials, and judging the relationship between the inventory and the required amount of raw materials; if it is determined that the inventory is greater than or equal to the required amount of raw materials, determining that the beverage can be produced according to the user request; obtaining current environmental data, the current environmental data including temperature data and humidity data; generating beverage preparation parameters based on the beverage type and the current environmental data, and starting a preparation process for the beverage based on the beverage preparation parameters, the beverage preparation parameters including the amount of ice cubes for iced drinks and the temperature of hot drinks; if it is determined that the preparation status of the preparation process is completed, pushing the beverage to the discharge port.

[0007] By adopting the above technical solution, by obtaining the drink type and quantity requested by the user, calculating the corresponding amount of raw materials, and determining whether the raw material inventory is sufficient, the feasibility of drink preparation can be ensured, avoiding production failures or interruptions due to insufficient raw materials, thereby improving production efficiency and user experience. Furthermore, by obtaining current environmental data and generating drink preparation parameters based on the drink type and environmental data, drink preparation can be intelligently and personalized. Preparation parameters, such as the amount of ice for iced drinks and the temperature of hot drinks, can be dynamically adjusted according to different environmental conditions and drink characteristics to ensure that the taste and quality of the drink meet the user's expectations, enhancing the user experience. Furthermore, by real-time monitoring of the preparation status of the preparation process and automatically pushing the drink to the dispensing port after preparation is completed, drink preparation can be automated and convenient, reducing manual operation and waiting time, and improving production efficiency and service quality. This intelligent, personalized, and automated production management method can significantly enhance the user experience of self-service beverage machines.

[0008] Optionally, the beverage preparation parameters are generated by integrating the beverage type and the current environmental data, specifically including: determining corresponding basic preparation parameters according to the beverage type; inputting the temperature data and the humidity data into a preset preparation parameter adjustment model to obtain corresponding preparation parameter adjustment values; and superimposing the basic preparation parameters and the preparation parameter adjustment values ​​to obtain beverage preparation parameters for the current environment.

[0009] By adopting the above technical solution and introducing basic preparation parameters and preset preparation parameter adjustment models, it is possible to achieve refined and dynamic adjustment of beverage preparation parameters, and generate the optimal preparation parameter combination according to different beverage types and environmental conditions. Among them, the basic preparation parameters provide standardized preparation configurations for different types of beverages, such as the extraction temperature and time of coffee, the sugar content and ingredient ratio of milk tea, etc., to ensure the basic quality and taste of the beverages. The preparation parameter adjustment model uses a machine learning algorithm to learn the correlation between environmental factors and preparation parameters, and predicts the preparation parameter adjustment value based on real-time environmental data. By superimposing the basic preparation parameters with the adjustment values, the beverage preparation parameters can be obtained, so that under different environmental conditions, the best taste and quality beverages can always be provided to users, thereby improving the adaptability and intelligence level of the self-service beverage machine.

[0010] Optionally, the beverage preparation parameters also include a raw material addition sequence, and starting the preparation process for the beverage according to the beverage preparation parameters specifically includes: obtaining raw material inventory information, the raw material inventory information including each raw material and the raw material type, inventory quantity and remaining shelf life corresponding to each raw material; based on the raw material inventory information, calculating the priority of each target raw material of the same raw material type, the priority is negatively correlated with the remaining shelf life of the target raw material and positively correlated with the inventory quantity of the target raw material; based on the raw material addition sequence, selecting the final raw material corresponding to each raw material type in order of each priority from high to low; adding and mixing the final raw material in the raw material addition sequence, and starting the preparation process for the beverage.

[0011] By adopting the above technical solution and introducing a raw material priority and delivery sequence optimization mechanism, efficient and refined management of raw material use can be achieved, reducing raw material waste and shelf life risks, and improving cost-effectiveness. Specifically, by obtaining information such as the inventory level and shelf life of raw materials, and based on certain priority calculation rules, such as the shorter the shelf life and the larger the inventory level, the higher the priority, the urgency of the use of each raw material can be reasonably assessed, and the delivery sequence of raw materials can be optimized accordingly, giving priority to raw materials with close to expiration dates and large inventory levels. This dynamic raw material selection and sorting mechanism can minimize the loss of expired raw materials and inventory backlogs, and improve the turnover efficiency and utilization rate of raw materials.

[0012] Optionally, calculating the corresponding required amount of raw materials based on the beverage type and the quantity of beverages specifically includes: obtaining the unit beverage raw material ratio corresponding to the beverage type based on the beverage type; calculating the required amount of raw materials based on the unit beverage raw material ratio and the quantity of beverages, wherein the required amount of raw materials is the product of the unit beverage raw material ratio and the quantity of beverages.

[0013] By adopting this technical solution and introducing a mechanism for converting unit beverage ingredient ratios and quantities, standardized and scaled beverage production management can be achieved, ensuring precise ingredient ratios and dosage control for orders of varying quantities. This approach also simplifies the beverage production process and operations, improving production efficiency and order processing capabilities, particularly with large-volume or peak-time orders. This large-scale, standardized production management model helps improve the operational efficiency of self-service beverage machines.

[0014] Optionally, after obtaining the inventory quantity of each of the raw materials and determining the relationship between the inventory quantity and the required amount of raw materials, the method further includes: sending a raw material shortage prompt to the user to inform the user that the currently available beverages are limited; determining a list of available beverages based on the inventory quantity of each of the raw materials; and sending the list of available beverages to the user terminal so that the user terminal can display the list of available beverages.

[0015] By implementing the above technical solution and introducing low-ingredient reminders and a dynamic list of available drinks, the self-service beverage machine achieves intelligent and personalized service, enhancing user experience and interaction. This intelligent product management and information push mechanism effectively guides users in selecting appropriate drinks, avoiding the frustration of choosing unavailable drinks and improving user satisfaction. Furthermore, this user-friendly interaction enhances the interactivity and intelligence of the self-service beverage machine, providing users with a more attentive and personalized service experience, and improving brand image and loyalty. This service optimization mechanism is a key means of enhancing the core competitiveness of self-service beverage machines.

[0016] Optionally, before inputting the temperature data and the humidity data into the preset preparation parameter adjustment model to obtain the corresponding preparation parameter adjustment values, the method further includes: obtaining historical preparation data of each beverage type, the historical preparation data including ambient temperature, ambient humidity, beverage preparation parameters and user feedback information, and annotating the historical preparation data to obtain a training data set; dividing the training data set into a training set and a validation set; based on the training set, using a machine learning algorithm to train the initial preparation parameter adjustment model to obtain the preset preparation parameter adjustment model, the machine learning algorithm including but not limited to neural networks, support vector machines and random forests; based on the validation set, verifying the preset preparation parameter adjustment model, and adjusting the hyperparameters of the preset preparation parameter adjustment model according to the verification results until the performance of the preset preparation parameter adjustment model reaches the preset standard.

[0017] By adopting the above technical solution and introducing a preset preparation parameter adjustment model training and optimization mechanism based on machine learning, the intelligent and adaptive adjustment of the preparation parameters of self-service beverage machines can be achieved, the quality and taste of beverage production can be continuously improved, and it can adapt to different environments and user needs.

[0018] Optionally, if it is determined that the preparation status of the preparation process is completed, after pushing the beverage to the discharge port, the method also includes: obtaining the user's material collection feedback information; if the material collection feedback information is not obtained within the preset time threshold, controlling the discharge device to retract the beverage that has been pushed to the discharge port; if the material collection feedback information is obtained within the preset time threshold, recording the end of the current sales process, and updating the inventory of each raw material based on the current sales process.

[0019] By adopting the above technical solutions, and introducing user feedback detection and intelligent inventory update mechanisms, we can achieve closed-loop management and automated control of the self-service beverage machine sales process, improving operational efficiency and resource utilization. This automated, intelligent, closed-loop sales process management model can significantly enhance the operational management and service support capabilities of self-service beverage machines.

[0020] According to a second aspect of the present application, a production management device for vendable beverages is provided, the device comprising: an acquisition module and a processing module, wherein: the acquisition module is configured to acquire a user request input by a user, the user request including a beverage and the beverage type and quantity corresponding to the beverage; the processing module is configured to calculate the corresponding required amount of raw materials based on the beverage type and the beverage quantity; the acquisition module is further configured to acquire the inventory of each of the raw materials and determine the relationship between the inventory and the required amount of raw materials; the processing module is further configured to determine that the beverage can be produced according to the user request if it is determined that the inventory is greater than or equal to the required amount of raw materials; the acquisition module is further configured to acquire current environmental data, the current environmental data including temperature data and humidity data; the processing module is further configured to generate beverage preparation parameters based on the beverage type and the current environmental data, and initiate a preparation process for the beverage based on the beverage preparation parameters, the beverage preparation parameters including the amount of ice cubes for iced drinks and the temperature of hot drinks; and the processing module is further configured to push the beverage to a discharge port if it is determined that the preparation process is in a preparation state of production completion.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the drink type and quantity requested by the user, calculating the corresponding amount of raw materials required, and determining whether the raw material inventory is sufficient, the feasibility of drink production can be ensured, avoiding production failures or interruptions due to insufficient raw materials, thereby improving production efficiency and user experience. At the same time, by obtaining current environmental data and generating drink preparation parameters based on the drink type and environmental data, drink production can be intelligently and personalized. According to different environmental conditions and drink characteristics, preparation parameters such as the amount of ice for iced drinks and the temperature of hot drinks can be dynamically adjusted to ensure that the taste and quality of the drink meet the user's expectations, thereby enhancing the user experience. Furthermore, by real-time monitoring of the preparation status of the preparation process and automatically pushing the drink to the dispensing port after production is completed, drink production can be automated and convenient, reducing manual operation and waiting time, and improving production efficiency and service quality. This intelligent, personalized, and automated production management method can significantly enhance the user experience of self-service beverage machines.

[0024] 2. By introducing basic preparation parameters and preset preparation parameter adjustment models, it is possible to achieve refined and dynamic adjustment of beverage preparation parameters, and generate the optimal preparation parameter combination according to different beverage types and environmental conditions. Among them, the basic preparation parameters provide standardized preparation configurations for different types of beverages, such as the extraction temperature and time of coffee, the sugar content and ingredient ratio of milk tea, etc., to ensure the basic quality and taste of the beverages. The preparation parameter adjustment model uses a machine learning algorithm to learn the relationship between environmental factors and preparation parameters, and predicts the preparation parameter adjustment value based on real-time environmental data. By superimposing the basic preparation parameters with the adjustment values, the beverage preparation parameters can be obtained, so that under different environmental conditions, the best taste and quality beverages can always be provided to users, thereby improving the adaptability and intelligence level of the self-service beverage machine.

[0025] 3. By introducing a raw material priority and delivery sequence optimization mechanism, efficient and refined management of raw material use can be achieved, reducing raw material waste and shelf life risks, and improving cost-effectiveness. Specifically, by obtaining information such as raw material inventory and shelf life, and based on certain priority calculation rules (e.g., the shorter the shelf life and the larger the inventory, the higher the priority), the urgency of each raw material can be reasonably assessed, and the delivery sequence of raw materials can be optimized accordingly, giving priority to raw materials with near expiration dates and large inventory levels. This dynamic raw material selection and sorting mechanism can minimize raw material expiration losses and inventory backlogs, and improve raw material turnover efficiency and utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1This is a flow chart of a method for managing the production of a drink available for sale disclosed in an embodiment of the present application; Figure 2 This is a module diagram of a production and management device for saleable beverages disclosed in an embodiment of the present application; Figure 3 This is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0027] Description of the accompanying drawings: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0029] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0030] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0031] This application provides a method for managing the production of beverages that can be sold. Figure 1 , Figure 1 This is a flow chart of a method for managing the production of beverages for sale provided in an embodiment of the present application. The method is applied to a self-service beverage machine. The method includes steps S101 to S107, which are as follows: Step S101: obtaining a user request input by a user, wherein the user request includes a drink and a drink type and a drink quantity corresponding to the drink.

[0032] In step S101, a user accesses the user interface of the beverage preparation management system running on the self-service beverage machine via a terminal device such as a mobile phone, tablet computer, or self-service ordering machine. The user interface displays available beverages, such as coffee, milk tea, and juice, using images, text, and videos. It also provides detailed descriptions of each beverage and diagrams for its preparation, making it easy for the user to understand and select. After browsing the beverage categories, the user clicks on a specific drink image or name to access the customization page. On this customization page, the user can select attributes such as flavor, temperature, and sweetness based on their taste preferences, and adjust the desired quantity using the numeric input box or the "+" and "-" buttons. After completing the beverage customization, the user clicks the Confirm Order button to submit the customized beverage request to the self-service beverage machine. Upon receiving the user's request, the self-service beverage machine parses the request data, extracting key information such as the beverage name, type, and quantity, and passes this information to subsequent processing modules for ingredient calculation and process control.

[0033] Step S102: Calculating the corresponding required amount of raw materials according to the beverage type and the beverage quantity.

[0034] In step S102, the corresponding required amount of raw materials is calculated based on the beverage type and the number of beverages, specifically including: obtaining the unit beverage raw material ratio corresponding to the beverage type based on the beverage type; calculating the required amount of raw materials based on the unit beverage raw material ratio and the number of beverages, wherein the required amount of raw materials is the product of the unit beverage raw material ratio and the number of beverages.

[0035] Specifically, the self-service beverage machine maintains a beverage ingredient ratio database, which stores standard ingredient ratios for various beverage types. For example, the standard ratio for a latte might be: 30ml of espresso, 150ml of milk, and 10g of sugar per serving. The self-service beverage machine extracts the beverage type information from the user request, such as "hot latte," and then uses this beverage type as a search criterion to query the beverage ingredient ratio database for the corresponding unit beverage ingredient ratio. After retrieving the unit beverage ingredient ratio, the self-service beverage machine extracts the number of drinks requested, such as "2 cups," and multiplies the amount of each ingredient in the unit ratio by the number of drinks to calculate the total amount of each ingredient required to make the requested drink. This calculation takes into account that different ingredients may have different units of measurement. For example, liquid ingredients are typically measured in milliliters (ml), while solid ingredients are typically measured in grams (g). The self-service beverage machine converts the calculated results based on the units of measurement for each ingredient recorded in the ratio database to ensure the accuracy of the ingredient amounts and ultimately calculate the required amount.

[0036] Step S103: obtaining the inventory quantity of each of the raw materials, and determining the relationship between the inventory quantity and the required raw material quantity.

[0037] In step S103, the self-service beverage machine integrates an intelligent inventory management system that tracks and records, in real time, the incoming and outgoing inventory of various ingredients, as well as current inventory information. Ingredient inventory data is typically stored in a dedicated ingredient inventory database, which shares and interacts with the beverage production management system. The self-service beverage machine passes the required quantity of each ingredient calculated in step S102 as a parameter to the inventory management system, requesting the current inventory data for the corresponding ingredient. Based on the received ingredient name and required quantity, the inventory management system queries the ingredient inventory database for the real-time inventory of each ingredient and returns the query result to the self-service beverage machine. After obtaining the real-time inventory data for each ingredient, the self-service beverage machine compares the inventory quantity with the required quantity calculated in step S102. This numerical comparison allows the self-service beverage machine to determine whether the current inventory is sufficient. If the inventory quantity of a particular ingredient is less than the required quantity, the self-service beverage machine generates an insufficient ingredient alarm. If the inventory quantity of all ingredients is greater than or equal to the required quantity, the self-service beverage machine determines that the current inventory is sufficient and can continue with the subsequent beverage production process.

[0038] After step S103, the method further includes: sending a raw material shortage prompt to the user to inform the user that the currently available drinks are limited; determining a list of available drinks based on the inventory of each of the raw materials; and sending the list of available drinks to the user terminal so that the user terminal can display the list of available drinks.

[0039] Specifically, when the self-service beverage machine detects insufficient inventory of certain ingredients in step S103, it immediately generates an insufficient ingredient notification message. This message includes the name of the insufficient ingredient, the current inventory level, and the required quantity, informing the user of the current shortage. The self-service beverage machine pushes the insufficient ingredient notification message to the user terminal in real time via a communication connection established with the user terminal. Upon receiving the message, the user terminal uses pop-up windows, audio prompts, vibrations, and other methods to alert the user of the drink restrictions caused by the insufficient ingredient. The self-service beverage machine recalculates the types and quantities of drinks currently available based on the inventory levels. Specifically, the self-service beverage machine iterates through all beverage recipes in the beverage ingredient ratio database. For each recipe, the self-service beverage machine compares the required quantity of each ingredient with the current inventory levels. If the inventory levels of all ingredients are greater than or equal to the recipe requirements, the drink can be prepared and the self-service beverage machine adds it to the list of available drinks. If the inventory levels of any ingredient are less than the recipe requirements, the drink cannot be prepared and the self-service beverage machine removes it from the list of available drinks. After calculating the available beverage list, the self-service beverage machine converts the list data into a format suitable for display on the user terminal and transmits the available beverage list data to the user terminal via a communication connection. After receiving the available beverage list data, the user terminal can browse the adjusted available beverage list to understand the current selection of beverages and select and place an order based on their taste preferences.

[0040] Step S104: If it is determined that the inventory amount is greater than or equal to the required amount of raw materials, it is determined that the beverage can be prepared according to the user request.

[0041] In step S104, the self-service beverage machine compares the required quantity of each ingredient required to prepare the user's requested drink in step S103 with its current inventory. If the inventory of each ingredient is greater than or equal to the required quantity, the self-service beverage machine marks the comparison result as sufficient. The self-service beverage machine checks the comparison results for all required ingredients. If all ingredients are marked as sufficient, the self-service beverage machine determines that the current inventory fully meets the requirements for preparing the drink requested by the user and can prepare the drink as requested.

[0042] Step S105: Acquire current environmental data, which includes temperature data and humidity data.

[0043] In step S105, multiple temperature and humidity sensors are deployed in the beverage preparation area to collect real-time environmental data. The temperature and humidity data collected by the sensors is uploaded to the self-service beverage dispenser in real time via a wired or wireless data transmission module. The self-service beverage dispenser also has an environmental data reception and analysis module deployed to receive environmental data from the data collection gateway.

[0044] Step S106: generating beverage preparation parameters based on the beverage type and the current environmental data, and starting a preparation process for the beverage according to the beverage preparation parameters, wherein the beverage preparation parameters include the amount of ice cubes for iced drinks and the temperature of hot drinks.

[0045] In step S106, the beverage type and the current environmental data are combined to generate beverage preparation parameters, specifically including: determining the corresponding basic preparation parameters according to the beverage type; inputting the temperature data and the humidity data into a preset preparation parameter adjustment model to obtain corresponding preparation parameter adjustment values; and superimposing the basic preparation parameters and the preparation parameter adjustment values ​​to obtain beverage preparation parameters for the current environment.

[0046] Specifically, the self-service beverage machine maintains a beverage preparation parameter database that stores basic preparation parameters for various beverage types, including ingredient ratios, brewing temperatures, brewing times, and ingredient placement order. These parameters are typically determined by beverage experts based on experience and experimentation, and represent the optimal parameter combination for making that type of beverage under standard environmental conditions. When a user selects a specific beverage type (such as a hot latte or iced Americano), the machine retrieves the corresponding basic preparation parameters from the beverage preparation parameter database. These parameters include the required ingredient amounts, equipment temperature settings, and brewing time. For iced drinks, this also includes the standard amount of ice required, and for hot drinks, the standard brewing temperature. After obtaining these basic preparation parameters, the machine inputs the current ambient temperature and humidity data into a preset preparation parameter adjustment model. This preset preparation parameter adjustment model is a machine learning-based algorithm trained using extensive historical data. The model takes ambient temperature and humidity data as input and outputs adjusted values ​​for various basic preparation parameters, such as temperature, time, and ice quantity. The preset preparation parameter adjustment model considers the impact of environmental factors on beverage taste and quality and dynamically calculates the necessary adjustments to basic preparation parameters under current environmental conditions. For example, in a high-temperature environment, the preset preparation parameter adjustment model might output a negative temperature adjustment value, indicating a need to lower the preparation temperature; in a humid environment, the model might output a positive time adjustment value, indicating a need to extend the preparation time. The self-service beverage machine combines the basic preparation parameters with the adjustment values ​​output by the preparation parameter adjustment model to determine the optimal beverage preparation parameters for the current environment. For example, if the basic preparation parameters for a hot latte are 75°C, and the adjustment model outputs a temperature adjustment value of -2°C, the final preparation temperature will be 73°C. The self-service beverage machine sends the generated beverage preparation parameters to the beverage preparation system, initiating the specific preparation process. Based on the received parameters, the preparation system automatically adjusts the temperature settings, timer duration, ice usage, and other factors of the preparation equipment to ensure optimal beverage preparation. After the beverage is prepared, the self-service beverage machine records the parameters and environmental data of the preparation process and uses them as new sample data to regularly retrain and optimize the preparation parameter adjustment model.

[0047] The beverage preparation parameters also include the order of raw material addition, and the preparation process for the beverage is started according to the beverage preparation parameters, specifically including: obtaining raw material inventory information, the raw material inventory information including each raw material and the raw material type, inventory and remaining shelf life corresponding to each raw material; based on the raw material inventory information, calculating the priority of each target raw material of the same raw material type, the priority is negatively correlated with the remaining shelf life of the target raw material and positively correlated with the inventory of the target raw material; based on the order of raw material addition, selecting the final raw material corresponding to each raw material type in order of each priority from high to low; adding and mixing the final raw material in the order of raw material addition, and starting the preparation process for the beverage.

[0048] Specifically, the self-service beverage machine tracks and records inventory information for various ingredients in real time, including ingredient name, type (such as coffee beans, milk, syrup), current inventory level, arrival time, and estimated expiration date. This information is stored in an ingredient inventory database. When generating beverage preparation parameters, the machine determines the required ingredients and their standard dosage based on the recipe. The machine also determines the optimal order for adding these ingredients to ensure the drink's layered texture and taste. For example, when making a latte, espresso is typically added first, followed by steamed milk, and finally, milk froth. For each ingredient to be added, the machine queries the inventory database for similar ingredients and calculates a priority score for each instance based on pre-set priority calculation rules. Priority calculations take into account both the remaining shelf life and inventory level of the ingredient: the shorter the remaining shelf life, the higher the priority; the larger the inventory level, the higher the priority. This is because ingredients nearing their expiration date should be used first to avoid waste due to expiration. However, if large inventories of ingredients are not used promptly, they can lead to inventory backlogs and excessive storage space. The self-service beverage machine ranks similar instances of each type of raw material according to preset priority calculation rules, and the instance with the highest priority is selected as the final delivery target for that type of raw material. In this way, the self-service beverage machine can meet the requirements of the beverage recipe while optimizing the combination of raw materials with the best inventory status, ensuring beverage quality while minimizing raw material waste and inventory pressure. After determining the final delivery instance of each type of raw material, the self-service beverage machine will place the corresponding raw materials into the production container according to the order of raw material delivery during the production process, and perform the necessary mixing, stirring, extraction and other operations to finally complete the beverage.

[0049] In one possible embodiment, before inputting the temperature data and the humidity data into the preset preparation parameter adjustment model to obtain the corresponding preparation parameter adjustment values, the method further includes: obtaining historical preparation data of each beverage type, the historical preparation data including ambient temperature, ambient humidity, beverage preparation parameters and user feedback information, and annotating the historical preparation data to obtain a training data set; dividing the training data set into a training set and a validation set; based on the training set, using a machine learning algorithm to train the initial preparation parameter adjustment model to obtain the preset preparation parameter adjustment model, the machine learning algorithm includes but is not limited to a neural network, a support vector machine and a random forest; based on the validation set, verifying the preset preparation parameter adjustment model, and adjusting the hyperparameters of the preset preparation parameter adjustment model according to the verification results until the performance of the preset preparation parameter adjustment model reaches the preset standard.

[0050] Specifically, the kiosk collects historical preparation data for each beverage type. This data is derived from actual production records over a period of time. Each record includes the ambient temperature and humidity during production, preparation parameters (such as temperature, time, and ice quantity), and user feedback (such as ratings and reviews). This data reflects the impact of different preparation parameter combinations on beverage quality and user satisfaction under different environmental conditions. The kiosk preprocesses and annotates the collected historical preparation data. Preprocessing includes data cleaning, format conversion, and feature extraction, aiming to convert the raw data into a format suitable for machine learning algorithm input. The annotation process assigns quality assessment labels (such as "good," "fair," and "poor") to each data entry based on user feedback to guide model training. The kiosk divides the annotated dataset into training and validation sets in a specific ratio (e.g., 8:2). The training set is used for model training and parameter optimization, while the validation set is used for model performance evaluation and hyperparameter adjustment. The kiosk selects one or more machine learning algorithms, such as neural networks, support vector machines, and random forests, to construct an initial preparation parameter adjustment model. The model inputs are ambient temperature and humidity, and its outputs are adjusted values ​​for various preparation parameters. The model's structure and hyperparameters (such as the number of hidden layers, number of neurons, and learning rate) can be initially set based on experience or heuristics. The kiosk uses the training set data to train the model for adjusting the initial preparation parameters. The training process employs a supervised learning approach, predicting the quality of the beverage based on the input environmental conditions and preparation parameters. The predictions are compared with the actual quality labels, and the prediction error is calculated. Using algorithms such as backpropagation, the model parameters are adjusted to minimize the prediction error. This process is repeated multiple times. The kiosk uses the validation set data to evaluate the performance of the trained model. Evaluation metrics can include accuracy, precision, recall, and F1 score. If the model's performance on the validation set does not meet the preset standards, the kiosk analyzes the reasons and attempts to adjust the model's hyperparameters, such as increasing the number of hidden layers and adjusting the learning rate. Training and validation are then repeated until the model performance meets the preset standards.

[0051] Step S107: If it is determined that the preparation status of the preparation process is completed, the beverage is pushed to the discharge port.

[0052] In step S107, after initiating the beverage preparation process in step S106, the self-service beverage machine monitors the operating status and key parameters of the preparation equipment in real time, such as temperature, pressure, flow rate, and time, to determine the progress of the preparation process and the beverage's production status. This status information is collected and reported by the equipment's sensors and controllers, and the self-service beverage machine obtains relevant data through its communication interface with the equipment. The self-service beverage machine has pre-set status judgment conditions and thresholds based on the standard steps and key nodes of the preparation process. For example, for a hot latte, the self-service beverage machine might set the following status judgment conditions: coffee grounds are added, water temperature reaches 95°C, extraction time reaches 25 seconds, milk froth temperature reaches 65°C, and milk froth time reaches 15 seconds. When all these conditions are met, the self-service beverage machine considers the hot latte to be ready. During the preparation process, the self-service beverage machine compares the status information reported by the equipment with the pre-set judgment conditions in real time and updates the beverage's production status based on the comparison results. For example, when the device reports that coffee grounds have been added, the self-service beverage machine updates the drink's preparation status to "Coffee grounds added complete"; when the device reports that the water temperature has reached 95°C, the self-service beverage machine updates the preparation status to "Water temperature reaches standard for extraction," and so on. When the self-service beverage machine detects that a drink's preparation status meets all preset completion conditions, it is deemed ready and can be delivered to the dispensing port.

[0053] After step S107, the method further includes: obtaining the user's material collection feedback information; if the material collection feedback information is not obtained within the preset time threshold, controlling the discharging device to retract the beverage that has been pushed to the discharging port; if the material collection feedback information is obtained within the preset time threshold, recording the end of the current sales process, and updating the inventory of each of the raw materials based on the current sales process.

[0054] Specifically, after the self-service beverage machine delivers a drink to the dispenser, it starts a timer to monitor user feedback on the drink's removal. This feedback can come from a variety of channels, such as the dispenser sensor, the confirmation button on the user's terminal, or inquiries from the voice interaction system. The self-service beverage machine selects the appropriate feedback channel based on the specific device configuration and interaction method. The self-service beverage machine has a preset time threshold to determine whether the user has promptly removed the drink. If the self-service beverage machine receives a user's removal confirmation signal within the preset time threshold (e.g., the dispenser sensor detects the drink has been removed, the user clicks the confirmation button, or the user responds with a voice message "taken"), the user is deemed to have successfully removed the drink, and the sales process can be completed normally. If the self-service beverage machine does not receive any removal feedback within the preset time threshold, it is assumed that the user may have forgotten to remove the drink or changed their mind. To prevent the drink from spoiling or occupying the dispenser, the self-service beverage machine controls the dispenser to retract the delivered drink.

[0055] Reference Figure 2 The present application also provides a production management device for saleable beverages, which is a self-service beverage machine. The self-service beverage machine includes an acquisition module 201 and a processing module 202, wherein: the acquisition module 201 is used to obtain a user request input by a user, the user request includes a beverage and the beverage type and beverage quantity corresponding to the beverage; the processing module 202 is used to calculate the corresponding required raw material quantity according to the beverage type and the beverage quantity; the acquisition module 201 is also used to obtain the inventory of each of the raw materials and determine the relationship between the inventory and the required raw material quantity; the processing module 202 is also used to, if confirmed, If the inventory quantity is greater than or equal to the required raw material quantity, it is determined that the drink can be prepared according to the user request; the acquisition module 201 is also used to obtain current environmental data, and the current environmental data includes temperature data and humidity data; the processing module 202 is also used to combine the beverage type and the current environmental data to generate beverage preparation parameters, and start the preparation process for the beverage according to the beverage preparation parameters, and the beverage preparation parameters include the amount of ice cubes for ice drinks and the temperature of hot drinks; the processing module 202 is also used to push the drink to the discharge port if it is determined that the preparation status of the preparation process is completed.

[0056] In one possible embodiment, the processing module 202 integrates the beverage type and the current environmental data to generate beverage preparation parameters, specifically including: the processing module 202 determines the corresponding basic preparation parameters according to the beverage type; the processing module 202 inputs the temperature data and the humidity data into a preset preparation parameter adjustment model to obtain corresponding preparation parameter adjustment values; the processing module 202 superimposes the basic preparation parameters with the preparation parameter adjustment values ​​to obtain beverage preparation parameters for the current environment.

[0057] In a possible embodiment, the beverage preparation parameters also include the order of raw material addition, and the processing module 202 starts the preparation process for the beverage according to the beverage preparation parameters, specifically including: the acquisition module 201 obtains the raw material inventory information, and the raw material inventory information includes each raw material and the raw material type, inventory and remaining shelf life corresponding to each raw material; the processing module 202 calculates the priority of each target raw material of the same raw material type based on the raw material inventory information, and the priority is negatively correlated with the remaining shelf life of the target raw material and positively correlated with the inventory of the target raw material; the processing module 202 selects the final raw material corresponding to each raw material type in order from high to low priority based on the order of raw material addition; the processing module 202 adds and mixes the final raw material in the order of raw material addition, and starts the preparation process for the beverage.

[0058] In one possible embodiment, the processing module 202 calculates the corresponding required amount of raw materials based on the beverage type and the quantity of beverages, specifically including: the processing module 202 obtains the unit beverage raw material ratio corresponding to the beverage type based on the beverage type; the processing module 202 calculates the required amount of raw materials based on the unit beverage raw material ratio and the quantity of beverages, wherein the required amount of raw materials is the product of the unit beverage raw material ratio and the quantity of beverages.

[0059] In one possible embodiment, after the acquisition module 201 acquires the inventory quantity of each of the raw materials and determines the relationship between the inventory quantity and the required amount of raw materials, the method further includes: the processing module 202 sends a raw material shortage prompt to the user to inform the user that the current saleable beverages are limited; the processing module 202 determines a list of saleable beverages based on the inventory quantity of each of the raw materials; the processing module 202 sends the list of saleable beverages to the user terminal so that the user terminal can display the list of saleable beverages.

[0060] In one possible embodiment, the processing module 202 inputs the temperature data and the humidity data into a preset preparation parameter adjustment model, and before obtaining the corresponding preparation parameter adjustment value, the method further includes: the acquisition module 201 obtains the historical preparation data of each beverage type, the historical preparation data including ambient temperature, ambient humidity, beverage preparation parameters and user feedback information, and labels the historical preparation data to obtain a training data set; the processing module 202 divides the training data set into a training set and a validation set; the processing module 202 uses a machine learning algorithm based on the training set to train the initial preparation parameter adjustment model to obtain the preset preparation parameter adjustment model, the machine learning algorithm includes but is not limited to a neural network, a support vector machine and a random forest; the processing module 202 verifies the preset preparation parameter adjustment model based on the validation set, and adjusts the hyperparameters of the preset preparation parameter adjustment model according to the verification result until the performance of the preset preparation parameter adjustment model reaches the preset standard.

[0061] In one possible embodiment, if the processing module 202 determines that the preparation status of the preparation process is completed, then after pushing the beverage to the discharge port, the method further includes: the acquisition module 201 acquires the user's material collection feedback information; if the processing module 202 does not acquire the material collection feedback information within a preset time threshold, then the discharge device is controlled to retract the beverage that has been pushed to the discharge port; if the processing module 202 acquires the material collection feedback information within the preset time threshold, then the end of the current sales process is recorded, and the inventory of each of the raw materials is updated based on the current sales process.

[0062] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0063] This application also provides an electronic device. Figure 3 , Figure 3 3. This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0064] The communication bus 302 is used to implement the connection and communication between these components.

[0065] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0066] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0067] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0068] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program for a method for managing the production of salable beverages.

[0069] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a method for producing and managing a drinkable beverage stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0070] The present application further provides a computer-readable storage medium storing instructions, which, when executed by one or more processors 301 , enable the electronic device 300 to perform one or more of the methods described in the above embodiments.

[0071] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only 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 system, 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 service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0073] Units described as separate components may or may not be physically separate, and 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.

[0074] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0075] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0076] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0077] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for managing the production of a drinkable beverage, characterized in that: The method comprises: Obtaining a user request input by a user, the user request including a beverage and a beverage type and a beverage quantity corresponding to the beverage; Calculating the corresponding required amount of raw materials according to the beverage type and the beverage quantity; Obtaining the inventory of each of the raw materials, and determining the relationship between the inventory and the required amount of the raw materials; If it is determined that the inventory amount is greater than or equal to the required amount of raw materials, it is determined that the drink can be prepared according to the user request; Acquiring current environmental data, wherein the current environmental data includes temperature data and humidity data; generating beverage preparation parameters based on the beverage type and the current environmental data, and initiating a preparation process for the beverage according to the beverage preparation parameters, wherein the beverage preparation parameters include the amount of ice cubes for iced drinks and the temperature of hot drinks; If it is determined that the preparation status of the preparation process is completed, the beverage is pushed to the discharge port.

2. The method according to claim 1, characterized in that The beverage preparation parameters are generated by integrating the beverage type and the current environment data, specifically including: Determining corresponding basic preparation parameters according to the beverage type; Inputting the temperature data and the humidity data into a preset preparation parameter adjustment model to obtain corresponding preparation parameter adjustment values; The basic preparation parameters are superimposed on the preparation parameter adjustment values ​​to obtain beverage preparation parameters for the current environment.

3. The method according to claim 1, characterized in that The beverage preparation parameters also include the order of adding raw materials. The starting of the preparation process for the beverage according to the beverage preparation parameters specifically includes: Obtaining raw material inventory information, the raw material inventory information including each raw material and the raw material type, inventory quantity, and remaining shelf life corresponding to each raw material; Calculating, based on the raw material inventory information, the priority of each target raw material of the same raw material type, wherein the priority is negatively correlated with the remaining shelf life of the target raw material and positively correlated with the inventory amount of the target raw material; Based on the order of the raw materials being put in, selecting the final raw materials corresponding to the respective raw material types in descending order of priority; The final raw materials are added and mixed according to the order in which the raw materials are added, and a preparation process for the beverage is started.

4. The method according to claim 1, wherein Calculating the corresponding required amount of raw materials according to the beverage type and the beverage quantity specifically includes: According to the beverage type, obtaining the unit beverage raw material ratio corresponding to the beverage type; The required amount of raw materials is calculated based on the unit beverage raw material ratio and the number of beverages, wherein the required amount of raw materials is the product of the unit beverage raw material ratio and the number of beverages.

5. The method according to claim 1, wherein After obtaining the inventory of each of the raw materials and determining the relationship between the inventory and the required amount of the raw materials, the method further includes: Sending a reminder of insufficient raw materials to the user to inform the user that the current beverages available for sale are limited; Determine a list of drinks available for sale based on the inventory of each of the raw materials; The list of available beverages is sent to a user terminal so that the user terminal can display the list of available beverages.

6. The method according to claim 2, characterized in that Before inputting the temperature data and the humidity data into a preset preparation parameter adjustment model to obtain corresponding preparation parameter adjustment values, the method further includes: Obtaining historical preparation data for each beverage type, the historical preparation data including ambient temperature, ambient humidity, beverage preparation parameters, and user feedback information, and annotating the historical preparation data to obtain a training data set; Dividing the training data set into a training set and a validation set; Based on the training set, the initial preparation parameter adjustment model is trained using a machine learning algorithm to obtain the preset preparation parameter adjustment model, the machine learning algorithm including but not limited to a neural network, a support vector machine, and a random forest; Based on the validation set, the preset preparation parameter adjustment model is verified, and the hyperparameters of the preset preparation parameter adjustment model are adjusted according to the validation results until the performance of the preset preparation parameter adjustment model reaches the preset standard.

7. The method according to claim 1, characterized in that If it is determined that the preparation status of the preparation process is completed, after pushing the beverage to the discharge port, the method further includes: Obtaining the user's feedback information on material collection; If the material taking feedback information is not obtained within the preset time threshold, the discharging device is controlled to retract the beverage that has been pushed to the discharging port; If the material collection feedback information is obtained within the preset time threshold, the end of the current sales process is recorded, and the inventory of each raw material is updated based on the current sales process.

8. A production and management device for saleable beverages, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire a user request input by a user, wherein the user request includes a drink and a drink type and a drink quantity corresponding to the drink; The processing module (202) is used to calculate the corresponding required amount of raw materials according to the beverage type and the beverage quantity; The acquisition module (201) is further used to acquire the inventory of each of the raw materials and determine the relationship between the inventory and the required raw material quantity; The processing module (202) is further configured to determine that the beverage can be prepared according to the user request if it is determined that the inventory amount is greater than or equal to the required amount of raw materials; The acquisition module (201) is further used to acquire current environmental data, wherein the current environmental data includes temperature data and humidity data; The processing module (202) is further configured to generate beverage preparation parameters based on the beverage type and the current environmental data, and to start a preparation process for the beverage according to the beverage preparation parameters, wherein the beverage preparation parameters include the amount of ice cubes for iced drinks and the temperature of hot drinks; The processing module (202) is further configured to push the beverage to a discharge port if it is determined that the preparation status of the preparation process is completed.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

Images