Unmanned movable automatic cooking robot and system

Abstract

The invention provides an unmanned movable automatic cooking robot and system, the robot comprises a plurality of modules such as a movable chassis and a machine body, the movable chassis realizes movement, a central control unit coordinates all the modules to work, a cooking execution module carries an intelligent fire control model to complete cooking, and a safety guarantee module ensures operation safety. The automatic driving module realizes automatic driving and obstacle avoidance, and the energy management module provides energy; a system matched with the system covers a user interaction module, a cooking task scheduling module and the like, food material requirements can be predicted through data analysis, a purchase plan is optimized, and convenient and intelligent cooking services are provided for users; the invention aims to solve the problems that the use scene is limited, the fire control is not accurate enough, the dish quality is low, and food material overstock or stockout is easily caused, and provides more efficient, convenient and intelligent cooking service through multi-module cooperation and data-driven optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent cooking, and in particular, to a movable automatic cooking robot and system without human intervention. Background Art

[0002] With the progress of technology, intelligent cooking technology and robotics technology have been continuously developing; in terms of intelligent cooking, from the initial simple electronic cooking devices, such as rice cookers and microwave ovens, it has developed to today's intelligent kitchen appliances with multiple preset cooking modes, which can achieve a certain degree of automated cooking. For example, an intelligent stir-fry machine can complete operations such as stir-frying and seasoning according to the set program; robotics technology has also gradually expanded from the industrial production field to the daily life service field, and mobile robots have been widely used in logistics distribution, cleaning, etc., and their navigation, obstacle avoidance, and human-computer interaction capabilities have been continuously improved; the integration trend of these two fields is becoming increasingly obvious, laying a foundation for the development of movable automatic cooking robots and systems without human intervention;

[0003] However, common automatic cooking devices are usually fixed in a specific position, lacking mobility, with limited usage scenarios, and it is difficult to meet the cooking needs of users at different locations; moreover, during the cooking process, the control of the cooking heat is often not precise enough, mainly relying on user presets or simple program settings, and unable to dynamically adjust according to the real-time state of the ingredients, which affects the quality of the dishes; in addition, the existing cooking systems are not intelligent enough in ingredient management, unable to accurately predict ingredient requirements, easily leading to ingredient backlogs or shortages, and are also not convenient enough in human-computer interaction, unable to well meet the personalized needs of users;

[0004] Therefore, there is an urgent need in this field for a movable automatic cooking robot and system without human intervention to solve the above technical problems. Summary of the Invention

[0005] The present invention provides a movable automatic cooking robot and system without human intervention, aiming to solve the problems of limited usage scenarios, inaccurate heat control, low dish quality, and easy ingredient backlogs or shortages; through multi-module collaboration and data-driven optimization, the present invention can effectively solve the above problems and provide more efficient, convenient, and intelligent cooking services.

[0006] On the one hand, the present invention provides a movable automatic cooking robot without human intervention, including:

[0007] A mobile chassis, a body, a central control unit, a communication module, a cooking execution module, a safety guarantee module, an autonomous driving module, and an energy management module;

[0008] The mobile chassis is used to realize the moving function of the robot and is connected to the body; the mobile chassis and the body form a humanoid robot;

[0009] The central control unit is respectively connected to the mobile chassis, the communication module, the cooking execution module, the safety guarantee module and the energy management module, and is used to control the coordinated operation of each module;

[0010] The communication module is used for data interaction with external devices;

[0011] The cooking execution module is arranged in the machine body, equipped with an intelligent fire control model, and is used to complete cooking operations;

[0012] The safety guarantee module is used to monitor and ensure the safe operation of the robot;

[0013] The automatic driving module is used to perform automatic driving and obstacle avoidance actions according to the instructions of the central control unit;

[0014] The energy management module provides energy for each module of the robot and manages the energy state.

[0015] According to an unmanned movable automatic cooking robot provided by the present invention, the mobile chassis includes driving wheels, driving motors, steering mechanisms and obstacle avoidance sensors; the driving motors are connected to the driving wheels and are used to drive the wheels to rotate; the steering mechanisms control the steering of the driving wheels; the obstacle avoidance sensors are arranged around the mobile chassis and are connected to the central control unit, and are used to detect obstacles and transmit information to the central control unit.

[0016] According to an unmanned movable automatic cooking robot provided by the present invention, the cooking execution module includes cooking utensils, heating devices, seasoning dispensing devices and food ingredient storage devices;

[0017] The cooking utensils are connected to the heating devices, and the heating devices are controlled by the central control unit and are used to heat the cooking utensils; the seasoning dispensing devices and the food ingredient storage devices are both connected to the central control unit, the seasoning dispensing devices dispense seasonings according to the instructions of the central control unit, and the food ingredient storage devices are used to store food ingredients;

[0018] The intelligent fire control model is:

[0019]

[0020] Among them, n represents the number of different key food ingredient types involved in the current cooked dish; T tar,i is the ideal cooking temperature of the i-th food ingredient in the current cooking stage; T cur is the actual temperature inside the current cooking utensil; S i represents the proportion of the i-th food ingredient in the dish; t ela represents the elapsed cooking time, that is, the already cooked duration; t total is the preset total cooking time of this dish, that is, the preset total cooking duration; trem is the remaining cooking time, obtained by subtracting the elapsed cooking time t from the preset total cooking time t total from the preset total cooking time t ela to obtain;

[0021] The intelligent heat control model is used to comprehensively consider the characteristics of ingredients, cooking time and cooking stage to achieve heat regulation.

[0022] According to an unmovable automatic cooking robot provided by the present invention, the safety protection module includes a smoke sensor, a temperature sensor, a leakage protection device and an emergency braking device;

[0023] The smoke sensor, temperature sensor and leakage protection device are respectively used to monitor the smoke, temperature and leakage conditions. When an abnormality is detected, a signal is transmitted to the central control unit; the emergency braking device is connected to the central control unit. In an emergency, the central control unit controls the emergency braking device to start, so that the robot stops running.

[0024] According to an unmovable automatic cooking robot provided by the present invention, the automatic driving module is used to control the driving speed and direction of the vehicle, and includes a path navigation unit and an obstacle avoidance unit;

[0025] The path navigation unit is used to automatically calculate the path according to the starting point and the destination;

[0026] The obstacle avoidance unit is used to avoid obstacles and traffic signals according to the road conditions on the planned path;

[0027] The central control unit is used to calculate the data transmitted back by the path navigation unit and the obstacle avoidance unit, make a conclusion and issue an instruction to the automatic driving module to control the vehicle to automatically drive and avoid obstacles.

[0028] According to an unmovable automatic cooking robot provided by the present invention, the energy management module includes a battery pack, a charging circuit and an energy recovery unit;

[0029] The battery pack is used to supply power to the robot; the charging circuit is connected to the battery pack and is used to charge the battery pack; the energy recovery unit is connected to the drive motor of the mobile chassis, and recovers and stores energy into the battery pack during the deceleration or braking process of the robot.

[0030] On the other hand, the present invention provides a system for an unmovable automatic cooking system, including:

[0031] a user interaction module, a cooking task scheduling module, a food ingredient supply chain management module and a data analysis and optimization module;

[0032] The user interaction module is used for the user to interact with the system;

[0033] The cooking task scheduling module is used to schedule cooking tasks according to user instructions and robot status;

[0034] The food supply chain management module is used to manage the procurement, inventory and distribution of food ingredients, and establish a food ingredient procurement plan;

[0035] The data analysis and optimization module is internally provided with an association rule mining model and a time series analysis model, which are used to analyze the user's historical cooking habits and food ingredient consumption data, so as to predict food ingredient demand and optimize the food ingredient procurement plan.

[0036] According to the present invention, a movable unmanned automatic cooking system is provided, and the user interaction module includes a user interface unit, a voice interaction unit and an order generation unit;

[0037] The user interface unit is used to display system information and receive user input; the voice interaction unit realizes the recognition of voice instructions and voice replies; the order generation unit generates a cooking order according to user input and transmits it to the cooking task scheduling module;

[0038] The cooking task scheduling module includes a task receiving unit, a task assignment unit and a task monitoring unit;

[0039] The task receiving unit receives the cooking order from the user interaction module; the task assignment unit assigns the task to a suitable robot according to the position, status and task priority of the robot; the task monitoring unit monitors the task execution progress in real time and feeds back the information to the user interaction module.

[0040] According to the present invention, a movable unmanned automatic cooking system is provided, and the food supply chain management module includes an inventory management unit, a procurement management unit and a distribution management unit;

[0041] The inventory management unit monitors the quantity of food ingredients in stock in real time; the procurement management unit generates a procurement order according to the inventory data and the predicted demand for food ingredients; the distribution management unit arranges the distribution of food ingredients and tracks the distribution status; finally, a food ingredient procurement plan for each cycle is formed.

[0042] According to the present invention, a movable unmanned automatic cooking system is provided, and the data analysis and optimization module includes a data collection unit, a data analysis unit and an optimization decision unit;

[0043] The data collection unit collects various types of food ingredient usage data during the operation of the system;

[0044] The data analysis unit is connected to the data collection unit, and analyzes the historical usage data of various types of food ingredients through the association rule mining model and the time series analysis model to predict the demand for food ingredients;

[0045] The optimization decision-making unit optimizes the food procurement plan for the next cycle according to the predicted food ingredient requirements.

[0046] The prediction process of the data analysis unit includes:

[0047] Using the association rule mining model and the time series analysis model to deeply analyze the historical usage data of various food ingredients; where support(X→Y) represents the support degree of the rule X→Y, which means the proportion of transactions that contain both X and Y in all transactions; σ(X∪Y) is the number of transactions that contain X and Y; N represents the total number of transactions; through the association rule mining model, the potential associations between different dish selections and between dishes and food ingredients can be analyzed. is the predicted value at time t + 1, which is used to predict the food ingredient requirements. is the autoregressive coefficient, which is used to reflect the influence degree of data at different past times on the current predicted value; y t+1-i is the actual value at time t + 1 - i; p is the autoregressive order, which is used to determine the time span of considering historical data; ∈ t+1 is white noise, which represents unpredictable random interference factors and is set by experience; α is the weight coefficient, and its value range is (0, 1), which is artificially preset according to historical actual business conditions and is used to balance the influence degree of the support degree on the predicted value.

[0048] Finally, the predicted food ingredient requirements are obtained The food procurement plan for the next cycle is optimized through the optimization decision-making unit.

[0049] Compared with the prior art, the beneficial effects of the present application are as follows:

[0050] 1. Existing cooking equipment has a single function and a fixed position, while the unmanned movable automatic cooking robot of the present invention integrates multiple functions such as movement, cooking, and autonomous driving; the movable chassis and the autonomous driving module enable it to shuttle flexibly and can also provide cooking services in scenarios such as outdoor gatherings and remote areas, breaking through the usage limitations of traditional cooking equipment.

[0051] 2. The fire control of existing cooking equipment is simple and it is difficult to ensure the quality of dishes; the intelligent fire control model of the present invention dynamically adjusts the fire considering multiple factors, precisely controls the temperature at each stage of cooking for different food ingredients, improves the cooking quality, and meets the high requirements of users for delicious food.

[0052] 3. The smoke, temperature sensors and leakage protection devices of the present invention monitor abnormalities in real time, and the emergency braking device can respond quickly in case of danger, avoiding safety accidents, and the safety measures are more perfect than those of existing cooking equipment.

[0053] 4. In terms of food ingredient and energy management, the data analysis and optimization module of the present invention predicts the demand for food ingredients through association rule mining and time series analysis, reducing food ingredient waste and out-of-stock situations; the energy recovery unit of the energy management module recovers braking energy, improving energy utilization efficiency and reducing operating costs.

[0054] 5. The user interaction module of the present invention provides convenient and diverse interaction methods; users can easily place orders through the user interface or voice interaction, and the system provides personalized recommendations based on the user's historical data. The cooking task scheduling module can also provide real-time progress feedback, greatly enhancing the user experience. Description of the Drawings

[0055] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0056] In the drawings:

[0057] Figure 1 is a schematic structural diagram of a movable unmanned automatic cooking robot provided by an embodiment of the present invention;

[0058] Figure 2 is a schematic structural diagram of a movable unmanned automatic cooking system provided by an embodiment of the present invention. Detailed Embodiments

[0059] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] An embodiment of the present invention provides a movable unmanned automatic cooking robot. Please refer to Figure 1 , including: a mobile chassis, a body, a central control unit, a communication module, a cooking execution module, a safety guarantee module, an autonomous driving module, and an energy management module;

[0062] The mobile chassis is used to implement the movement function of the robot and is connected to the body; the mobile chassis and the body form a humanoid robot;

[0063] The central control unit is respectively connected to the mobile chassis, the communication module, the cooking execution module, the safety guarantee module, and the energy management module, and is used to control the coordinated operation of each module;

[0064] The communication module is used for data interaction with external devices;

[0065] The cooking execution module is set inside the body and is equipped with an intelligent fire control model to complete cooking operations;

[0066] The safety guarantee module is used to monitor and ensure the safe operation of the robot;

[0067] The automatic driving module is used to perform automatic driving and obstacle avoidance actions according to the instructions of the central control unit;

[0068] The energy management module provides energy for each module of the robot and manages the energy state.

[0069] The principle and beneficial effects of this embodiment are as follows: This embodiment provides a movable unmanned automatic cooking robot. Its core principle lies in the collaborative work of multiple modules to achieve functions such as the robot's autonomous movement, intelligent cooking, safety guarantee, and energy management. The following is a detailed analysis of its principle and beneficial effects:

[0070] The mobile chassis is the basis for the robot to achieve the movement function. By connecting with the body, it ensures that the robot can move freely in different environments. The design of the mobile chassis enables the robot to move flexibly in complex environments such as kitchens and restaurants, adapting to different working scenarios;

[0071] The central control unit is the "brain" of the robot, responsible for coordinating and controlling the work of each module. It is connected to the mobile chassis, communication module, cooking execution module, safety guarantee module, and energy management module to ensure that each module can work collaboratively. The efficient coordination ability of the central control unit ensures that the robot can work according to the preset instructions and programs, improving the overall operation efficiency and stability;

[0072] The communication module is responsible for data interaction with external devices, such as receiving user instructions and uploading the robot's status information. Through the communication module, the robot can perform real-time data exchange with users, other devices, or cloud systems to achieve remote control and monitoring, enhancing the robot's intelligence level;

[0073] The cooking execution module is the core functional module of the robot. It is equipped with an intelligent fire control model and can automatically adjust parameters such as fire and time according to different dish requirements to complete cooking operations. The application of the intelligent fire control model enables the robot to precisely control the cooking process, ensuring the taste and quality of the dishes and reducing the error of manual operation;

[0074] The safety assurance module is used to monitor the operating status of the robot, detect and handle potential safety hazards in a timely manner, such as obstacle detection, emergency stop, etc.; the presence of the safety assurance module greatly reduces the risk of accidents during the operation of the robot, ensuring the safety of the robot and the surrounding environment; the autonomous driving module, according to the instructions of the central control unit and combined with environmental perception data, realizes the automatic driving and obstacle avoidance functions of the robot; the autonomous driving module enables the robot to navigate autonomously in a complex environment, avoid obstacles, and improve the autonomy and adaptability of the robot;

[0075] The energy management module is responsible for providing energy for each module of the robot, and real-time monitoring and managing the usage status of energy to ensure that the robot will not be interrupted due to insufficient energy during the working process; the introduction of the energy management module improves the endurance of the robot and ensures the stable operation of the robot during long-term work;

[0076] Through the collaborative work of multiple modules, the robot can achieve highly automated and intelligent cooking operations, reducing the need for manual intervention; the mobile chassis and autonomous driving module of the robot enable it to move flexibly in different environments and adapt to various working scenarios; the safety assurance module and energy management module ensure the safety and reliability of the robot during operation and reduce the risk of accidents; the collaborative work of the intelligent heat control model and the central control unit enables the robot to complete cooking tasks efficiently and precisely, improving the quality and consistency of the dishes.

[0077] Furthermore, this unmanned movable automatic cooking robot is set in a humanoid shape. Its appearance design simulates the human form and has a flexible joint structure, making its actions more natural and smooth, and enabling it to better integrate into various life scenarios; the two hands of the humanoid robot are designed as multi-functional robotic arms, which are equipped with high-precision sensors and dexterous grasping devices, and can not only accurately grasp ingredients and cooking utensils, but also complete fine cooking actions such as stir-frying and stirring, and work in coordination with the heating device, seasoning dispensing device, etc. in the cooking execution module to achieve efficient cooking;

[0078] A vision system is integrated at the head position of the robot. This vision system has a high-definition camera function and image recognition algorithms, and can identify objects such as obstacles, traffic signals, ingredients, and cooking utensils in the surrounding environment, providing more accurate environmental information for the obstacle avoidance unit of the autonomous driving module;

[0079] In addition, the body part of the humanoid robot has been optimized in design. Components such as the battery pack and charging circuit of the energy management module are arranged inside to ensure stable energy supply for each module while guaranteeing the overall structural stability of the robot. Moreover, the surface of the body is made of a special material that is fireproof, high-temperature resistant, and has a certain degree of flexibility, which can not only ensure safety but also simulate the texture of human skin to a certain extent, enhancing the user's interaction experience.

[0080] At the same time, the humanoid appearance and flexible joints enable the robot to move naturally, making it easier to integrate into life scenarios and reducing the sense of distance between the user and the machine. The head visual system allows for eye contact, and the special material on the body simulates the texture of the skin, enhancing the interactive affinity. Combined with voice interaction, it realizes multi-modal interaction, meets the emotional needs of users, and improves satisfaction.

[0081] The multi-functional robotic arm, combined with high-precision sensors and gripping devices, can accurately grasp and operate food ingredients and utensils, complete fine cooking actions, improve cooking accuracy and efficiency in complex cooking steps, make the cooking process smoother, and result in higher-quality dishes.

[0082] The humanoid structure and visual system enable the robot to be more agile in avoiding obstacles, identifying traffic signals, and finding paths in complex indoor and outdoor environments, such as in narrow aisles with mobile chassis or outdoor gathering venues, by virtue of its flexible joints and accurate environment recognition, making it more adaptable to changing scenarios than traditional robots.

[0083] To further optimize the above embodiments, the mobile chassis includes drive wheels, drive motors, steering mechanisms, and obstacle avoidance sensors. The drive motor is connected to the drive wheels and is used to drive the wheels to rotate. The steering mechanism controls the steering of the drive wheels. The obstacle avoidance sensors are arranged around the mobile chassis and are connected to the central control unit, used to detect obstacles and transmit information to the central control unit.

[0084] To further optimize the above embodiments, the cooking execution module includes cooking utensils, heating devices, seasoning dispensing devices, and food ingredient storage devices.

[0085] The cooking utensils are connected to the heating devices, and the heating devices are controlled by the central control unit to heat the cooking utensils. Both the seasoning dispensing devices and the food ingredient storage devices are connected to the central control unit. The seasoning dispensing devices dispense seasonings according to the instructions of the central control unit, and the food ingredient storage devices are used to store food ingredients.

[0086] The intelligent cooking temperature control model is:

[0087]

[0088] Where n represents the number of different key food ingredient types involved in the current cooking dish; T tar,i Is the ideal cooking temperature of the i-th food ingredient in the current cooking stage; Tcur is the actual temperature inside the current cooking appliance; S i represents the proportion of the i-th ingredient in the dish; t ela indicates the elapsed cooking time, that is, the cooking duration; t total is the preset total cooking time for this dish, that is, the preset total cooking duration; t rem is the remaining cooking time, which is obtained by subtracting the cooking duration t total from the preset total cooking duration t ela to get;

[0089] The intelligent heat control model is used to comprehensively consider the ingredient characteristics, cooking time, and cooking stage to achieve heat adjustment.

[0090] It should be noted that in one embodiment, to make the dish of Kung Pao Chicken, the chicken, peanuts, carrots, etc. in Kung Pao Chicken can be regarded as different key ingredients. Assuming there are 3 in total, n = 3; when stir-frying the chicken, the ideal temperature is 180°C, that is, T tar,1 = 180; when stir-frying the peanuts, the ideal temperature is 150°C, that is, T tar,2 = 150; the actual temperature T cur inside the current cooking appliance can be obtained in real time through a temperature sensor. Assuming the current temperature is T cur = 120°C; in Kung Pao Chicken, the proportion of chicken is [40%], the proportion of peanuts is [20%], the proportion of carrots is [15%], etc. (the sum of the proportions of each ingredient is 100%); at the current moment, the preset cooking duration of Kung Pao Chicken is 15 minutes, and it has been cooked for 5 minutes, that is, it represents t total = 15, t ela = 5, then the remaining cooking time t rem = 10;

[0091] The numerator part in the model represents calculating the comprehensive temperature adjustment requirement by combining the gap between the current temperature and the ideal temperature of different ingredients, their proportions in the dish, and the remaining cooking time; the denominator part is used for normalization to ensure the rationality of the result; This part represents that as the cooking time increases, the amplitude of heat adjustment will gradually increase because higher precision of heat is required in the later stage of cooking. In the early stage, the temperature can be increased appropriately quickly, and in the later stage, fine adjustment is required to avoid overcooking or undercooking of the ingredients; to meet the requirements of different cooking stages.

[0092] To further optimize the above embodiment, the safety protection module includes a smoke sensor, a temperature sensor, a leakage protection device, and an emergency braking device;

[0093] The smoke sensor, temperature sensor, and leakage protection device are respectively used to monitor the smoke, temperature, and leakage conditions. When an abnormality is detected, the signal is transmitted to the central control unit; the emergency braking device is connected to the central control unit. In an emergency, the central control unit controls the emergency braking device to start, causing the robot to stop running.

[0094] It should be noted that, further, the cooking robot has a function and fault self-monitoring function, and can perform its own function self-monitoring at fixed times, under the master's instructions, or in case of faults. Once it is found that there is a problem with the function implementation, it will report the warranty to the background system and submit an accurate fault information report, waiting for repair. If it cannot be repaired in a short time, the system will automatically allocate a new robot for use in the family.

[0095] The central control unit is also connected to a voice interaction module, which can achieve direct voice communication with the user. The central control unit is connected to the battery management module, which monitors its own power in real time and automatically evaluates the available time of the remaining power according to the current actions and daily transaction arrangements. When the power is insufficient, it automatically replenishes the electric energy, and it will also automatically charge when there is nothing to do, always ensuring that its own power can supply the continuous operation of the robot itself. The central control unit is connected to the electrical fire hazard monitoring module to monitor the electrical fire hazards in the internal lines. Once it is found that there is a problem with the circuit, it will start to alarm, and in case of necessity, it will urgently cut off the power supply of the hazard part to ensure that the entire robot itself will not catch fire or burn due to these reasons.

[0096] The central control unit can enable the cooking robot to have a function and fault self-monitoring function, and can perform its own function self-monitoring at fixed times, under the master's instructions, or in case of faults. Once it is found that there is a problem with the function implementation, it will report the warranty to the background system and submit an accurate fault information report, waiting for repair.

[0097] To further optimize the above embodiments, the autonomous driving module is used to control the driving speed and direction of the vehicle, including a path navigation unit and an obstacle avoidance unit;

[0098] The path navigation unit is used to automatically calculate the path according to the starting point and the destination;

[0099] The obstacle avoidance unit is used to avoid obstacles and traffic signals according to the road conditions on the planned path;

[0100] The central control unit is used to calculate the data transmitted back by the path navigation unit and the obstacle avoidance unit, make a conclusion, and issue an instruction to the autonomous driving module to control the vehicle to drive automatically and avoid obstacles.

[0101] It should be noted that a driving motor is provided on the driving wheel of the robot, and a control signal receiving device and a control signal input device are also connected to one side of the driving motor; used to control the driving speed and direction of the vehicle, the path navigation unit is used to automatically calculate the path according to the starting point and the destination; the obstacle avoidance unit is used to avoid obstacles and traffic signals according to the road conditions on the planned path; the communication module is used to communicate with the background cloud service system, the intelligent unmovable automatic cooking robot and the user client and feedback data; the central control unit is used to calculate the data transmitted back by the path navigation unit, the obstacle avoidance unit and the communication module and make a conclusion and control the vehicle to drive through the driving module. The intelligent unmovable automatic cooking robot is mainly used outdoors, such as in the case of outdoor family dinners and dinners in open spaces, etc. The main driving areas are roads in the city and roads in the community, etc. When the robot is driving in the community, the driving speed does not exceed 5 km / h. When driving on other roads, it drives within the speed limit stipulated by national laws and regulations according to the real-time road conditions.

[0102] The obstacle avoidance unit includes a traffic sign recognition module, and the traffic sign recognition module includes a video recognition device for shooting the road picture in front of the vehicle, distinguishing whether there are traffic lights and zebra crossings in front, and identifying the state of the traffic lights. At the same time, according to the pixel value of the zebra crossing in the video picture, calculate the real-time spatial distance between the vehicle and the zebra crossing, and send the continuously shortening spatial distance value between the two to the central control unit; the central control unit combines the state information of the traffic lights in front, according to the preset critical value of the red light running distance, compares the continuously shortening spatial distance value between the vehicle and the zebra crossing, and judges whether the vehicle will run a red light; the video recognition device includes a video acquisition module and a video recognition module, the video acquisition module is used to shoot the road picture in front of the vehicle, and the video recognition module is used to analyze and calculate the acquired picture; the video recognition module includes a traffic light recognition module and a zebra crossing recognition module, the traffic light recognition module is used to recognize the state of the traffic lights in the acquired picture; the zebra crossing recognition module is used to analyze and calculate the spatial distance between the zebra crossing in the acquired picture and the vehicle.

[0103] This technical solution specifically describes a technical solution for an obstacle avoidance unit. By installing a camera device on the vehicle chassis 1, the front road image is captured, traffic lights and zebra crossings in the image are identified, the real-time spatial distance between the vehicle and the zebra crossing ahead is calculated through analysis of the image pixel values, and the status of the traffic lights is identified, and the spatial distance value and the status information of the traffic lights are sent to the host computer; as the vehicle continues to move forward, the distance between the vehicle and the zebra crossing ahead gets closer and closer, and the host computer continuously obtains continuously changing real-time spatial distance values, and at the same time judges whether the vehicle will run a red light. If it will run a red light, the vehicle speed is limited or the vehicle is locked through the ECU, preventing and stopping the behavior of the automatic transport vehicle running a red light, which is beneficial to maintaining traffic order. The camera device of this technical solution can obtain navigation pattern information and real-time road condition information by using a lens and a charge-coupled device (CCD), or obtain navigation pattern information by using a lens and a complementary metal-oxide semiconductor (CMOS).

[0104] It should be particularly noted that the navigation pattern information can be obtained in two different ways. For example, through a lens and a charge-coupled device (CCD, Charge-coupled Device), and optionally through a lens and a complementary metal-oxide semiconductor (CMOS, Complementary Metal-Oxide Semiconductor). Through these sensors, the vehicle navigation device can capture navigation pattern information.

[0105] The central control unit is also connected to the drive module. The drive module includes an ECU. If the central control unit determines that the vehicle will run a red light, it controls the vehicle through the ECU; the central control unit is also connected to the cloud service system. The central control unit can send the judgment result of whether the vehicle will run a red light to the cloud service system, and the cloud service system can send back command instructions to the central control unit to control the vehicle through the central control unit connected to the ECU; this technical solution specifically discloses the role of the ECU in the obstacle avoidance process. The ECU in this technical solution refers to the ECU (Electronic Control Unit) electronic control unit, also known as the "vehicle computer", "on-board computer", etc. In terms of use, it is a special microcomputer controller for automobiles. It is the same as an ordinary computer, consisting of a microprocessor (CPU), a memory (ROM, RAM), an input / output interface (I / O), an analog-to-digital converter (A / D), and large-scale integrated circuits such as shaping and driving. In a simple sentence, "the ECU is the brain of the car".

[0106] The obstacle avoidance unit includes an obstacle recognition module, which includes a detection device for detecting the distance of obstacles, a roadblock information storage device for storing the azimuth, distance, and nature of detected roadblocks, an obstacle avoidance operation code storage device for storing obstacle avoidance operation codes, and a comparator for comparing the stored roadblock information storage device with the stored obstacle avoidance operation codes in the obstacle avoidance operation code storage device to replace the control signal input device and instruct the vehicle to perform obstacle avoidance movement; the detection device is at least one transmitting unit and receiving unit arranged around the body of the electric vehicle; the transmitting unit includes an ultrasonic transmitting element and a trigger circuit, and the receiving unit includes an ultrasonic receiving element and a front-end amplification circuit; the path navigation unit includes a memory, an input / output port, and a processor; the processor includes: a navigation information acquisition module for acquiring navigation pattern information and planning the driving path of the non-motor vehicle lane; an automatic navigation control module for controlling the vehicle driving parameters according to the navigation pattern information acquired by the navigation information acquisition module.

[0107] Furthermore, the present invention can achieve automated indoor road walking and road memory, avoiding all obstacles during obstacle avoidance walking, including people or objects. And during the operation process, it is powered entirely by electricity, monitors its own battery power in real time, automatically evaluates the available time of the remaining battery power according to the current actions and daily task arrangements, automatically replenishes the battery power when the battery power is insufficient, and also automatically charges when there is no work, always ensuring that its own battery power can supply the continuous operation of the robot itself.

[0108] To further optimize the above embodiments, the energy management module includes a battery pack, a charging circuit, and an energy recovery unit;

[0109] The battery pack is used to supply power to the robot; the charging circuit is connected to the battery pack and is used to charge the battery pack; the energy recovery unit is connected to the drive motor of the mobile chassis and recovers energy during the deceleration or braking process of the robot and stores it in the battery pack.

[0110] It should be noted that the energy management module can also have a CPU, a voltage monitoring unit, a current monitoring unit, a battery status monitoring unit, and a bus communication unit; it includes a solar charging module arranged around the body and on the upper end surface for charging the battery pack and a wired charging device realized through a generator. This technical solution specifically describes the structural form of a battery monitoring chip. Preferably, this battery monitoring chip can use a chip with the model number: BQ27510. The bus communication unit of this technical solution can refer to the lithium battery power management system for electric vehicles disclosed in [Chinese Utility Model] CN201220020977.6. The battery status monitoring unit of this technical solution can refer to a battery intelligent management system disclosed in [Chinese Utility Model] CN201020199399.8.

[0111] When the robot decelerates or brakes, the drive motor is converted into a generator. The rotation of the drive wheels of the mobile chassis drives the rotation of the motor rotor. The internal magnetic field of the motor cuts the magnetic induction lines to generate an induced electromotive force, and then generates an electric current. The energy recovery unit collects this part of the current, first converts the alternating current into direct current through a rectifier circuit, and then adjusts it to the voltage and current values suitable for charging the battery pack through a step-down or step-up circuit, and finally stores it in the battery pack. For example, when the robot decelerates when encountering a red light, the electric energy generated by the drive motor can be effectively recovered, realizing energy reuse, improving the energy utilization efficiency of the robot, and reducing the charging frequency.

[0112] Embodiment 2:

[0113] An embodiment of the present invention provides a movable unmanned automatic cooking system, which is applied to the robot in Embodiment 1. Please refer to Figure 2 , including:

[0114] A user interaction module, a cooking task scheduling module, a food ingredient supply chain management module, and a data analysis and optimization module;

[0115] The user interaction module is used for the user to interact with the system; [[ID=)15]]

[0116] The cooking task scheduling module is used to schedule cooking tasks according to user instructions and the robot state;

[0117] The food ingredient supply chain management module is used to manage the procurement, inventory, and distribution of food ingredients, and establish a food ingredient procurement plan;

[0118] The data analysis and optimization module is internally provided with an association rule mining model and a time series analysis model, which are used to analyze the user's historical cooking habits and food ingredient consumption data, so as to predict food ingredient demand and optimize the food ingredient procurement plan.

[0119] It should be noted that the user interaction module includes a user interface unit, a voice interaction unit, and an order generation unit; <)

[0120] The user interface unit is used to display system information and receive user input; the voice interaction unit realizes the recognition of voice instructions and voice replies; the order generation unit generates a cooking order according to user input and transmits it to the cooking task scheduling module;

[0121] The cooking task scheduling module includes a task receiving unit, a task allocation unit, and a task monitoring unit;

[0122] The task receiving unit receives the cooking order from the user interaction module; the task allocation unit allocates the task to a suitable robot according to the position, state, and task priority of the robot; the task monitoring unit monitors the task execution progress in real time and feeds back the information to the user interaction module.

[0123] It should be noted that in actual application scenarios, the user interface unit of the user interaction module can be designed as a touch display screen to clearly display system information such as various dish information, recommended packages, and robot status. The user completes the input operation by clicking on the screen to select dishes, cooking preferences, etc. The voice interaction unit integrates advanced voice recognition chips and voice synthesis technology, which can accurately recognize user voice commands, such as "I want a serving of Kung Pao Chicken", and reply with clear voice to confirm the information. The order generation unit integrates information such as the dishes selected by the user and special requirements to generate a cooking order, which is transmitted to the cooking task scheduling module through the network.

[0124] The task receiving unit of the cooking task scheduling module monitors the network port in real time. Once an order is received, it passes it to the task assignment unit. The task assignment unit can adopt a heuristic algorithm. Based on the real-time position of the robot, using GPS positioning information; status, such as whether it is busy and remaining battery power; and task priority, such as processing urgent orders first and other factors, comprehensively evaluate and assign the task to the most suitable robot after evaluation. The task monitoring unit obtains the work progress of the robot, such as the progress of ingredient preparation and cooking stage, etc., through real-time communication with the robot, and feeds this information back to the user interaction module in real time to display the real-time status of the order on the user interface, so that the user can understand the cooking process at any time.

[0125] To further optimize the above embodiments, the food ingredient supply chain management module includes an inventory management unit, a procurement management unit, and a distribution management unit;

[0126] The inventory management unit monitors the quantity of food ingredients in stock in real time; the procurement management unit generates a procurement order based on the inventory data and the predicted demand for food ingredients; the distribution management unit arranges the distribution of food ingredients and tracks the distribution status; finally, a food ingredient procurement plan for each cycle is formed.

[0127] It should be noted that in actual operation, the inventory management unit can rely on weight sensors and liquid level sensors installed on the food ingredient storage device to monitor the quantity of food ingredients in stock in real time. For solid food ingredients, the weight sensor can accurately measure the change in their weight. Once the weight is lower than the preset safety inventory threshold, an alarm will be triggered and the data will be transmitted to the procurement management unit. For liquid food ingredients, the liquid level sensor can monitor the liquid level to achieve real-time monitoring of the inventory.

[0128] After receiving the inventory data, the procurement management unit combines the predicted demand for food ingredients by the data analysis and optimization module, and uses a specific algorithm to generate a procurement order. For example, using the Economic Order Quantity (EOQ) model, comprehensively considering factors such as procurement cost, storage cost, and predicted food ingredient usage, calculate the most reasonable procurement quantity. At the same time, the procurement management unit establishes an Electronic Data Interchange (EDI) system with multiple food ingredient suppliers and automatically sends the procurement order to the suppliers to improve procurement efficiency.

[0129] The distribution management unit is responsible for arranging the distribution of food ingredients and cooperating with professional logistics distribution platforms. During the distribution process, Internet of Things technology is used to install GPS locators and temperature sensors (for food ingredients with preservation requirements) on each food ingredient distribution package. Through GPS positioning, the distribution management unit can track the distribution status in real time, such as vehicle location, driving route, etc.; the temperature sensor is used to monitor the temperature during transportation to ensure the quality of food ingredients. The distribution management unit will also reasonably plan the distribution route according to factors such as distribution distance and traffic conditions to ensure that the food ingredients are delivered on time and safely. In addition, the distribution management unit will feedback the distribution status to the inventory management unit and the procurement management unit in real time to adjust the inventory data and subsequent procurement plans in a timely manner, forming a complete and efficient food ingredient supply chain management system.

[0130] To further optimize the above embodiments, the data analysis and optimization module includes a data collection unit, a data analysis unit, and an optimization decision-making unit;

[0131] The data collection unit collects various types of food ingredient usage data during the operation of the system;

[0132] The data analysis unit is connected to the data collection unit and analyzes the historical usage data of various food ingredients through an association rule mining model and a time series analysis model to predict the demand for food ingredients;

[0133] The optimization decision-making unit optimizes the food ingredient procurement plan for the next cycle according to the predicted demand for food ingredients;

[0134] The prediction process of the data analysis unit includes:

[0135] Using the association rule mining model and the time series analysis model to deeply analyze the historical usage data of various food ingredients; where support(X→Y) represents the support degree of the rule X→Y, which means the proportion of transactions that contain both X and Y in all transactions; σ(X∪Y) is the number of transactions that contain X and Y; N represents the total number of transactions; through the association rule mining model, potential associations between different dish selections and between dishes and food ingredients can be analyzed; is the predicted value at time t+1, used to predict the demand for food ingredients; is the autoregressive coefficient, used to reflect the influence degree of data at different past times on the current predicted value; y t+1-i is the actual value at time t+1-i; p is the autoregressive order, used to determine the time span of considering historical data; ∈ t+1is white noise, representing unpredictable random interference factors, which is set by experience; α is the weight coefficient, and its value range is (0, 1), which is artificially preset according to historical actual business conditions and is used to balance the influence degree of support on the predicted value;

[0136] Finally, the predicted demand for ingredients is obtained Optimize the ingredient procurement plan for the next cycle by optimizing the decision-making unit.

[0137] It should be noted that in one embodiment, it is assumed that within a certain week, the unmanned movable automatic cooking system has collected the following data: a total of 100 cooking tasks have been completed, involving 50 different dishes, and 30 household users have been served; the following uses these data as an example;

[0138] The data collection unit collects data from each module; for example, it obtains records of the dishes selected by users, cooking time, etc. from the user interaction module; obtains task allocation situations and the duration of tasks completed by robots from the cooking task scheduling module; obtains data such as the procurement volume of ingredients and inventory changes from the ingredient supply chain management module; it is assumed that within a week, the dish of Kung Pao Chicken has been selected by users 10 times, with an average cooking time of 30 minutes each time, the procurement volume of Kung Pao Chicken ingredients during this period is 50 servings, and the remaining amount at the lowest inventory is 5 servings;

[0139] Use the association rule mining model to analyze the association between dish selection and other factors; for example, to explore the association between selecting Kung Pao Chicken (X) and selecting rice (Y) as the staple food; after statistics, among these 100 cooking tasks, the number of times of selecting both Kung Pao Chicken and rice, σ(X∪Y), is 8 times, and the total number of tasks N = 100, then the support degree support(X→Y) = 0.08, which indicates that in this system, users who select Kung Pao Chicken have an 8% probability of selecting rice at the same time. If the support degree exceeds the preset threshold (such as 0.05), the system can consider that there is a certain association between the two; subsequently, when a user selects Kung Pao Chicken, rice can be preferentially recommended as the staple food to improve the user experience;

[0140] Use the time series analysis model to predict the demand for ingredients; taking the prediction of the procurement volume of Kung Pao Chicken ingredients next week as an example, assume that the autoregressive order p = 2 is determined through historical data, and the autoregressive coefficient is obtained according to the calculation The usage amount of Kung Pao Chicken ingredients this week, y t is 10 servings, and the usage amount last week, y t-1 is 8 servings. Assuming that white noise is ignored and the weight coefficient is selected as α = 0.5, then the predicted value servings; at the same time, considering a certain safety stock, the system can appropriately increase the procurement volume. For example, if the safety stock coefficient is set to 1.2, then the procurement volume of Kung Pao Chicken ingredients next week is 8.44 * 1.2 = 10.128, and rounding up is 11 servings;

[0141] The optimization decision-making unit generates optimization strategies according to the data analysis results; for example, based on the discovery of the association between Kung Pao Chicken and rice through association rule mining, it sends an instruction to the user interaction module to recommend rice at a prominent position on the interface when the user selects Kung Pao Chicken; according to the predicted ingredient demand from time series analysis, it sends a purchasing instruction to the ingredient supply chain management module to adjust the purchasing plan for the ingredients of Kung Pao Chicken; at the same time, it feeds back these optimization strategies to the cooking task scheduling module so that it can arrange tasks more reasonably, such as giving priority to allocating the cooking task of this dish to the robot with sufficient ingredient inventory when the predicted purchase quantity of the ingredients of Kung Pao Chicken increases.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An unmovable automatic cooking robot, characterized in that, Comprising: A mobile chassis, a body, a central control unit, a communication module, a cooking execution module, a safety guarantee module, an automatic driving module, and an energy management module; The mobile chassis is used to realize the moving function of the robot and is connected to the body; the mobile chassis and the body form a humanoid robot; The central control unit is respectively connected to the mobile chassis, the communication module, the cooking execution module, the safety guarantee module, and the energy management module, and is used to control the coordinated work of each module; The communication module is used for data interaction with external devices; The cooking execution module is arranged in the body and is equipped with an intelligent fire control model for completing cooking operations; The safety guarantee module is used to monitor and guarantee the safe operation of the robot; The automatic driving module is used for automatic driving and obstacle avoidance actions according to the instructions of the central control unit; The energy management module provides energy for each module of the robot and manages the energy state.

2. The unmovable automatic cooking robot according to claim 1, wherein The mobile chassis includes driving wheels, driving motors, a steering mechanism, and obstacle avoidance sensors; the driving motors are connected to the driving wheels and are used to drive the wheels to rotate; the steering mechanism controls the steering of the driving wheels; the obstacle avoidance sensors are arranged around the mobile chassis and are connected to the central control unit, and are used to detect obstacles and transmit information to the central control unit.

3. The unmovable automatic cooking robot according to claim 1, characterized in that, The cooking execution module includes cooking utensils, heating devices, a seasoning dispensing device, and a food storage device; The cooking utensils are connected to the heating devices, and the heating devices are controlled by the central control unit and are used to heat the cooking utensils; the seasoning dispensing device and the food storage device are both connected to the central control unit, the seasoning dispensing device dispenses seasonings according to the instructions of the central control unit, and the food storage device is used to store food; The intelligent fire control model is: Among them, n represents the number of different key ingredient types involved in the current cooked dish; T tart,i is the ideal cooking temperature of the i-th ingredient in the current cooking stage; T cur is the actual temperature inside the current cooking appliance; S i represents the proportion of the i-th ingredient in the dish; t ela represents the elapsed cooking time, i.e., the cooked duration; t total is the preset total cooking time for this dish, i.e., the preset total cooking duration; t rem is the remaining cooking time, obtained by subtracting the cooked duration t total from the preset total cooking time t ela ; The intelligent fire control model is used to comprehensively consider the characteristics of the ingredients, the cooking time, and the cooking stage to achieve fire adjustment.

4. The unmovable automatic cooking robot according to claim 1, wherein The safety guarantee module includes a smoke sensor, a temperature sensor, a leakage protection device, and an emergency braking device; The smoke sensor, the temperature sensor, and the leakage protection device are respectively used to monitor smoke, temperature, and leakage conditions, and when abnormalities are detected, signals are transmitted to the central control unit; the emergency braking device is connected to the central control unit, and in an emergency, the central control unit controls the emergency braking device to start, causing the robot to stop running.

5. The unmovable automatic cooking robot according to claim 1, characterized in that, The automatic driving module is used to control the driving speed and direction of the vehicle and includes a path navigation unit and an obstacle avoidance unit; The path navigation unit is used to automatically calculate the path according to the starting point and the destination; The obstacle avoidance unit is used to avoid obstacles and traffic signals according to the road conditions on the planned path; The central control unit is used to calculate the data transmitted back by the path navigation unit and the obstacle avoidance unit, make a conclusion, and issue an instruction to the automatic driving module to control the vehicle to drive automatically and avoid obstacles.

6. The unmovable automatic cooking robot according to claim 1, wherein The energy management module includes a battery pack, a charging circuit, and an energy recovery unit; The battery pack is used to power the robot; the charging circuit is connected to the battery pack and is used to charge the battery pack; the energy recovery unit is connected to the drive motor of the mobile chassis and recovers energy during the deceleration or braking process of the robot and stores it in the battery pack.

7. An unmovable automatic cooking system, applied to an unmovable automatic cooking robot as claimed in claims 1-6, characterized in that, It includes: A user interaction module, a cooking task scheduling module, a food ingredient supply chain management module, and a data analysis and optimization module; The user interaction module is used for the user to interact with the system; The cooking task scheduling module is used to schedule cooking tasks according to user instructions and the robot status; The food ingredient supply chain management module is used to manage the procurement, inventory, and distribution of food ingredients and establish a food ingredient procurement plan; The data analysis and optimization module is internally provided with an association rule mining model and a time series analysis model, which are used to analyze the user's historical cooking habits and food ingredient consumption data, so as to predict food ingredient demand and optimize the food ingredient procurement plan.

8. The unmanned movable automatic cooking system according to claim 7, characterized in that, The user interaction module includes a user interface unit, a voice interaction unit, and an order generation unit; The user interface unit is used to display system information and receive user input; the voice interaction unit realizes the recognition of voice instructions and voice replies; the order generation unit generates a cooking order according to user input and transmits it to the cooking task scheduling module; The cooking task scheduling module includes a task receiving unit, a task assignment unit, and a task monitoring unit; The task receiving unit receives the cooking order from the user interaction module; the task assignment unit assigns the task to a suitable robot according to the position, status, and task priority of the robot; the task monitoring unit monitors the task execution progress in real time and feeds back the information to the user interaction module.

9. The unmanned movable automatic cooking system according to claim 7, characterized in that, The food ingredient supply chain management module includes an inventory management unit, a procurement management unit, and a distribution management unit; The inventory management unit monitors the quantity of food ingredient inventory in real time; The procurement management unit generates a procurement order according to the inventory data and the predicted food ingredient demand; The distribution management unit arranges the food ingredient distribution and tracks the distribution status; finally, a food ingredient procurement plan for each cycle is formed.

10. A kind of unmanned movable automatic cooking system according to claim 9, characterized in that, The data analysis and optimization module includes a data collection unit, a data analysis unit, and an optimization decision-making unit; The data collection unit collects various food ingredient usage data during the operation of the system; The data analysis unit is connected to the data collection unit and analyzes the historical various food ingredient usage data through the association rule mining model and the time series analysis model to predict food ingredient demand; The optimization decision-making unit optimizes the food ingredient procurement plan for the next cycle according to the predicted food ingredient demand; The prediction process of the data analysis unit includes: Using the described association rule mining model and the described time series analysis model to deeply analyze historical data on the usage of various types of ingredients; where support(X→Y) represents the support degree of the rule X→Y, which means that in all transactions, the proportion of transactions that contain both X and Y in the total transactions; σ(X∪Y) is the number of transactions that contain X and Y; N represents the total number of transactions; through the described association rule mining model, potential associations between different dish selections and between dishes and ingredients can be analyzed; is the predicted value at time t + 1, used to predict ingredient demand; is the autoregressive coefficient, used to reflect the influence degree of data at different past times on the current predicted value; y t+1-i is the actual value at time t + 1 - i; p is the autoregressive order, used to determine the time span of considering historical data; ∈ t+1 is white noise, representing unpredictable random interference factors, set by experience; α is the weight coefficient, and its value range is (0, 1), artificially preset according to historical actual business conditions, used to balance the influence degree of support on the predicted value; Finally, the predicted food ingredient requirements are obtained. The food ingredient procurement plan for the next cycle is optimized by the optimized decision-making unit.

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