3 results about "Critical control point" patented technology

The application belongs to the technical field of artificial intelligence and food safety information, and discloses a question and answer system of a meat product whole-chain safety knowledge graph based on deep learning, which is composed of a knowledge graph construction module and a system question and answer module; wherein the knowledge graph construction module comprises a data acquisition unit, a data processing unit, an entity recognition unit, a relationship extraction unit, a critical control point selection unit and a knowledge graph construction unit; the system question and answer module comprises a user question analysis unit, a query statement generation unit, a standard answer generation unit and an intelligent question and answer Web system construction unit. The question and answer system of the meat product whole-chain safety knowledge graph based on deep learning is adopted, a more easy-to-use and more intelligent natural language question and answer system is developed by constructing the meat product whole-chain safety knowledge graph, and finally the professional, scattered and complex meat product safety knowledge is efficiently, accurately and friendly provided to the end users.

The invention provides an intelligent central kitchen food safety monitoring method based on multi-dimensional sensing data, and relates to the technical field of food safety monitoring. The method comprises the steps that multifunctional partitions are divided according to the technological process, and key control points and control limit values are determined to generate a partition control framework; generating batch identifiers for the raw materials and the products and collecting multi-dimensional sensing data; time-space correction is completed through an anchor event and a synchronization signal, a material flow graph is constructed, and a batch boundary is deduced; extracting hazard feature data based on batch boundaries and performing fusion calculation on risk indexes; and finally, generating a treatment plan and verifying an execution state to obtain a monitoring result reflecting batch risk and treatment information. The problem that in the prior art, multi-dimensional sensing data fusion of the whole process of a central kitchen and dynamic evaluation and closed-loop verification of food safety risks cannot be achieved is solved.

The invention provides a data-driven manufacturing quality key control point identification method, which comprises the following steps of: generating a fault weight of each fault node according to a quality loss function on the basis of historical data including fault nodes, severity and maintenance cost; based on the mapping relationship between the fault nodes and the manufacturing quality control points, collecting a mapping fault node set of each manufacturing quality control point, and calculating the weight of the manufacturing quality control points in a maximum pooling manner; taking the fault weight and the manufacturing quality control point weight as weight input, performing weighted association rule mining on the mapping relation, and generating an association rule set of the control point and the fault node; and constructing a double-layer directed weighted network based on the association rule set, calculating topological characteristics of nodes based on the double-layer directed weighted network, analyzing the topological characteristics, performing importance ranking, and determining a key control point set.