Intelligent modular production method and system for personalized customized healthy drinks

Abstract

The invention discloses an intelligent modular production method and system for personalized customized healthy drinks, and relates to the field of food interaction software, and the method comprises the steps: collecting customization demand information submitted by a user side, obtaining basic information of drinks, obtaining basic preference information of the healthy drinks based on a preference algorithm, and carrying out the customization of the healthy drinks. Meanwhile, drink health attribute data is generated in combination with a health value evaluation module, an acquisition platform acquires commodity information acquired from a supply end, comprehensive evaluation information of customized health drinks is acquired based on health drink customization preference information and in combination with a health and cost two-dimensional algorithm, and the comprehensive evaluation information is sent to a platform end for recording; based on the integrated model, the user demands are analyzed, the fuzzy demands are converted into specific data, the beverage is highly customized, the health demands of the user are accurately matched, allergens and unhealthy components are effectively avoided, and differentiated health demands are met.

Description

Technical Field

[0001] This invention relates to the field of food interactive software, specifically to a method and system for intelligent modular production of personalized health drinks. Background Technology

[0002] With the upgrading of health consumption and the rise of personalized demands, the beverage market is transforming from standardized products to a "health + customization" model. Consumers' demands for beverages are no longer limited to basic flavors, but are paying more attention to ingredient compatibility, nutritional balance, and health risk avoidance. Coupled with the popularization of big data and intelligent production technologies, the beverage industry is being driven to upgrade towards a precise and modular customization model, making personalized health drinks a new focus of market competition.

[0003] Current beverage customization services on the market mostly focus on adjusting basic flavors (such as sweetness and ice content) or limited ingredients, resulting in a relatively simple service model. Some brands record user needs through manual inquiries and then make limited combinations based on existing products, but a systematic and intelligent processing flow has not yet been formed. The depth of customization is insufficient to cover consumers' complex health data (such as allergens and nutritional needs) and personalized preferences.

[0004] The existing customization process generally involves: collecting users' taste or ingredient requirements, directly matching and adjusting existing inventory products, and using a fixed markup model for pricing (e.g., charging extra for adding specific ingredients). Production planning is mainly based on historical sales or manual estimates. The entire process lacks quantitative analysis of user health data and has not established a dynamic assessment mechanism for cost and health value, relying heavily on manual decision-making.

[0005] Existing technologies have several drawbacks. First, they fail to convert users' vague health needs into structured data, resulting in low customization accuracy and difficulty in meeting diverse health requirements. Second, they lack a dual-dimensional assessment of health and cost, which can easily lead to problems such as "health standards met but prices too high" or "low cost but high health risks." Summary of the Invention

[0006] This application provides a method, system, and equipment for the intelligent modular production of personalized health drinks, which addresses the technical problems of low customization accuracy, inability to transform vague health demands into structured data, and lack of dual-dimensional assessment of health and cost in existing technologies.

[0007] In view of the above problems, this application provides a method, system and equipment for intelligent modular production of personalized health drinks.

[0008] The first aspect of this application provides a method and system for intelligent modular production of personalized health drinks. The system is applied to a method for intelligent modular production of personalized health drinks. The method includes: collecting customization request information submitted by a user to obtain basic beverage information; obtaining basic health drink preference information based on a preference algorithm; simultaneously generating health attribute data of the beverage in conjunction with a health value assessment module; collecting product information obtained from the supply side through a platform; obtaining comprehensive evaluation information of customized health drinks based on health drink customization preference information and a dual-dimensional algorithm combining health and cost; sending the comprehensive evaluation information to the platform for recording; analyzing user needs based on an integrated model to obtain preferred beverage information that balances popularity, cost, and health value; sending this information to the platform and the supply side respectively; receiving recommendation response results and production response results; obtaining health drink production orders based on a cost-health balance order generation algorithm; sending the production orders to a personalized production module to generate health customization results; sending these results to the supply side for secondary optimization response; obtaining the final pricing and health plan based on an optimized production algorithm; and synchronizing these results to the platform.

[0009] A second aspect of this application provides a smart modular production method and system for personalized health drinks, comprising: a processor coupled to a memory for storing a program, wherein when the program is executed by the processor, the system performs the functions of the system described in the first aspect.

[0010] A third aspect of this application provides a method and system for the intelligent modular production of personalized health drinks, wherein a computer program is stored on the storage medium, and the computer program, when executed by a processor, performs the functions of the system described in the first aspect.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] This application embodiment transforms vague requirements into specific data, enabling highly personalized customization of beverages, accurately matching users' health needs, effectively avoiding allergens and unhealthy ingredients, and meeting differentiated health demands.

[0013] This application embodiment balances health and cost, avoiding problems such as high price and low health or low price and high risk, optimizing production plans to reduce inventory, increasing merchant profits, and ensuring user experience.

[0014] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 The flowchart provided for this application;

[0017] Figure 2 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0018] In the diagram: 300, electronic device; 301, memory; 302, processor; 303, communication interface; 304, bus architecture. Detailed Implementation

[0019] This application provides a smart modular production method, system, and equipment for personalized health drinks, which addresses the technical problems of slow response to existing customization needs, high costs, and unstable profits.

[0020] To address the aforementioned technical problems, the overall approach of the technical solution provided in this application is as follows:

[0021] This application embodiment collects user-customized demand information, processes it using a preference algorithm to obtain basic preference information, transforms fuzzy demands into structured data, achieves highly personalized customization of healthy beverages, meets differentiated needs, and uses a price algorithm combined with a pricing ratio algorithm to balance personalization and cost control. Then, through an integrated model, it obtains information on popular beverages to drive production response, optimize merchants' costs and production processes, and improve profits and competitiveness.

[0022] After introducing the basic principles of this application, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0023] Example 1

[0024] like Figure 1 As shown, this application provides a smart modular production method for personalized health drinks, the method comprising:

[0025] S100: Collect customized demand information submitted by users, obtain basic beverage information, obtain basic preference information for healthy beverages based on preference algorithms, and generate beverage health attribute data in conjunction with the health value assessment module;

[0026] Step S100 in the method provided in this application embodiment includes:

[0027] S110: Collect users' basic beverage flavor requirements and obtain basic beverage flavor information;

[0028] S120: Collect user's basic beverage ingredient requirements and obtain beverage basic ingredient information;

[0029] S130: Collect basic user health data to obtain user health profile information;

[0030] S140: Collect user beverage allergen information to obtain basic beverage allergy information;

[0031] S150: Output basic beverage information, including basic flavor information, basic ingredient information, health profile information, and basic allergy information.

[0032] S160: Calculate the basic information of the beverage based on the health value assessment module to obtain the health attribute data of the beverage;

[0033] The health value assessment module includes:

[0034] Nutrition matching function:

[0035]

[0036] This is used to calculate the degree of match between beverage ingredients and the user's daily nutritional needs, where c k For beverage ingredient content, t k For the user's target intake, w k For nutritional weighting, w k Dynamically generated based on user health profiles, for example:

[0037] For diabetic users: Carbohydrate w=0.6, Protein w=0.3, Fat w=0.1

[0038] Correspondingly, for the average adult user: carbohydrates w = 0.4, protein w = 0.3, fat = 0.3;

[0039] By using machine learning models, inputting user data such as age, medical history, and exercise intensity, personalized weight values ​​are output.

[0040] Health risk coefficient acquisition function:

[0041] G l =∑h∈F (T h ·J h )

[0042] Where T h J represents the upper limit of the safe daily intake of component h. h Let h be the user's sensitivity coefficient to ingredient h, and F be the set of ingredients the user needs to avoid. The system collects user information such as taste preferences, ingredient requirements, health indicators (e.g., age, weight, medical history), and allergy information through questionnaires or the app interface. For example, a user with diabetes who indicates a preference for sweet drinks and a lactose allergy will have this information aggregated into basic data and then calculated using the health value assessment module. This module uses a nutritional matching function to determine whether the beverage's ingredients meet the user's daily nutritional needs, and a health risk coefficient acquisition function to identify unhealthy ingredients such as high sugar and high salt. For example, for a diabetic user, the system will lower the recommendation priority of high-sugar drinks.

[0043] S200: The data collection platform acquires product information from the supply side, and based on customized health drink preferences, combines health and cost-based dual-dimensional algorithms to obtain comprehensive evaluation information on customized health drinks;

[0044] Step S200 in the method provided in this application embodiment includes:

[0045] S210: Collect commodity price information and health attribute data from the supply side;

[0046] S220: Calculating the cost index C based on the pricing capacity algorithm I ;

[0047] S230: Calculating the Health Index H based on the Health Value Scoring Algorithm I ;

[0048] S240: A comprehensive index S is obtained based on cost index and health index. I ;

[0049] S250: Based on the cost index and health index, the chalk sets cost balance threshold and health balance threshold. When the cost index is greater than the cost balance threshold or the health balance threshold is greater than the health balance threshold, a two-dimensional warning message is issued. The two-dimensional warning message is output as a comprehensive evaluation information for customized health drinks.

[0050] Where C I The main components are (base price + floating price * customization complexity) / capacity;

[0051] H I =α·N M +β·(1-G l ), where α and β are the set weights, NM For nutritional matching, G l The health risk coefficient is represented by α and β, which are user-configurable preference weights (ranging from 0 to 1, and α + β = 1).

[0052] In actual use, if users want to optimize health, they can set α=0.7 and β=0.3; conversely, if they want to prioritize cost-effectiveness, they can set α=0.3 and β=0.7.

[0053] The system obtains product information such as beverage ingredients, price, and volume from suppliers, and then conducts a two-dimensional evaluation. On the one hand, it uses a pricing-to-volume algorithm to calculate a cost index based on the formula (base price + floating price * customization complexity) / volume, thus reflecting the cost-effectiveness of the beverage. On the other hand, it uses a health value scoring algorithm to calculate a health index by combining nutritional matching degree and health risk coefficient. Then, the system integrates these two indices to generate comprehensive evaluation information and sets a balance threshold between cost and health. When the cost of a beverage is too high or the health risk coefficient exceeds the threshold, the system will issue a warning.

[0054] S300: Send comprehensive evaluation information to the platform for recording, analyze user needs based on the integrated model, obtain information on preferred beverages that take into account popularity, cost and health value, and send them to the platform and the supply side respectively.

[0055] Step S300 in the method provided in the application embodiment includes:

[0056] S310: Record information and price information of preferred beverages, analyze information on price of customized health drinks, and obtain information on customized beverages;

[0057] S320: Popular beverage information is sent to the platform for recommendation response. After obtaining the popular beverage information, the platform generates promotional images based on the popular beverage information and AI image model, and sends the promotional images to users for promotion. The platform records the changes in order transaction data before and after the promotion of related beverages to obtain information on the impact of the promotion.

[0058] S330: Popular beverage information is sent to the supply side for production response. The production response mainly involves the supply side obtaining a production forecast list of popular beverages based on trend analysis algorithms after obtaining the popular beverage information.

[0059] The system records comprehensive evaluation information into the platform database, and then uses an integrated model to analyze user needs. This model combines factors such as popularity, cost, and health value to select the optimal beverage combination. After receiving this information, the platform uses an AI image model to generate beautiful promotional images and pushes them to users, such as promotions for low-sugar fruit tea launched in summer. At the same time, the supply side uses trend analysis algorithms to predict potential production quantities and generate a list of estimated production of popular beverages.

[0060] S400: Receives recommendation response results and production response results, and obtains health beverage production orders based on the balanced order generation algorithm;

[0061] Step S400 in the method provided in the application embodiment includes:

[0062] S410: Based on the production response results and recommendation response results, obtain health beverage production orders using an order generation algorithm;

[0063] S420: The order generation algorithm includes:

[0064] O = Q * (K1 * R + K2 * P)

[0065] Where O represents the output production order quantity for healthy beverages, Q represents the basic estimated quantity in the "Popular Beverage Production Forecast List" in the production response results, i.e., the potential production quantity predicted by the supply side based on trend analysis algorithms, and R represents the "Promotion Impact Coefficient" in the recommendation response results, quantifying the effect of platform-side promotion on order volume improvement. Specifically...

[0066]

[0067] Where ΔO 推广前 To promote the increase in orders per unit time;

[0068] ΔO 推广后 This represents the increase in orders per unit of time after the promotion.

[0069] f is a local minimum (f≠0), and the range of R is [0, 2]. R>1 indicates that the promotion will increase the number of orders, and R<1 indicates that the promotion will not be effective.

[0070] The aforementioned unit of time refers to the time set by the user (including suppliers and the platform), and information is collected based on the time they set.

[0071] Based on the above structure, in actual use, when the demand for beverages is high, K2 = 0.6 and K1 = 0.4 can be set to prioritize production. Conversely, K1 = 0.6 and K2 = 0.4 can be set to limit the corresponding promotion effect.

[0072] S500: Sends production orders to the personalized production module to generate health customization results, sends them to the supply side for secondary optimization response, obtains the final pricing and health plan based on the optimized production algorithm, and synchronizes them to the platform.

[0073] The system will calculate the promotion impact coefficient R based on the changes in order transaction data before and after the promotion. If the order volume increases significantly after the promotion, the R value will be greater than 1, which means that the promotion effect is good. The system will multiply this coefficient by the basic estimated quantity to obtain the final production order quantity. For example, if the basic estimated quantity is 1,000 bottles and the R value is 1.5, then the actual production order quantity is 1,500 bottles.

[0074] Step S500 in the method provided in the application embodiment includes:

[0075] S510: Receives personalized production information submitted by the user to the platform. The personalized production module simultaneously receives health beverage production orders and personalized production information, analyzes the health beverage production orders to obtain customized order information, and outputs the customized order information and personalized production information as health customization results.

[0076] S520: Send the health customization results to the supply side for secondary customization response. The secondary customization response specifically involves building an optimized production algorithm based on the health customization results, obtaining the best pricing information, and outputting the best pricing information as the final pricing result.

[0077] S530: After obtaining the final pricing result, the system automatically triggers the printing command, sends the printing command to the user end and the supplier end respectively, generates a printing confirmation record and synchronizes it to the platform end, so that production, finance and quality inspection departments can quickly obtain key information.

[0078] The system receives personalized requests from users, such as adjusting sugar content or adding specific ingredients. It then generates customized solutions based on production orders. The supply side uses optimized production algorithms to balance costs and health benefits based on these solutions and determines the final pricing. Finally, the system automatically generates printing instructions and synchronizes key data such as production lists and pricing information with departments such as production, finance, and quality inspection, thereby achieving full-process digital management.

[0079] By using a multi-dimensional evaluation algorithm, the system can accurately match users' health needs, reducing the risk of users ingesting unhealthy ingredients. At the same time, with the help of a dual-dimensional early warning mechanism, the system can avoid recommending beverages with low cost-effectiveness or high health risks. Moreover, the introduction of the promotion impact coefficient allows production plans to be adjusted more flexibly according to market feedback, reducing the risk of inventory backlog. In addition, the personalized customization function improves user satisfaction, and the optimized production algorithm balances the company's costs and profits. Ultimately, through data-driven decision-making, the system achieves a win-win situation for health, cost, and user experience.

[0080] Example 2

[0081] Based on the same inventive concept as the intelligent modular production method and system for personalized health drinks in the foregoing embodiments, this application provides an intelligent modular production method and system for personalized health drinks, wherein the intelligent modular production method and system for personalized health drinks includes:

[0082] On the platform side, it provides an interactive platform for merchants and users;

[0083] The supply side consists of beverage suppliers and merchants;

[0084] The user end refers to consumers who make purchases through the platform.

[0085] Example 3

[0086] Based on the same inventive concept as the intelligent modular production method and system for personalized health drinks in the foregoing embodiments, this application also provides a computer-readable storage medium for the intelligent modular production method and system for personalized health drinks, wherein a computer program is stored on the storage medium, and the computer program, when executed by a processor, implements the method as in Embodiment 1.

[0087] Exemplary electronic devices

[0088] The following is for reference. Figure 2 To describe the electronic device of this application,

[0089] Based on the same inventive concept as the intelligent modular production method and system for personalized health drinks in the foregoing embodiments, this application also provides an intelligent modular production device for personalized health drinks, including: a processor coupled to a memory for storing a program, wherein when the program is executed by the processor, the system performs the steps of the method described in Embodiment 1.

[0090] The electronic device 300 includes: a processor 302. For ease of illustration, Figure 2The term is represented by a single thick line, but this does not imply that there is only one bus or a single type of intelligent modular production method and system for customized health drinks.

[0091] Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of programs according to the present application.

[0092] Communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0093] Memory 301 can be ROM or other types of static storage devices capable of storing static information and instructions, RAM or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory can exist independently and be connected to the processor via bus architecture 304. Memory can also be integrated with the processor.

[0094] The memory 301 stores computer execution instructions for implementing the scheme of this application, and the processor 302 controls the execution. The processor 302 executes the computer execution instructions stored in the memory 301, thereby realizing the intelligent modular production method and system for personalized health drinks provided in the above embodiments of this application.

[0095] Those skilled in the art will understand that the various numerical designations, such as "first," "second," etc., used in this application are merely for descriptive convenience and are not intended to limit the scope of this application, nor do they indicate a chronological order. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates an "or" relationship between the preceding and following related objects as a personalized, customized, intelligent, modular production method and system for health drinks. "At least one" refers to one or more. "At least two" refers to two or more. "At least one," "any one," or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0096] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0097] The instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0098] The various illustrative logic units and circuits described in this application may be implemented or operate the described functions using a general-purpose processor, digital signal processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor may be a microprocessor, and optionally, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented using a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0099] The steps of the methods or algorithms described in this application can be directly embedded in hardware, a software unit executed by a processor, or a combination of both. The software unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other storage medium of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and storage medium can be disposed in an ASIC, which can be disposed in a terminal. Optionally, the processor and storage medium can also be disposed in different components within the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative examples of this application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent modular production of personalized health drinks, characterized in that, The method includes: Collect customized request information submitted by users to obtain basic beverage information, obtain basic preference information for healthy beverages based on preference algorithms, and generate beverage health attribute data in conjunction with the health value assessment module; The data collection platform acquires product information from the supply side, and based on customized health drink preferences, it combines health and cost-based algorithms to obtain comprehensive evaluation information for customized health drinks. The comprehensive evaluation information is sent to the platform for recording. Based on the integrated model, user needs are analyzed to obtain information on the preferred beverages that take into account popularity, cost and health value. This information is then sent to the platform and the supply side respectively. Receive recommendation response results and production response results, and obtain health beverage production orders based on the balanced order generation algorithm; Production orders are sent to the personalized production module to generate health-customized results, which are then sent to the supply side for secondary optimization. Based on the optimized production algorithm, the final pricing and health plan are obtained and synchronized to the platform.

2. The intelligent modular production method for personalized health drinks according to claim 1, characterized in that, The process involves collecting user-defined demand information to obtain basic beverage information, using a preference algorithm combined with health value assessment to obtain customized health beverage preferences, and simultaneously generating beverage health attribute data using the health value assessment module, including: Collect users' basic beverage flavor requirements to obtain basic beverage flavor information; Collect users' basic beverage ingredient requirements and obtain basic beverage ingredient information; Collect basic user health data to obtain user health profile information; Collect users' beverage allergen information to obtain basic beverage allergy information; The basic information of a beverage includes its basic flavor information, basic ingredient information, health profile information, and basic allergy information. The basic information of the beverage is calculated based on the health value assessment module to obtain the health attribute data of the beverage. The health value assessment module includes: Nutrition matching function: This is used to calculate the degree of match between beverage ingredients and the user's daily nutritional needs, where c k For beverage ingredient content, t k For the user's target intake, w k Nutritional weight; Health risk coefficient acquisition function: G l =∑ h∈F (T h ·J h ) Used to assess the potential health risks of beverage ingredients to users, where T h J represents the upper limit of the safe daily intake of component h. h This represents the user's sensitivity coefficient to component h.

3. The intelligent modular production method for personalized health drinks according to claim 1, characterized in that, The data collection platform acquires product information from the supply side, and based on customized health drink preferences, combines health and cost-based dual-dimensional algorithms to obtain comprehensive evaluation information for customized health drinks, including: Collect commodity price information and health attribute data from the supply side; The cost index G is calculated based on the pricing ratio algorithm. I ; Health Index H is calculated based on the health value scoring algorithm. I ; The comprehensive index S is obtained based on the cost index and the health index. I ; Where C I The value is (base price + floating price * customization complexity) / capacity; H I =α·N M +β·(1-G l ), where α and β are the set weights, N M For nutritional matching, G l Health risk coefficient; Based on the cost index and health index, cost balance thresholds and health balance thresholds are set respectively. When the cost index exceeds the cost balance threshold or the health balance threshold exceeds the health balance threshold, a dual-dimensional early warning message is issued. The dual-dimensional early warning message is used as the comprehensive evaluation information for customized health drinks.

4. The intelligent modular production method for personalized health drinks according to claim 1, characterized in that, The comprehensive evaluation information is sent to the platform for recording. Based on the comprehensive evaluation information of customized healthy drinks and the analysis of user needs using the popularity evaluation module, information on preferred beverages that balance cost and health value is obtained and sent to both the platform and the supply side, including: Record information on preferred beverages and their prices, analyze information on customized health drinks and their prices, and obtain information on customized beverages. Information on popular beverages is sent to the platform for recommendation response. After obtaining the information on popular beverages, the platform generates promotional images based on the information and AI image models, and then distributes the promotional images to users. The platform records changes in order transaction data before and after the promotion of related beverages to obtain information on the impact of the promotion. Information on popular beverages is sent to the supply side for production response. The production response mainly involves the supply side obtaining a production forecast list of popular beverages based on trend analysis algorithms after receiving the information.

5. The intelligent modular production method for personalized health drinks according to claim 1, characterized in that, The process of receiving recommendation response results and production response results, and obtaining health beverage production orders based on a balanced order generation algorithm, includes: Based on the production response results and recommendation response results, a health beverage production order is generated using an order generation algorithm. The order generation algorithm includes: O = Q * (K1 * R + K2 * P) Where O represents the output health beverage production order quantity, Q represents the basic estimated quantity in the "Popular Beverage Production Forecast List" in the production response results, i.e., the potential production quantity predicted by the supply side based on trend analysis algorithms, and R represents the "Promotion Impact Coefficient" in the recommendation response results, quantifying the effect of platform-side promotion on order volume improvement. Specifically... Where ΔO 推广前 To promote the increase in orders per unit time; ΔO 推广后 This represents the increase in orders per unit of time after the promotion. f is a local minimum (f≠0), and the range of R is [0, 2]. R = 1 indicates that the promotion will increase the number of orders, and R < 1 indicates that the promotion will not be effective. P represents the "production feasibility coefficient" in the production response results, reflecting the degree of matching between the actual production capacity on the supply side and the estimated inventory. It is calculated as follows: Where C is the actual production capacity, which is the maximum production capacity corresponding to the production resources that can be allocated on the supply side; C is the estimated demand, which is the capacity demand in the production forecast list; and P is in the range of [0, 1]. P = 1 means that the actual production capacity fully matches the estimated demand, and P < 1 means that the production capacity is insufficient. K1 and K2 are weighting coefficients, and K1+K2=1, used to balance the impact of recommendation response and production response.

6. The intelligent modular production method for personalized health drinks according to claim 1, characterized in that, The production order for health beverages is sent to the personalized production module for a customized response, resulting in a health-customized product. This customized result is then sent to the supply side for a secondary customization response. Based on the optimized production algorithm, the final pricing result is obtained and sent to the platform, including: The personalized production module receives personalized production information submitted by users to the platform. It simultaneously receives health beverage production orders and personalized production information, analyzes the health beverage production orders to obtain customized order information, and outputs the customized order information and personalized production information as health customization results. The health customization results are sent to the supply side for secondary customization response. The secondary customization response specifically involves building an optimized production algorithm based on the health customization results, obtaining the best pricing information, and outputting the best pricing information as the final pricing result. After obtaining the final pricing result, the system automatically triggers a printing command, which is sent to both the user and the supplier. A printing confirmation record is generated and synchronized to the platform, facilitating quick access to key information for departments such as production, finance, and quality inspection.

7. A smart modular production system for personalized health drinks, characterized in that, The system includes: On the platform side, it provides an interactive platform for merchants and users; The supply side consists of beverage suppliers and merchants; The user end refers to consumers who make purchases through the platform.

8. A smart modular production equipment for personalized health drinks, characterized in that: include: A processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the steps of the system as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the system according to any one of claims 1 to 6.

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