A fire dynamic compensation and feeding control method and device of an intelligent cooking robot
By integrating smoke, temperature, and acoustic sensors into the intelligent cooking robot, the concentration of white smoke, sound, and temperature characteristics during the cooking process are monitored in real time, and the heat is dynamically adjusted. This solves the problem of matching heat control with the state of the ingredients, thereby improving the quality of cooking.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN JINYUANKANG IND CO LTD
- Filing Date
- 2026-06-24
- Publication Date
- 2026-07-24
AI Technical Summary
Existing intelligent cooking robots cannot accurately control the heat and match the state of the ingredients, resulting in the Maillard reaction not being able to start effectively, and there is a lack of real-time detection methods for the evaporation of free water on the surface of the ingredients.
By collecting monitoring data on cooking actions using smoke sensors, temperature sensors, and acoustic sensors, the concentration of white smoke, sound energy, and temperature characteristics are analyzed to determine the surface moisture state of the ingredients. The cooking temperature is then dynamically adjusted based on the heat compensation information and the dehydration critical point.
It achieves precise control over the moisture content of ingredients, improves the adaptability of heat and ingredient condition, ensures the normal initiation of the Maillard reaction, and enhances the quality of stir-frying.
Smart Images
Figure CN122449997A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of cooking robot technology, and in particular to a method for dynamic heat compensation and ingredient control of an intelligent cooking robot. Background Technology
[0002] Intelligent cooking robots are cooking devices that automatically handle ingredient delivery, heat control, and stir-frying, aiming to automate and standardize food preparation by replacing manual labor. Current technology generally executes cooking tasks programmatically, often overlooking a crucial culinary physics phenomenon: the large amount of free water adhering to the surface of ingredients rapidly vaporizes upon contact with the hot pan, forming a water vapor film that hinders the Maillard reaction. This water vapor film keeps the surface temperature of the ingredients locked at around 100°C, preventing the Maillard reaction from occurring and thus failing to produce the characteristic caramelized flavor of stir-fried dishes. The Maillard reaction only initiates when the surface free water completely evaporates and the water vapor film ruptures. Due to the lack of effective detection methods for this "surface dehydration critical point," it's impossible to adjust the heat in time at the moment the water film ruptures, resulting in a mismatch between heat control and the actual state of the ingredients.
[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0004] The main objective of this invention is to provide a method for dynamic heat compensation and ingredient control in an intelligent cooking robot, aiming to improve the accuracy of heat control. To achieve the above objective, this invention provides a method for dynamic heat compensation and ingredient control in an intelligent cooking robot, applicable to cooking robots. The method includes the following steps: Acquire cooking action events, and control sensors to collect monitoring data of the target dish based on the cooking action events; Based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data, the surface moisture status data of the target dish is determined. The firing temperature compensation information is determined based on the surface moisture state data, and the surface dehydration critical point is determined based on the monitoring data; The cooking robot is controlled based on the heat compensation information and the surface dehydration critical point.
[0005] Optionally, the sensor includes: a smoke sensor, a temperature sensor, and an acoustic sensor, and the step of controlling the sensor to collect monitoring data of the target dish based on the cooking action event includes: When the cooking action event is a feeding action, the smoke sensor is controlled to collect white smoke concentration data; The temperature sensor is controlled to collect temperature data of the food; the acoustic sensor is controlled to collect sound data of the food. The white smoke concentration data, the food temperature data, and the food sound data are used as the monitoring data.
[0006] Optionally, the step of determining the surface moisture state data of the target dish based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data includes: The initial preset time period is determined based on the occurrence time and preset duration of the cooking action event; Extract the maximum value of the white smoke concentration data within the initial preset time period as the white smoke concentration feature value; The sound data of the dishes within the initial preset time period are subjected to frequency domain transformation, and the frequency band energy of low-frequency water boiling is extracted as the sound feature value; Calculate the peak value of the temperature difference change rate corresponding to the temperature data of the dishes within the initial preset time period, and use it as the temperature feature value; The surface moisture state data are determined based on the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value.
[0007] Optionally, the step of determining the surface moisture state data based on the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value includes: Obtain the exhaust fan speed coefficient, wherein the exhaust fan speed coefficient is negatively correlated with the exhaust fan wind force; The surface moisture evaluation value is obtained by weighting the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value according to the exhaust fan speed coefficient. The surface moisture state data is determined based on the surface moisture evaluation value.
[0008] Optionally, the step of determining the firing compensation information based on the surface moisture state data includes: Determine the relationship between the surface moisture state data and the preset moisture threshold; When the surface moisture state data is greater than the preset moisture threshold, the heat compensation information is determined to be high-power compensation information. The high-power compensation information includes: a first compensation power and a first compensation duration, wherein the first compensation power is greater than the rated maximum power. When the surface moisture state data is less than or equal to the preset moisture threshold, the heating compensation information is determined to be conventional compensation information, which includes: The second compensation power and the second compensation duration, wherein the second compensation power is less than or equal to the rated maximum power.
[0009] Optionally, the step of determining the surface dehydration critical point based on the monitoring data includes: During the continuous acquisition process of the sensor, the ratio of the frequency band energy of high-frequency frying to the frequency band energy of low-frequency water boiling in the sound data of the dish is calculated in real time and used as the frequency domain energy ratio. Determine whether the frequency domain energy ratio crosses a preset high-frequency transition threshold and whether the duration is greater than a preset duration; When the frequency domain energy ratio crosses the preset high-frequency transition threshold and the duration is greater than the preset duration, the surface dehydration critical point is determined.
[0010] Optionally, the step of controlling the cooking robot based on the heat compensation information and the surface dehydration critical point includes: In response to the surface dehydration critical point, the heating power of the cooking robot is stepped back from the current compensated power to the normal cooking power. Obtain the lowest temperature value of the dish during the compensated heating period, and calculate the temperature drop between the lowest temperature value and the instantaneous temperature value before feeding. The extended cooking time is calculated based on the temperature drop and the surface moisture status data, and the extended cooking time is added to the preset cooking time of the current stage.
[0011] Furthermore, to achieve the above objectives, the present invention also provides a dynamic heat compensation and ingredient feeding control device for an intelligent cooking robot, characterized in that the dynamic heat compensation and ingredient feeding control device for the intelligent cooking robot includes: The monitoring module is used to acquire cooking action events and control the sensors to collect monitoring data of the target dish based on the cooking action events. The extraction module is used to determine the surface moisture status data of the target dish based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data. The analysis module is used to determine the heat compensation information based on the surface moisture state data, and to determine the surface dehydration critical point based on the monitoring data; The control module is used to control the cooking robot based on the heat compensation information and the surface dehydration critical point.
[0012] Furthermore, to achieve the above objectives, the present invention also provides a dynamic heat compensation and ingredient feeding control device for an intelligent cooking robot. The dynamic heat compensation and ingredient feeding control device for the intelligent cooking robot includes: a memory, a processor, and a dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot stored in the memory and executable on the processor. The dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot is configured to implement the steps of the dynamic heat compensation and ingredient feeding control method for the intelligent cooking robot described in any one of the above.
[0013] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a dynamic heat compensation and ingredient feeding control program for an intelligent cooking robot. When the dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot is executed by a processor, it implements the steps of the dynamic heat compensation and ingredient feeding control method for the intelligent cooking robot described in any of the above claims.
[0014] This invention proposes a method for dynamic heat compensation and ingredient control of an intelligent cooking robot. This method acquires cooking action events, controls sensors to collect monitoring data of the target dish based on these events, determines the surface moisture state data of the target dish based on the white smoke concentration, sound energy, and temperature characteristics during an initial preset time period in the monitoring data, determines heat compensation information based on the surface moisture state data, and determines the surface dehydration critical point based on the monitoring data. The cooking robot is then controlled based on the heat compensation information and the surface dehydration critical point, thereby enabling precise control of the moisture content of the ingredients entering the pan and improving the adaptability of heat control to the state of the ingredients. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the structure of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control device in the hardware operating environment of the embodiment of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the dynamic heat compensation and ingredient feeding control of the intelligent cooking robot of the present invention. Figure 3 This is a flowchart illustrating the second embodiment of the dynamic heat compensation and ingredient feeding control of the intelligent cooking robot of the present invention. Figure 4 This is a flowchart illustrating the third embodiment of the intelligent cooking robot of the present invention, which features dynamic compensation for heat and control of ingredient feeding.
[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0018] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control device in the hardware operating environment of the embodiment of the present invention.
[0019] like Figure 1 As shown, the intelligent cooking robot's dynamic heat compensation and ingredient control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interaction device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interaction device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interaction device 1003 may also connect to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0020] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the dynamic heat compensation and ingredient control equipment of the intelligent cooking robot. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0021] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot.
[0022] exist Figure 1In the intelligent cooking robot's dynamic heat compensation and ingredient feeding control device shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the intelligent cooking robot's dynamic heat compensation and ingredient feeding control device of the present invention can be set in the intelligent cooking robot's dynamic heat compensation and ingredient feeding control device. The intelligent cooking robot's dynamic heat compensation and ingredient feeding control device calls the intelligent cooking robot's dynamic heat compensation and ingredient feeding control program stored in the memory 1005 through the processor 1001, and executes the intelligent cooking robot's dynamic heat compensation and ingredient feeding control method provided in the embodiment of the present invention.
[0023] This invention provides a method for dynamic heat compensation and ingredient control of an intelligent cooking robot, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for dynamic heat compensation and ingredient control of an intelligent cooking robot according to the present invention.
[0024] In this embodiment, the dynamic heat compensation and ingredient control method of the intelligent cooking robot includes: Step S1: Obtain the stir-frying action event, and control the sensor to collect monitoring data of the target dish according to the stir-frying action event; In this embodiment, the cooking action event can include adding ingredients, adding seasonings, etc. Specifically, when the cooking action event of adding ingredients is detected, the sensors are activated to collect monitoring data of the target dish. It should be explained that the target dish here refers to the ingredients in the pot. Preferably, the cooking robot is equipped with a multi-compartment independently controlled feeding box, and each feeding box outlet is equipped with an infrared beam sensor. When the control unit drives the electric flip door of a feeding box to open, a corresponding cooking action event is generated. Based on this event, the control unit synchronously triggers the sensor acquisition process. The sensors include a smoke sensor deployed above the pot, a temperature sensor array attached to the inner and outer sides of the bottom of the pot, and an acoustic sensor installed in the non-high-temperature area of the robot. The monitoring data includes three categories: white smoke concentration data, dish temperature data, and dish sound data. In the cooking initialization stage, the system also needs to perform empty pot preheating: heating the pot with no ingredients at standard power, collecting the temperature difference between the inner and outer sides as a reference difference for storage, and calibrating the ambient noise of each sensor.
[0025] Step S2: Determine the surface moisture status data of the target dish based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data. In this embodiment, the initial preset time period is determined based on the occurrence time and preset duration of the cooking action event, for example, from 0 to 10 seconds after the ingredient feeding action. Within this time period, the system extracts three types of feature values: the maximum value is extracted from the white smoke concentration data as the white smoke concentration feature value; the energy of the low-frequency water boiling band is extracted from the food sound data through frequency domain transformation as the sound energy feature value; and the peak value of the temperature difference change rate in the food temperature data is calculated as the temperature feature value. The temperature difference change rate is the rate of change of the real-time difference between the outer and inner temperatures of the bottom of the pot. Subsequently, the system performs weighted fusion of the white smoke concentration feature value, the sound energy feature value, and the temperature feature value to obtain a surface moisture evaluation value, thereby determining the surface moisture state data.
[0026] Step S3: Determine the heat compensation information based on the surface moisture state data, and determine the surface dehydration critical point based on the monitoring data; In this embodiment, the process of determining the cooking temperature compensation information is as follows: Surface moisture state data is compared with a preset moisture threshold. When the surface moisture state data is greater than the preset moisture threshold, it indicates that the food surface contains a large amount of free water, requiring strong dehydration film compensation. In this case, the cooking temperature compensation information is determined to be high-power compensation information, including a first compensation power and a first compensation duration. The first compensation power is greater than the rated maximum power, for example, 150% to 200% of the rated maximum power. When the surface moisture state data is less than or equal to the preset moisture threshold, the cooking temperature compensation information is determined to be conventional compensation information, including a second compensation power and a second compensation duration. The second compensation power is less than or equal to the rated maximum power.
[0027] The process for determining the surface dehydration critical point is as follows: During continuous sensor acquisition, the ratio of high-frequency frying frequency band energy to low-frequency water boiling frequency band energy in the food sound data is calculated in real time and used as the frequency domain energy ratio. When this ratio crosses a preset high-frequency transition threshold, for example, the high-frequency energy exceeds 1.5 times the low-frequency energy, and the duration is greater than a preset duration, such as 0.2 seconds, it is determined that the water film on the food surface has completely evaporated, and the Maillard reaction is initiated. This is then determined as the surface dehydration critical point.
[0028] Step S4: Control the cooking robot according to the heat compensation information and the surface dehydration critical point.
[0029] In this embodiment, the control process includes three stages. The first stage is the compensation heating stage: based on the compensation power and compensation duration in the heat compensation information, the heating unit is controlled to operate at the compensation power for the corresponding duration. The second stage is the step drop stage: in response to the surface dehydration critical point, the heating power is instantly and step-dropped from the current compensation power to the normal cooking power, for example, 60% to 100% of the rated power, to prevent the food from burning. The third stage is the delay correction stage: the lowest temperature value of the dish during the compensation heating period is obtained, the temperature drop between this lowest temperature value and the instantaneous temperature value before adding the ingredients is calculated, the extended cooking time is calculated based on the temperature drop and the surface moisture state data, and the extended cooking time is added to the preset cooking time of the current stage. For compound recipes with multiple ingredients added in batches, optionally, the above steps are repeated until all ingredients are cooked.
[0030] Furthermore, the dynamic heat compensation and ingredient control method of the intelligent cooking robot described in this embodiment of the invention is based on the following physical process: when room temperature or low temperature ingredients are added to a preheated pot, the temperature difference between the inner and outer sides of the pot bottom changes drastically; free water on the surface of the ingredients vaporizes at high temperature, generating detectable white smoke and acoustic signals in a specific frequency band; after the surface water film has completely evaporated, the ingredients enter the Maillard reaction stage, and the acoustic spectrum characteristics undergo a significant transition. Therefore, the applicability of this technical solution depends on whether the dish to be cooked meets the triggering conditions of the above physical process.
[0031] The technical solution of this invention is particularly applicable to the following types of Chinese stir-fry dishes: The first category is stir-fried dishes, which are dishes where the ingredients are directly stir-fried at high temperature without being marinated or with only simple seasoning. Typical examples of this type of dish include stir-fried pork slices with green peppers, stir-fried pork with vegetables, garlic broccoli, and dry-fried green beans. In stir-fried dishes, the surface of the ingredients usually has a certain amount of free water attached, especially fresh vegetables or refrigerated meats. When placed in a high-temperature wok, they produce obvious thermal disturbance and release white smoke, meeting the conditions for temperature difference rate of change to trigger compensation. At the same time, stir-fried dishes aim for a crispy outside and tender inside texture, which requires strict control over the timing of the Maillard reaction after surface dehydration. This invention can precisely control this critical point by using the function of acoustic frequency shift detection of the surface dehydration critical point.
[0032] The second category is complex dishes involving stir-frying followed by re-stir-frying. These dishes involve some ingredients being first stir-fried in oil or pre-stir-fried until partially cooked, then combined with other ingredients in the final stir-fry. Typical examples of this type include shredded pork with garlic sauce (Yu Xiang Rou Si), and similar variations include stir-frying shredded pork with bamboo shoots and wood ear mushrooms, twice-cooked pork, and Kung Pao chicken. Dishes involving stir-frying followed by re-stir-frying typically involve two or more separate ingredient additions. The intelligent ingredient addition timing control function of this invention can precisely trigger the ingredient addition actions at each stage based on the heat recovery status of the pot, avoiding the problem of insufficient heating in the first stage or overheating in the second stage due to fixed-time ingredient addition.
[0033] The third category is dry-fried dishes, which involve gradually drying out the moisture in the ingredients over medium or low heat to achieve a dry and fragrant texture. Typical examples of this type of dish include dry-fried green beans, dry-fried king oyster mushrooms, and dry-fried beef noodles. In the cooking process of dry-fried dishes, the evaporation of moisture from the ingredients is a continuous physical process, with the white smoke concentration signal and acoustic spectrum characteristics constantly changing. The multimodal moisture assessment mechanism of this invention can track the degree of dehydration of the ingredients in real time, providing reliable feedback for adjusting the heat.
[0034] In this embodiment, by acquiring cooking action events, the system controls sensors to collect monitoring data of the target dish based on these events. Based on the white smoke concentration characteristic value, sound energy characteristic value, and temperature characteristic value of the initial preset time period in the monitoring data, the surface moisture state data of the target dish is determined. Based on the surface moisture state data, heat compensation information is determined, and the surface dehydration critical point is determined based on the monitoring data. The cooking robot is then controlled based on the heat compensation information and the surface dehydration critical point, thereby enabling precise control of the moisture content of the ingredients entering the pot and improving the adaptability of heat control to the state of the ingredients.
[0035] Furthermore, based on the first embodiment, a second embodiment of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control method of the present invention is proposed. In this embodiment, referring to... Figure 3 The sensors include: a smoke sensor, a temperature sensor, and an acoustic sensor. The step of collecting monitoring data of the target dish based on the cooking action event control sensor includes: Step S11: When the cooking action event is a feeding action, control the smoke sensor to collect white smoke concentration data; Specifically, the smoke sensor is either a photoelectric smoke sensor or an infrared smoke detection module, which can be installed above the pot or in the smoke exhaust channel. When the infrared sensor detects food falling, the control unit immediately triggers the smoke sensor to begin collecting data. The white smoke concentration signal is normalized to a range of 0 to 1, where 1 represents the maximum amount of white smoke. The smoke sensor captures the white smoke generated when moisture on the surface of the food vaporizes rapidly; its concentration is positively correlated with the evaporation rate of free water on the food surface.
[0036] Step S12: Control the temperature sensor to collect food temperature data; control the acoustic sensor to collect food sound data; Optionally, the temperature sensor here can be a far-infrared temperature sensor, which can generally collect the temperature of the bottom of the pot, preferably the temperature of the food side rather than the heat source side. The acoustic sensor is a high-temperature resistant directional microphone array, installed in a non-high-temperature area of the robot, such as the upper arm or the control panel side, and equipped with a physical oil-proof and breathable membrane. The control board integrates a digital signal processor with a built-in fast Fourier transform algorithm to convert the cooking sound in the time domain into frequency domain energy distribution data in real time.
[0037] Step S13: Use the white smoke concentration data, the dish temperature data, and the dish sound data as the monitoring data.
[0038] In this embodiment, the data from the three types of sensors are aligned and packaged with a unified timestamp to form a monitoring data frame. Each monitoring data frame includes: the white smoke concentration value of the smoke channel, the inner and outer temperature array values of the temperature channel, and the frequency domain energy distribution vector of the acoustic channel. The monitoring data frame is transmitted to the control unit in real time via a communication bus, providing data basis for subsequent surface moisture status evaluation and fire compensation decisions.
[0039] Furthermore, based on the first or second embodiment, a third embodiment of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control method of the present invention is proposed. In this embodiment, referring to... Figure 4 The initial preset time period is determined based on the occurrence time and preset duration of the cooking action event; Step S21: Extract the maximum value of the white smoke concentration data within the initial preset time period, and use it as the white smoke concentration feature value; Step S22: Perform frequency domain transformation on the sound data of the dish within the initial preset time period, and extract the frequency band energy of low-frequency water boiling as the sound feature value; In this embodiment, the frequency domain transformation employs the Fast Fourier Transform algorithm. After converting the time-domain sound signal to the frequency domain, the energy integrals of the low-frequency water boiling band, such as the water boiling sound in the range of 500Hz to 2000Hz, and the water bursting and vaporizing sound in the range of 1kHz to 3kHz, are extracted as sound feature values. The energy of the low-frequency water boiling band reflects the degree of intense vaporization of free water on the surface of the food; higher energy indicates more intense water evaporation. This acoustic feature is not affected by the physical interference of the exhaust fan's airflow field and can maintain stable detection performance even under strong wind conditions, thus maintaining stable monitoring results.
[0040] Step S23: Calculate the peak value of the temperature difference change rate corresponding to the temperature data of the dish within the initial preset time period, and use it as the temperature feature value; Optionally, the calculation process for the temperature difference change rate is as follows: First, calculate the real-time temperature difference ΔT = T_out - T_in, where T_out is the temperature on the heating side collected by the temperature sensor on the outer side of the pot, and T_in is the temperature on the food side collected by the temperature sensor array on the inner side. Then, calculate the temperature difference change rate d(ΔT) / dt. When room temperature or refrigerated food is added to the hot pot, the temperature at the bottom of the pot drops sharply, and the temperature difference change rate shows a positive peak. This peak value within the initial preset time period is used as the temperature characteristic value. When the temperature difference change rate exceeds the positive threshold, it is determined as an event of a large amount of cold food being added.
[0041] Step S24: Determine the surface moisture state data based on the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value.
[0042] In this embodiment, the determination process incorporates a weighted correction using an exhaust fan speed coefficient. This coefficient is negatively correlated with the exhaust fan's airflow rate; that is, the higher the airflow rate, the smaller the coefficient. Based on this coefficient, the white smoke concentration characteristic value, sound characteristic value, and temperature characteristic value are weighted and calculated to obtain the surface moisture evaluation value. When the exhaust fan is at its highest speed, the white smoke is rapidly drawn away, causing the white smoke concentration characteristic value to attenuate and become distorted. In this case, the system automatically increases the weight of the sound characteristic value in the weighted calculation to compensate for the insufficiency of the photoelectric signal with acoustic energy.
[0043] Furthermore, the step of determining the surface moisture state data based on the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value includes: Obtain the exhaust fan speed coefficient, wherein the exhaust fan speed coefficient is negatively correlated with the exhaust fan wind force; The surface moisture evaluation value is obtained by weighting the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value according to the exhaust fan speed coefficient. The surface moisture state data is determined based on the surface moisture evaluation value.
[0044] In this embodiment, the surface moisture state data is determined based on the mapping relationship between the surface moisture evaluation value and the preset evaluation value range.
[0045] Furthermore, based on any of the above embodiments, a fourth embodiment of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control method of the present invention is proposed. In this embodiment, the step of determining the heat compensation information based on the surface moisture state data includes: Determine the relationship between the surface moisture state data and the preset moisture threshold; In this embodiment, optionally, the preset moisture threshold is determined based on the baseline physical parameters of the dish, for example, taking 1.0 times the standard mass flow integral as the dividing point. Surface moisture state data is represented by the moisture index Ω. When Ω is greater than 1.3, it is judged as a high surface water ingredient, and when Ω is less than or equal to 1.3, it is judged as a normal moisture content ingredient.
[0046] When the surface moisture state data is greater than the preset moisture threshold, the heat compensation information is determined to be high-power compensation information. The high-power compensation information includes: a first compensation power and a first compensation duration, wherein the first compensation power is greater than the rated maximum power. In this embodiment, the high-power compensation information corresponds to the strong compensation stage for the dehydration film. The first compensation power is 150% to 200% of the rated maximum power, forcibly evaporating the free water on the surface of the food with high heat flux density. The first compensation duration is calculated by the following formula: t_comp = α × (m / m0), where m is the total mass of the food in the pot estimated by the mass sensing module, m0 is the reference mass, which can commonly be set to 200g, and α is an empirical coefficient, generally 1.2 to 2.0. In addition, the actual compensation power is dynamically adjusted according to the amount of white smoke: P_comp = min(P_max × 2.0, P_max × (1.5 + k_smoke × S_smoke)), where k_smoke is an empirical coefficient, generally 0.3 to 0.8. If the white smoke concentration remains below the low threshold during the compensation process, such as less than 0.1 for 0.5 seconds, the compensation is terminated early to prevent temperature overshoot. Temperature overshoot is due to the need for heat conduction through the pot during the heating process.
[0047] When the surface moisture state data is less than or equal to the preset moisture threshold, the heating compensation information is determined to be conventional compensation information, which includes: The second compensation power and the second compensation duration, wherein the second compensation power is less than or equal to the rated maximum power.
[0048] In this embodiment, the standard compensation information applies to ingredients with low surface moisture content, such as drained vegetables or room-temperature ingredients. The second compensation power is less than or equal to the rated maximum power, for example, 80% to 120% of the rated power. The second compensation duration is shortened proportionally according to the weight of the ingredients. If the temperature difference rate triggers compensation but the amount of white smoke remains close to zero and the internal temperature drop is not significant, it is determined to be a sensor false trigger or an empty feeding event, the compensation is canceled, and an alarm is issued.
[0049] Furthermore, based on any of the above embodiments, a fifth embodiment of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control method of the present invention is proposed. In this embodiment, The step of determining the surface dehydration critical point based on the monitoring data includes: During the continuous acquisition process of the sensor, the ratio of the frequency band energy of high-frequency frying to the frequency band energy of low-frequency water boiling in the sound data of the dish is calculated in real time and used as the frequency domain energy ratio. In this embodiment, the high-frequency frying band refers to the sound energy of pure oil frying above 4kHz (corresponding to the crisp sizzling sound of the Maillard reaction stage), and the low-frequency water boiling band refers to the sound energy of water boiling and popping below 3kHz. The frequency domain energy ratio R_sizzle = P_HF / P_LF, where P_HF is the instantaneous energy of the high-frequency frying band and P_LF is the instantaneous energy of the low-frequency water boiling band. When there is a large amount of free water on the surface of the food, the low-frequency water boiling sound dominates, and the R_sizzle value is extremely small. When the water film dries completely, the low-frequency popping sound stops abruptly, and the high-frequency frying sound emerges instantly, and the R_sizzle value increases rapidly.
[0050] Determine whether the frequency domain energy ratio crosses a preset high-frequency transition threshold and whether the duration is greater than a preset duration; When the frequency domain energy ratio crosses the preset high-frequency transition threshold and the duration is greater than the preset duration, the surface dehydration critical point is determined.
[0051] In this embodiment, the surface dehydration critical point marks the complete evaporation of the water film on the food surface and the formal initiation of the Maillard reaction. At this moment, the system immediately triggers a power step-down command, instantly reducing the heating power from the compensated power to the normal cooking power, optionally 80% to 100% of the rated power, to prevent the food from scorching due to continuous high temperatures. Simultaneously, the system records the current moment as a key node in this cooking stage for subsequent delay correction calculations.
[0052] Furthermore, based on any of the above embodiments, a sixth embodiment of the intelligent cooking robot's dynamic heat compensation and ingredient feeding control method of the present invention is proposed. In this embodiment, the step of controlling the cooking robot according to the heat compensation information and the surface dehydration critical point includes: In response to the surface dehydration critical point, the heating power of the cooking robot is stepped back from the current compensated power to the normal cooking power. In this embodiment, the step drop is an instantaneous power switch, not a gradual adjustment. Once the acoustic frequency shift determination confirms the critical point of surface dehydration, the control unit immediately switches the drive signal of the heating unit (electromagnetic or infrared heating device) from the compensation level to the normal level in the next control cycle (e.g., within 100ms). The normal cooking power after the drop is determined according to the requirements of the current stage of the recipe, typically 80% to 100% of the rated power.
[0053] Obtain the lowest temperature value of the dish during the compensated heating period, and calculate the temperature drop between the lowest temperature value and the instantaneous temperature value before feeding. In this embodiment, the instantaneous temperature before feeding is the T_in value collected by the inner temperature sensor array just moments before the food is dropped. During the compensation heating period, the system continuously tracks the lowest point of the inner temperature. The extended cooking time is calculated based on the temperature drop and the surface moisture status data, and the extended cooking time is added to the preset cooking time of the current stage.
[0054] In this embodiment, the formula for calculating the extended cooking time is: Δt_ext = β × (T_in0 - T_in_min), where β is an empirical coefficient that can range from 0.5 to 1.5 seconds / ℃, retrieved from local storage or the cloud depending on the type of ingredients and the cooking stage. The system adds Δt_ext to the preset cooking time for the current stage. For example, if the original plan is to stir-fry meat slices for 60 seconds, and Δt_ext is calculated to be 49.6 seconds, the system will automatically extend it to approximately 110 seconds. For cases where multiple ingredients are added in stages, the system continuously monitors the temperature rise process inside the food. When T_in rises to 90% of the target stable temperature difference value, the next addition action is triggered. If the infrared sensor at the addition port detects that the ingredients have not fallen, an alarm is issued and subsequent steps are paused. After cooking, the system generates a multimodal thermal response feature code for this cooking based on the temperature change curve, white smoke integral data, and acoustic frequency shift inflection point recorded throughout the cooking cycle, and uploads it to the cloud for subsequent recipe self-optimization.
[0055] Furthermore, this invention also proposes a dynamic heat compensation and ingredient feeding control device for an intelligent cooking robot, characterized in that the dynamic heat compensation and ingredient feeding control device for the intelligent cooking robot includes: The monitoring module is used to acquire cooking action events and control the sensors to collect monitoring data of the target dish based on the cooking action events. The extraction module is used to determine the surface moisture status data of the target dish based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data. The analysis module is used to determine the heat compensation information based on the surface moisture state data, and to determine the surface dehydration critical point based on the monitoring data; The control module is used to control the cooking robot based on the heat compensation information and the surface dehydration critical point.
[0056] Furthermore, this invention also proposes a dynamic heat compensation and ingredient feeding control device for an intelligent cooking robot. The device includes a memory, a processor, and a dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot stored in the memory and executable on the processor. The program is configured to implement the steps of the dynamic heat compensation and ingredient feeding control method for the intelligent cooking robot described above.
[0057] Furthermore, this embodiment of the invention also proposes a storage medium storing a dynamic heat compensation and ingredient feeding control program for an intelligent cooking robot. When the processor executes the dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot, it implements the steps of the dynamic heat compensation and ingredient feeding control method for the intelligent cooking robot described above.
[0058] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0059] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0060] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0061] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A method for dynamic heat compensation and ingredient control of an intelligent cooking robot, characterized in that, The method for dynamic heat compensation and ingredient control of a cooking robot, applied to a cooking robot, includes the following steps: Acquire cooking action events, and control sensors to collect monitoring data of the target dish based on the cooking action events; Based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data, the surface moisture status data of the target dish is determined. The firing temperature compensation information is determined based on the surface moisture state data, and the surface dehydration critical point is determined based on the monitoring data; The cooking robot is controlled based on the heat compensation information and the surface dehydration critical point.
2. The method for dynamic heat compensation and ingredient control of the intelligent cooking robot as described in claim 1, characterized in that, The sensors include: a smoke sensor, a temperature sensor, and an acoustic sensor. The step of collecting monitoring data of the target dish based on the cooking action event control sensor includes: When the cooking action event is a feeding action, the smoke sensor is controlled to collect white smoke concentration data; The temperature sensor is controlled to collect temperature data of the food; the acoustic sensor is controlled to collect sound data of the food. The white smoke concentration data, the food temperature data, and the food sound data are used as the monitoring data.
3. The method for dynamic heat compensation and ingredient control of the intelligent cooking robot as described in claim 1, characterized in that, The step of determining the surface moisture state data of the target dish based on the characteristic values of white smoke concentration, sound energy, and temperature during the initial preset time period in the monitoring data includes: The initial preset time period is determined based on the occurrence time and preset duration of the cooking action event; Extract the maximum value of the white smoke concentration data within the initial preset time period as the white smoke concentration feature value; The sound data of the dishes within the initial preset time period are subjected to frequency domain transformation, and the frequency band energy of low-frequency water boiling is extracted as the sound feature value; Calculate the peak value of the temperature difference change rate corresponding to the temperature data of the dishes within the initial preset time period, and use it as the temperature feature value; The surface moisture state data are determined based on the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value.
4. The method for dynamic heat compensation and ingredient control of the intelligent cooking robot as described in claim 3, characterized in that, The step of determining the surface moisture state data based on the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value includes: Obtain the exhaust fan speed coefficient, wherein the exhaust fan speed coefficient is negatively correlated with the exhaust fan wind force; The surface moisture evaluation value is obtained by weighting the white smoke concentration characteristic value, the sound characteristic value, and the temperature characteristic value according to the exhaust fan speed coefficient. The surface moisture state data is determined based on the surface moisture evaluation value.
5. The method for dynamic heat compensation and ingredient control of the intelligent cooking robot as described in claim 1, characterized in that, The step of determining the heat compensation information based on the surface moisture state data includes: Determine the relationship between the surface moisture state data and the preset moisture threshold; When the surface moisture state data is greater than the preset moisture threshold, the heat compensation information is determined to be high-power compensation information. The high-power compensation information includes: a first compensation power and a first compensation duration, wherein the first compensation power is greater than the rated maximum power. When the surface moisture state data is less than or equal to the preset moisture threshold, the heating compensation information is determined to be conventional compensation information, which includes: The second compensation power and the second compensation duration, wherein the second compensation power is less than or equal to the rated maximum power.
6. The method for dynamic heat compensation and ingredient control of the intelligent cooking robot as described in claim 1, characterized in that, The step of determining the surface dehydration critical point based on the monitoring data includes: During the continuous acquisition process of the sensor, the ratio of the frequency band energy of high-frequency frying to the frequency band energy of low-frequency water boiling in the sound data of the dish is calculated in real time and used as the frequency domain energy ratio. Determine whether the frequency domain energy ratio crosses a preset high-frequency transition threshold and whether the duration is greater than a preset duration; When the frequency domain energy ratio crosses the preset high-frequency transition threshold and the duration is greater than the preset duration, the surface dehydration critical point is determined.
7. The method for dynamic heat compensation and ingredient feeding control of the intelligent cooking robot as described in any one of claims 1 or 6, characterized in that, The step of controlling the cooking robot based on the heat compensation information and the surface dehydration critical point includes: In response to the surface dehydration critical point, the heating power of the cooking robot is stepped back from the current compensated power to the normal cooking power. Obtain the lowest temperature value of the dish during the compensated heating period, and calculate the temperature drop between the lowest temperature value and the instantaneous temperature value before feeding. The extended cooking time is calculated based on the temperature drop and the surface moisture state data, and the extended cooking time is added to the preset cooking time of the current stage.
8. A dynamic heat compensation and ingredient feeding control device for an intelligent cooking robot, characterized in that, The intelligent cooking robot's dynamic heat compensation and ingredient control device includes: The monitoring module is used to acquire cooking action events and control the sensors to collect monitoring data of the target dish based on the cooking action events. The extraction module is used to determine the surface moisture status data of the target dish based on the white smoke concentration characteristic value, sound energy characteristic value and temperature characteristic value of the initial preset time period in the monitoring data; The analysis module is used to determine the heat compensation information based on the surface moisture state data, and to determine the surface dehydration critical point based on the monitoring data; The control module is used to control the cooking robot based on the heat compensation information and the surface dehydration critical point.
9. A dynamic heat compensation and ingredient feeding control device for an intelligent cooking robot, characterized in that, The intelligent cooking robot's dynamic heat compensation and ingredient feeding control device includes: a memory, a processor, and a dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot stored in the memory and executable on the processor. The dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot is configured to implement the steps of the dynamic heat compensation and ingredient feeding control method for the intelligent cooking robot as described in any one of claims 1 to 7.
10. A storage medium, characterized in that, The storage medium stores a dynamic heat compensation and ingredient feeding control program for an intelligent cooking robot. When the processor executes the dynamic heat compensation and ingredient feeding control program for the intelligent cooking robot, it implements the steps of the dynamic heat compensation and ingredient feeding control method for the intelligent cooking robot as described in any one of claims 1 to 7.