Automatic dish preparing and charging system for unmanned intelligent kitchen

Abstract

The invention provides an automatic dish preparation and charging system for an unmanned intelligent kitchen, and relates to the technical field of intelligent kitchen automation, and the system comprises a cloud dish database, a dish scheduling and automatic dish supplement module, a dish preparation module, an automatic charging module, and a dish preparation task management module. The experimental group combines the same kind of pretreatment through the system, the cutting and matching time is shortened from 42 minutes to 28 minutes, the pickling time is shortened from 30 minutes to 18 minutes, the total consumed time is shortened from 72 minutes to 46 minutes, the improvement rate reaches 36%, and the scheduling and automatic dish supplementing module supports selection of dishes according to the dish sections and automatic generation of a scheduling plan. Food material requirements are automatically calculated, and an inventory management system is docked in real time. The automatic feeding system adopts a flow sensor and a weight sensor to calibrate and control feeding errors.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent kitchen automation, and more specifically, particularly relates to an automatic vegetable preparation and ingredient adding system for an unmanned intelligent kitchen. Background Art

[0002] In today's catering industry, especially in large units, factory canteens, chain fast food kitchens, and group meal distribution centers, etc., the traditional vegetable preparation and ingredient adding methods face many problems that need to be solved urgently, seriously restricting the quality and efficiency of catering services, which are specifically manifested as follows:

[0003] 1. Vegetable preparation link:

[0004] Low efficiency: Traditional kitchen vegetable preparation mainly relies on manual operation. For example, in a large factory canteen, 4 chefs manually cut and prepare, and on average, they can only process 30 servings of ingredients per hour, and they need to work overtime for 2 hours every day to meet the demand. This low-efficiency vegetable preparation method is difficult to handle a large number of orders. During the peak hours in a chain fast food kitchen, 30% of the orders are overdue due to delayed vegetable preparation, seriously affecting the meal delivery efficiency and customer experience.

[0005] Lack of standardization: Manual vegetable preparation lacks a unified standardized process. There are differences in the knife skills, ingredient processing methods, and marinating times of different chefs, resulting in unstable dish quality. For example, in a traditional kitchen, it is difficult to accurately control operations such as "cut potatoes into slices 3 mm thick" and "marinate chicken wings for 30 minutes", affecting the consistency of the dishes.

[0006] 2. Ingredient adding link:

[0007] Insufficient accuracy: Chefs add seasonings based on experience, and the ingredient adding error is relatively large. Taking a traditional manual kitchen as an example, the deviation of the single-serving salt amount can reach ±6 g, and there are as many as 15 complaints per month due to overly salty or light tastes, seriously affecting the stability of the dish taste and customer satisfaction. Even some existing semi-automatic systems, such as a certain brand of intelligent seasoning machine, although they support ingredient adding during cooking, still require manual input of the dish number, and the ingredient adding error (for liquids) is as high as ±3.5 g, which cannot meet the requirements of precise seasoning delivery in catering production.

[0008] Single function: The functions of existing semi-automatic systems are limited. They only support ingredient adding during cooking and cannot cover the ingredient adding scenarios before and after cooking, making it difficult to meet the diverse dish production requirements. At the same time, vegetable preparation still relies on paper lists, which is cumbersome and error-prone.

[0009] 3. Inventory management link:

[0010] High ingredient loss: The traditional inventory management method is backward, lacking scientific prediction and monitoring means. Manual inventory counts need to be carried out 2 times a week, and the ingredient loss rate is as high as 8% due to over-purchasing or under-purchasing, resulting in unnecessary cost waste.

[0011] Lack of system docking: The existing semi-automatic system is not docked with the inventory system, and it is impossible to grasp the remaining amount of ingredients in real time. There have been 2 supply disruptions due to insufficient inventory, which has had a serious impact on the continuity of catering services.

[0012] 4. Overall operation and management link:

[0013] High labor cost: Under the traditional catering operation mode, the labor input is large. For example, in a large factory canteen, 8 chefs are required every day (4 for preparing dishes + 4 for cooking), and the labor cost is as high as 1600 yuan per day. This has brought a heavy burden to catering enterprises today when labor costs are increasing day by day.

[0014] Difficulty in meeting diverse needs: As people's attention to healthy eating continues to increase, there are more demands for the healthy grading of dishes. The traditional catering system cannot conveniently screen dishes according to health needs and is difficult to meet the increasingly diverse dietary needs of consumers.

[0015] In summary, the traditional catering dish preparation and ingredient adding methods have many deficiencies in terms of efficiency, accuracy, inventory management, operation cost, and meeting diverse needs, and cannot meet the needs of the rapid development of the modern catering industry. Therefore, it is of great practical significance to develop an automatic dish preparation and ingredient adding system for unmanned intelligent kitchens that is efficient, accurate, intelligent, and can optimize the catering production process in all aspects. Summary of the Invention

[0016] In order to solve the above technical problems, the present invention provides an automatic dish preparation and ingredient adding system for unmanned intelligent kitchens to solve the above problems.

[0017] An automatic dish preparation and ingredient adding system for unmanned intelligent kitchens, comprising:

[0018] Cloud dish database;

[0019] Dish production scheduling and automatic replenishment module;

[0020] Dish preparation module;

[0021] Automatic ingredient adding module.

[0022] Preferably, the production scheduling and replenishment module includes:

[0023] Production scheduling plan generation unit, supporting the selection of dishes and portions according to meal periods (breakfast / lunch / dinner), and automatically associating cooking duration and equipment requirements;

[0024] Ingredient demand calculation unit, generating an ingredient list associated with dishes based on the single-portion ingredient amount of the dish SOP × the number of scheduled production portions;

[0025] Inventory real-time docking interface, synchronize the remaining ingredients in the inventory management system, and trigger the replenishment logic (generate a purchase order when the remaining amount < safety threshold).

[0026] Preferably, the prepared dish task management module includes:

[0027] Preprocessing task visualization interface, display tasks classified by "vegetables / meats / seasonings", and mark the operation sequence (such as cutting → marinating → deep-frying);

[0028] Task scheduling engine, merge the preprocessing of similar ingredients (such as multiple dishes all need shredded potatoes), and allocate task priorities according to the cooking countdown;

[0029] Progress monitoring unit, display the task completion status in real time, and support manual adjustment of task allocation to handle emergencies.

[0030] Preferably, the feeding scenarios of the automatic feeding system include:

[0031] Feeding before cooking: After weighing the ingredients, position the portion basin under the feeder, and add preprocessing seasonings (such as soy sauce for marinating) according to the SOP sequence;

[0032] Feeding during cooking: Link the cooking equipment (such as a stir-fry machine), and automatically inject liquid seasonings according to the preset time point (such as the 3rd minute of stir-frying);

[0033] Feeding after cooking: Complete powder sprinkling (such as pepper salt) or oil pouring (such as sesame oil) through a robotic arm or a sprinkling device, and support visual recognition to locate the position of the dish.

[0034] Preferably, the cloud dish database includes:

[0035] Dynamic update mechanism, support the R & D team to modify the SOP online (such as adjusting the salt amount by ±10%), and automatically synchronize the version change to the terminal;

[0036] Health grading labels (such as low oil / low salt / high protein), support screening dishes according to health requirements during production scheduling;

[0037] Cooking parameter library, store equipment adaptation parameters (such as oven temperature 220°C × 15 minutes), and automatically match the equipment model.

[0038] Preferably, the control logic of the automatic feeding system includes:

[0039] Dual-sensor calibration: Joint calibration of the flow sensor (volume) + weight sensor (mass), with an error < ±0.5g;

[0040] Anti-misoperation mechanism: When the portion basin is not correctly positioned, the feeding instruction is automatically frozen;

[0041] Seasoning Remaining Warning: When the remaining amount in the storage bin < 20%, the system pushes a replenishment reminder to the mobile device.

[0042] Preferably, the production scheduling and dish replenishment module supports:

[0043] Backtracking of historical production scheduling data, automatically predicting the amount of ingredients used in the same time period next week, and optimizing the safety stock threshold;

[0044] Supplier docking interface, generating an electronic receipt according to the purchase order and automatically entering it into the inventory system.

[0045] Preferably, the preprocessing steps of the dish preparation task management module include:

[0046] Standardized operation guidelines: Displaying knife skills requirements (such as "cut potatoes into 3mm thick slices") and marinating duration (such as "marinate chicken wings for 30 minutes") through pictures and texts / videos;

[0047] Intelligent tool linkage: The cutting table is equipped with a weight sensor to real-time verify whether the amount of ingredients processed meets the SOP.

[0048] Preferably, it further includes a hygiene monitoring module:

[0049] Ultraviolet disinfection of the hopper of the feeding machine, automatically starting for 30 seconds of sterilization after each feeding;

[0050] Visual recognition by cameras in the dish preparation area, detecting violations such as mixing raw and cooked ingredients and operation overtime, and giving real-time alarms.

[0051] Preferably, the cloud dish database is docked with the production scheduling module through a microservices architecture, supporting distributed deployment for multiple canteens. A single system can manage the production scheduling of more than 500 dishes simultaneously, and the response time < 200ms.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] In the present invention, the dish preparation task management module realizes the reasonable merging and distribution of tasks through the preprocessing task visualization interface, task scheduling engine and progress monitoring unit. For example, in Experimental Example 2, for processing 6 types of ingredients for 4 dishes, the experimental group merged similar preprocessing through the system. The cutting time was shortened from 42 minutes to 28 minutes, the marinating time was shortened from 30 minutes to 18 minutes, the total time consumption was reduced from 72 minutes to 46 minutes, and the improvement rate reached 36%. And for every 10% increase in the task merging rate, the dish preparation time is shortened by 8 - 12 minutes. In the scenario of a large factory canteen, the dish preparation time is reduced from 180 minutes in the traditional mode to 95 minutes, with an improvement amplitude of 47%; the dish preparation time of meat patties in a chain fast food kitchen is shortened from 45 minutes to 28 minutes, and the efficiency is improved by 38%.

[0054] In the present invention, the production scheduling and automatic dish replenishment module supports automatically generating a production scheduling plan by selecting dishes and portions according to meal periods, automatically calculating the ingredient requirements, and connecting to the inventory management system in real time. When the remaining amount of ingredients is lower than the safety threshold, the replenishment logic is triggered to generate a purchase order, realizing the full-process automation management from dish production scheduling to ingredient replenishment, saving the manual calculation and communication costs, and improving the overall operation efficiency.

[0055] In the present invention, the automatic feeding system uses dual sensors of a flow sensor and a weight sensor for calibration to control the feeding error. Experimental example 1 shows that the error of liquid seasonings is < ±0.5 g, and the error of powdered seasonings is < ±1 g, meeting the standard of GB31644-2018 "Compound Seasonings". In practical applications, such as in the scenario of a large factory canteen, the feeding error is reduced from ±7.2 g in the traditional mode to ±0.6 g, with an increase of 91%, effectively ensuring the stability and consistency of the dish taste.

[0056] In the present invention, the prepared dish task management module provides standardized operation guidelines, displays knife skills requirements, marinating duration, etc. through pictures, texts or videos, and is linked with intelligent tools. For example, a weight sensor is built into the cutting and preparation table to verify in real time whether the amount of processed ingredients meets the standard operating procedure (SOP), ensuring the accuracy of the prepared dish link.

[0057] In the present invention, the production scheduling and dish replenishment module automatically predicts the ingredient usage in the same time period of next week through historical production scheduling data, optimizes the safety inventory threshold, and connects to the supplier interface. An electronic receipt is generated according to the purchase order and automatically entered into the inventory system, effectively reducing the ingredient loss rate. Compared with the inventory loss rates of 8% in traditional kitchens and 5% in semi-automatic systems, the present invention reduces the inventory loss rate to 2%.

[0058] In the present invention, through precise feeding and standardized prepared dish processes, the dish consistency is significantly improved. The results of the blind test of taste consistency show that the mean value of Kung Pao Chicken in the present invention is 8.9, the standard deviation is 0.45, and the Kendall coefficient is 0.92 (highly consistent). Compared with the traditional group with a mean value of 7.5, a standard deviation of 1.12, and a Kendall coefficient of 0.68 (moderately consistent), the dish consistency is improved by 35%, and the consumer complaint rate is decreased by 78%. In the scenario of a large factory canteen, the dish consistency (blind test score) is improved from 7.8 ± 1.2 to 9.2 ± 0.5, with an increase of 18%.

[0059] In the present invention, the health monitoring module performs ultraviolet disinfection on the feeding machine lanes and automatically starts sterilization for 30 seconds after each feeding; the cameras in the vegetable preparation area detect violations such as the mixing of raw and cooked ingredients and operation overtime through visual recognition and give real-time alarms, effectively ensuring food safety. The cloud dish database and the production scheduling module are docked through a microservices architecture, supporting distributed deployment in multiple canteens. A single system can manage the production scheduling of more than 500 dishes simultaneously, with a response time < 200 ms, facilitating the unified management and operation of large-scale catering venues. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a schematic diagram of the system of the present invention;

[0061] Figure 2 is a schematic diagram of the dish production scheduling and automatic supplementary dish module in the present invention;

[0062] Figure 3 is a schematic diagram of the vegetable preparation module in the present invention;

[0063] Figure 4 is a schematic diagram of the automatic feeding module in the present invention;

[0064] Figure 5 is a schematic diagram of the cloud dish database in the present invention;

[0065] Figure 6 is a schematic diagram of the control logic of the automatic feeding module in the present invention;

[0066] Figure 7 is a schematic diagram of the supporting functions of the dish production scheduling and automatic supplementary dish module in the present invention;

[0067] Figure 8 is a schematic diagram of the content of the preprocessing task visualization interface in the present invention;

[0068] Figure 9 is a schematic diagram of the health monitoring module in the present invention;

[0069] Figure 10 is a schematic diagram of the docking between the cloud dish database and the dish production scheduling and automatic supplementary dish module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and examples. The following examples are used to illustrate the present invention but cannot be used to limit the scope of the present invention.

[0071] Please refer to Figures 1 - 10 , the present invention provides an automatic vegetable preparation and feeding system for an unmanned intelligent kitchen, including:

[0072] A cloud dish database;

[0073] Dish Production Scheduling and Automatic Reordering Module;

[0074] Ingredient Preparation Module;

[0075] Automatic Feeding Module.

[0076] The production scheduling and reordering module includes:

[0077] A production scheduling plan generation unit that supports selecting dishes and portions according to meal periods (breakfast / lunch / dinner), and automatically associates cooking duration and equipment requirements;

[0078] An ingredient requirement calculation unit that generates an ingredient list associated with dishes based on the single-portion ingredient quantity of the dish SOP × the number of scheduled portions;

[0079] A real-time inventory docking interface that synchronizes the remaining ingredients in the inventory management system and triggers the replenishment logic (generates a purchase order when the remaining quantity < safety threshold).

[0080] The ingredient preparation task management module includes:

[0081] A preprocessing task visualization interface that displays tasks classified by "vegetables / meats / seasonings" and marks the operation sequence (such as cutting → marinating → frying);

[0082] A task scheduling engine that combines the preprocessing of similar ingredients (such as multiple dishes all requiring shredded potatoes) and assigns task priorities according to the cooking countdown;

[0083] A progress monitoring unit that displays the task completion status in real time and supports manual adjustment of task allocation to handle emergencies.

[0084] The feeding scenarios of the automatic feeding system include:

[0085] Feeding before cooking: After weighing the ingredients, position the portion basin under the feeder and add preprocessed seasonings (such as soy sauce for marinating) in the SOP order;

[0086] Feeding during cooking: Link the cooking equipment (such as a stir-fry machine) and automatically inject liquid seasonings according to preset time points (such as the 3rd minute of stir-frying);

[0087] Feeding after cooking: Complete powder sprinkling (such as pepper salt) or oil pouring (such as sesame oil) through a robotic arm or a sprinkling device, and support visual recognition to locate the position of the dish.

[0088] The cloud dish database includes:

[0089] A dynamic update mechanism that supports the R & D team to modify the SOP online (such as adjusting the salt content by ±10%), and automatically synchronizes the version change to the terminal;

[0090] Health grading labels (such as low oil / low salt / high protein), which support filtering dishes according to health requirements during production scheduling;

[0091] Cooking parameter library, storing device adaptation parameters (such as oven temperature 220°C × 15 minutes), and automatically matching device models.

[0092] The control logic of the automatic feeding system includes:

[0093] Dual-sensor calibration: Joint calibration of a flow sensor (volume) and a weight sensor (mass), with an error < ±0.5 g;

[0094] Anti-misoperation mechanism: When the portion basin is not correctly positioned, the feeding instruction is automatically frozen;

[0095] Seasoning remaining warning: When the remaining amount in the storage bin < 20%, the system pushes a replenishment reminder to the mobile terminal.

[0096] Support for the production scheduling and dish replenishment module:

[0097] Historical production scheduling data backtracking, automatically predicting the amount of ingredients used in the same time period next week, and optimizing the safety inventory threshold;

[0098] Supplier docking interface, generating an electronic receipt based on the purchase order and automatically entering it into the inventory system.

[0099] The preprocessing steps of the vegetable preparation task management module include:

[0100] Standardized operation guidelines: Displaying knife skills requirements (such as "cut potatoes into 3-mm thick slices") and marinating duration (such as "marinate chicken wings for 30 minutes") through pictures, texts, or videos;

[0101] Intelligent tool linkage: A weight sensor is built into the cutting and preparation table to continuously verify whether the amount of processed ingredients meets the SOP.

[0102] It also includes a hygiene monitoring module:

[0103] Ultraviolet disinfection of the feeding machine's cargo channel, automatically starting for 30 seconds of sterilization after each feeding;

[0104] Visual recognition by cameras in the vegetable preparation area to detect violations such as mixing raw and cooked ingredients and operation overtime, and real-time alarm.

[0105] The cloud dish database is connected to the production scheduling module through a microservices architecture, supporting distributed deployment in multiple canteens. A single system can manage the production scheduling of more than 500 dishes simultaneously, with a response time < 200 ms.

[0106] Example 1: Application in a large factory canteen (2000 people dining per day):

[0107] Scenario description: A canteen in an automobile factory, supplying 3 meals a day, with 8 - 10 dishes per meal. In the traditional mode, 8 chefs are required (4 for vegetable preparation + 4 for cooking).

[0108] 1. System deployment:

[0109] Hardware: 2 automatic feeding machines (6 liquid + 4 powder product channels), 4 intelligent vegetable preparation tables (integrated with weight sensors), 1 central control panel;

[0110] Software: The cloud database accesses 120 common factory-supplied dishes (such as Kung Pao Chicken and Yu-Shiang Shredded Pork), and sets the inventory safety threshold (such as potatoes ≥ 200 kg).

[0111] 2. Operation process:

[0112] (1) Production scheduling and dish replenishment:

[0113] At 17:00 on Monday, the cafeteria administrator checks the Tuesday menu through the APP (Lunch: 600 servings of Kung Pao Chicken, 500 servings of Yu-Shiang Shredded Pork);

[0114] The system automatically calculates:

[0115] For Kung Pao Chicken: Chicken breast 600 × 200 g = 120 kg, cucumber 600 × 100 g = 60 kg, dried chili peppers 600 × 5 g = 3 kg;

[0116] For Yu-Shiang Shredded Pork: Pork tenderloin 500 × 150 g = 75 kg, wood ear 500 × 30 g = 15 kg, broad bean paste 500 × 20 g = 10 kg;

[0117] Connect to the inventory system: The inventory of chicken breast is 80 kg (safety threshold 50 kg), triggering a purchase order (40 kg, arriving at 7:00 the next day).

[0118] (2) Vegetable preparation task management:

[0119] At 7:30 on Tuesday, the vegetable preparation table shows the tasks:

[0120]

[0121] The system sorts according to the cooking time sequence: The pre-treatment needs to be completed before 10:00 (the stir-frying machine starts at 10:30).

[0122] (3) Automatic feeding:

[0123] Feeding before cooking (marinating): The chicken breast is weighed at 120 kg;

[0124] The system calculates the marinating seasonings: Light soy sauce 120 kg × 0.8% = 960 g, cooking wine 120 kg × 0.5% = 600 g;

[0125] After the portion basin is positioned, the feeding machines No. 1 (light soy sauce) and No. 2 (cooking wine) are injected in sequence, taking 45 seconds;

[0126] Ingredient addition during cooking (stir-frying): When the temperature of the cooking machine reaches 180°C (at the 2nd minute), the system automatically adds doubanjiang (10 kg, divided into 5 pots, 2 kg per pot), with a time error < 5 seconds;

[0127] Ingredient addition after cooking (sprinkling sesame seeds): The robotic arm is equipped with a dispensing device, which visually identifies the position of the plate and sprinkles 5 g of sesame seeds on each plate (error ±0.2 g).

[0128] The implementation effects are as follows:

[0129]

[0130] Example 2: Chain fast-food kitchen (output: 500 servings per hour):

[0131] Scenario description: A certain chain hamburger store needs to quickly serve meals during peak hours (11:00 - 13:00). In the traditional mode, 30% of the orders are timed out due to vegetable preparation delays.

[0132] 1. System optimization:

[0133] Dynamic production scheduling: Connect to the POS system to synchronize the order volume in real time (e.g., at 10:30, it is predicted that 200 beef hamburgers need to be produced from 11:00 - 11:30);

[0134] Compression of vegetable preparation tasks: The original 30-minute meat patty marinating process is changed to "pre-treatment + pre-marinating". The system prompts to thaw the meat patties 2 hours in advance, and the marinating liquid is automatically injected by the dosing machine (saving 15 minutes).

[0135] 2. Key data:

[0136]

[0137] Example 3: Group meal distribution center (10,000 servings per day):

[0138] Scenario description: A certain group meal enterprise provides meals for schools and needs to ensure a stable supply of 2,000 servings per hour. In the traditional mode, a 2% return rate is caused by seasoning deviations.

[0139] 1. System enhancement:

[0140] Dual-sensor calibration: After each batch of raw materials arrives, the dosing machine is automatically calibrated (e.g., for peanut oil with a density of 0.92 g / ml, the flow sensor and weight sensor are jointly calibrated);

[0141] Abnormal warning: When the dosing error of a certain lane exceeds ±1 g continuously for 3 times, the system automatically switches to the standby lane and pushes a maintenance work order.

[0142] 2. Long-term operation data (30 days):

[0143] Index Mean value Standard deviation Industry standard Error of single portion of seasoning (liquid) 0.42g 0.15g ±5g Error of single portion of seasoning (powder) 0.85g 0.20g ±3g Return rate 0.3％ 2% (enterprise target)

[0144] Comparative Example 1: Traditional manual kitchen (2,000 people / day):

[0145] Vegetable preparation: 4 chefs manually cut and prepare, with an average of 30 servings per hour, and 2 hours of overtime per day;

[0146] Ingredient addition: Chefs add ingredients based on experience, with a deviation of ±6 g in the salt content per serving, and 15 monthly complaints (taste too salty / light);

[0147] Inventory: Inventories are counted twice a week, and the food material loss rate is 8% (due to over - or under - procurement).

[0148] Comparative Example 2: Existing semi - automatic system (a certain brand of intelligent seasoning machine):

[0149] Function: Only supports ingredient addition during cooking. The dish number needs to be manually input, and vegetable preparation still relies on paper lists;

[0150] Data: Used in a pilot canteen for 3 months. The time required for vegetable preparation scheduling is still 15 minutes per meal period, and the ingredient addition error is ±3.5 g (liquid);

[0151] Limitation: Not connected to the inventory system, and there were 2 instances of supply disruptions due to insufficient inventory.

[0152] Comparative experiment data:

[0153] Index Traditional kitchen Semi - automatic system The present invention Vegetable preparation efficiency (portions / hour) 30 45 60 Feeding error (g) ±7.2 ±3.5 ±0.6 Labor cost (yuan / day) 1600 1200 600 Inventory loss rate 8％ 5％ 2％ Taste consistency (standard deviation) 1.2 0.8 0.5

[0154] Experimental Example 1: Ingredient addition accuracy test:

[0155] Purpose: To verify the error of the automatic ingredient addition system in different scenarios.

[0156] Method:

[0157] Select 3 seasonings: light soy sauce (liquid), salt (powdered), honey (viscous liquid);

[0158] Set the single - serving dosage: 5 g, 3 g, 8 g;

[0159] Test 100 times and record the actual dosage and error.

[0160] Result:

[0161]

[0162] Conclusion: The error of liquid seasonings is < ±0.5 g, and that of powdered seasonings is < ±1 g, meeting the standard of GB31644 - 2018 "Compound Seasonings".

[0163] Experimental Example 2: Efficiency of combining vegetable preparation tasks:

[0164] Purpose: To verify the impact of task combination on vegetable preparation time.

[0165] Method:

[0166] Scenario: 4 dishes, a total of 6 types of ingredients need to be processed (potatoes, carrots, chicken breasts, tenderloins, green peppers, onions);

[0167] Control group: Prepare ingredients separately according to the dishes;

[0168] Experimental group: The system combines similar pre-treatments (e.g., potatoes + carrots are shredded together).

[0169] Result:

[0170] Link Control group time (minutes) Experimental group time (minutes) Improvement rate Cutting and preparation 42 28 (combining 3 items) 33％ Marinating 30 18 (sharing marinating liquid feeding) 40％ Total time consumption 72 46 36％

[0171] Conclusion: For every 10% increase in the task combination rate, the ingredient preparation time is shortened by 8 - 12 minutes.

[0172] Experimental Example 3: Blind taste test for taste consistency:

[0173] Purpose: To compare the dish consistency between the present invention and traditional kitchens.

[0174] Method:

[0175] Samples: Kung Pao Chicken (30 portions of the present invention, 30 portions of traditional);

[0176] Judges: 10 professional chefs + 50 consumers, scoring according to saltiness, aroma, and color (1 - 10 points);

[0177] Indicators: Mean ± Standard Deviation, Kendall's Coefficient of Concordance (consistency).

[0178] Result:

[0179] Group Mean value Standard deviation Kendall coefficient The present invention 8.9 0.45 0.92 (highly consistent) Traditional group 7.5 1.12 0.68 (moderately consistent)

[0180] Conclusion: The dish consistency of the present invention is improved by 35%, and the consumer complaint rate is decreased by 78%.

[0181] In summary, the automatic ingredient preparation and feeding system of this unmanned intelligent kitchen demonstrates significant advantages and innovation, and comprehensively optimizes the catering production process.

[0182] From the perspective of functional modules, all parts cooperate closely. The cloud-based dish database not only provides rich dish information, but also has a dynamic update mechanism, health grading labels, and a cooking parameter library, laying a solid foundation for the entire system. The production scheduling and automatic replenishment module realizes the automation from production scheduling plan generation to ingredient procurement, and can also predict ingredient usage based on historical data to optimize inventory management. The ingredient preparation task management module greatly improves the ingredient preparation efficiency through preprocessing task visualization, task scheduling, and progress monitoring. The automatic dosing module operates precisely in different dosing scenarios and ensures accurate dosing through mechanisms such as dual-sensor calibration. The hygiene monitoring module ensures food safety from dosing machine disinfection to detection of violations in the ingredient preparation area.

[0183] Its remarkable effects can be clearly seen through multiple embodiments. In large factory canteens, the ingredient preparation time is significantly shortened, dosing errors are reduced, labor costs are decreased, and dish consistency is improved. In chain fast-food kitchens, through dynamic production scheduling and ingredient preparation task compression, the problem of meal delivery during peak hours is effectively solved, and the order overtime rate is greatly reduced. In group meal distribution centers, with the system strengthened, the seasoning error is strictly controlled, and the return rate is reduced.

[0184] Compared with traditional manual kitchens and existing semi-automatic systems, this system has been greatly improved in key indicators such as ingredient preparation efficiency, dosing accuracy, labor cost, inventory loss rate, and taste consistency. Various experimental examples further verify the advantages of the system in dosing accuracy, ingredient preparation task merging efficiency, and taste consistency.

[0185] This automatic ingredient preparation and dosing system for unmanned intelligent kitchens brings an intelligent, standardized, and efficient solution to the catering industry through innovative functional designs, efficient operation modes, and reliable performance. It can effectively meet the needs of catering venues of different scales, has broad application prospects and promotion value, and is expected to drive the entire catering industry to develop towards a more intelligent, efficient, and high-quality direction.

[0186] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are chosen and described in order to better explain the principles of the invention and its practical application, and to enable those of ordinary skill in the art to understand the invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. An automatic food preparation and feeding system for an unmanned intelligent kitchen, characterized in that: include: Cloud-based food database; Dish scheduling and automatic food replenishment module; Food preparation module; Automatic feeding module.

2. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The dish scheduling and automatic dish replenishment module includes: Production scheduling plan generation unit; Ingredient demand calculation unit: Generates a list of ingredients associated with dishes based on the single ingredient quantity of the dish SOP × the number of scheduled portions; Real-time inventory docking interface: synchronize the food balance of the inventory management system and trigger the replenishment logic.

3. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The food preparation module comprises: Pre-processing task visualization interface: display tasks by "vegetables / meat / seasonings" classification, and mark the operation sequence; Task scheduling engine: merge similar ingredients for pre-processing and assign task priorities based on cooking countdown; Progress monitoring unit: displays the task completion status in real time and supports manual adjustment of task allocation to deal with emergencies.

4. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The feeding scenarios of the automatic feeding module include: Add ingredients before cooking: After the ingredients are weighed, position the portion bowl under the feeder and add pre-processing seasonings according to the SOP sequence; Adding ingredients during cooking: Linking cooking equipment to automatically inject liquid seasoning according to preset time points; Add ingredients after cooking: use a robotic arm or a spreading device to sprinkle powder or oil, and support visual recognition to locate the position of the dish.

5. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The cloud-based dish database includes: Dynamic update mechanism: supports R&D teams to modify SOP online, and version changes are automatically synchronized to terminals; Health classification label: supports filtering dishes according to health needs during production scheduling; Cooking parameter library: stores device adaptation parameters and automatically matches device models.

6. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The control logic of the automatic feeding system includes: Dual sensor calibration: flow sensor (volume) + weight sensor (mass) joint calibration, error < ±0.5g; Anti-misoperation mechanism: When the portion bowl is not positioned correctly, the feeding instruction is automatically frozen; Seasoning remaining warning: When the remaining amount in the storage bin is less than 20%, the system will push a replenishment reminder to the mobile terminal.

7. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The production scheduling and food replenishment module supports: Historical production scheduling data backtracking: automatically predict the amount of food used in the same time period next week and optimize the safety stock threshold; Supplier docking interface: Generate electronic receipt based on purchase order and automatically enter it into the inventory system.

8. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: The preprocessing steps of the food preparation task management module include: Standardized operation instructions: Use pictures / videos to show knife skills and marinating time; Smart tool linkage: The cutting table has a built-in weight sensor to verify in real time whether the food processing volume complies with the SOP.

9. The automatic food preparation and feeding system for an unmanned intelligent kitchen as claimed in claim 1, characterized in that: Also includes hygiene monitoring module: The feeding machine aisle is sterilized by ultraviolet light, and sterilization starts automatically for 30 seconds after each feeding; The camera in the food preparation area uses visual recognition to detect violations such as mixing raw and cooked ingredients, and operation timeouts, and generates real-time alarms.

10. The system according to any one of claims 1 to 9, characterized in that: The cloud-based dish database is connected to the dish scheduling and automatic replenishment module through a microservice architecture, supporting distributed deployment in multiple canteens. A single system can manage the scheduling of 500+ dishes at the same time, with a response time of <200ms.

Images