Control system and method of self-service beverage dispenser

Abstract

The invention discloses a self-service beverage dispenser control system and method, and relates to the technical field of self-service beverage dispenser control, and the method comprises the steps: obtaining user preference and user information; identifying user information to verify the identity through a biological identification method, and obtaining user health data; obtaining a user health demand weight based on the user health data; generating a personalized beverage recommendation list through user preferences and user health demand weights; adjusting a beverage formula in real time based on the personalized beverage recommendation list; and based on the adjusted beverage formula, making is started through non-contact payment. According to the method, the health data of the user is acquired through the biological recognition technology, and the personalized beverage recommendation list is generated in combination with the user preference and the health demand weight, so that the intelligent level and the user experience of the equipment are improved, the application scene of the equipment in the fields of health management and personalized service is expanded, and the method has remarkable market value and application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent self-service beverage machine control, and in particular to a control system and method of a self-service beverage machine. Background Art

[0002] In recent years, with the rapid development of artificial intelligence (AI), big data and Internet of Things (IoT) technologies, traditional self-service equipment is gradually transforming towards intelligence and personalization. In the beverage industry, self-service beverage machines have been widely used for their convenience and efficiency. However, the functions of traditional self-service beverage machines are relatively simple, mainly relying on users to manually select fixed beverage recipes, which cannot meet the diverse and personalized needs of users. At the same time, the application of health management and precise recommendations in consumption scenarios has gradually emerged, and consumers have an increasing demand for the health attributes, taste preferences and ingredient customization of beverages. In the prior art, some smart beverage machines have tried to introduce user interactive functions, such as selecting beverage types through touch screens, adjusting sweetness, or adding IoT functions to achieve remote control, but these functions still remain at the level of simple parameter input by users, lacking a deep combination of personalized recommendations and health data. In addition, the intelligence of payment methods has gradually become the key to user experience, but the payment function and beverage production process in existing devices are mostly separated, and a complete closed loop from recommendation to production has not been achieved.

[0003] Although the existing smart self-service beverage machines can meet users' needs for personalized beverages and convenient payment to a certain extent, there are still significant shortcomings. First, the existing technology lacks a personalized recommendation mechanism based on health data, and is unable to dynamically adjust beverage ingredients according to the user's health status. For example, some users may have special needs for sugar intake, but traditional self-service beverage machines cannot personalize low-sugar beverage recommendations based on the user's physiological data (such as blood sugar levels). Secondly, the existing technology has poor linkage between beverage recommendations and the production process. After the user selects the beverage, the machine only makes it according to a fixed formula. It is difficult to dynamically adjust the formula according to the user's actual health needs and preferences, thereby reducing the user experience satisfaction. The above problems expose the technical shortcomings of current smart beverage machines in the integration of health management and personalized services. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method for a self-service beverage machine to solve the problems in the prior art of lacking a personalized recommendation mechanism based on health data and poor linkage between beverage recommendation and production process.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a control method for a self-service beverage machine, which includes obtaining user preferences and user information; verifying the identity by identifying the user information through a biometric method and obtaining user health data; obtaining the user's health requirement weight based on the user health data; generating a personalized beverage recommendation list through the user preferences and the user's health requirement weight; adjusting the beverage formula in real time based on the personalized beverage recommendation list; and starting the production by non-contact payment based on the adjusted beverage formula.

[0008] As a preferred solution of the control method for the self-service beverage machine of the present invention, wherein: the user preferences include beverage preferences, taste preferences, and ingredient preferences;

[0009] The user information includes basic information and physical signs information.

[0010] As a preferred solution of the control method for the self-service beverage machine of the present invention, wherein: the specific steps of verifying the identity by identifying the user information through a biometric method and obtaining the user health data are as follows.

[0011] Collect the user's facial image through the camera of the self-service beverage machine;

[0012] Extract the user's facial features from the user's facial image through a high-precision face recognition model;

[0013] Match the extracted user's facial features and the user information through the cosine similarity formula. The expression is:

[0014]

[0015] where S is the similarity score, V is the user's facial features, and V db is the user information;

[0016] Set the similarity threshold T through the positive and negative sample similarity distribution;

[0017] When S≥T, it means that the match is successful and the identity verification is successful;

[0018] When S<T, it means that the match fails and re-verification is required;

[0019] Obtain the user's unique identifier through identity verification;

[0020] Based on the user's unique identifier, connect to the hospital medical database through the API, send a health data query request, and obtain the user health data.

[0021] As a preferred solution of the control method for the self-service beverage machine of the present invention, wherein: the specific steps of obtaining the user's health requirement weight based on the user health data are as follows.

[0022] Obtain ideal health values ​​based on the difference between the user's health data and health benchmark values;

[0023] Based on the user's health data and ideal health value, the user's health demand weight is obtained, and the expression is:

[0024]

[0025] Among them, w k is the user health demand weight of the kth user health data, h k is the kth user health data, c k is the ideal health value of the kth user health data, h i is the i-th user health data, c i is the ideal health value of the i-th user health data, n is the total number of user health data, k is the index variable of user health data, i is the index variable of user health data, and α is the sensitivity parameter.

[0026] As a preferred solution of the control method of the self-service beverage machine of the present invention, wherein: the personalized beverage recommendation list is generated according to the user preference and the user health demand weight, and the specific steps are as follows:

[0027] Based on the user's health demand weight and user preference, the matching value between the user's health demand weight and the beverage and the degree of compatibility between the user's preference and the beverage are quantified, so as to calculate the personalized recommendation score of each beverage, which is expressed as:

[0028]

[0029] Among them, R(d) is the personalized recommendation score of the d-th beverage, f k (d) is the matching value between the kth user health data and the dth beverage, β is the adjustment factor of user preference on recommendation, and g j (d,p j ) is the degree of compatibility between the preference value of the jth user and the dth beverage, p j is the preference value of the jth user preference, j is the index variable of the user preference, γ is the penalty factor, U is the ideal decision vector, D is the actual decision vector, and m is the total number of preference targets;

[0030] The beverages are sorted in descending order based on the personalized recommendation score R(d) to obtain a personalized beverage recommendation list.

[0031] As a preferred solution of the control method of the self-service beverage machine of the present invention, wherein: based on the personalized beverage recommendation list, the beverage formula is adjusted in real time to obtain new beverage ingredients, the specific steps are as follows:

[0032] Get the initial drink recipe and current ingredient values ​​through a personalized drink recommendation list;

[0033] According to the user's health demand weight and user preference, the current ingredient value in the initial beverage formula is dynamically adjusted, and the expression is:

[0034]

[0035] in, is the new component value of the tth component, x t is the current component value of the tth component, λ t is the adjustment weight of the tth component, δ t is the adjusted sensitivity parameter of the tth component, r t is the ideal target value of the tth component, ъ t is the adjustment amplitude parameter of the tth component, t is the index variable of the component;

[0036] Based on the new ingredient values, the beverage recipe is adjusted by automated machines.

[0037] As a preferred solution of the control method of the self-service beverage machine of the present invention, wherein: based on the adjusted beverage formula, the production is started by contactless payment, and the specific steps are as follows:

[0038] Display adjusted beverage recipes through self-service beverage machines;

[0039] After confirming the beverage formula, the self-service beverage machine dynamically calculates the beverage price based on the beverage formula. The expression is:

[0040]

[0041] Where N is the drink price, B is the base price, is the new component value of the tth component, is the ingredient value of the tth ingredient of the initial beverage recipe, C is the additional service charge, and M is the total number of ingredients;

[0042] Based on the calculated drink price, payment is completed via contactless payment;

[0043] After receiving the payment information, a unique order number is generated, and the self-service beverage machine starts making beverages according to the adjusted beverage recipe.

[0044] In a second aspect, the present invention provides a control system for a self-service beverage machine, including a user information collection module, a biometric identification and identity authentication module, a health needs analysis module, a beverage recommendation module, a formula dynamic adjustment module and a beverage production module; the user information collection module is used to obtain user preferences and user information; the biometric identification and identity authentication module is used to identify user information through biometric identification methods to verify identity and obtain user health data; the health needs analysis module is used to obtain user health needs weights based on user health data; the beverage recommendation module is used to generate a personalized beverage recommendation list based on user preferences and user health needs weights; the formula dynamic adjustment module is used to adjust the beverage formula in real time based on the personalized beverage recommendation list; the beverage production module is used to start production through contactless payment based on the adjusted beverage formula.

[0045] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the control method of the self-service beverage machine as described in the first aspect of the present invention is implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the control method of the self-service beverage machine as described in the first aspect of the present invention is implemented.

[0047] The beneficial effects of the present invention are as follows: the present invention obtains user health data through biometric technology, and generates a personalized beverage recommendation list based on user preferences and health needs weights, adjusts beverage recipes in real time, and uses contactless payment to complete the production process, solving the technical problem that traditional self-service beverage machines cannot provide accurate services based on user health status and personalized needs, improving the intelligence level of the equipment and user experience, and expanding its application scenarios in the field of health management and personalized services, and has significant market value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 is a flow chart of the control method of the self-service beverage machine in Example 1;

[0050] Figure 2 This is a system diagram of the control system of the self-service beverage machine in Example 1. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a control method for a self-service beverage machine, comprising the following steps:

[0055] S1. User preferences include beverage preferences, taste preferences, and ingredient preferences.

[0056] It should be noted that beverage preferences: the types of beverages that users like, such as coffee, juice, tea, carbonated drinks, etc.;

[0057] Taste preference: user’s preference for beverage taste, such as sweet, sour, bitter, bland, etc.;

[0058] Ingredient preference: user’s requirements or restrictions on beverage ingredients, such as low sugar, lactose-free, high protein, low fat, etc.

[0059] S1.1. The user information includes basic information and vital signs information.

[0060] It should be noted that basic information includes the user's name, gender, age, height, weight, etc.

[0061] Vital sign information: data information related to the user's health, such as blood sugar level, blood pressure, heart rate, BMI (body mass index), etc.

[0062] The above information is obtained with the user's consent and is used for legal purposes.

[0063] S2. Use biometrics to identify user information to verify identity and obtain user health data.

[0064] S2.1. Collect the user's facial image through the self-service beverage machine camera.

[0065] S2.2. Extract the user's facial features from the user's facial image through a high-precision face recognition model.

[0066] It should be noted that the high-precision face recognition model is widely used in the field of high-precision face recognition, such as financial payment, security monitoring, identity authentication, etc., and has high performance and robustness.

[0067] The specific process of extracting the user's facial features through the high-precision face recognition model is as follows:

[0068] The input layer receives the user's facial image;

[0069] Extract local features through the convolutional layer;

[0070] Fuse the local features into global features through the pooling layer and the fully connected layer;

[0071] The output layer outputs the user's facial features.

[0072] S2.3. Match the extracted user's facial features and user information through the cosine similarity formula. The expression is:

[0073]

[0074] where S is the similarity score, V is the user's facial features, and V db is the user information.

[0075] S2.4. Set the similarity threshold T through the positive and negative sample similarity distributions;

[0076] When S ≥ T, it indicates a successful match and successful identity verification;

[0077] When S < T, it indicates a failed match and requires re-verification.

[0078] It should be noted that the similarity threshold T is set through the positive and negative sample similarity distributions;

[0079] Calculate the similarity between the feature vectors of the same user through the cosine similarity formula to obtain the positive sample similarity distribution;

[0080] Calculate the similarity between the feature vectors of different users through the cosine similarity formula to obtain the negative sample similarity distribution.

[0081] S2.5. Obtain the user's unique identifier through identity verification.

[0082] S2.6. Based on the user's unique identifier, connect to the hospital medical database through the API, send a health data query request, and obtain the user's health data.

[0083] S3. Based on the user's health data, obtain the user's health demand weight.

[0084] S3.1. Obtain ideal health value based on the difference between the user's health data and the health benchmark value.

[0085] It should be noted that the expression for obtaining the ideal health value is:

[0086]

[0087] Among them, c k is the ideal health value of the kth user health data, s k is the basic value of health demand of the kth user’s health data, λ1 is the adjustment coefficient of the current health data, λ2 is the adjustment coefficient of the health goal, and h k is the kth item of user health data, is the health benchmark value of the kth user health data, is the adjustment factor for the kth user’s health data;

[0088] It should also be noted that health benchmark values ​​usually come from statistical data provided by medical institutions or authoritative health databases, and are the normal range or recommended values ​​of health indicators (such as blood sugar, blood pressure, etc.) for specific populations.

[0089] S3.2. Based on the user's health data and ideal health value, obtain the user's health demand weight, expressed as:

[0090]

[0091] Among them, w k is the user health demand weight of the kth user health data, h k is the kth user health data, c k is the ideal health value of the kth user health data, h i is the i-th user health data, c i is the ideal health value of the i-th user health data, n is the total number of user health data, k is the index variable of user health data, i is the index variable of user health data, and α is the sensitivity parameter.

[0092] It should be noted that h k and c k : Focus on a single user's health data (the kth item) to calculate the numerator, which represents the weighted contribution of the kth health need.

[0093] h i and c i : Traverse all user health data (from the 1st item to the nth item) for denominator normalization to ensure that the sum of all weights is 1.

[0094] Difference: h k and c k Only for a single indicator; while h i and c i It is a collection of all indicators to ensure the relativity of each weight value.

[0095] It should also be noted that k and i are essentially index variables of the user's health data, but k represents the single health data whose weight is currently being calculated, and i represents the traversal index of all health data used for normalization processing.

[0096] S4. Generate a personalized beverage recommendation list based on user preferences and user health needs weights.

[0097] S4.1. Based on the user health demand weight and user preference, the matching value between the user health demand weight and the beverage and the degree of compatibility between the user preference and the beverage are quantified, thereby calculating the personalized recommendation score for each beverage, which is expressed as:

[0098]

[0099] Among them, R(d) is the personalized recommendation score of the d-th beverage, f k (d) is the matching value between the kth user health data and the dth beverage, β is the adjustment factor of user preference on recommendation, and g j (d,p j ) is the degree of compatibility between the preference value of the jth user and the dth beverage, p j is the preference value of the jth user preference, j is the index variable of the user preference, γ is the penalty factor, U is the ideal decision vector, D is the actual decision vector, and m is the total number of preference targets.

[0100] It should be noted that f k The specific expression of (d) is:

[0101]

[0102] Among them, f k (d) is the matching value between the kth user health data and the dth beverage, is the sensitivity parameter of the kth user health data, q t (d) is the actual value of the tth ingredient of the dth beverage;

[0103] It should also be noted that g j (d,p j ) is expressed as:

[0104]

[0105] Among them, g j (d,p j ) is the degree of compatibility between the preference value of the jth user and the dth beverage, q t is the actual value of the tth component.

[0106] S4.2. Sort the beverages in descending order based on the personalized recommendation score R(d) to obtain a personalized beverage recommendation list.

[0107] S5. Based on the personalized beverage recommendation list, adjust the beverage recipe in real time and obtain new beverage ingredients.

[0108] S5.1. Obtain the initial beverage recipe and current ingredient values ​​through the personalized beverage recommendation list.

[0109] S5.2. Dynamically adjust the current component values ​​in the initial beverage formula according to the user's health demand weight and user preference. The expression is:

[0110]

[0111] in, is the new component value of the tth component, x t is the current component value of the tth component, λ t is the adjustment weight of the tth component, δ t is the adjusted sensitivity parameter of the tth component, r t is the ideal target value of the tth component, ъ t is the adjustment parameter of the tth component, and t is the index variable of the component.

[0112] It should be noted that adjusting the weight λ t The expression is:

[0113]

[0114] Among them, p t is the preference value of the tth component, p e is the preference value of the e-th component, f t (ρ) is the matching value between the tth ingredient and the ρth beverage recipe, f e (ρ) is the matching value between the e-th ingredient and the ρ-th beverage recipe, g t (p t ,f t (ρ)) is p t and f t (ρ) The degree of adaptation between e (p e ,f e (ρ)) is p e and fe (ρ), M is the total number of components, e is the index variable of the component, Φ t is the correction term for the tth component;

[0115] It should also be noted that t and e are essentially component index variables, but t is a fixed specific component, while e is a loop index used to traverse the preference values ​​of all components.

[0116] S5.3. Adjust the beverage recipe using an automated machine based on the new ingredient values.

[0117] S6. Based on the adjusted beverage recipe, production is started through contactless payment.

[0118] S6.1. Display the adjusted beverage recipe through a self-service beverage machine.

[0119] S6.2. After confirming the beverage formula, the self-service beverage machine dynamically calculates the beverage price based on the beverage formula. The expression is:

[0120]

[0121] Where N is the drink price, B is the base price, is the new component value of the tth component, is the ingredient value of the tth ingredient in the initial beverage recipe, C is the additional service charge, and M is the total number of ingredients.

[0122] It should be noted that the expression calculation process is as follows:

[0123] By calculating the new component values ​​for all components With the initial composition value Take the absolute value of the difference, take the total and divide it by the total number of components M to get the average change ratio of the component adjustment, add 1 to this ratio and multiply it by the base price B to reflect the price increase caused by the component adjustment, add the additional service fee C to get the final price N.

[0124] It should also be noted that the additional service fee C is charged to compensate for the additional costs incurred by personalized services, special ingredients, convenient payment or additional services (such as environmentally friendly packaging).

[0125] S6.3. Based on the calculated beverage price, payment is completed through a contactless payment method.

[0126] It should be noted that contactless payment methods include NFC payment, QR code payment, contactless bank card payment, face payment, fingerprint payment and RFID payment.

[0127] S6.4. After receiving the payment information, a unique order number is generated, and the self-service beverage machine starts making beverages according to the adjusted beverage formula.

[0128] This embodiment also provides a control system for a self-service beverage machine, including: a user information collection module, a biometric identification and identity authentication module, a health needs analysis module, a beverage recommendation module, a formula dynamic adjustment module and a beverage production module;

[0129] User information collection module, used to obtain user preferences and user information;

[0130] Biometric identification and identity verification module, used to identify user information through biometric identification methods to verify identity and obtain user health data;

[0131] A health needs analysis module is used to obtain the user's health needs weight based on the user's health data;

[0132] The beverage recommendation module is used to generate a personalized beverage recommendation list based on user preferences and user health needs weights;

[0133] A dynamic recipe adjustment module is used to adjust beverage recipes in real time based on a personalized beverage recommendation list;

[0134] The beverage preparation module is used to initiate preparation based on the adjusted beverage recipe through contactless payment.

[0135] This embodiment also provides a computer device, which is applicable to the control method of a self-service beverage machine, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the control method of the self-service beverage machine proposed in the above embodiment.

[0136] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0137] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the control method of the self-service beverage machine proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0138] In summary, the present invention uses: biometric technology to obtain user health data, and generates a personalized beverage recommendation list based on user preferences and health needs weights, adjusts beverage recipes in real time, and uses contactless payment to complete the production process. It solves the technical problem that traditional self-service beverage machines cannot provide accurate services based on user health status and personalized needs, improves the intelligence level of the equipment and user experience, and expands its application scenarios in the field of health management and personalized services. It has significant market value and application prospects.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A control method for a self-service beverage machine, characterized in that: including, obtaining user preferences and user information; identifying user information through biometric methods to verify identity and obtaining user health data; obtaining user health demand weights based on user health data; generating a personalized beverage recommendation list through user preferences and user health demand weights; real-time adjusting the beverage formula based on the personalized beverage recommendation list; initiating production through non-contact payment based on the adjusted beverage formula.

2. The control method of the self-service beverage machine according to claim 1, characterized in that: The user preferences include beverage preferences, taste preferences, and ingredient preferences; The user information includes basic information and physical sign information.

3. The control method of the self-service beverage machine according to claim 2, characterized in that: The step of identifying user information through biometric methods to verify identity and obtaining user health data is as follows: collecting the user's facial image through the camera of the self-service beverage machine; extracting the user's facial features from the user's facial image through a high-precision face recognition model; matching the extracted user's facial features with the user information through the cosine similarity formula, and the expression is: Among them, S is the similarity score, V is the user's facial features, and V db is user information; setting a similarity threshold T through the similarity distribution of positive and negative samples; when S≥T, it indicates successful matching and successful identity verification; when S<T, it indicates failed matching and requires re-verification; obtaining the user's unique identifier through identity verification; based on the user's unique identifier, connecting to the hospital medical database through the API, sending a health data query request, and obtaining the user's health data.

4. The control method of the self-service beverage machine according to claim 3, characterized in that: The step of obtaining the user's health demand weights based on the user's health data is as follows: obtaining the ideal health value based on the difference between the user's health data and the health benchmark value; obtaining the user's health demand weights based on the user's health data and the ideal health value, and the expression is: Among them, w k is the user health demand weight of the kth user health data, h k is the kth user health data, c k is the ideal health value of the kth user health data, h i is the i-th user health data, c i is the ideal health value of the i-th user health data, n is the total number of user health data, k is the index variable of user health data, i is the index variable of user health data, and α is the sensitivity parameter.

5. The control method of the self-service beverage machine according to claim 4, characterized in that: The step of generating a personalized beverage recommendation list through the user's preferences and the user's health demand weights is as follows: quantifying the matching value between the user's health demand weights and beverages and the degree of adaptation between the user's preferences and beverages based on the user's health demand weights and user preferences, so as to calculate the personalized recommendation score of each beverage, and the expression is: Among them, R(d) is the personalized recommendation score of the d-th beverage, f k (d) is the matching value between the kth user health data and the dth beverage, β is the adjustment factor of user preference on recommendation, and g j (d,p j ) is the degree of compatibility between the preference value of the jth user and the dth beverage, p j is the preference value of the jth user preference, j is the index variable of the user preference, γ is the penalty factor, U is the ideal decision vector, D is the actual decision vector, and m is the total number of preference targets; sorting the beverages in descending order based on the personalized recommendation score R(d) to obtain a personalized beverage recommendation list.

6. The control method of the self-service beverage machine according to claim 5, characterized in that: The step of real-time adjusting the beverage formula based on the personalized beverage recommendation list to obtain new beverage ingredients is as follows: obtaining the initial beverage formula and the current ingredient values through the personalized beverage recommendation list; dynamically adjusting the current ingredient values in the initial beverage formula according to the user's health demand weights and user preferences, and the expression is: in, is the new component value of the tth component, x t is the current component value of the tth component, λ t is the adjustment weight of the tth component, δ t is the adjusted sensitivity parameter of the tth component, r t is the ideal target value of the tth component, ъ t is the adjustment amplitude parameter of the tth component, t is the index variable of the component; adjusting the beverage formula through an automated machine according to the new ingredient values.

7. The control method of the self-service beverage machine according to claim 6, characterized in that: The step of initiating production through non-contact payment based on the adjusted beverage formula is as follows: displaying the adjusted beverage formula through the self-service beverage machine; after confirming the beverage formula, the self-service beverage machine dynamically calculates the beverage price according to the beverage formula, and the expression is: Where N is the drink price, B is the base price, is the new component value of the tth component, is the ingredient value of the tth ingredient of the initial beverage recipe, C is the additional service charge, and M is the total number of ingredients; completing the payment through non-contact payment based on the calculated beverage price; after receiving the payment information, generating a unique order number, and the self-service beverage machine initiates production according to the adjusted beverage formula.

8. A control system for a self-service beverage machine, based on the control method for a self-service beverage machine according to any one of claims 1 to 7, characterized in that: including a user information collection module, a biometric and identity verification module, a health demand analysis module, a beverage recommendation module, a formula dynamic adjustment module, and a beverage production module; User information collection module, used to obtain user preferences and user information; Biometric identification and identity verification module, used to identify user information through biometric identification methods to verify identity and obtain user health data; A health needs analysis module is used to obtain the user's health needs weight based on the user's health data; The beverage recommendation module is used to generate a personalized beverage recommendation list based on user preferences and user health needs weights; A dynamic recipe adjustment module is used to adjust beverage recipes in real time based on a personalized beverage recommendation list; The beverage preparation module is used to initiate preparation based on the adjusted beverage recipe through contactless payment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control method of the self-service beverage machine according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method of the self-service beverage machine according to any one of claims 1 to 7 are implemented.

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