Method and system for automated verification of food import eligibility based on data structuring alignment

Abstract

The application provides a food input qualification automatic verification method and system based on data structured comparison. The method calls the MQC rejection rule library in the memory through the processor, and sequentially performs seven-level logic verification on the structured data of the food to be verified. The system uses a text matching algorithm, a numerical comparison algorithm, and a supply chain hash verification technology to identify potential metabolic interference characteristics in the data. When any "rejection signal" is triggered in the verification process, the system automatically generates an "admission interception instruction". Only when all verification steps are passed, the system generates an "MQC qualification certification digital voucher", allowing the data to be written into the subsequent energy regulation system.

Description

Technical Field

[0001] This invention relates to the field of digital information processing and automated control technology, and in particular to an automated verification method and system for food input qualifications based on structured data comparison. Background Technology

[0002] In digital management and energy control systems, the accuracy and compliance of input data are prerequisites for stable system operation. However, existing food data input methods suffer from significant problems of unstructured and ambiguous data. Limited data dimensions: Existing technologies mostly record data based on calorie or macronutrient (carbohydrate / fat / protein) values, and cannot identify "hidden metabolic disruptors" (such as specific additives, oxidized lipids, or highly processed structures).

[0003] Lack of automated verification mechanism: Current screening mainly relies on manual reading of labels or subjective judgment, and lacks a technical solution that can transform complex biochemical rules into computer-executable logic gates.

[0004] Uncontrollable system-level risks: When food data containing high glycemic load or mitochondrial toxic components is incorrectly written into the energy management system, it can cause the system's algorithm model to fail or generate incorrect regulatory instructions.

[0005] Therefore, there is an urgent need for a technical solution that can automatically parse food metadata and perform automated comparison and interception based on a preset mitochondrial quality control (MQC) rule base, so as to achieve data cleaning and quality control at the input end. Summary of the Invention

[0006] The purpose of this invention is to solve the above-mentioned problems by providing an automated verification method and system for food input qualifications based on data structure comparison.

[0007] The technical solution of this application is implemented as follows: This invention aims to solve the following technical problems at the data processing and system control levels, but does not involve individual health diagnosis or disease treatment decisions: How to transform the abstract "mitochondrial-friendly" standard into a machine-readable database and verification algorithm; How to identify and block unstructured food data containing specific keywords (such as hidden sugars, interfering agents) through automated means (such as OCR or API); How to establish a sequential verification process based on multiple logic gates (AND / OR / NOT) to achieve hierarchical locking and authentication of input data; How to construct a pre-access module that can serve as a "firewall" for an energy regulation system.

[0008] This invention proposes an automated verification method and system for food input qualifications based on structured data comparison. The method uses a processor to call the MQC rejection rule base in memory to sequentially perform seven levels of logical verification on the structured data of the food to be verified. The system utilizes text matching algorithms, numerical comparison algorithms, and supply chain hash verification technology to identify potential metabolic interference features in the data. When any "rejection signal" is triggered during the verification process, the system automatically generates an "access interception instruction"; only when all verification steps pass, the system generates an "MQC qualification certification digital certificate," allowing the data to be written into the subsequent energy control system. The rule base adopts a technical architecture design and includes the following three aspects: 1. Efficient storage and rapid location of rules are achieved using key-value pair mapping or hash index structures; 2. The keyword matrix is ​​constructed as a searchable table based on an inverted index, supporting multi-dimensional combined queries; 3. Threshold parameter sets are encapsulated in the form of callable arrays or mapping tables for easy dynamic loading and flexible invocation; the overall design ensures the accuracy of rule matching and the flexibility of parameter configuration.

[0009] Specifically, this invention provides an automated verification method for food input qualifications based on structured data comparison. The method is executed by a computing device equipped with a processor and includes the following steps: (1) Data parsing and structuring steps: Receive the metadata of the food to be verified through the data interface and parse it into structured data containing the ingredient list field, nutrient value field and production process label field; (2) Rule base calling steps: Load the preset mitochondrial quality control (MQC) veto rule base from the memory. The rule base includes a blacklist keyword matrix, a metabolic interference feature library and a processing intensity quantification threshold. (3) Multi-dimensional logic verification steps: Based on the structured data, the following computer-implemented verification steps are executed sequentially, wherein the verification steps constitute a series logic gate structure; when any step triggers a rejection signal, the system triggers a short-circuit termination mechanism; the output result is a data write permission control instruction: (a) Sugar load text matching: The ingredient list field is matched against the "high sugar stress" entries in the blacklist keyword matrix. If a match is found, a first-level rejection signal is generated. (b) Calculation of carbohydrate structure properties: Identify the main matrix properties based on the fields in the ingredient list, or calculate the numerical ratio of carbohydrates to dietary fiber. If the identification result falls into the "highly digestible starch matrix" definition domain, a first-level rejection signal is generated. (c) Lipid oxidation risk comparison: Identify the type of oil in the ingredient list field and retrieve its oxidation risk index in the "Lipid Stability Database". If the index is higher than the preset safety threshold, generate a first-level rejection signal. (d) Additive interference feature identification: The ingredient list field is compared with the metabolic interference feature library. If a non-nutritive ingredient marked as "mitochondrial membrane potential interfering agent" or "metabolic signal blocking agent" is found, a first-level rejection signal is generated. (e) Processing Intensity Index Calculation: Based on the complexity of the production process label field or ingredient composition, the processing intensity algorithm is called to calculate the industrialization index of the food. If the index exceeds the preset MQC prototype threshold, a first-level rejection signal is generated. (4) Result generation and locking steps: When any level of rejection signal is triggered during the verification process, the processor immediately terminates the subsequent verification or marks an exception, and generates an "MQC admission interception instruction". A "MQC Qualification Digital Certificate" is generated only if no rejection signal is triggered in any of the verification steps.

[0010] Glycolic load database definition: As a further improvement, the “high sugar stress terms” in step (a) are stored in the MQC rejection rule base, and the dataset includes, but is not limited to: sucrose, high fructose corn syrup (HFCS), crystalline fructose, maltose syrup, glucose syrup, concentrated fruit juice (non-reduced), brown rice syrup, invert sugar syrup and other carbohydrate keywords with a glycemic index (GI) exceeding a preset value.

[0011] Carbohydrate structure algorithm: As a further improvement, the verification step of step (b) includes: identifying whether the main structure of the food is refined grain flour or free sugar matrix; if it is identified as a carbohydrate source with an incomplete plant cell structure, it is determined as a "high-risk matrix structure label" and a rejection signal is triggered.

[0012] Lipid database: As a further improvement, the "lipid stability database" in step (c) includes oxidative stability scores for various edible oils; when the input data shows that the oil contains hydrogenated vegetable oil, refined seed oil, or trans fatty acid content that is not zero, the system determines that its oxidative risk index exceeds the standard.

[0013] Additive Blacklist: As a further improvement, step (d) targets “mitochondrial membrane potential disruptors” including, but not limited to: artificial colors, artificial flavors, specific preservatives (such as sodium benzoate), monosodium glutamate (MSG) and other artificial sweeteners, which are predefined as having potential metabolic signaling interference properties.

[0014] Processing intensity algorithm: As a further improvement, the processing intensity algorithm in step (e) is based on the NOVA food classification system or equivalent logic, and generates a quantitative industrialization index by calculating the ratio of isolated extracts to whole foods in the ingredient list.

[0015] As a further improvement, the method further includes: (f) toxicity load data verification: obtaining the test report data of the food batch and comparing it numerically with a preset mitochondrial toxicity safety threshold; if the threshold is exceeded, a first-level rejection signal is generated; and / or (g) Supply chain digital identity verification: Verify whether the food has a valid supply chain traceability hash value (HashID) or verifiable source metadata. If the verification fails, a secondary rejection signal is generated. Furthermore, the result generation and locking step (4) further includes: When any first-level or second-level rejection signal is triggered during the verification process, the processor immediately terminates the subsequent verification or marks an anomaly, and generates an "MQC admission interception instruction". A "MQC Qualification Digital Certificate" is generated only if no rejection signal is triggered in any of the verification steps.

[0016] Traceability technology: As a further improvement, step (g) uses blockchain distributed ledger technology or encrypted database query technology to verify the existence of the source metadata of food.

[0017] SaaS / App Architecture: This invention further provides an automated verification system for food input qualifications, the system comprising: Data acquisition terminal: Equipped with an optical character recognition (OCR) module or barcode scanning interface, used to extract unstructured ingredient and process information from food physical packaging or digital labels and convert it into machine-readable structured data; Cloud-based rule engine: Stores the aforementioned MQC veto rule library and is configured to receive the structured data and perform multi-dimensional logical verification steps; Status Locking Module: Based on the verification results, it generates a visual "red light / green light" passage status indicator on the user terminal interface, or sends a data write permission / prohibition command to the associated external energy control system. Dynamic update interface: used to receive the latest scientific research data and periodically update the blacklist keyword matrix and threshold parameter set.

[0018] As a further improvement, the system is configured to act as a front-end data access gateway for an individual energy regulation system based on mitochondrial quality control, allowing only food data holding "MQC Qualification Digital Certificates" to be written into the main database of the energy regulation system, thereby preventing inferior energy input from interfering with the accuracy of the system's algorithm model.

[0019] The advantages or beneficial effects of the above technical solutions include at least the following: Standardized digitization: This invention transforms vague health standards into precise database matching logic, eliminating the subjectivity of human judgment and reducing the rate of erroneous data writing, thereby reducing the propagation of backend model errors.

[0020] Millisecond-level automated screening: This invention further combines OCR with algorithms to achieve real-time verification and grading of massive amounts of food data.

[0021] System-level security defense: As a "firewall" for the energy regulation system, it effectively prevents inferior energy data from contaminating the core algorithm model, ensures the accuracy of subsequent energy regulation, improves database consistency and integrity, and optimizes data access control efficiency. Attached Figure Description

[0022] Figure 1 A flowchart is provided for an embodiment of the present invention to illustrate an automated verification method for food input qualifications based on structured data comparison. Detailed Implementation

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information. Furthermore, the thresholds provided in this invention are exemplary, are all configurable parameters, and support dynamic loading.

[0024] Please see Figure 1 As shown, this invention provides an automated verification method for food input qualifications based on structured data comparison. The method is executed by a computing device equipped with a processor and includes the following steps: (1) Data parsing and structuring steps: Receive the metadata of the food to be verified through the data interface and parse it into structured data containing the ingredient list field, nutrient value field and production process label field; (2) Rule base calling steps: Load the preset mitochondrial quality control (MQC) veto rule base from the memory. The rule base includes a blacklist keyword matrix, a metabolic interference feature library and a processing intensity quantification threshold. (3) Multi-dimensional logic verification steps: Based on the structured data, the following computer-implemented verification steps are executed sequentially, wherein the verification steps constitute a series logic gate structure; when any step triggers a rejection signal, the system triggers a short-circuit termination mechanism; the output result is a data write permission control instruction: (a) Sugar load text matching: The ingredient list field is matched against the "high sugar stress" entries in the blacklist keyword matrix. If a match is found, a first-level rejection signal is generated. (b) Calculation of carbohydrate structure properties: Identify the main matrix properties based on the fields in the ingredient list, or calculate the numerical ratio of carbohydrates to dietary fiber. If the identification result falls into the "highly digestible starch matrix" definition domain, a first-level rejection signal is generated. (c) Lipid oxidation risk comparison: Identify the type of oil in the ingredient list field and retrieve its oxidation risk index in the "Lipid Stability Database". If the index is higher than the preset safety threshold, generate a first-level rejection signal. (d) Additive interference feature identification: The ingredient list field is compared with the metabolic interference feature library. If a non-nutritive ingredient marked as "mitochondrial membrane potential interfering agent" or "metabolic signal blocking agent" is found, a first-level rejection signal is generated. (e) Processing Intensity Index Calculation: Based on the complexity of the production process label field or ingredient composition, the processing intensity algorithm is called to calculate the industrialization index of the food. If the index exceeds the preset MQC prototype threshold, a first-level rejection signal is generated. (4) Result generation and locking steps: When any level of rejection signal is triggered during the verification process, the processor immediately terminates the subsequent verification or marks an exception, and generates an "MQC admission interception instruction". A "MQC Qualification Digital Certificate" is generated only if no rejection signal is triggered in any of the verification steps.

[0025] Glycolic load database definition: As a further improvement, the “high sugar stress terms” in step (a) are stored in the MQC veto rule base, and the dataset includes, but is not limited to: sucrose, high fructose corn syrup (HFCS), crystalline fructose, maltose syrup, glucose syrup, concentrated fruit juice (non-reduced), brown rice syrup, invert sugar syrup and other carbohydrate keywords with a glycemic index (GI) exceeding a preset value.

[0026] Carbohydrate structure algorithm: As a further improvement, the verification step of step (b) includes: identifying whether the main structure of the food is refined grain flour or free sugar matrix; if it is identified as a carbohydrate source with an incomplete plant cell structure, it is determined as a "high-risk matrix structure label" and a rejection signal is triggered.

[0027] Lipid database: As a further improvement, the "lipid stability database" in step (c) includes oxidative stability scores for various edible oils; when the input data shows that the oil contains hydrogenated vegetable oil, refined seed oil, or trans fatty acid content that is not zero, the system determines that its oxidative risk index exceeds the standard.

[0028] Specifically, in one embodiment, the specific oxidative stability scoring rules of the lipid stability database are as follows: 1. Base Score (S) base The system presets a basic stability score (out of 100) for each type of oil: Primary steady state (Score 90-100): short / medium chain saturated fatty acids (such as coconut oil, MCT oil), long chain saturated fats (such as butter, cocoa butter); Secondary steady state (Score 70-89): mainly monounsaturated fatty acids (such as olive oil and avocado oil); Level 3 Susceptibility (Score < 50): Vegetable oils with > 30% polyunsaturated fatty acids (such as soybean oil, corn oil, and sunflower oil).

[0029] 2. The dynamic deduction formula system reads food label data and executes the following calculation formula to generate the final stability score (S). final ): S final = S base -(P trans х50)-(P process х20), where P trans (Trans fat coefficient): Boolean value; 1 if trans fatty acids or hydrogenated vegetable oil are detected; 0 otherwise; P process (Process coefficient): If it is a cold pressing / primary pressing process, the value is 0; if it is a refining / leaching / high temperature treatment process, the value is 1.

[0030] 3. Determine when S final When the preset risk threshold T1 is reached (e.g., T1 is a configurable parameter that supports dynamic loading, such as 60 or 70), the system generates a "Level 1 Oxidation Risk Rejection" signal.

[0031] Additive Blacklist: As a further improvement, step (d) targets “mitochondrial membrane potential disruptors” including, but not limited to: artificial colors, artificial flavors, specific preservatives (such as sodium benzoate), monosodium glutamate (MSG) and other artificial sweeteners, which are predefined as having potential metabolic signaling interference properties.

[0032] Processing intensity algorithm: As a further improvement, the processing intensity algorithm in step (e) is based on the NOVA food classification system or equivalent logic, and generates a quantitative industrialization index by calculating the ratio of isolated extracts to whole foods in the ingredient list.

[0033] In one embodiment, the "processing intensity algorithm" satisfies the following calculation model: Industrialization Index (I ind Calculation formula: I ind =(N isolate / N total )х100+(W additive х10), where N isolate (Number of isolated extracts): The number of items in the ingredient list that belong to "industrially extracted ingredients" (e.g., soy protein isolate, maltodextrin, fructose syrup, refined vegetable oil); N total (Total Number of Ingredients): The total number of items in a food ingredient list; W additive (Additive Weighting): Based on a library of metabolic disruptors, high-risk additives (such as aspartame and artificial colors) are weighted (W). additive The weight is 2; the weight of common additives is 1; and the weight of no additives is 0. When multiple additives are present in food, W additive This is the sum of the weights of each additive.

[0034] Specifically, a library of metabolic disruptor signatures (specific examples): Mitochondrial toxic agents (Interference Category Library): Sodium benzoate (interferes with mitochondrial DNA), potassium sorbate (at high concentrations); Signal blocking agents: Sucralose, Acesulfame K; Intestinal barrier disruptors: Carboxymethyl cellulose (CMC), polysorbate-80 (P8O), etc.

[0035] In other embodiments, the method may further include: (f) Toxicity load data verification: Obtain the test report data for this food batch and compare it with the preset mitochondrial toxicity safety threshold. If the threshold is exceeded, a first-level rejection signal is generated; and / or (g) Supply chain digital identity verification: Verify whether the food has a valid supply chain traceability hash value (HashID) or verifiable source metadata. If the verification fails, a secondary rejection signal is generated. At this point, the result generation and locking step (4) specifically includes: When any first-level or second-level rejection signal is triggered during the verification process, the processor immediately terminates the subsequent verification or marks an anomaly, and generates an "MQC admission interception instruction". A "MQC Qualification Digital Certificate" is generated only if no rejection signal is triggered in any of the verification steps.

[0036] Traceability technology: As a further improvement, step (g) uses blockchain distributed ledger technology or encrypted database query technology to verify the existence of the source metadata of food.

[0037] SaaS / App Architecture: This invention further provides an automated verification system for food input qualifications, the system comprising: Data acquisition terminal: Equipped with an optical character recognition (OCR) module or barcode scanning interface, used to extract unstructured ingredient and process information from food physical packaging or digital labels and convert it into machine-readable structured data; Cloud-based rule engine: Stores the aforementioned MQC veto rule library and is configured to receive the structured data and perform multi-dimensional logical verification steps; Status Locking Module: Based on the verification results, it generates a visual "red light / green light" passage status indicator on the user terminal interface, or sends a data write permission / prohibition command to the associated external energy control system. Dynamic update interface: used to receive the latest scientific research data and periodically update the blacklist keyword matrix and threshold parameter set.

[0038] As a further improvement, the system is configured to act as a front-end data access gateway for an individual energy regulation system based on mitochondrial quality control, allowing only food data holding "MQC Qualification Digital Certificates" to be written into the main database of the energy regulation system, thereby preventing inferior energy input from interfering with the accuracy of the system's algorithm model.

[0039] This invention constructs the food screening process into seven cascaded automated data processing modules. These modules primarily include four core functions: First, addressing issues such as character misidentification and confusion between similar-looking characters during Optical Character Recognition (OCR), a correction mechanism based on an error pattern library and edit distance algorithm is employed to accurately restore the original text. Second, standardized mapping of ingredient names is implemented by establishing a standardized dictionary covering industry standards and abbreviations, aligning various non-standard inputs to a standardized terminology database. Third, synonym normalization is performed, merging different words expressing the same concept using a thesaurus and semantic similarity model to eliminate data redundancy. Finally, a data noise filtering mechanism removes garbled characters, irrelevant symbols, and duplicate content, ensuring the purity of the input data. The application of this module will provide a solid and reliable foundation for subsequent data analysis and mining. Specifically, (a) Glycemic Load Matching Module Technical implementation: The system loads a pre-set "high sugar stress term library" (containing 50+ keywords such as sucrose, fructose syrup, and maltodextrin).

[0040] Processing logic: Perform a full text search on the food ingredient list. If any term is matched, the system generates a Level 1 rejection signal (Signal_Reject_L1) to terminate the subsequent process.

[0041] (ii) Carbohydrate Matrix Identification Module Technical implementation: The system calculates the "matrix integrity index" based on the order and proportion of components.

[0042] Processing logic: Identify whether the main component belongs to the "refined powder" or "free starch" category. If the identification result shows that the main matrix has a highly digestible structure, it is determined to be inconsistent with the MQC rules, triggering a rejection signal.

[0043] (iii) Lipid Oxidation Risk Comparison Module Technical implementation: Call the "Oil and Fat Oxidation Stability Database".

[0044] Processing logic: Extract the oil names (e.g., soybean oil, sunflower oil) from the ingredients and retrieve their corresponding oxidation risk scores. If the score is higher than a preset threshold, or if the keyword "hydrogenation" is detected, the system classifies it as a "Type-A lipid input with risk level R1" and blocks it.

[0045] (iv) Additive Feature Library Filtering Module Technical implementation: Establish a "mitochondrial interference feature library" (labeling non-nutritive components with membrane potential interference and signal blocking properties).

[0046] Processing logic: The component data is compared with the feature library. If specific codes such as sodium benzoate, aspartame, or artificial coloring are found, the system triggers an interception command.

[0047] (v) Processing Intensity Algorithm Calculation Module Technical implementation: A digital algorithm based on NOVA classification logic is used.

[0048] Processing logic: Calculate the ratio of "Isolates" to "Whole Foods" in the ingredient list. If the calculated Industrial Index exceeds the MQC prototype threshold, it is classified as a "reconstructed food" and rejected.

[0049] (vi) Toxicity Threshold Validation Module Technical implementation: The interface calls third-party testing data or batch reports.

[0050] Processing logic: Verify whether pesticide residue and heavy metal levels are below the MQC safety threshold (system preset safety threshold T2). If they exceed the threshold, generate a blocking signal.

[0051] (vii) Digital Identity and Traceability Verification Module Technical implementation: Based on blockchain hash or supply chain database query.

[0052] Processing logic: Verify whether the food product has valid source metadata. If any data field is missing or fails the proof of existence test, the system marks it as "unknown risk" and refuses access.

[0053] System Hardware Architecture: In a preferred embodiment, the system of the present invention includes: Data acquisition terminal: Integrated with an optical character recognition (OCR) engine to scan food packaging and automatically extract text information; or integrated with an API interface for direct connection to e-commerce or food databases.

[0054] Cloud-based rule engine: Deployed on a remote server, it stores the dynamically updated MQC rule base and comparison algorithm, and is responsible for performing the above seven-level verification calculations.

[0055] User Interface (UI): Used to present visual verification results (e.g., red lock icon / green pass certificate) and provide alternative recommendations.

[0056] This invention can be widely applied to: The "scan code to check ingredients" function in smartphone applications; An automated product selection and filtering system for a health e-commerce platform; Data input gateway for the personal energy management system; Digital auditing tools for third-party food certification bodies, and other areas.

[0057] Through the above technical solution, the present invention achieves the following: 1. Standardized digitalization: Transforming vague health standards into precise database matching logic eliminates the subjectivity of human judgment.

[0058] 2. Millisecond-level automated screening: By combining OCR with algorithms, real-time verification and grading of massive amounts of food data are achieved.

[0059] 3. System-level security defense: As a "firewall" for the energy regulation system, it effectively prevents inferior energy data from contaminating the core algorithm model, ensuring the accuracy of subsequent energy regulation.

[0060] Those skilled in the art should understand that the above embodiments are merely for illustrative purposes and are not intended to limit the scope of this application. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of this application.

Claims

1. A method for automated verification of food input qualifications based on structured data comparison, characterized in that: The method is executed by a computing device equipped with a processor and includes the following steps: (1) Data parsing and structuring steps: Receive the metadata of the food to be verified through the data interface and parse it into structured data containing the ingredient list field, nutrient value field and production process label field; (2) Rule base calling step: Load the preset mitochondrial quality control rejection rule base from the memory. The rule base includes a blacklist keyword matrix, a metabolic interference feature library and a processing intensity quantification threshold. (3) Multi-dimensional logic verification steps: Based on the structured data, the following computer-implemented verification steps are executed sequentially. The verification steps constitute a series logic gate structure. When any step triggers a rejection signal, the system triggers a short-circuit termination mechanism. The output result is a data write permission control instruction. The data write permission control instruction is used to control the data access status of the backend algorithm model to realize permission management of the food data input process. (a) Sugar load text matching: The ingredient list field is traversed and matched with the "high sugar stress" entries in the blacklist keyword matrix. If a match is found, a first-level rejection signal is generated. (b) Calculation of carbohydrate structure properties: Identify the main matrix properties based on the ingredients list fields, or calculate the numerical ratio of carbohydrates to dietary fiber. If the identification result falls into the "highly digestible starch matrix" domain, a first-level rejection signal is generated. (c) Lipid oxidation risk comparison: Identify the type of oil in the ingredient list field and retrieve its oxidation risk index in the "Lipid Stability Database". If the index is higher than the preset safety threshold, generate a first-level rejection signal. (d) Additive interference feature identification: The ingredient list field is compared with the metabolic interference feature library. If a non-nutritive ingredient marked as "mitochondrial membrane potential interfering agent" or "metabolic signal blocking agent" is found, a first-level rejection signal is generated. (e) Processing Intensity Index Calculation: Based on the complexity of the production process label field or ingredient composition, the processing intensity algorithm is called to calculate the industrialization index of the food. If the index exceeds the preset MQC prototype threshold, a first-level rejection signal is generated. (4) Result generation and locking steps: When any level of rejection signal is triggered during the verification process, the processor immediately terminates the subsequent verification or marks an exception, and generates an "MQC admission interception instruction". A "MQC Qualification Digital Certificate" is generated only if no rejection signal is triggered in any of the verification steps.

2. The method according to claim 1, characterized in that, The "high sugar stress term" in step (a) is stored in the MQC veto rule base, and its dataset includes, but is not limited to: sucrose, high fructose corn syrup (HFCS), crystalline fructose, maltose syrup, glucose syrup, concentrated fruit juice (non-reduced), brown rice syrup, invert sugar syrup and other carbohydrate keywords with a glycemic index exceeding a preset value.

3. The method according to claim 1, characterized in that, The verification step in step (b) includes: identifying whether the main structure of the food is refined grain flour or free sugar matrix; if it is identified as a carbohydrate source with an incomplete plant cell structure, it is determined to be a "high-risk matrix structure label" and a rejection signal is triggered.

4. The method according to claim 1, characterized in that, The "lipid stability database" in step (c) contains oxidative stability scores for various edible oils; when the input data shows that the oil contains hydrogenated vegetable oil, refined seed oil, or trans fatty acid content that is not zero, the system determines that its oxidative risk index exceeds the standard.

5. The method according to claim 1, characterized in that, The "mitochondrial membrane potential disruptors" targeted in step (d) include, but are not limited to, artificial colors, artificial flavors, specific preservatives, monosodium glutamate and other artificial sweeteners, which are predefined as having potential metabolic signaling interference properties.

6. The method according to claim 1, characterized in that, The processing intensity algorithm in step (e) is based on the NOVA food classification system or equivalent logic. It generates a quantitative industrialization index by calculating the ratio of separated extracts to whole ingredients in the ingredient list.

7. The method according to claim 1, characterized in that, Following step (e), the procedure further includes: (f) Toxicity load data verification: Obtain the test report data for this food batch and compare it with the preset mitochondrial toxicity safety threshold. If the threshold is exceeded, a first-level rejection signal is generated; and / or (g) Supply chain digital identity verification: Verify whether the food has a valid supply chain traceability hash value or verifiable source metadata. If the verification fails, a secondary rejection signal is generated. Furthermore, step (4) of generating and locking the result further includes: When any first-level or second-level rejection signal is triggered during the verification process, the processor immediately terminates the subsequent verification or marks an anomaly, and generates an "MQC admission interception instruction". A "MQC Qualification Digital Certificate" is generated only if no rejection signal is triggered in any of the verification steps.

8. The method according to claim 7, characterized in that, Step (g) employs blockchain distributed ledger technology or encrypted database query technology to verify the existence of the food's source metadata.

9. An automated verification system for food input qualifications, characterized in that, The system includes: Data acquisition terminal: Equipped with an optical character recognition module or barcode scanning interface, used to extract unstructured ingredient and process information from food physical packaging or digital labels and convert it into machine-readable structured data; Cloud-based rule engine: stores the MQC veto rule library as described in claim 1, and is configured to receive the structured data and perform multi-dimensional logical verification steps; Status Locking Module: Based on the verification results, it generates a visual "red light / green light" passage status indicator on the user terminal interface, or sends a data write permission / prohibition command to the associated external energy control system. Dynamic update interface: used to receive the latest scientific research data and periodically update the blacklist keyword matrix and threshold parameter set.

10. The system according to claim 9, characterized in that, The system is configured as a front-end data access gateway for an individual energy regulation system based on mitochondrial quality control, allowing only food data holding "MQC Qualification Digital Certificates" to be written into the main database of the energy regulation system, thereby preventing inferior energy input from interfering with the accuracy of the system's algorithm model.

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