A robot-based beverage quality management method, device, and electronic equipment

Abstract

This application relates to a robot-based beverage quality management method, device, and electronic device, implemented through Agent AI. The method includes: acquiring the current quality parameters of the beverage upon completion of beverage preparation by the robot; acquiring the standard quality parameters corresponding to the beverage category; analyzing the causes of deviations between the current and standard quality parameters when discrepancies exist; generating control instructions based on the causes of deviations; and adjusting the preparation parameters of the beverage prepared by the robot according to the control instructions to adjust the quality parameters of the prepared beverage. This application can immediately take corrective measures when quality deviations occur, preventing substandard beverages from being served to users and ensuring a positive user experience with the chain brand's beverages.

Description

Technical Field

[0001] This application belongs to the field of computer science, and specifically relates to a robot-based beverage quality management method, device, and electronic device. Background Technology

[0002] One of the core competitive advantages of chain brands is ensuring the consistency of the quality of their beverages, meaning that customers can enjoy the same taste, temperature, and appearance of beverages no matter which store they visit.

[0003] Factors such as batch differences in bean varieties, equipment wear and tear, and changes in environmental temperature and humidity can all affect the quality of beverages. However, traditional beverage quality control methods rely on manual sampling, which is infrequent and has a narrow coverage, making it impossible to achieve real-time and comprehensive quality monitoring. This makes it impossible to take immediate corrective measures when quality deviations occur, resulting in a large number of substandard beverages being served to customers, which reduces customers' experience with chain brand beverages. Summary of the Invention

[0004] The purpose of this application is to provide a robot-based beverage quality management method, apparatus, and electronic device to overcome or at least partially solve the above-mentioned problems.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: A robot-based beverage quality management method, implemented through Agent AI, includes the following steps: When the robot finishes making the beverage, the current quality parameters of the beverage are obtained; Obtain the standard quality parameters corresponding to the beverage category; When there is a deviation between the current quality parameter and the standard quality parameter, the cause of the deviation is analyzed based on the current quality parameter and the standard quality parameter. Generate control instructions based on the causes of the deviation; The robot adjusts the production parameters when making the beverage according to the control instructions, so as to adjust the quality parameters of the beverage.

[0006] In one embodiment of this application, the current quality parameters include at least beverage concentration, beverage ingredients, output temperature, beverage appearance, extraction time, and beverage weight; the beverage appearance includes at least milk foam thickness, color, and latte art integrity; the standard quality parameters include at least standard concentration range, standard temperature range, standard output, standard extraction time, and corresponding allowable deviation range; and the production parameters include at least grind size, water temperature, and extraction time.

[0007] In one embodiment of this application, the robot includes a near-infrared spectrometer, a temperature sensor, an image recognition system, and a weight sensor; obtaining the current quality parameters of the beverage includes: The beverage concentration and composition were obtained using the near-infrared spectrometer. The serving temperature of the beverage is obtained through the temperature sensor; The appearance of the beverage is obtained through the image recognition system; The weight of the beverage is obtained using the weight sensor.

[0008] In one embodiment of this application, before obtaining the standard quality parameters corresponding to the beverage category, the method further includes: Obtain the store quality parameters of each store in the chain when making beverages; Determine standard quality parameters from the aforementioned store quality parameters; The standard quality parameters are stored in the quality standard database and synchronized to all chain stores across the network.

[0009] In one embodiment of this application, the method further includes: Obtain the store quality parameters of each chain store when making beverages within a preset time range; A quality analysis report is generated based on the store quality parameters. When a store with persistent deviations is identified based on the quality analysis report, a designated entity is notified to intervene in the store with persistent deviations.

[0010] In one embodiment of this application, after notifying a designated object to intervene in the stores with persistent deviations when the quality analysis report determines that a store with persistent deviations has been identified, the method further includes: If the number of stores with persistent deviations reaches a preset number of stores, obtain the production environment information of the robots making the beverage in the stores with persistent deviations; the production environment information includes at least temperature, humidity, and water hardness; Generate network-wide control instructions based on the aforementioned production environment information; The network-wide control command is sent to the robot in the store where the similarity to the production environment information reaches a preset similarity, so that the robot can adjust the production parameters when making the beverage according to the network-wide control command.

[0011] In one embodiment of this application, generating the control command based on the cause of the deviation includes: When the deviation is caused by insufficient grind of coffee beans, an adjustment command is generated to increase the grind fineness of the robot to re-process and inspect the coffee. When the cause of the deviation is abnormal water temperature control, a control command is generated to adjust the power of the robot's heater in order to remake and test it. When the cause of the deviation is an extraction time deviation, an adjustment command is generated to extend or shorten the extraction time of the robot to re-process and detect it.

[0012] A robot-based beverage quality management device, implemented through Agent AI, includes: The current quality parameter acquisition module is used to acquire the current quality parameters of the beverage when the robot finishes making the beverage; The standard quality parameter acquisition module is used to acquire the standard quality parameters corresponding to the beverage category of the beverage. The deviation cause analysis module is used to analyze the cause of the deviation based on the current quality parameter and the standard quality parameter when there is a deviation between the current quality parameter and the standard quality parameter. The control instruction generation module is used to generate control instructions based on the cause of the deviation; The quality parameter adjustment module is used to adjust the production parameters of the robot when making the beverage according to the control instructions, so as to adjust the quality parameters of the beverage.

[0013] An electronic device includes: a processor; and a memory for storing processor-executable instructions. The processor is configured to execute the instructions to implement the aforementioned robot-based beverage quality management method.

[0014] A computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the above-described robot-based beverage quality management method.

[0015] The embodiments of this application have at least the following beneficial effects: In this embodiment, when the robot finishes making the beverage, it acquires the current quality parameters of the beverage and the standard quality parameters corresponding to the beverage category. When there is a deviation between the current quality parameters and the standard quality parameters, it analyzes the cause of the deviation based on the current quality parameters and the standard quality parameters, and generates control instructions based on the cause of the deviation to adjust the production parameters of the robot when making the beverage. In this way, the quality parameters of the beverage can be adjusted. Thus, corrective measures can be taken immediately when the quality deviation of the beverage occurs, avoiding the serving of unqualified beverages to users and ensuring the user's experience with the chain brand's beverages. Attached Figure Description

[0016] Figure 1This is a flowchart illustrating the steps of a robot-based beverage quality management method provided in this application embodiment; Figure 2 This is a flowchart illustrating a robot-based beverage quality management system provided in this application embodiment. Figure 3 This is a schematic diagram of the structure of a robot-based beverage quality management device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0018] Reference Figure 1 This document illustrates a flowchart of a robot-based beverage quality management method provided in an embodiment of this application. Implemented through Agent AI, the method includes the following steps: Step 101: When the robot finishes making the beverage, obtain the current quality parameters of the beverage.

[0019] Step 102: Obtain the standard quality parameters corresponding to the beverage category.

[0020] In specific implementations, the robot (robot control system) can be a humanoid robot, or it can be other types of robots capable of providing anthropomorphic interactive services; this application embodiment does not impose any limitations on this. The robot is equipped with an Agent (AI, Artificial Intelligence) that can be used to manage the quality of beverages produced by the robot. The robot has a quality standard database, which stores at least the standard quality parameters for various types of beverages (such as coffee, milk tea, and ice cream), for example, the standard concentration range, standard temperature range, and standard output of Americano coffee, as well as the allowable deviation range for each standard quality parameter, serving as a benchmark for quality assessment and management of the beverages.

[0021] In this embodiment of the application, when the robot receives a user's beverage order request, it will prepare the corresponding beverage according to the beverage information in the order request (such as beverage category, flavor, specifications, temperature and sweetness). Subsequently, when the robot finishes preparing the beverage, it can obtain the current quality parameters of the prepared beverage, and obtain the standard quality parameters corresponding to the beverage category from the quality standard database.

[0022] In one embodiment of this application, the current quality parameters may include at least the beverage concentration, beverage ingredients, output temperature, beverage appearance, extraction time, and beverage weight; wherein, the beverage appearance may include at least the thickness of the milk foam, color, and the integrity of the latte art; the standard quality parameters may include at least the standard concentration range, standard temperature range, standard output, standard extraction time, and the corresponding allowable deviation range; the production parameters may include at least the grind size, water temperature, and extraction time.

[0023] Step 103: When there is a deviation between the current quality parameter and the standard quality parameter, analyze the reasons for the deviation based on the current quality parameter and the standard quality parameter.

[0024] In this embodiment, the robot can compare the current quality parameters of a beverage with the corresponding standard quality parameters, either individually or in combination. When a deviation exists, the robot analyzes the cause of the deviation based on the current and standard quality parameters, either individually or in combination. The cause of the deviation may include insufficient coffee bean grind or abnormal water temperature control. For example, if the beverage's concentration and extraction time differ from the standard concentration range and standard extraction time of the standard quality parameters, the deviation can be determined to be due to insufficient coffee bean grind. If, except for the beverage's output temperature being lower than the standard temperature range, all other parameters are the same, the deviation can be determined to be due to abnormal water temperature control.

[0025] In some embodiments, the robot can compare each of the current quality parameters of the beverage (such as beverage concentration, beverage ingredients, output temperature, beverage appearance, extraction time, and beverage weight) with each of the standard quality parameters (standard concentration range, standard temperature range, standard yield, standard extraction time, and allowable deviation range). If the comparison reveals that any one of the parameters exceeds the allowable deviation range, it can be determined that there is a deviation in that parameter of the beverage, and the cause of the deviation can be analyzed. For example, if the comparison reveals that the output temperature of the beverage deviates from the standard temperature range, it can be determined that the cause of the deviation is abnormal water temperature control.

[0026] In other embodiments, the robot can simultaneously compare multiple interrelated current quality parameters of a beverage with standard quality parameters and analyze whether the trends between these parameters correspond to certain faults. For example, by comparing the beverage concentration and extraction time with standard concentration ranges and standard extraction times, if both low concentration and short extraction time are found, it can be judged as insufficient grinding. If the beverage concentration is normal but the output temperature is too high and the milk foam is too thin, it can be judged as abnormal water temperature control.

[0027] When the robot detects that multiple current quality parameters (e.g., two or more) of a beverage deviate from the standard beverage parameters simultaneously, a combined comparison method will be used for deviation analysis to accurately identify the cause of the deviation and improve the accuracy of the deviation analysis. If the robot detects that only a single current quality parameter of the beverage deviates from the standard beverage parameter, then a combined comparison is not necessary.

[0028] In practical applications, robots can pre-collect potential deviation combinations that may occur when beverages have quality problems (such as a combination of issues with beverage concentration and extraction time, or a combination of issues with beverage concentration, beverage weight, and extraction time). When the beverage is finished, if the robot detects that multiple current quality parameters of the beverage deviate from the standard beverage parameters at the same time, it can combine and compare the current quality parameters with the standard beverage parameters according to the problem combination, thereby analyzing the cause of the deviation in a timely manner.

[0029] Step 104: Generate control instructions based on the cause of the deviation.

[0030] Step 105: Adjust the production parameters of the robot when making the beverage according to the control instructions, so as to adjust the quality parameters of the beverage.

[0031] In this embodiment, the robot can automatically generate corresponding control commands based on the analyzed causes of deviations, such as adjusting the grinding degree, adjusting the water temperature, and adjusting the extraction time. The robot can then adjust the production parameters of the beverage according to the control commands, thereby adjusting the beverage parameters and ensuring that the beverage served to the user is a high-quality beverage that meets the standards.

[0032] This application embodiment uses a robot control system to perform real-time quality management of beverages. The entire process requires no human intervention, achieving a closed loop of detecting deviations, analyzing the causes of deviations, and automatically adjusting, ensuring that the quality of each beverage from the robot remains stable within the standard range, thus guaranteeing the beverage experience of the chain brand.

[0033] In this embodiment, when the robot finishes making the beverage, it acquires the current quality parameters of the beverage and the standard quality parameters corresponding to the beverage category. When there is a deviation between the current quality parameters and the standard quality parameters, it analyzes the cause of the deviation based on the current quality parameters and the standard quality parameters, and generates control instructions based on the cause of the deviation to adjust the production parameters of the robot when making the beverage. In this way, the quality parameters of the beverage can be adjusted. Thus, corrective measures can be taken immediately when the quality deviation of the beverage occurs, avoiding the serving of unqualified beverages to users and ensuring the user's experience with the chain brand's beverages.

[0034] In one embodiment of this application, the robot includes a near-infrared spectrometer, a temperature sensor, an image recognition system, and a weight sensor; obtaining the current quality parameters of the beverage may include: The beverage concentration and composition were obtained using the near-infrared spectrometer. The serving temperature of the beverage is obtained through the temperature sensor; The appearance of the beverage is obtained through the image recognition system; The weight of the beverage is obtained using the weight sensor.

[0035] In this embodiment, a multi-dimensional quality detection sensor network is set up. Specifically, multiple sensors are deployed in the production process of each coffee robot. The sensors may include at least: a near-infrared spectrometer for detecting beverage concentration and beverage ingredients; a temperature sensor for detecting the production temperature; an image recognition system for acquiring the beverage appearance, such as the thickness, color, and integrity of the latte art; and a weight sensor for detecting the weight.

[0036] By collecting multi-dimensional current quality parameters of beverages through sensors installed on the robot, it is possible to quickly determine whether the beverage is within the standard range based on the current quality parameters, thus ensuring the beverage experience of chain brands.

[0037] In one embodiment of this application, before obtaining the standard quality parameters corresponding to the beverage category, the method may further include: Obtain the store quality parameters of each store in the chain when making beverages; Determine standard quality parameters from the aforementioned store quality parameters; The standard quality parameters are stored in the quality standard database and synchronized to all chain stores across the network.

[0038] In this embodiment of the application, a cross-store quality benchmarking system is set up, which can periodically compare the store quality parameters of each category in each store to identify stores with excellent quality performance. Then, standard quality parameters can be determined from the store quality parameters of stores with excellent quality performance, and the standard quality parameters are stored in the quality standard database and synchronized to stores across the entire chain, especially stores with poor quality performance, so as to realize cross-store quality benchmarking and improvement.

[0039] In one embodiment of this application, the method may further include: Obtain the store quality parameters of each chain store when making beverages within a preset time range; A quality analysis report is generated based on the store quality parameters. When a store with persistent deviations is identified based on the quality analysis report, a designated entity is notified to intervene in the store with persistent deviations.

[0040] In this embodiment, a quality reporting and early warning system is provided, which can periodically obtain the quality parameters of each chain store when making beverages within a preset time range, and generate a quality analysis report based on the store quality parameters. The quality analysis report can be in the form of daily, weekly, or monthly reports. Then, by analyzing the quality analysis report, it can be determined whether there are stores with continuous deviations, and an early warning is triggered when the quality continues to deviate, notifying the operation and management personnel to intervene manually, thereby ensuring the quality of beverages of the chain brand.

[0041] In one embodiment of this application, after notifying a designated object to intervene in the stores with persistent deviations when the quality analysis report determines that a store with persistent deviations has been identified, the method further includes: If the number of stores with persistent deviations reaches a preset number of stores, obtain the production environment information of the robots making the beverage in the stores with persistent deviations; the production environment information includes at least temperature, humidity, and water hardness; Generate network-wide control instructions based on the aforementioned production environment information; The network-wide control command is sent to the robot in the store where the similarity to the production environment information reaches a preset similarity, so that the robot can adjust the production parameters when making the beverage according to the network-wide control command.

[0042] In this embodiment, when the same type of beverage exhibits continuous quality deviations across multiple stores and the number reaches a preset threshold (e.g., more than five stores), the system infers that the deviations may be related to the environment in which the beverages are prepared, rather than operational issues in individual stores. In this case, the system automatically collects environmental information about the beverage preparation process of the robots in the stores with deviations, such as temperature, humidity, and water hardness. Based on this information, the system generates corresponding network-wide control instructions, such as uniformly lowering the extraction water temperature or extending the pre-soaking time in high-temperature summer conditions. These instructions are then sent only to robots in stores whose environmental information matches the deviations at a preset similarity level. This achieves precise batch control, preventing unnecessary adjustments from being made to stores that could negatively impact the quality of their products.

[0043] For example, if more than five stores have low concentration and high output temperature for Americano coffee for three consecutive days, and the system detects that these stores are located in areas experiencing high temperatures, it can generate network-wide control instructions to lower the extraction water temperature and adjust the grind size. These instructions will then be pushed to stores nationwide whose production environment information is similar to those of the five stores to a preset level, thus preventing the same deviations from occurring in advance and ensuring the quality of the chain brand's beverages.

[0044] In one embodiment of this application, generating the control command based on the cause of the deviation may include: When the deviation is caused by insufficient grind of coffee beans, an adjustment command is generated to increase the grind fineness of the robot to re-process and inspect the coffee. When the cause of the deviation is abnormal water temperature control, a control command is generated to adjust the power of the robot's heater in order to remake and test it. When the cause of the deviation is an extraction time deviation, an adjustment command is generated to extend or shorten the extraction time of the robot to re-process and detect it.

[0045] In this embodiment, corresponding control instructions can be generated based on the cause of the deviation. Specifically, when the cause of the deviation is insufficient grind of coffee beans, a control instruction to increase the grind fineness of the robot is generated for re-processing and testing. When the cause of the deviation is abnormal water temperature control, a control instruction to adjust the heater power of the robot is generated for re-processing and testing. When the cause of the deviation is extraction time deviation, a control instruction to extend or shorten the extraction time of the robot is generated for re-processing and testing. In this way, by generating control instructions according to the cause of the deviation and testing the re-processed beverage to determine whether the re-processed beverage meets the standard, the next beverage can be re-processed based on the control instructions to meet the standard, shortening the response cycle from detection to correction and effectively ensuring the continuous stability of the product quality.

[0046] Reference Figure 2 This is a flowchart of a robot-based beverage quality management system provided in this application embodiment. The specific process may include: After the coffee is made, it enters the quality inspection stage; a multi-dimensional quality inspection sensor network collects data, including: a near-infrared spectrometer to detect concentration and components, a temperature sensor to detect the output temperature, an image recognition system to detect appearance, and a weight sensor to detect the output quantity.

[0047] The robot's Agent AI quality analysis engine performs a comprehensive analysis to determine if the quality is within the standard range. If it is, the quality data is recorded and the product is delivered. If it is outside the standard range, Agent AI analyzes the cause of the deviation and determines if the deviation can be automatically adjusted. If it can be automatically adjusted, the robot's automatic adjustment instruction generation module generates adjustment instructions, the robot control system executes the adjustment, and the product is remade and tested. If it cannot be automatically adjusted, an early warning is triggered, and operations management personnel are notified.

[0048] In addition, the system will conduct cross-store quality benchmarking, regularly compare data from each store, determine whether quality differences are found between stores, and if differences exist, synchronize the parameters of the benchmark store to the stores with poor quality; if no differences exist, generate a quality analysis report.

[0049] The embodiments of this application can analyze beverage quality online in real time and have real-time parameter adjustment capabilities. When quality deviations occur, corrective measures can be taken immediately to prevent qualified beverages from being released to users, thus ensuring a good user experience.

[0050] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of this application.

[0051] Reference Figure 3 The diagram illustrates a structural block diagram of a robot-based beverage quality management device provided in this embodiment of the application. Implemented via Agent AI, the device may specifically include the following modules: The current quality parameter acquisition module 301 is used to acquire the current quality parameters of the beverage when the robot finishes making the beverage; The standard quality parameter acquisition module 302 is used to acquire the standard quality parameters corresponding to the beverage category of the beverage. The deviation cause analysis module 303 is used to analyze the cause of the deviation based on the current quality parameter and the standard quality parameter when there is a deviation between the current quality parameter and the standard quality parameter. The control instruction generation module 304 is used to generate control instructions based on the cause of the deviation. The quality parameter adjustment module 305 is used to adjust the production parameters of the robot when making the beverage according to the control command, so as to adjust the quality parameters of the beverage.

[0052] In one embodiment of this application, the current quality parameters include at least beverage concentration, beverage ingredients, output temperature, beverage appearance, extraction time, and beverage weight; the beverage appearance includes at least milk foam thickness, color, and latte art integrity; the standard quality parameters include at least standard concentration range, standard temperature range, standard output, standard extraction time, and corresponding allowable deviation range; and the production parameters include at least grind size, water temperature, and extraction time.

[0053] In one embodiment of this application, the robot includes a near-infrared spectrometer, a temperature sensor, an image recognition system, and a weight sensor; the current quality parameter acquisition module 301 is used for: The beverage concentration and composition were obtained using the near-infrared spectrometer. The serving temperature of the beverage is obtained through the temperature sensor; The appearance of the beverage is obtained through the image recognition system; The weight of the beverage is obtained using the weight sensor.

[0054] In one embodiment of this application, the apparatus further includes: a standard quality parameter determination module, used for: Obtain the store quality parameters of each store in the chain when making beverages; Determine standard quality parameters from the aforementioned store quality parameters; The standard quality parameters are stored in the quality standard database and synchronized to all chain stores across the network.

[0055] In one embodiment of this application, the device further includes: an intervention module, used for: Obtain the store quality parameters of each chain store when making beverages within a preset time range; A quality analysis report is generated based on the store quality parameters. When a store with persistent deviations is identified based on the quality analysis report, a designated entity is notified to intervene in the store with persistent deviations.

[0056] In one embodiment of this application, the device further includes: a network-wide control module, used for: If the number of stores with persistent deviations reaches a preset number of stores, obtain the production environment information of the robots making the beverage in the stores with persistent deviations; the production environment information includes at least temperature, humidity, and water hardness; Generate network-wide control instructions based on the aforementioned production environment information; The network-wide control command is sent to the robot in the store where the similarity to the production environment information reaches a preset similarity, so that the robot can adjust the production parameters when making the beverage according to the network-wide control command.

[0057] In one embodiment of this application, the control instruction generation module 304 is used for: When the deviation is caused by insufficient grind of coffee beans, an adjustment command is generated to increase the grind fineness of the robot to re-process and inspect the coffee. When the cause of the deviation is abnormal water temperature control, a control command is generated to adjust the power of the robot's heater in order to remake and test it. When the cause of the deviation is an extraction time deviation, an adjustment command is generated to extend or shorten the extraction time of the robot to re-process and detect it.

[0058] In this embodiment, when the robot finishes making the beverage, it acquires the current quality parameters of the beverage and the standard quality parameters corresponding to the beverage category. When there is a deviation between the current quality parameters and the standard quality parameters, it analyzes the cause of the deviation based on the current quality parameters and the standard quality parameters, and generates control instructions based on the cause of the deviation to adjust the production parameters of the robot when making the beverage. In this way, the quality parameters of the beverage can be adjusted. Thus, corrective measures can be taken immediately when the quality deviation of the beverage occurs, avoiding the serving of unqualified beverages to users and ensuring the user's experience with the chain brand's beverages.

[0059] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment. This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 1001, a device interface 1002, a memory 1003, and a bus 1004; Memory 1003 is used to store computer programs; The processor 1001 executes the above steps when executing the program stored in the memory 1003.

[0060] The bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0061] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0062] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0063] This application also provides a storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the robot-based beverage quality management method of the foregoing embodiments.

[0064] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. The structure required to construct such a device is obvious from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0065] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0066] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0067] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0068] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the sequencing device according to this application. This application can also be implemented as a device or apparatus program for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0069] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0071] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0073] It should be noted that the various data-related processes in the embodiments of this application are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

Claims

1. A robot-based beverage quality management method, characterized in that, Implemented via Agent AI, the method includes: When the robot finishes making the beverage, the current quality parameters of the beverage are obtained; Obtain the standard quality parameters corresponding to the beverage category; When there is a deviation between the current quality parameter and the standard quality parameter, the cause of the deviation is analyzed based on the current quality parameter and the standard quality parameter. Generate control instructions based on the causes of the deviation; The robot adjusts the production parameters when making the beverage according to the control instructions, so as to adjust the quality parameters of the beverage.

2. The method according to claim 1, characterized in that, The current quality parameters include at least the beverage concentration, beverage ingredients, output temperature, beverage appearance, extraction time, and beverage weight; the beverage appearance includes at least the thickness, color, and integrity of the latte art; the standard quality parameters include at least the standard concentration range, standard temperature range, standard output, standard extraction time, and corresponding allowable deviation range; the production parameters include at least the grind size, water temperature, and extraction time.

3. The method according to claim 1, characterized in that, The robot includes a near-infrared spectrometer, a temperature sensor, an image recognition system, and a weight sensor; acquiring the current quality parameters of the beverage includes: The beverage concentration and composition were obtained using the near-infrared spectrometer. The serving temperature of the beverage is obtained through the temperature sensor; The appearance of the beverage is obtained through the image recognition system; The weight of the beverage is obtained using the weight sensor.

4. The method according to claim 1, characterized in that, Before obtaining the standard quality parameters corresponding to the beverage category, the method further includes: Obtain the store quality parameters of each store in the chain when making beverages; Determine standard quality parameters from the aforementioned store quality parameters; The standard quality parameters are stored in the quality standard database and synchronized to all chain stores across the network.

5. The method according to claim 2, characterized in that, The method further includes: Obtain the store quality parameters of each chain store when making beverages within a preset time range; A quality analysis report is generated based on the store quality parameters. When a store with persistent deviations is identified based on the quality analysis report, a designated entity is notified to intervene in the store with persistent deviations.

6. The method according to claim 5, characterized in that, After notifying a designated entity to intervene in the stores exhibiting persistent deviations upon identifying them based on the quality analysis report, the method further includes: If the number of stores with persistent deviations reaches a preset number of stores, obtain the production environment information of the robots making the beverage in the stores with persistent deviations; the production environment information includes at least temperature, humidity, and water hardness; Generate network-wide control instructions based on the aforementioned production environment information; The network-wide control command is sent to the robot in the store where the similarity to the production environment information reaches a preset similarity, so that the robot can adjust the production parameters when making the beverage according to the network-wide control command.

7. The method according to claim 2, characterized in that, The step of generating control instructions based on the cause of the deviation includes: When the deviation is caused by insufficient grind of coffee beans, an adjustment command is generated to increase the grind fineness of the robot to re-process and inspect the coffee. When the cause of the deviation is abnormal water temperature control, a control command is generated to adjust the power of the robot's heater in order to remake and test it. When the cause of the deviation is an extraction time deviation, an adjustment command is generated to extend or shorten the extraction time of the robot to re-process and detect it.

8. A robot-based beverage quality management device, characterized in that, Implemented via Agent AI, the device includes: The current quality parameter acquisition module is used to acquire the current quality parameters of the beverage when the robot finishes making the beverage; The standard quality parameter acquisition module is used to acquire the standard quality parameters corresponding to the beverage category of the beverage. The deviation cause analysis module is used to analyze the cause of the deviation based on the current quality parameter and the standard quality parameter when there is a deviation between the current quality parameter and the standard quality parameter. The control instruction generation module is used to generate control instructions based on the cause of the deviation; The quality parameter adjustment module is used to adjust the production parameters of the robot when making the beverage according to the control instructions, so as to adjust the quality parameters of the beverage.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the instructions to implement the robot-based beverage quality management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the robot-based beverage quality management method as described in any one of claims 1 to 7.

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