6 results about "Critical control point" patented technology

The invention relates to the technical field of freeze-drying control, and discloses an automatic control method and system for freeze-drying, rotating and pre-freezing. According to the method, a freeze-drying process dynamic model is constructed by monitoring operation parameters in a rotary pre-freezing stage, and characteristic indexes of the freeze-drying process dynamic model are analyzed; comparing the index with a standard reference value to generate a deviation analysis result, mining abnormal signals, screening key control points, monitoring environmental parameters in real time, and calculating response coefficients of the environmental parameters influencing the key control points; evaluating a potential fault mode based on the response coefficient, positioning a fragile link in the process, and carrying out performance detection to find out a specific problem point; formulating a control planning target according to the problem point, analyzing a corresponding control mode, querying a standard of the control mode, and generating a control operation instruction according to the standard; after the instruction is applied to equipment execution control, freeze-dried product quality data is collected for consistency verification, and finally a process quality report is generated. According to the method, multi-dimensional monitoring, fault early warning and accurate regulation and control of the freeze-drying, rotating and pre-freezing process are achieved.

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.

The embodiment of the invention discloses a food processing quality inspection method and system based on a fingerprint spectrum, and the method comprises the steps: collecting initial fingerprint spectrum data and production process monitoring information of food to be subjected to quality inspection processing, and then carrying out the correlation analysis of the two types of data through combining the key control point information of a food processing technology, generating a database index tag set containing unique identification of the characteristics of the processed food to be subjected to quality inspection; executing multi-dimensional retrieval in a food fingerprint spectrum shared database according to the label set, obtaining node fingerprint spectrum data with similar characteristics and generating a retrieval result; and finally, combining a characteristic difference comparison result of the node fingerprint spectrum data and the initial fingerprint spectrum data to generate a quality inspection identification result which is used for guiding quality control and process optimization in a food processing process.

A system for producing yogurt using pectin obtained from banana peels, comprising: a pectin extraction unit for pretreatment, drying, grinding and Acid extraction from banana peels; a formulation unit for mixing extracted pectin with milk; a fermentation unit for pasteurizing, inoculating and incubating; and a security unit that implements HACCP protocols for monitoring critical control points.