A student nutrition meal take recipe generation and analysis method based on dynamic cascade calculation

Abstract

The application discloses a kind of nutrition with quantity recipe and nutrition analysis method and system of campus student meal. The method comprises: establishing "recipe-set meal-dish-raw material" four-level recipe data model and "raw material-commodity" mapping model, and built-in 《WS / T554-2017 student meal nutrition standard》And food nutrition database;User is based on week view Configuration daily meal set meal and dish, and set estimated number of diners;System is based on dish raw material consumption, real-time cascade calculation dish, set meal and corresponding meal nutrient content, and automatically check whether it conforms to the nutrition standard of the age and gender of the meal student. At the same time, according to the number of diners, the total consumption of each raw material required is automatically summarized and calculated, and is converted into estimated purchase quantity in units of grams, jin, kilograms;Finally, daily / weekly nutrition analysis report covering five dimensions of food category intake, nutrient intake, three meals energy proportion, energy source distribution and protein source distribution is automatically generated. The application realizes the precision, dynamic and automation of student meal nutrition catering, significantly improves the scientificity of recipe compilation and the accuracy of procurement plan.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, specifically to an intelligent dietary management method and system, and in particular to a method and system for generating and analyzing student nutrition meal plans based on dynamic cascade computing. Background Technology

[0002] Scientific and reasonable nutritional meal planning is key to ensuring students' healthy growth. Currently, the management of student meal menus in school canteens mainly relies on manual experience or basic information technology tools, which has many technical limitations.

[0003] Traditional management methods, such as manual calculations using Excel spreadsheets, are not only inefficient but also prone to errors. Chefs or nutritionists must manually consult food nutrition facts labels, calculate the nutrient content of each dish and meal, and roughly compare it with national standards such as the "WS / T554-2017 Nutritional Standards for Student Meals." The entire process relies heavily on personal experience, making it impossible to perform detailed analysis by age group and gender. Furthermore, adjustments to the menu are disconnected from nutritional calculations and ingredient procurement plans, making it difficult to ensure data consistency.

[0004] There are also some nutrition meal planning apps on the market, but these apps usually have relatively fixed functions and suffer from the following common technical defects: The calculation model is rigid: it mostly involves one-time batch calculations based on fixed template recipes. When users need to fine-tune the amount of a certain ingredient in a dish, the system cannot automatically and accurately recalculate all affected nutritional and procurement data. Users often need to manually re-trigger the entire calculation process or make multiple modifications, which is cumbersome and prone to introducing new errors.

[0005] The data structure is flat and lacks dynamic correlation: the internal data model of the system is usually relatively simple, failing to build a multi-level, strongly correlated structured data model such as "raw materials -> dishes -> set meals -> meal times". Therefore, when the underlying data (raw material usage) changes, the system lacks an effective mechanism to automatically detect and update all relevant upper-level data, resulting in inconsistencies between recipes, nutrition analysis reports, and purchase lists.

[0006] The analysis is limited in scope and lacks synergy: Nutritional analysis functions often focus on calculating macro-level indicators such as total calories and total protein, making it difficult to achieve automated benchmarking analysis against national standards for each item and for different population groups. More importantly, the nutrition calculation module and the procurement planning module are technically independent, and changes in the recipe cannot be automatically and synchronously reflected in the precise quantity of ingredients procured (including the conversion of different units of measurement) in real time, creating "data silos."

[0007] In summary, existing technical solutions have inherent technical shortcomings in areas such as data structure design, consistency maintenance of data updates, and real-time linkage between multiple modules, making it difficult to achieve precise, dynamic, and efficient management of student nutrition meal planning. Therefore, an innovative solution that can technically solve the aforementioned data correlation and dynamic response problems is urgently needed. Summary of the Invention

[0008] In a first aspect, embodiments of this disclosure provide a method for generating and analyzing student nutrition meal plans based on dynamic cascaded calculations, comprising the following steps: S1. Constructing the Basic Data Model: Building a multi-level structured data model including ingredients, dishes, set meals, and meal times, with each level forming a bottom-up referencing structure through relationships; establishing a mapping relationship between ingredients and procureable goods; and storing a food nutrition database containing the nutritional content per unit of each ingredient. S2. Receive user instructions to adjust the amount of at least one ingredient in any dish; S3. In response to the adjustment instruction, based on the association relationship of the data model and the food nutrition database, automatically trigger a series of cascaded recalculation operations starting from the raw material layer and moving upwards along the hierarchy to update the nutrient content of all upper-level objects affected by the adjustment, and simultaneously update the total raw material procurement demand according to the adjusted usage. S4. Based on the nutrient content obtained after cascade recalculation and the updated total raw material procurement demand, generate a nutrition analysis report and a food procurement plan respectively.

[0009] Preferably, in the multi-level data model, raw materials are the lowest level unit, dishes are composed of one or more raw materials, set meals are composed of one or more dishes, and meals are composed of one or more set meals or individual dishes.

[0010] Preferably, the cascaded recalculation operation includes: Recalculate the nutrient content of the target dish containing the adjusted ingredients; Based on the updated nutrient content of the target dish, the total nutrient content of each set meal containing the target dish is recalculated. Based on the updated total nutrient content of the meal plan, the total nutrient content of the corresponding meal will be recalculated.

[0011] Preferably, the total raw material procurement requirement is calculated based on the adjusted raw material usage and the estimated number of diners.

[0012] Preferably, the estimated number of diners is obtained dynamically in the following way: Obtain the total number of students enrolled in the school, and combine this with course calendar data and student leave data to calculate the actual number of diners during a specific meal time in the future.

[0013] Preferably, the method further includes: A nutrition standard database is constructed, which stores recommended or limited nutritional intake values ​​for student groups according to age and gender. The nutrition analysis report is generated by comparing the recalculated total nutrient content of each meal with the standard values ​​of the student's dining attributes matched in the nutrition standard database.

[0014] Preferably, the nutrition analysis report is a multi-dimensional nutrition achievement analysis report, including at least two of the following dimensions: The following data are considered: whether the intake of different types of food meets the standard, whether the intake of nutrients meets the standard, the distribution of energy in the three meals, the distribution of energy sources, and the distribution of protein sources.

[0015] Preferably, when generating a food procurement plan, the following are also included: Based on the mapping relationship between the raw materials and the purchasable commodities, the total procurement demand of raw materials is converted into the procurement quantity in units of commodities, and unit conversion between grams, kilograms, and kilometres is performed.

[0016] Preferably, the cascaded recalculation process is triggered in real time: after the user completes the input of the dosage adjustment command, the system immediately and automatically executes the cascaded recalculation operation and feeds back the updated results of the nutrition analysis report and the food procurement plan to the user interface in real time.

[0017] Secondly, this disclosure also provides a system for generating and analyzing student nutrition meal plans based on dynamic cascaded calculations, used to implement the aforementioned method for generating and analyzing student nutrition meal plans based on dynamic cascaded calculations. The system includes: The data model building module is used to build and maintain a multi-level structured data model that includes raw materials, dishes, set meals, and meal times, as well as a raw material-commodity mapping relationship and a food nutrition database. The instruction receiving module is used to receive user instructions on adjusting the amount of ingredients used in dishes. The dynamic cascaded calculation engine is connected to the data model construction module and the instruction receiving module. In response to the adjustment instruction, it automatically performs cascaded recalculation along the hierarchical association of the data model and outputs the updated nutrient content and total raw material procurement requirements. The report generation module, which is connected to the dynamic cascaded computing engine, is used to generate nutrition analysis reports and food procurement plans based on the recalculation results.

[0018] Diners prediction module: Responsible for acquiring and processing data from relevant business systems, dynamically calculating and estimating the number of diners, and providing key input parameters for the dynamic cascaded computing engine.

[0019] The present invention has the following advantages: (1) Consistency, real-time performance, and automation of data updates have been achieved. This invention constructs a multi-level, strongly correlated data structure model of "raw materials-dishes-set meals-meal times" and innovatively introduces a dynamic cascading computing engine, enabling any adjustment to the amount of raw materials at the bottom level to automatically trigger and complete the synchronous recalculation and update of all related data from the bottom up in real time. This fundamentally solves the core technical problems of data inconsistency and slow adjustment feedback caused by loose data models and rigid computing modes in existing technologies, ensuring that the data between nutritional analysis, recipe planning, and purchasing lists are always accurately synchronized.

[0020] (2) Significantly improves the accuracy and overall efficiency of the dietary management process. This invention automates the entire chain from recipe preparation to procurement plan generation. The system can dynamically obtain the estimated number of diners and automatically convert the total raw material demand into procurement quantities in different units (grams, kilograms, kilograms), completely eliminating errors in manual estimation and unit conversion. At the same time, the automatic generation of multi-dimensional nutrition reports frees nutritionists from tedious manual calculations and benchmarking. In summary, this solution can significantly reduce (e.g., more than 85%) the cost of manual intervention and time in related processes, making management decisions more accurate and efficient.

[0021] (3) It provides in-depth, customizable scientific decision support and forms an intelligent management closed loop. This system not only performs basic nutritional calculations, but also automatically completes refined nutritional compliance analysis by age group and gender based on authoritative national standards (such as WS / T554-2017), and generates comprehensive reports covering multiple dimensions such as food types, nutrient intake, and energy source proportions. This elevates nutritional assessment from a static, macroscopic level to a dynamic, microscopic, and personalized level. Ultimately, this invention organically integrates recipe design, nutritional assessment, procurement and supply into a data-driven, real-time linked, and self-optimizing intelligent business closed loop, laying a solid technical foundation for the scientific and refined dietary management of school canteens. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0023] Figure 1A flowchart of a method for generating and analyzing student nutrition meal recipes based on dynamic cascade calculation, provided in an embodiment of the present invention; Figure 2 This is a system runtime data flow diagram for the nutritional recipe and nutritional analysis service provided in an embodiment of the present invention; Figure 3 This is a structural diagram of a student nutrition meal quantity-based recipe generation and analysis system based on dynamic cascade calculation, provided as an embodiment of the present invention. Detailed Implementation

[0024] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings to make the technical solution of the present invention clearer and more complete. It should be noted that the described embodiments are for illustrative purposes only and are not intended to limit the present invention. Other implementation methods that can be made by those skilled in the art based on the content of the present invention without creative effort should all fall within the protection scope of the present invention.

[0025] In this application and its claims, unless otherwise expressly stated, the terms "comprising," "including," and similar expressions should be understood to indicate the presence of the listed items without excluding the presence or addition of other items. The words "an," "a," and similar terms should not be construed as limited to the singular in this application, and may also include multiple items.

[0026] Furthermore, the accompanying drawings in this application are merely illustrative and not necessarily drawn to scale. The same reference numerals denote components with the same or similar functions. To clearly illustrate the present invention, specific details are provided in the following embodiments. Those skilled in the art should understand that these details are not essential for implementing the present invention, and other methods may be used to implement it without affecting the basic idea of ​​the invention.

[0027] The overall flow of the method described in this invention is as follows: Figure 1 As shown, the details are as follows: I. Overall System Data Flow The data flow diagram for nutrition recipes and nutrition analysis services is as follows: Figure 2 As shown, the system involved in this invention is typically deployed on a cloud server or a local server (corresponding to the admin end in the diagram), and users access it through a client (such as a web browser or mobile application, corresponding to the canteen end and the supplier end). The core logical flow of the system begins with the construction of the basic data model, runs through user interaction and dynamic calculation, and ends with the generation of reports and plans.

[0028] II. Construction of the Basic Data Model The implementation of this invention first relies on a set of data structures with clear hierarchical and relational relationships. These structures are implemented through tables in a relational database and foreign key constraints between them, forming the data foundation that supports "dynamic cascading computation".

[0029] (1) Core data structure: Four-level association model The system defines and maintains four core entities: "Ingredients," "Dishes," "Set Meals," and "Meal Sessions," and establishes the relationships between them through association tables. Raw materials: Represent the most basic food building blocks (such as "beef" or "rice"). Each raw material is associated with its standard nutritional content data (such as protein and energy value per 100 grams).

[0030] Dish: Composed of one or more ingredients in specific quantities (in grams), representing a specific dish (e.g., "Braised Beef with Potatoes"). The compositional relationship between the dish and its ingredients (i.e., which ingredients are used in the dish and how many grams of each) is precisely recorded in the association table.

[0031] Set meals: Composed of one or more dishes, representing a fixed dietary combination (e.g., "one meat dish, one vegetable dish, one soup, and one staple food"). The relationship between set meals and individual dishes is also defined through an association table.

[0032] Meal Session: Corresponds to a specific dining occasion (e.g., "Lunch on October 26, 2024"). A meal session can directly include multiple dishes or one or more set meals; this flexible configuration is achieved through an association table.

[0033] The above structure forms a bottom-up reference chain (raw materials -> dishes -> set meals -> meal times). When the underlying data (such as the amount of a certain raw material used) changes, the system can locate and update all affected upper-level data step by step according to these predefined relationships. This is the prerequisite for realizing "cascaded recalculation".

[0034] (2) Key mapping relationships and basic database To integrate nutrition calculations with procurement management, the system also needs to establish the following key mappings and pre-configure basic data: Raw material-product mapping: Each raw material is associated with a standard product (SKU) in the procurement system, and the conversion factor for its packaging specifications is recorded. For example, the raw material "beef" is mapped to the product "chilled fresh beef (specification: 10 kg / box)," and the conversion relationship "1 box = 10,000 grams" is recorded. This mapping serves as a bridge for automatically generating procurement quantities and converting units.

[0035] Food nutrition databases: a pre-established, authoritative source that stores the standard content of various nutrients (such as energy, protein, fat, carbohydrates, vitamins, and minerals) per unit weight (usually per 100 grams) of all raw materials. This database serves as the benchmark for any nutritional calculations.

[0036] The Student Nutrition Meal Standard Database is established based on standards such as "WS / T554-2017 Student Meal Nutrition Standards." It stores recommended daily or per-meal nutrient intakes (or ranges), food type requirements, and energy source proportions, categorized by age group and gender. This database serves as a benchmark for nutritional compliance analysis; some typical data are shown in Table 1.

[0037] Table 1 Individual Item Compliance Assessment (By Age Group-Gender)

[0038] By constructing the above data model, the system provides structured data support for subsequent real-time calculations, dynamic linkages, and comprehensive analysis.

[0039] II. Receiving User Commands and Triggering Dynamic Cascading Calculation This step is the core of the invention, which involves building a responsive data processing engine. When the user adjusts the raw material usage at the lowest level of the data model, it can automatically and in real time trigger and execute a series of chain calculations upward along the data hierarchy, ensuring consistent updates of all related data.

[0040] (1) Command capture and event triggering Users can adjust the quantities of ingredients in dishes through an interactive interface. For example, in the dish "Braised Beef with Potatoes" (ID:D001), the amount of "Beef" (ID:F001) can be changed from 40 grams to 41 grams, and the amount of "Potatoes" (ID:F002) can be changed from 60 grams to 59 grams. After the user confirms the operation, the instruction receiving module generates a structured change event. This event carries the core parameters: the target dish identifier (dish_id) and a list of ingredient quantity pairs that have been changed [(food_id, new_quantity)].

[0041] (2) Core algorithm flow of the dynamic cascaded computing engine The dynamic cascaded computing engine, as the core processing unit of the system, listens for the aforementioned events and immediately initiates a multi-stage cascaded computing process. Its logic is bottom-up, propagating and recalculating based on preset data relationships (foreign key constraints).

[0042] Phase 1: Leaf Node Update (Nutritional Data Update at the Vegetable Level) The engine first locates the target dish D001. For each ingredient in the dish... The system retrieves the unit nutrient content vector from a pre-set food nutrient database. Each component of this vector represents the content of a nutrient (such as energy, protein, or fat) per gram of raw material.

[0043] Total nutritional content vector of the updated dishes Calculated using formula (1): (1) in, It is a raw material The new amount (grams) used in this dish. Once the calculation is complete, the engine will immediately add the new... The value is updated (or cached) in the dish record. This step is usually completed within a very short time (e.g., within 1 second) after the user's action, achieving a "real-time" response.

[0044] Phase 2: First-level Propagation of Package-Level Nutritional Data Next, the engine queries the set meal-menu relationship table to find all set meals that contain the updated dish D001 (e.g., set meal S_SL_01). For each affected set meal... Its nutritional content is the sum of the nutritional content of all its constituent dishes.

[0045] Set up a package Include The first dish, the The nutritional content of each dish is The total nutritional content of this meal Updated to the form defined in formula (2): (2) The calculation results are also updated in real time to the corresponding package records.

[0046] ③ Phase Three: Second-level Propagation of Meal-Level Nutritional Data The engine further queries the meal-set-and-dish relationship table to determine which meal instances (such as "Monday Lunch") contain the newly updated set-and-dish S_SL_01 or directly contain dish D001. For each affected meal... Its total nutritional content is the sum of the nutritional content of all its components (which may be a set meal or individual dishes).

[0047] Assuming meals Depend on It consists of several components, each component The nutritional content (which may be a set meal or a standalone dish) is: The total nutritional content of a meal Updated to the form defined by formula (3): (3) This step ensures the immediate accuracy of top-level nutritional analysis data.

[0048] Phase 4: Parallel Recalculation of Procurement Demand While performing the cascaded calculations of the aforementioned nutritional data, the engine initiates a parallel thread or transaction specifically to handle updates to the procurement demand. This is the key to achieving "synchronous" updates in this invention.

[0049] The system first determines which raw materials have had their usage changed based on the change events (F001 and F002 in this example). For each affected raw material... The system needs to obtain its estimated number of diners. (This can be dynamically obtained by connecting to the academic affairs system and attendance data, such as for 150 people). Raw materials Total demand (gram) is defined as being calculated according to formula (4): (4) in, It is a raw material The adjusted per capita consumption (grams / person). For example, if the new per capita consumption of beef (F001) is 41 grams, then the new total demand is 41 × 150 = 6150 grams.

[0050] (3) Atomicity and real-time performance of the overall process The entire cascading calculation process is encapsulated within a database transaction or an execution context with similar guarantees to ensure "atomicity": either all relevant nutrition and procurement data are successfully updated, or a complete rollback occurs in the event of an error, eliminating data inconsistencies. The end-to-end latency from user-triggered action to the interface displaying the updated result is controlled within seconds.

[0051] III. Generating Nutritional Analysis Reports and Food Procurement Plans The final output of this invention manifests as two types of structured business documents: a scientific nutritional analysis report and a precise food procurement plan. Both reports are automatically generated by the report generation module based on the latest and consistent data output by the dynamic cascaded computing engine, through a series of standardized data processing and transformation rules.

[0052] (1) The generation mechanism of multidimensional nutrition analysis reports The report generation module first obtains the total nutritional content data of each meal, which has already been updated by the cascaded computing engine, based on the user-selected meal range (such as a single day, a single meal, or a week). Then, it combines this data with accurate student demographic data (such as the actual number of diners by age group and gender) and executes the following analysis process: ① Calculation of average intake per population group Assume there is a total There are three different student groups (divided by age and gender), the first The number of diners in each group is For a given meal, the total content of a certain nutrient (such as energy) is: The system does not simply divide the total content by the total number of people. Instead, it calculates the average intake per person for each group based on a pre-defined nutrient allocation logic (such as a weighted average based on the number of people or ensuring that the intake of each group meets its lower limit). .

[0053] In this embodiment, total nutrition is allocated according to the proportion of the number of people, as defined in formula (5): (5) in, For the target group The standardization factor can usually be set to 1, which simplifies the distribution to the absolute proportion of the population. When needed by the user, the report generation module processes the latest data output by the dynamic cascaded computing engine: More sophisticated models can assign different nutritional requirement weights to different groups. .

[0054] ② Item-by-item assessment and scoring Get Afterwards, the system queries the student nutrition meal standard database to obtain group information. Recommended intake range for this nutrient or a single threshold Then, a compliance determination is performed, and the determination of the compliance status is shown in formula (6): (6) Then, the system iterates through all nutrients (such as energy, protein, calcium, iron, vitamin A, etc.) and all groups to complete the entire process. Individual item assessment. For food type and energy source distribution (protein energy ratio)... The proportion of energy supplied by fat Carbohydrate energy ratio For composite indicators such as ( ), the system compares the actual ratio calculated from the underlying data with the standard ratio range based on the same principle.

[0055] ③ Multi-dimensional summary and comprehensive score generation all Each individual result is categorized into several pre-defined dimensions (e.g., A. Food type intake; B. Nutrient intake; C. Dietary proportions; D. Energy source distribution; F. Protein source distribution). Each dimension... Include Each evaluation item, its dimensional score The calculation is shown in formula (7): (7) Under the aforementioned predicted dimensions, typical summative scores for each dimension are shown in Table 2.

[0056] Table 2 Scores of the Five Dimensions

[0057] Finally, a single-day comprehensive score for this meal is generated. Its definition is shown in formula (8): (8) The report generation module will ,each The system integrates all individual judgment details (actual value, standard value, status) and outputs a formatted analysis report with both text and graphics. For periodic reports (such as weekly reports), the system calculates the arithmetic mean of the comprehensive scores for each day within the period to generate a comprehensive periodic score.

[0058] A typical comprehensive score is shown in Table 3.

[0059] Table 3. Weekly Rating Examples

[0060] (2) The mechanism for generating precise food procurement plans Another core function of the report generation module is to seamlessly transform nutritional meal planning data into executable procurement instructions, the process of which is as follows: ① Summary of raw material requirements Based on the user-specified planning period (e.g., the next week), the system extracts the total required weight (in grams) of each ingredient i in all dishes from all scheduled meal data. This is essentially the summation of the amount of the ingredient used in all meals and all dishes within the cycle, as defined in formula (9): (9) in, It is the amount of ingredient i used per person in the dish (grams / person). This is the estimated number of diners for this meal.

[0061] ②Unit conversion and product association get Next, the system performs unit conversion. First, it retrieves the SKU and specifications of the purchased goods associated with raw material i by querying the raw material-commodity mapping table. The key conversion steps are as follows: Converted to jin (Chinese unit of weight):

[0062] Convert to kilograms:

[0063] If the associated product is a packaged product (e.g., "10 kg / box"), let its packaging specifications be... If the weight is kilograms per piece, then the number of packages required is... As defined in formula (10), the rounding up method is used to ensure sufficient procurement quantity: (10) (3) Procurement list generation and output The system summarizes and sorts the above calculation results by product SKU, generating a structured draft purchase order. Each line in the list clearly lists: product SKU, product name, total required weight (grams / kg / jin), suggested quantity, specifications, and other information. This list can be directly exported or pushed to the procurement system, completing a fully automated closed loop from "nutritional meal planning" to "precision procurement".

[0064] The above-described formulaic and rule-based detailed description fully elucidates how the present invention transforms dynamically calculated data into decision support information with direct business value, demonstrating the completeness and feasibility of the technical solution at the application level.

[0065] For example, a total beef requirement of 6150 grams is automatically converted to 6150 / 500 = 12.3 jin (approximately 6.65 kg) or 6150 / 1000 = 6.15 kg. For packaged goods (such as SKU_B001, 10 kg / box), the system will further calculate that 6.15 / 10 = 1 box needs to be purchased (rounded up). The final result is a clear and directly executable purchase list.

[0066] IV. Implementation Results and Experimental Comparison Analysis To objectively evaluate the practical effectiveness of the system described in this invention, we selected three schools in a certain city that used different menu management methods (School A used the system of this invention, School B used commercially available nutritional meal planning software, and School C used traditional manual management via Excel spreadsheets) as comparison subjects and conducted a one-month follow-up experiment. The experiment mainly compared four key indicators: menu compilation efficiency, nutritional analysis accuracy, procurement plan accuracy, and labor cost consumption. The data comparison is shown in Table 4. Table 4. Comparison of Monthly Results of Three Recipe Management Methods

[0067] From the data shown in Table 4, the following conclusions can be drawn: ① In terms of efficiency and responsiveness: The system of this invention, thanks to its "dynamic cascaded computing engine," achieves automatic global updates within seconds after user data adjustments, whereas traditional methods suffer from severe lag and a large amount of repetitive work. This directly demonstrates the significant effectiveness of this invention in solving the technical problems of "data update consistency" and "slow feedback."

[0068] ② In terms of accuracy and scientific rigor: This invention achieves multi-dimensional and refined automated nutritional analysis with an extremely low error rate through a structured data model and a built-in authoritative standard library. In contrast, existing tools have limited analytical dimensions, rely on manual intervention, and are prone to errors. This verifies the advantages of this invention in improving the "precision" and "scientific rigor" of nutritional assessment.

[0069] ③ Regarding business closed-loop and cost: This invention integrates recipe, nutrition, and procurement data, enabling the automatic and accurate generation of procurement plans with a significantly lower deviation rate than comparable products. Simultaneously, end-to-end automation drastically reduces related manual input. This powerfully demonstrates the beneficial effects of this invention in achieving "management closed loop" and "cost reduction and efficiency improvement."

[0070] In summary, the experimental data fully demonstrate that the present invention has made comprehensive improvements in key technical indicators compared with the prior art. Through innovative technical solutions (dynamic cascaded calculation, multi-dimensional analysis, and automated closed loop), it effectively solves many defects pointed out in the background art and achieves comprehensive technical effects of improving efficiency, ensuring accuracy, and saving costs.

[0071] V. System Composition and Interaction like Figure 3 As shown, the present invention also provides a system for implementing the above method, wherein each module communicates with each other through a predefined interface: Data model building module: responsible for initializing and maintaining the aforementioned multi-level structured data model, mapping relationships, and various databases.

[0072] Command receiving module: As the interaction interface between the system and the user, it captures any operation commands from the user on the recipe data.

[0073] The dynamic cascaded computing engine is the core processing unit of this system. It communicates with the data model building module and the instruction receiving module. Once it receives a data change instruction, it automatically executes the cascaded recalculation logic based on the relationships in the data model.

[0074] Report generation module: Receives the latest results from the dynamic cascaded computing engine and generates a food procurement plan according to predetermined rules.

[0075] The diners prediction module is responsible for acquiring and processing data from relevant business systems (academic affairs system, attendance system), dynamically calculating and estimating the number of diners, and providing key input parameters for the dynamic cascaded calculation engine.

[0076] In summary, this invention, through the construction of a specific data structure, the design of a responsive dynamic computing engine, and the integration of multi-source data and business rules, realizes a highly automated, data-linked, and real-time feedback-enabled refined management system for student nutrition meals. Those skilled in the art can implement and apply the system based on the above description and specific software engineering practices.

[0077] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0078] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0079] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0080] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0081] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0082] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0083] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0084] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for generating and analyzing student nutrition meal plans based on dynamic cascaded computation, characterized in that, Includes the following steps: S1. Constructing the Basic Data Model: Building a multi-level structured data model including ingredients, dishes, set meals, and meal times, with each level forming a bottom-up referencing structure through relationships; establishing a mapping relationship between ingredients and procureable goods; and storing a food nutrition database containing the nutritional content per unit of each ingredient. S2. Receive user instructions to adjust the amount of at least one ingredient in any dish; S3. In response to the adjustment instruction, based on the association relationship of the data model and the food nutrition database, automatically trigger a series of cascaded recalculation operations starting from the raw material layer and moving upwards along the hierarchy to update the nutrient content of all upper-level objects affected by the adjustment, and simultaneously update the total raw material procurement demand according to the adjusted usage. S4. Based on the nutrient content obtained after cascade recalculation and the updated total raw material procurement demand, generate a nutrition analysis report and a food procurement plan respectively.

2. The method according to claim 1, characterized in that, In the multi-level data model, raw materials are the lowest level unit, dishes are composed of one or more raw materials, set meals are composed of one or more dishes, and meals are composed of one or more set meals or individual dishes.

3. The method according to claim 1 or 2, characterized in that, The cascaded recalculation operation includes: Recalculate the nutrient content of the target dish containing the adjusted ingredients; Based on the updated nutrient content of the target dish, the total nutrient content of each set meal containing the target dish is recalculated. Based on the updated total nutrient content of the meal plan, the total nutrient content of the corresponding meal will be recalculated.

4. The method according to claim 1, characterized in that, The total raw material procurement requirement is calculated based on the adjusted raw material usage and the estimated number of diners.

5. The method according to claim 4, characterized in that, The estimated number of diners is obtained dynamically through the following methods: Obtain the total number of students enrolled in the school, and combine this with course calendar data and student leave data to calculate the actual number of diners during a specific meal time in the future.

6. The method according to claim 1, characterized in that, The method further includes: A nutrition standard database is constructed, which stores recommended or limited nutritional intake values ​​for student groups according to age and gender. The nutrition analysis report is generated by comparing the recalculated total nutrient content of each meal with the standard values ​​of the student's dining attributes matched in the nutrition standard database.

7. The method according to claim 6, characterized in that, The nutrition analysis report is a multi-dimensional nutrition achievement analysis report, including at least two of the following dimensions: The following data are considered: whether the intake of different types of food meets the standard, whether the intake of nutrients meets the standard, the distribution of energy in the three meals, the distribution of energy sources, and the distribution of protein sources.

8. The method according to claim 1, characterized in that, When generating a food procurement plan, the following are also included: Based on the mapping relationship between the raw materials and the purchasable commodities, the total procurement demand of raw materials is converted into the procurement quantity in units of commodities, and unit conversion between grams, kilograms, and kilometres is performed.

9. The method according to claim 1, characterized in that, The cascaded recalculation process is triggered in real time: after the user completes the input of the dosage adjustment command, the system immediately and automatically executes the cascaded recalculation operation and feeds back the updated results of the nutrition analysis report and the food procurement plan to the user interface in real time.

10. A student nutrition meal quantity-based recipe generation and analysis system based on dynamic cascaded computation, characterized in that, The system for implementing the method as described in any one of claims 1 to 9 comprises: The data model building module is used to build and maintain a multi-level structured data model that includes raw materials, dishes, set meals, and meal times, as well as a raw material-commodity mapping relationship and a food nutrition database. The instruction receiving module is used to receive user instructions on adjusting the amount of ingredients used in dishes. The dynamic cascaded calculation engine is connected to the data model construction module and the instruction receiving module. In response to the adjustment instruction, it automatically performs cascaded recalculation along the hierarchical association of the data model and outputs the updated nutrient content and total raw material procurement requirements. The report generation module is connected to the dynamic cascaded computing engine and is used to generate nutritional analysis reports and food procurement plans based on the recalculation results. Diners prediction module: Responsible for acquiring and processing data from relevant business systems, dynamically calculating and estimating the number of diners, and providing key input parameters for the dynamic cascaded computing engine.

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