3619 results about "Food material" patented technology

The food material cleaning tank comprises an outer tank and a draining tank detachably arranged in the outer tank, the bottom of the draining tank is provided with an aeration channel capable of being communicated with an external air source, and the inner bottom wall of the draining tank is provided with aeration holes which are communicated with the aeration channel and used for blowing air into the draining tank. An air guide pipe with an upward air outlet end is arranged below the draining groove, and an assembly opening for the upper end of the air guide pipe to be detachably inserted into so as to be communicated with the aeration channel is formed in the outer bottom wall of the draining groove. The aeration holes in the inner bottom wall of the draining tank directly blow air into the draining tank, and the blown air cannot be blocked by the bottom wall of the draining tank, so that boiling turbulent flow is formed in the draining tank, pesticide residues and dirt on the surface of an object to be cleaned are powerfully stripped, and the cleaning effect is improved; the aeration channel at the bottom of the draining tank is detachably connected with the air guide pipe at the bottom of the outer tank, so that whether the air channel is communicated or not can be selected according to needs.

The invention discloses an electric heating cooker uniform temperature control method and system for gradient temperature difference regulation and control, and relates to the technical field of temperature control, the method comprises the following steps: dividing an internal heating area of a target electric heating cooker, arranging temperature sensors, and acquiring meal temperature data of N subareas in real time through a subarea temperature sensor array; constructing a gradient temperature difference model based on meal heat conduction information and food material characteristic information, and performing deviation calculation on the meal temperature data of the N sub-regions based on the gradient temperature difference model to obtain temperature deviation data of the N sub-regions; and a PID controller is adopted to perform heating power analysis on the temperature deviation data of the N sub-regions, and the heating power adjustment amount of the N sub-regions is determined to control the target electric heating cooker to perform uniform-temperature heating control. The technical effects of realizing uniform temperature control of the electric heating cooker, effectively solving the problem of local overheating or half-cooking caused by a traditional heating mode, ensuring uniform heating of meals and improving the heating quality are achieved.

The invention relates to the technical field of selenium-rich food safety, and discloses a selenium-rich food safety supervision system based on a block chain. The system comprises a production environment monitoring unit which acquires multi-dimensional environment parameters such as soil selenium content, air pollutant index and temperature and humidity of a selenium-rich food raw material planting base and a production and processing place in real time, and generates a comprehensive environment safety index through dynamic acquisition and fusion processing; and when the index is lower than a preset threshold value, a block chain alarm instruction is triggered and a supply chain tracing unit is started. And the supply chain tracing unit responds to the instruction, carries out block chain evidence storage on full-link circulation information (including supplier qualification certificates, logistics temperature control records and the like) of the selenium-rich food from raw material collection to terminal sales, and analyzes the evidence storage information to construct a complete link map. And the quality verification unit synchronously receives the environmental safety index and the supply chain link map, performs multi-source information cross verification in combination with selenium-rich food real-time spectral feature detection data, outputs a quality compliance judgment result and generates a block chain verification label.

The invention belongs to the field of industrial automation, and provides a system based on multi-mode perception and intelligent regulation and control. By integrating various sensors on the robot and applying technologies such as multi-modal sensing fusion, bionics, real-time feedback, multi-source data fusion, reinforcement learning and block chains, accurate grabbing and sorting of food materials, dynamic regulation and control of parameters in the processing process, online quality monitoring, multi-robot collaborative scheduling and quality tracing are achieved. The invention aims to solve the problems of difficult material grabbing, inflexible parameter regulation and control, inaccurate quality monitoring and the like in food processing, improve the automation and intelligence level of the food processing industry, and meet the production requirements of high efficiency, safety and high quality.

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 the technical field of equipment control, and discloses an intelligent control method and system for high-frequency vibration food material purification equipment. The method comprises the following steps: optically scanning a food material pollution area to generate a pollution distribution diagram; pollution area parameters are quantitatively calculated, and a vibration purification parameter table is obtained; a vibration element is driven to form a directional cleaning waveform; collecting turbidity data to analyze the pollutant separation rate; vibration and flushing parameters are adjusted, and a linkage mechanism is established; the cleanliness is evaluated through outlet scanning, linkage parameters are optimized according to differences, and closed-loop control is achieved. Based on the real-time state information of food material pollution, accurate configuration of the vibration parameters, cooperative control of multiple purification technologies and closed-loop feedback optimization of the purification effect are achieved, and therefore the food material purification efficiency and effect and the energy utilization rate are improved.

The invention discloses a blockchain traceability catering supply chain risk early warning method and system, and the method comprises the steps: deploying a multi-source sensor in a cold chain transportation link, collecting the temperature, humidity and vibration intensity data of food materials in real time, and carrying out the real-time binding with the GPS track of a transportation vehicle, and forming a space-time related transportation environment data set; setting a dynamic threshold based on the type of the transported food material, starting redundant sensor cross validation when sensor data in the transportation environment data set continuously exceeds the dynamic threshold for N times, and if the deviation between the validation data and the transportation environment data set exceeds a preset tolerance, marking the transportation environment data set as an abnormal data segment and triggering block chain evidence storage; and compressing the abnormal data segment and the GPS track fragment associated with the abnormal data segment into a data block, writing the data block into a lightweight alliance chain after the data block is subjected to double signature by a transportation party node and a receiving party node, and generating a traceability code containing a timestamp. According to the method, the supply chain risk disposal efficiency is improved through fusion of space-time gridding multi-source data and risk multi-dimensional assessment.

The invention discloses a food material position determination method and device based on image recognition and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: obtaining an image of a food material placed on a shelf of an oven, and carrying out the visual angle segmentation processing of the image, and obtaining a first image and a second image; and performing edge detection on the first image to obtain a boundary position of a plane where the food material is located, and determining first position information of the food material in the plane based on a relative position between the boundary position and the food material. The second image is input into the first image recognition model and the second image recognition model, and second position information of the baking tray where the food materials are placed is determined according to the relative relation between the first recognition result and the second recognition result output by the first image recognition model and the second image recognition model. And determining the spatial position of the food material in the oven according to the first position information and the second position information.

The invention relates to the technical field of automatic control, in particular to an electric food warmer control system which comprises a temperature data monitoring module, a thermal response prediction and adjustment module, a warmer bottom power balance regulation and control module, a food material heating opportunity optimization module and a heating strategy execution module. According to the method, the heat conduction characteristic of the pot body material and the specific heat capacity parameter of the food material are combined, the temperature change trend is accurately calculated, accurate temperature control is achieved, the pot bottom temperature gradient is analyzed, heat distribution is balanced, local overheating or insufficient heating is reduced, the temperature error interval is predicted through historical thermal response data, the heating strategy is dynamically adjusted, and the temperature control precision is improved; power distribution is optimized according to the temperature difference of the partition at the bottom of the pot, heat energy utilization efficiency is improved, heat absorption characteristics of food materials are matched, heating opportunity is optimized, uniform heating is ensured, cooking quality is improved, starting and stopping states of heating elements are dynamically adjusted, power output is optimized, system stability is improved, energy waste is reduced, and the service life of the heating elements is prolonged.

The invention relates to an intelligent lean optimization method and system for a food raw material formula and a medium, and the method comprises the following steps: building a problem according to a user demand, and inputting the problem into a large model; analyzing the large model according to answers output by the questions to obtain food raw material formula knowledge; structuring the food raw material formula knowledge and establishing a knowledge graph; and according to the taste relation in the knowledge graph and the food raw material dosage, calculating to obtain a corresponding product flavor index, and combining a deep reinforcement learning algorithm to dynamically adjust the food raw material dosage through iteration to realize multi-target optimization of the product flavor index, so that the optimized product flavor index is matched with a preset target value. Compared with the prior art, the accuracy and richness of the obtained food raw material formula knowledge are improved, the complex and changeable market requirements can be met, and the efficiency and precision of food raw material formula optimization are remarkably improved.

The embodiment of the invention discloses an intelligent unmanned aerial vehicle food material delivery method and system, and the method comprises the following steps: obtaining to-be-delivered order information and parameter information of an unmanned aerial vehicle, determining a target delivery strategy according to the to-be-delivered order information and the parameter information of the unmanned aerial vehicle, and carrying out the food material delivery according to the target delivery strategy. Real-time environment information images of the unmanned aerial vehicle are obtained in real time in the distribution process; and comparing the real-time environment information image with an initial environment information image to obtain incremental image information, and adjusting a target distribution strategy or controlling an unmanned aerial vehicle to perform intelligent obstacle avoidance according to the incremental image information. According to the method, global distribution path planning and local intelligent obstacle avoidance are combined, manual frequent intervention and adjustment are not needed, the autonomy and efficiency of unmanned aerial vehicle distribution are improved, the method can adapt to complex scenes, the environment adaptability is higher, the safety of the unmanned aerial vehicle can be guaranteed, and food distribution can be efficiently completed.

The invention provides a heating method and device for dynamically controlling the cooking degree of dishes and electronic equipment, and relates to the technical field of food material analysis, and the method comprises the steps: obtaining food material images according to a preset time interval; analyzing the food material image based on a ripening degree identification model, and outputting a real-time ripening degree numerical value of the current food material; comparing the real-time ripening degree value with a preset NI target ripening degree value to obtain a ripening degree difference value; according to the ripening degree difference value, a corresponding dynamic heating curve is matched from a ripening degree-heating curve database, the dynamic heating curve is converted into a control instruction, a temperature control system is driven to adjust heating parameters until the ripening degree difference value is not smaller than zero, and dish heating is completed. According to the method, the resource consumption and the monitoring real-time performance of the system are balanced by adopting a time-sharing variable-period image acquisition strategy, and the ripening degree recognition robustness is improved by combining a two-channel convolutional neural network of hue evolution characteristics and morphological parameters.

The invention discloses a recognition method, device and system for putting food materials in two hands and a storage medium, and belongs to the technical field of household appliances. The method comprises the following steps: acquiring multiple frames of first images; performing hand and food material detection and recognition on the multiple frames of first images to obtain hand and food material detection and recognition results corresponding to the first images in the multiple frames of first images; according to the hand and food material detection and recognition results corresponding to the first images in the multiple frames of first images, tracking the two-hand actions of the user, and determining whether the action of at least one hand in the two-hand actions of the user is food material putting or not; and under the condition that at least one of the actions of the two hands of the user is to put the food materials, determining a target shelf area in which the two hands put the food materials on the basis of a target image of a bounding box in which the hand and food material detection and recognition result in the multi-frame first image comprises the food material taking by the hand. By detecting the hands and the food materials and tracking the actions of the two hands of the user, accurate recognition of the food materials put into the two hands is achieved.

The invention discloses a nutrition recipe intelligent generation system and method, and belongs to the technical field of nutrition information processing and intelligent recommendation. The system is suitable for individual nutrition management of individuals of all ages, and particularly, optimization design is carried out for infant stages of 0-72 months. The system intelligently generates a personalized nutrition recipe based on individual basic information, growth indexes, nutrition standards, available food materials, user preferences and feedback and other multi-dimensional data, and supports nutrition intake evaluation and dynamic optimization. The system comprises core modules of data input, nutritional requirement calculation, recipe generation, nutrition analysis and interactive display and the like, and expands and supports functions of sensitization screening, regional and seasonal adaptation, work and rest linkage, family sharing and the like. Through fusion of individual growth data and environmental conditions, the system realizes scientific and high-adaptability nutrition recommendation, improves feeding convenience and intervention accuracy, and has a wide application prospect.

The invention discloses a chef machine intelligent recommendation system based on machine learning, and relates to the technical field of machine learning. The system comprises a user command input and conversion unit used for acquiring a voice cooking demand instruction input by a user and converting the voice cooking demand instruction into a cooking demand text; the keyword extraction unit is used for extracting four types of cooking keywords from the cooking demand text to obtain a keyword candidate set; the categories of the cooking keywords comprise food materials, processes, nutrition and scenes; and the cooking recommendation unit is used for performing similarity retrieval and matching from a pre-established cooking keyword database according to the extracted keyword candidate set, and presenting results to the user according to a sequence of similarity from high to low. According to the method, complex and diversified natural language input can be efficiently processed, and personalized and precise menu recommendation is realized.

1. Name of the Design Product: Vegetable Cutter Attachment. 2. Use of the Design Product: It is used to be installed on a cooking machine to complete operations such as slicing, shredding and dicing of food materials by using the power of the cooking machine. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Combined State Diagram of Design 1. 5. The front view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the front view of Component 1 of Design 2 is omitted; the front view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the front view of Component 1 of Design 3 is omitted; the front view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the front view of Component 1 of Design 4 is omitted; the rear view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the rear view of Component 1 of Design 2 is omitted; the rear view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the rear view of Component 1 of Design 3 is omitted; the rear view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the rear view of Component 1 of Design 4 is omitted; the left view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the left view of Component 1 of Design 2 is omitted; the left view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the left view of Component 1 of Design 3 is omitted; the left view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the left view of Component 1 of Design 4 is omitted; the right view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the right view of Component 1 of Design 2 is omitted; the right view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the right view of Component 1 of Design 3 is omitted; the right view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the right view of Component 1 of Design 4 is omitted; the top view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the top view of Component 1 of Design 2 is omitted; the top view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the top view of Component 1 of Design 3 is omitted; the top view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the top view of Component 1 of Design 4 is omitted; the bottom view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the bottom view of Component 1 of Design 2 is omitted; the bottom view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the bottom view of Component 1 of Design 3 is omitted; the bottom view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the bottom view of Component 1 of Design 4 is omitted; the perspective view of Component 1 of Design 2 is the same as that of Component 1 of Design 1, so the perspective view of Component 1 of Design 2 is omitted; the perspective view of Component 1 of Design 3 is the same as that of Component 1 of Design 1, so the perspective view of Component 1 of Design 3 is omitted; the perspective view of Component 1 of Design 4 is the same as that of Component 1 of Design 1, so the perspective view of Component 1 of Design 4 is omitted. 6. Design 1 is designated as the basic design. 7. The design product is composed of component 1, the body of the vegetable cutter attachment, and component 2, the replaceable vegetable cutting cylinder. Component 1 is used to provide the structural basis and the power transmission basis, and component 2 is used to be replaceably installed in component 1 to complete different operations such as slicing, shredding, and dicing of food ingredients.

The invention discloses a food material putting recognition method, device and system and a storage medium, and belongs to the technical field of household appliances, and the method comprises the steps: obtaining multiple frames of first images, carrying out the hand and food material detection recognition of the multiple frames of first images, and obtaining the hand and food material detection recognition results corresponding to the first images in the multiple frames of first images; according to hand and food material detection and recognition results corresponding to the first images in the multiple frames of first images, whether the action of the user is food material putting or not is determined, and the food material putting position comprises a rack edge or a non-rack edge; and under the condition that the action of the user is determined to be food material putting, determining a target shelf area where the food material is put based on a target image of a bounding box of the hand-held food material in the multi-frame first image and a food material detection and recognition result. By detecting the hand and the food materials, the action of the hand can be recognized more accurately, so that whether the food materials are put in or not is judged, and the recognition accuracy is improved.

The utility model discloses a meat cutting module and a cutting machine, and belongs to the technical field of household appliances. The meat cutting module comprises a meat cutting container, a meat cutting food material guide-in piece, a meat cutting structure and a gear box, the meat cutting structure is arranged in the meat cutting container and comprises a knife comb part and a chopping block part, and each comb tooth assembly comprises a comb tooth shaft rod and a plurality of comb teeth which are slidably hung on the comb tooth shaft rod; each group of knife and chopping block assembly comprises a knife and chopping block shaft lever and a plurality of blades connected to the knife and chopping block shaft lever in a sliding manner, and each comb tooth is arranged between two adjacent blades; the gear box comprises a gear transmission shaft, a gear set and an output part. According to the meat cutting module, the distance between the blades and the cutter comb can be adjusted.

The invention provides a cooking control method, device and system and a storage medium. The cooking control method comprises the following steps: acquiring image information of a cooking utensil in which a target cooking food material is placed; determining initial nutritional ingredient information of the target cooking food material based on an image recognition result of the image information; in the cooking process, the current operation parameter information of the cooking utensil and the current state parameter information of the target cooking food material are monitored; performing nutrient loss prediction based on the initial nutrient information, the current operation parameter information and the current state parameter information to obtain estimated nutrient information; and adjusting a preset cooking strategy based on the target nutrition information and the estimated nutritional ingredient information to obtain a target cooking strategy. According to the invention, image recognition can be carried out on the target cooking food material, the target cooking food material is automatically monitored and the nutritional ingredients are tracked and predicted, the prediction accuracy is high, and the content of the residual nutritional ingredients after cooking can be increased.

The invention discloses a cooking method of a pressure cooker, which comprises the following steps: a boosting cooking stage: starting a heating device to enable the cooking pressure of the pressure cooker to reach a first preset cooking condition; in the variable-pressure wall breaking stage, a heating device is turned off, a cooling device is turned on to enable the cooking pressure of the pressure cooker to be rapidly reduced from the first preset cooking condition to a second preset cooking condition, so that cell walls of cooking food materials are broken under the action of pressure difference formed in a short time, and the second preset cooking condition at least comprises that the cooking pressure is larger than normal pressure. When the cooking pressure is rapidly reduced from the first preset cooking condition to a second preset cooking condition, the rapid pressure reduction rate is at least 1.8 times of the natural pressure reduction rate. Through the method, food materials which are not easy to cook and have thick cell walls can be cooked in a short time, meanwhile, nutrient substances are fully dissolved out, and the cooking taste is improved.

The utility model provides a cooking utensil which comprises a body, a heating assembly and a fan assembly, the body is provided with a heat dissipation cavity and a cooking cavity, the heat dissipation cavity is communicated with the cooking cavity, the heating assembly and the fan assembly are connected with the body, the heating assembly is used for heating food materials in the cooking cavity, and at least one part of the fan assembly is located in the heat dissipation cavity. The other part of the fan assembly is located in the cooking cavity, and the fan assembly can blow air in the heat dissipation cavity into the cooking cavity. External fresh air can flow into the cooking cavity through the heat dissipation cavity, so that the cooking cavity can continuously introduce external air in the working process. Therefore, cooking parameters such as humidity and oxygen content in the cooking cavity are adjusted by means of the external air so as to meet the cooking requirements of specific food, and the technical defects that in the prior art, the cooking parameters such as humidity and oxygen content cannot be adjusted, the food cooking requirements cannot be met, and the quality of finished food is poor are overcome.

The invention discloses a control method of a cooking navigation system, and the method comprises the steps: obtaining food material image information of a to-be-cooked food material through responding to a cooking navigation starting instruction of an object; performing semantic analysis on the food material image information to obtain estimated menu information; performing parameter estimation processing based on the estimated menu information to obtain cooking parameter information; under the condition that the object cooks the to-be-cooked food material based on the cooking parameter information, obtaining cooking image information of the to-be-cooked food material, current working state information of target cooking equipment corresponding to the cooking parameter information and current cooking environment information of a cooking environment; cooking state analysis is carried out based on the cooking image information, the current working state information and the current cooking environment information, cooking correction information is obtained, and the object is made to carry out cooking correction based on the cooking correction information. By monitoring the whole cooking process, interacting feedback with the object in real time and optimizing the cooking process, the problem of high cooking difficulty caused by modeling and singleness of cooking suggestions is solved.

The embodiment of the invention provides a steam device, a high-temperature steam box and a cooking method of the high-temperature steam box, the steam device comprises a steam generator or a plurality of steam generators which are sequentially connected to perform multi-stage evaporation, and the steam generator or the last-stage steam generator of the plurality of steam generators is provided with a plurality of steam outlets. The plurality of steam outlets are respectively communicated to the same or different cooking areas in the cooking cavity of the high-temperature steam box so as to respectively or simultaneously feed high-temperature steam into the cooking cavity; the multiple steam outlets comprise at least one first steam outlet directly communicated with the cooking area and at least one second steam outlet capable of being switched between closing and opening. According to the steam device, the steam outlets in different forms are formed, opening and closing of the steam outlets can be adjusted according to cooking requirements, high-temperature steam directly acts on food materials or fills the whole cooking cavity to achieve different cooking modes, and the requirements of different food materials for different cooking modes are met.

The invention relates to the technical field of energy-saving food crushing, and discloses a food crushing device which comprises a crushing device shell and a collecting box, the collecting box is slidably connected to the bottom of the crushing device shell, and an adjusting assembly is arranged at the top of the crushing device shell; the adjusting assembly comprises a positioning block, the positioning block is fixedly connected to the top of the crushing device shell, a grinding disc is arranged on the inner wall of the top of the positioning block, a servo motor is fixedly installed at the bottom of the positioning block, and a power transmission rod is fixedly connected to an output shaft of the servo motor; a second positioning ring is driven by an electric cylinder to slide up and down, a connecting rod is driven to be linked with a T-shaped connecting plate, the distance between a crushing disc and a grinding disc is continuously adjustable, and by dynamically adjusting the distance between the crushing disc and the grinding disc, the equipment can automatically match power output according to the hardness and target granularity of food materials; energy waste caused by'high-power idling 'or'repeated crushing' of traditional fixed-spacing equipment is avoided, and accurate utilization of energy is achieved from the source.

Food material processing equipment based on water jet comprises a sorting assembly, a food material overturning assembly I, a food material overturning assembly II, a cleaning assembly, a discharging guide rail and a material blocking assembly, the discharging end of the sorting assembly is higher than the feeding end of the food material overturning assembly I, and the sorting assembly and the food material overturning assembly I communicate with each other through the discharging guide rail; the feeding end of the food material overturning assembly II is higher than the discharging end of the food material overturning assembly I, and the feeding end and the discharging end are communicated through a discharging guide rail. The food material overturning assembly I comprises a workbench III, a revolution assembly and an autorotation assembly, the revolution assembly is rotationally installed on the workbench III, and the autorotation assembly is arranged at the rotating end of the revolution assembly. By arranging the food material overturning assembly I, quasi-cylindrical food materials can be driven to rotate through the rotating shaft, and at the moment, the rotating food materials can be cleaned. And the sheet-shaped food materials can fall into the cross-shaped overturning box, then the food materials are driven to be overturned, and then the overturned food materials are cleaned. In this way, the food materials do not need to be manually turned over, and the food material cleaning efficiency is improved.

The invention discloses a food material baking method based on image recognition, a storage medium and an electronic device, and relates to the technical field of smart home, the food material baking method based on image recognition comprises the following steps: determining a pixel region corresponding to a preset type of food material in an image collected from the interior of an oven; determining barycentric coordinates of the preset type of food materials in a first coordinate system according to the pixel region; wherein the first coordinate system is an equipment coordinate system of image acquisition equipment in the oven; the physical distance between the center position of a heating device of the oven and the center-of-gravity position of the preset type of food materials is determined through the center-of-gravity coordinates; according to the physical distance and the pixel area, determining first heat received by the preset type of food materials within target time, and according to the first heat, determining a baking strategy for the preset type of food materials.

The invention relates to the technical field of nutritious food configuration, and discloses a system and a method for configuring personalized nutritious food, and the method for configuring personalized nutritious food comprises the following steps: collecting spectral characteristic data and chemical component data of a food material sample by adopting a hyperspectral imaging and mass spectrum combined technology, and obtaining holographic chemical fingerprint information; processing the collected spectral data by using a multi-scale feature extraction algorithm, extracting multi-level feature information of the food materials, and carrying out standardization and dimension reduction processing on the feature data; according to the method, high-precision prediction of the nutrient content of the food material is realized through combination of hyperspectral imaging and mass spectrometry technologies and a deep learning model, so that the nutrient content prediction accuracy and the configuration scheme accuracy are improved, the nutrient balance achievement rate is also improved, and meanwhile, the limitation of a static database is broken through; real-time nutritional ingredient analysis of any food material is achieved, the method can adapt to individual differences and freshness changes of the food materials, and accurate configuration based on actual food material characteristics is provided.

The invention relates to the field of cooking control, in particular to a control method and device of a cooking utensil, equipment and a storage medium. The method comprises the following steps: identifying weight monitoring parameters of food materials in the cooking utensil, carrying out multi-time-point water content attenuation analysis on the food materials, and constructing a real-time water content attenuation curve; identifying real-time cooking environment state parameters, performing quantitative correlation analysis according to the real-time water content attenuation curve, and constructing a water content change quantitative correlation model; acquiring a monitoring image in the cooking utensil, and performing deep target visual identification and personalized taste preference mining to generate personalized taste features of a multi-food-material user; and performing real-time cooking state identification according to the real-time water content attenuation curve and the water content change quantitative correlation model, and performing rolling time window water attenuation prediction to obtain water attenuation prediction values of a plurality of time windows. The cooking utensil parameters are dynamically adjusted based on the moisture change in the cooking process, and therefore the cooking effect and quality are improved.

The embodiment of the invention relates to a model training method and device, computer equipment and a storage medium, gradient information uploaded by a plurality of terminals and role information corresponding to each terminal are received, the gradient information is generated by training a local model of each terminal, the local model is trained through health data of the terminal, and the role information corresponding to each terminal is obtained. The generation module is used for generating food recommendation information corresponding to the role information; for each piece of gradient information, determining a weight based on the corresponding role information, and performing weighted aggregation on the plurality of pieces of gradient information according to the weight; and generating a global model updating parameter based on the gradient information after weighted aggregation, and sending the global model updating parameter to each terminal, so that the terminal performs updating training on the local model according to the global model updating parameter. Therefore, after gradient information of different user terminals is obtained and weighted aggregation is carried out, optimization training is carried out on the local model of each terminal, and after optimization, the model of each terminal dynamically adapts to health requirements of different users, so that the accuracy of model output is improved.

The invention relates to the technical field of real-time control software, and discloses a multi-sensor-based oven constant temperature control system and method, and the system comprises a multi-sensor detection module, a temperature control module connected with the multi-sensor detection module through a CNA bus, and an air circulation module connected with the multi-sensor detection module and the temperature control module. And the safety protection module is connected with the multi-sensor detection module and related control devices of the oven. The method corresponds to the system. According to the oven constant temperature control system and method based on the multiple sensors, through cooperation of all the modules and steps, the temperature detection accuracy and comprehensiveness are improved, the oven environment and actual food heating are both considered, meanwhile, dynamic intelligent temperature control is achieved, environment interference is reduced, temperature overshoot and lag are avoided, different food materials are adapted, in addition, airflow distribution is optimized, and the energy consumption is reduced. The temperature uniformity is improved, the safety and the reliability are enhanced, all-directional protection is formed, and stable operation of the system is guaranteed.