A fire power regulation method and device of intelligent kitchen electrical appliances and the intelligent kitchen electrical appliances

Abstract

This application discloses a method, device, and smart kitchen appliance for controlling the heat of a smart kitchen appliance. The method includes: acquiring current temperature data, current smoke data, and current image data; determining the current cooking mode and its corresponding current rating weight; determining a food status score based on the current temperature data, current smoke data, current image data, and current rating weight; generating heat control parameters based on the food status score; and adjusting the heat of the smart kitchen appliance based on the heat control parameters. In this application embodiment, by collecting three types of data—temperature, smoke, and image—and combining them with the rating weight corresponding to the current cooking mode, a real-time food status score is calculated. Then, heat control parameters are generated based on this score, achieving precise and intelligent heat control. This breaks through the reliance on traditional experience, quantifies the food status through multi-dimensional data, avoids the limitations of single-sensor monitoring, comprehensively captures key cooking information, and reduces problems such as burnt exterior and undercooked interior, and stale texture.

Description

Technical Field

[0001] This application relates to the field of smart device technology, and in particular to a method, device and smart kitchen appliance for controlling the heat of a smart kitchen appliance. Background Technology

[0002] With the improvement of people's living standards and the promotion and popularization of technologies such as the Internet, big data, artificial intelligence, and voice interaction, more and more traditional lifestyles are gradually changing, and the use of kitchen appliances is gradually moving towards intelligence. In the cooking process, heat control is a core element affecting the quality of dishes, cooking efficiency, and operational safety. Traditional cooking heat control mainly relies on the cook's experience and manual operation. The cook needs to adjust the gas stove valve or induction cooker power setting in real time according to the type of ingredients, cooking stage, and taste preferences to control the heat.

[0003] However, existing intelligent cooking equipment only has simple heat adjustment functions and cannot fully and accurately adjust the heat according to the actual cooking state of the ingredients. This results in insufficient intelligence and precision in heat control, making it difficult to meet the comprehensive needs of modern cooking for high-quality dishes, efficient operation, and reliable safety. Summary of the Invention

[0004] To address existing technical problems, this invention provides a method, device, electronic device, and storage medium for controlling the heat of intelligent kitchen appliances. By collecting three core data types—temperature, smoke, and images—and combining them with the scoring weights corresponding to the current cooking mode, a real-time status score of the ingredients is calculated. Based on this score, heat control parameters are generated, and the heat of the kitchen appliance is automatically adjusted. This breaks through the reliance on traditional experience, quantifies the state of ingredients through multi-dimensional data, and achieves precise and intelligent heat control. At the same time, it avoids the limitations of single-sensor monitoring, comprehensively captures key cooking information, and reduces problems such as burnt outside and undercooked inside, and stale taste.

[0005] In a first aspect, embodiments of this application provide a method for controlling the heat output of a smart kitchen appliance, the method comprising: Acquire current temperature data, current smoke data, and current image data; Determine the current cooking mode and its corresponding current rating weight; Based on current temperature data, current smoke data, current image data, and current scoring weights, determine the food condition score; Firepower control parameters are generated based on the food condition score; The power of smart kitchen appliances is adjusted based on the power control parameters.

[0006] In one optional embodiment, the food quality score is determined based on current temperature data, current smoke data, current image data, and current scoring weights, including: Determine the target temperature data based on the current cooking mode and ingredient information; Temperature ratio data is determined based on the ratio of current temperature data to target temperature data; Determine smoke threshold data based on the current cooking mode and ingredient information; The smoke proportion data is determined based on the ratio of the current smoke data to the smoke threshold data. The focal spot area ratio data of the current image data is determined based on the preset target detection model. The food condition score is determined based on temperature ratio data, smoke ratio data, scorch area ratio data, and the current scoring weight.

[0007] In one optional embodiment, determining the current cooking mode and the corresponding current rating weight includes: Obtain the initial score weights; the initial score weights include the initial temperature weight, the initial smoke weight, and the initial image weight; If the current cooking mode is frying, increase the initial smoke weight and decrease the initial image weight. Determine the current scoring weight based on the increased initial smoke weight, decreased initial image weight, and initial temperature weight. Alternatively, if the current cooking mode is stewing, increase the initial temperature weight and determine the current scoring weight based on the increased initial temperature weight and initial image weight. Alternatively, if the current cooking mode is neither frying nor stewing, determine the initial scoring weight as the current scoring weight.

[0008] In an optional embodiment, before determining the food condition score based on current temperature data, current smoke data, current image data, and current scoring weights, the method further includes: Apply a sliding window filter to the current temperature data to obtain the filtered temperature data. The filtered temperature data is used as the current temperature data. Perform Kalman filtering on the current smoke data to obtain filtered smoke data; The filtered smoke data is used as the current smoke data.

[0009] In an optional embodiment, after acquiring the current temperature data, current smoke data, and current image data, the method further includes: Determine the pot bottom temperature based on the current temperature data; If the temperature of the pot bottom exceeds the safe temperature threshold, a fire shut-off command will be generated.

[0010] In one optional embodiment, acquiring current temperature data, current smoke data, and current image data includes: Determine the condition of the ingredients based on the current image data; If the time indicated by the food status that there is no food in the pot is greater than the empty pot time threshold, and the current temperature data is greater than the empty pot temperature threshold, a first heat adjustment command is generated; the first heat adjustment command is used to instruct the stove to reduce the heat level.

[0011] In one optional embodiment, the smart kitchen appliance is equipped with a temperature detection device, a smoke detection device, and an image detection device; acquiring current temperature data, current smoke data, and current image data includes: Acquire the communication status of temperature detection equipment, smoke detection equipment, and image detection equipment; If the communication status indicates normal communication, acquire the current temperature data, current smoke data, and current image data; normal communication indicates that the temperature detection device, smoke detection device, and image detection device are all experiencing communication failures; or; if the communication status indicates communication failure, generate a second power adjustment command; communication failure indicates that any one of the temperature detection device, smoke detection device, and image detection device is experiencing communication failures; the second power adjustment command is used to instruct the stove to maintain a medium power level.

[0012] In one optional embodiment, generating fire control parameters based on food condition scores includes: The type of food is determined based on the current image information and a preset target recognition algorithm; The target status score is determined based on information about the type of ingredients and the current cooking mode. The state score error is determined based on the difference between the target state score and the food ingredient state score; The controller output signal is determined by performing a proportional-integral-derivative superposition operation on the state scoring error. Fire control parameters are generated based on the controller output signal.

[0013] Secondly, embodiments of this application provide a fire control device for a smart kitchen appliance, the device comprising: The acquisition module is used to acquire current temperature data, current smoke data, and current image data; The first determining module is used to determine the current cooking mode and the corresponding current scoring weight; The second determining module is used to determine the food status score based on the current temperature data, current smoke data, current image data, and current scoring weight; The parameter generation module is used to generate fire control parameters based on the food condition score; The firepower adjustment module is used to adjust the firepower of smart kitchen appliances based on firepower control parameters.

[0014] Thirdly, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the fire control method of the intelligent kitchen appliance of the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the fire control method for intelligent kitchen appliances of the first aspect.

[0016] Fifthly, embodiments of this application provide a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the fire control method for intelligent kitchen appliances according to the first aspect.

[0017] The fire control method, device, electronic device, and storage medium for smart kitchen appliances provided in this application have the following technical effects: The process involves acquiring current temperature data, current smoke data, and current image data; determining the current cooking mode and its corresponding current rating weight; determining the food condition score based on the current temperature data, current smoke data, current image data, and current rating weight; generating heat control parameters based on the food condition score; and adjusting the heat of the smart kitchen appliance based on the heat control parameters. In this embodiment, by collecting three core data types—temperature, smoke, and image—and combining them with the rating weight corresponding to the current cooking mode, a real-time food condition score is calculated. Then, heat control parameters are generated based on this score, ultimately automatically adjusting the heat of the kitchen appliance. This breaks through traditional reliance on experience, quantifies the food condition through multi-dimensional data, and achieves precise and intelligent heat control. Simultaneously, it avoids the limitations of single-sensor monitoring, comprehensively captures key cooking information, and reduces problems such as burnt exterior and undercooked interior, and stale texture. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for controlling the heat of a smart kitchen appliance according to an embodiment of this application. Figure 1 ; Figure 3 This is a flowchart illustrating a method for controlling the heat of a smart kitchen appliance according to an embodiment of this application. Figure 2 ; Figure 4 This is a flowchart illustrating a method for determining the state score of food ingredients according to an embodiment of this application; Figure 5 This is a schematic flowchart of an empty pot detection method provided in an embodiment of this application; Figure 6 This is a schematic flowchart of an overheat emergency stop method provided in an embodiment of this application; Figure 7 This is a flowchart illustrating a fault self-testing method provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a fire control device for a smart kitchen appliance provided in an embodiment of this application; Figure 9 This is a hardware structure block diagram of a server for a method of controlling the firepower of a smart kitchen appliance, as provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] Please see Figure 1 , Figure 1This is a schematic diagram of an application environment provided in an embodiment of this application. The fire control system of the smart kitchen appliance includes a multimodal sensing unit 101, a data processing unit 102, and a fire control unit 103.

[0023] In one possible embodiment, the multimodal sensing unit 101 includes an infrared temperature sensor for acquiring temperature data, a smoke particle sensor for acquiring smoke data, and an RGB camera for acquiring image data.

[0024] The multimodal sensing unit 101 is communicatively connected to the data processing unit 102, and transmits the collected temperature data, smoke data and image data to the data processing unit 102.

[0025] In one possible embodiment, the data processing unit 102 acquires current temperature data, current smoke data, and current image data; determines the current cooking mode and the corresponding current rating weight; determines the food status rating based on the current temperature data, current smoke data, current image data, and current rating weight; generates fire control parameters based on the food status rating; and adjusts the firepower of the smart kitchen appliance based on the fire control parameters.

[0026] In one possible embodiment, the fire control unit 103 may be specifically configured as a gas solenoid valve, which adjusts the opening and closing degree of the gas solenoid valve based on the fire control parameters to achieve fire control.

[0027] In this embodiment, by collecting three core data types—temperature, smoke, and images—and combining them with the scoring weights corresponding to the current cooking mode, a real-time status score of the ingredients is calculated. Then, based on this score, fire control parameters are generated, and the firepower of the kitchen appliances is automatically adjusted. This breaks through the reliance on traditional experience, quantifies the status of ingredients through multi-dimensional data, and achieves precise and intelligent control of firepower. At the same time, it avoids the limitations of monitoring with a single sensor, comprehensively captures key cooking information, and reduces problems such as burnt outside and undercooked inside, and stale taste.

[0028] The following describes a specific embodiment of a fire control method for intelligent kitchen appliances according to this application. Figure 2 This is a flowchart illustrating a method for controlling the heat of a smart kitchen appliance according to an embodiment of this application. Figure 1 This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown in the embodiments or drawings... Figure 2 As shown, it may include: S201: Obtain current temperature data, current smoke data, and current image data.

[0029] S202: Determine the current cooking mode and the corresponding current rating weight.

[0030] S203: Determine the food condition score based on the current temperature data, current smoke data, current image data, and current scoring weight.

[0031] S204: Generate fire control parameters based on food condition scores.

[0032] S205: Adjusts the firepower of smart kitchen appliances based on firepower control parameters.

[0033] Figure 3 This is a flowchart illustrating a method for controlling the heat of a smart kitchen appliance according to an embodiment of this application. Figure 2 The method may include: S301: Acquire current temperature data, current smoke data, and current image data.

[0034] In this embodiment, current temperature data is obtained through an infrared temperature sensor, current smoke data is obtained through a smoke particle sensor, and current image data is obtained through an RGB camera.

[0035] In one optional embodiment, before determining the food quality score based on the current temperature data, current smoke data, current image data, and current scoring weights, the acquired data needs to be preprocessed. The preprocessing steps specifically include: S3011: Perform sliding window filtering on the current temperature data to obtain filtered temperature data.

[0036] S3012: Determine the filtered temperature data as the current temperature data.

[0037] S3013: Perform Kalman filtering on the current smoke data to obtain filtered smoke data.

[0038] S3014: Determine the filtered smoke data as the current smoke data.

[0039] Preferably, the current temperature data is filtered using a sliding window to eliminate noise, with a window size of N=5, and the calculation formula is as follows: ;in, This represents the raw data acquired at the i-th sampling time. The raw data typically contains the real signal we want and the noise we don't need.

[0040] Preferably, the current smoke data is corrected for sensor drift using a Kalman filter, and the state equation is as follows: ;in, : A is the prior estimate, representing the predicted estimate of the system state at time k using only information up to time k-1; A is the state transition matrix, which describes how the system state naturally evolves from time k-1 to time k without external control input; B is the control matrix, which describes how the control input u(k) affects the system state. ; This represents the optimal estimate of the system state at time k-1, after comprehensively utilizing all observation information up to time k-1.

[0041] S302: Determine the current cooking mode and the corresponding current rating weight.

[0042] In one optional embodiment, determining the current cooking mode and the corresponding current rating weight includes: S3021: Obtain the initial score weights.

[0043] In this embodiment of the application, the initial scoring weight includes the initial temperature weight. Initial smoke weights and initial image weights Specifically, it can be 0.5, 0.3, or 0.2.

[0044] S3022: Determine if the current cooking mode is frying mode. If yes, execute S3024; otherwise, execute.

[0045] S3023: Increase the initial smoke weight, decrease the initial image weight, and determine the current scoring weight based on the increased initial smoke weight, decreased initial image weight, and initial temperature weight.

[0046] In one possible embodiment, if the current cooking mode is frying mode, the initial smoke weight is increased and the initial image weight is decreased, and the current scoring weight is determined based on the increased initial smoke weight, the decreased initial image weight and the initial temperature weight.

[0047] In frying mode, the smoke provides a better indication of the food's doneness; therefore, increasing the smoke weight is crucial. =0.4, reduce image weight =0.1, which can prioritize capturing cooking state information caused by sudden changes in oil fumes, avoid misjudgment due to local deviations in scorch images, and suppress the interference of sudden changes in oil fumes on the score, making the score more consistent with the actual cooking logic of frying.

[0048] S3024: Determine if the current cooking mode is stewing mode. If yes, execute S3024; otherwise, execute.

[0049] S3025: Increase the initial temperature weight, and determine the current scoring weight based on the increased initial temperature weight and the initial image weight.

[0050] In one possible embodiment, if the current cooking mode is stewing mode, the initial temperature weight is increased, and the current scoring weight is determined based on the increased initial temperature weight and the initial image weight.

[0051] In stewing mode, the steam is mostly water vapor, which doesn't reflect the doneness of the food. Temperature, on the other hand, is a better indicator of doneness. Therefore, increasing the weight of temperature is crucial. =0.7, smoke detection off =0, which is adapted to the characteristics of long-term low temperature and less oil smoke in stewing mode, and avoids invalid data from affecting the accuracy of the score.

[0052] S3026: Determine the initial rating weight as the current rating weight.

[0053] In another possible embodiment, if the current cooking mode is neither frying nor stewing, the initial scoring weight is determined to be the current scoring weight.

[0054] For conventional cooking methods such as steaming and stir-frying, which are not deep-frying or stewing, the initial weights can evenly cover the monitoring value of the three types of data: temperature, smoke, and image, and meet the comprehensive judgment requirements of this type of mode for multi-dimensional state information. Therefore, the original weights of 0.5, 0.3, and 0.2 can be used as the current scoring weights.

[0055] The proportion of effective information from various sensor data differs across cooking modes. The above solution, by dynamically adjusting weights, ensures that high-value data dominates the scoring, while reducing or eliminating the proportion of low-value or invalid data. This avoids the problem of invalid data diluting effective information under a single, fixed weight, allowing the food condition score to more realistically and accurately reflect the actual doneness of the food, providing a reliable basis for subsequent heat control.

[0056] S303: Determine the food condition score based on current temperature data, current smoke data, current image data, and current scoring weights.

[0057] S304: Generate fire control parameters based on food condition scores.

[0058] In one optional embodiment, generating fire control parameters based on food condition scores includes: S3041: Determine the type of food based on the current image information and the preset target recognition algorithm.

[0059] S3042: Determine the target state score based on the type of ingredients and the current cooking mode.

[0060] S3043: Determine the state score error based on the difference between the target state score and the food ingredient state score.

[0061] S3044: Perform proportional-integral-derivative superposition calculation on the state scoring error to determine the controller output signal.

[0062] S3045: Generates fire control parameters based on the controller output signal.

[0063] First, the type of food is determined through image recognition, such as steak, fries, and ribs. Then, combined with the current cooking mode, such as frying, stewing, or stir-frying, a target state score is customized. For example, the target score for frying steak in the frying mode is 0.83, while the target score for frying fries in the frying mode can be set to 0.67. The ideal cooking state of different foods is quantified through target scores, making the reference standard for heat control more precise.

[0064] The error is determined by the difference between the target score and the current score. Then, the proportional (P), integral (I), and derivative (D) operations are performed on the state score error using the PID algorithm to determine the controller output signal. Finally, the fire control parameters are generated based on the controller output signal.

[0065] S305: Adjusts the firepower of smart kitchen appliances based on firepower control parameters.

[0066] Figure 4 This is a flowchart illustrating a method for determining the condition score of food ingredients according to an embodiment of this application. In an optional embodiment, the food ingredient condition score is determined based on current temperature data, current smoke data, current image data, and current score weights, including: S401: Determine the target temperature data based on the current cooking mode and ingredient information.

[0067] Different cooking modes and different ingredients have different target temperatures. Therefore, it is necessary to determine the target temperature data based on the current cooking mode and ingredient information.

[0068] S402: Determine the temperature ratio data based on the ratio of the current temperature data to the target temperature data.

[0069] In this embodiment of the application, based on the current temperature data and target temperature data max The ratio determines the temperature ratio data. .

[0070] S403: Determine smoke threshold data based on the current cooking mode and ingredient information.

[0071] In one possible embodiment, the smoke threshold data is also related to the cooking mode, and the corresponding smoke threshold data is determined based on the current cooking mode.

[0072] S404: Determine the smoke proportion data based on the ratio of the current smoke data to the smoke threshold data.

[0073] In this embodiment of the application, based on the current smoke data and smoke threshold data The ratio determines the smoke proportion data. .

[0074] S405: Determine the focal spot area ratio data of the current image data based on the preset target detection model.

[0075] In this embodiment, the focal spot area ratio data of the current image data is determined based on a preset target detection model. .

[0076] S406: Determine the food condition score based on temperature ratio data, smoke ratio data, scorch area ratio data, and current scoring weight.

[0077] Finally, based on temperature ratio data, smoke ratio data, scorch area ratio data, and the current scoring weights, the food condition score is determined. .

[0078] In one possible embodiment, in a steak-frying scenario, the user selects the frying mode, and the system sets the target temperature data. =180℃, smoke threshold data =50ppm. Temperature sensor detects the surface temperature of the steak. =120℃; smoke sensor detects concentration =20ppm; Camera-recognized focal spot area percentage =5%. Therefore, the ingredient condition score. And the target score corresponding to the steak-frying scene. =0.83, error PID output =0.83 0.33 + 0.01 ∫0.33dτ+0.2 ≈0.26; As a result, the firepower increased by 26%, and the medium-high fire was maintained until the surface scorch marks expanded to 15%.

[0079] In another possible embodiment, in the French fry scenario, the user selects the frying mode, and the system sets... =190℃, =200ppm; Current oil temperature =195℃; Smoke concentration =80ppm; Camera identifies the percentage of golden area on the surface of the fries. =30%. Therefore, determine the ingredient condition score. And the target score corresponding to the French fry scene. =0.67, error PID output ≈ 0.02, reduce firepower by 2% to stabilize oil temperature.

[0080] The above scheme, based on the cooking mode and the target temperature and smoke threshold customized for the ingredients, can ensure that the various ratio data can truly correspond to the doneness of the ingredients, so as to accurately control the heat.

[0081] Figure 5 This is a flowchart illustrating an empty pot detection method provided in an embodiment of this application, which acquires current temperature data, current smoke data, and current image data, including: S501: Determine the status of the ingredients based on the current image data.

[0082] Based on a preset image recognition algorithm, the current image data is identified to determine the state of the ingredients; specifically, it is only necessary to identify whether there are ingredients in the pot.

[0083] S502: Determine if the food condition meets the empty pot condition. If yes, execute S503; otherwise, execute S505.

[0084] In one possible embodiment, the empty pot condition specifically refers to a pot having no food for a period exceeding an empty pot time threshold, and the current temperature exceeding an empty pot temperature threshold. By using image recognition, time thresholds, and temperature thresholds to determine multiple conditions, false triggers caused by a single condition are avoided, accurately identifying dangerous scenarios of dry burning in an empty pot, and effectively reducing safety risks such as cookware damage and fires.

[0085] S503: Generate the first firepower adjustment command.

[0086] In this embodiment, the first firepower adjustment command is used to instruct the stove to reduce the firepower level.

[0087] S504: Get the current image data.

[0088] Figure 6 This is a flowchart illustrating an overheating emergency stop method provided in an embodiment of this application. After acquiring current temperature data, current smoke data, and current image data, the method may include: S505: Determine the bottom temperature data of the pot based on the current temperature data.

[0089] The entire infrared image is obtained through an infrared temperature sensor. Basic filtering is performed first, followed by geometric correction and temperature compensation to ensure accurate correspondence between the image and the actual measured area's position and temperature. Then, a preset image recognition algorithm separates the effective area of ​​the pot bottom from the infrared image, avoiding temperature interference from the background (such as the stovetop, air, and food). Specifically, this can be achieved by using the temperature difference between the pot bottom area and the food / background areas, setting a temperature threshold, and filtering out pixel areas exceeding the threshold, which are then identified as the pot bottom area.

[0090] S506: Determine if the temperature data of the bottom of the pot is greater than the safe temperature threshold. If yes, proceed to S507; otherwise, proceed to S508.

[0091] S507: Generate fire shutdown command.

[0092] S508: Get the current temperature data.

[0093] When the infrared temperature sensor detects that the temperature of the bottom of the pot exceeds the safe temperature threshold, specifically, if the gas stove exceeds 350°C or the induction cooker exceeds 300°C, it generates a fire shut-off command to immediately shut off the solenoid valve or IGBT drive.

[0094] In one alternative embodiment, the smart kitchen appliance is equipped with a temperature detection device, a smoke detection device, and an image detection device. Figure 7 This is a flowchart illustrating a fault self-diagnosis method provided in an embodiment of this application, which acquires current temperature data, current smoke data, and current image data, including: S601: Obtain the communication status of the temperature detection device, smoke detection device, and image detection device.

[0095] S602: Determine if the communication status is normal. If yes, execute S603; otherwise, execute S604.

[0096] S603: Acquire current temperature data, current smoke data, and current image data.

[0097] S604: Generate a second firepower adjustment command.

[0098] In one optional embodiment, if the communication status indicates normal communication, the current temperature data, current smoke data, and current image data are acquired. In this embodiment, normal communication indicates that the temperature detection device, smoke detection device, and image detection device are all communicating normally, thus the current temperature data, current smoke data, and current image data are acquired normally.

[0099] In one optional embodiment, if the communication status indicates a communication abnormality, a second firepower adjustment command is generated. The second firepower adjustment command is used to instruct the stove to maintain a medium firepower level. At this time, the intelligent firepower control is inaccurate, so it switches to a conservative fire control mode and fixes the medium firepower output.

[0100] In this embodiment of the application, communication anomaly indicates that any one of the temperature detection device, smoke detection device, and image detection device is experiencing a communication anomaly.

[0101] This application also provides a fire control device for intelligent kitchen appliances. Figure 8 This is a schematic diagram of the structure of a fire control device for a smart kitchen appliance provided in an embodiment of this application, as shown below. Figure 8 As shown, the device 700 includes: The acquisition module 701 is used to acquire current temperature data, current smoke data, and current image data; The first determining module 702 is used to determine the current cooking mode and the corresponding current scoring weight; The second determining module 703 is used to determine the food status score based on the current temperature data, current smoke data, current image data and current scoring weight; The parameter generation module 704 is used to generate fire control parameters based on the food condition score. The firepower adjustment module 705 is used to adjust the firepower of smart kitchen appliances based on firepower control parameters.

[0102] In one alternative implementation, it further includes: The third determination module is used to determine the target temperature data based on the current cooking mode and ingredient information; The fourth determination module is used to determine the temperature ratio data based on the ratio of the current temperature data to the target temperature data; The first acquisition module is used to determine smoke threshold data based on the current cooking mode and ingredient information; The fifth determining module is used to determine the smoke ratio data based on the ratio of the current smoke data to the smoke threshold data; The sixth determination module is used to determine the focal spot area ratio data of the current image data based on the preset target detection model; The seventh determination module is used to determine the food status score based on temperature ratio data, smoke ratio data, scorch area ratio data, and the current scoring weight.

[0103] In one alternative implementation, it further includes: The second acquisition module is used to acquire the initial scoring weights; the initial scoring weights include the initial temperature weight, the initial smoke weight, and the initial image weight. The eighth determining module is used to: if the current cooking mode is frying, increase the initial smoke weight and decrease the initial image weight, and determine the current scoring weight based on the increased initial smoke weight, decreased initial image weight, and initial temperature weight; or if the current cooking mode is stewing, increase the initial temperature weight and determine the current scoring weight based on the increased initial temperature weight and initial image weight; or if the current cooking mode is neither frying nor stewing, determine the initial scoring weight as the current scoring weight.

[0104] In an optional embodiment, it further includes: The first filtering module is used to perform sliding window filtering on the current temperature data to obtain filtered temperature data. The ninth determination module is used to determine the filtered temperature data as the current temperature data; The second filtering module is used to perform Kalman filtering on the current smoke data to obtain filtered smoke data. The tenth determination module is used to determine the filtered smoke data as the current smoke data.

[0105] In an optional embodiment, it further includes: The eleventh determination module is used to determine the bottom temperature data of the pot based on the current temperature data; The first instruction generation module is used to generate a fire-off instruction if the temperature data of the bottom of the pot is greater than the safe temperature threshold.

[0106] In an optional embodiment, it further includes: The twelfth determination module is used to determine the status of the ingredients based on the current image data; The second instruction generation module is used to generate a first firepower adjustment instruction if the food status indicator shows that there is no food in the pot for a longer period than the empty pot time threshold, and the current temperature data is greater than the empty pot temperature threshold; the first firepower adjustment instruction is used to instruct the stove to reduce the firepower level.

[0107] In one optional embodiment, the smart kitchen appliance is equipped with a temperature detection device, a smoke detection device, and an image detection device; it also includes: The third acquisition module is used to acquire the communication status of the temperature detection device, the smoke detection device, and the image detection device. The fourth acquisition module is used to acquire the current temperature data, current smoke data, and current image data if the communication status indicates normal communication; normal communication indicates that the temperature detection device, smoke detection device, and image detection device are all experiencing communication failures; or; if the communication status indicates communication failure, a second firepower adjustment command is generated; communication failure indicates that any one of the temperature detection device, smoke detection device, and image detection device is experiencing communication failure; the second firepower adjustment command is used to instruct the stove to maintain a medium firepower level.

[0108] In an optional embodiment, it further includes: The thirteenth determination module is used to determine the type of food based on the current image information and a preset target recognition algorithm; The fourteenth determination module is used to determine the target status score based on the ingredient type information and the current cooking mode. The fifteenth determination module is used to determine the state score error based on the difference between the target state score and the food ingredient state score; The sixteenth determination module is used to perform proportional-integral-derivative superposition calculation on the state scoring error to determine the controller output signal; The first parameter generation module is used to generate fire control parameters based on the controller output signal.

[0109] The apparatus and method embodiments in this application are based on the same application concept.

[0110] The methods and embodiments provided in this application can be executed on a computer terminal, server, or similar computing device. Taking running on a server as an example, Figure 9 This is a hardware structure block diagram of a server for a method of controlling the heat of a smart kitchen appliance, as provided in an embodiment of this application. Figure 9 As shown, the server 800 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 810 (CPUs 810 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 830 for storing data, and one or more storage media 820 (e.g., one or more mass storage devices) for storing application programs 823 or data 822. The memory 830 and storage media 820 may be temporary or persistent storage. The program stored in the storage media 820 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 810 may be configured to communicate with the storage media 820 and execute the series of instruction operations stored in the storage media 820 on the server 800. Server 800 may also include one or more power supplies 860, one or more wired or wireless network interfaces 850, one or more input / output interfaces 840, and / or one or more operating systems 821, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0111] The input / output interface 840 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 800. In one example, the input / output interface 840 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 840 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0112] Those skilled in the art will understand that Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 800 may also include... Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown.

[0113] This application provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the above-described data processing method.

[0114] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in a server to store at least one instruction, at least one program, code set, or instruction set related to implementing a fire control method for a smart kitchen appliance in the method embodiment. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the fire control method for the smart kitchen appliance.

[0115] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0116] As can be seen from the embodiments of the intelligent kitchen appliance fire control method, device, electronic device, or storage medium provided in this application, this application acquires current temperature data, current smoke data, and current image data; determines the current cooking mode and the corresponding current scoring weight; determines the food status score based on the current temperature data, current smoke data, current image data, and current scoring weight; generates fire control parameters based on the food status score; and adjusts the firepower of the intelligent kitchen appliance based on the fire control parameters. In the embodiments of this application, by collecting three core data types—temperature, smoke, and image—and combining them with the scoring weight corresponding to the current cooking mode, a real-time food status score is calculated. Then, fire control parameters are generated based on this score, and finally, the firepower of the kitchen appliance is automatically adjusted. This breaks through the reliance on traditional experience, quantifies the food status through multi-dimensional data, achieves precise and intelligent firepower control, avoids the limitations of single-sensor monitoring, comprehensively captures key cooking information, and reduces problems such as burnt exterior and undercooked interior, and stale taste.

[0117] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0119] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0120] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for controlling the heat output of an intelligent kitchen appliance, characterized in that, include: Acquire current temperature data, current smoke data, and current image data; Determine the current cooking mode and its corresponding current rating weight; Based on the current temperature data, the current smoke data, the current image data, and the current scoring weight, a food quality score is determined; Firepower control parameters are generated based on the food condition score. The power of the smart kitchen appliance is adjusted based on the aforementioned power control parameters.

2. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, The process of determining the food quality score based on the current temperature data, the current smoke data, the current image data, and the current scoring weight includes: Determine the target temperature data based on the current cooking mode and ingredient information; Temperature ratio data is determined based on the ratio of the current temperature data to the target temperature data; The smoke threshold data is determined based on the current cooking mode and the ingredient information; The smoke ratio data is determined based on the ratio of the current smoke data to the smoke threshold data; The focal spot area ratio data of the current image data is determined based on a preset target detection model. The food quality score is determined based on the temperature ratio data, the smoke ratio data, the scorch area ratio data, and the current scoring weight.

3. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, Determining the current cooking mode and the corresponding current rating weight includes: Obtain initial scoring weights; the initial scoring weights include initial temperature weights, initial smoke weights, and initial image weights; If the current cooking mode is frying, increase the initial smoke weight and decrease the initial image weight, and determine the current rating weight based on the increased initial smoke weight, decreased initial image weight, and initial temperature weight; or; if the current cooking mode is stewing, increase the initial temperature weight and determine the current rating weight based on the increased initial temperature weight and initial image weight; or; if the current cooking mode is neither frying nor stewing, determine the initial rating weight as the current rating weight.

4. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, Before determining the food condition score based on the current temperature data, the current smoke data, the current image data, and the current scoring weight, the method further includes: The current temperature data is subjected to sliding window filtering to obtain filtered temperature data; The filtered temperature data is determined as the current temperature data; Perform Kalman filtering on the current smoke data to obtain filtered smoke data; The filtered smoke data is determined as the current smoke data.

5. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, After acquiring the current temperature data, current smoke data, and current image data, the process further includes: Determine the pot bottom temperature data based on the current temperature data; If the temperature data of the pot bottom is greater than the safe temperature threshold, a fire shut-off command is generated.

6. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, The acquisition of current temperature data, current smoke data, and current image data includes: The status of the ingredients is determined based on the current image data; If the time when the food status indicator indicates that there is no food in the pot is greater than the empty pot time threshold, and the current temperature data is greater than the empty pot temperature threshold, a first firepower adjustment command is generated; the first firepower adjustment command is used to instruct the stove to reduce the firepower level.

7. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, The smart kitchen appliance is equipped with a temperature detection device, a smoke detection device, and an image detection device. The acquisition of current temperature data, current smoke data, and current image data includes: Obtain the communication status of the temperature detection device, the smoke detection device, and the image detection device; If the communication status indicates normal communication, the current temperature data, the current smoke data, and the current image data are acquired; normal communication indicates that the temperature detection device, the smoke detection device, and the image detection device are all experiencing communication failures; or; if the communication status indicates communication failure, a second power adjustment command is generated; communication failure indicates that any one of the temperature detection device, the smoke detection device, and the image detection device is experiencing communication failures; the second power adjustment command is used to instruct the stove to maintain a medium power level.

8. The method for controlling the heat of an intelligent kitchen appliance according to claim 1, characterized in that, The generation of fire control parameters based on the food ingredient status score includes: The type of food is determined based on the current image information and a preset target recognition algorithm; A target status score is determined based on the ingredient type information and the current cooking mode. The state score error is determined based on the difference between the target state score and the food ingredient state score; The controller output signal is determined by performing a proportional-integral-derivative superposition operation on the state scoring error. The fire control parameters are generated based on the output signal of the controller.

9. A heat control device for an intelligent kitchen appliance, characterized in that, The device includes: The acquisition module is used to acquire current temperature data, current smoke data, and current image data; The first determining module is used to determine the current cooking mode and the corresponding current scoring weight; The second determining module is used to determine the food status score based on the current temperature data, the current smoke data, the current image data, and the current scoring weight; The parameter generation module is used to generate fire control parameters based on the food ingredient status score; The firepower adjustment module is used to adjust the firepower of the smart kitchen appliance based on the firepower control parameters.

10. A smart kitchen appliance, characterized in that, The intelligent kitchen appliance includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the fire control method of the intelligent kitchen appliance as described in any one of claims 1-8.

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