3657 results about "Food item" patented technology

The invention discloses a food safety sampling inspection data verification method and system and a computer storage medium thereof, particularly relates to the technical field of food safety supervision informatization, and is used for solving the problem of false consistency caused by silent failure when an external data source fails in an existing verification system. Field-level binding is realized by establishing a dependency mapping table of a sampling inspection system and an external data source; constructing a service response causal graph model, and dynamically marking a failure data source based on the deviation between the anti-fact response and the actual response; analyzing the verification field set and positioning a key verification field; degrading the weight of the key field to be below a threshold value and activating a manual auditing flag bit; scanning a preset coupling relationship between fields, freezing associated fields which do not trigger rules, verifying and constructing a conflict topology; when the check engine is executed, dynamically skipping the weight reduction field, suspending the freezing field, and generating a to-be-rechecked report in combination with the artificial flag bit and the topological graph; error data transmission is blocked from the source, and the food safety risk missed judgment rate is remarkably reduced.

The invention provides a frying and baking equipment heat preservation automatic control method and system, and relates to the technical field of food processing equipment control, and the method comprises the steps: collecting temperature signals of different positions through a plurality of groups of thermocouples and infrared thermal imaging sensors which are arranged in a frying and baking area, constructing a dynamic three-dimensional temperature field model, and obtaining a dynamic three-dimensional temperature field model; calculating a temperature standard deviation and a maximum temperature gradient in real time according to the dynamic three-dimensional temperature field model; and when the temperature standard deviation is larger than or equal to a preset threshold value, according to the heat distribution difference of the three-dimensional temperature field model, a multi-region differential compensation strategy is generated, the power distribution proportion of each heating unit is dynamically adjusted, and the duty ratio parameter of pulse width modulation is optimized in real time. Through multi-sensor accurate monitoring, dynamic regulation and control and intelligent stop judgment, uniform and stable temperature control, energy conservation and consumption reduction are achieved, the service life of equipment is prolonged, meanwhile, the use safety of a user is guaranteed, and the cooking experience is improved.

The invention relates to the field of food drying, in particular to a multi-layer circulating food drying machine which adopts a stage treatment technology combining condensation dehumidification and adsorption dehumidification and utilizes a preheating mechanism to recover condensation waste heat. The drying device structurally comprises a plurality of layers of independently sealed drying areas, each area is provided with a bottom air inlet groove, a top exhaust groove and a bottom exhaust groove, and a hot air path is optimized through a flow guide plate; the condensation mechanism adopts a spiral runway-shaped condensation pipe and a cooling fin to realize primary dehumidification, and the adsorption mechanism alternately works through a rotatable fan-shaped adsorption area to maintain the adsorption efficiency; the preheating mechanism absorbs condensation heat dissipation to preheat external air and then supplements the external air to the circulating system. Through hot air cyclic utilization, multi-stage dehumidification and waste heat recovery, the drying efficiency is remarkably improved, the energy consumption is reduced, meanwhile, the adaptability to food with different water contents is enhanced, and uniform and rapid drying is achieved.

The invention relates to the technical field of image recognition, and discloses a food freshness detection method and system based on image recognition, and the method comprises the steps: collecting an initial image, carrying out the white balance correction through a white reference card, and obtaining a corrected image with an accurate color; performing target area segmentation on the corrected image, extracting a food main area and eliminating background interference; pixel sampling is carried out on the main area image, the main area image is converted to Lab, HSV and RGB color spaces, and redness, yellowness, brightness, hue angle and red-green ratio are extracted to form a multi-dimensional color feature set; establishing a difference feature vector based on the feature set and a preset standard sample, and comparing the deviation degrees of the target food and the standard sample on the multi-dimensional color features; and finally, judging the freshness grade of the food according to the deviation degree. According to the invention, lossless and rapid food freshness grading detection is realized.

The invention provides a zero-sample food image recognition method based on semantic decoupling and a diffusion model, and aims to solve the problems of semantic interval and generation feature offset in the field of zero-sample food image recognition in the prior art. According to the method, semantic precise decoupling of food material composition and a cooking process in text description is realized by constructing a food material-cooking double-branch attention network, and implicit association of a food material combination and a cooking mode is captured by using a cross-modal fusion strategy, so that the accuracy of semantic condition control is remarkably improved; on the basis, a regional perception diffusion feature synthesizer is designed, visual features with spatial sensitivity are generated through a progressive denoising process, and the defect of a traditional generation model in the aspect of food texture detail reduction is effectively overcome. Compared with a traditional method, the scheme has remarkable advantages in the aspects of feature generation quality, semantic relation modeling and complex scene adaptability, and a new zero sample detection solution is provided for the field of food calculation.

The invention relates to a manufacturing method for delicious pork slices. To overcome the influence of too much intake of pork products on a human body, the manufacturing method for the delicious pork slices is provided. The manufacturing method comprises the steps of cutting the pork into the pork slices with the thickness of 2-4 mm; immersing the pork slices in an immersion liquid for 10-30 minutes; heating the immersion liquid with the pork slices and cooking for 0.5-1 h; fishing the pork slices out; draining the liquid; packaging and sterilizing. The manufacturing method is characterized in that, based on 100 parts by weight of the pork slices, the immersion liquid comprises, by weight, 1-5 parts of red yeast, 1-10 parts of rice wine, 20-30 parts of soy sauce, 1-5 parts of dried ginger, 10-20 parts of white sugar, 1-5 parts of spices, 1-5 parts of sodium bicarbonate, 15-30 parts of seaweed and 50-60 parts of water. The pork slices processed by the method can effectively prevent damp and hot proliferation in a stomach after being taken, and gives health to a human body while guaranteeing nutrition of the human body.

A kit for creating a miniature replica of a food or beverage item, the kit including a base object that forms a base of the miniature replica of the food or beverage item, the base object comprising a miniature replica of a component of the food or beverage item, a resin container storing a resin configured to be applied to the base object and to adhere to the base object, the resin container comprising a miniature replica of a container associated with the food or beverage item, and one or more ingredient objects configured to be at least partially embedded in the resin, the one or more ingredient objects comprising miniature replicas of one or more ingredients associated with the food or beverage item, the base object and the resin comprising complementary properties that enable the ultraviolet light to penetrate the resin and thereby cure the resin.

The invention relates to the technical field of food supervision, and discloses an AI-based food supervision efficiency optimization method and system. The method comprises the following steps: firstly, receiving a multi-source food supervision data stream in a target area, then analyzing data by using a risk feature extraction model to generate a risk level map and a probability matrix, then constructing an adaptive supervision strategy model to generate a supervision instruction set, and then simulating a risk linkage effect and optimizing parameters through a risk diffusion path prediction model. And finally, iteratively optimizing and outputting a supervision decision action sequence by using a multi-agent reinforcement learning framework. The system comprises a data receiving module, a processing analysis module, a strategy generation module, a risk simulation and parameter optimization module and a parameter iteration and decision output module. The method can accurately evaluate the risk, optimize the supervision strategy, predict the risk diffusion, improve the food supervision efficiency, and guarantee the food safety.

The invention relates to the technical field of image processing, in particular to a food production line cleanliness monitoring method and system based on machine vision. The method comprises the steps of obtaining a Lab image of a to-be-detected conveyor belt of a food production line, determining an abnormal score value of a target pixel point in the Lab image, and constructing a data item edge weight of an energy function according to the abnormal score value of the pixel point in the Lab image; determining a periodic displacement amount of a periodic structure of the Lab image in the motion direction of the conveyor belt to be detected, and determining a periodic consistency factor of adjacent pixel pairs according to the periodic displacement amount; according to the method, a data item edge weight of an energy function is established, a smooth item edge weight of the energy function is established according to texture features and a period consistency factor, the energy function is established by using the data item edge weight and the smooth item edge weight, segmentation enabling the energy function to be minimized is performed on the Lab image, and a cleanliness monitoring result can be determined according to the minimum value of the energy function. According to the technical scheme, the cleanliness of the conveying belt of the food production line can be automatically monitored.

The invention discloses a food detection system and method based on computer vision, and relates to the technical field of food visual detection and intelligent identification, and the system comprises an image collection module which obtains initial food image data from a production line through a preset image collection device, processes the data through an edge detection algorithm to extract contour features, and sends the contour features to an image processing module; meanwhile, a boundary point coordinate extraction process is fused to obtain appearance boundary description of the food; the boundary clustering module is used for grouping boundary points by adopting a clustering algorithm according to the extracted appearance boundary description, carrying out processing under an iterative convergence condition in combination with a distance metric function and cluster center initialization, and judging that the shape is irregular if the grouping number exceeds a preset threshold value, so as to obtain a shape classification label; according to the food detection system and method based on computer vision, the accuracy and adaptability of food quality detection are remarkably improved, and the quality control requirement of a multi-scene production line is met.

The invention discloses a food detection state visual output system and method based on machine vision, and relates to the technical field of machine vision, and the system comprises a multi-source data synchronization module, a semantic analysis module, a visual rendering module, an interaction instruction processing module and an abnormity reinspection module. The multi-source data synchronization module generates a synchronization signal; the semantic analysis module extracts a multi-modal feature vector based on the synchronizing signal, and generates a feature vector which is matched with a user role and contains a semantic tag in combination with a pre-constructed lightweight knowledge graph; the visual rendering module dynamically generates an interactive interface according to semantic tags in the feature vectors; the interaction instruction processing module receives a positioning operation signal triggered by a user on an interaction interface to generate a traceability request instruction; and the exception rechecking module performs rechecking according to the traceability request instruction and the rendering instruction. According to the invention, the problems of insufficient surface defect detection precision, difficulty in quality tracing and lack of process closed-loop control in industrial production can be solved.

The invention relates to a double-layer preservative film for prolonging the shelf life of frozen beef and mutton after unfreezing and a preparation method of the double-layer preservative film. According to the preparation method of the angelica dahurica extract-gliadin nano-particles, the angelica dahurica extract-gliadin nano-particles can be used for preparing a double-layer preservative film, and the preservative film is a natural component and is environmentally friendly and safe. The preservative film is of a double-layer structure, the mechanical performance is enhanced through the double-layer film structure, and meanwhile the preservative film has excellent oxygen and water vapor barrier performance and an antibacterial effect. According to the preservative film disclosed by the invention, slow release of active ingredients is realized by utilizing the Pycnogenin-gliadin nanoparticles, so that the antioxidant and antibacterial effects are remarkably improved, and the quality guarantee period of meat is prolonged. In addition, the preservative film does not need to be in direct contact with food in practical application, potential safety hazards of the food are further reduced, the operation process is simplified, pollution is avoided, a user can conveniently use the preservative film, the preservative film has the advantages of efficient preservation and environmental protection, and the preservative film has good application prospects and value.

The invention relates to the technical field of machine vision and food quality detection, in particular to a food defect real-time detection method and system based on image processing. The method specifically comprises the following steps: integrating multi-modal data, and adaptively adjusting a weight coefficient to improve data quality; multi-stage noise reduction is carried out, key frames are extracted, and a key frame verification mechanism is enhanced through a 2FA control variable; constructing a YOLO-BioNet model, and optimizing the detection efficiency and precision in a complex scene through dynamic convolution kernel allocation and a spiking neural network; integrating Kalman filtering, LSTM (Long Short Term Memory) time sequence modeling and a deep reinforcement learning model, analyzing a food surface change track and predicting a defect state; real-time data and historical information are fused, and a dynamic threshold value is calculated through a Bayesian network; performing linkage response between an automatic sorting instruction and a production line; a distributed storage and graph neural network optimization model is adopted, and defect tracing is supported. The method has remarkable advantages in the aspects of real-time performance, detection accuracy and self-adaptive capability.

The invention discloses a food image denoising method based on dynamic coding and efficient channel perception hybrid upsampling, and the method is characterized in that a dynamic coding module CAMixer and a channel perception hybrid upsampling module E-CAMixUp are cooperatively integrated, and an efficient channel perception hybrid denoising network ECAMixDNet is formed. According to the method, the CAMixer utilizes a learnable attention mechanism to adaptively adjust attention so as to contain more useful textures, the representation capability of convolution is improved, and the E-CAMixUp realizes high-quality image reconstruction under multi-scale noise perception through the collaborative design of dual-path feature reconstruction and adaptive channel interactive filtering, so that the image reconstruction efficiency is improved. The problem that artifacts are easily introduced in the reconstruction stage by an inter-channel noise distribution difference suppression mechanism is solved. The network extracts multi-scale features through a Swin Transform and an RBF attention mechanism, a decoder is combined with a residual module to realize high-quality reconstruction, food texture and color sensitivity are effectively kept, and visual restoration quality is improved.

A control system for a drive through includes processing circuitry. The processing circuitry is configured to determine, using image data obtained from multiple cameras, a characteristic of a vehicle in a lane of the drive through. The processing circuitry can operate a point of sale unit to provide an indication of the characteristic of the vehicle. The processing circuitry can, responsive to detection of one or more of a plurality of vehicles using the image data, perform at least one of (i) initiating an alert via the point of sale unit for staff of the drive through, (ii) initiating an action to open an additional lane, or (iii) initiating a kitchen action to prepare one or more food or beverage items.

The present invention discloses a method and system for detecting foreign objects in food based on deep learning, including an image acquisition module and a foreign object analysis module connected thereto; the image acquisition module: for acquiring RGB images and penetration images of food; the foreign object analysis module: inputting the food images corresponding to the RGB images, the food images corresponding to the penetration images, color features and shape features into a foreign object detection model, relating to the technical field of food detection, and solving the technical problem that when using X-ray images of food and analyzing the foreign objects therein, the sensitivity for detecting low-density foreign objects in food X-ray images is too low, resulting in difficulty in meeting the current high-quality requirements for food; taking the color situation in food as one of the judgment bases and taking the shape features of food as one of the judgment bases, and judging through the trained foreign object detection model can avoid a large missed detection rate when detecting foreign objects in food by a single image.

The invention discloses a data association analysis method and device based on food safety event emergency disposal. The method comprises the following steps: S1, acquiring food data; s2, constructing a database linked list according to the food data; s3, obtaining a food safety traceability flow chart according to the association of the food safety links in the database linked list; s4, according to the food safety traceability flow chart, obtaining a food safety traceability information model through Bayesian network learning; and S5, according to the origin of the scene where the food emergency occurs, through the food safety traceability information model, obtaining traceability information of the food safety emergency. By adopting the technical scheme of the invention, the direct influence and potential threats of the food safety emergencies are effectively mastered, and the problem that the emergency disposal efficiency of the food safety emergencies is influenced is avoided.

The invention relates to the technical field of group meal food traceability, and particularly provides a group meal food whole-course traceability method and system, and the method comprises the steps: carrying out the traversing of a pre-constructed traceability map according to a food traceability request when the food traceability request is received; based on a first preset evaluation dimension, performing confidence evaluation on each candidate traceability chain path to obtain a plurality of first confidence evaluation results, and screening out a target traceability chain path from all the candidate traceability chain paths according to all the first confidence evaluation results; analyzing whether the target traceability chain path is broken, if yes, repairing the target traceability chain path according to the auxiliary information set and outputting the repaired target traceability chain path, and if not, directly outputting the target traceability chain path; according to the method, the problem that the generated tracing chain is incomplete or inaccurate due to data missing or cross-link data association errors existing in the acquired tracing data can be effectively solved.

The utility model discloses a food conveying belt which comprises a machine frame, a first supporting frame and a second supporting frame, and a conveying belt body is installed on the inner side of the machine frame. The first supporting frame is fixed to the right side of the lower portion of the rack, a cleaning mechanism is arranged on the inner side of the first supporting frame and comprises a mounting base, a cleaning brush, a connecting plate, a limiting rod and a first spring, the cleaning brush is tightly attached to the lower portion of the conveying belt body, and the limiting rod is slidably connected into the mounting base through the first spring; and the second supporting frame is fixed to the left side of the upper portion of the rack, a guiding mechanism is arranged in the second supporting frame, and the guiding mechanism comprises a first connecting wheel, a second connecting wheel, a fixing column and a second guiding plate. According to the food conveying belt, food residues on the conveying belt can be automatically cleaned, and the food conveying belt is clean, sanitary, convenient to mount and dismount, simple and rapid to operate, convenient to clean, capable of separating food according to the size of the food and convenient to convey in rows.

The invention belongs to the technical field of food detection, and particularly relates to an oyster freshness distinguishing method based on an electronic nose and prior information. According to the method, oyster smell data collected by an electronic nose system is combined with traditional oyster physicochemical indexes, and the prior weight of an oyster freshness prediction model is set by calculating the correlation coefficient of the oyster smell data collected by the electronic nose system and oyster physicochemical index information closely related to freshness change; and subsequently, the weight is adjusted through a compression and incentive mechanism to construct a reliable classification model, so that the screening capability, reliability and accuracy of the freshness key characteristics at the accurate distinguishing moment are improved, and high-accuracy oyster freshness distinguishing is realized for the oyster freshness.

The utility model discloses a large-scale continuous marinating system, and belongs to the technical field of food processing.The large-scale continuous marinating system comprises a marinating container which is arranged in a sealed mode, a solid-liquid separation device is connected to a discharging opening in the bottom of the marinating container through a discharging pipe, and a belt conveying device used for sending out separated eggs is arranged on one side of the solid-liquid separation device; the solid-liquid separation device is also connected with a blending tank for collecting separated brine, and the brine treated in the blending tank is conveyed to a marinating container for use through a pipeline; the marinating container is provided with a temperature detector for conveniently controlling the temperature in the marinating container, and the top of the marinating container is also connected with a pressurizing pipe for conveniently applying pressure to the temperature detector and a pressure releasing pipe for releasing pressure outwards. The soft boiled egg marinating equipment is used for solving the problems that soft boiled eggs marinated by the existing marinating equipment are poor in quality, the marinating equipment needs manual assistance, the processing efficiency is low, the eggs are easy to break, the cleaning is difficult and the like.

The invention provides an intelligent cooking supervision optimization system based on digital twinning and a construction method, and solves the problems of insufficient whole-process supervision of a traditional kitchen, lagging of real-time early warning and difficulty in operation standardization. The system constructs a virtual model through high-precision modeling (geometry, physics, behaviors and rules); the whole stir-frying link is uniformly coded by using an industrial internet identification analysis technology, so that data tracking and tracing are realized; a real-time data acquisition, transmission and analysis mechanism is established, and seamless virtual-real mapping between physical equipment and a digital platform is realized; and a visual control interface is constructed to support ordering and remote control. A scheduling rehearsal mechanism based on an improved ant colony algorithm is innovatively proposed, multi-task multi-pot dish distribution scheduling is optimized, trial and error cost is reduced, and meal delivery efficiency and system stability are improved. The system provides support for digital and intelligent transformation of the catering industry, and improves food safety supervision and kitchen operation efficiency.

A container straw includes a tube whose first and second ends are sealed with first and second end caps, respectively, and a plurality of swellable food items sealed within the tube. The second end of the tube is angled to complementarily fit against an angled end face of the second end cap.

The present disclosure relates to low density, high protein, grain-based ready-to-eat food pieces, compositions including such food pieces, and methods of making such food pieces. The pieces include a grain flour in an amount of about 28% to about 55% by dry weight, about 28% to about 40% by dry weight protein, where at least a portion of the protein ingredients include a protein with a PDCAAS greater of at least 60%, such as pea protein, fava bean protein, or soybean protein, and about 10% to about 35% by dry weight low DE carbohydrate.

A container includes a base including cells each sized and shaped for receiving a food item therein, and a land positioned between adjacent cells, and a lid releasably engaged with the base when the container is in a closed configuration, the lid including a cover and a retaining column extending from the cover to a distal column end. The distal column end engages the land of the base when the container is in the closed configuration, the retaining column includes outwardly-extending retainers. When the container is in the closed configuration, each of the outwardly-extending retainers is located over one of the adjacent cells to limit movement of the food item received in the respective cell. Each outwardly-extending retainer is spaced a distance from the distal column end such that a clearance is defined between the outwardly-extending retainers and the plurality of cells when the container is in the closed configuration.

The invention discloses an intelligent food transportation monitoring method and system based on a multi-dimensional dynamic coupling algorithm, and the method comprises the steps: collecting food transportation data in real time through wireless communication; constructing a digital twinborn model of the food transportation process, and fusing the digital twinborn model and the risk prediction model; simulating a transportation environment change trend to obtain a current multi-dimensional dynamic coupling environment influence factor; obtaining a food heat accumulation amount, and predicting a food quality deterioration probability factor; judging whether the food quality deterioration probability factor exceeds a threshold value or not, and predicting a transportation risk multi-dimensional quantitative index by combining the multi-dimensional dynamic coupling environment influence factor and the food quality deterioration probability factor; generating an optimization strategy, and outputting an intervention scheme; according to the method, the defect of the traditional technology in the aspect of risk prediction of food transportation monitoring is overcome, and through accurate risk prediction and timely intervention measures, the deterioration loss of food in the transportation process is remarkably reduced, the transportation resource allocation is optimized, and the transportation efficiency is improved.

The invention relates to controllable fermentation equipment and process for prefabricated dough, and relates to the technical field of food fermentation. Comprising a fermentation box, a right-angle supporting plate, a stainless steel tray, a linkage block and an integrated controllable part, and the integrated controllable part regulates and controls the temperature and humidity of dough supported in the stainless steel tray through the right-angle supporting plate and the linkage block; according to the invention, the following problems in the prior art can be solved: in the prior art, the size of a dough placing area is fixed and cannot be adaptively adjusted according to the quantity and size of to-be-fermented dough, so that the equipment adaptability is poor; and secondly, aiming at the situation that when a small amount of dough is fermented, the dough only occupies one-layer or two-layer space of the fermentation box, so that the positions of other layers in the fermentation box are in a vacant state, and no matter how many dough is fermented, the humidity and temperature of the whole fermentation box need to be adjusted, so that resources are wasted.

The invention discloses an aviation food truck docking identification method and system based on feature fusion, and the method comprises the steps: obtaining multi-sensor data of an aviation food truck, extracting cabin door positioning information, and carrying out the feature fusion processing to form a docking-pose feature matrix; performing pattern recognition based on the pose feature matrix to obtain track feature parameters, recognizing a cabin door geometric feature spectrum, generating a cabin door inversion feature library through inversion processing, and performing differential comparison to determine space focus features; identifying a cabin door center position and an obstacle interference position by using a space focus feature, constructing a docking failure preset condition to generate a reverse verification factor, and forming a composite space weight map; acquiring a butt joint gradient information identification position deviation area based on the weight map, and converting the butt joint gradient information identification position deviation area into a path optimization trigger area through deviation conversion parameters; adjusting pose information is formed through the trigger area, a recognition coordinate system is constructed for collision conflict detection, a butt joint recognition result is generated, and safe and accurate butt joint of the aviation food truck and the aircraft cabin door is achieved.

The invention provides a device for detecting pesticide residues in food, and relates to the technical field of food detection equipment, the device comprises a loading box, a sample crushing cylinder, an inner cylinder, a crushing mechanism, a freezing box, a first sampling bottle, a second sampling bottle and a rotating mechanism; the top of the loading box is covered with a cover body, and the sample crushing cylinder is fixedly connected to the inner side wall of the loading box; the inner cylinder body is arranged in the sample crushing cylinder, crushing mechanisms are arranged inside and outside the inner cylinder body, the sample crushing cylinder is used for sampling food skin, and the inner cylinder body is used for sampling the middle part in food; the freezing box is arranged in the loading box, is positioned on the lower side of the sample crushing cylinder, and is used for freezing a sample to be rechecked; the rotating mechanism drives the sample and the extraction solvent to be fully fused, so that the pesticide residue extraction is more sufficient, the accuracy of the detection result is improved, and the accurate separation and extraction mode is beneficial to more comprehensive and accurate evaluation of the pesticide residue condition of the food.