AI辅助食品标签审核数据库

食品标签审核数据信息存储在系统中，以支持AI自动审核与复核管理。数据结构主要包括： 一、标签基础信息表：存储食品标签的基本信息，关键字段包括唯一标识、产品名称、标签类型（电子标签/实物标签）、上传图片、配料表、营养成分表、法规标准条款等。 二、审核结果记录表：存储AI审核输出结果，关键字段包括审核准确率、识别条款、合规性判断、原始记录生成时间、AI审核结果、人工复核结果等。 三、审核数据库：存储历史审核案例，关键字段包括审核标准、审核尺度、常见问题库、审核结论一致性记录等。 索引设计包括主索引，以及辅助索引如标签类型、审核日期等。 通过状态字段管理审核流程（0：待审核，1：AI审核完成，2：人工复核完成，3：废弃），便于流程追踪。按创建时间分区存储，提高查询效率。以唯一标识作为主键，确保审核记录全局唯一性。Food label audit data is stored in the system to support AI-powered automatic audit and review management. The data structure mainly includes: 1. Label Basic Information Table: Stores basic information of food labels, with key fields such as unique identifier, product name, label type (electronic label/physical label), uploaded images, ingredient list, nutrition facts table, and regulatory standard clauses. 2. Audit Result Record Table: Stores the output results of AI audits, with key fields including audit accuracy, identified clauses, compliance judgment, original record generation time, AI audit results, and manual review results. 3. Audit Database: Stores historical audit cases, with key fields including audit standards, audit stringency, common problem library, and audit conclusion consistency records. The index design includes primary indexes and secondary indexes such as label type and audit date. The audit process is managed via a status field (0: Pending Audit, 1: AI Audit Completed, 2: Manual Review Completed, 3: Discarded) to facilitate process tracking. Data is partitioned and stored by creation time to improve query efficiency. The unique identifier is used as the primary key to ensure the global uniqueness of all audit records.