Intelligent dish matching device for automatic frying pan

Abstract

The invention relates to an intelligent dish matching device for an automatic frying pan, and aims to improve the cooking efficiency and food safety management in the catering industry. The device comprises a vegetable cleaning cabinet, a mechanical arm, a mechanical clamping jaw, a guide rail system, a frying pan and a control system. The clean vegetable cabinet realizes automatic supplement of food materials through a landslide system, supports classified storage of the food materials and shortens the vegetable preparation time; a mechanical arm is combined with a guide rail system, the grabbing range is expanded through a six-degree-of-freedom rotating joint and motor driving, and efficient operation between a food cabinet and a frying pan is achieved; the frying pan has a rotating function, and the angle can be adjusted according to cooking requirements so as to be matched with feeding of food materials and pouring of finished products. And the food material identifier, the wok identifier and the finished dish identifier are tracked in real time through an industrial internet identifier analysis technology, the food quality and safety are ensured, and intelligent scheduling of tasks is realized in combination with a multi-objective optimization algorithm. The device supports parallel operation of multiple pots, the meal delivery speed is effectively increased, and the requirements of the catering industry for efficient and intelligent production are met.

Description

Technical Field

[0001] The present invention relates to fields such as mechanical structure, industrial Internet identity resolution, algorithm model, digital twin and other technologies, and specifically to the construction of an intelligent dish preparation device for an automatic cooking pot. Background Art

[0002] With the rapid development of the catering industry, the market has put forward higher requirements for the scale, standardization and efficiency of food production. As the core model of intensive production, central kitchens play an important role in improving production efficiency and ensuring food safety. However, the existing central kitchen equipment still has deficiencies in automation and intelligence, especially in meeting the needs of efficient dish matching and intelligent cooking. It still relies on manual operation or semi-automatic equipment, making it difficult to achieve full automation and efficient food preparation, thus restricting the overall efficiency and quality consistency of dish production.

[0003] In traditional central kitchens, due to the diverse cooking requirements of multiple dishes, the processing methods and time requirements of different ingredients are different, so it is difficult for existing equipment to accurately control the time and order of side dishes, which affects the production quality and taste of the dishes. Especially in batch production, manual operations are inevitably prone to human errors, which leads to challenges in food safety and standardization. High labor costs and a reduction in the number of workers have further increased the operating pressure of catering companies, putting them at a disadvantage in cost control and market competition. They urgently need to improve efficiency and reduce dependence on labor through intelligent equipment. In addition, the side dish process that relies on manual or simple mechanical devices is difficult to achieve efficient collaboration with automatic cooking devices, which affects the overall speed of serving meals and cannot fully meet the catering industry's demand for efficient production.

[0004] In this context, the intelligent side dish device for automatic cooking pots has become an urgently needed innovative solution. The research and development of this device aims to realize the full automation of the side dish process, and can accurately control the ratio and delivery time of different ingredients, so as to achieve a seamless connection with the automatic cooking pot. The introduction of the intelligent side dish device can significantly improve the overall efficiency of the central kitchen, ensure the precise control of the order, time and proportion of the delivery of ingredients, and make the quality of each dish more stable. In addition, the device can monitor and automatically adjust the key parameters of the side dish process in real time, effectively improve the efficiency and consistency of the dish production process, and meet the market demand for fast food delivery and high-quality dishes.

[0005] The intelligent food preparation device for automatic cooking pots can not only effectively address the pain points of central kitchens in terms of automation and efficiency, but also achieve precise control and automatic optimization of production processes through Internet of Things technology and artificial intelligence algorithms. The introduction of this device not only improves the production efficiency and food safety of central kitchens, but also provides consumers with more diversified and personalized dining options, thus providing technical support and innovative ideas for the sustainable development of the modern catering industry. Summary of the invention

[0006] The present invention focuses on the shortcomings of the current catering industry in the field of informatization and intelligence, and proposes an intelligent comprehensive system covering the entire kitchen cooking process to address the problem of low meal delivery efficiency. The system achieves full-process traceability and optimized scheduling from food material management to finished product output by establishing a set of efficient identification resolution systems and advanced intelligent scheduling algorithms, significantly improving meal delivery efficiency and the stability of dish quality. The identification resolution system covers ingredient identification, wok identification, and fried finished product identification, ensuring that every link in the dish production process can be fully recorded and traced, laying the foundation for efficient management, transparent quality control, and digital upgrading of smart kitchens.

[0007] In the design of the system, the ingredient label not only records the source, type, storage location, inventory and shelf life of each ingredient, but also supports real-time tracking and scheduling, providing data support for the precise management of ingredients and inventory optimization. The wok label covers key data such as the real-time status, temperature, heat setting and specific frying time of the wok, ensuring that each frying stage of each dish can be traced back to the specific pot and operating parameters. The finished stir-fry product label summarizes the information of the ingredients and the wok, forming a complete production record of the dish, so that the source of each ingredient, the time of delivery and the operating temperature of the finished stir-fry product can be traced, which improves the transparency of the finished product and the food safety management capabilities.

[0008] The core advantage of the system lies in its innovative multi-task and multi-pot scheduling algorithm under the constraints of side dish resources, which combines the greedy algorithm, priority algorithm and shortest job first (SJF) algorithm to achieve intelligent optimization of multi-pot parallel operations. This combined scheduling strategy can not only dynamically allocate tasks according to the priority and cooking needs of different dishes, but also schedule the frying tasks of the three pots in real time, ensuring that the ingredients of each dish can be delivered in stages as needed, minimizing the idle time of the robotic arms and pots. In addition, when the task scheduling complexity is high, the system also combines genetic algorithms to optimize the global allocation of tasks, and generates high-quality scheduling plans through operations such as "selection", "crossover" and "mutation", so as to complete a large number of tasks in a short time and achieve the global optimal meal delivery efficiency.

[0009] At the same time, the idea of ​​building a digital twin system is proposed, which can monitor and manage the entire cooking process, provide real-time feedback and optimize the status of each dish, and help operators achieve more precise control, thereby improving the efficiency of serving meals. In general, the present invention provides a complete solution for the intelligent development of the catering industry, supports information management, quality traceability and scientific research, and provides a technical demonstration for the digital upgrade and identification resolution talent training in the catering field.

[0010] To achieve the above objectives, the present invention proposes the following technical solutions: First, the modeling design of the relevant physical model ensures the scientificity and practicality of the system, and reasonably arranges the spatial position of the system, determines the relative position of each physical component, and optimizes the operating efficiency of the entire system. At the same time, a unique identification coding rule is formulated to systematically identify and encode the side dishes, woks, and fried products in all links. A unique identification code is generated through the identification registration system and uploaded to the industrial Internet identification resolution node, which can help consumers and regulatory agencies track the source of food and ensure the safety of food. This is particularly important when a food safety incident occurs, and it helps to quickly trace the source of the problematic food. Then, the software and hardware are combined, and a detailed functional analysis is performed on each physical component to ensure its compatibility with the software system. Then, a software architecture that matches the hardware is designed to realize functions such as data collection and processing, algorithm calculation, and user interface interaction. Finally, the relevant scheduling algorithm is used, combined with machine learning and optimization algorithms, to ensure that the cooking robot can efficiently serve meals and improve overall operational capabilities.

[0011] Step 1: Modeling and design of physical model

[0012] Based on the entire workflow of the entire cooking robot, the present invention is divided into four major modules, namely, a clean vegetable cabinet, a mechanical arm, a slide rail, and a cooking pot. First, the SOLIDWORK modeling software is used to refine the model of each module, and then the corresponding physical materials are determined according to the use environment of the product to ensure strength, wear resistance and thermal stability, so as to improve the overall performance and meet the application requirements. Then, the relative positions of the clean vegetable cabinet, the mechanical arm, and the cooking pot are determined by the extension range of the mechanical arm. When the mechanical arm is too close to the target object, if the distance is too close, its movement speed will be accelerated, the force will increase, and it may cause collision, extrusion or scratching, resulting in damage, deformation or rupture of the surface of the object. At the same time, the joints and transmission parts of the mechanical arm may be damaged, such as joint deformation or component damage. On the contrary, if the mechanical arm is too far away from the target object, the moving time is extended, resulting in an increase in the overall operation cycle, and the coordination between the joints may decrease, thereby affecting the operation accuracy. Therefore, it is very important to select a suitable relative position. The present invention has been verified by a large number of experiments to determine the optimal position relative to this product to optimize operating efficiency and safety. This design not only improves the working efficiency of the system, but also ensures the safety of the robot arm and materials, providing a solid foundation for future smart kitchens.

[0013] Step 2: Design of food labeling and coding

[0014] In order to ensure food safety, it is proposed to apply industrial Internet identification resolution to this product. By analyzing the identification of ingredients, woks, and finished products, the whole process of traceability can be realized. When food safety incidents occur, such as when food is detected to contain harmful substances or is contaminated, identification code resolution can quickly and accurately determine the batch, production time, sales area, and other information of the problematic food. At the same time, consumers can obtain food quality information by scanning the identification code on the food box, including quality inspection reports of raw materials, quality control measures during processing, and quality inspection results of finished products.

[0015] Step 3: Communication connection design

[0016] The present invention relates to three hardware components that need to communicate: a robotic arm, a gripper, and a slide motor. Through the interface design of these three hardware, it is intended to achieve efficient communication and collaborative work between modules to ensure the overall performance of the system and the smoothness of operation. This interface design not only promotes the real-time transmission of data, but also enhances the coordination between modules, laying a solid foundation for the stable operation of the system. The design of the interface follows the principle of standardization, covering multiple aspects such as data transmission, control instructions, and status feedback, to ensure the real-time transmission and processing of information between different modules. At the same time, the interface design takes into account future scalability and compatibility, providing convenient conditions for the enhancement of subsequent functions and the access of new equipment. Through a flexible interface architecture, the modules can interact effectively to ensure that the system can quickly respond to user needs and optimize the operating process. The subsequent section will elaborate on the specific communication mechanism and protocol to support the stable operation of the system and the reliable transmission of data.

[0017] Step 4: Multi-task multi-pot scheduling algorithm under the constraints of side dish resources

[0018] Improving efficiency is one of the core challenges in the cooking process, especially in the collaborative scheduling between the robot arm and multiple pots. This paper proposes an efficient algorithm that aims to ensure the best operating effect in a dynamic multi-tasking environment by optimizing the scheduling of ingredients by the robot arm. The algorithm comprehensively considers factors such as the type of ingredients, cooking time, the state of the pot, and the working capacity of the robot arm to ensure that each link can be coordinated and orderly, thereby maximizing the speed of serving meals.

[0019] Specifically, before the ingredients enter the cooking process, the system will evaluate the cooking time and method required, and generate detailed scheduling instructions for the robot arm to execute based on these parameters. Taking three woks as an example, when the three woks are running at the same time, the algorithm dynamically adjusts the operation order of the robot arm through an intelligent scheduling mechanism to avoid resource waste and conflicts. The algorithm will reasonably allocate tasks according to the priority of the dishes, so that the robot arm can complete the scheduling of the three woks in the shortest time, improving overall work efficiency and equipment utilization.

[0020] In addition, the algorithm also applies a multi-objective optimization strategy to balance the speed of serving and food quality. To achieve this goal, the algorithm designs a comprehensive objective function, taking serving time and cooking quality as multiple optimization objectives. Specifically, multi-objective optimization aims to achieve the goals of minimizing the idle time of the robot arm, minimizing the total cooking time of all dishes, and minimizing the idle time of the pot, so as to ensure the efficiency and effectiveness of each operation in the entire cooking process. Through this optimization method, the system can effectively balance the tasks of the three pots so that each cooking operation is completed in the best state. This design not only effectively shortens the serving time, but also ensures the uniformity and stability of the quality of the dishes, meeting the dual needs of efficient serving and food safety. This intelligent regulation enables the cooking process to achieve higher efficiency under the conditions of multi-pot parallel operation, and provides reliable technical support for the intelligentization of modern catering.

[0021] Through a large number of experiments and simulation tests, the present invention analyzes the system performance under different scheduling strategies. The experimental results show that the cooking robot using this algorithm performs well in terms of food delivery speed and operation accuracy, especially in the context of multi-pot synchronous cooking, showing extremely high stability and reliability. This achievement provides strong technical support for the development of smart kitchens, indicating that this algorithm has significant advantages in dealing with multi-pot and multi-task environments, and can achieve efficient and reliable side dish operations.

[0022] Compared with the prior art, the advantages of the present invention are:

[0023] 1. This invention proposes, designs and builds a physical platform for an intelligent dish preparation device for automatic cooking pots for the first time, providing the catering industry with a practical case for scientific research on quality and safety, digital upgrading of the industry and training of talents in identification resolution.

[0024] 2. The present invention designs an ingenious mechanical model of a clean vegetable cabinet. By designing a suitable slope, automatic replenishment of vegetables can be achieved, thereby greatly improving the cooking efficiency of the mechanical arm.

[0025] 3. The present invention can efficiently achieve the goal of how a robotic arm can use multiple pots to fry multiple dishes in the shortest time through reasonable design of the vegetable clean cabinet, reasonable spatial arrangement of the vegetable clean cabinet, the robotic arm, and the frying pan, and reasonable application of dynamic programming and the shortest job first algorithm.

[0026] 4. The present invention proposes and designs an identification scheme suitable for information security in the cooking process. By pasting your own information identification code on the ingredients, woks, and fried products, the problem of information traceability throughout the entire process is effectively solved.

[0027] 5. The present invention proposes a virtual platform construction scheme suitable for the entire cooking process. By using operating platforms such as Unity and 3DMAX, a virtual platform with real-time synchronization, precise mapping, and high fidelity characteristics is constructed, and the optimal decision of the physical system is achieved through automatic deduction of the virtual platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Intelligent side dish system

[0029] Figure 2 Clean vegetable cabinet in the intelligent food preparation system

[0030] Figure 3 Schematic diagram of six-axis robotic arm

[0031] Figure 4 Standardized lunch box design

[0032] Figure 5 Robotic arm slide design

[0033] Figure 6 Design of drive motor to control left and right movement of robotic arm

[0034] Figure 7 Flowchart of dynamic scheduling of multi-pot cooking tasks

[0035] Figure 8 Flowchart of genetic optimization scheduling for intelligent cooking of a large number of dishes DETAILED DESCRIPTION

[0036] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail and in depth below with reference to examples.

[0037] This paper focuses on four core modules: hardware design and construction, food identification and coding, communication connection, and algorithm research, and builds an efficient and intelligent cooking robot system. Figure 1 As shown. In terms of hardware design and modular construction, the system ensures stability and reliability, providing a solid physical foundation for high-frequency operations. Food identification and coding technology, by giving each ingredient an independent label, not only records information such as the source, inventory, location and shelf life of the ingredient, but also supports real-time tracking and management of the quantity of ingredients, providing an accurate data basis for the overall scheduling of ingredients. This identification system achieves comprehensive food safety protection by tracing back to the specific source and status of the ingredients, and can display detailed information such as the specific storage location, frying time, and side dish details of each ingredient on the label of the fried finished product, ensuring that the entire process of each dish is transparent and traceable.

[0038] The communication connection part uses an efficient data exchange mechanism to enable the various modules to work together to ensure smooth operation, especially in the case of multi-pot parallel operation, which significantly improves the system response speed and stability. The core algorithm design gives full play to the multi-objective optimization capabilities in the three-pot side dish task. Specifically, the algorithm introduces a combination strategy of greedy algorithm, priority algorithm and shortest job first (SJF) algorithm to achieve a balance between multiple factors such as meal delivery time, working efficiency of the robot arm and pots. By comprehensively evaluating the status of the three pots, the algorithm dynamically allocates tasks and accurately feeds materials to ensure that each pot is replenished with ingredients in a timely manner at different cooking stages, thereby achieving seamless connection of the cooking process of each dish, greatly improving the overall cooking efficiency and dish quality.

[0039] The organic combination of the four core modules enables the present invention to take a key step in achieving efficient and intelligent kitchen management. This system not only effectively guarantees efficient food delivery, but also realizes the whole process traceability from food material management to finished product output, providing strong support for the intelligent upgrade, food safety control and efficient food delivery of the catering industry. For the present invention, the specific operation method is as follows:

[0040] Step 1: Selection and design of kitchen cooking system hardware

[0041] Step 101: First, design the clean food cabinet according to system requirements

[0042] The design of the clean vegetable cabinet combines practicality and efficiency. It uses durable stainless steel plates to meet the hygiene requirements of the kitchen environment and is easy to clean and maintain. The cabinet is rationally divided into three independent areas to store vegetables, seasonings and meat respectively to prevent cross contamination and ensure the freshness of the ingredients. The partition design shortens the search time of the robot arm when grasping, effectively improves the overall cooking efficiency, and facilitates inventory management. The chef can monitor and update the food information more timely to reduce waste.

[0043] The overall dimensions of the clean food cabinet are 2.2 meters long, 1.5 meters high, and 1.1 meters wide, taking into full consideration the needs of dishes and the working range of the robotic arm. The cabinet is divided into four layers vertically, and each dish can store 6 to 8 portions, ensuring the storage capacity of 36 dishes, meeting the diverse needs of customers and reducing preparation time. The layered and partitioned layout improves the efficiency of food classification and storage, allowing chefs to quickly obtain the required ingredients, optimize kitchen operation processes, and improve customer dining experience.

[0044] In addition, the cabinet is innovatively designed with a sliding slope system. The sliding slope angle is optimized through simulation, so that after each dish is picked up, the subsequent dishes will automatically slide down and be replenished, thereby maintaining a continuous supply of ingredients and avoiding operational delays. Figure 2The sliding system enhances the fluidity of ingredients, ensures the smooth operation of the robotic arm during peak hours, significantly improves the speed of serving food, and provides strong intelligent support for the modern catering industry.

[0045] Step 102: Select the robot arm and the robot gripper according to the system requirements

[0046] The cooking robot system includes a robot body, a controller and a mechanical gripper. The robot body simulates the design of a human arm and has 6 rotation joints, each of which represents a degree of freedom, including shoulder, elbow and wrist joints. It can achieve a variety of postures to meet the needs of complex cooking operations, such as Figure 3 As shown. Its working range is a sphere with a radius of 610mm, which can cover a large operating space. When installing the robot, tools should be prevented from entering the cylindrical space directly above and below to improve safety and operational flexibility.

[0047] The mechanical gripper has high gripping force and flexibility, which can meet the needs of grasping a variety of ingredients. The gripper material is wear-resistant and corrosion-resistant, suitable for high-frequency operations in the kitchen environment. Its opening and closing control has high precision, and it can adjust the force according to the characteristics of the ingredients to ensure the stability of the grip and the integrity of the ingredients. In addition, the gripper is compact and lightweight, which can adapt to small spaces, improve operating efficiency and reduce interference. This design optimizes the durability of the equipment and the overall efficiency of the system while ensuring grasping accuracy.

[0048] Step 103 Design rails and lunch boxes according to system requirements

[0049] Ingredients are standardized to ensure that the ingredients are of consistent specifications and quality each time they are cooked, thereby reducing inconsistencies in taste and appearance caused by differences in ingredients, such as Figure 4 This standardized food material approach not only improves the dining experience of customers, but also simplifies the operation process of the robotic arm, enabling it to identify, grab and process food materials more quickly, thereby improving overall work efficiency, especially helping to quickly serve meals during peak hours.

[0050] In order to further optimize the operating range of the robot arm, the present invention designs a set of high-strength aluminum alloy guide rail system, which cooperates with the motor to support the movement of the robot arm on the guide rail. The specific design is as follows: Figure 5 The rails cover the entire working area of ​​the clean food cabinet, allowing the robot arm to move flexibly between different heights and lateral positions. The surface of the rails has been specially treated to reduce friction, ensuring smooth operation of the robot arm.

[0051] As the core component of the guide rail system, the motor provides stable and efficient power support, such as Figure 6As shown. The motor has a maximum efficiency of up to 96%, IP65 protection level, can adapt to complex environments, and an operating temperature range of -25°C to +90°C, suitable for a wide range of working conditions. Its rated input speed is 3000rpm, the maximum output speed is 8000rpm, and it provides a rated thrust of 400N, ensuring stable operation under high loads. The motor has an average life of 20,000 hours and has high-precision output (flange accuracy meets DIN 42955-R standards, and the reverse clearance is less than 6 arc minutes). Combined with an intelligent control system, the robotic arm can reach the target position accurately and quickly, significantly improving the system's operational stability and overall efficiency.

[0052] Step 104: Select a cooking pot according to system requirements

[0053] In smart kitchen scenarios, wok design plays a key role in efficient cooking. Smart woks are generally made of stainless steel or high-temperature resistant materials to ensure that they do not deform or rust during frequent heating and cooling. The durable pot body not only supports multiple cooking of different dishes, but also can withstand high temperatures and cooking loads. The size and depth of the wok are usually carefully designed to meet the needs of placing a variety of ingredients, while reducing splashing when the robotic arm puts in dishes or seasonings, thereby ensuring safety and efficiency.

[0054] In the intelligent cooking system, the heating and rotation functions of the wok are its core, usually using a fast and uniform heating method, such as electromagnetic induction heating technology. This heating method allows the pot to quickly reach the set cooking temperature and maintain a stable temperature to meet the cooking needs of different dishes. The constant temperature control system can also adjust the temperature according to the placement of ingredients to ensure that the ingredients are evenly heated at each stage to avoid taste or nutrient loss caused by temperature fluctuations.

[0055] The rotating design allows the ingredients to be fully stir-fried during the cooking process, heated evenly, and avoids partial overheating or burning. The rotation angle and speed of the pot can be adjusted according to different ingredients and cooking steps. The robotic arm can put the ingredients in order within a predetermined time, and the rotating pot ensures the uniform mixing and consistency of the ingredients through the stir-frying action.

[0056] Step 105 Arrange the space position reasonably according to system requirements

[0057] In order to ensure that the robot arm can grab and place food efficiently and accurately, the position layout of the robot arm, the clean vegetable cabinet and the cooking pot is particularly critical. The relative position of the robot arm and the clean vegetable cabinet needs to be arranged reasonably, avoiding both too far away to cause time delays in the grabbing process and too close to avoid insufficient operating space and collision risks. The location of the clean vegetable cabinet should be designed within the operating radius of the robot arm to ensure that it can easily cover all food areas. At the same time, cooperate with the movement of the slide system to shorten the movement path of the robot arm and improve work efficiency. The cooking pot is located directly in front of the robot arm or at the other end of the slide to reduce complex rotation operations and ensure that the process from grabbing to placing is smooth and efficient. The entire layout comprehensively considers the movement trajectory, working range and safety of the robot arm to ensure that it will not be interfered with by other equipment or environment while working efficiently, providing a stable operating environment for the entire cooking system.

[0058] Step 2: Design of food identification and coding

[0059] After the design is completed, the present invention establishes a complete set of identification and resolution systems for the intelligent cooking system, realizing the full process traceability and scheduling from raw materials to finished products, ensuring the high quality and transparency of dish production. The identification system consists of three parts: ingredient identification, wok identification, and finished product identification. Each part records the key data in the production process, constructing a complete traceability chain, so that each process of the dish can be recorded and traced back in detail, providing support for the efficient management and quality tracking of the intelligent kitchen.

[0060] The ingredient label not only covers the source, type, storage location, storage time and shelf life of each ingredient, but also records the real-time quantity of each ingredient. The detailed record of this label helps the system to accurately dispatch ingredients. By tracking the changes in inventory, the system can optimize the order of ingredients according to demand and shelf life, and ensure that the required ingredients are quickly located in the preparation process. This tracking of quantity information lays the foundation for the overall ingredient dispatching of the system, effectively avoiding waste and shortages, and improving the efficiency of inventory management and the sustainability of dish production.

[0061] The wok logo plays an important role in ensuring food safety in the logo resolution system. It mainly records the real-time status of each wok and key cooking parameters, such as temperature, heat setting, frying stage and specific frying time. After each cooking is completed, these data will be bound to the record of the finished product to form a complete traceability chain, ensuring that the frying process of each dish is clear and traceable. The detailed information provided by the wok logo can quickly locate the root cause of the problem when a food safety problem occurs. For example, by analyzing the frying temperature and time, the system can determine whether it meets the safe cooking standards, thereby effectively controlling and managing potential risks.

[0062] The stir-fried finished product label is the core part of the entire traceability system. It summarizes the information in the ingredient label and the wok label to form a complete production record of the stir-fried finished product. The stir-fried finished product label records in detail the key information such as the source of the ingredients, the placement location, the time of the ingredients, the specific wok number, the heat and duration of the stir-frying, etc. of each dish. Through the stir-fried finished product label, consumers can clearly understand the source of the dish, the production process and the processing information of each ingredient, thereby further improving the transparency of food and consumer trust. The stir-fried finished product label not only provides detailed information for the display of the meal, but also provides a basis for quality traceability after the meal.

[0063] Through the organic combination of the three major logos of ingredients, woks and finished products, the logo analysis system provides technical support for the whole process of food production traceability and scheduling, ensuring the efficient operation, quality control and strong traceability of the food production process. This system enables the smart kitchen to not only serve food quickly, but also meet the dual needs of food quality and transparency.

[0064] The Industrial Internet Identifier Resolution Application Service Platform is designed to facilitate and quickly connect to more enterprises, and the coding rules are relatively simple and flexible. It is mainly composed of an identifier prefix, a separator, and an identifier suffix. The identifier prefix is ​​coded by the secondary node platform, including the country code (fixed), industry code (fixed), and enterprise code; the identifier prefix and identifier suffix are separated by " / "; the identifier suffix is ​​completely customized by the enterprise, with various rules, unique coding, certain meaning, and no duplication.

[0065] The identification prefix consists of three parts: the national top-level node code, the identification resolution secondary node code and the enterprise code, with the character '.' as a separator in the middle and ending with the character " / ". The national top-level node code for identification resolution is "88", the secondary node code for identification resolution of the food industry is "109", and the enterprise node code used in this invention is "80044833".

[0066] The coding rule of the identification suffix adopts the form of "catering equipment code. Catering link code. Related element code". The specific coding structure is: "88.109.80044833 / catering equipment code. Catering link code. Related element code", as shown in Table 1. Among them, the catering equipment code starts from "001" and increases according to the number of equipment. The catering link code is a fixed value, including four main links: meal preparation link, dish transportation link, frying link and meal delivery link, corresponding to codes 001 to 004 respectively. The information such as ingredients, frying pan equipment, and fried finished products is in the related element code.

[0067] Table 1 Identification prefix encoding

[0068] National top node code: 88 Secondary node code: 109 The node code of the invention enterprise: 80044833 Rules: 88.109.80044833 / Food preparation equipment code.Food preparation link code.Related element code

[0069] The food ingredients of the kitchen cooking robot system are coded from 0 to 999, as shown in Table 2 below.

[0070] Table 2 Food coding examples

[0071]

[0072]

[0073] The wok codes are 01-03, as shown in Table 3.

[0074] Table 3 Wok coding example

[0075]

[0076]

[0077] The codes for the fried finished products are 000-099, as shown in Table 4.

[0078] Table 4 Example of coding for fried finished products

[0079]

[0080] Step 3: Communication connection design

[0081] Step 301: Select the robot communication method according to system requirements

[0082] The robot arm provides a variety of communication methods, including wired and wireless communications, to meet the needs of different scenarios. In wired communication, the robot arm supports network cables, RS485 to serial ports, and USB serial ports. Network cable communication has high speed and stability, suitable for large-scale data exchange; RS485 is suitable for long-distance transmission and environments with high anti-interference requirements; USB serial ports are used for equipment debugging or temporary data transmission. In general, wired communication ensures data integrity and transmission efficiency, but in space-constrained smart kitchens, the wiring is complex and lacks flexibility, which is not suitable for frequent movement of equipment.

[0083] In terms of wireless communication, the robot arm supports WiFi STA and WiFi AP modes. WiFi AP mode allows the robot arm to act as an independent access point to build a local communication system, but it may cause excessive load when connecting multiple devices and has limited applicability. In contrast, WiFi STA mode is more suitable for smart kitchen scenarios. In this mode, the robot arm can access the kitchen central network and realize real-time data interaction with smart devices without direct device connection, which has high flexibility and scalability.

[0084] WiFi STA mode can also support remote control and cloud data transmission, which is convenient for real-time monitoring and intelligent scheduling. In summary, although wired communication has superior stability, the WiFi STA mode in wireless communication is more suitable for the efficient and flexible operation needs of smart kitchens, and improves the intelligence and automation level of the system.

[0085] Step 302: Select the guide rail motor and mechanical gripper communication method according to system requirements

[0086] In the application of smart kitchen, the present invention selects RS485 to serial port as the communication method of the guide motor and the mechanical gripper because it has advantages in anti-interference, long-distance transmission, multi-point communication and low cost.

[0087] RS485 communication uses differential signal transmission, and its strong anti-interference ability allows it to remain stable in kitchens with complex electromagnetic environments. This communication method can support long-distance transmission of 1,200 meters, which is suitable for flexible deployment of rail systems in kitchens and avoids the problem of wireless signal attenuation. In addition, RS485 supports multi-point communication, and a master device can connect to up to 32 slave devices, which facilitates multi-device collaboration and improves the simplicity and efficiency of system integration. Compared with other communication methods, the low-cost advantage of RS485 can also effectively control the overall construction and maintenance costs of smart kitchens.

[0088] In summary, the characteristics of RS485 to serial port in anti-interference, transmission distance, multi-point communication and cost control enable it to provide an efficient and reliable solution for the coordinated control of guide motors and mechanical grippers in smart kitchens.

[0089] Step 4: Multi-task multi-pot scheduling algorithm under the constraints of side dish resources

[0090] In order to achieve a single robotic arm coordinating three woks to complete the frying task of multiple dishes in the shortest time, the problem can be modeled as a scheduling problem or a multi-objective optimization problem. In this scenario, the robotic arm needs to reasonably allocate its motion path and the order of using the woks to minimize the overall time cost and ensure the cooking quality of each dish. At the same time, according to the number of dishes to be fried and their complexity, two different frying strategies can be designed for optimization under a dynamic demand environment. This optimization can not only improve cooking efficiency, but also reasonably allocate the resource utilization of the robotic arm and the pots in the scenario of multi-tasking, thereby maximizing the level of intelligent operation in the kitchen.

[0091] When the number of dishes is less than 10, the greedy algorithm is an efficient choice. It quickly finds the global optimal solution by selecting the local optimal solution in each step. Combined with the priority, it can ensure that time-sensitive dishes or dishes with strict cooking temperature requirements are processed first. The shortest time job priority algorithm further optimizes the frying order, so that simple and less time-consuming dishes can be processed in a shorter time, reducing the idle time of the robot arm and the wok. The combined algorithm has a fast execution speed and is suitable for small-scale dish needs that require quick response.

[0092] When the number of dishes is greater than 10, the complexity of the problem increases significantly, and a simple greedy algorithm may not be able to find the global optimal solution. At this time, the genetic algorithm is an algorithm suitable for multi-objective optimization. It simulates the biological evolution process and continuously generates better solutions, and can find near-optimal solutions in large-scale problems. Combined with the priority algorithm, it can ensure that important dishes are given priority, avoiding problems such as long waiting times or temperature out of control.

[0093] Step 401: Priority algorithm + shortest time job priority algorithm + greedy algorithm

[0094] In this intelligent cooking system, in order to achieve multi-pot parallel work and complete the frying task of multiple dishes in the shortest time, the combination strategy of greedy algorithm, priority algorithm and shortest job first (SJF) algorithm is a suitable and efficient scheduling solution. Based on the workflow of this system, each pot can fry a dish independently, and the robotic arm will complete the preliminary ingredient placement before each dish starts frying. The frying process of each dish needs to be carried out in stages, such as adding oil first, then meat, and finally adding vegetables and other side dishes. In this multi-stage ingredient mode, the robotic arm must work in coordination with the pot to ensure that the dishes are placed in stages as needed to achieve the best frying effect.

[0095] In this scheme, the introduction of the greedy algorithm is mainly used to solve the local optimal problem in the scheduling process. The core idea of ​​the greedy algorithm is to give priority to the task that can be completed the fastest at the moment. Specifically, when multiple woks are working in parallel, the greedy algorithm selects the dish with the shortest cooking time and places it in the idle wok to ensure that each wok is in working condition as much as possible, thereby reducing the idle time of the robot arm and the wok. In the specific process of scheduling, assuming that any of the three woks is idle, the greedy algorithm will assign the wok to the dish with the shortest cooking time according to the remaining cooking time of each dish. The mathematical model of this strategy can be expressed as:

[0096]

[0097] Among them, T iis the total cooking time of the i-th dish. By giving priority to the shortest task at the moment, the greedy algorithm ensures the minimization of the overall scheduling time. This scheduling method is suitable for situations where the number of dishes is small and the time is tight, and it can quickly find the global optimal solution.

[0098] However, due to the differences in cooking characteristics of different dishes, some dishes may have higher temperature or time requirements, so the priority algorithm plays a key role in this scenario. The priority algorithm assigns a weight (priority value) to each dish to ensure that dishes with higher priority are given priority in scheduling. Combined with the priority algorithm, the greedy algorithm first considers high-priority dishes when selecting dishes, and then schedules them based on the completion time. The combined scheduling goal can be expressed as:

[0099]

[0100] Where P i It is the priority weight of each dish. By combining priority with time, the time requirements of the task and the requirements of the dish can be taken into account to ensure that each dish is processed at the right time.

[0101] The shortest job first (SJF) algorithm further optimizes the overall efficiency of this scheduling scheme. The SJF algorithm prioritizes the dishes that take the shortest time to cook, ensuring that the system completes as many simple, less time-consuming dishes as possible in a short period of time, and avoids the robot arm and the pot being idle due to waiting for longer tasks. The objective function of this scheduling is:

[0102]

[0103] where t i Indicates the cooking time of the current stage. By selecting short-time tasks for priority execution, the SJF algorithm reduces the time wasted due to waiting and enables the system to complete simple tasks in a shorter time, thereby improving the overall work efficiency. Combining the priority algorithm and the greedy algorithm, the SJF algorithm ensures the continuous use of the pot by giving priority to short-time tasks during the scheduling process, further optimizing resource utilization.

[0104] In this smart kitchen scenario, the priority algorithm, SJF algorithm and greedy algorithm are combined to form a hierarchical and complementary scheduling solution. The greedy algorithm ensures local optimality by selecting the shortest time task in real time; the priority algorithm assigns different processing weights to each dish to ensure that the order of tasks meets their respective requirements; and the SJF algorithm ensures the efficient operation of the system by giving priority to short-time tasks. On the whole, this combination not only meets the rapid response requirements of frying a small number of dishes, but also adapts to the complex scheduling requirements of multiple tasks of three pots working in parallel, realizes the seamless connection between the robotic arm and the wok, and maximizes efficiency in actual operation. The specific flow chart is as follows Figure 7 shown.

[0105] Step 402: Priority Algorithm + Genetic Algorithm

[0106] In the intelligent cooking system, when the number of dishes exceeds 10, the complexity of task scheduling increases significantly. It is difficult to find the global optimal solution within a reasonable time by relying solely on the greedy algorithm. Therefore, the combination strategy of genetic algorithm and priority algorithm can more effectively solve this multi-objective optimization problem. As a random optimization algorithm, genetic algorithm imitates the biological evolution process and continuously generates high-quality solutions through operations such as "selection", "crossover" and "mutation", so as to find a near-optimal scheduling solution under complex conditions. Combined with the priority algorithm, it can ensure that certain important dishes (such as dishes with special requirements for temperature or time) are given priority to avoid the deterioration of cooking quality of key dishes due to long waiting time or temperature control errors.

[0107] In this scheme, the priority algorithm first sets different priorities for each dish to indicate the importance or time sensitivity of the dish. By setting priorities, the dishes can be preliminarily sorted so that high-priority dishes have greater selection rights during the optimization process of the genetic algorithm. This priority sorting provides guidance for the genetic algorithm, helps to speed up the algorithm convergence and reduce calculation time. The mathematical model of the priority algorithm can be expressed as:

[0108]

[0109] Where P i Indicates the priority of each dish, R i It represents the response time of the dishes in the scheduling (i.e. the time to start cooking). This formula can ensure that high-priority dishes are scheduled as early as possible, thus avoiding the impact of their quality due to long waiting time.

[0110] The main role of the genetic algorithm in this scheduling scheme is to optimize the overall task allocation and scheduling time. The operation process of the genetic algorithm includes initial population generation, fitness evaluation, selection, crossover and mutation operations. First, the initial population is a set of randomly generated task allocation schemes, each of which represents a possible cooking order and delivery schedule. The fitness function is used to evaluate the pros and cons of each scheme, and give priority to those with shorter total cooking time or those that can meet the needs of high-priority dishes to enter the next round of iteration. The fitness function can be expressed as:

[0111]

[0112] Where T i represents the total cooking time of each dish, and α is a weight parameter used to balance the relationship between the total cooking time and the priority. Through this function, the genetic algorithm can generate a better scheduling plan to minimize the overall time.

[0113] In each round of iteration, the "selection" operation selects solutions with higher fitness from the current population so that high-quality solutions can be passed on to the next generation. The "crossover" operation generates new task combinations by exchanging part of the scheduling order of two solutions, which helps to explore more possible scheduling methods and avoid falling into local optimal solutions. The "mutation" operation increases the diversity of the population by randomly adjusting the order or time of individual tasks, making the genetic algorithm more robust. The introduction of crossover and mutation enables the genetic algorithm to have global search capabilities, so that it can find near-optimal scheduling solutions in a huge search space.

[0114] When explaining the mathematical model of genetic algorithms, we can first understand it from its core principle of "survival of the fittest". The fitness function is used to measure the pros and cons of each solution and select operations based on its value. Suppose there are two different task allocation solutions, S1 and S2, and their fitness are f(S1) and f(S2) respectively. If f(S1)>f(S2), then S1 is more likely to be retained in the next generation. The formula for the crossover operation can be used

[0115] C=S1×(1-r)+S2×r

[0116] Where C is the new solution and r is a randomly generated weight ranging from 0 to 1. The mutation operation can be expressed as

[0117] M=C×Δ

[0118] Its Δ is a small disturbance value used to break the existing pattern. Through crossover and mutation, the genetic algorithm can generate more diverse scheduling solutions and continuously approach the optimal solution.

[0119] In the smart kitchen scenario, the combination of the priority algorithm and the genetic algorithm can not only ensure that important dishes are given priority, but also find the task allocation solution with the best overall time through the global search characteristics of the genetic algorithm. When there are a large number of dishes, the combined algorithm can effectively balance the efficiency of task allocation and the quality requirements of dishes, achieving efficient use of woks and precise control of robotic arms. This solution can be applied to large-scale cooking tasks to ensure that customer needs can be quickly responded to during peak hours. The specific flow chart is as follows: Figure 8 shown.

[0120] Step 5: Build a twin system

[0121] When the physical layer was built, we proposed the idea of ​​building a virtual cooking robot using Unity and other related simulation software. The introduction of the prediction and deduction functions of the virtual layer can greatly improve the overall performance and adaptability of the system. By building a virtual model, we can simulate the various processes of the system in actual operation, including robot arm operation, wok temperature control, food delivery, etc., so as to discover possible process bottlenecks or operational conflicts in advance. Through these simulations and optimizations, we can not only shorten the debugging time of the system, but also foresee possible failures, provide data support for subsequent operations, and further enhance the reliability of the system.

[0122] Virtual layer prediction can also optimize resource scheduling and improve the system's response speed and work efficiency. For example, during the simulation process, the demand fluctuations of different dishes at different times can be analyzed to optimize the utilization rate of the robotic arm and the wok. In this way, the system can dynamically adjust resources in actual operation to ensure rapid response to customer needs during peak hours, and optimize energy consumption during off-peak hours to save costs. Through the deduction of the virtual layer, optimization strategies can be formulated in advance to improve overall resource utilization and achieve smarter resource management.

Claims

1. An intelligent dish preparation device for an automatic cooking pot, characterized in that: The device includes a clean vegetable cabinet, a robotic arm, a robotic gripper, a guide rail system, a frying pan, and a control system: 1) The clean vegetable cabinet has multiple partitioned areas for storing different types of food by classification, and the food can be automatically replenished through a sliding system; 2) The robotic arm is combined with a guide rail system. The robotic arm is equipped with a six-degree-of-freedom rotation joint and a precisely controlled mechanical gripper for grabbing and placing food. The guide rail system is made of high-strength aluminum alloy and driven by a motor to support the robotic arm to move between multiple working areas, thereby expanding the grabbing range and improving work efficiency. 3) The frying pan is designed with intelligent rotation characteristics on the basis of supporting automatic heating and rotation functions. Through the rotation angle design, it can achieve efficient cooperation with the robotic arm, thereby improving the overall food delivery efficiency and automation level; 4) The control system is based on the industrial Internet identification resolution technology, which realizes the unique identification, data collection and full traceability management of the whole process materials; 5) The control system uses a multi-objective optimization algorithm to intelligently manage the task allocation and operation scheduling of each module.

2. The intelligent dish preparation device according to claim 1, characterized in that: Each partition area in the clean vegetable cabinet has an independent storage level, and the standardized storage capacity of each dish is 6-8 portions, supporting a total capacity of 36 dishes.

3. The intelligent dish preparation device according to claim 1, characterized in that: The slope of the landslide system is optimized through simulation to ensure that the food can automatically slide down and be replenished after being clamped, thereby improving the continuity of meal delivery.

4. The intelligent dish preparation device according to claim 1, characterized in that: The robotic arm can grab and place food ingredients through a six-degree-of-freedom rotating joint and a precisely controlled mechanical gripper. The guide rail system is made of high-strength aluminum alloy and driven by a motor, which supports the rapid horizontal movement of the robotic arm to dynamically adapt to the multiple working areas of the clean vegetable cabinet to obtain ingredients, thereby expanding the grabbing range of the robotic arm. Through the coordinated design of the guide rail and the robotic arm, the system can cover the entire storage area of ​​the clean vegetable cabinet and the operating range of the wok, realize efficient collaboration of multi-task operations, and improve the system's operating efficiency and flexibility.

5. The intelligent dish preparation device according to claim 1, characterized in that: The motor of the guide rail system has a transmission efficiency of 96% and an IP65 protection level, supporting the precise movement and efficient operation of the robotic arm in complex kitchen environments.

6. The intelligent dish preparation device according to claim 1, characterized in that: During the cooking process, the wok can rotate to a preset angle in the direction of the robotic arm according to the demand for placing ingredients, so that the robotic arm can place ingredients accurately according to the steps; when the frying process is completed, the wok automatically rotates to the opposite direction of the robotic arm to pour out the finished dish. Through the cooperation of the robotic arm and the wok rotation function, the efficient connection between the placing of ingredients and the output of dishes is achieved, which improves the operating efficiency and the speed of serving the food.

7. The intelligent dish preparation device according to claim 1, characterized in that: The control system uses the identification resolution system to track and manage the food identification, wok identification and fried product identification in real time, and ensures food safety and transparency of the production process through identification resolution technology; 1) The food label records the source, quantity, storage location and shelf life of the food; 2) The wok mark records the wok's temperature, frying time, service life and heat setting; 3) The stir-fried finished product label summarizes and displays the source of ingredients, wok information and the production process of the final product.

8. The intelligent dish preparation device according to claim 1, characterized in that: The control system integrates a combination strategy of a priority algorithm, a greedy algorithm and a shortest job first (SJF) algorithm to achieve intelligent scheduling of multiple pots in parallel when the number of dishes is less than 10.

9. The intelligent dish preparation device according to claim 1, characterized in that: When the number of dishes exceeds 10, the control system combines the genetic algorithm with the priority algorithm to optimize the global scheduling plan of the dishes to ensure the efficient operation of multiple pots and multiple tasks.

10. The intelligent dish preparation device according to claim 1, characterized in that: The system constructs a digital twin model through virtual layer simulation to achieve real-time monitoring, prediction and optimization of the cooking process, further improving operational accuracy and efficiency.

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