Intelligent control method and system for household fuel gas smoking oven

Abstract

The invention discloses an intelligent control method and system for a household fuel gas smoking oven, and the method and system determine the control effect coefficient of the household fuel gas smoking oven based on the maturity of food, the heat distribution diagram of the household fuel gas smoking oven and the energy consumption of the household fuel gas smoking oven, thereby guaranteeing the accuracy of the control effect coefficient of the household fuel gas smoking oven. Furthermore, according to the intelligent control model, the household gas smoking furnace and the food, corresponding smoking balance parameters are matched, food parameters are determined based on the food type and the food maturity state, and the heating temperature and the smoke output amount of the household gas smoking furnace are dynamically regulated and controlled based on the food parameters and the smoking balance parameters. According to the intelligent control method, the intelligent control device and the intelligent control system, multiple interactions of the food parameters and the smoking balance parameters are compatible, multi-dimensional control in the food parameters and the smoking balance parameters is guaranteed, the accuracy of dynamic regulation and control of the heating temperature and the smoke output amount of the gas smoking oven is guaranteed, and intelligent control over the household gas smoking oven is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of household gas smokers, and particularly to an intelligent control method and system for a household gas smoker. Background Art

[0002] With the development of technology, household gas smokers have gradually been applied to people's lives. Users use household gas smokers to heat food, and some smoke will be generated during the use of household gas smokers. The output of the smoke is regarded as the smoke output. In the prior art, the household gas smoker realizes the heating of food through temperature control, without considering the control of the smoke output, and cannot achieve dynamic control of the smoke output. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides an intelligent control method and system for a household gas smoker.

[0004] An embodiment of the present invention provides an intelligent control method for a household gas smoker, including: Determining a detection space based on the location of the household gas smoker; Determining the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the detection of the detection space; Determining the maturity of the food according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple maturity parameters of the food; Determining a control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; Matching a control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and matching an intelligent control model of the household gas smoker based on the control mode and the maturity state of the food; Matching corresponding smoking balance parameters according to the intelligent control model, the household gas smoker, and the food, determining food parameters based on the type of food and the maturity state of the food, and dynamically regulating the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoking balance parameters to trigger the intelligent control of the household gas smoker.

[0005] In addition, an embodiment of the present invention also provides an intelligent control system for a household gas smoker, and the intelligent control system for the household gas smoker includes: A detection space module for determining a detection space based on the location of the household gas smoker; A detection module for determining the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the detection of the detection space; A maturity module for determining the maturity of the food according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple maturity parameters of the food; A control module for determining the control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; An intelligent module for matching the control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and matching the intelligent control model of the household gas smoker based on the control mode and the maturity state of the food; A dynamic regulation module for matching corresponding smoking balance parameters according to the intelligent control model, the household gas smoker, and the food, determining food parameters based on the type of food and the maturity state of the food, and dynamically regulating the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoking balance parameters to trigger the intelligent control of the household gas smoker.

[0006] In the embodiment of the present invention, through the method in the embodiment of the present invention, the detection space is determined based on the location of the household gas smoker; the current working mode of the household gas smoker and the usage scenario of the household gas smoker are determined according to the detection of the detection space; the maturity of the food is determined according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple maturity parameters of the food; the control effect coefficient of the household gas smoker is determined based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker, which comprehensively considers the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker, realizes the multi-dimensional control of the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker, and ensures the accuracy of the control effect coefficient of the household gas smoker.

[0007] Further, the control mode of the household gas smoke oven is matched according to the control effect coefficient of the household gas smoke oven, the smoke output of the household gas smoke oven, and the heating power of the household gas smoke oven. Based on this control mode and the ripeness state of the food, an intelligent control model of the household gas smoke oven is matched. According to the intelligent control model, the household gas smoke oven, and the food, the corresponding smoke balance parameters are matched. Based on the type of food and the ripeness state of the food, the food parameters are determined. Based on the food parameters and the smoke balance parameters, the heating temperature and the smoke output of the household gas smoke oven are dynamically regulated, which is compatible with the multiple interactions of the food parameters and the smoke balance parameters, ensuring the control in multiple dimensions of the food parameters and the smoke balance parameters, ensuring the accuracy of the dynamic regulation of the heating temperature and the smoke output of the gas smoke oven, realizing the intelligent control of the household gas smoke oven, and further realizing the dynamic management and control of the food and the household gas smoke oven. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0009] Figure 1 is a schematic flowchart of the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 2 is a schematic flowchart of S11 in the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 3 is a schematic flowchart of S12 in the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 4 is a schematic flowchart of S13 in the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 5 is a schematic flowchart of S14 in the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 6 is a schematic flowchart of S15 in the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 7 is a schematic flowchart of S16 in the intelligent control method of the household gas smoke oven in the embodiment of the present invention; Figure 8 is a schematic structural composition diagram of the intelligent control system of the household gas smoke oven in the embodiment of the present invention; Figure 9It is a hardware diagram of an electronic device shown according to an exemplary embodiment. Detailed implementation manners

[0010] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0011] Please refer to Figures 1 to 9 , an intelligent control method for a household gas smoke oven, which is applied to the intelligent control scenario of a household gas smoke oven; the intelligent control method for a household gas smoke oven includes: Step S11: Determine the detection space based on the location of the household gas smoke oven; Step S12: Determine the current working mode of the household gas smoke oven and the usage scenario of the household gas smoke oven according to the detection of the detection space; Step S13: Determine the maturity of the food according to the current working mode of the household gas smoke oven, the usage scenario of the household gas smoke oven, and multiple maturity parameters of the food; Step S14: Determine the control effect coefficient of the household gas smoke oven based on the maturity of the food, the heat distribution map of the household gas smoke oven, and the energy consumption of the household gas smoke oven; Step S15: Match the control mode of the household gas smoke oven according to the control effect coefficient of the household gas smoke oven, the smoke emission amount of the household gas smoke oven, and the heating power of the household gas smoke oven, and match the intelligent control model of the household gas smoke oven based on the control mode and the maturity state of the food; Step S16: Match the corresponding smoke balance parameters according to the intelligent control model, the household gas smoke oven, and the food, determine the food parameters based on the type of food and the maturity state of the food, and dynamically adjust the heating temperature and smoke emission amount of the household gas smoke oven based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoke oven.

[0012] Refer to Figure 2 , in step S11, determine the detection space based on the location of the household gas smoke oven; In the specific implementation process of the present invention, the specific steps may be: S111: Collect the control signal of the household gas smoke oven; determine the position information of the household gas smoke oven according to the analysis of the control signal of the household gas smoke oven; S112: Determine the location where the household gas smoke oven is located according to the position information of the household gas smoke oven; S113: Determine a detection space based on the location of the household gas smoker, the location of the user, and the surrounding environment of the household gas smoker.

[0013] In an embodiment of the present application, a control signal of the household gas smoker is collected; the position information of the household gas smoker is determined according to the analysis of the control signal of the household gas smoker, and the position information of the household gas smoker is introduced.

[0014] At this time, the system needs to collect control signals from the household gas smoker. These signals are usually transmitted by wired or wireless means and contain information such as the working state of the smoker, user settings, operation instructions, etc. At the same time, the system needs a receiver that can receive control signals from the smoker. This receiver is built into the smoker and is also an independent device connected to the smoker by wired or wireless means. The received signals need to be identified to determine their source and type, which is usually achieved by comparing features such as the signal format, frequency, encoding, etc. The identified signals need to be stored for subsequent analysis and processing. The storage method is temporary (such as in-memory caching) and also permanent (such as database records).

[0015] Optionally, the user is using a smart household gas smoker that is equipped with a Bluetooth module for communicating with a control application (installed on a smartphone). When the user starts the smoker through the control application, the application sends a Bluetooth signal containing a start instruction to the smoker. In this example, the control application on the smartphone is the initiator of signal collection, and the Bluetooth module of the smoker is the signal receiver. The received signal is identified as a start instruction from the control application and is stored in the built-in memory of the smoker for subsequent processing.

[0016] The system needs to analyze the collected control signals to extract information related to the position of the household gas smoker. The received signals are converted from the original format to a readable format, which involves operations such as decryption, decoding, or data conversion. Information related to the position is extracted from the decoded signals. This information can be direct (such as GPS coordinates) or indirect (such as the location description of the smoker set by the user through the application). If the extracted information is indirect, the system needs to use additional algorithms or data sources to calculate the actual position of the smoker, which involves operations such as map matching, distance calculation, and path planning.

[0017] Optionally, in the example of the user's intelligent household gas smoker, the start instruction sent by the control application itself does not contain direct GPS coordinate information; however, if the user has specified the location of the smoker in the application when setting it up (for example, by selecting the home kitchen as the location of the smoker), then the system can use this information to determine the location of the smoker; in this example, the parsing process involves extracting the location information set by the user from the start instruction and associating this information with the identifier of the smoker; since the user has specified the location of the smoker, the system does not need to perform additional location calculations to determine the actual location of the smoker.

[0018] In summary, by collecting and parsing the control signals of the household gas smoker, the system can indirectly or directly determine the location information of the smoker, which is crucial for subsequent intelligent control operations such as determining the detection space and adjusting the smoking parameters; in practical applications, these steps need to be adjusted and optimized according to the specific model of the smoker, the control method, and the user's requirements.

[0019] Furthermore, determining the location where the household gas smoker is located based on the location information of the household gas smoker ensures the accuracy of the location where the household gas smoker is located.

[0020] At this time, the system needs to use the location information of the household gas smoker collected and parsed previously to determine the actual physical location of the smoker, which usually involves further processing and verification of the location information to ensure its accuracy and reliability.

[0021] The system needs to verify the received location information to ensure its reliable source and that it has not been interfered with or tampered with, which includes checking the integrity of the information, the matching of check codes, and comparison with other known information, etc.; if the location information exists in an encoded or encrypted form, the system needs to decode or decrypt it to obtain its original form; for example, if the location information is represented by GPS coordinates, the system needs to convert it into a more intuitive address or a location point on the map; the system needs to match the parsed location information with a known geographical location database to determine the exact location of the smoker, which involves interacting with a map service, a geographic information system (GIS), or a location database in a smart home system.

[0022] Specifically, the user is using a household gas smoker that supports intelligent control, which is built with a GPS module and can obtain its location information in real time; when the user starts the smoker, it will send the location information to the control application on the user's smartphone via Wi-Fi or Bluetooth.

[0023] The control application will first verify whether the received location information is from the user's smoker (e.g., by checking whether the sender ID of the information matches the ID of the smoker); if the location information is sent in the form of GPS coordinates, the control application will use the built-in map service or call a third-party map API to convert these coordinates into more intuitive address information, such as "No. XX, XX Street"; the control application will match this address information with the user's home address or the common location of the smoker previously set by the user in the application to confirm whether the smoker is at the location expected by the user.

[0024] Through this process, the system can accurately determine the location of the household gas smoker, providing key information for subsequent intelligent control operations (such as determining the detection space, adjusting smoking parameters, etc.); in practical applications, these steps need to be adjusted and optimized according to the specific smoker model, control method, and user requirements.

[0025] Therefore, determining the detection space based on the location of the household gas smoker, the user's location, and the surrounding environment of the household gas smoker takes into account the overall situation of the location of the household gas smoker, the user's location, and the surrounding environment of the household gas smoker, ensuring the accuracy of the detection space.

[0026] At this time, the system needs to comprehensively consider the location of the household gas smoker, the user's current location, and the environmental factors around the smoker to determine a reasonable detection space. This detection space should be able to cover the smoker and the influence range of the smoke, heat, etc. generated by it, while ensuring that the user can safely and conveniently monitor the smoking process.

[0027] The system needs to obtain the accurate location information of the household gas smoker, which can be achieved through technologies such as GPS, Bluetooth beacons, Wi-Fi positioning, etc.; at the same time, the system also needs to obtain the user's current location information, which is usually achieved through the positioning function on the user's smartphone or other intelligent devices.

[0028] The system needs to use sensors or external data sources to obtain the environmental factors around the smoker, such as temperature, humidity, wind speed, air quality, etc. These environmental factors are crucial for determining the size and shape of the detection space because they will affect the diffusion of smoke, the transfer of heat, and the safety risks.

[0029] Based on the acquired location information and the evaluated surrounding environment, the system calculates a reasonable detection space through an algorithm. This space should be large enough to contain the smoking oven and the influence range of the smoke, heat, etc. generated by it. At the same time, the monitoring needs and safety of the user should also be considered. When determining the detection space, it is necessary to consider the trade-off between multiple factors, such as the balance between the space size and the monitoring accuracy, and the balance between safety and convenience. At the same time, once the detection space is determined, the system needs to record it for subsequent use. As the location information, environmental factors, or user needs change, the system needs to dynamically update the detection space.

[0030] Specifically, the user is using an intelligent household gas smoking oven to cook food at home, and at the same time, the user is using a control application on a smartphone in the living room to monitor the smoking process. The smoking oven is built-in with a GPS module, which can obtain its location information in real time and send it to the control application through Wi-Fi. The user's smartphone sends the user's current location information to the control application through its built-in GPS and Wi-Fi positioning functions.

[0031] The smoking oven is built-in with a GPS module, which can obtain its location information in real time and send it to the control application through Wi-Fi. The user's smartphone sends the user's current location information to the control application through its built-in GPS and Wi-Fi positioning functions.

[0032] Based on the location of the smoking oven, the location of the user, and environmental factors such as temperature, humidity, wind speed, and air quality in the kitchen, the control application calculates a reasonable detection space through an algorithm. The kitchen is a relatively enclosed space with a low wind speed, so the detection space will be set as a small area inside the kitchen to ensure that the smoke does not spread to other areas such as the living room. If there is a smoke alarm installed in the kitchen, the control application will also match the detection space with the coverage range of the smoke alarm to ensure that the alarm can be triggered in time when the smoke concentration exceeds the standard.

[0033] Through this process, the system can comprehensively consider various factors to determine a reasonable detection space, providing key information for subsequent intelligent control operations. In practical applications, these steps need to be adjusted and optimized according to the specific model of the smoking oven, the control method, and the user's needs.

[0034] Reference Figure 3 In step S12, according to the detection of the detection space, the current working mode of the household gas smoking oven and the usage scenario of the household gas smoking oven are determined. In the specific implementation process of the present invention, the specific steps can be: S121: Divide the detection space into multiple spatial regions, and perform multi-dimensional detection on the multiple spatial regions; collect multiple working parameters and multiple scenario parameters based on the multi-dimensional detection of the multiple spatial regions; determine the current working mode of the household gas smoke oven and the usage scenario of the household gas smoke oven according to the multiple working parameters and the multiple scenario parameters.

[0035] In the embodiment of the present application, the detection space is divided into multiple spatial regions, and multi-dimensional detection is performed on the multiple spatial regions; multiple working parameters and multiple scenario parameters are collected based on the multi-dimensional detection of the multiple spatial regions; the current working mode of the household gas smoke oven and the usage scenario of the household gas smoke oven are determined according to the multiple working parameters and the multiple scenario parameters, which takes into account the overall consideration of multiple working parameters and multiple scenario parameters and ensures the accuracy of the current working mode of the household gas smoke oven and the usage scenario of the household gas smoke oven.

[0036] At this time, in the household scenario, the system needs to perform layout analysis on the entire detection space to identify different functional areas, such as cooking areas, storage areas, ventilation areas, etc.; the system also needs to identify obstacles in the space, such as door panels, pipes, etc., which affect the diffusion of smoke, the distribution of temperature, etc.; based on the layout analysis and obstacle identification, the system divides the detection space into multiple small spatial regions, which can be physically separated or logically divided according to functional or environmental factors; optionally, the detection space is the cooking space of a household gas smoke oven, and the system identifies the center of the household gas smoke oven as the cooking area and the area near the pipe as the ventilation area; based on these identification results, the system divides the cooking space of the household gas smoke oven into two spatial regions, namely the cooking area and the ventilation area.

[0037] Deploy multiple sensors in each spatial region, such as temperature and humidity sensors, smoke sensors, light sensors, sound sensors, etc., to achieve multi-dimensional monitoring of the space environment; the sensors collect the environmental parameter data they are responsible for in real time and transmit these data to the system for processing and analysis; optionally, in the cooking area, temperature and humidity sensors and smoke sensors are deployed; in the ventilation area, light sensors and sound sensors are deployed, and these sensors collect data such as temperature and humidity, smoke concentration, light intensity, and sound level in the kitchen in real time.

[0038] The system collects working parameters from a household gas smoker, such as heating power, smoke generation amount, working time, etc.; the system combines the environmental parameter data collected by sensors to generate scene parameters, such as average temperature and humidity in the space, smoke concentration distribution, lighting conditions, etc.; optionally, the system collects working parameters from the smoker with a medium heating power, stable smoke generation amount, and a working time of 30 minutes; at the same time, combining the sensor data, the system generates scene parameters with an average temperature of 25°C, an average humidity of 50%, and a smoke concentration within the safe range in the kitchen.

[0039] The system matches the collected working parameters and scene parameters with a preset working mode database to identify the current working mode of the household gas smoker; the system also matches the collected scene parameters with a preset usage scenario database to determine the usage scenario of the household gas smoker; optionally, the system identifies the current working mode of the household gas smoker as the "baking mode" because the heating power is moderate, the smoke generation amount is stable, and the working time is long; at the same time, the system determines that the usage scenario is "daily cooking" because the temperature and humidity, smoke concentration, and lighting conditions in the kitchen meet the environmental requirements of daily cooking.

[0040] Reference Figure 4 , in step S13, determine the ripeness of the food according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple ripeness parameters of the food; In the specific implementation process of the present invention, the specific steps may be: S131: Locate the food in the household gas smoker; determine multiple location ripeness parameters based on the detection of the food; determine multiple ripeness parameters according to the multiple location ripeness parameters, the type of the food, and the form of the food; determine the ripeness of the food based on the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and the multiple ripeness parameters of the food.

[0041] In the embodiment of the present application, locate the food in the household gas smoker; determine multiple location ripeness parameters based on the detection of the food; determine multiple ripeness parameters according to the multiple location ripeness parameters, the type of the food, and the form of the food; determine the ripeness of the food based on the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and the multiple ripeness parameters of the food, which takes into account the overall consideration of the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and the multiple ripeness parameters of the food, and ensures the accuracy of the ripeness of the food.

[0042] At this time, cameras, infrared sensors, etc. are installed inside or near the household gas smoke oven to capture images of food or detect the thermal radiation of food; image recognition technology (such as deep learning algorithms) is used to analyze the captured food images to identify the position, shape, and size of the food; the recognized food position information is recorded to provide basic data for subsequent steps. Optionally, there is a rectangular steak inside the household gas smoke oven, and the system captures an image of the steak through the camera and uses image recognition technology to identify the position of the steak (such as in the center of the smoke oven), shape (rectangular), and size (20 cm long and 10 cm wide).

[0043] Multiple detection points are set at different positions of the food (such as the surface, center, edge, etc.); temperature sensors, humidity sensors, etc. are used to collect the temperature, humidity, and other ripeness parameters of each detection point; the collected ripeness parameter data is recorded to provide an analysis basis for subsequent steps; Optionally, 5 detection points are set on the steak: the center point and the four corner points; the system uses temperature sensors to collect the temperature data of each detection point, such as the temperature at the center point is 65 °C, and the temperatures at the four corner points are 63 °C, 64 °C, 64 °C, and 63 °C respectively.

[0044] The collected ripeness parameters are analyzed to calculate statistics such as the average value, maximum value, and minimum value; according to the type and form of the food, a ripeness model is established to correlate the ripeness parameters with the ripeness degree parameters; the ripeness model is used to calculate multiple ripeness degree parameters of the food, such as the overall ripeness degree and local ripeness degree; Optionally, the system calculates the average temperature (64 °C) and temperature standard deviation (1 °C) based on the temperature data of the steak, and calculates the overall ripeness degree (close to medium-rare) and local ripeness degree of the steak (the four corner points are slightly lower than the center point, but both are close to medium-rare) using the preset ripeness model according to the type (meat) and form (rectangular) of the steak.

[0045] Analyze the current working mode of the household gas smoke oven, such as baking, smoking, etc.; judge the requirements for the ripeness of the food according to the usage scenarios of the household gas smoke oven, such as daily cooking, festival celebrations, etc.; combine the multiple ripeness degree parameters of the food, the working mode, and the usage scenario of the household gas smoke oven to comprehensively judge whether the ripeness of the food meets the requirements; Optionally, the household gas smoke oven is currently in the "baking mode" for daily cooking; the system judges that the ripeness of the steak meets the requirements of daily cooking according to the overall ripeness degree and local ripeness degree of the steak, combined with the requirements for the ripeness of the food in the "baking mode" (such as golden brown on the surface and juicy inside); through the above steps, the system can accurately locate the food in the household gas smoke oven, collect and analyze the ripeness parameters, and finally determine the ripeness of the food, which provides an intelligent cooking experience for users and ensures that the food is presented to consumers in the best state.

[0046] ReferenceFigure 5 , S14: Determine the control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; In the specific implementation process of the present invention, the specific steps may be: S141: Determine the heat distribution map of the household gas smoker based on the heat detection of the household gas smoker; S142: Collect the energy consumption of the household gas smoker, and correlate the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; Determine the control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker.

[0047] In the embodiment of the present application, the heat distribution map of the household gas smoker is determined based on the heat detection of the household gas smoker, and the heat distribution map is introduced.

[0048] At this time, temperature sensors are installed at different positions inside the household gas smoker. These sensors can be thermistors, thermocouples, or other types of temperature sensors; The number, position, and type of sensors should be optimized according to the size, shape, and intended use of the smoker to ensure that the heat distribution inside the furnace can be comprehensively and accurately detected.

[0049] The sensors collect temperature data regularly (such as every second, every few seconds, or every minute) and transmit the data to the central processing unit (such as a microcontroller, computer, or smartphone application) via wired or wireless means; The data collection frequency should be determined according to the complexity of the cooking process and the requirements for heat distribution accuracy; After receiving the sensor data, the central processing unit performs preprocessing, such as removing noise, calibration, and standardization; In order to generate the heat distribution map, the system needs to use interpolation algorithms (such as linear interpolation, bilinear interpolation, or more advanced interpolation methods) to estimate the temperature values that are not directly measured between the sensors.

[0050] Based on the processed temperature data and interpolation results, the system generates a heat distribution map inside the household gas smoker. This map can be two-dimensional (such as a plan view) or three-dimensional (such as a stereogram), depending on the sensor layout and the system's capabilities; The heat distribution map usually uses color coding to represent the temperature of different regions. The darker the color, the higher the temperature, and the lighter the color, the lower the temperature.

[0051] Specifically, there is a household gas smoker with 10 temperature sensors installed inside, located in the upper, middle, and lower layers as well as the four corners of the oven cavity. These sensors send data wirelessly to a smartphone app. During the process of cooking a piece of beef, the system collects temperature data at a frequency of once per second. After the collected data is preprocessed, the system uses the bilinear interpolation algorithm to estimate the temperature values that are not directly measured between the sensors.

[0052] Finally, the system generates a two-dimensional heat distribution map, which uses color coding to represent the temperature in different areas of the oven. For example, the color at the bottom of the oven cavity is darker, indicating a higher temperature; while the color at the top of the oven cavity is lighter, indicating a lower temperature. By observing this heat distribution map, users can intuitively understand the temperature changes and distribution inside the oven, and thus adjust the cooking strategy, such as adjusting the heat, turning the food, or changing the position of the food, to ensure that the food is evenly heated and achieves the best cooking effect.

[0053] In addition, the system can also automatically adjust the heating elements of the smoker according to the changes in the heat distribution map to achieve a more intelligent and efficient cooking process. For example, if the system detects that the temperature in a certain area of the oven cavity is too high, it will automatically reduce the heating power in that area to prevent the food from overheating or burning.

[0054] Furthermore, collect the energy consumption of the household gas smoker and correlate it with the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker. Determine the control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker, taking into account the overall situation of the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker, and ensure the accuracy of the control effect coefficient of the household gas smoker.

[0055] At this time, the household gas smoker is equipped with an energy consumption measurement module, such as an electricity meter or a power sensor, for real-time collection of energy consumption data during the cooking process. The collected data can include total energy consumption, instantaneous power, changes in energy consumption over time, etc.

[0056] Correlate the collected energy consumption data with the maturity of the food (such as determined through step S131), which includes analyzing the relationship between energy consumption and the food's maturity speed and maturity uniformity. At the same time, correlate the energy consumption data with the heat distribution map of the household gas smoker and analyze the relationship between energy consumption and heat distribution uniformity and heat loss. Correlation analysis can use technical means such as statistical methods, machine learning algorithms, or expert systems.

[0057] Based on the results of the correlation analysis, calculate the control effect coefficient of the household gas smoking oven. This coefficient is a comprehensive indicator used to evaluate the performance of the smoking oven in terms of heat distribution, energy consumption efficiency, and food maturity control during the cooking process. The calculation of the control effect coefficient can be based on a preset scoring standard or algorithm, considering multiple factors such as energy consumption efficiency, heat distribution uniformity, and accuracy of food maturity. The calculated control effect coefficient can be a numerical value (such as a floating point number between 0 and 1) or a grade (such as excellent, good, average, poor).

[0058] Specifically, there is a household gas smoking oven cooking a piece of beef. During the cooking process, the system collects the energy consumption data of the smoking oven in real time, including the total energy consumption and the instantaneous power. At the same time, the system also determines the maturity of the beef through step S131 and generates a heat distribution map of the household gas smoking oven through step S141. Next, the system performs data correlation analysis. The analysis finds that as the cooking time progresses, the energy consumption gradually increases, and the maturity of the beef also improves accordingly. In addition, the heat distribution map shows that the heat distribution in the oven cavity is relatively uniform, without obvious hot spots or cold spots.

[0059] Based on these analysis results, the system calculates the control effect coefficient of the household gas smoking oven. The scoring standard includes energy consumption efficiency (accounting for 40%), heat distribution uniformity (accounting for 30%), and accuracy of food maturity (accounting for 30%). The system calculates the control effect coefficient to be 0.85 (with a full score of 1) according to these weights and the corresponding scoring standard, indicating that the household gas smoking oven performs well during the cooking process, can effectively control heat distribution and energy consumption, and ensure that the maturity of the food meets expectations. Users can evaluate the performance of the smoking oven based on this control effect coefficient and make adjustments or optimizations when needed.

[0060] Reference Figure 6 S15: Match the control mode of the household gas smoking oven according to the control effect coefficient of the household gas smoking oven, the smoke emission amount of the household gas smoking oven, and the heating power of the household gas smoking oven, and match the intelligent control model of the household gas smoking oven based on the control mode and the maturity state of the food; In the specific implementation process of the present invention, the specific steps can be as follows: S151: Collect the control effect coefficient of the household gas smoking oven; S152: Monitor the household gas smoking oven in real time and collect the smoke emission amount of the household gas smoking oven and the heating power of the household gas smoking oven; S153: Perform multi-dimensional matching on the control effect coefficient of the household gas smoking oven, the smoke emission amount of the household gas smoking oven, and the heating power of the household gas smoking oven; S154: Match the control mode of the household gas smoking furnace based on the control effect coefficient of the household gas smoking furnace, the smoke output of the household gas smoking furnace, and the heating power of the household gas smoking furnace; S155: Associate the control mode with the maturity state of the food; S156: Match the intelligent control model of the household gas smoking furnace according to the control mode and the maturity state of the food.

[0061] In an embodiment of the present application, collect the control effect coefficient of the household gas smoking furnace; monitor the household gas smoking furnace in real time, and collect the smoke output of the household gas smoking furnace and the heating power of the household gas smoking furnace, and perform multi-dimensional matching on the control effect coefficient of the household gas smoking furnace, the smoke output of the household gas smoking furnace, and the heating power of the household gas smoking furnace.

[0062] At this time, collect the control effect coefficient of the household gas smoking furnace. At the same time, the system first configures real-time monitoring parameters, including monitoring frequency (such as per second, every few minutes, etc.), monitoring data types (smoke output, heating power, etc.), and storage methods of monitoring data (such as memory storage, database storage, etc.); the smoke output is usually detected by a smoke sensor, which can sense the concentration or flow rate of the smoke; the heating power is obtained through a power sensor or directly from the internal control system of the smoking furnace; before deploying the sensor, calibration is required to ensure the accuracy of the data.

[0063] The system regularly collects data from the sensor according to the preset monitoring frequency; the data includes real-time smoke output values, heating power values, and timestamps and other information; the collected raw data needs to be preprocessed, such as removing noise, smoothing, or data standardization, etc., to improve the accuracy and reliability of the data.

[0064] Optionally, the household gas smoking furnace is cooking a smoked fish; in step S152, the system monitors the operating state of the smoking furnace in real time; the system is configured to collect data every 5 seconds and store the data in memory for quick access; the smoke sensor is deployed at the outlet of the smoking furnace to detect the smoke output; at the same time, the power sensor is connected to the heating element of the smoking furnace to measure the heating power; before cooking starts, these sensors have been calibrated to ensure the accuracy of the data.

[0065] The system starts to collect data from the sensor at a frequency of every 5 seconds; for example, at a certain moment, the smoke output value collected by the system is 30 units / second, and the heating power value is 1500 watts; the collected data contains some small fluctuations or noises, and the system reduces these fluctuations through smoothing to obtain a more stable data curve; the processed data is stored in memory and continuously updated to reflect the latest state; the system can access these data at any time for subsequent analysis and decision-making.

[0066] If the system detects an abnormal change in the smoke output or heating power (such as a sudden increase or decrease), it will trigger an alarm to remind the user to check the status of the smokehouse or take corresponding corrective measures; for example, if the heating power suddenly drops to a very low level, the system will issue an alarm to prompt the user to check whether the heating element is faulty; through this practical example, it can be seen that step S152 plays a key role in the intelligent monitoring process of the household gas smokehouse, providing real-time operating status data for the system and helping the system make more accurate and efficient decisions.

[0067] Furthermore, the system has collected data on the control effect coefficient, smoke output, and heating power of the household gas smokehouse. These data are real-time and historical data stored in the previous steps; the system needs to determine the matching dimensions, which include time, cooking stage, food type, smokehouse model, etc.; the matching dimensions will determine how the data are combined and compared.

[0068] According to the determined matching dimensions, the system groups the data; for example, the data can be grouped according to the cooking stage (such as preheating, cooking, and keeping warm); within each group, the system further matches the data of the control effect coefficient, smoke output, and heating power; the system needs to formulate matching rules for determining which data points are considered to be matched, and these rules are based on the data range, change trend, relative relationship, etc.; the system outputs the matching results, which include the matched data points, the degree of matching (such as similarity, correlation coefficient, etc.), and any abnormal or unexpected situations.

[0069] Specifically, a household gas smokehouse is cooking a smoked rib, and the system has collected data on the control effect coefficient, smoke output, and heating power; the data collected by the system include a control effect coefficient of 0.85 (indicating good control effect), a smoke output of 25 units / second, and a heating power of 1600 watts. These data are real-time and continuously updated as the cooking process progresses; the system decides to match according to the cooking stage; the cooking process is divided into a preheating stage, a cooking stage, and a keeping warm stage.

[0070] During the preheating phase, the system collected a set of data points, including the values of the control effect coefficient, the smoke output, and the heating power. As cooking entered the cooking phase, the system collected another set of data points. The system established the following matching rules: within the same cooking phase, the data points of the control effect coefficient, the smoke output, and the heating power should exhibit similar trends of change, and their values should be within a preset range. During the preheating phase, the system found that the control effect coefficient remained stable, the smoke output gradually increased, and the heating power also gradually rose to the preset cooking level, and these data points conformed to the expected matching rules. However, in the middle of the cooking phase, the system detected a sudden drop in the heating power, and the smoke output and the control effect coefficient were also affected accordingly. The system considered this a mismatched situation and output a corresponding warning.

[0071] Based on the matching results, the system decided to adjust the control mode in the middle of the cooking phase to increase the heating power and stabilize the smoke output to ensure that the smoked ribs could be evenly heated and achieve the expected taste and flavor. At the same time, the system would adjust the parameters of the intelligent control model to better meet the requirements of the current cooking phase. Through this practical example, it can be seen that step S153 plays an important role in the intelligent control process of the household gas smoking furnace. It helps the system identify abnormal situations during the cooking process and guides the subsequent steps to take corresponding corrective measures by matching the control effect coefficient, the smoke output, and the heating power in multiple dimensions.

[0072] Furthermore, the control mode of the household gas smoking furnace is matched based on the control effect coefficient of the household gas smoking furnace, the smoke output of the household gas smoking furnace, and the heating power of the household gas smoking furnace. The control mode is associated with the maturity state of the food. The intelligent control model of the household gas smoking furnace is matched according to the control mode and the maturity state of the food, ensuring the accuracy of the intelligent control model of the household gas smoking furnace.

[0073] At this time, the system has collected and matched the data of the control effect coefficient, the smoke output, and the heating power of the household gas smoking furnace. The system integrates these data together to form a comprehensive data set for subsequent control mode matching. At the same time, the system has collected and matched the data of the control effect coefficient, the smoke output, and the heating power of the household gas smoking furnace. The system integrates these data together to form a comprehensive data set for subsequent control mode matching.

[0074] The system selects a suitable matching algorithm to match the collected data with the patterns in the control mode library; the matching algorithm is based on methods such as similarity calculation, machine learning models, or expert systems; the system executes the matching algorithm and compares the collected data with each pattern in the control mode library; the result of the comparison is a similarity score, a probability value, or a ranking list; according to the matching result, the system selects the optimal control mode; the optimal mode is the one with the highest similarity, the largest probability, or the top-ranked one; the system applies the selected control mode to the household gas smoker and adjusts its operating parameters to meet the requirements of that mode.

[0075] Optionally, the household gas smoker is cooking a smoked beef, and the system has collected and matched data on the control effect coefficient, smoke output, and heating power; the system integrates these data into a dataset, including a control effect coefficient of 0.88 (indicating excellent control effect), a smoke output of 30 units per second, and a heating power of 1800 watts.

[0076] The system has a library containing multiple control modes; for example, there is a "slow roasting mode" for smoked beef, which cooks food with a lower heating power and a stable smoke output to achieve the best taste and flavor; the system selects a matching algorithm based on similarity calculation; this algorithm compares the collected data with the patterns in the control mode library and calculates the similarity score between them; the system executes the matching algorithm and compares the collected data with the "slow roasting mode"; the algorithm calculates a similarity score of 0.92, indicating that the collected data is very well matched with the "slow roasting mode"; according to the matching result, the system selects the "slow roasting mode" as the optimal control mode; the system applies the "slow roasting mode" to the household gas smoker, adjusts the heating power to a preset lower level, and keeps the smoke output stable, so that the smoked beef can be cooked under the best conditions and achieve the expected taste and flavor.

[0077] Furthermore, the system has matched an optimal control mode according to the control effect coefficient, smoke output, and heating power of the household gas smoker; the system first identifies the selected control mode and obtains its relevant parameter settings and cooking strategies; at the same time, the system monitors the maturity state of the food in real time, which can be achieved in various ways, such as using a temperature sensor to measure the internal temperature of the food, using computer vision technology to detect changes in the color and texture of the food, or inferring based on preset cooking time and stages.

[0078] The system formulates association rules to link the control mode with the maturity state of the food. These rules are based on factors such as food type, cooking requirements, smoker model, and previous cooking experience. The system performs real-time association analysis to compare and match the current control mode with the maturity state of the food. The result of the analysis is an association degree indicator, which is used to measure the consistency and adaptability between the control mode and the food maturity state. According to the result of the association analysis, the system generates adjustment suggestions, which include adjusting the parameter settings of the control mode, extending or shortening the cooking time, changing the smoke output, etc., to ensure that the food can reach the expected maturity state.

[0079] Optionally, a household gas smoker is cooking a smoked chicken breast. The system has matched the "medium-temperature slow-roasting mode" according to the control effect coefficient, smoke output, and heating power. The system identifies the selected control mode as the "medium-temperature slow-roasting mode" and obtains its relevant parameter settings, such as the heating power being maintained between 1600 - 1700 watts and the smoke output being stable. The system uses a temperature sensor to monitor the temperature inside the chicken breast in real time and observes that the temperature is gradually rising and approaching the preset cooking completion temperature.

[0080] The system formulates association rules, stipulating that when the temperature inside the chicken breast reaches the preset value, the food is considered to have reached the maturity state. At the same time, the system also considers the influence of cooking time and smoke output on the food maturity state. The system performs real-time association analysis to compare the parameter settings of the "medium-temperature slow-roasting mode" with the maturity state of the chicken breast. The analysis result shows that the current control mode is highly consistent with the maturity state of the food, and the food is being cooked as expected.

[0081] Since the association analysis result shows that the control mode is consistent with the food maturity state, the system does not generate adjustment suggestions. However, if the system detects any deviation, it will suggest adjusting the heating power or smoke output to ensure that the food can reach the expected maturity state. In this example, the user does not receive any adjustment suggestions, so there is no need to fine-tune the control mode. However, if the user observes that the food is cooking too fast or too slow, they can manually adjust the parameter settings of the control mode according to the association analysis result and adjustment suggestions provided by the system.

[0082] Through this actual example, it can be seen that step S155 plays a crucial role in the intelligent control process of the household gas smoker. It helps the system ensure that the food is cooked as expected and achieves the best taste and flavor by performing association analysis between the control mode and the food maturity state. At the same time, the system also provides a mechanism for user feedback and adjustment, allowing users to fine-tune the control mode according to the actual situation to meet personalized cooking needs.

[0083] The system has a predefined intelligent control model library, which contains a variety of control models based on different algorithms and strategies, such as PID control, fuzzy logic control, neural network control, etc.; each model is optimized for different cooking scenarios, food types or smoker characteristics; the system analyzes the current control mode and the maturity state of the food, and extracts key information, which includes parameter settings of the control mode, cooking stage of the food, temperature curve, etc.

[0084] The system selects a suitable matching algorithm to match the current control mode and food maturity state with the models in the intelligent control model library; the matching algorithm is based on factors such as similarity calculation, model performance evaluation, user preferences, etc.; the system applies this algorithm to select the optimal intelligent control model from the model library; at the same time, after selecting the model, the system needs to adjust and optimize the model parameters according to the current control mode and food maturity state, which includes adjusting the gain of the controller, optimizing fuzzy logic rules, training neural networks, etc.

[0085] The system applies the adjusted intelligent control model to the household gas smoker and adjusts its operating parameters in real time to achieve precise cooking control; the system continuously monitors the performance of the intelligent control model, including indicators such as control accuracy, stability, energy consumption, etc.; according to the monitoring results, the system provides feedback to the user or automatically adjusts the model parameters to optimize the performance.

[0086] Specifically, a household gas smoker is cooking a smoked pork chop. The system has matched the "high-temperature quick-roasting mode" according to the control effect coefficient, smoke output and heating power, and monitors through a temperature sensor that the internal temperature of the pork chop is gradually rising and approaching the preset cooking completion temperature. The system has a library containing a variety of intelligent control models, such as models based on PID control, models based on fuzzy logic control, and models based on neural network control, and these models are optimized for different cooking requirements and smoker characteristics.

[0087] The system analyzes the parameter settings of the "high-temperature quick-roasting mode" (such as heating power range, smoke output, etc.) and the maturity state of the pork chop (such as internal temperature, cooking stage, etc.), and extracts key information for model matching; the system selects a matching algorithm based on similarity calculation and model performance evaluation; this algorithm compares the current control mode and food maturity state with the models in the model library and selects the optimal intelligent control model; in this example, the system selects the model based on PID control because it performs well in rapid heating and precise control; the system adjusts the gain of the PID controller according to the "high-temperature quick-roasting mode" and the maturity state of the pork chop to ensure rapid response and stable control.

[0088] The system applies the adjusted PID control model to the household gas smoke oven, and adjusts the heating power and the smoke output in real time to achieve precise cooking control; as the internal temperature of the pork chop rises, the controller gradually reduces the heating power to prevent overcooking; the system continuously monitors the performance of the PID control model, including control accuracy and stability; if the system detects any deviation or instability, it will automatically adjust the controller parameters or provide feedback to the user to optimize the cooking process.

[0089] Through this practical example, it can be seen that step S156 plays a crucial role in the intelligent control process of the household gas smoke oven; it realizes precise cooking control by matching the current control mode and the food maturity state with the models in the intelligent control model library and adjusting the model parameters in real time, which not only improves the cooking accuracy and stability, but also optimizes the energy consumption and cooking efficiency.

[0090] Reference Figure 7 , S16: According to the intelligent control model, the household gas smoke oven and the food, match the corresponding smoke balance parameters, determine the food parameters based on the type of food and the maturity state of the food, and dynamically adjust the heating temperature and the smoke output of the household gas smoke oven based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoke oven. In the specific implementation process of the present invention, the specific steps can be: S161: Associate the intelligent control model, the household gas smoke oven and the food; match the corresponding smoke balance parameters based on the intelligent control model, the household gas smoke oven and the food. S162: Determine the food parameters according to the type of food and the maturity state of the food; dynamically adjust the heating temperature and the smoke output of the household gas smoke oven based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoke oven.

[0091] In the specific implementation process of the present invention, associating the intelligent control model, the household gas smoke oven and the food; matching the corresponding smoke balance parameters based on the intelligent control model, the household gas smoke oven and the food; determining the food parameters according to the type of food and the maturity state of the food; dynamically adjusting the heating temperature and the smoke output of the household gas smoke oven based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoke oven realizes the intelligent control of the household gas smoke oven.

[0092] At this time, the system first identifies the currently selected intelligent control models, which are based on different algorithms such as PID control, fuzzy logic control, neural network control, etc. Each model has its unique control strategy and advantages; the system analyzes the specific model, performance parameters and characteristics of the household gas smoke oven, which includes the type of heating element, the structure of the smoke exhaust system, the accuracy of temperature control, etc. These information are crucial for subsequent matching of the smoke balance parameters.

[0093] The system identifies the type of food to be cooked and analyzes its characteristics; the types of food include meat, fish, vegetables, etc. Each type of food has different requirements for smoking, such as flavor absorption ability, texture retention, cooking time, etc.; based on the intelligent control model, the characteristics of the household gas smoke oven and the type of food, the system formulates association rules, which define the matching principles of the smoke balance parameters under different combinations to ensure that the food can obtain the best smoking effect; according to the association rules, the system matches the corresponding smoke balance parameters, which include the heating temperature range, the amount of smoke exhaust, the smoking duration, the division of smoking stages, etc.; the matching process takes into account the cooking requirements of the food, the performance limitations of the smoke oven and the characteristics of the control model. Before actual application, the system verifies the matched smoke balance parameters, which can be achieved by simulating the cooking process, referring to historical data or user feedback; according to the verification results, the system fine-tunes the parameters to ensure the best cooking effect.

[0094] Optionally, a household gas smoke oven based on fuzzy logic control, model FS-3000, is used to prepare to cook a piece of smoked beef; the system identifies that the currently used is a fuzzy logic control model, which is good at dealing with non-linear and time-varying control problems and is suitable for a complex system such as a household gas smoke oven; the FS-3000 type smoke oven adopts an efficient heating element, which can quickly heat up and maintain a stable temperature; the smoke exhaust system adopts a precision regulating valve, which can accurately control the amount of smoke exhaust; the temperature control accuracy reaches ±5°C.

[0095] The food to be cooked is smoked beef, which requires a strong smoking flavor and a tender and juicy texture; beef has relatively high requirements for smoking and requires appropriate heating temperature and sufficient smoking time to achieve the ideal cooking effect; based on the fuzzy logic control model, the characteristics of the FS-3000 type smoke oven and the requirements of smoked beef, the system formulates association rules; the rules stipulate that a higher heating temperature and smoke exhaust amount are adopted at the initial stage of cooking to promote the rapid absorption of the smoking flavor; as the cooking progresses, the heating temperature and smoke exhaust amount are gradually reduced to maintain the texture of the beef and avoid overcooking.

[0096] According to the association rules, the system matches the smoking balance parameters; the heating temperature range is set from 150°C to 200°C, with a relatively high initial temperature (about 190°C), gradually decreasing to the later stage (about 160°C); the smoke output is set at a medium level, slightly higher in the initial stage to promote flavor absorption and slightly lower in the later stage to maintain the texture of the beef; the smoking duration is set at 4 hours, divided into three stages: initial, middle, and later; before actual cooking, the system verifies the matched smoking balance parameters by simulating the cooking process; according to the simulation results, the system fine-tunes the parameters to ensure that the beef can achieve an ideal smoking effect and texture during cooking; through this practical example, it can be seen that step S161 plays a crucial role in the intelligent control process of the household gas smoker; it provides a solid foundation for subsequent intelligent regulation by closely associating the intelligent control model, the household gas smoker, and the food, and matching the corresponding smoking balance parameters.

[0097] Furthermore, the system first identifies the type of food to be cooked, which can be achieved through user input, food recognition sensors, or a preset food database; the food type determines the key factors to be considered during the smoking process, such as flavor absorption ability, cooking time, texture maintenance, etc.; the system continuously monitors the maturity state of the food, which is usually achieved through built-in temperature sensors, humidity sensors, or image recognition technology; the maturity state includes changes in the internal temperature, humidity, and color of the food, and this information is crucial for dynamically regulating the smoking process.

[0098] Based on the monitoring results of the food type and maturity state, the system determines the food parameters, which include the target internal temperature, cooking time range, degree of smoked flavor absorption, etc.; the food parameters are the basis for subsequent intelligent regulation; the system reviews the previously matched smoking balance parameters according to the intelligent control model, the household gas smoker, and the food type, which include the heating temperature range, smoke output, smoking duration, etc., and they provide a benchmark for dynamic regulation.

[0099] Combining the food parameters and the smoking balance parameters, the system formulates a dynamic regulation strategy, which includes specific rules for adjusting the heating temperature and smoke output at different cooking stages to ensure that the food can achieve an ideal smoking effect and texture; according to the dynamic regulation strategy, the system adjusts the heating temperature and smoke output of the household gas smoker in real time, which is achieved by sending control signals to the heating elements and smoke output system of the smoker; the triggering and execution of the intelligent control are real-time and can respond to changes in the maturity state of the food. During the cooking process, the system continuously monitors the maturity state and smoking effect of the food and adjusts the control strategy as needed, and this feedback mechanism ensures the accuracy and flexibility of the intelligent control.

[0100] Specifically, a smoked chicken breast is being cooked using a household gas smoker; the system identifies that the food to be cooked is a chicken breast, which belongs to the category of meat, has a good absorption capacity for smoked flavor, requires a moderate cooking time, and needs to maintain a tender texture; the system monitors the temperature change inside the chicken breast through an in-built temperature sensor; initially, the temperature inside the chicken breast is low; as cooking progresses, the temperature gradually rises.

[0101] Based on the type and maturity status monitoring results of the chicken breast, the system determines the target internal temperature to be 74°C (ensuring food safety), the cooking time range to be 2 to 3 hours, and the smoked flavor absorption level to be medium; the system reviews the previously matched smoked balance parameters, including the heating temperature range set at 120°C to 180°C, the smoke output set at a medium level, and the smoking duration of 2.5 hours; combining the food parameters and the smoked balance parameters, the system formulates a dynamic regulation strategy; in the initial stage of cooking, a relatively high heating temperature (about 160°C) and smoke output are adopted to promote the rapid absorption of smoked flavor; as the internal temperature of the chicken breast rises, the heating temperature (to about 140°C) and smoke output are gradually reduced to maintain the texture of the chicken breast and avoid overcooking.

[0102] The system adjusts the heating temperature and smoke output of the household gas smoker in real-time according to the dynamic regulation strategy; in the initial stage of cooking, the heating element quickly heats up to 160°C, and the smoke output system starts with a medium smoke output; as cooking progresses, the system gradually reduces the heating temperature and smoke output until the target internal temperature and smoked effect are achieved. During the cooking process, the system continuously monitors the internal temperature and smoked effect of the chicken breast; if it is found that the temperature rises too quickly or the smoked flavor absorption is insufficient, the system will timely adjust the control strategy, such as reducing the heating temperature or increasing the smoke output, to ensure that the final smoked effect meets the expectations.

[0103] Please refer to Figure 8 , Figure 8 which is a schematic diagram of the structural composition of the intelligent control system of the household gas smoker in the embodiment of the present invention.

[0104] As Figure 8 shown, an intelligent control system of a household gas smoker, the intelligent control system of the household gas smoker includes: A detection space module 21 for determining a detection space based on the location of the household gas smoker; A detection module 22 for determining the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the detection of the detection space; A maturity module 23 for determining the maturity of the food according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple maturity parameters of the food; A control module 24 for determining a control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; An intelligent module 25 for matching a control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and matching an intelligent control model of the household gas smoker based on the control mode and the maturity state of the food; A dynamic regulation module 26 for matching corresponding smoke balance parameters according to the intelligent control model, the household gas smoker, and the food, determining food parameters based on the type of the food and the maturity state of the food, and dynamically regulating the heating temperature and the smoke output of the household gas smoker based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoker.

[0105] Please refer to Figure 9 and, with reference to Figure 9 below, an electronic device 40 according to this embodiment of the present invention will be described. Figure 9 The shown electronic device 40 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0106] As Figure 9 shown, the electronic device 40 is presented in the form of a general-purpose computing device. The components of the electronic device 40 may include, but are not limited to: at least one of the above-mentioned processing units 41, at least one of the above-mentioned storage units 42, and a bus 43 connecting different system components (including the storage unit 42 and the processing unit 41).

[0107] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 41, so that the processing unit 41 executes the steps according to various exemplary embodiments of the present invention described in the "Embodiment Method" part of this specification.

[0108] The storage unit 42 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 421 and / or a cache storage unit 422, and may further include a read-only storage unit (ROM) 423.

[0109] The storage unit 42 may further include a program / utility 424 having a set (at least one) of program modules 425. Such program modules 425 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, and the implementation of a network environment is included in each or some combination of these examples.

[0110] The bus 43 can represent one or more of several types of bus structures, including a memory unit bus or a memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of the various bus structures.

[0111] The electronic device 40 can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 40, and / or communicate with any device that enables the electronic device 40 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 44. Also, the electronic device 40 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 45. As Figure 9 shown, the network adapter 45 communicates with other modules of the electronic device 40 through the bus 43. It should be understood that although Figure 9 not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup and planning systems, etc.

[0112] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a multi-parameter sensor device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0113] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium can include: a read-only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc. And it stores computer program instructions, and when the computer program instructions are executed by a computer, the computer executes the method according to the above.

[0114] In addition, the above has introduced in detail the intelligent control method and system of the household gas smoking furnace provided by the embodiments of the present invention. In this article, specific examples have been used to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An intelligent control method for a household gas smoke oven, characterized in that, Including: Determine a detection space based on the location of the household gas smoker; Determine the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the detection of the detection space; Determine the ripeness of the food according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple ripeness parameters of the food; Determine the control effect coefficient of the household gas smoker based on the ripeness of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; Match the control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and match the intelligent control model of the household gas smoker based on the control mode and the ripeness state of the food; Match the corresponding smoke balance parameters according to the intelligent control model, the household gas smoker, and the food, determine the food parameters based on the type of the food and the ripeness state of the food, and dynamically adjust the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoker.

2. The intelligent control method of the household gas smoking furnace according to claim 1, characterized in that, The determination of the detection space based on the location of the household gas smoker includes: Collect the control signal of the household gas smoker; determine the location information of the household gas smoker according to the analysis of the control signal of the household gas smoker; Determine the location where the household gas smoker is located according to the location information of the household gas smoker; Determine the detection space according to the location where the household gas smoker is located, the location of the user, and the surrounding environment of the household gas smoker.

3. The intelligent control method of the household gas smoking furnace according to claim 1, characterized in that, The determination of the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the detection of the detection space includes: Divide the detection space into multiple space regions, and perform multi-dimensional detection on the multiple space regions; collect multiple working parameters and multiple scenario parameters according to the multi-dimensional detection of the multiple space regions; determine the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the multiple working parameters and the multiple scenario parameters.

4. The intelligent control method of the household gas smoke oven according to claim 1, characterized in that, The determination of the ripeness of the food according to the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple ripeness parameters of the food includes: Locate the food in the household gas smoker; determine the ripeness parameters at multiple positions based on the detection of the food; determine multiple ripeness parameters according to the ripeness parameters at multiple positions, the type of the food, and the form of the food; determine the ripeness of the food based on the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple ripeness parameters of the food.

5. The intelligent control method of the household gas smoke oven according to claim 1, characterized in that, The determination of the control effect coefficient of the household gas smoker based on the ripeness of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker includes: Determine the heat distribution map of the household gas smoker based on the heat detection of the household gas smoker; Collect the energy consumption of a household gas smoker, and correlate it with the ripeness of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; determine the control effect coefficient of the household gas smoker based on the ripeness of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker.

6. The intelligent control method of the household gas smoking furnace according to claim 1, characterized in that, Match the control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and match the intelligent control model of the household gas smoker based on this control mode and the ripeness state of the food, including: Collect the control effect coefficient of the household gas smoker; Monitor the household gas smoker in real time, and collect the smoke output of the household gas smoker and the heating power of the household gas smoker; Perform multi-dimensional matching on the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker; Match the control mode of the household gas smoker based on the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker.

7. The intelligent control method of the household gas smoke oven according to claim 6, characterized in that, Match the control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and match the intelligent control model of the household gas smoker based on this control mode and the ripeness state of the food, further including: Correlate this control mode and the ripeness state of the food; Match the intelligent control model of the household gas smoker according to this control mode and the ripeness state of the food.

8. The intelligent control method of the household gas smoking furnace according to claim 1, characterized in that Match the corresponding smoking balance parameters according to the intelligent control model, the household gas smoker, and the food, determine the food parameters based on the type of food and the ripeness state of the food, and dynamically adjust the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoking balance parameters to trigger the intelligent control of the household gas smoker, including: Correlate the intelligent control model, the household gas smoker, and the food; match the corresponding smoking balance parameters based on the intelligent control model, the household gas smoker, and the food.

9. The intelligent control method of the household gas smoking furnace according to claim 8, characterized in that Match the corresponding smoking balance parameters according to the intelligent control model, the household gas smoker, and the food, determine the food parameters based on the type of food and the ripeness state of the food, and dynamically adjust the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoking balance parameters to trigger the intelligent control of the household gas smoker, further including: Determine the food parameters according to the type of food and the ripeness state of the food; dynamically adjust the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoking balance parameters to trigger the intelligent control of the household gas smoker.

10. An intelligent control system for a household gas smoke oven, characterized in that, The intelligent control system of the household gas smoker is applied to the intelligent control method of the household gas smoker as described in any one of claims 1-9, and the intelligent control system of the household gas smoker includes: A detection space module for determining a detection space based on the location of the household gas smoker; A detection module for determining the current working mode of the household gas smoker and the usage scenario of the household gas smoker according to the detection of the detection space; A maturity module for determining the maturity of food based on the current working mode of the household gas smoker, the usage scenario of the household gas smoker, and multiple maturity parameters of the food; A control module for determining the control effect coefficient of the household gas smoker based on the maturity of the food, the heat distribution map of the household gas smoker, and the energy consumption of the household gas smoker; An intelligent module for matching the control mode of the household gas smoker according to the control effect coefficient of the household gas smoker, the smoke output of the household gas smoker, and the heating power of the household gas smoker, and matching the intelligent control model of the household gas smoker based on the control mode and the maturity state of the food; A dynamic regulation module for matching corresponding smoke balance parameters according to the intelligent control model, the household gas smoker, and the food, determining food parameters based on the type of food and the maturity state of the food, and dynamically regulating the heating temperature and smoke output of the household gas smoker based on the food parameters and the smoke balance parameters to trigger the intelligent control of the household gas smoker.

Images