4812 results about "Food material" patented technology

The invention provides a food material distribution intelligent control method and system based on big data, and the method comprises the steps: collecting the temperature and humidity of a distribution compartment in a distribution process in real time, collecting the position information and speed of a distribution vehicle, and obtaining the sensitive parameters of distributed food materials in the distribution vehicle; all reachable paths are obtained, a prediction model is used for predicting the remaining fresh-keeping time of the food materials distributed on all the reachable paths according to the current compartment internal environment parameters, the fresh-keeping state data of the food materials and the following path parameters, and the path parameters comprise weather and road conditions; screening out reachable paths of which the difference value between the remaining fresh-keeping time and the arrival time of the reachable paths does not exceed a preset time threshold value from the reachable paths; and obtaining the reachable path with the minimum cost calculated through the environment-sensitive supply chain cost model from the screened reachable paths. By means of the method and the corresponding system, it is ensured that the food materials are delivered at the lowest cost and keep the best freshness after delivery is completed, and the quality loss caused by transportation is reduced.

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 embodiment of the invention relates to an automatic cooking method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first food material image shot by a to-be-cooked food material; performing food material type detection on the first food material image by using a preset food material detection model, and determining attributes of food materials; generating an initial cooking scheme based on the attributes of the food materials; obtaining dietary habit data of the target user; analyzing the dietary habit data by using a preset dietary habit model, and determining a dietary habit type of the target user; based on the dietary habit type, adjusting parameters of the initial cooking scheme to obtain a target cooking scheme matched with the dietary habit type; and based on the target cooking scheme, controlling the cooking equipment to cook the food materials. According to the embodiment of the invention, the method achieves the automatic detection of the type of the to-be-cooked food material, automatically generates the cooking scheme matched with the diet habit of the user, better meets the personalized demands of the user, and improves the cooking efficiency.

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 discloses a food material cooking control method and device, equipment and a storage medium. The method comprises the following steps: acquiring a cooking food material image; performing image detection on the cooking food material image by adopting a pre-trained lightweight food material detection model to obtain a food material type and a food material position of at least one target food material output by the model; according to the food material type and the food material position of each target food material, cooking parameters are determined; and executing a corresponding equipment control action based on the cooking parameters to obtain a cooking result. According to the technical scheme, the flexibility of the food material cooking process is improved, effective and accurate identification of the food materials is achieved, accurate determination of the cooking parameters is improved, and therefore the cooking quality is improved.

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 belongs to the technical field of conveying belt cleaning, and particularly relates to a raw material conveying device for food processing and a conveying method thereof.The raw material conveying device comprises a conveying part, a supporting part, a hot compress mechanism, a powder suction mechanism, a scraping mechanism and a wiping mechanism; the conveying part comprises a rack, a conveying belt and a driving assembly, and the conveying belt is arranged on the side wall of the rack and is driven by the driving assembly; the supporting part comprises a containing box and a lifting bucket, the containing box is fixedly installed on the side wall of the rack, and the lifting bucket is arranged in the containing box in a sliding mode; the hot compress mechanism comprises a heating box and a discharging pipe, the heating box is fixedly installed on the inner wall of the containing box, the discharging pipe is arranged in the lifting hopper, and one end of the discharging pipe is connected with an inner cavity of the heating box; the powder suction mechanism comprises a powder suction hopper and a connecting pipe, and the powder suction hopper is arranged in the containing box in a sliding mode; through cooperation of the structures, the cleaning modes are switched according to residues on the outer wall of the conveying belt, the application range is widened, and meanwhile the cleaning effect and the food raw material conveying quality are kept.

The invention discloses a refrigeration cabinet and an operation control method of the refrigeration cabinet. A weight sensor used for detecting the weight of stored articles and a temperature sensor used for detecting the temperature of a chamber are arranged in the storage chamber of the refrigeration cabinet. When it is detected that the door body is opened, the compressor is controlled to stop running; calculating a weight change value according to the object weight detected by the weight sensor before and after the door body is opened; according to the compartment temperature detected by the temperature sensor, the temperature change rate is calculated; and after it is detected that the door body is closed, the operation state and operation parameters of the compressor are controlled according to the door opening duration of the door body, the weight change value and the temperature change rate. By the adoption of the refrigerator, door opening and closing actions, food material weight changes and compartment temperature changes can be effectively combined to control the rotating speed of the compressor, the cooling speed is increased, and the storage quality of food is improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent electric cooker control method and system based on the Internet of Things, and the method comprises the steps: obtaining instruction information remotely sent by a user, and converting the instruction information into coded information; the method comprises the following steps: acquiring type information and weight information of food in an intelligent electric cooker, fusing the type information and the weight information of the food to obtain a plurality of gene sequences, and taking a plurality of genes in the sequences as preferable genes; randomly modifying the value of a preferred gene in a preset range, and performing fitness evaluation on the modified gene sequence in combination with the coding information until an optimal gene sequence is obtained; and converting the optimal gene sequence into a cooking control instruction, and controlling the intelligent electric cooker to perform cooking operation according to the cooking control instruction. According to the method and the device, the optimal cooking scheme is automatically generated and selected by analyzing the user instruction, identifying the type and the weight of the food materials and adopting gene sequence optimization and fitness evaluation, so that personalized and intelligent cooking experience is realized, and the use experience of the user is improved.

The invention belongs to the technical field of refrigeration equipment, and provides refrigeration equipment and a food material recognition method.The refrigeration equipment comprises a box body, the box body is provided with a plurality of storage areas used for containing food; the image acquisition equipment is arranged in at least one storage area; the control device is connected with the image acquisition equipment and is configured to respond to triggering of an image acquisition condition, an image in the storage area is acquired based on the image acquisition equipment, and an object in the image comprises one or more food materials; determining shielded food materials in the image according to food material contours of the food materials in the image; sheltering object removal processing and missing part complementation processing are carried out on sheltered food materials in the image, and a processed image is obtained; and identifying food material information of the shielded food materials in the processed image. The problem that a traditional refrigerator is low in recognition accuracy due to food material shielding can be solved.

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 provides an intelligent refrigerator article storage management method and system. The method comprises the steps that accurate basic data is provided for dynamic storage optimization and healthy diet recommendation; the partition and environmental parameters are dynamically adjusted according to the storage requirements of the food materials, and the preservation time is prolonged; the fresh-keeping state and the storage environment of the food materials are evaluated in real time, and the partition optimization effect is ensured; recording food material taking behaviors of the user, and generating a healthy intake model; and generating personalized diet recommendation and reminding in combination with the health demand and the storage state of the user. According to the intelligent refrigerator article storage management method and system, the food material storage efficiency is optimized through the intelligent technology, the food material utilization rate is increased, and meanwhile the personalized healthy diet requirement of a user is met.

A bulk food product gravity-feed dispenser includes a compartment configured to receive and store bulk food material. A valve separates the compartment from an outlet. The valve operates to open and close to selectively permit bulk food material to pass from the compartment through the outlet. An electromechanical motor is communicatively connected to a controller and mechanically connected to the valve. The controller operates in response to receipt of a dispense instruction input to produce a control signal to the electromechanical motor. The electromechanical motor operates to move the valve between a closed position and an open position to dispense a portion of the bulk food material.

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 discloses a control method and device of cooking equipment and the cooking equipment. The cooking equipment comprises an air frying module and a microwave module. The control method of the cooking equipment comprises the following steps: acquiring characteristic parameters of food materials; determining operation parameters of the cooking equipment according to the characteristic parameters of the food materials and a preset mapping relation table; wherein the preset mapping relation table is a corresponding relation table of the characteristic parameters of the food materials and the preset operation parameters of the cooking equipment; controlling the air frying module or the microwave module to work according to the operation parameters so as to improve the cooking speed of the food materials. According to the control method of the cooking equipment, the operation parameters of the cooking equipment can be determined according to the characteristic parameters of the food materials and the preset mapping relation table, and the air frying module or the microwave module in the cooking equipment is controlled to work according to the operation parameters; the dehydration speed of food formed by processing the food materials is increased, the cooking process is accelerated, the cooking time is shortened, and the cooking effect of crispy outside and tender inside is achieved.

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 the technical field of intelligent barbecue control, and particularly discloses an automatic food material cooking method applied to an intelligent barbecue oven and the intelligent barbecue oven, and the method comprises the following steps: based on a temperature sensor matched with the type of a food material in a barbecue state currently, collecting temperature data of the food material in a barbecue process; the method comprises the steps of collecting temperature data corresponding to food materials, analyzing the collected temperature data corresponding to the food materials to obtain a temperature analysis result of the food materials, and generating accurate temperature control parameters of the intelligent barbecue oven according to the temperature analysis result of the food materials and preset barbecue temperature required by the food materials so as to accurately control intelligent barbecue equipment to work and control the temperature of the food materials in the barbecue process. Therefore, the accuracy of temperature control in the food material barbecuing process is improved, the barbecuing efficiency of the food materials is improved, food meeting the taste of a user is barbecued, and the user experience feeling is improved.

The invention discloses an intelligent dietary nutrition nursing management system and method based on health data, and relates to the technical field of medical nutrition management. The method includes generating nutritional requirement feature information of a user; analyzing the loss rates of the key nutritional ingredients of the target food material in different processing modes, and obtaining a nutritional loss weight value corresponding to each processing mode; establishing an adaptability matching model according to the nutrition demand feature information and the nutrition loss weight value, and selecting a processing mode meeting the user demand; dynamically adjusting the recommended diet scheme according to the selected processing mode in combination with the current real-time health state data of the user; and recording feedback data of the user on the recommended diet scheme and subsequent changes of the health state, and dynamically optimizing the matching model. According to the invention, a more personalized and scientific diet management scheme is provided, and the problems of low diet scheme recommendation precision and insufficient dynamic adaptive capacity in the prior art are effectively solved.

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 utility model relates to the technical field of food processing, in particular to a sterilization device for food material preparation, which comprises a sterilization cylinder, a sterilization cylinder, an annular distribution device and a control device, and is characterized in that annular distribution is embedded in the inner wall of the sterilization cylinder; when food materials are sterilized, a motor is started to drive a turning plate to rotate through a rotating shaft to push the food materials to move, meanwhile, when the rotating shaft rotates, under the action of a mounting plate, a trigger rod, a push plate and a plurality of trigger blocks, a placement disc is driven to reciprocate up and down, the food materials on the placement disc can be continuously vibrated up and down, and meanwhile, the food materials are sterilized. When a placing disc moves up and down, a turning assembly is driven to operate, under the action of a round rod, a fixing plate and a transverse rod, a turning plate is driven to rotate in a reciprocating mode when the placing disc moves up and down, and through up-down reciprocating movement of the placing disc and reciprocating rotation of the turning plate, position changes of food materials in different directions during sterilization are facilitated; the problem of dead angles during sterilization of the food materials is avoided, so that the sterilization effect of the food materials is 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 utility model relates to the technical field of blades, in particular to a meat mincer blade structure convenient to adjust and replace and a meat mincer, the blade structure comprises a cutter shaft, and a sliding groove is formed in the side wall of the cutter shaft; the first blade assembly is fixedly connected with the cutter shaft, and the first blade assembly is located on the lower portion of the cutter shaft; and each second blade assembly comprises a second shaft sleeve and a second blade, a clamping block is arranged on the inner wall face of each second shaft sleeve, the clamping blocks are connected with the sliding grooves in a sliding mode, and the clamping blocks are detachably connected with the cutter shaft. Due to the fact that the second blade assembly and the third blade assembly which are detachably connected are arranged, the number of the blades on the cutter shaft can be increased, namely more blades can be increased, more cutting faces are achieved, food materials can be processed more quickly, and the time needed for mincing meat is shortened.

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.